Reich's DNA Study Shows Human Selection Spiked In Bronze Age

赖希DNA研究显示人类自然选择在青铜时代激增 古基因组数据表明，自然选择在距今4000年至2000年间急剧加速，推动了认知表现预测指标的显著提升与特征适应。 通过分析包含22000多个古代与现代基因组的庞大数据集，David Reich及其团队证实，人类自然选择在农业革命后并未停止，反而在距今4000年至2000年间的青铜时代急剧加速。该研究揭示了影响免疫功能、代谢特征、体脂调节及认知预测指标的强烈选择信号；其中，认知表现评分在过去1万年里上升了约整整一个标准差。此外，Reich还提出了一个统一演化模型，认为尼安德特人与现代人共享了一次走出高加索的中石器时代扩散，其主要差异在于各自被哪些当地古人类种群在遗传上所淹没。 古代 DNA 测序样本已从 2010 年的约 10 个暴增至如今超过 20,000 个；对过去 1 万年间约 1000 万个基因变异位点的分析表明，98% 的频率变动归因于遗传漂变和人群迁移，仅有 2% 由定向自然选择驱动。 (5:00) 人类基因组的自然选择在青铜时代（距今 4,000 至 2,000 年前）急剧加速，并呈现出显著的生物学冲击，主要富集在免疫功能、脂肪代谢以及疾病易感性相关性状上。 (15:00) 预测认知表现的多基因评分在过去 1 万年里提升了约整整一个标准差，且绝大部分选择压力集中在青铜时代，近 2000 年来则趋于平缓；该演化轨迹在古代欧洲人 DNA 与现代中国人群队列的研究中表现出高度一致的跨人群统计关联。 (30:00) 过去 1 万年间肥胖、BMI 和 2 型糖尿病的遗传风险稳步下降；农耕社会面临的是持续数月的多年期大饥荒，短期的脂肪储存无法抵御这种灾难，进而削弱了传统“节俭基因”的生存优势。 (50:00)

Harvard geneticist David Reich argues that ancient DNA reveals human biology did not remain quiescent after the out-of-Africa expansion; instead, directional natural selection intensified dramatically during the Bronze Age in response to dense settlement, epidemic diseases, and shifting social pressures. Isolating Directional Selection from Demographic Drift (5:00) For decades, evolutionary biology assumed natural selection largely stalled after modern humans migrated from Africa, given the absence of fixed allele differences between global populations separated for 40,000 to 50,000 years. Reich and researcher Ali Akbari tested this by scaling ancient DNA datasets to 22,000 individuals across the last 18,000 years. By modeling ancient populations as an archipelago of isolated communities between migration waves, the team partitioned allele frequency shifts: 98% of genetic variance across time is driven by genetic drift and population turnover, leaving a subtle 2% signal attributable to directional natural selection. Applying a relatedness matrix adapted from medical genetics across 10 million genomic sites, the researchers identified 479 independent loci under natural selection at 99% statistical confidence, and approximately 3,800 sites at 50% confidence. When cross-referenced with modern genome-wide association studies (GWAS) such as the UK Biobank, loci with strong selection statistics exhibited a five-fold enrichment for trait-associated mutations, confirming that ancient selection predominantly modified active biological functions rather than background genetic noise. The Bronze Age Acceleration and Immune Pressures (15:00) Contrary to expectations that the adoption of agriculture 11,000 to 12,000 years ago drove the primary biological transformation, the data indicates that natural selection accelerated most sharply between 5,000 and 2,000 years ago during the Bronze and Iron Ages. Surging population densities, urban clustering, and close proximity to domesticated animals exposed genomes adapted to hunter-gatherer existence to an unprecedented mismatch with zoonotic pathogens. Selection coefficients during this window frequently reached 0.5% to 1%, sufficient to rapidly double allele frequencies across dozens of generations. This evolutionary shock produced stark reversals and sweeps in disease and dietary loci. For example, a risk variant in the TYK2 gene linked to severe tuberculosis climbed to 9% to 10% frequency before plummeting over the last 3,000 years as the disease became endemic. Similarly, traits such as lactase persistence, FADS1 fatty acid processing variants, and polygenic skin depigmentation in ancestral Europeans showed their most pronounced evolutionary momentum within this Bronze Age window. Polygenic Evolution in Metabolism and Cognitive Predictors (30:00) Over the last 10,000 years, the genetic data shows a steady, directional decline in risk factors associated with obesity, body mass index, and type 2 diabetes. Reich links this to an updated thrifty gene hypothesis: early agricultural societies maintained more predictable, day-to-day caloric supplies than the acute, boom-and-bust cycles experienced by hunter-gatherers, relaxing the adaptive need for heavy fat storage. Consequently, populations with longer historical exposure to farming retain greater modern genetic protection against metabolic disorders. Polygenic predictors for cognitive performance and educational attainment also experienced substantial positive selection that peaked between 5,000 and 2,000 years ago, moving roughly one standard deviation over 10,000 years before plateauing over the last two millennia. Reich cautions that educational polygenic scores are highly pleiotropic—correlating with delayed reproduction, executive function, and walking pace—rather than measuring isolated intelligence. To prove this signal was not a methodological artifact of European biobanks, the team validated the effect sizes against modern Chinese cohorts, finding a statistically robust five-to-six standard deviation correlation. A Unified Wave-Front Model for Archaic Humans (85:00) Reich challenges prevailing evolutionary consensus models that treat Neanderthals and Denisovans as simple sister lineages, arguing that accumulated anomalies resemble Ptolemaic epicycles. Although Neanderthal nuclear DNA clusters with Denisovans, late Neanderthal mitochondrial DNA and Y chromosomes were completely replaced by early modern human lineages 200,000 to 300,000 years ago, coinciding with the shared spread of Levallois stone tool technology. Reich hypothesizes that a pioneering population carrying Middle Stone Age toolmaking expanded from the Caucasus or East Africa 300,000 to 400,000 years ago. In Europe, this pioneer wavefront was genetically swamped by local archaics, retaining a 95% archaic nuclear genome while fixing modern uniparental markers via social or natural selection. In Africa, where the local archaic population had diverged for 1.5 million years, biological incompatibilities limited archaic admixture to roughly 20%, resulting in anatomically modern humans. Under this framework, Neanderthals represent the culturally linked European counterpart of the same expansion. Reich emphasizes that recent human evolution relied on natural selection sifting through vast reservoirs of pre-existing standing polygenic variation rather than waiting for novel mutations. However, because complex behavioral and metabolic adaptations involve extensive biological trade-offs, trait trajectories fluctuate as environmental and social contexts change across millennia.

哈佛大学古DNA学者David Reich团队通过大规模古基因组测序与新统计模型提出，人类生物学演化在走出非洲后并未停滞，而是在距今约5,000至2,000年前的青铜时代迎来了激烈的加速选择期。这一转向由农业扩张后的高密度定居、病原体冲击及社会结构剧变所驱动，不仅剧烈重塑了免疫与代谢机制，也在认知行为相关的多基因位点留下了清晰的选择印记。 技术与统计突破：分离漂变与选择信号 (120:00) 长期以来，演化生物学界主流观点认为人类走出非洲后自然选择趋于停滞，而古DNA领域的早期研究也因样本规模受限而陷入进展瓶颈。Reich实验室利用液相捕获富集技术（in-solution enrichment）靶向超100万个关键位点，克服了古遗骸提取物中99%以上为微生物DNA的污染限制，将样本库扩充至上万例；研究员Ali Akbari在此基础上构建了基于时空“群岛”的亲缘关系矩阵模型。该统计方法将人群迁徙与遗传漂变等全基因组宏观背景剥离，在分析的约1,000万个变异位点中，证明98%的等位基因频率变动源于遗传漂变和种群流动，仅有2%归因于定向自然选择。该框架以超过99%的置信度锁定了至少479个独立受选位点，并在与英国生物样本库（UK Biobank）等现代表型关联分析对比时发现，选择统计量高于5的区域展现出5倍的表型变异富集，证实人类基因组普遍处于自然选择的微妙作用之下。 青铜时代选择激增：重塑免疫与代谢机制 (15:00) 数据显示，人类演化中最显著的生物学冲击并未发生在11,000年前农业初兴时期，而是集中在距今5,000至2,000年前的青铜时代。当时人口急剧增长，聚落密度跃升，人畜紧密接触导致原本适应狩猎采集的人类基因组突然面临严重的病原体与环境错配。最强烈的正选择信号高度聚集于免疫系统（富集4至5倍）与代谢功能：例如导致重症结核病风险的TYK2基因变异，其频率曾在距今8,000年前持续上升至9%—10%，却在近3,000年结核病流行加剧后遭自然选择剧烈逆转并清除；涉及脂肪酸转换的FADS1基因、乳糖耐受变异以及欧洲人群的肤色变浅多基因表型，也均在距今4,000至2,000年间经历了峰值选择。与此同时，与肥胖、高BMI和2型糖尿病相关的遗传风险位点在过去1万年中持续下降，表明农业带来的相对稳定日常热量供给，削弱了此前狩猎采集时代应对饥荒波动的“节约基因”贮脂需求。 认知预测指标激增与集体大脑假说之争 (30:00) 通过追踪与现代受教育年限及认知表现相关的数千个微效等位基因，研究团队发现这些多基因评分在距今5,000至2,000年的青铜时代达到了选择强度的顶峰（单位时间选择强度约达2个标准差），而在最近2,000年间则几乎停滞；1万年来的累积净位移相当于现代变异尺度的1个标准差（约将中位数人群推升至如今的第85百分位）。为排除现代西方GWAS的统计偏差，团队利用现代中国人群的GWAS数据与古代欧洲DNA轨迹进行交叉比对，发现了高达5至6个标准差的强相关性。Reich指出，这一实证发现直接挑战了Joseph Henrich提出的“集体大脑”假说（即复杂社会分工会降低对个体智力的选择压力）；但他也强调，受教育年限等评分具有广泛的多效性，与推迟育龄、情绪控制和低BMI等行为及生理特征高度交织，冰岛近一个世纪的数据甚至显示其遗传预测值因生育模式变化而下降，表明这一选择可能反映了资源投资与繁殖策略的多维权衡，而非单一维度的抽象智力筛选。 深层演化：常备变异与古人类扩散模型 (85:00) 在更深的时间尺度上，Mallick等人的研究显示现代人群中不存在距今40万至50万年以内的通用单基因完全硬扫荡，表明现代人类的适应性演化主要依赖于走出非洲前奠基群体中早已存在的庞大“常备多基因变异”（standing variation）。针对古人类谱系的矛盾现象——尼安德特人全基因组与丹尼索瓦人聚类，但其Y染色体和线粒体DNA却在距今20万至30万年前被早期现代人类完全替换，Reich提出了一个统一扩散模型：距今30万至40万年前，发明勒瓦娄哇（Levallois）石器技术的人群自高加索或东非扩散，进入欧洲时发生波前杂交，其核DNA虽被当地庞大的本土古老人群稀释覆盖达95%，但现代文化技术与单系遗传标记通过社会选择得以留存；该先锋群体南下非洲时则与更早分化（约150万年）的古老非洲谱系混合并保留了约80%先锋血统。Reich认为该假说虽然仍待证实，但比在传统模型上不断修补外轮的解释更具自洽性。 Reich指出，人类对自身的演化理解常受到学科壁垒与当代价值偏见的影响。虽然古DNA数据推翻了“人类走出非洲后演化停滞”或“文明降低个体生物学选择压力”的先验理论，但利用现代多基因评分去反推古代复杂行为特征仍存在高度不确定性。微弱选择系数的长期演化效应、表型背后的适应性代价，以及深层古人类谱系交融的精确路径，仍是悬而未决的核心科学问题。

## Background 本期播客由 Dwarkesh Patel 于 2026 年 5 月 8 日在 RSS/网络平台发布。节目简介指出，哈佛大学古遗传学家 David Reich 与合作者 Ali Akbari 发表了全新重磅论文，颠覆了人类在农业革命后自然选择趋于停滞的学界共识，揭示出自然选择在约 3000 年前的青铜时代剧烈加速，并在录制结束后于白板上提出了关于尼安德特人本质的全新假说。 ## TL;DR 通过将古 DNA 测序规模扩增至约 1.6 万个古代个体并开发全新的亲缘关系矩阵统计模型，Reich 团队成功剥离了占变异主导地位的 98% 遗传漂变与迁徙噪声，在人类基因组中锁定了数百个承受强烈定向选择的位点。数据显示，自然选择不仅没有休眠，反而在青铜时代向铁器时代过渡的窗口期（距今 5000 至 2000 年前）经历了戏剧性的全面加速，重塑了包括结核病防御（TYK2）、代谢调节（FADS1）、浅肤色以及认知表现预测因子在内的多项关键性状。此外，Reich 还提出了关于古人类演化的颠覆性模型，认为距今 30 万年前掌握中石器/勒瓦娄哇技术的先进人群在向欧洲和非洲双向扩散时，分别与两地古老原住民发生不同程度的混合，从而将尼安德特人与现代人重新定义为共享相同文化与单亲遗传谱系的“双生表亲”。 ## Atmosphere & style 对谈沉浸在浓厚的科学前沿探索与智识激荡氛围中，既有严密推演数量遗传学公式的极致理性，又不乏颠覆经典教条时的震撼与敬畏。Dwarkesh 的提问精准而具穿透力，不断从演化人类学和科学哲学层面发起诘难，而 Reich 则展现出顶尖学者的坦诚与严谨——毫不避讳自己过去先验假设被冷酷数据击碎的经历，甚至在录音结束后意犹未尽地在白板上手绘推演。整场交流充满了范式转移前夕的思想张力，让听众如同亲临科学史上“哥白尼革命”重构托勒密本轮的关键时刻。 ## Bulletin - `04:45` 戴维·赖克（David Reich）与合作者阿里·阿克巴里（Ali Akbari）基于1.6万例古人类基因组研发了全新统计模型，彻底推翻自然选择在农耕革命后陷入休眠的学术共识，证实近万年间自然选择反而显著加速。 - `15:45` 青铜时代（距今约5,000至2,000年前）是人类生物学演化的剧烈拐点：人口稠密与病原体频发引发进化错配，导致免疫、代谢以及认知表现相关基因位点承受了剧烈的定向选择。 - `30:00` 在过去10,000年间，与认知表现和受教育年限相关的多基因评分被自然选择系统性推升了约1个整标准差，而这一飞跃绝大部分发生在青铜时代的2,000年窗口期内。 - `01:30:00` 赖克提出了关于尼安德特人起源的颠覆性假说：现代人与尼安德特人并非平行孤立谱系，而是约30万年前发源于高加索或近东并掌握中石器时代石器技术的同源扩张人群，仅在欧亚和非洲遭遇了不同古人类的不对称基因淹没。 ## Key takeaways - `04:45` 长期以来人类演化学界认为近万年间定向选择停滞，但赖克团队发现这是因为全基因组变异频率变动的98%被遗传漂变与迁徙噪音所掩盖，实际定向选择在底层汹涌运作。 - `10:00` 研究团队通过建立时空群岛模型与亲缘关系矩阵，剥离了大规模人群替换（如颜那亚牧民西迁）引起的背景震荡，成功锁定数千个受自然选择推动的特定位点。 - `11:42` 强选择位点在免疫性状（如抗病基因）中呈现4至5倍的极度富集，代谢调节位点同样显著富集，颠覆了农业定居初期才是演化最大变局的传统观念。 - `20:00` 青铜时代的生态剧变导致演化错配：例如TYK2基因在结核病暴发后由正向选择骤变为负向淘汰，FADS1代谢基因与ABO血型系统也经历了剧烈的频率洗牌。 - `25:00` 欧洲人群皮肤浅色化的最密集选择期同样发生在距今4,000至2,000年前的青铜时代，而非最初步入农业阶段的8,500年前。 - `30:00` 与认知表现及受教育年限相关的多基因评分在青铜时代经历爆发式选择，在消除祖源变化影响后整体推移了1个标准差，反驳了分工细化削弱个体智商选择的“集体智慧假说”。 - `40:00` 中国人群GWAS效应值与欧洲过去万年古DNA演化轨迹呈现高达5至6个标准差的强正相关，强力证实认知性状的选择信号并非欧洲现代样本的统计伪影。 - `47:30` 所谓受教育年限的选择本质上可能筛选的是生活史策略（迟延满足、推迟生育与精细化育儿的K-策略），而现代充裕社会如冰岛在近百年中则呈现相反的选择倾向。 - `52:15` 早期农民虽然面临长周期周期性饥荒，但狩猎采集者高频的暴食/断粮周期更依赖短期脂肪堆积，这解释了为何农业定居后对体脂和糖尿病易感变异施加了持续的抑制选择。 - `01:10:00` 人类早在5万至10万年前就已齐备构建文明的全部认知遗传原料，但直到距今1.2万年前全新世极其罕见稳定的气候降临，长周期的农业定居才得以在全球多地相继引爆。 - `01:24:40` 尼安德特人与现代人共享先进的勒瓦娄哇石器技术与单亲遗传标记（线粒体DNA与Y染色体），而核基因组却偏向丹尼索瓦人，现有“小规模混血”的标准模型正陷入类似托勒密本轮的修补危机。 - `01:30:00` 赖克假定约30万年前发源于高加索或近东的先驱人群向西扩张时核基因被欧洲本土古人类稀释了95%，但其母系或父系文化特质将单亲标记完整保留，最终演化为尼安德特人。 - `01:40:00` 种群生殖不兼容性随隔离时间的平方递增：30万年前扩张人群与欧亚古人类分化较短因此易于渗透，但与分化约120万年的超古老非洲支系杂交受到严重生殖屏障限制。 - `02:04:12` 团队将古DNA选择统计量与英国生物样本库（UK Biobank）50万人现代GWAS数据对撞，在统计量大于5时表型关联富集度激增至60-70%并进入平台期，确凿排除了背景净化选择伪影。 - `02:10:00` 结合测序成本的百万倍下降与液相杂交靶向捕获技术（1240k试剂盒），科学界攻克了骨骼中99%以上土壤微生物污染的物理瓶颈，使古DNA研究迈入工业化大样本时代。 ## Detailed walkthrough ### `00:00` 古DNA研究的初心与样本规模的突破 哈佛大学古人类DNA领域的领军学者戴维·赖克（David Reich）再次做客访谈。赖克将自己定义为一名致力于通过基因组探究人类历史、揭示古代人群之间以及古代人与现代人之间关联的遗传学家。两年前的初次访谈引发了巨大反响，因为大众首次直观感受到现代生物技术正以颠覆性的速度重塑我们对人类历史的认知。此次重逢，则是基于赖克实验室刚出炉的一篇极具开创性的预印本研究。回顾古DNA领域16至17年前起步时的愿景，学界的初衷不仅是梳理人群迁徙，更渴望“通过从古人类遗骸中提取DNA并追踪其随时间的变化，深入了解人类生物学特性的演变”。 然而在过去十几年中，这一生物学层面的梦想几乎陷入停滞。古DNA固然在历史层面上取得了巨大成功——它揭示了大量颠覆传统考古认知的剧烈迁徙、人群替代、基因混合以及显著的性别偏向过程，证明如今生活在某一地区的人群往往并非几千或几万年前当地居民的后代。但当试图研究诸如肤色深浅、成年后消化牛奶的乳糖耐受能力或特定行为特征等生物学特性的基因频率变化时，学者们却屡屡受挫。其核心瓶颈在于样本量：单个个体的全基因组虽然能反映出其背后由2个父母、4个祖父母乃至成千上万祖先交织而成的庞大族谱，极其适合精确定位该个体在人类历史谱系中的位置，但对于某个具体突变位点而言，一个人至多只能提供来自父母的两个等位基因样本。如果缺乏极大规模的样本库，根本无法绘制出等位基因频率随时间演变的高分辨率图谱。 观察基因频率的变化之所以至关重要，是因为过去数万年的人类历史实际上构成了一场宏大的“自然实验”。当人群遭遇环境剧变——例如转向农业定居、近距离接触驯化动物、从寒温带迁往炎热地带，或是由平原登上高海拔环境时，环境压力会迫使群体产生适应性演化。能够帮助机体适应低氧高海拔、或促成有利行为模式的突变位点，会在正向选择下系统性地向某一方向抬升频率。唯有当古代样本量积累到足够庞大时，统计学才能将这些微小而决定性的几率波动（哪怕仅是几个百分点的偏移）与背景随机噪声剥离开来，捕捉到环境变迁对人类生物特性的真实推力。 ```mermaid flowchart TD accTitle: 单样本分析与群体等位基因分析的对比 accDescr: 对比单个古DNA样本在回溯人群历史与追踪单基因位点演变上的巨大差异。 subgraph SingleGenome["单个古DNA个体样本"] A["全基因组序列"] --> B["包含成千上万祖先信息"] B --> C["高精度定位人群历史与混合"] A --> D["单个功能突变位点"] D --> E["仅能提供1至2个等位基因样本"] E --> F["无法反映群体频率变化趋势"] end subgraph BigData["工业化大规模古代样本库"] G["成千上万古代个体序列"] --> H["高分辨率时间序列"] H --> I["捕捉基因频率细微偏移"] I --> J["识别自然选择与适应性突变"] end ``` ### `04:45` 破解自然选择“静止假说”：98%漂变与汹涌的定向选择 七年前，当时在赖克实验室担任博士后、如今已成为实验室常驻资深研究员的阿里·阿克巴里（Ali Akbari）启动了一项研究，试图利用实验室规模化产出的古代DNA来解析人类生物学的演变轨迹。赖克实验室的一个核心方向就是推动古DNA测序流程的“工业化”——在大幅压低成本的同时确保高通量和高质量，从而建立起前所未有的庞大古人类样本库。这使得重新检验长期被学界视为主流共识的演化假说成为可能。过去数十年间，人类演化领域盛行一种观点，即自然选择在过去数十万年间基本处于“休眠”或停滞状态。 这一主流观点此前有着看似坚实的实证支持。例如，欧洲人群与东亚人群大约在4万到5万年前源自共同走出非洲和中东的祖先群体，其间经历了约1,500至2,000个世代的分化。若在全基因组尺度上对比这两大人群，不同突变的频率虽有差异，但几乎找不到任何一个在两群体间呈现100%完全分化的基因变异。在漫长的几千代演化中，即便考虑到帮助消化酒精或牛奶等潜在适应性突变，人们也理应看到某些基因被迅速推升至极高频率，但实际数据却表明，其分化程度与纯粹随机的遗传漂变预期完全一致。这让很多学者误以为，早期智人早在数十万年前就已达到了某种适应峰值，此后的演化只剩下清除有害突变的净化选择，几乎不存在把有利突变推向新表型平衡点的“定向选择”（directional selection）。 赖克与阿克巴里的全新突破正在于利用庞大的古代DNA数据集，对全基因组约1000万个可变突变位点的频率变化成因进行了精确解构与分配。他们的分析揭示了一个极具挑战性的统计现实：在人类基因组变异频率的所有变动中，有高达98%完全是由人群迁徙、群体结构与遗传漂变等非选择性因素所主导，适应性的定向选择在总变异波动中仅占微不足道的极小比例。正因这种信号极其微弱且被掩盖在剧烈漂变的噪声之下，过去的检测方法几乎全军覆没。然而，一旦穿透这层由98%漂变构成的迷雾，数据显示定向选择实际上在整个人类基因组中“汹涌肆虐”（rampant in the genome）。 - **欧洲与东亚群体的分化时间**：约40,000至50,000年前脱离共同祖先，跨越1,500至2,000个世代。 - **全基因组分析规模**：覆盖了约10,000,000（1000万）个具有多态性的突变位点。 - **方差分解结果**：变异频率波动的98%归因于遗传漂变和人群迁徙结构，仅约2%属于适应性定向选择。 ### `08:15` 为何人群替换不等于自然选择：全基因组波动与位点特异性 面对“定向选择仅占微小比例”的界定，主持人德瓦凯什（Dwarkesh Patel）提出了一个尖锐的疑问：为什么不能把人群之间的混合（admixture）或替代（replacement）本身直接算作群体层面的自然选择？如果历史上一波携带特定基因的人群彻底替代了另一波人群，这种群际竞争与胜出在广义上难道不属于过去一万年里的自然选择吗？ 赖克坦承，人群替换在宏观历史意义上确实可能蕴含着竞争选择的意味，但他强调了遗传学在机制划分和统计识别上的本质差异。人群被替代的原因往往非常复杂，极可能是由于文化、制度或技术优势（例如拥有某种工具、武器或耕作模式），而不必然是由某个生物学上的基因突变赋予的直接适应优势。退一步讲，即便其中确实存在基因的贡献，人群替代在生物学上引发的也是整套基因组的整体漂移——所有数千万个位点的频率同时发生系统性跃变。 因此，从统计遗传学的严格定义出发，研究人员要寻找的是能够在全基因组背景之外独立驱动演化的“特定功能位点”。在群体发生大规模迁徙与替代的动荡时期，全基因组频率会出现剧烈波动，这在统计学上反而是噪音最大、最难剥离自然选择信号的时期。想要精确捕捉自然选择的足迹，最理想的窗口反而是那些迁徙暂歇、群体结构相对平稳的历史阶段，唯有在此时，个别突变位点逆风抬升的异常轨迹才能被清晰识破。 ### `10:00` 区分群体迁徙与自然选择：时空中的“群岛模型” 在研究过去人类基因频率的变化时，最核心的难题是如何将真正的自然选择与人口大迁徙造成的基因流动彻底区分开来。欧洲和近东的人口历史不能被视作一个铁板一块的整体，而应理解为一片分布在时间和空间当中的“群岛”（an archipelago of little populations in space and time）。在这个模型中，大迁徙与种群大融合之间往往夹杂着数百年相对平静的孤立期——例如不列颠岛上的一个小群体可能孤立繁衍了几百年，匈牙利平原上的另一个小群体也相对隔绝了几百年。在这些大自然提供的“天然微型实验场”里，没有外来基因的大规模冲刷，研究人员可以直接观测：某个特定突变的出现频率是否在这些不同的孤立群体中，都在缓慢而一致地朝着同一个方向漂移？如果所有群体中的突变频率箭头都指向同一方向，那么就可以确凿地判定自然选择正在发生（"if all the arrows point in the same direction, we win"）。 ```mermaid flowchart TD accTitle: 群岛模型区分自然选择与人口迁徙 accDescr: 展示如何在孤立种群微型实验中剥离颜那亚草原迁徙等群体流动背景，从而识别自然选择信号。 subgraph MigrationImpact["外来迁徙干扰（约 4500 年前）"] Yamnaya["颜那亚草原游牧人群大规模西迁"] -->|涌入 40% - 80% DNA| PopShift["基因突变频率剧烈突变（非选择主导）"] end subgraph Archipelago["时空群岛模型（分析孤立窗口）"] Britain["不列颠孤立种群<br/>（稳定繁衍数百年）"] --> CheckA["位点频率是否上升？"] Hungary["匈牙利孤立种群<br/>（稳定繁衍数百年）"] --> CheckB["同各位点频率是否上升？"] Others["其他区域孤立种群<br/>（稳定繁衍数百年）"] --> CheckC["同各位点频率是否上升？"] end CheckA & CheckB & CheckC -->|所有箭头指向同一方向| SelectionVerified["确认存在自然选择效应<br/>（解释超出随机漂移的变化）"] ``` 与之形成鲜明对比的是大规模人群迁移造成的剧烈变动。距今约 4,500 年前，来自黑海和里海北部草原的颜那亚（Yamnaya）游牧人群大规模涌入欧洲，当地人群中有 40%、50% 甚至多达 80% 的 DNA 在短时间内被草原牧民血统所取代。这一时期几乎所有基因突变的频率都发生了剧烈震荡，但那主要是由于草原人群与欧洲本地人群在数万年的隔绝中各自演化出了不同的基因频率，属于迁徙融合的副产物，而非自然选择的结果。因此，统计模型必须严格剥离这种中性漂移和人群混合的背景噪声，去检验频率偏离随机预期的净变化。 ### `11:42` 基因组全息震颤：免疫与代谢特征的富集 研究团队在该阶段筛选出了大量受选择影响的候选位点。在置信度达到 50% 的标准下，大约锁定了 7,200 个 DNA 变异位点，这意味着其中约有一半（约 3,600 个）是真实的自然选择位点。这绝不意味着 7,200 个位点之外的基因组区域就没有经历选择；如果将置信度阈值放宽到 25%，候选位点的数量会跃升至数万个，其中同样包含大量真实信号。事实上，多项深入分析表明，人类的整个基因组都在因自然选择而“震颤”（"the genome is vibrating with natural selection"）。虽然自然选择在所有频率变动中仅贡献了约 2% 的份额，但其力量无孔不入，几乎将 DNA 上的每一个位点或多或少地向某个方向拖拽。 为了探究自然选择究竟在筛选哪些生理功能，研究人员聚焦于置信度最高、最为明确的数百个核心位点，并将其与全基因组关联分析（GWAS）数据库中记录的约 100 种人类表型进行跨组对比，涵盖免疫系统、自身免疫、行为、新陈代谢等各个领域： - **免疫性状极度富集**：与免疫系统相关的基因变异呈现出惊人的聚集效应，其富集程度高达 4 到 5 倍（vast enrichment by about a 4 or 5 fold）。 - **代谢性状显著富集**：涉及肥胖症、体脂分布以及 2 型糖尿病等代谢调节的位点，也显示出了极其强烈的自然选择富集信号。 - **行为与精神表型信号微弱**：在这一轮强信号筛选中，与行为特征或精神疾病相关的位点几乎没有表现出任何可检测的富集现象。 ### `13:48` 行为性状的“隐形”之谜：多基因微效架构与统计功效 对于行为和认知表型缺乏富集信号的现象，绝不能草率地得出“过去一万年间人类行为与认知没有受到选择”的结论。研究团队拥有充分的证据表明，这种表象实际上是由不同性状背后的底层遗传架构（genetic architecture）差异以及现有统计功效（statistical power）的局限性所导致的。 医学遗传学早已证实，免疫性状通常由相对较少数量、但效应极强的主效基因（small numbers of genes of strong effect）所调控；一旦环境施加选择压力，这些个别位点就会产生巨大的频率跃升，从而轻松被最严格的筛选算法捕捉。相反，人类的行为、认知以及精神特质是由成千上万个效应极其微弱的微效基因（very large number of genes of weak effect）共同塑造的“多基因性状”（polygenic traits）。在全基因组水平上，这种微弱的单点拖拽力量很难在单个位点上跨过极高置信度的统计门槛。因此，依赖极端强选择信号构建的测试池能够极其敏锐地捕获免疫基因，却对弥漫在全基因组中的微弱行为选择信号视而不见；行为特征不仅真实存在选择，而且同样深深刻写在基因组的演化历程中。 ### `15:45` 青铜时代的剧变与进化错配：加速的选择 这项研究彻底颠覆了学术界此前长达数十年的共识——即过去数万年或数十万年间，人类的自然选择已经趋于沉寂。过去学者们普遍认为自然选择长期休眠，是因为在欧洲人和东亚人之间，极少看到频率差异达到 100% 的等位基因；若自然选择系数长期保持在 1% 以上，有利变异就会在短短几十代内迅速翻倍，经历上千代的积累，两大洲人群之间理应出现大量彻底固定的基因差异。 研究团队指出，这一经典悖论可以通过至少两个核心因素来解答，其中最重要的便是：欧洲和近东的人群在过去几千年里正处于一个“自然选择剧烈加速”的特殊历史窗口。 | 历史时期 | 距今时间 | 生存模式与环境特征 | 自然选择强度与特征 | |:--- |:--- |:--- |:--- | | **新石器早期及以前** | 距今 10,000–5,000 年前 | 农业在近东发明（约 1.1–1.2 万年前）并传入欧洲；早期定居与食物生产展开 | 自然选择处于相对基础的平稳速率 | | **青铜时代及之后** | 距今 5,000–2,000 年前 | 人口密度急剧攀升，城市化起步；与家畜极其紧密地杂居，人畜共患病频繁暴发 | 免疫与代谢性状的选择强度**剧烈加速**；部分位点选择方向出现剧烈逆转 | 这种演化加速的根本推手在于剧烈的“进化错配”（evolutionary mismatch）。几乎所有受检的古 DNA 样本都属于农业或畜牧业人群，但他们的基因蓝图在不久前（仅一万年前）还完全适应于旧石器时代的狩猎采集生活。当他们被迅速推入青铜时代——高密度定居、恶劣的城市卫生条件、病原体在人类与驯化动物之间无休止地相互传播——他们古老的生物学系统遭遇了前所未有的环境冲击。 为了直观展示这些剧变，Ali Akbari 与合作者专门搭建了一款名为 AGES 的在线基因组浏览器工具，供学者查询超过 1,000 万个位点的演化轨迹与选择证据。数据显示，虽然多数位点在较长时间尺度上保持着相对恒定的选择速率，但在距今 5,000 至 2,000 年前（即青铜时代向铁器时代迈进的核心期），有相当一部分关键位点经历了选择强度的几何级放大，甚至是演化方向的彻底急转弯。 ### `20:00` 免疫基因的剧烈翻转与青铜时代之谜 青铜时代与铁器时代伴随着爆炸性的人口增长以及新技术前所未有的密集应用，而这种剧烈的生存环境改变在人类基因组中刻下了清晰的印记。大卫·赖希（David Reich）以 *TYK2* 基因变异为例说明了这种冲击：该变异是当今全球致死人数最多的传染病——重症结核病（Tuberculosis）的重要风险因素。在距今 8,000 至 6,000 年前，该变异在欧洲局部地区的等位基因频率一度飙升至 9% 到 10%，显现出极强的正向自然选择；然而在距今 3,000 年以内的窗口期，它的频率却断崖式下跌，受到同样强烈的负向淘汰。赖希推测，病原体测序和相关证据表明结核病可能在大约两三千年前才演变成广泛的地方性流行病，在此之前该变异或许能抵抗某种其他严重疾病，但一旦结核病大肆肆虐，致死代价便彻底逆转了自然选择的方向。 德瓦尔凯什（Dwarkesh Patel）在插入赞助内容后探讨了青铜时代的奇特之处：如果说成体乳糖耐受性（lactase persistence）在青铜时代大幅增强是由于人类不再只吃牛肉，而是开始全面利用牛奶、羊毛等次级动物产品，那么某些理论上早在农业发端时就应发挥作用的基因，为何也集中在青铜时代经历爆发式演化？例如负责将植物脂肪酸合成为人体必需长链脂肪酸的 *FADS1* 基因，在人类从狩猎采集肉食转向谷物淀粉饮食时本就至关重要，但数据却显示它在 5,000 至 3,000 年前才遭遇了异乎寻常的超强选择压力。 赖希解释说，*FADS1* 变异相当于一种“素食与肉食平衡”的代谢适应机制，他的前同事伊恩·马西森（Iain Mathieson）早在 2015 年便证实该位点经历了极强选择，且其古老版本甚至可追溯到古老人群。类似古老变异经历潮汐波动的现象也出现在 ABO 血型系统中：尽管 A 与 B 型等位基因在人类与长臂猿分化前就已共存，但在青铜时代，B 型血的频率却显著提升了近 10%，不断挤占 A 型血的份额。 - *TYK2* 基因的轨迹展示了选择压力的骤变：先因防御古代未知病原体而快速上升，后因结核病全面爆发而急剧下降。 - *FADS1* 基因适应植物性饮食，虽然存在时间极其久远，却同样在 5,000 到 3,000 年前迎来了最剧烈的选择窗口。 - ABO 血型系统中 B 型等位基因的扩张，进一步印证了古代传染病谱系在青铜时代经历的重塑。 ```mermaid flowchart TD accTitle: TYK2 基因在青铜时代前后的选择反转机制 accDescr: 展示环境与病原体演变如何导致 TYK2 基因频率在青铜时代经历急剧的先增后降 subgraph EarlyStage["新石器时代早期（约 8,000-6,000 年前）"] A["古代未知病原体威胁"] --> B["TYK2 变异提供生存优势"] B --> C["等位基因频率快速飙升至 9%-10%"] end subgraph BronzeAge["青铜时代至铁器时代（约 3,000 年前至今）"] D["结核病（TB）演变为地方性流行病"] --> E["TYK2 变异成为重症结核高危因子"] E --> F["强烈负向自然选择导致频率急剧暴跌"] end C -.-> D ``` ### `25:00` 被严重低估的青铜时代：身体与认知的大撕裂 青铜时代的演化冲击不仅限于代谢和血液系统。多发性硬化症（Multiple Sclerosis）的风险变异以及导致病理性铁过载的血色沉着病（Hemochromatosis）相关变异，都在青铜时代前后遭遇了剧烈的选择拐点，并在欧洲南北呈现出显著分异——北欧表现为极强的正向与反向选择，南欧则相对温和。对于复杂的表型性状，欧洲人在过去一万年间经历了皮肤浅色化（depigmentation）的过程，而赖希团队的最新数据证明，浅肤色选择强度最密集的时期同样锁定在距今 4,000 到 2,000 年前，此后逐渐放缓。这颠覆了学术界长久以来的共识：人们原以为距今约 11,000 至 8,500 年前的新石器时代农业革命是人类演化的最大转折点，但基因组的真实读数却证明，约 5,000 年前开启的青铜时代才是真正的剧烈撕裂期（wrenching process），其对人类生物学特性的改造幅度远大于定居农业的最初阶段。 德瓦尔凯什提出了一项对照：赖希与巴蒂亚（Bhatia）等人在 2014 年分析了近三万名现代非裔美国人的基因组（约 80% 西非血统与 20% 欧洲血统），结果发现在经历两三百年极其严酷的蓄奴制与异地生存环境剧变后，基因组各区域的混合比例并未出现偏离中性预期的选择偏差。赖希指出，这恰恰突显了“复利效应”在时间尺度上的决定性作用：现代非裔族群仅经历了大约五代人，即便每代存在高达 2% 的强选择，累积效应也只有约 10%，很难从统计噪声中被检出；然而青铜时代的剧变持续了整整 3,000 年，上百代人的自然选择通过复利叠加，最终释放出巨大的生物学效应。 这种强烈的选择模式不仅主导了肤色等外貌特征，也深入到了更为复杂的认知性状。赖希的研究将现代白人测试中影响认知表现（Cognitive Performance，如智力测验分数与受教育程度相关等位基因）的成百上千个变异位点汇总分析，发现这些位点在过去一万年中受选择最强烈的爆发期，同样整整齐齐地落在了距今 4,000 到 2,000 年前的青铜与铁器时代。这一发现揭示出：伴随密集型社会分工与文明构建的推进，人类的认知预测基因在大约几千年前经历了一场深刻而迅猛的基因组重塑。 - 欧洲人皮肤浅色化的最强选择窗口期发生在距今 4,000 至 2,000 年前，而非农业初期的 8,500 年前。 - 现代非裔美国人研究未能检测到明显选择信号，证明两三百年（约 5 代人）的时间过短，而青铜时代跨越了数千年，使得代际复利效应得以全面显现。 - 包括智力测试预测因子在内的复杂认知性状变异，其受自然选择推动的最剧烈阶段同样集中在青铜时代的 4,000 到 2,000 年前。 ### `30:00` 青铜时代认知表型自然选择的爆发与统计校正 现代遗传学通过全基因组关联分析（GWAS）构建了预测智商测试表现的多基因评分（polygenic score）。令人惊讶的是，这个评分与预测受教育年限、家庭财富等表型的遗传预测因子高度相关。在远古时代，既不存在学校，也不存在现代意义上的财富积累，但如果追踪这些现代遗传预测因子在过去谱系中的演化轨迹，会发现它们呈现出系统性的定向变动：在过去 10,000 年间，以现代人类的变异尺度来衡量，该性状在消除祖源变化影响后的群体内发生了大约整整一个标准差（1 standard deviation）的遗传漂移。 为了厘清这一演化历程中选择压力的起伏，研究团队利用滑动窗口法，在 18,000 年的数据集上拖拽一个跨度为 2,000 年的时间窗口，分段测量选择强度。数据揭示了一个违背直觉的模式：对认知表现的自然选择并没有在工业化之后或现代文明中加速，而是在青铜时代达到顶峰（距今 5,000 至 2,000 年前，尤其是距今 4,000 到 2,000 年之间）。在此期间，平均选择强度达到了惊人的两个标准差；相反，在过去 2,000 年中，选择效应几乎彻底停滞，完全没有显著的自然选择信号。 ```mermaid flowchart TD accTitle: 剔除迁徙干扰的局部群体选择分析 accDescr: 展示研究如何将不同时空的局域人群划分为孤岛群，并在控制祖源恒定的前提下测定自然选择的共同演化方向。 subgraph Archipelago["时空孤岛采样 (空间与时间切片)"] A["英国局域人群<br/>(距今4000-3000年前)"] B["匈牙利局域人群<br/>(青铜时代中晚期)"] C["意大利局域人群<br/>(距今2000-1500年前)"] end subgraph Method["Ali Akbari 开发的方法论"] D["控制短期内未受大规模迁徙颠覆的局域祖源"] E["检测多基因等位基因频率是否在各群岛中朝同一方向变动"] end Archipelago --> Method Method --> F["剥离98%的迁徙置换效应<br/>精确捕获2%由自然选择驱动的变动"] ``` 这项由 Ali Akbari 主导的方法论的核心难点在于剥离大规模人群迁徙造成的假象。欧洲古代样本构成异质且剧烈变动，狩猎采集者、早期农夫与草原牧民在不同时期的比例大相径庭。Akbari 的模型将整个样本库细分为时空上的“微型群岛”（例如距今 4,000 至 3,000 年前的英国定居点、匈牙利或意大利的特定时间切片）。在每个祖源相对连续、未发生毁灭性迁徙替换的微环境中，观察基因突变是否统一朝特定方向演进。 - **效应尺度的量级**：从整个人类群体分布看，中位数跃升一个标准差意味着原本处于中位数（第 50 百分位）的特征值，被推升到了第 85 百分位的高度。 - **迁徙与选择的比例解耦**：在等位基因频率变动中，迁徙解释了约 98% 的变异，而真正的定向自然选择仅占约 2%；然而这微小的 2% 纯选择力量，足以在万年尺度上推移整个标准差。 - **古代基线的基准差异**：若单纯用现代英国白人的认知表型多基因评分去测算远古人群，欧洲狩猎采集者的预测值比现代均值低约 3 个标准差；早期农夫迁入后直接跳升至现代均值水平（0 点），而随后的草原牧民基准值又相对偏低。这种大幅震荡反映的是迁徙带来的族群“基准点”（set points）更替，唯有 Akbari 的新模型才能从这些震荡中证明自然选择在所有地区均朝着同一方向持续推动。 ### `35:00` “集体智慧假说”的破灭与受教育年限的表型迷思 该研究结果直接冲击了人类学中广为流传的“集体智慧假说”（collective intelligence hypothesis）。该假说认为，随着社会分工细化与复杂化，个体只需要掌握极小范围的技能即可生存（正如现代人只需学会调试麦克风与提问），而远古狩猎采集者必须独立掌握从觅食、解毒、搭建庇护所到生火的庞大生存知识库；因此，文明的演进理论上应导致对个体智力的选择压力放松、甚至因“外包给社会集体大脑”而发生反向选择。Dwarkesh 提到 Joseph Henrich 的著作中描绘的古人巨大认知负担，指出传统常识会预期狩猎采集时期的智力选择要求最高。 然而冷酷的基因数据彻底反驳了这种直觉：文明破晓与定居社会的建立不仅没有削弱对认知表型的筛选，反而伴随着人类历史上对这类遗传变异最强烈的选择压力。针对该现象，Reich 指出了多基因评分本身的复杂性与多效性（pleiotropy），警告不应将现代标签简单等同于演化原力： - **冰岛百年的反向演化案例**：2017 年一项针对冰岛人群跨越一个世纪的著名研究显示，预测受教育年限（years of schooling）的遗传得分在仅仅 100 年内就下降了约 0.1 个标准差，呈现出极快的逆向选择。 - **混杂表型与延迟满足**：预测受教育年限的遗传变异与女性初育年龄（age at first birth）呈现强烈的正相关。如果统计模型控制了初育年龄，受教育年限的选择信号就会彻底消失。这暗示遗传层面真正被选择的或许并不是“受教育能力”或抽象的“智力”，而是延迟满足、长远规划能力，或是生育决策倾向。 - **多效性关联网络**：受教育年限的多基因评分目前已经在数百万人中完成测绘，它同时与身体质量指数（BMI）、肥胖率、步行速度（walking pace）以及家庭财富呈现显著关联。青铜时代剧烈变迁的生态与社会阶层化，究竟是在筛选认知水平本身，还是在筛选一系列伴随而来的代谢、行为或时间偏好特征，仍需要更审慎的双重审视。 ### `40:00` 跨人群验证：为何受教育年限的强选择信号并非统计假象 研究团队最初检测到“受教育年限”（years of schooling）相关的基因预测值在过去一万年中显著上升时，第一反应并非兴奋，而是深度的自我怀疑与难以置信。这类全基因组关联分析（GWAS）通常基于当代英国白人（如英国生物样本库 UK Biobank）的数据，研究者很容易怀疑这一结果是否只是某种未被校正的人群分层偏误或特定文化背景下的统计假象。毕竟，决定现代人受教育年限的底层特质非常复杂，可能涵盖执行功能、延迟满足倾向，乃至整体的行为节律。 为了验证这是否为真正的生物学演化信号，团队设计了一项严格的交叉人群检验：他们提取了完全在中国人群中测定的受教育年限 GWAS 位点及效应值，并将这些效应值与过去一万年欧洲古代 DNA 中相应等位基因的频率演化轨迹进行对比。欧洲与东亚人群在数万年间基本相互独立，如果这些信号只是现代欧洲样本中的方法学噪音，两者之间绝不可能存在统计相关性。然而结果极其惊人：中国人群受教育年限的效应大小，与欧洲过去一万年古 DNA 的基因频率变化轨迹之间，呈现出高达 5 到 6 个标准差的强正相关，其吻合程度甚至不亚于直接使用欧洲现代数据进行预测。这一跨越欧亚大陆的铁证让研究人员彻底确信，古人类基因组中所经历的自然选择是真实存在的，它所推动的底层遗传变异，在当今世界不同的人群中都一致地表现为更长受教育年限的倾向。 ```mermaid flowchart LR accTitle: 跨人群GWAS与古DNA演化轨迹验证 accDescr: 展示如何通过中国人群GWAS与欧洲古DNA轨迹交叉验证自然选择信号的真实性 subgraph Europe["欧洲演化数据"] A["欧洲古DNA样本<br/>（覆盖过去10,000年）"] --> B["等位基因频率<br/>演化轨迹"] end subgraph China["独立现代对照"] C["现代中国人群GWAS<br/>（受教育年限表型）"] --> D["测定各遗传变异的<br/>效应量 (Effect Size)"] end B --- E{"跨大陆统计对比"} D --- E E --> F["呈现5-6个标准差的强关联<br/>排除欧洲特有统计假象"] ``` - **信号跨大陆一致性**：在中国人群中测得的受教育年限遗传效应，能以极高精度预测欧洲过去万年间的基因频率上升趋势。 - **关联特质的复合性**：受教育年限不仅关联智力，还与肥胖率、步速、家庭财富等多项生理与行为特征高度遗传相关，暗示其背后存在更通用的神经认知或行为调控机制。 --- ### `44:00` 远古猎人的智力悖论与古代社会的价值坐标 这一发现随即引出了一个尖锐的演化谜题：如果智力或认知功能具有普遍的适应优势，为何在农业文明和青铜时代之前，智力相关的基因评分没有在人类演化中被迅速“拉满”？数据显示，中石器时代的欧洲狩猎采集者在智力测试预测值上，比现代人低了整整三个标准差。Dwarkesh 提出质疑：面对严苛莫测的大自然，狩猎采集者需要辨识植物、追踪猎物、制造复杂工具并搭建庇护所，正如约瑟夫·亨里奇（Joseph Henrich）等学者所言，现代人往往严重低估了原始生存任务的认知复杂度。既然演化能在短短几百上千年内大幅改变身体结构，为何智力这一生存利器在旧石器时代却没有经历极端的定向选择？ Reich 指出，现代人审视演化时极易陷入当代社会的认知偏见——我们生活在一个空前崇拜智商测试、逻辑分析和学历教育的时代，并想当然地认为高智力在一切时空下都是绝对优势。但回溯古代文献便能发现截然不同的价值尺度：在《希伯来圣经》、基督教《旧约》乃至荷马史诗等古代经典中，受到最高赞誉的美德是力量、勇气、虔诚或美貌，而非抽象智力。具有讽刺意味的是，《旧约》故事成形与写就的青铜时代，恰恰是人类基因组中智力选择压力达到顶峰的时期，然而当时人们的显性价值观却依然偏向武力和神圣感。生存与繁衍的法则极为残酷，在漫长的旧石器时代，维系小群落生存的核心可能更多依赖于文化代际传递的积累（即集体智力），而非单个个体在抽象认知或延迟满足上的突飞猛进。 --- ### `47:30` 迟延满足与多子多福：生命史策略的演化摆动 为了从生物学机制上解释这种选择压力的变迁，Reich 提出了基于生活史理论（Life History Theory）与资源分配策略的假说。那些与更长受教育年限、更高智商、更慢步速、更低肥胖率以及更多家庭财富共同变异的基因组合，其生理核心可能是一种特定的人生策略：倾向于推迟生育、积累社会资源，并对极少数后代进行精细化的高强度抚育（类似于生态学中的 K-策略）。相反，另一组遗传倾向则促使个体尽早生育、尽可能多地生育后代，而降低在每个子女身上的单一投入（类似于 r-策略）。 这种策略在演化上的优劣从来不是绝对的，而是随着生态与社会环境的改变而剧烈摇摆。例如冰岛过去一百年的遗传数据就显示，自然选择正在系统性地“淘汰”那些预测长教育年限的高认知基因簇。在现代福利社会的充裕环境下，抚养更多孩子并不需要父母积累极高的个体资本即可存活，采取“广种薄收”生育策略的个体在基因传递上反而占据了绝对优势。哺乳动物在漫长孕期中对少数幼崽倾注心血，而鱼类则将成千上万枚卵排入河流任凭大部分被捕食，两种模式都能在特定生态位中胜出。青铜时代的到来或许建立了一种前所未有的制度与财富网络，使得推迟满足、倾注资本深耕后代的人类策略获得了前所未有的繁殖优势，从而在基因库中掀起了一场剧烈的选择风暴。 - **冰岛近百年的反向演化**：20 世纪以来的冰岛社会对受教育年限基因呈现明显的负选择，低抚育投入、高生育率的生活史策略在物质充裕的环境中更具生殖优势。 - **性状协同网络**：肥胖率、智商、财富积累和受教育年限在基因组层面上通过共享的变异节点紧密咬合，反映了深层的生理折衷（trade-off）。 ### `50:00` 复杂特质的谱系平衡与精神分裂症的演化之谜 在讨论自然选择对人类特质的塑造时，David Reich 指出，许多复杂表型并非被单向推向极端，而是在多维度谱系中来回摆动。以精神分裂症（schizophrenia）和双相情感障碍（bipolar disease）为例，常理上很难理解这类严重精神疾病为何能在基因池中存留而不被淘汰。Reich 推测，我们今天所定义的疾病，实质上可能是某种心理谱系的一端；在特定语境下，谱系上的某些特征甚至具有适应优势。例如在萨满传统或重视通灵、异象的宗教环境中，具备高度想象力、神经质甚至轻度焦虑的个体反而可能受到高度尊崇。这些或许是亚临床状态的精神分裂或双相倾向，在某些时代提供了演化价值，在另一些时代则变成劣势。 Dwarkesh Patel 顺势提到了 Julian Jaynes 在《二分心智的崩溃与意识的起源》（*The Origins of Consciousness in the Bicameral Mind*）中提出的假说：在荷马时代之前，古人可能普遍处于一种类似精神分裂的状态，将脑海中涌现的心声或幻觉直接视为神明的训诫与真实对话，古代文献中也大量记载了这种对异象深信不疑的行为模式。Reich 对此表示赞同，指出即使在现代宗教社区中，体验超自然感应或与神明对话依然受到高度赞扬。 不过从现代群体遗传学的数据来看，精神分裂症和双相情感障碍的大多数风险突变在整体上依然属于负向选择： - 精神疾病相关的风险变异通常呈现出突变频率极低（low frequency）且单个效应极微弱（small effects）的特征。 - 复杂特质的演化权衡极其微妙，既存在不同环境下的利弊切换，也存在广泛的背景多效性选择。 --- ### `52:15` 农业革命与对抗脂肪的自然选择：“节约基因假说”的反思 过去一万年里另一个引人注目的选择信号，是基因层面上对体脂率的持续抑制。全基因组关联分析显示，与肥胖、高体重指数（BMI）、高腰臀比以及 2 型糖尿病风险相关的多基因变异组合，在欧洲和近东地区的人群中经历了约一个标准差的显著下降。 对于这一现象，Reich 提到了学界经典的“节约基因假说”（thrifty genes hypothesis）。传统理论认为，狩猎采集者需要高效将热量转化为体脂以度过饥荒；而步入农业社会后，较稳定的食物产出削弱了对储存大量体脂的依赖，过高体脂反而带来代谢负担，从而诱发了针对体脂与糖尿病倾向的负向选择。这与现代人群的流行病学分布相呼应：相比经历较少农耕世代的美洲原住民或非裔美国人群体，欧洲人群由于拥有更长久的农业适应史，在遗传上对 2 型糖尿病具备相对更强的保护机制。 Dwarkesh 提出了一个尖锐的反思：现代人类学常有一种流行叙事，认为狩猎采集者的饮食其实比单一依靠农作物的早期农民更加稳定多样，既然他们不依赖单一作物且能随时迁徙，为何基因演化反而揭示出农民的生活比采集者拥有“更稳定的食物供给”？ Reich 解释称，这本质上是一个**时间尺度（timescale）**的错位： - **狩猎采集的暴食/挨饿周期（Boom-Bust Cycle）**：在狩猎采集社会中，一次成功的捕猎会带来暴饮暴食与极短时间内的体脂堆积，随后可能面临连续数天甚至数周完全没有肉食的断粮期。体内快速合成脂肪的能力直接决定了两周后能否存活。 - **农业社会的饥荒周期**：农业社会固然会遭遇农作物绝收，古人骨骼上的骨痕也反映出营养压力，但这类大饥荒的发生频率通常是每隔 3 到 5 年一次。人体通过一次进食储存的脂肪不可能支撑三年之久。因此，应对周期性大饥荒的生存策略与日常应对两周断粮的体脂堆积完全属于两种不同的选择机制。 ```mermaid flowchart TD accTitle: 两种食物获取模式的时间尺度与演化选择对比 accDescr: 对比狩猎采集社会的短周期暴食挨饿与农业社会的长周期饥荒对脂肪存储基因的不同选择压力 subgraph Forager ["狩猎采集模式：短期高频波动（数天至数周）"] F1["捕获大型猎物<br/>暴饮暴食"] --> F2["短期脂肪快速堆积"] F2 --> F3["持续数天至数周无肉食"] F3 --> F4["选择压力：强力支持高体脂与节约基因"] end subgraph Farming ["早期农业模式：长期低频波动（数年）"] A1["常态化粮食存储<br/>日均碳水供应相对平稳"] --> A2["体脂过度堆积成为代谢负担"] A2 --> A3["每隔3-5年发生极端旱涝饥荒"] A3 --> A4["短期脂肪无法抵抗三年大饥<br/>选择转向清除高BMI/糖尿病风险变异"] end ``` --- ### `55:50` 智力与复杂特质的“天花板”有多高？ Dwarkesh 进而转向一个关于演化潜力的深刻思考：在青铜时代及人类祖先环境中，免疫系统和抗病能力对生存繁衍的贡献显然压倒了极端智力，因此承受的自然选择压力也远大于智力。如果过去几十万年里人类并未将智力推向极限，那是否意味着智力这一特质在“顶部”还存在巨大的未开发空间？特别是当今人类训练 AI 模型时，全部能量与算力仅服务于智力最大化，无需耗费资源在维持免疫防线或防范精神分裂上，这是否意味着人类本身的智力潜能远远没有见顶？ Reich 肯定了这一判断。他指出，包括身高在内的绝大多数复杂特质，如果只沿单一方向进行极限选择，其均值完全可以移动到极其极端的水平——理论上若将所有正向身高突变叠加在一人身上，其高度甚至能“媲美摩天大楼”。然而自然选择从未这样做，是因为在真实生物环境中，任何单项特质推向极端都会带来灾难性的权衡代价（trade-offs）。但从遗传架构来看，只要选择压力持续单向施加，群体的均值就必然会迅速发生显著移动。 --- ### `57:40` 瓶颈期后的庞大潜能：复杂特质与突变极限的终结 Dwarkesh 进一步追问：走出非洲时的人类有效种群规模极度压缩（当时可能仅有上万人甚至数千人），为何如此微小的一批奠基者人群，其基因库内部居然蕴藏着如此惊人的潜在变异（latent variation），足以在数万年间支撑起认知能力、疾病抗性、身高等如此繁复多样的演化弹性？ Reich 从多基因遗传架构（polygenic architecture）和种群规模两个维度拆解了这一机制： 1. **复杂特质无需等待新突变**： 与受单一基因控制的孟德尔性状不同，诸如身高、BMI 或精神分裂症易感性这类复杂特质是由成千上万个微效等位基因共同决定的。当环境剧变发生时，种群根本不需要等待新突变诞生；原始基因池中原本就存在的等位基因频率只需发生微小协同滑动，便能在数百到数千年内迅速将整个群体的适应稳态（adaptive set point）重置到全新水平。 2. **罕见单基因特质的时间瓶颈**： 相反，某些高度特殊的适应性状——如成年后消化牛奶的乳糖耐受基因，或是抵抗疟疾的镰刀型细胞贫血症等位基因——往往依赖单一或极少数关键点突变。如果这类突变在原始种群中不存在，小群体（如仅有一万人时）就必须耗费数十乃至上百个世代去被动等待突变偶然产生。 3. **现代超大种群下的“突变非限制状态”**： 当今人类总人口已达到 80 亿，人类演化的数学逻辑彻底改写： - 每人每代平均产生约 30 个新的单核苷酸点突变（point mutations）。 - 全球每一代产生的新突变总数约为 $80\text{ 亿} \times 30 = 2400\text{ 亿}$ 个。 - 而人类单倍体基因组仅包含约 30 亿个碱基对。 这意味着：**在当今世界上，人类基因组中每一个可能发生的碱基突变，在每一代人中都会独立且重复出现大约 100 次**。人类在遗传演化层面上已经彻底摆脱了“突变匮乏”（mutation-limited）的束缚。 ### `01:00:00` 青铜时代的选择加速源于人口规模扩大吗？ Dwarkesh Patel 提出了一个直觉假设：青铜时代自然选择剧烈加速，是否仅仅是因为人口规模终于突破了临界值？在公元前 3000 年左右，全球人口达到了约 5000 万人，不同地区间的基因流动也大幅增加；如果早期种群规模仅有数万人，人们往往需要等待数十乃至数百代才能迎来一次新的突变。那么，是否是更庞大的人口和更高的基因流动，才让那些选择系数较弱、原先被遗传漂变所掩盖的基因变异终于能够被自然选择“看清”并发挥作用？ David Reich 认为这个设想非常引人深思，但在数量遗传学上并不成立。事实上，当种群规模达到 100 万人左右时，几乎所有可能发生的突变在短短数代之内就会全部出现，而这一人口规模在青铜时代之前、甚至在欧洲及其他地区的农业起源阶段就已经达到了。虽然在极小种群中自然选择确实效率低下——比如规模为 1,000 的群体，等位基因频率每代随机漂变的幅度大约是千分之一（0.1%），弱于该漂变尺度的选择会被噪音淹没；但当前研究所检测到的选择系数非常强，通常在 0.5% 到 1% 或更高。这种量级的强烈选择，即使在 1,000 到 10,000 人的较小种群中也能高效运作。那些唯有在大规模群体中才能被选择识别、而在小群体中无法生效的微弱选择，其选择系数通常低至万分之一乃至十万分之一，需要数万甚至十万代（即数十万到数百万年）才能显著提升频率。因此，青铜时代演化加速的核心瓶颈是时间跨度（时代的选择压力变化），而非人口阈值。 - **突变出现频率与群体规模**：种群有效规模达到 100 万左右时，所有可能的点突变在几代之内就会反复出现。 - **选择系数与群体规模的匹配**： - 极弱选择（$s \approx 10^{-4} \sim 10^{-5}$）：依赖超大群体才能摆脱漂变，但需要几十万年甚至几百万年才能扩散，与青铜时代数千年的时间尺度不符。 - 研究中观察到的选择（$s \ge 0.5\% \sim 1\%$）：属于强选择，在规模仅为数千人的群体中即可高效起效。 - **核心结论**：一旦跨过基本的人口门槛，决定演化速度的主导因素是演化时间跨度和环境压力强度，而非绝对人口数量。 --- ### `01:03:00` 5万年前的人类认知革命：为何找不到“关键基因突变”？ Dwarkesh 转向了另一个看似矛盾的重大谜团：根据 David Reich 团队 2016 年发表的 Mallick 等人的论文，现代人类与约 5 万年前的古人类之间，竟然找不到任何固定的基因差异（fixed differences）。大约在 5 万到 10 万年前，人类考古记录中迎来了所谓的“文化加速期”或“认知革命”，壁画、具象艺术、穿孔念珠项链以及工具形态的突飞猛进集中爆发。许多人长期推测，背后必定存在某种生物学上的“基因开关”突变——比如某种突然席卷整个人类的关键基因变异，赋予定型复杂的具象语言或高级认知能力。 为了检验这一假说，Swapan Mallick 与 Reich 等人在全基因组范围内进行了穷尽式搜索，试图寻找所有现存人类最近共同祖先可追溯到 10 万至 20 万年前的标志性选择性清除（selective sweeps）。然而令人震惊的是，全基因组范围内根本找不到任何早于 40 万到 50 万年前的选择性清除信号。这意味着，在现代人类具象文化全面爆发的关键阶段，并没有出现过任何席卷全人类的单点关键基因突变。 ```mermaid flowchart TD accTitle: 人类认知革命假说对比 accDescr: 对比单一基因突变假说与多基因微调模型在考古证据与全基因组数据下的表现。 subgraph HypoA["传统突变假说（被证伪）"] A1["约 5-10 万年前出现单点突变"] --> A2["关键基因发生选择性清除"] A2 --> A3["全人类迅速普及具象语言与复杂艺术"] end subgraph HypoB["多基因多位点模型（支持证据）"] B1["现存全人类未见 40-50 万年以内的选择性清除"] --> B2["微效多基因变异（Polygenic）协同位移"] B2 --> B3["基因库如黏土般蕴藏充足可塑性"] end ``` 如果在这段时期确实发生过生物学适应，它绝不是单基因驱动的，而是“多基因协同”（polygenic adaptation）——成百上千个微效突变朝着同一方向微调，使群体认知与行为走向新的设定点。 - **Mallick et al. 2016 关键发现**：在现代人类基因组中，无法找到距今 40 万至 50 万年以内的全球性选择性清除事件。 - **谱系深层分化**：约 5 万到 10 万年前的人类群体是西非人、绝大多数东非人以及所有走出非洲人群的共同祖先；而南部非洲的科伊桑人（Khoisan）和中非雨林狩猎采集者的分化甚至可追溯至 20 万年前。 - **智力与潜能一致性**：无论谱系分化多么古老，如今所有人群的后代在现代教育与大学中都能表现出同等能力，没有任何关键认知突变为某一部分人群独有而另一部分缺失。 --- ### `01:05:30` 人类基因库即黏土：500多项复杂性状的快速位移 从距今 5 万到 10 万年前仅约 1 万人的瓶颈群体，演化出当今地球上形态各异、生息于各个角落的数十亿人类，所有多样性在当时实际上已经以隐性或潜在的形式储存在那个小种群中。人类遗传学界的一种共识是：人类基因库就像一团“黏土”，其内部蕴含的变异已经足够丰富，几乎可以塑造成任何性状；在不同的环境与选择压力下，这些复杂性状的群体均值会发生显著漂移与重塑。 Reich 指出，他们最新的研究系统扫描了超过 500 个复杂性状，发现其中约 100 个性状在欧亚大陆过去 18,000 年、10,000 年直至 5,000 年（跨越末次盛冰期、新石器时代及青铜时代）的演化中，发生了朝系统性方向演进的显著遗传改变。这确凿地证明，人类基因组一直在对所处生活环境做出强烈且迅速的多基因响应，且近万年来的选择强度甚至超越了早期狩猎采集时期。 - **多性状普查**：在评估的 500 多个复杂性状中，大约 100 个在过去 1.8 万年间显示出具有统计显著性的定向位移。 - **基因潜能预存**：早在 5 万年前人类走出非洲之前，产生现代人类绝大多数性状多样性的基因原料就已经齐备。 --- ### `01:07:00` 既然基因早就齐备，为什么农业在冰期结束前从未出现？ 如果人类早在 5 万年前在生物学和遗传层面上就已经“完全就位”，拥有了从事象征艺术、复杂规划与农业技术所需的所有遗传成分，且人类早在 4 万年前就已到达新几内亚、1.5 万年前到达美洲，欧亚与非洲各地也均有人群分布，为什么没有任何地区在 1.1 万至 1.2 万年前之前独立发明农业？为什么农业只在末次盛冰期结束、进入被称为全新世（Holocene）的暖期之后，才在全球多个中心爆发式地涌现？ 这个极具冲击力的问题突显出文化演化、环境变迁与生物演化之间的脱节：遗传潜能的具备是必要条件，但决非充分条件。即便硬件系统早数十万年前就已经具备，全球气候剧烈波动的冰河时期依然死死压制了农业定居生活方式的萌芽，直到全新世稳定温暖的气候窗口降临，沉睡于“基因黏土”中的潜能才在世界各地被全面激活。 ### `01:10:00` 气候反常的稳定期与漫长的“农业引信” 在与气候学家和考古学家的反复交流中，David Reich 经常抛出一个让他百思不得其解的核心疑问：为什么农业会在全球如此多彼此隔绝的地区，近乎在同一历史时刻相继爆发？我们是否真的生活在一个极其不可思议的地质纪元？专家们给出的答案是肯定的——放眼过去整整 200 万年的宏观时间尺度，大约从 12,000 年前开始，地球进入了一个不仅气候回暖、而且稳定性极高、极其反常的时期。池塘底部的沉积物同位素测温数据显示，全新世以来，无论是年际波动、十年际还是百年际的气候振荡，幅度都急剧收敛。奇迹般稳定的气候降临之后，分散各地的不同人类族群便仿佛收到指令一般，纷纷独立转向了农耕。 ```mermaid flowchart TD accTitle: The Holocene Climate and Agriculture Explosion accDescr: Flowchart showing deep cognitive readiness followed by Holocene climate stability triggering agriculture worldwide. subgraph Deep_Past["深层演化准备（20万 - 30万年前）"] A["认知与行为工具包齐备<br/>（中石器时代/勒瓦娄哇技术）"] --> B["谱系分化并扩散全球<br/>（科伊桑人、雨林猎人、各大洲祖先）"] end subgraph Trigger["环境催化（约12,000年前）"] C["全新世气候超级稳定期<br/>（两百万年来罕见的极低波动）"] end subgraph Result["全球独立农业爆发"] B & C --> D["近东：谷物农业"] B & C --> E["美洲：玉米驯化"] B & C --> F["新几内亚与东亚：块茎与水稻"] end ``` Dwarkesh 质疑道，美洲驯化玉米、旧大陆培育谷物，所处的生态环境天差地别，仅仅用全球气候方差下降来解释多地独立起源似乎仍然令人费解。Reich 赞同这确实是一个极其反常、反直觉的事实。除了南非的科伊桑人（Khoisan）和中非雨林狩猎采集者外，当今全世界绝大多数人的共同祖先可以追溯到大约 70,000 年前。既然这些分散到西非、东非、美洲、欧洲、南亚、东亚和新几内亚的后裔都能独立建立农耕与阶级社会，说明现代人类建立文明所需的全部智力、认知与行为“原材料”早在离开非洲前甚至二三十万年前就已齐备。然而，从硬件就绪到最终引爆，中间却拖延了长达 4 万到 6 万年、甚至近 30 万年的漫长“引信”（a very long fuse）。 - **深层认知的古老同质性**：无论是 30 万年前分化的科伊桑人祖先，还是现代各地族群，所有人完全具备上大学、掌握现代复杂社会技能的认知潜力，说明核心认知工具包在数十万年前就已经固化。 - **全新世的气候奇迹**：冰芯与池塘底部沉积物同位素证据表明，全新世以前的更新世冰期伴随着极其剧烈、难以预测的气候剧变，使得任何长周期的定居农业在生态上根本无法立足。 --- ### `01:15:00` 特奥蒂瓦坎的震撼与尼安德特人的演化谜题 Dwarkesh 进一步探讨：在公元前 10,000 年到公元前 5,000 年的早期新石器时代，人口并未出现大爆炸；是否有可能更早的农耕社会其实早已存在，只是因为缺乏铜和锡等青铜时代冶金技术而湮灭在历史尘埃中？Reich 明确反驳了这种假设。他指出考古学不可能彻底漏掉这种痕迹，并以美洲文明作为反例：新大陆的宏伟文明完全是在纯石器时代的技术背景下建立的。Reich 回忆自己 20 岁游历墨西哥特奥蒂瓦坎（Teotihuacan）古城时的极大震撼——其规模之宏大完全可与古埃及金字塔媲美，但他们既没有冶金术，也没有大型役畜，甚至连轮子都没有发明。任何持有“旧大陆技术优越论”的人只要亲眼目睹特奥蒂瓦坎的巨石建筑，偏见都会瞬间瓦解。这些与欧亚人群分化了数万年的先民，仅靠石器时代的工具包就建立了庞大都市，进一步佐证了人口和文明的爆发并非被金属技术单一卡死，而是受制于那根漫长的历史引信。 随后，对话转向了当今人类古基因组学中最令人困惑的大图景谜题：现代人与古老型人类（尼安德特人和丹尼索瓦人）之间错综复杂的真实关系。全基因组数据给出的传统主干树状图非常清晰：丹尼索瓦人与尼安德特人是“姐妹群”，它们在 50 万至 60 万年前拥有共同祖先，而这个古老支系在 70 万至 80 万年前与智人祖先分离。然而，大量反常的生物学与文化特征却彻底打乱了这个图景： 1. **中石器时代技术**：尼安德特人与现代人都掌握勒瓦娄哇（Levallois）石核剥片技术，这是认知上的一次重大飞跃，但东亚的丹尼索瓦人分布区却没有发现这种技术。 2. **细胞器与性染色体替换**：尼安德特人的线粒体 DNA（mtDNA）和 Y 染色体居然不是丹尼索瓦人类型的，而是在二三十万年前通过与现代人祖先谱系的杂交被完全替换成了现代人版本，频率飙升至 100%。 3. **深层混合事件**：两者的基因组内部都记录了二三十万年前现代人与古人类混血的印记。 尽管全基因组比对坚定地将尼安德特人和丹尼索瓦人聚在一起，但尼安德特人与现代人之间却存在着丹尼索瓦人完全不具备的紧密纽带。主流学界过去认为这仅仅是一次现代人早期支系向尼安德特人渗透了约 5% 基因的边缘事件，但 Reich 透露，他目前日思夜想的核心假说正是：这看似微不足道的 5% 信号，背后掩盖的极有可能是一场剧烈得多的深层人口风暴。 - **丹尼索瓦人头骨新突破**：自两人上次录制节目以来，学界已正式确认并匹配到了丹尼索瓦人的实体头骨化石，为这一神秘人群补充了关键形态学拼图。 - **全基因组与局部特征的尖锐冲突**：全基因组中性位点判定尼安德特人偏向丹尼索瓦人；但功能性技术（勒瓦娄哇石器）以及完全单亲遗传标志物（线粒体与 Y 染色体）却将尼安德特人与现代人牢牢锁在一起。 ### `01:20:00` 基因组深处的古老祖先：为什么你某些基因位点更接近尼安德特人 戴维·赖克（David Reich）提出了一个颠覆直觉的观点：尼安德特人在某些深层维度上其实具有高度的“现代性”（deeply modern）。尽管他们在遗传上很大程度被更古老的古人类基因淹没，但他们所承载的现代人类影响远超学界以往的认知。他们与现代人类共同拥有的非洲中期石器时代（Middle Stone Age）和旧石器时代中期（Middle Paleolithic）的技术革命，在本质上构成了他们身份认同的核心。 ```mermaid flowchart TD accTitle: 基因位点溯祖时间与古人类分化关系 accDescr: 展示人类基因组内部两个等位基因的古老溯祖时间与尼安德特人分化时间的关系 subgraph Ancestry ["100万至200万年前：人类祖先深层变异"] CA["任意两个基因拷贝的共同祖先<br/>（常染色体位点）"] end subgraph Split ["50万至80万年前：古人类谱系分化"] Ancestry --> Modern["现代人类祖先谱系"] Ancestry --> Archaic["古老型人类（尼安德特人 / 丹尼索瓦人）"] end subgraph Present ["现代个体基因组（如3号染色体）"] Modern --> Mat["来自母亲的染色体拷贝"] Modern --> Pat["来自父亲的染色体拷贝"] Mat -.->|"在特定位点上亲缘关系更近"| Archaic end ``` 当德瓦凯什·帕特尔（Dwarkesh Patel）询问尼安德特人与现代人祖先早期的杂交事件发生于何时，赖克指出那发生在距今 20 万至 30 万年前。随后赖克给出了一个让德瓦凯什直呼“太疯狂了”的遗传学事实：任意两个现代人基因的平均共同祖先时间，实际上可以追溯到 100 万至 200 万年前。如果对比一个人从母亲那里继承的 3 号染色体片段和从父亲那里继承的同源片段，这两个基因拷贝汇聚到共同祖先的典型时间也在 100 万到 200 万年前，这一时间远远早于现代人谱系与尼安德特人、丹尼索瓦人分化的年代。 这意味着现代人类种群内部的遗传多样性，完全包容了古老型人类的分化时间。在人体基因组的许多具体位点上，一个人来自母亲那一方的 DNA 与尼安德特人的亲缘关系，甚至可能超过与其亲生父亲同位点 DNA 的亲缘关系。面对德瓦凯什关于这是否类似于“同胞姐妹在某些片段与你更近、在另一些片段与外人更近”的追问，赖克解释这属于标准的溯祖过程：在 50 万、70 万乃至 100 万年前，人类祖先种群内部就已经具备极其丰富的遗传多样性，某些现代人继承了其中一部分祖先的变异，另一些人继承了另一部分；尼安德特人与现代人在进化尺度上的分化时间相对靠后，使得这种不完全谱系分选广泛存在。 - 现代人类体内任意两个等位基因的平均共同祖先时间约为 100 万至 200 万年前。 - 现代人谱系与尼安德特人、丹尼索瓦人的分化时间晚于这一深层祖先变异时间，大约在 50 万至 80 万年前。 - 早期古人类之间的基因交流（包括现代人类祖先对尼安德特人的基因输入）发生于距今 20 万至 30 万年前。 --- ### `01:22:40` 白板上的“异端”假说：尼安德特人究竟是不是现代人？ 正式访谈录音结束后，戴维·赖克意犹未尽，在录音室的白板上自发画起了示意图，阐述他关于尼安德特人起源的全新“异端假说”。德瓦凯什见状立刻掏出 iPhone 进行录制，并特别提醒听众，虽然纯音频可以听懂，但在 YouTube 或 Spotify 上观看白板手绘能获得更直观的理解。 赖克指出，人类学界目前关于古老人类与现代人类关系的思考方式可能根本就走偏了。2010 年以来的“标准模型”基于全基因组测序：丹尼索瓦人与尼安德特人在大约 50 万到 60 万年前拥有共同祖先，构成姐妹群，而这对支系在更早的 70 万到 80 万年前与现代人祖先谱系分道扬镳。但在 20 万至 30 万年前，曾发生过一次现代人向尼安德特人祖先输入基因的事件，给后来的尼安德特人留下了约 5% 的现代人 DNA。 然而考古学记录呈现出截然不同的图景。在物质文化与行为表现上，尼安德特人与现代人展现出极高的相似度，反倒与东亚的丹尼索瓦人差异巨大。历史上学者长期认为尼安德特人是现代人的姐妹群，2010 年丹尼索瓦人全基因组测序证明其与尼安德特人亲缘更近时，曾给整个古人类学界带来巨大的困惑。赖克在此提出一个大胆的逆向构想：或许尼安德特人在本质上其实是一群“文化意义上的现代人”，即便其核基因组有高达 95% 来自丹尼索瓦人相关的古老型人类。 - **标准基因组模型**：现代人谱系于 70 万至 80 万年前率先分离；尼安德特人与丹尼索瓦人于 50 万至 60 万年前分离互为姐妹群。 - **早期基因交流事件**：距今 20 万至 30 万年前，现代人祖先曾向尼安德特人贡献约 5% 的核基因组。 - **考古学与遗传学的悖论**：文化与行为上尼安德特人极度接近现代人，核基因组上却与丹尼索瓦人聚为一支。 --- ### `01:25:00` 中石器时代革命与勒瓦娄哇技术：横跨欧亚的文化纽带 促使赖克构建新模型的核心动力，源自考古学上被称为“非洲中期石器时代革命”（Middle Stone Age Revolution）的重大现象。大约在 30 万至 40 万年前，一种全新的打制石器技术率先在现代高加索地区（如格鲁吉亚一带）或东非出现。这种革命性的石器制作模式在欧洲被称为“旧石器时代中期”（Middle Paleolithic），其典型代表为勒瓦娄哇技术（Levallois technology）。 这种技术的独特之处在于工匠并不直接就地取材粗糙打制，而是极其精细地预先修整石核，并常常从远离使用地点的优质燧石矿源长途搬运石料。伴随着这项石器革命的，还有用火行为的显著普及，以及对高品质矿石资源前所未有的远距离流通调配。赖克强调，这一系列的文化和技术飞跃，完全是由现代人类与尼安德特人所共享的，而在东亚的古老人类（如丹尼索瓦人）遗址中完全缺失。 赖克推测，高加索或近东一带生活着一支率先发明了勒瓦娄哇技术与旧石器时代中期文化的先锋人群。距今 20 万至 30 万年前，正是这支携带先进石器技术的现代人类分支开始向欧洲腹地扩张，并在前进道路上与欧洲本土的原住民古老型人类遭遇并杂交，而这正是全基因组中检测到的那次 5% 基因渗入事件的历史真相。 ```mermaid flowchart LR accTitle: 勒瓦娄哇技术扩张与人群迁徙假说 accDescr: 描述近东与高加索地区发明的先进石器技术人群向欧洲扩张并杂交的过程 subgraph Origin ["创新策源地（约30-40万年前）"] A["高加索 / 近东先锋人群<br/>· 发明勒瓦娄哇技术<br/>· 远距离搬运优质燧石<br/>· 广泛用火"] end subgraph Expansion ["向欧洲扩张（约20-30万年前）"] A --> B["扩张前沿人群<br/>（携带现代人文化与单系遗传标记）"] C["欧洲本土古老型人类<br/>（类西马·德洛斯·韦索斯种群）"] B & C --> D["尼安德特人形成<br/>· 95% 欧洲古老核基因<br/>· 保留现代人石器技术与线粒体/Y染色体"] end ``` - 勒瓦娄哇技术特征：精心预制石核、长距离转运优质燧石（Flint）、远超以往的广域用火。 - 起源时间与地点：距今 30 万至 40 万年前，最初见于高加索（格鲁吉亚一带）及东非。 - 地理分布分界：该文化模式横贯欧洲与非洲，但完全不见于东亚古人类遗存中。 --- ### `01:26:45` 线粒体与Y染色体的替换谜题：胡瑟裂谷与前沿基因淹没 全基因组的研究看似给出了定论，但单系遗传标记（线粒体 DNA 与 Y 染色体）的数据却揭示出一个极度惊人的反常现象。如果观察整套常染色体，尼安德特人与丹尼索瓦人聚在一起；但如果提取线粒体 DNA（母系遗传）或 Y 染色体（父系遗传），尼安德特人却直接与现代人类聚在一起。丹尼索瓦人与现代人的线粒体及 Y 染色体分化时间超过 70 万至 80 万年，与早期分化历史相符；然而尼安德特人与现代人的线粒体 DNA 和 Y 染色体分化时间，竟然只有 30 万至 45 万年左右。 赖克指出，主流学术界目前默认的推论极其反常：仅占全基因组 5% 的外来现代人类基因流，竟然在尼安德特人群体中将母系线粒体和父系 Y 染色体双双推到了 100% 的固定频率。在纯中性概率下，两套独立的单亲遗传标记同时被替换的几率极低（大致为 5% × 5% = 0.25%），这在第一性原理上显得极不合逻辑，只是因为大量零散论文的累加才让大家勉强接受。 西班牙拥有 30 万至 40 万年历史的胡瑟裂谷（Sima de los Huesos，即“骨头坑”）遗址提供了关键化石证据。该遗址出土的人类核基因组已经呈现出尼安德特人的典型特征，然而其线粒体 DNA 却完全是丹尼索瓦人式的。这证实欧洲早期的原住民确实拥有丹尼索瓦人般的线粒体，后来确实遭遇了一场现代人近亲谱系的大规模冲击，将其线粒体和 Y 染色体彻底洗牌。 为解释这一悖论，赖克提出了一套“波前扩张与基因淹没”（Wavefront Expansion and Genetic Swamping）模型： 1. **地貌与亚群划分**：将欧洲大陆划分为上百个相互连接的地方小种群（Demes），现代人类先驱从右下角的近东或中东地区切入欧洲。 2. **前沿杂交与不对称渗入**：在哺乳动物和鸟类的物种扩散生态模拟中，当一个先锋小群体向大面积定居原住民的领地扩张时，即便前沿每次只发生微弱的杂交，随着前进波前的推进，拓荒者也会被原住民庞大的种群数量迅速“遗传淹没”（Genetic Swamping）。 3. **文化与母系传承的保留**：若该现代人先驱群体具有母系社会特质（Matrilineal），其先进的石器制作技术由母亲向后代传授，母系线粒体 DNA 就会随着文化传统被顽强保留并扩散。当这支队伍最终推进到欧洲大西洋彼岸时，其核基因组已经有 95% 以上被欧洲本土古人类同化，但他们依然保持着来自源头人群的石器技术与单亲遗传标记。 - **胡瑟裂谷（Sima de los Huesos）化石证据**：年代距今 30 万至 40 万年，核基因组偏向尼安德特人，线粒体 DNA 却偏向丹尼索瓦人。 - **单亲标记替换悖论**：常规模型认为 5% 的低频基因流导致了 mtDNA 与 Y 染色体 100% 的替代，概率仅约 0.25%，在统计学上近乎不可思议。 - **生态学波前模型**：先锋扩张种群（Pioneers at the wave front）由于体量远小于沿途定居种群，常染色体在推进过程中被本土基因彻底淹没，但其文化传统与特定单系基因借助特定社会结构（如母系传承）得以传遍整个欧亚西部。 ### `01:30:00` 走出近东与高加索的革命：尼安德特人与现代人的“同源模型” 大卫·赖希（David Reich）在此详细阐述了他针对人类演化提出的颠覆性假说。传统的学术共识认为尼安德特人是一个与现代人平行的古人类支系，两者仅发生过小规模混血；然而，基因组数据中却存在一个令人困扰的反常现象：尼安德特人的常染色体与丹尼索瓦人聚类，但其线粒体 DNA 和 Y 染色体却与现代人完全一致。赖希指出，如果要解释为何尼安德特人与现代人共享单亲遗传物质，同时现代人常染色体中仅有约 5% 的微弱输入，就必须重新构想整个演化树。不仅尼安德特人经历过人口替换，现代人自身同样是两大人群深层混合的产物：约 150 万年前分化的古老非洲人类群体，与一支更晚近的早期现代人群体，在大约 20 万至 30 万年前发生了约 20% 对 80% 的大融合。 赖希认为，驱动这整个格局变动的关键枢纽，是一群大约在 20 万至 30 万年前生活在高加索或东北非、发明了中石器时代（Middle Stone Age）或勒瓦娄哇技术（Levallois technology）的核心人群。这个掌握先进石器技术的人群向四面八方剧烈扩张：一面向欧洲扩散，遭遇当地原本与丹尼索瓦人同源的古老欧洲人类，最终与当地群体混血并发生了 95% 的遗传淹没（仅保留 5% 的现代人血统），这一过程造就了尼安德特人；另一面则深入非洲腹地，与非洲本土更古老的人类相遇，形成了现代人类的祖先。之所以在欧洲发生了 95% 的古人类替换，而在非洲却只被当地古人类替换了 20%，原因在于基因分化程度的差异：欧洲本地古人类与扩张群体的分化时间仅为 70 万至 80 万年，生殖隔离较弱；而非洲本土古人类分化时间长达 150 万年，巨大的遗传不兼容性形成了显著的基因流动屏障。 这一假说彻底重构了尼安德特人的定义：现代人与尼安德特人并不是两个毫无关系的遥远物种，而是由同一场发源于 20 万至 30 万年前的伟大扩张孕育出的“双生表亲”。两者共享了同一批文化发明、石器工具箱，甚至共享了线粒体和 Y 染色体；唯一的差异仅在于他们离开扩散源地后，分别与不同区域的本土古人类发生了不对称的基因混合。 ```mermaid flowchart TD accTitle: 现代人与尼安德特人起源的扩散混合模型 accDescr: 展示约20万至30万年前发源于近东或高加索的核心人群分别向欧洲和非洲扩散并与当地古人类混合的过程。 subgraph Origin ["核心源地（约20-30万年前）"] Core["先进技术人群<br/>发明中石器时代/勒瓦娄哇技术<br/>带入 Y 染色体与线粒体"] end subgraph EuropeRoute ["欧洲扩散路径"] EuroArchaic["欧洲本土古人类<br/>（分化约70-80万年，与丹尼索瓦同源）"] Neanderthal["尼安德特人<br/>95% 本土古人类常染色体<br/>+ 5% 核心扩张基因/单亲标记/文化"] end subgraph AfricaRoute ["非洲扩散路径"] AfriArchaic["非洲本土古老群体<br/>（分化约150万年，生殖隔离严重）"] ModernHumans["现代人祖先<br/>80% 核心扩张人群<br/>+ 20% 非洲古老血统"] end Core -->|"向欧洲扩张"| Neanderthal EuroArchaic -->|"贡献 95% 遗传背景"| Neanderthal Core -->|"向非洲扩张"| ModernHumans AfriArchaic -->|"贡献 20% 遗传背景"| ModernHumans ``` ### `01:33:28` 非洲深层古老结构的基因印记 德瓦克什·帕特尔（Dwarkesh Patel）紧接着追问这支分化了 150 万年的神秘古老非洲群体的细节：他们生活在非洲的什么区域？那些没有与扩张人群混合的分支后来怎样了？有没有可能幸存下来？赖希澄清说，这些推论并非直接来自于古 DNA 测序，因为非洲的高温湿热环境极其不利于古 DNA 保存，目前学术界缺乏数十万年前的古非洲人类 DNA 样本。这一结论完全是基于对现代全球人群——尤其是非洲不同族群——的高分辨率现生基因组变异分析得出的。 至少有三到五项独立的研究通过分析现生人群的基因多态性一致发现：当代人类绝不能用一个长期匀质分布的单一祖先群体来解释。数据明确指示人类祖先在 100 多万年前就已经分化为至少两个、甚至更多的孤立支系，随后在大约数十万年前重新聚合，形成了在解剖学意义上现代人的共同祖先。包括南非的科伊桑人（Khoisan）和中非雨林狩猎采集者在内的所有现代人群，其基因组深层都留有这场远古聚合的烙印。 至于这群分化了 150 万年的古老人类究竟生活在何处，赖希直言“谁也无法确定具体位置”。非洲是人类演化长达 700 万年的巨大摇篮，东非、西非、南非和中非都分布着不同的生态区位。当掌握先进技术的群体席卷非洲大陆时，面对的并不是荒无人烟的处女地，而是一个遍布各种各样分化程度不一的古人类图景，扩散者在行进途中与遇见的形形色色的古老原住民发生了复杂的基因交织。 - 现代人类的基因变异模式否定了过去认为的单一种群稳定演化模型，强力支持深层亚结构聚合假说。 - 科伊桑人、中非雨林狩猎采集者以及非裔与非非裔人群，均普遍携带早期古老非洲分化人群的混合指纹。 ### `01:35:12` 5% 的示踪剂：颜那亚类比与文化/基因的扩散 德瓦克什对这个模型提出了一个核心疑问：如果 30 万年前现代人的祖先只是与欧洲原住民发生混合，并没有在人口数量上替换对方（欧洲原住民保留了 95%），这难道不意味着勒瓦娄哇技术主要是作为一种“纯文化现象”跨群体传播的吗？ 赖希反驳了这种非此即彼的二元划分。他指出，技术的扩散并非脱离血缘的纯文化空投，基因本身同样在移动，只不过在漫长的迁徙中被稀释了。为了形象地解释这一点，赖希举了青铜时代欧亚草原人群“颜那亚人”（Yamnaya）的经典案例：颜那亚人向西冲入欧洲时建立了绳纹器文化（Corded Ware），其基因在欧洲经历了约 25% 的稀释；随后其后裔反向横跨中亚、穿越兴都库什山脉进入南亚次大陆，一路上不断与当地人口混血。如今在印度人群中，颜那亚祖源成分通常只剩下 10%、5%，甚至在部分群体中仅有 2%。 然而，正是这看似微不足道的 5% 或 2% 的微量基因，充当了不可磨灭的“示踪染料”（tracer dye）。这抹染料忠实地追踪了印欧语系在欧亚大陆的扩散路径，串联起从大西洋沿岸到印度半岛跨越万里的语言和精神文化。赖希强调，尼安德特人基因组中那来自核心扩张人群的 5%“绝不能对其嗤之以鼻”（you shouldn't sneeze at it），它虽被古欧洲人类的基因汪洋所淹没，却正是将勒瓦娄哇技术、先进工具以及关键生物学特征带入尼安德特人祖先群体的活化石。 ### `01:36:58` 解开全基因组与单亲遗传的矛盾：母系/父系扩张与社会选择 德瓦克什进一步追问其机制：既然常染色体被稀释到了只剩 5%，为什么在漫长的种群淹没中，线粒体 DNA（母系遗传）或 Y 染色体（父系遗传）能够享有如此特殊的特权，被完好无损地固定下来？ 赖希坦言，过去十五年来，尤其是最近三四年，尼安德特人的遗传图谱一直让他深感困扰：为什么常染色体把尼安德特人和丹尼索瓦人聚在一起，而单亲遗传的线粒体和 Y 染色体却毫无争议地倒向现代人？在整个自然界的所有其他物种中，都几乎找不到如此极端的遗传脱节。赖希指出，如果扩张人群具有极强的单系连续性（母系或父系氏族扩张），就可以解释其中一个单亲染色体的留存： 1. **母系扩张假说**：如果迁徙群体的社会结构是母系的，他们吸收了欧洲当地的男性古人类，但孩子留在母系氏族中并由母亲的文化抚育长大，这样线粒体 DNA 就会完整保留； 2. **父系扩张假说**：如果迁徙群体是父系的，男性扩张者迎娶当地古人类女性，后代在父系氏族中接受教养，则 Y 染色体就会完整保留。 然而，单靠母系或父系扩张只能保住“一个”单亲遗传部分，无法同时解释为什么 Y 染色体和线粒体 DNA **两者**都聚向了现代人。赖希提出了两种可能的叠加机制：自然选择或“社会选择”（social selection）。 在人类社会中，父系或母系制度是常态而非例外。更为关键的是两性生殖成功率的极度不对称：在传统社会中，绝大多数适龄女性都能生育后代，但男性的生殖成功率却呈现出巨大的方差——相当一部分男性一生无嗣，而少数占主导地位的男性却拥有极多妻妾和子女。在这种男性为争夺配偶展开激烈竞争的环境下，“女性配偶选择”（female mate choice）便起到了决定性的筛选作用。如果欧洲本地古人类男性的后代在竞争本土女性时处于劣势，而源自先进技术扩张群体的男性（或非古老支系男性）更容易受到青睐并获得社会地位，那么社会选择便会强力清除欧洲本土古人类男性的 Y 染色体，从而在短短数千年间实现 Y 染色体和线粒体基因组的双重固定与替换。 ### `01:40:00` 线粒体替换的谜团与生殖不兼容性的平方增长法则 德瓦凯什对模型中的母系遗传细节提出了疑问：当现代人类先祖扩散到新边疆并与当地古人类繁衍时，后代如果来自迁入群体的母亲，就会携带现代人类的线粒体 DNA（mtDNA）；如果来自当地古老种群的母亲，则会携带古人类的 mtDNA。那么，为什么携带古老 mtDNA 的后代最终未能留存下来？戴维·赖克坦言这是整套假说中“最脆弱的一环”，甚至坦率地承认“这套论证很可能是错的，但我只是在分享我脑海中的思考”。赖克指出，相比于同时解释 mtDNA 和 Y 染色体的双重替代，单独解释 mtDNA 容易得多：一种可能是古老种群的线粒体在生理机能上适应度较低；另一种更符合人类现实的解释则是社会学层面的歧视——群体内部可能依据父母是古人类还是现代人而产生歧视与分层。 紧接着，德瓦凯什追问了两次历史接触的巨大反差：为什么在距今约 300,000 年前，我们的祖先谱系与他们互动后，整体保留的主要是古老谱系的常染色体；而到了 70,000 年前再次接触时，古人类的遗传谱系却走向了彻底灭绝？赖克解释道，两次事件呈现截然不同的结果不仅不令人意外，反而极其符合演化生物学规律。种群分化时间与生物学生殖不兼容性的产生密切相关： - 当两个群体仅分化了数万年时（例如阿迪蒂·巴蒂亚此前论文中研究的西非人与欧洲人，分化约 70,000 年），彼此之间完全不存在生殖隔离或负向自然选择。 - 即使是现代南部非洲分化近 200,000 年的科伊桑人与班图人（如纳尔逊·曼德拉所属的科萨人谱系），基因也是完全相容的。 - 然而，当两个种群分化长达 700,000 至 800,000 年时（如尼安德特人与现代人），就会迅速演化出严重的生物学不兼容性。 赖克指出，生殖不兼容性通常涉及成对的相互作用基因（即杜布赞斯基-马勒不兼容性模型），因此其发生概率大致是“隔离时间的平方”。在 300,000 年前的那次扩张中： 1. **欧亚方向**：迁徙人群与欧亚古人类（丹尼索瓦人的先祖）仅分化了大约 400,000 年，遗传屏障相对渗透性极高，因此基因能够顺畅渗入并扩散。 2. **非洲方向**：迁徙人群遭遇的非洲本土古老群体则已分化长达 1,200,000 年，几乎逼近了无法繁育后代的生理极限。 在这种极端分化下，跨群体结合产生的后代往往适应度极低、极易夭折或不育，导致非洲内部的基因交流屏障高得多。 ```mermaid flowchart TD accTitle: 300,000年前核心人群扩张与遗传渗透差异 accDescr: 说明高加索周边核心种群向欧亚和非洲扩张时，由于与当地古人类分化时间差异导致的相容性截然不同。 subgraph Origin["扩张源头 (~300,000年前)"] Core["中石器/勒瓦娄哇技术人群<br/>(高加索/近东周边)"] end subgraph Eurasia["欧亚大陆方向"] EurasianArch["欧亚古人类 (分化~400,000年)"] Core -->|"低分化 / 高遗传渗透性"| EurasianArch EurasianArch --> Neanderthal["常染色体被古人类淹没<br/>但保留mtDNA/Y染色体与技术<br/>(演化为尼安德特人)"] end subgraph Africa["非洲方向"] AfricanArch["超古老非洲支系 (分化~1,200,000年)"] Core -->|"极高生殖屏障 / 不育与夭折"| AfricanArch AfricanArch --> ModernHumans["杂交极其受限<br/>主要保留核心人群遗传主干<br/>(演化为现代智人)"] end ``` --- ### `01:44:20` 300,000年前的技术爆炸与欧亚-非洲双向扩散 赖克进一步强调，这一假说最迷人的地方在于“时间上的惊人重合”。在距今大约 300,000 年前的同一时间窗口，欧亚大陆和非洲同时发生了深刻的石器工业革命——勒瓦娄哇技术（Levallois technology）及中石器时代石器工具组合（Middle Stone Age toolkit）的登场。考古学界传统上有一种观点认为，这种极其复杂、需要精确预制石核的勒瓦娄哇技术是在不同地区独立演化出来的。但在赖克看来，技术形态的高度一致性、线粒体与 Y 染色体发生置换的时间、以及常染色体混合信号发生的时间完全吻合。 德瓦凯什提出疑问：“如果不采纳你的假说，我们就必须相信尼安德特人独立发明了石器时代新技术？”赖克回应说这并非天方夜谭，就如同农业在世界多个地区独立起源一样；但他坚信，将这一系列现象统一解释为一个单一源头人群的向外技术爆发与人群扩散，在逻辑上更为自洽且合理：“它可能并不正确，但我只是想告诉你，我们当前习以为常的模型未必就是世界的真相。这个假说解释了多得多的现象，而且在结构上并不比传统假说更复杂。” - **三大重合证据链**： - **技术工具链**：欧亚大陆与非洲几乎在同一地层年代出现高度相似的勒瓦娄哇预制石核剥片技术。 - **单亲遗传链**：尼安德特人的线粒体 DNA 和 Y 染色体均在距今 300,000 年左右被与现代人类祖先极近的谱系所完全替换。 - **年代学重合**：人群扩散、基因混合事件与技术跃迁在时间轴上高度收敛于约 30 万年前。 --- ### `01:46:15` 托勒密本轮 vs 哥白尼革命：古人类学模型的范式危机 赖克在此做了一个极为精妙的科学史类比：当今古人类学构建的演化模型，正在陷入类似于托勒密天文学“本轮（epicycles）”的繁琐修补困境。古人类学原本设立的“标准模型”坚称：现代智人是一个完全独立的谱系，而尼安德特人与丹尼索瓦人是互为姊妹群的古老旁支。然而随着古 DNA 测序的发展，矛盾的数据不断涌现： 1. 先是发现现代人向尼安德特人有过早期基因流动； 2. 随后又发现极度分化的“超古老谱系”（super-archaic lineage）渗入丹尼索瓦人； 3. 全基因组明明显示尼安德特人与丹尼索瓦人更接近，但尼安德特人的线粒体与 Y 染色体却与智人几乎一模一样。 学界为了保住传统演化树，只得不断往里塞入额外的补丁——例如假定极端强烈的自然选择恰好在数万年间完全洗刷并替换了单亲染色体。赖克直言：“这让人想起古代天文学。为了在‘太阳绕着地球转’的前提下解释行星的怪异运动，托勒密天文学家发明了一个又一个本轮，让模型变得冗长而别扭。直到哥白尼提出万物绕日运行，整个体系才瞬间回归清爽与简单。古人类学界不断添加复杂修饰来迎合旧模型，而我们提出的这一替代模型，虽然未必像日心说那样翻天覆地，但它明显更加简单，且能自然解释这许多矛盾。” 德瓦凯什紧接着提出了一个深刻的科学哲学问题：古希腊萨摩斯岛的阿里斯塔克斯（Aristarchus）早在公元前就提出了日心说，但当时的雅典学者将其驳回，理由是“如果地球真的绕日公转，我们在一年中理应看到恒星产生相对视差；若看不到视差，唯一的可能就是恒星离我们极其遥远，远到了不可思议的程度——而这在当时被认为极其荒谬”。德瓦凯什追问赖克：“如果你的这套假说就像日心说一样，那么它背后那个让同行觉得‘不可思议、难以接受’的恒星视差等价物是什么？学界为什么对这个模型犹豫不决？” 赖克思索后回答道，核心的障碍在于跨学科的壁垒：采纳这个模型，就必须承认**非洲与欧亚大陆在 300,000 年前的重大文化变革与遗传扩散存在直接的因果联动**。目前人类遗传学领域割裂严重：一部分学者专注于通过现代人类基因组挖掘非洲内部极其深层的古老亚结构（substructure），另一部分学者则深耕欧亚大陆的古 DNA 数据。这两个学术圈子长期各说各话，“至今还没有人真正将非洲内部的现代人群亚结构研究，与基于古 DNA 的演化图景严密拼接在一起”。 ### `01:50:00` 中石器时代的演化飞跃与固定突变的出现 基于古 DNA 研究中远古人类与现代人类亲缘关系的最新成果，不同古人类群体的亚群分化时间在年代尺度上高度吻合。David Reich 指出，这种模型并非天方夜谭，反而极为简洁自洽（parsimonious）。Dwarkesh Patel 提出，当时不同人类群体均能够掌握并采纳石器时代技术，这似乎说明即便不同人类谱系存在分化，其遗传差异也并未大到无法相互学习生产方式的程度。 ```mermaid flowchart TD accTitle: 中石器时代遗传突变与人群扩散模型 accDescr: 展示距今30至40万年前关键突变爆发后人群向欧洲及非洲的扩散分化过程 subgraph Origin["关键演化起源（约 30-40 万年前）"] Mutation["高加索 / 中东 / 东北非核心人群<br/>产生关键适应性突变"] end subgraph Expansion["人群向外扩散与分化"] Euro["进入欧洲"] --> ArchaicEuro["与欧洲本土古人类深度混血<br/>基因被稀释，保留适应性突变<br/>演化为尼安德特人"] Africa["进入非洲"] --> ArchaicAfrica["与非洲深度分化的古人类混血<br/>演化为解剖学意义上的现代人"] end Mutation --> Euro Mutation --> Africa ``` Reich 提出了另一种遗传驱动的假说：人类基因的共同祖先并非集中在距今 10 万或 15 万年前，而主要回溯至距今 40 万到 50 万年前。如果在高加索、中东或东北非的某个核心种群中，发生了赋予技术制造能力的关键突变，该种群随后向外扩张。当其迁入欧洲时，尽管本地基因池迅速稀释了其遗传构成（swamped by local genes），那些核心突变却因自然选择的优势被保留下来。虽然大部分学界目光都聚焦在距今 5 万到 10 万年前解剖学现代人类行为（行为现代性）的爆发，但许多考古学家认为距今 30 万年前的中石器时代技术飞跃同样深远，甚至更为关键。 在固定差异（fixed differences）方面，现代人与距今 5 万年前的人类之间几乎不存在固定突变差异，但若回溯至距今 30 万到 40 万年前，所有现代人类的基因变异便开始汇聚于同一个共同祖先。此时，若与同时代其他谱系对比，该单一种群祖先之前产生的新突变就会表现为固定差异。这表明具备现代人体骨骼特征的早期智人与尼安德特人的典型形态，恰好都在这一时刻共同登上历史舞台。 - 解剖学现代人与行为现代人存在巨大的时间断层：距今 30 万年前人类骨骼形态已经现代化，但随后的 20 万年间，古人类依然在制造与尼安德特人无异的勒瓦娄哇（Levallois）石器，未见明显超越。 - 直到距今 5 万至 10 万年前，考古学记录中才显现出文化与艺术层面的“加速（quickening）”，关于这一加速是纯文化演进还是基因突变驱动，学术界至今仍争论不休。 --- ### `01:53:23` 被数据推翻的偏见：从尼安德特人基因到自然选择停滞说 谈及科学探索中的心路历程，Reich 感慨自己的直觉和先验假设在研究中屡遭数据推翻，这种经历甚至让他感到“心有余悸（almost traumatized）”。参与尼安德特人基因组计划时，他最初坚信非洲以外的人群只是非洲人群遗传变异的一个子集，完全没有与古人类混血的痕迹。但当测序结果明确显示尼安德特人与非非洲人群的关系明显比非洲人群更近时，他极度怀疑这是实验误差，并与同事耗费数年时间试图“证明该结果是错的”，然而数据却愈发坚挺。 在自然选择的研究上，他们再次经历了类似的认知反转。长期以来，学界普遍认为人类在过去几十万年间自然选择十分微弱，特别是农业革命之后更是趋于沉寂。2015 年，Reich 团队与 Iain Mathieson 等人发表了一项开创性研究，通过比对约 200 名古代欧洲及中东人群与当代欧洲人的样本，寻找频率偏离中性漂变预期的位点。尽管当时成功发现了 12 个强选择位点，令人振奋，但后续研究的发展却一度令人沮丧。 - 2024 年哥本哈根团队针对更优质古 DNA 数据集开展研究，样本量与数据质量显著提升，却仅检测出 21 个强选择位点。 - 这一结果让学界陷入悲观，怀疑自然选择的检测已触及渐近线上限（asymptote），进而认为人类基因组在全新世确实没有经历大规模的方向性适应选择。 --- ### `01:57:48` 1.6 万样本突破极限：古代 DNA 统计方法学的革新 面对这一瓶颈，Reich 与哈佛大学合作者 Ali Akbari 决定从数据规模与统计方法两个维度进行彻底突破。首先是数据通量的飞跃：团队将数据量大幅扩增了约 14 倍，在本项研究中一口气报道了约 10,000 例全新的古人类基因组数据，使分析涵盖的古 DNA 样本总数攀升至约 16,000 例，时间跨度覆盖过去 18,000 年。 | 维度 | 2015 年早期研究 (Mathieson 等) | 2026 年最新研究 (Akbari & Reich 等) | |:--- |:--- |:--- | | **样本规模** | 约 200 例古代个体 | 约 16,000 例（包含约 10,000 例全新测序样本） | | **地理跨度** | 欧洲与近东 | 聚焦欧洲与中东（拥有古 DNA 领域 70-80% 的数据沉淀） | | **检测手段** | 传统等位基因频率极端离群值检验 | 借鉴医学疾控的全新关联预测算法（基于个体亲缘图谱） | | **核心结论** | 发现 12 个位点，暗示选择作用微弱 | 颠覆选择沉寂说，揭示青铜时代强烈的自然选择加速 | 虽然数据主要来自欧洲与中东，但 Reich 强调这并非因为该地区演化地位更重要，而是由于历史原因，全球现存古 DNA 数据的 70% 至 80% 都集中于此，使其成为天然的演化高精实验室。 除了海量数据，团队还引入了一套原本用于医学领域寻找疾病风险因子的全新统计方法学。该方法不再依赖传统的群体平均频率估算，而是利用 16,000 个个体构成的稠密亲缘关系网络，直接建模并预测个体的基因型分布，从而在极高分辨率下捕捉极微弱的等位基因频率波动。 ### `02:00:00` 区分遗传漂变与自然选择的新统计模型 为了精确捕捉人类基因组在时间尺度上的演进，大卫·赖希（David Reich）与合作者阿里·阿克巴里（Ali Akbari）构建了一个全新的统计学预测模型。这项分析纳入了多达 22,000 个人类基因组样本，其中包含 16,000 名古代人和 6,000 名现代人，覆盖了全基因组范围内的 1,000 万个 DNA 位点。模型的核心逻辑在于将“全基因组层面的群体历史背景”与“特定位点的自然选择效应”彻底剥离：首先，研究团队基于全基因组构建了一张巨大的亲缘关系矩阵，借此预测任意两个个体之间的遗传相似度。这张矩阵完整吸收了人类历史上发生过的所有种群瓶颈、随机遗传漂变（genetic drift）以及跨地域人群的大规模混合（admixture）——这些事件对整个基因组的影响是全方位的。 在剥离了中性漂变和人口学历史的底噪之后，研究人员向模型提出了一个核心备择假设：是否存在某种自然选择的力量，在不同地理空间和漫长时间轴上，始终将特定突变的等位基因频率“吹向同一个方向”？赖希坦言，假设自然选择在所有时期和地点都保持恒定速率是一个“近乎笨拙的简化假设（a dumb assumption）”，因为真实的自然选择压力必定随时间剧烈波动；但这种极简假设却构成了一道极具辨识力的统计基准——只要加入恒定选择系数能比单纯依赖亲缘关系矩阵显著更好地拟合数据，就足以证实该位点经历了真实的定向自然选择。德瓦尔凯什·帕特尔（Dwarkesh Patel）对此进行了总结与确认：模型通过将全基因组的漂变背景与特定位点的定向选择系数叠加，从而能够极为敏锐地检验出哪些位点的频率变迁无法仅用人口历史来解释。 ```mermaid flowchart TD accTitle: 古 DNA 自然选择检测与验证模型架构 accDescr: 展示了结合 22,000 个基因组、亲缘关系矩阵与定向选择系数的模型，并通过英国生物样本库进行独立验证的完整流程。 subgraph InputData["输入数据集"] G["22,000 个个体基因组<br/>（16,000 古代 + 6,000 现代）"] L["1,000 万个 DNA 突变位点"] end subgraph TwoPartModel["双组件预测模型"] GRM["组件一：亲缘关系矩阵<br/>吸收瓶颈、漂变与人群混合"] SEL["组件二：定向选择系数<br/>检验跨时空恒定单向驱动"] Fit["拟合检验：引入选择系数<br/>是否显著优于纯中性模型？"] end subgraph Validation["独立功能验证（UK Biobank）"] UKB["500,000 人表型-基因组关联<br/>（GWAS 涵盖数百种复杂性状）"] Score["选择统计量（Z-score）梯度滑动"] Plat["统计量 > 5 时达到富集平台期<br/>（性状富集度从 15% 飙升至 60-70%）"] end G --> GRM L --> SEL GRM --> Fit SEL --> Fit Fit --> Score UKB --> Score Score --> Plat ``` --- ### `02:02:18` 惊人的发现：数百个独立受选择位点与初期的怀疑 当这套算法在 22,000 个样本的 1,000 万个位点上全速运转时，输出的结果彻底颠覆了研究团队的预期：基因组中呈现出定向变化的位点数量并非寥寥无几，而是多达数千个。然而，在基因组中紧密排列的突变之间存在严重的连锁不平衡，相邻位点往往互相干扰，形成统计上的假阳性聚集。为此，团队对信号进行了严苛的物理降噪与独立聚类，在每个连锁区域只保留最强的一个主导信号，屏蔽其余关联位点。即便在如此严谨的过滤后，他们依然确认了至少 479 个具有 99% 置信度的独立受选择位点；若将判定阈值放宽至置信度大于 50%，受选择位点的总数竟高达约 3,800 个。 面对如此庞大的数字，赖希的第一反应是强烈的难以置信。在以往古人类学和演化遗传学的单次全基因组扫描中，科学家们倾尽全力往往也只能发现一二十个潜在的选择信号，而这次一口气冒出数百乃至数千个位点，“这简直是个疯狂的数字（a crazy number of results）”。赖希回忆道，他们最初认定这肯定是统计漏洞或算法失误导致的假象，于是在接下来的整整两年时间里，团队“竭尽全力试图推翻这些结果（trying to make the results go away）”。然而令人惊叹的是，伴随着一次又一次极为挑剔的压力测试和纠错分析，这些信号不但没有消失，反而在统计学上变得愈发坚不可摧。 - **479 个独立核心位点**：在基因组独立区块中，达到 99% 统计置信度的定向选择位点。 - **3,800 个候选位点**：置信度超过 50% 的潜在选择位点总数，揭示了 selection 在人类基因组中无所不在的痕迹。 - **历史对比**：此前同类全基因组扫描中，单次研究能够捕获的受选择位点通常至多仅有二十余个。 --- ### `02:04:12` 利用英国生物样本库（UK Biobank）进行独立功能验证 为了从根本上检验这些海量信号究竟是真实的生物学印记还是未知的算法伪影，团队必须引入一种完全不依赖古代 DNA 样本的外部独立证据。关键的突破源于合作者阿里·阿克巴里构想出的一个绝妙构思：将古 DNA 检测出的选择信号与庞大的现代全基因组关联分析（GWAS）数据库直接对撞。团队调取了英国生物样本库（UK Biobank）中约 50 万英国受试者的全基因组测序与深层表型数据，该数据库针对高血压、身高、体脂等数百种生理及疾病性状进行了细致的统计关联。在基线状态下，人类 1,000 万个位点中约有 15%（约 150 万个位点）能被证实与至少一种表型性状存在统计学关联。 阿克巴里与赖希将他们发明的“定向自然选择统计量”（该统计量大致服从高斯正态分布，数值代表偏离无选择状态的标准差倍数）作为滑动游标，从 1、2、3 一路提高到 4 和 5。令人震撼的现象出现了：随着选择统计阈值的抬高，这些位点与现代复杂性状的功能关联富集度呈现出戏剧性的陡峭攀升。当统计量超过 5 时，这些突变影响可测性状的比例从基准的 15% 激增至 60% 到 70%，形成了近 5 倍的功能富集；而一旦阈值突破 5，富集曲线便进入了绝对的平稳平台期。这种利用现代医学表型作为外部独立标尺来校准真阳性率的方法，彻底改变了传统仅靠理论显著性评判选择信号的模式——当统计量达到平台期，就意味着该阈值之上的选择信号几乎 100% 全部是真实存在的演化适应。 > “这是阿里想出的一个绝妙点子。它彻底摆脱了传统的假阳性控制思路，因为我们直接利用了一组完全来自独立维度、通过截然不同方式测定的表型相关信息，来反向读取突变为真实的概率。” —— 大卫·赖希 --- ### `02:07:25` 排除背景选择干扰与古 DNA 技术的跨越式演进 尽管与现代 GWAS 的强关联提供了震撼的佐证，但团队依然面临一个巨大的演化生物学诘难：这种富集是否源于“背景选择（background selection）”？在人类基因组中，基因编码区不断承受着清除轻微有害新突变的“净化选择”，这被称为背景选择。由于编码区既集中了大量疾病相关性状位点，又频繁经历有害突变的剔除，人们很容易怀疑是背景选择同时人为放大了表型关联和所谓的时间变化信号。为了排除这一致命干扰，研究团队在基因组内部进行了严格的对照实验：他们将 DNA 按照承受背景选择的严苛程度划分为不同的切片，并在相同背景选择强度以及相同等位基因频率的片段内分别重复富集分析。结果显示，每一个切片均重现了完全相同的演化模式，且都在统计量达到约 5 时平稳进入平台期，无可辩驳地证明这些信号绝非背景选择伪影，而是切切实实由适应性环境压力推动的定向演化。 当帕特尔转而追问，为何过去十余年间所能测序的古代人类数量发生了如此暴力的阶梯式飞跃（样本规模从个位数飙升数个数量级）时，赖希指出了技术与成本的双重革命。首要的驱动力是测序成本的天文数字级暴跌：自 2000 年代末高通量测序初现以来，基因测序的单位成本整体下降了整整 100 万倍；即使从 2010 年古代 DNA 实验室初具体系算起至今，成本又再次下降了一到两个数量级。另一项至关重要的核心技术突破则是液相杂交富集（in-solution enrichment / hybridization capture）技术，它允许科学家在古骨样本中从高达 99% 的土壤微生物环境 DNA 污染中，像钓鱼一样精准“钓取”出占比极低的人类内源性 DNA 片段，从而使大规模古代群体基因组学研究成为可能。 - **对照排除伪影**：按背景选择强度与等位基因频率进行精细分层切片后，选择统计量与表型关联的 5 倍富集模式依然完全稳固。 - **测序成本百万倍下降**：高通量测序成本自 2000 年代末以来骤降 6 个数量级（$10^6$），2010 年后又降低了 10 至 100 倍。 - **液相杂交捕获技术**：突破了微量古 DNA 被微生物污染掩盖的物理瓶颈，奠定了成千上万样本高通量分析的技术基石。 ### `02:10:00` 杂交捕获技术：破解古DNA样本的高污染瓶颈 在古人类DNA研究的早期阶段，科学家们面临着极其严峻的样本质量难题。从古代骨骼中提取出的DNA样本中，真正属于人类的DNA比例极低，通常不足10%，甚至往往低于1%。按照当时的技术水平，如果采取“暴力穷举”（brute-force sequencing）式的全基因组测序，绝大多数测序算力和资金都会白白浪费在无用信息上，其高昂的经济成本令人望而却步。为了让绝大多数被测序的片段都能用于实际分析，David Reich团队开发出一种巧妙的靶向捕获方法：他们人工合成了一整套短链DNA探针，专门锁定人类基因组中具有高度多样性与分析价值的位点。 该技术靶向了人类基因组中超过100万个高度变异的关键位点（即著名的1240k试剂盒相关技术）。在筛选这些位点时，团队专门纳入了大量已知的生物学功能靶点，特别是全基因组关联分析（GWAS）中证实与现代人表型、性状紧密相关的变异位点。在实际操作中，研究人员将提取出的古DNA液体样本“冲洗”过这批人工合成的探针阵列，探针便会特异性结合那些富含历史演化信息的目标人类DNA片段。通过洗脱与富集，即便原始样本中只有1%是人类DNA，最终测序产物中绝大部分也是团队高度关注的目标序列，从而彻底打破了经济成本的瓶颈。 ```mermaid flowchart LR accTitle: 古DNA杂交捕获与富集流程 accDescr: 展示从高度污染的骨骼样本中通过人工合成探针捕获目标人类变异位点的过程 subgraph Input["原始样本提取"] A["骨骼提取DNA<br/>(微生物 >99%, 人类 <1%)"] end subgraph Capture["靶向杂交"] B["人工合成探针库<br/>(>100万个GWAS关键位点)"] C["杂交结合目标序列"] end subgraph Output["高效测序"] D["洗脱非目标杂质"] E["富集后人类目标位点测序"] end A --> C B --> C C --> D D --> E ``` ### `02:11:15` 微生物背景、病原体遗存与测序通量的工业化跃升 针对样本中剩余超过99%的非人类DNA，David Reich解释称，这绝大部分源于个体死亡后在其遗体上定植繁衍的细菌和真菌等微生物。虽然在研究人类演化时这些微生物序列常被当作背景噪音过滤掉，但在古病理学领域，它们本身却是一座蕴藏丰富的金矿。例如，科研人员通过提取古人类牙齿及遗骸骨骼中的病原体DNA，成功重构了疟疾、黑死病（鼠疫）以及乙型肝炎（Hepatitis B）等致命瘟疫的历史传播轨迹。 在克服了靶向捕获的技术障碍后，Reich实验室进一步引入了自动化机器人系统，将样本制备与测序流程全面工业化。这一系列技术突破带来了数据产量的爆发式增长：测序通量从最初的每年几十个样本，迅速攀升到每年数百个，最终跃升至每年数千个。如今，仅Reich团队自己的实验室，每年就能产出超过5,000个个体的全基因组尺度数据，全球其他几家顶尖实验室也已具备相似的生产力。 - **数据爆炸式增长**：2010年全球公开发表的人类古基因组序列仅有约10例，而到如今已突破20,000例大关，实现了数个数量级的飞跃。 - **科研范式的重塑**：这种“半指数级乃至超指数级的跃升”使得过去根本无法触及的历史谜题迎刃而解；正如David Reich在对谈尾声时所感叹的，"我们在2014年能提出的科学问题，与我们今天能够探究的问题早已完全不可同日而语。" ## Notable quotes > `00:00` 通过从古人类遗骸中提取DNA并追踪其随时间的变化，深入了解人类生物学特性的演变 — David Reich > `10:00` if all the arrows point in the same direction, we win — David Reich > `11:42` the genome is vibrating with natural selection — David Reich > `57:40` 在当今世界上，人类基因组中每一个可能发生的碱基突变，在每一代人中都会独立且重复出现大约 100 次 — David Reich > `01:10:00` a very long fuse — David Reich > `01:41:52` you shouldn't sneeze at it — David Reich > `01:46:15` 这让人想起古代天文学。为了在‘太阳绕着地球转’的前提下解释行星的怪异运动，托勒密天文学家发明了一个又一个本轮，让模型变得冗长而别扭。直到哥白尼提出万物绕日运行，整个体系才瞬间回归清爽与简单。古人类学界不断添加复杂修饰来迎合旧模型，而我们提出的这一替代模型，虽然未必像日心说那样翻天覆地，但它明显更加简单，且能自然解释这许多矛盾。 — David Reich > `01:46:15` 至今还没有人真正将非洲内部的现代人群亚结构研究，与基于古 DNA 的演化图景严密拼接在一起 — David Reich > `02:02:18` a crazy number of results — David Reich > `02:02:18` trying to make the results go away — David Reich > `02:04:12` 这是阿里想出的一个绝妙点子。它彻底摆脱了传统的假阳性控制思路，因为我们直接利用了一组完全来自独立维度、通过截然不同方式测定的表型相关信息，来反向读取突变为真实的概率。 — David Reich > `02:11:15` 我们在2014年能提出的科学问题，与我们今天能够探究的问题早已完全不可同日而语。 — David Reich ## Mentioned David Reich, Ali Akbari, Dwarkesh Patel, Iain Mathieson, Swapan Mallick, Aditi Bhatia, Joseph Henrich, Julian Jaynes, UK Biobank, Cursor, Jane Street, Crusoe, AGES, 1240k capture reagent, Yamnaya, Corded Ware culture, Neanderthals, Denisovans, Khoisan, Sima de los Huesos, Teotihuacan, Levallois technology, The Origins of Consciousness in the Bicameral Mind, Hebrew Bible, Iliad / Homer ## Video description <p><a target="_blank" href="https://reich.hms.harvard.edu/">David Reich</a> is back.</p><p>He and collaborator <a target="_blank" href="https://reich.hms.harvard.edu/people/ali-akbari">Ali Akbari</a> just <a target="_blank" href="https://reich.hms.harvard.edu/sites/reich.hms.harvard.edu/files/inline-files/2026_Akbari_Nature_selection_0.pdf">published a paper</a> that overturns a long-standing consensus about human evolution — that natural selection has been dormant in our species since the agricultural revolution.</p><p>By scaling ancient DNA sequencing and developing a new statistical method, they found that selection has actually sped up.</p><p>Selection went especially bonkers during the Bronze Age (around 3,000 years ago).</p><p>That’s when gene frequencies for everything from immune function to body fat to intelligence were most in flux.</p><p>Over the last 10,000 years, selection pushed the genetic predictor of cognitive performance up by roughly a full standard deviation — most of it between 4,000 and 2,000 years ago.</p><p>After we finished recording, David sketched out on a whiteboard his new heretical model about who the Neanderthals really were. Luckily, I took out my iPhone and managed to record it.</p><p>He thinks the standard story (that Neanderthals are some separate archaic lineage we interbred with a little) just doesn’t fit the evidence. Instead, he proposes that Neanderthals are essentially genetically-swamped modern humans.</p><p>A small population somewhere around the Caucasus invented Middle Stone Age technology roughly 300,000 years ago and expanded outward. The ones that moved into Europe interbred with local archaic humans, got genetically swamped, and became Neanderthals. The same expansion went into Africa, met much more diverged archaic Africans, and that mixture became us.</p><p>This means Neanderthals and modern humans share the same cultural ancestry — the only difference is which archaic humans they mixed with afterward.</p><p>David is a brilliant and rigorous scholar. It was a real delight to learn from him again.</p><p>Watch on <a target="_blank" href="https://youtu.be/sRKBGVFVYAw">YouTube</a>; read the <a target="_blank" href="https://www.dwarkesh.com/p/david-reich-2">transcript</a>.</p><p>Sponsors</p><p>* <a target="_blank" href="https://cursor.com/dwarkesh">Cursor</a> was super useful as I prepped for this episode. Whenever I had a question, I’d have Cursor kick off a few different models simultaneously and then compare their responses. I found that this led to better results than I could get out of any individual LLM. If you’ve only used Cursor for coding, you should try using it for research. Check it out at <a target="_blank" href="https://cursor.com/dwarkesh">cursor.com/dwarkesh</a></p><p>* <a target="_blank" href="https://janestreet.com/dwarkesh">Jane Street</a> uses an internal currency called “hive bucks” to allocate compute through a real-time auction – and anyone can change anyone else’s bids or even kill their jobs! Everyone just trusts each other to act in the firm’s best interest, which is what lets the system work in the first place. If this weird and high-trust culture sounds like your kind of thing, Jane Street’s hiring at <a target="_blank" href="https://janestreet.com/dwarkesh">janestreet.com/dwarkesh</a></p><p>* <a target="_blank" href="https://crusoe.ai/dwarkesh">Crusoe’s</a> ML infra team built fastokens, an open-source tokenizer that delivers a ~9x speedup over Hugging Face and up to 40% faster time-to-first token – on real production workloads! Crusoe achieved these results by parallelizing things and using some clever engineering to handle duplicates without cross-thread coordination. Learn more at <a target="_blank" href="https://crusoe.ai/dwarkesh">crusoe.ai/dwarkesh</a></p><p>Timestamps</p><p>(00:00:00) – Ancient DNA suggests strong selection over last 10,000 years</p><p>(00:15:45) – Natural selection intensified during the Bronze Age</p><p>(00:35:02) – Why didn’t evolution max out intelligence?</p><p>(00:57:21) – Evolution is limited by time, not population size</p><p>(01:09:02) – Why no farming before the Ice Age?</p><p>(01:17:13) – The Neanderthal puzzle David can’t stop thinking about</p><p>(01:54:10) – The methodology behind this breakthrough</p> <br/><br/>Get full access to Dwarkesh Podcast at <a href="https://www.dwarkesh.com/subscribe?utm_medium=podcast&#38;utm_campaign=CTA_4">www.dwarkesh.com/subscribe</a> [Source](https://api.substack.com/feed/podcast/196892360/60c5fc38586fe814f5fd91f0895a59c1.mp3)

[0:00] David Reich: 通过从古人类遗骸中提取DNA并追踪其随时间的变化，深入了解人类生物学特性的演变 [10:00] David Reich: if all the arrows point in the same direction, we win [11:42] David Reich: the genome is vibrating with natural selection [57:40] David Reich: 在当今世界上，人类基因组中每一个可能发生的碱基突变，在每一代人中都会独立且重复出现大约 100 次 [70:00] David Reich: a very long fuse [101:52] David Reich: you shouldn't sneeze at it [106:15] David Reich: 这让人想起古代天文学。为了在‘太阳绕着地球转’的前提下解释行星的怪异运动，托勒密天文学家发明了一个又一个本轮，让模型变得冗长而别扭。直到哥白尼提出万物绕日运行，整个体系才瞬间回归清爽与简单。古人类学界不断添加复杂修饰来迎合旧模型，而我们提出的这一替代模型，虽然未必像日心说那样翻天覆地，但它明显更加简单，且能自然解释这许多矛盾。 [106:15] David Reich: 至今还没有人真正将非洲内部的现代人群亚结构研究，与基于古 DNA 的演化图景严密拼接在一起 [122:18] David Reich: a crazy number of results [122:18] David Reich: trying to make the results go away [124:12] David Reich: 这是阿里想出的一个绝妙点子。它彻底摆脱了传统的假阳性控制思路，因为我们直接利用了一组完全来自独立维度、通过截然不同方式测定的表型相关信息，来反向读取突变为真实的概率。 [131:15] David Reich: 我们在2014年能提出的科学问题，与我们今天能够探究的问题早已完全不可同日而语。

[20:00] David Reich: The biology of the population might be not in the ideally adapted position... what you're seeing is the DNA of this population which is descended from hunter-gatherers only 10,000 years ago reacting to the shock of having been moved into an agricultural and Bronze Age and high population density and urban environment. [40:00] David Reich: There's no way by chance that the trajectory in Europeans over the last 10,000 years will have anything to do with the effect on the years of schooling in China today. But there's actually a huge statistical correlation... a five or six standard deviation correlation. [89:00] David Reich: The actual correct way to think about them may be in an important sense somehow the relations or the close cousins of modern humans.

[0:00] I am back with David Reich, who is a professor of ancient DNA at Harvard. How do you describe what it is that you study? I'm a geneticist, and I work on human history and how ancient people relate to each other and people living today. Great. And so we did an interview, was it two years ago at this point, which ended up being one of the most popular interviews I've ever done. I think people just found it really compelling that there's so much about human history we don't know and are just learning about now as a result of the kinds of techniques that your lab is using. And you have a new preprint that's very exciting, and I wanted to talk to you about it. So let's begin. Can you give me a little bit of context on what we're talking about today? Well, the dream was that when this field started, this ancient DNA field started, more than 16 or 17 years ago, that we were going to learn a lot about biology, learn about how people's biology changed over time by getting DNA out of ancient human remains and tracking changes over time. And that dream has really not been realized since the beginning of this field So while the field been a big success with regard to learning about human history it resulted in surprising findings about human migrations people not being descended from the people who lived in the same place hundreds or thousands or tens of thousands of years before, and mixture being common in human history, sex bias processes being common in human history, and things that were not expected from archaeology. And so the field's been a big success from that perspective. But what's not been successful is learning about biology and biological change. And one big reason for that has been that the sample sizes have been too small. So when you have a single person's DNA, it provides a tremendous amount of information about history. And that's because when you look at one person's DNA, it's not a single person. It's many people. It's your two parents. It's your four grandparents. It's your eight great-grandparents and 16 great-great-grandparents and so on. And going back in time, thousands, tens of thousands, even hundreds of thousands of ancestors going back in time contributed to people today. So when you look at the DNA of a single person's genome or a Neanderthal genome, you have effectively tens of thousands of ancestors all represented in your data And you can position that individual exquisitely with respect to other people from whom you have data But when you are interested in how a particular genetic variant that affects something like your skin pigmentation or affects your ability to digest cow milk into adulthood or affects a behavioral trait when you want to see how that changes over a time, a single person gives you only one sample or maybe two samples, the one that is in their mother and the one that's in their father. And so to get a high-resolution picture of how the frequency changes over time, you need to have very big sample sizes of truly very large numbers of people. And we just didn't have that until the last few years. So what motivates this study that we're, I think, talking about today and the work that hopefully another number of groups will be doing in the coming years is the fact that we now finally have those numbers and we can do something with the data to see how frequency changes over time. Can I ask you a question? I'll be asking a of questions to the next few hours. But why are frequency changes especially interesting? So what we're interested in is using the experiment of nature that's occurred in our history over the last tens of thousands of years to understand what's biologically significant in our DNA And if there has been a change in environment that a population has experienced for example people have shifted to agriculture or begun living close to domesticated animals or moved to a new environment from a cold place to a warm place or a low place to a high place then there pressure on the population to adapt to these new stresses these new needs. And the way you're going to detect that is you're going to see that the frequency of a genetic variant that, for example, might allow you to live at higher altitude, for example, or that might sort of nudge you to have a different behavioral pattern that might be advantageous in the new situation, that genetic variant might push systematically in some direction in a way that is enough that you can detect it. Now, it's very hard to detect slight shifts in frequency by a few percent or a 10 percent unless you have a very, very big sample size. And so what we're looking for are those changes in frequency that are too extreme to be due to chance, then that will tell us that there have been pushes against the biology as a result of the changes in environment that people have experienced. Interesting. Okay. So, what did you guys find? Seven years ago, Ali Akbari, who at the time was a postdoctoral scientist in my laboratory and a few years later became a permanent staff scientist in my laboratory, set out to use the data that we were producing to learn about biological change over time. [5:00] I think the reason he was interested in our laboratory rather than other places was that a focus of our laboratory has been generating truly large amounts of data from ancient humans. We've been really trying to industrialize the process, make it very inexpensive, make it high quality, and generate large numbers of samples with lots of good data for this purpose. So there's been this large amount of data that we've generated, and it made it possible to conceive again of asking the question about whether there's been frequency changes over time. So the mainstream view in human evolution in the last several decades has been that natural selection has been pretty quiescent over the last several hundred thousands of years of human history. And there's several lines of evidence that have been deployed to document this. One is that if you compare diverse populations from different continents around the world, for example, Europeans and East Asians, and you look at mutations that differ in frequency between these groups, all mutations differ a little bit in frequency, sometimes a lot. You can say, what are the most different mutations in terms of frequency between Europeans and East Asians? And there almost no genetic changes that are 100 different in frequency between Europeans and East Asians Europeans and East Asians descend from a common ancestral population 40 or 50 years ago that came out of Africa and the Middle East This population had a set of gene frequencies genetic frequencies and these variants bopped around randomly, a process known as genetic drift, or perhaps under selection in one direction or another. And the time that's passed since 40,000 or 50,000 years ago is sufficiently small on an evolutionary timescale, that there's just not much genetic differentiation on average between these two groups, Europeans and East Asians. But however, if there's been natural selection, for example, to help people in one place digest alcohol better, or for example, digest milk better, or do something else better, what you might expect is that there would be some mutation that would have rocketed up to very high frequency. And 40 or 50,000 years is a lot of time. It's maybe 1,500 or 2,000 generations. And so that might be enough time easily to see 100% different in frequency. And yet you don't see any more compared to what you'd expect by chance. So this made it seem that just selection has been quiescent. Maybe a few hundred thousand years ago, the ancestral human population got to some kind of optimum. And after that, there hasn't been much genetic change in one way or the other And there been small amounts of natural selection or there been selection to remove bad mutations that are constantly raining down on the genome but not what we call directional selection which is newly arising mutations or mutations being pushed in a systematic direction to help the population get to a different adaptive set point that's more favorable for the conditions that population is living in. So we were able to partition how much of the changes and frequencies of all the mutations that we're seeing in the DNA, we're looking at about 10 million positions that vary, is due to directional selection, adaptation, versus other factors, especially genetic drift. And 98% of it is other factors, especially genetic drift. So it's overwhelmingly migrations in population structure causing fluctuations in frequency. And as a result, it's super hard to actually detect the signals of natural selection, in adaptive natural selection, because they're a tiny fraction of the total frequency change. The vast majority of it are these migrations and mixtures. Nevertheless, there's so much natural selection, as our study has shown, that in fact, it's been rampant in the genome. Can I ask a clarifying question here So why are we discounting population admixture or replacement as selection Because if you think about it at a group level if one population replaces another population isn that selection i remember from the last episode you were explaining how there's been huge changes in what kinds of people are in a specific area one population came in and kind of replaced the previous one and then a new population came in and replaced the previous one and to the extent that the genetics are relevant to why that population replaced the other one. Why should that not count towards what we understand to be selection over the last 10,000 years? It could count and may count and probably should count in some respects. But it could also be that this population replacement is due to some cultural phenomenon, technology held by one of these groups, not others. And maybe there's genetic mutations that are contributing to this. Who knows? It's possible. But what you're seeing is a whole genome shift. And so what we're looking to see is whether there's one place in the DNA that is driving the change in a way that's different from the rest of the genome. And really, from a statistical point of view, what happens at these times of migration is there's just huge fluctuations in frequencies. And these are extremely uninformative times for looking and detecting natural selection. The best moments to detect natural selection is when migrations and population. [10:00] admixtures are not happening for a few hundred years. And during these times, you can actually see the mutation slowly blowing in one direction as a result. Really, the way we think about the history of Europe and the Middle East, and the way we think about it for the purpose of this study, is as an archipelago of little populations in space and time, each of which are pretty isolated from each other. So a little population in Britain, isolated for a few hundred years, a little population in Hungary, isolated for a few hundred years, between big events of migration and mixture. And in each of those little experiments of nature, we can ask, does this mutation slightly increase in frequency? Does that same mutation slightly increase in frequency? And if all the arrows point in the same direction, we win. And they're telling us that natural selection is occurring. So, for example, 4,500 years ago in Europe, almost all mutations go through huge frequency changes. And that's not because of natural selection. It's because of the step migration from the step north of the Black and Caspian Sea, 40%, 50%, 80% of the DNA becomes Yamnaya from step-pastor lists. And their frequencies of mutations were different, not because of selection necessarily, but just because they had evolved in different places for thousands and tens of thousands of years And then if you look at the descendant populations there huge changes in frequency And what you need to do is see oh is natural selection explaining a shift more than you would expect by chance Okay, in this next section, David explains the nitty-gritty of the methodology of this paper. It's honestly a bit technical, and I wanted you to get a sense of the results first, so I've moved that section to the end. If you want to understand the methodology, just stick around for the full episode. Okay, you found these locations that seem to be under selection. oh another clarifying question so you have you say 3 800 locations which we were 50 confident are have been under selection the last 10 000 years 7 200 which where we're 50 confident so i think we're getting about 7 200 positions in the dna that have 50 confidence of being real yeah so only half of those are real uh so 36 which don't know which ones so 3600 of them are real Okay, and does that also mean that outside of those 7 to 200, you're confident the other locations in the genome are not under selection? No. Okay. So if you look at the 25% probability cutoff, there will be tens of thousands, and there will be many real ones there too In fact multiple analyses we do suggest that the genome is vibrating with natural selection And there all sorts of weaker effects that are there that would be picked up in larger studies even than we've done. And that, in fact, almost every position in the DNA is correlated to a position and being dragged in one way or the other by natural selection. Instead of being quiescent, natural selection is everywhere. Even though it's only 2% of the frequency change, it's tugging the positions in one direction or the other everywhere. So we analyzed these positions that we had identified, these hundreds of positions, the ones we were super confident about. And we looked to see whether they were randomly distributed in the DNA or whether they had patterns. And what we did is we looked at maybe 100 or so traits where there had been genome-wide association studies for all sorts of different traits, like ones associated with immunity or autoimmunity or behavior or metabolism and basically other things. And for each of these, we could ask, are the genetic variations that are known to affect these traits from genome-wide association studies do they have an unusual number of genetic selection signals And what we found is there was a vast enrichment by about a four or five fold for immune traits That is there was a super concentration of selected signals in immune traits Whereas also we saw a strong enrichment for metabolic traits, things that might impact your obesity or fat traits or type 2 diabetes. And really almost no detectable enrichment, as far as we could tell, for behavioral traits or for psychiatric traits. And just to make sure I understand, this is not to say that behavioral traits or psychiatric traits or cognitive traits are not under selection. It's just that the individual sites where such traits are controlled are not especially likely to be among the locations that you've identified as under selection. Yeah, that's exactly right. So it might seem from the results of that analysis that, in fact, immune traits are highly selected and that there's been no selection for behavior in the last 18,000 years in this part of the world. But in fact, that's a wrong conclusion. And in fact, we have evidence that that's a wrong conclusion. And in fact, there's clear evidence of selection also on behavioral traits. And the reason we think we see, and we have evidence that this is so, much weaker signals for behavioral traits is that behavioral traits... [15:00] We know from other studies, medical studies, are underpinned by much larger numbers of genes than immune traits, which are underpinned by relatively small numbers of genes of strong effect. Behavioral traits are shaped genetically by very large number of genes of weak effect, and we just don't have the statistical power to detect these very weak signals there. So when we do an analysis where we look at our very strong signals of selection, that collection of very strong results is very effectively querying the immune traits, but is not very effectively querying the behavioral traits. It may still be the case, and I guess it is, that immune traits are the most selected category, but it is not at all the case. And in fact, we can prove it's not the case that behavioral traits are not selected. So we think there's two reasons why natural selection has – we've been able to prove, really, that there's two reasons why – how to reconcile the previous observations with our new observations. Remember, the previous observation is that natural selection seems to have been quiescent over a timescale of hundreds of thousands or many tens of thousands of years. Reason That you don see 100 different in frequency variants across Europeans and East Asians So now we seeing hundreds of positions that are rocketing up in frequency with selection rates 1 or more in a lot of cases So 1 or more selection rates will mean that there'll be a rapid doubling over periods of dozens of generations. And so over 1,500, 2,000 generations, like you see separating Europeans and East Asians, shouldn't you see many genetic variants that are 100% different in frequency across populations? So we were able to show that this is explained by at least two factors. So one of them is that we actually, in this part of the world, Europe and the Middle East, are in a period of accelerated natural selection. And one way to see this is to look at this enrichment pattern that we're observing, where immune traits are unusually associated with these selection signals. And we could compare the last 5,000 years of our time period, what's called the Bronze Age and further onward, to the previous 5,000 years. And what we see is that this intensification of selection around immune traits, similarly, the intensification around metabolic traits, has accelerated over this time period. So it's not like natural selection has been at the same rate over all places and times. In fact, it's increasing over the time period we're analyzing. And so plausibly, the whole time period has increased compared to previous periods So we in a period of intensified selection That not implausible because this is a population that went through a huge shock in terms of the way people live and the culture So this is a population that almost everybody we're analyzing are farmers or food producers in one way or another. Farming was invented for the first time anywhere in the world in the Middle East 11 or 12,000 years ago. The people who invented farming exploded into Europe after 8,500 years ago and spread across Europe and expanded rapidly. In the Bronze Age, there was an intensification of how people lived with much higher population densities, people living more and more next to their animals and getting their diseases and exchanging their diseases with them and with each other. And so this is a period of rapid, rapid change in terms of how people are living, resulting in different biological needs of this population. So it's not surprising, perhaps, that in the context of these dramatic changes, the biology of the population might be not in the ideally adapted position. That is that there might be what some people call an evolutionary mismatch where you take a genetic variation that evolved in hunter and put it into farmers or pastoralists and it not exactly right And so what you seeing is the DNA of this population which is descended from hunter only 10 years ago reacting to the shock of having been moved into an agricultural and bronze age and high population density and urban environment. And a hypothesis is that what we're seeing is the adaptation that occurs as a result of that. Interesting. Okay, so it might be helpful to, in a paper, you have many examples of this intensification of selection around the Bronze Age. And so feel free to navigate it yourself, but it might be helpful to go through some of these examples. So we look, one of the things we do in this work is we look carefully at many, many of these positions in the DNA. We actually have an internet browser that you could look at called the ages browser that Ali and a colleague of his, who's a co-author of our paper, built that allows you to query each of these 10 million positions and see the trajectories at each position and the evidence for selection. And one of the things that we see is that while for the most part, the signals of natural selection we detect are consistent with being constant natural selection over time, in a handful of them we're able to see that there's been a reversal or a radical change in natural selection. And very often that occurs in the period between 5,000 to 2,000 years ago. [20:00] which is the Bronze Age and the Iron Age, a period of rapid population growth and rapid movement to intensive use of many technologies that were not used that way before. So an example of this is the TIC2 genetic variant. That is a major risk factor for severe tuberculosis, which is the major infectious disease, the most important infectious disease killer in the world today. And if you look at this major risk factor for tuberculosis, this variant rockets up in frequency from 8,000 or 6,000 years ago to maybe 9% or 10% in this part of the world. And then it rockets down in frequency in the last 3,000 years. In both cases, there's very clear evidence of natural selection. In the first case, to increase in frequency, and then in the next case, to decrease in frequency. And a possible reason for this is maybe the spread of tuberculosis maybe becomes endemic in the population 2,000 or 3,000 years ago. That's potentially consistent with pathogen sequence data and other lines of evidence. And maybe this variant was protecting against something before then but then tuberculosis became significant after that point and it was so bad that it pushed in the opposite direction That speculative Oh interesting And the thing it was protecting against was probably another disease Maybe Prepping for this episode required a full lit review I needed to understand why other methods had failed to find evidence of natural selection over the last 10,000 years. What exactly did Reich and Agbari do differently? Honestly, this was quite subtle, because the most important points were distributed across a bunch of different papers. And it was frustrating to talk to LLMs about it because they kept getting confused. One of them would fail to understand an important crux, and so I switched over to a different model, and that one would get tripped up on the very next point. I ended up using Cursor to kick off a handful of models at the same time and compare their results after. I could have one model critique the response of another. This was super useful because while I'm not a geneticist, I do have enough taste to be able to say, hey, this answer makes sense, these ones don't. I also had Cursor turn this work into a so I could retain what I learned. Cursor started as a programming tool, but I found it really great for this kind of research. There's no other interface where I can get answers from a bunch of independent LLMs, all while reading the relevant paper on the same screen. Go to cursor.com slash Thwarkash to try it out. One of the big takeaways for me from the paper was just that something weird happened in the bronze age um and that as you said we not like across trade after trade uh the selection intensifies during the bronze age and this makes sense for some things for example why do we see lactase persistence uh where adults can process milk why is that intensified during this period oh well it makes sense this is the time when we don't we start using cattle not just for the meat but then also for milk and wool and other secondary products. So it makes sense. This is why lactose would matter. Lactose persistence would matter more. But there's other things which seem like they should have been relevant since the dawn of agriculture. I forget the exact name of the allele, but was it FAD-S1, which helps convert plant fatty acids into long chain fatty acids that your body needs? And that's also relevant when you move from a diet of meat as a hunter-gatherer to a diet of cereals. But that is also one I think you found was under a special selection, especially high selection, 5,000, 3,000 years ago. Yeah, so what's going on? Why is the bronze age so special across all these different traits that you observing Right So FADS 1 this variant is sort of a vegetarian slash meat adaptation And already in work prior to this actually Ian Matheson who was a former colleague who worked with me in 2015, identified this as a very strongly selected variant. And it's actually been ancient. You see copies in archaic humans, too. One of the findings of our paper is the ABO blood system. You know, you get your blood type, it's A, B, and O. The B variant has increased up to 10% at the expense of A. But previous work has shown that A and B were both already present in the ancestor of humans and gibbons, other apes. And so these mutations, some of them have been going back and forth and fluctuating over time in different time periods. But we're talking about changes in the Bronze Age. So this TIC2 variant for tuberculosis risk, multiple sclerosis risk variant inflected and increased in frequency before the Bronze Age, and then 2,000 or 3,000 years ago reversed at that period. And there's differences in Northern Europe where this process is super strong, very strong positive selection, very strong negative selection. And then in Southern Europe, only a little bit and not even very strong negative selection for hemochromatosis, which is iron pathogenic iron buildup. [25:00] that causes problems in Europe. That too has reversed around this period. In some of the complex traits that maybe we'll talk about later, these traits too have periods of intensification of natural selection. For example, depigmentation, which is the Europeans have depigmented, gotten lighter skin over the last 10,000 years. You can see it in our data. The period of strongest depigmentation is between about 4,000 to 2,000 years ago. And then after that, it's much less. And so this seems to be a very impactful, eventful, important period where a lot of the processes that we are seeing become very powerful. And it's surprising on first principles. You might think, before you walked into this genetic data, that the big change is going to be starting to grow plants and maybe farm animals. And that happens in the Neolithic, you know, beginning 11 or 12,000 years ago and spreads into Europe after 8,500 years ago. But actually, the intensification happens like 5,000 years ago, 4,000 years ago. And so, it's really interesting. This observation of that being a key point, that being an inflection point, tells us something about when humans at least in this part of the world were wrenched into a way of living that was so different from how the hunter ancestors lived that the organism had to adapt very strongly And that maybe the degree of that wrenching process moving into the Bronze Age was qualitatively greater than the degree of the wrenching process that happened from the initial transition to growing plants. Which is surprising, because our cartoon picture is that the big transition is farming. But the genetic data, the biological readout is saying our genome is reacting much more strongly to these events that happened 5,000 years ago. So you did some work with Bhatia and many other colleagues in 2014. You were looking at 20,000 or 30,000 African-American genomes today. And you were saying, look, there's some percentage, 80% West African DNA and then 20% European DNA. And can we look at their genomes today? And do we see that their allele frequencies are much different than what you just expect from this admixture? And you find, correct me if I'm wrong, but you found that they weren't. That is to say that over 200 300 years of extremely intense environment change you know going from you know chattel slavery and completely new environment There no effect of natural selection And so we see episodes like this where we don see natural selection but then the Bronze Age apparently must have had an even stronger effect where the change in environment is even stronger than what we see from Africans in Africa than being migrated to the new world and then living under slavery. That may be the case. it also may be the case that that period is just too short to see much effect. So what you're looking for in the Bhatia et al. paper, where we looked at about 30,000 African Americans and looked to see whether there is, instead of the average percentage of maybe around 80% West African ancestry, in some places in the DNA, more than 80%, in some places in the DNA, less than 80%, significantly, as you would expect if there was natural selection from some genetic variant from Europeans or from Africans, we didn't see any place in the DNA that was significantly different from what you would expect by chance. And so, one possible explanation for that is just that there's only a handful of generations, maybe five, over which the natural selection would operate. And so, maybe if the selection was 2 a generation you would still only see maybe a 10 compounded effect and there just not enough time to detect it But the Bronze Age is not 300 years it 3 years It the power of compound interest and you have enough time to begin to see a strong effect. But this really, really, really does seem to be a very impactful time in terms of human history. And you can see it in our complex traits. So, for example, if you look at pigmentation, for example, which is the strongest signal of selection for a complex trait in our data set. So you look at genetic mutations that are known to affect pigmentations. You add up their effect across all of the DNA. So there's dozens or hundreds of them. And you look to see in what time is the natural selection strongest. And the time period is really 2,000 to 4,000 years ago. And for some of these other traits as well, you see again, the time period over which the selection is strongest is 2,000, 4,000 years ago. So, for example, if you look at genetic variants that affect measures of cognitive performance, for example, such as performance on intelligence tests in white British people today. So, this is, of course, a very strange trait to measure in the past, because there were no intelligence tests and there was no school. [30:00] But it is a predictor today, and you could look at how it's changed in the past. And we see very strong natural selection for this combination of genetic variants that predicts people's performance in IQ tests and also is highly correlated to the predictor that predicts the number of years of school or the household wealth of people, all crazy traits in the past because there was no wealth in the past. There was no school in the past. But if you look at the predictors today, there is a strong movement in a systematic direction, a large effect about a standard deviation on the scale of modern variation. And then we can do this trick of looking to see whether there's periods of time when this natural selection has occurred more intensely or less intensely. What we do is we drag a 2,000-year window through our data, and we repeat our whole analysis, not on 18,000 years, but just on a short 2,000-year window. And we can measure the strength of selection in each of these 2,000-year windows. And what you see when you look at intelligence is you see that this maxes out in the Bronze Age, between 5,000, 4,000, 3,000, 2,000 years ago. And the impact in the last 2,000 years is almost nothing. There's no evidence of natural selection at all. You might think your bias coming into this my bias perhaps if there any signal of natural selection on this trade at all might be that it would be unusually strong in the last 2 years Maybe this is a time of industrialization Maybe this is a time of greater need for this particular trait But in fact there no evidence of natural selection at all in the last 2 years But there very strong evidence in between 2 and 4 years ago where instead of a one standard deviation strength of selection it a two standard deviation strength sort of averaged over this time period And the standard deviation here is how much the polygenic score for the trait itself moves? How much the polygenic score trait moves over a 10,000-year period within a population that is held constant in terms of its ancestry. Because what's actually we're doing is we're looking in our data set at a kind of heterogeneous group of people. There's Southern Europeans and Northern Europeans and hunter-gatherers and farmers. And at different times in the past, those groups are more or less represented. So the whole strength of the methodology Ali Akbari developed is it corrects for that changing ancestry over time. And as I mentioned before, really what's being asked here is we've divided up our whole data set into an archipelago of little populations in different places in space and time And we asking in each place in space and time a little pocket of people in Britain from 4 years ago to 3 years ago, a little pocket of people in Hungary, a little pocket of people in Italy from 2,000 years ago to 1,500 years ago. And each of these places where the ancestry is relatively similar without being too disrupted in that short period by migrations, we watch to see if the genetic changes blow in the same direction. And what we're doing here is we're measuring the strength of selection at each point in time after correcting for the big population changes that have occurred. Okay, so the effect here is huge then, because if you're saying one standard deviation, a standard deviation above the median would be somebody in the 85th percentile. So you're saying that the effect of selection has been so strong that compared to 10,000 years ago versus now, um you know the the median has gone to the 85th percentile um and that's just like a huge effect over the last 10 000 years on something like intelligence or um this the the thing that predicts household income or whatever um so these seem like the especially given that this is only two percent of the change in the low frequencies and then like the 98 is coming for migration So then it sort of stupendous to think about like well what is the impact of migration then If this alone can explain, or is driving a standard deviation change in these kinds of qualities, at least among the kind of variation we see in the world. One thing you can see in the data is the migration impact is huge. So for example, if you look at the trajectory for measures of cognitive performance, like scores on intelligence tests in white British people today, but you look at the predictor of that in people in ancient times, the estimate for the hunter-gatherers of Europe is like three standard deviations below the modern mean. So that's hugely different. And then you see a huge jump from them to the farmers, who are at the mean, at zero, and that's migration. So what you're seeing is those two groups had different set points for those traits. And then the step-pastoralists have a lower set value of this. And so you see huge fluctuations in the predictor of this trait over time. That doesn't prove selection. What that is just telling you is migration. But what our test is telling you is, in addition to those fluctuations due to migration, is there a consistent effect of natural selection blowing the trait in the same direction over all over all [35:00] places at times and that's what we're detecting yeah so there's this uh the person who has a collective intelligence hypothesis which is this idea that um the selection for intelligence has actually been in the opposite direction that as society has developed there's been more specialization if there's more specialization each person only needs to understand a smaller and smaller part of the world and um therefore actually the ancients were much smarter than us And we've sort of evolved out in intelligence. And your results seem to point in the opposite direction. That although there's not been a selection in the last 2000 years as society has gotten more complicated, at least when society began, there was more need for the kind of thing that predicts intelligence today. And the reason that's surprising is if you think about hunter-gatherers, yeah, reading your colleague Joseph Hendricks' book, the amount of information that they needed to hold on to and assess everything from how to process food to how to build shelters, fire, etc., compared to my world where I got to know how to set up mics and ask questions. It just like it seems like the demands of intelligence should have been like way higher in the ancestral environment And so it very surprising that the beginnings of civilization increase the selection on intelligence Right. So, you know, this is the power of data, right? Like, you know, I think, Joe, if you asked him prior to this work, what the hunter-gatherer selection would be and where their set point for, you know, this particular trait would have been, you know, I think he probably wouldn't have made a very strong prediction, but he would have said, well, maybe you would have expected it to have a high predicted value of this trait because these people were really having to do a lot of things and figure a lot of stuff out, maybe. and that maybe once you have more complex societies, there'll be more of a collective brain and maybe there'll be selection against this trait. And in fact, it's sort of the opposite in some ways. So it's the power of data. It's not what you expect. And after looking at this data, it's actually the value of data to try to make sense of all these things. It's very interesting, like the genetic predictor of intelligence, there's lots of kind of things that are confusing about it. So it's actually worth talking about it. or the genetic predictor of years of schooling, which is highly correlated to it and is measured even better. So if you look at the genetic predictor of years of schooling there another amazing study from 2017 from a group in Iceland that looked at this measure over the last 100 years in Iceland And it looked at older people and it looked at younger people, people born more recently in Iceland. And there's an estimated 0.1 standard deviation decrease in genetic predictor of intelligence in Iceland just within one century. It's an absolutely huge effect over a short period. And this is selection against years of schooling. If I said intelligence, I didn't mean to. It's selection against the genetic predictors of numbers of years of school. And so, one possible interpretation of this, sort of hand-wavy, is that actually what's being measured here is not selection for years of schooling or for actually real intelligence, but for another trait altogether that's correlated to both of them. So, for example, the predictor of numbers of years of schooling is very, very strongly correlated to the age at which women have their first kid. And if you control for that for numbers of years of schooling, all of the signal of years of schooling goes away. So maybe what you're measuring is women's decision about when to have children. And if you have children earlier, you don't go to school as much. If you have children later, you go to school more. Maybe it some kind of measurement of delaying gratification or putting things off or planning The same trait is correlated to body mass index to obesity or to walking pace So is this really like intelligence as we think about it? Or is it something else that manifests itself differently in different times in the past? Yeah. Okay, so obviously a trait like years of schooling was not itself a meaningful thing in the past. And the underlying things for it seem to have been under strong selection. So whatever in the genome predicts years of schooling seems to have been under strong selection. And how should we think about this? What is the actual thing that's changing in the genome? Yeah, well, I think that there's two things going on that you need to think about. So one of them is that years of schooling is connected to so many other things genetically. So if you look at the genetic predictor of years of schooling, this trait has been measured in millions of people now. It's actually correlated to really, really surprising things. It's correlated to the age at which women have their first kid. It's correlated to people's obesity. It's correlated to people's walking pace. It's correlated to people's household wealth. It's correlated to a variety of other traits that seem quite different from it. So if you think you're actually... [40:00] measuring years of genetic prediction of intelligence or actual studiousness or something like that, you should think again, because there's many things that it's correlated to. There seems to be some kind of general trait that maybe you could think of as executive function, or maybe propensity to defer gratification, or something, or I'm just waving my hands, that is under selection, and it pushes all these traits in the same direction, one way or the other. And in different times in the past, it's advantageous or disadvantageous. But when we found this signal of years of schooling being increased, the genetic propensity to go to school for more years, as it manifests itself in people, in white British people today, when we found the signal, we were sort of incredulous, like, how could this be? Maybe this is a problem. So we did a few tests to try to figure out whether this was real. And one of the tests we did is we looked for a study where this measurement of the numbers of years of school was done not in Europeans, but was done in Chinese people in China. And we looked at variants that had the effect size of many variants as they affected the number of years of school in China And we saw whether they had a relationship a correlation to the trajectory of those same genetic variants in Europeans over the last 10 years So these are two parts of the world where the populations have been essentially completely disconnected. And there's no way by chance that the trajectory in Europeans over the last 10,000 years will have anything to do with the effect on the years of schooling in China today. But there's actually a huge statistical correlation, a five or six standard deviation correlation between the effect size of variance, a number of years of school in China today, and the trajectory in Europe, just as strong, actually, as the effect size of variance in Europeans to the trajectory in Europeans. So we just could not see a way this could happen by chance. And once we saw that, we really felt quite convinced that this was a real signal and that really somehow there has been natural selection to increase the genetic changes that today manifest themselves as predicting more years of schooling. Okay, just to make sure I understood, you're saying you're, you know, you're looking at this ancient DNA in Europe and you saying well it seems to predict years of schooling for modern people in Europe or at least a selection on those ancient dna that ancient dna seems to predict more years of schooling in modern europe and then you also find well it also predicts how the same um variants predict more years of schooling for chinese people in china yeah and so this is not just some weird artifact from the way these g-wats were done in europe that seems to, these parts of the genome seem to robustly predict the kind of thing that actually leads to more years of schooling, at least in people today. Correct. Jane Street is pretty secretive, but I did learn about one internal mechanism which illustrates how high trust and weird their culture is. Researchers aren't given compute allocations. Instead, Jane Streeters use an internal currency called hive bucks to bid for compute in real-time auctions. Everybody can spend as many hive bucks as they want. But your hive buck bid is meant to represent the real dollar value of the experiment that you want to run. Now, notably during the auction, anybody can change anybody else's bid. And after the auction, people can even kill each other's jobs. People just trust each other to do this in a way that benefits the whole firm. As a result, Jane Street's allocations reflect a near real consensus on the highest priority uses of compute As Axel one of their ML engineers put it I think Jane Street like pretty bottom in terms of we have lots of different researchers who are all training their own models sequence models all sorts of other weird and wonderful things By the way with their new compute deal they just added a six billion dollar high buck stimulus to their internal economy. Jane Street is hiring researchers, engineers and interns. Go to jainestreet.com slash Thorcash to learn more. Okay, so stepping back, I want to understand, I think there's this question about what does this tell us about what actually changed in our environments um over the last 18 000 years and we talked a little about what happened after the bronze age i want to understand it's surprising to me we're talking about this during the collective intelligence part of the conversation but it's surprising to me that things like intelligence or lack of schizophrenia or so forth things just seem kind of robustly good we're not maxed out um before the bronze age and in fact there was so much the diversity among different populations was so big that you have uh the european hunter-gatherers um having three standard deviations less predicted uh value for you know what they would score on intelligence tests if it existed um [45:00] But, you know, they were existing in the real world in a place where intelligence matters. And so how can it be that this was not a true, you just look at the human body or any animal, just like there's evolution has been acting on it so strongly to make it functional, the things it needs to do. And this one thing, which seems so relevant, especially to what human hunter-gatherers needed to do, doesn't seem to have been under that strong selection in the Mesolithic or Paleolithic or those eras? I think that that's a great question. And like, as we talked about before, the human selection is very effective. It can move the mean value of traits within hundreds or thousands of years in one direction or the other if that's adaptive in a particular environment. And so you might wonder, isn't intelligence good, you know, in all contexts and places in time? And I think that there's a number of ways to think about that. First of all, I think we are speaking from the point of view of a society which intensely values this particular trait, you know, ability to score well on IQ tests or things like them, or to go to school for a long time or whatever it is And I think this is unprecedented in human history that we live in a time like this Like if you look at the you know Hebrew and Christian Bible and you look at how much intelligence is valued it basically not at all When the Bible is being written, especially the Old Testament, is exactly when selection for intelligence is the highest point it's apparently ever been. Yeah, exactly. But like, it's about strength or courage or religiosity or, right, those are the values, right? Or if you read Homer, or the other texts of other religions, it's not intelligence, it's beauty, it's like other things. And so, this value system, which has a hyper focus on, you know, smarts, is not obviously a trait value that's been common in the past. You might think that in certain communities, like, you know, some communities or not, there might be valuation of things that are more proximate to, you know, years of schooling. But really, broadly, it's not been a high value in the population. But obviously, the thing we're referring to is not or the thing we care about is not direct performance in an IQ test, especially in the past. I think the thing I'm trying to understand better is this is intelligence more broadly. And maybe just that IQ test intelligence is not that correlated with here is a new world environment and go figure out how to process food there and make shelter and everything else all the things which you know your colleagues like joseph hendricker talked about like the how modern people underestimate the difficulty of um doing this kind of thing with a small band of people anyway this is say like maybe that's not iq test intelligence and that's why we don't see that strong a selection effect on this thing but i just intuitively it seems like regardless of the value system, it just seems very valuable to have this trait maxed out. So I'm being very speculative. And let me give you two examples about how, about what this is, about in my head, how I'm thinking about this. And not that I'm a particularly good authority on these things. But as I mentioned, a lot of these traits, which are quite disparate, are highly correlated to each other. Obesity, years of schooling, walking pace, you know, performance and IQ tests, household wealth, all these crazy traits all seem to be governed to a substantial extent by a shared combination of genetic variants. And let's just think about what this might mean. So, in Iceland in the last hundred years, there's been selection against this combination of variants. And one possible interpretation is it's basically selection for two ways of investing in your children having many kids and not investing a lot in them or having few kids and investing more in them right So if you invest in deferring having kids but having more wealth having more resources and putting more into each kid, you're going to have a lower fertility, and you're going to have fewer kids. And that's going to result in lower fertility, but those kids might survive more and do better in society. Alternatively, you can just have as many kids as you can and invest less in them. They might have individually less good outcomes, but in a time of plenty, which is potentially Iceland in the 20th century, it might make sense to have more kids and invest less in them. And so there's a toggle between having more kids and investing less in them, and having more kids and investing less in one's life, and having fewer kids and investing more in excelling in various ways, or something like this. And so you can imagine that actually, at different times and in different places, in ecology, there's resource, there's different ways, like mammals often invest a lot with a pregnancy and a small number of children, whereas fish will spawn huge numbers of offspring into the river, the great majority of whom will be eaten. But that is an effective way to produce offspring in certain conditions. So there'll be a toggle, depending on the environmental conditions, back and forth between investing in large numbers of offspring with fewer and less investment. [50:00] numbers of offspring with more investment. And maybe we're just seeing that move back and forth over different places and times. Similarly, for schizophrenia and bipolar disease, how could this ever be advantageous? But maybe what we're seeing with these diseases is a kind of readout of some kind of spectrum of traits that actually, in some contexts, might be advantageous. Maybe being anxious or being imaginative or being neurotic might be helpful in a shamanistic tradition, you know, in a religious tradition which values people who can have visions or values people who can be creative. And maybe these are subclinical versions of schizophrenia or bipolar disease that in certain times may be advantageous and in other times may be disadvantageous. Maybe you're just seeing selection from different types of creativity or other thinking that can be valuable in different contexts. I'm waving my hands here, but my sense is that these complex traits have not pushed in one direction because there's advantage, there are spectrums where there's advantages to both ends of the spectrum, and there's multidimensional, you know, impacts of these different traits. Julian Jaynes has this famous theory in The Origins of Consciousness in the Bicameral Mind that I butchering this But fundamentally the way I understand it is that up until Homer basically everybody was schizophrenic in the sense that people genuinely thought that gods or whatever were real people that you communicating with And his claim is that ancient texts seem to show people behaving in this way. You're being asked to believe in visions. Yeah, exactly. And even today, I think there's valuation in some religious communities and communicating with God and having visions and having supernatural communions. And so, I just don't know. But I think it's super interesting to ask the question why certain traits are not always advantageous. For schizophrenia and bipolar disease, there is a sense in which most of the mutations are disadvantageous. in vintage. We can see that from the patterns of variation where the variants that are risk factors tend to be low frequency and they tend to be small effects. So another trait of fine under selection is the trend away from body fat since the agricultural evolution. Why is that? So this is what you see as a reduction in the combination of genetic mutations that make you at risk for obesity body mass index And similarly and very correlated to it higher fat massed higher waist to hip ratio higher type 2 diabetes risk And so there is clear selection by about a standard deviation on the scale of modern variation for these traits, reducing over the last 10,000 years in this part of the world. So what can be going on there? Why was there not selection for this combination of traits before? There's a longstanding idea known as the thrifty genes hypothesis. The idea is that once you have hunter-gatherer populations that move into a farming environment where there's plentiful food, there is no longer a need to the same extent to be able to build up body fat to survive in times of stress because there's more constant stores of food. And so as a result, there will be natural selection against body fat, which can be once you move into an agricultural environment and to periods of food plenty. And so maybe what you're seeing is that this group of people in Europe and the Middle East over the last 10,000 years has moved into a period of relatively more stable food where building up stores of fat are not as advantageous, and there's been selection against this combination of traits. Europeans actually are relatively better protected genetically against type 2 diabetes than some other populations around the world like African Americans and Native Americans that have perhaps not been as exposed to agriculture for as much time. So you may be seeing the effect of more exposure to more stable food accessibility. This is also another way in which the data goes against a common story. And the common story is that hunter-gatherers actually had much more stable diets because they were more varied, And so they weren't reliant on a single cereal or a single crop for their calories. And if one game went away, they had other things that they could scout for. They could move locations more easily because they weren't tied down to the land. And so they were more food stable. But in fact, if there's been selection against storage of body fat, that suggests that as unstable and as common as famines might have been in agricultural societies, it's at least more stable than what the hunter-gatherers had. I think there's a timescale issue. You're absolutely right. So I think, as I understand it, I'm no anthropologist, but my understanding is that when there's a hunt in some of these in traditional societies or communities that hunt, people will often gorge themselves and eat a huge amount and build up a sort of temporary store of fat and then go with multi- [55:00] days without eating meat sometimes until the next hunt. And so there is this sort of boom-bust access to high-value nutrition that is not true to the same extent in farming communities. On the flip side of this, these famines are, I think, something that occurs more commonly in agricultural societies. But the timescale and the tempo of them is very different from the hunting tempo. So maybe there's a famine every three years. And indeed, if you look at the bones of farmers, at least in some communities, there's more stress in them, maybe due to a famine every three years or a famine every five years. But selection might not be acting on that three-year time period. Your fat store from the latest hunt is not going to carry you through to the famine three years later. And so survival of famines is a different thing than building up body fat for being able to survive two weeks later. A kind of random question I have is, if you were mentioning, look, as compared to these other things which matter much more for fitness and the ancestral environment, the immune system, especially after the Bronze Age, all these other things have mattered more than intelligence And so they been under much more selective pressure than intelligence Right That makes you wonder whether there much more room at the top for intelligence as in if humans had been selected especially for intelligence they could have been much smarter and the reason that relevant is we're currently building ai systems which are trying to make as smart as possible and in fact the only goal of the training process is intelligence we don't have to worry about also at the same time making their immune systems powerful and we have lots of energy to spend on it right and at the same time making sure they're not schizophrenic i guess we kind of worry about that. But if intelligence has not been the dominant trait under selection for humans over the last 10, 20, 100,000 years, does that mean that there's more room at the top for this trait? I think there's more room at the top for a lot of these traits. I think that you can move height very extremely in one direction much more than it is today. You can move any of these traits very much more extreme in the other terms. There's probably very strong negatives to doing that. you're probably sacrificing other things. And I think that there's trade-offs probably. But I think it's highly likely that if natural selection was pushed any of these traits in more in one direction than it is, the mean would move. So all of this evolution since out of Africa is acting on alleles that already existed in the pool of human variants from that first group which we were talking about last time on the order of 10 people that um you know exploded out of uh, out of Africa. And it's, is it surprising that across all these different traits from cognitive profiles to, uh, resistance to different kinds of diseases to, um, height to whatever, that that one pool of people contained so much latent variation that they could supply the, you know, enough, you know, stretchiness to accommodate all these different traits that you're studying now? That's a rich question. And I think that the human population has within it for complex traits, a tremendous amount of variation. So within the human population, there's a huge amount of variation that affects height. There's a huge amount of variation that affects body mass index. If you take all these mutations and all set them to the high height variant, a person will be extremely tall, like as tall as a tall building, you know, if you, of course, which will never happen. But if you take all these variants that affect schizophrenia risk they will and you point them all in the same direction there will be extreme risk or extreme protection for schizophrenia So for complex traits ones underpinned by many mutations all the variation already exists to move the population to a different adaptive set point that's optimal in the environment which it's in. So if you push the population into a new environment within hundreds or thousands of years, the population can rapidly move to a new adaptive set point, there are some unusual traits like ability to digest cow's milk or protection against sickle cell anemia that require a single very important mutation that may not yet exist in the population. And then you have to wait for the mutation to occur in some people. And when the populations are relatively small, only 10,000 people, you might have to wait dozens or hundreds of generations for that mutation to arise. But when the populations are large, there's not mutation limiting anymore. Every mutation that can occur does occur. There's 8 billion people in the world. There are maybe 30 new mutations every generation. So that's like, what is it? It's like 240 billion new point mutations every generation. There's only 3 billion DNA bases in the genome. So every mutation that can occur does occur about 100 times every generation, and we're not mutation-limited anymore. [60:00] Not like you have to, that the mutations can arise again. They do arise again. But when the population is only 10,000, you have to wait dozens or hundreds of generations sometimes for the new mutation to occur. And so how likely is it that the thing that changes with the Bronze Age is just that the human population is big enough? So in 3,000 BC, you go to, I think, a population of 50 million-ish people. The population is big enough and the gene flow between different areas is high enough, such that things which don't have an overwhelming selection coefficient, which aren't overwhelmingly favored by evolution, are finally visible to selection? I think that's not likely to be true, but it's an extremely interesting thing to think about. So I think already when population sizes are on the order of a million or so, every mutation that can occur does occur within a few generations. And so that's well before the Bronze Age, if you take the population even of a place like Europe, but also of other places, or maybe it's at the dawn of the Bronze Age or the farming period. So the question you ask is, maybe when the population is small, natural selection doesn't work effectively. So a common thing that people think about with natural selection, and that is true is that in small populations selection doesn work effectively And that because mutations bop around in frequency from generation to generation a lot in a small population just randomly So if you have a population of size 1,000, mutations will bop around by a frequency of 1 over 1,000 every generation. And if the selection coefficient is less than that, it will be drowned in the random bopping around of frequencies due to genetic drift. But that is already for a population of 1,000, 0.1% selection coefficient is very weak. We're talking about 1% effects, and that's very strong. It will work very well even in a population of a size 1,000 or 10,000. If you are talking about mutations of the type that will start rising only in large populations but not small populations, those are selection coefficients that are on the scale of 1 over 10,000 or 1 over 100,000. And those ones will take 10,000 or 100,000 generations to rise in frequency. which is hundreds of thousands or millions of years. So that's not going to do anything over the timescale we're talking about. There's just a timescale issue. So we're talking about strong measurable selection coefficients on the order of half a percent or more in this study. And all of those are going to work in small populations or large populations. It not going to be affected by the population size Interesting But you saying more generally once you hit a given threshold of population the dominant factor is time span not population size Correct Okay Interesting It very interesting And it actually not widely understood Yeah Okay. So speaking of data contradicting what you might have otherwise assumed, one of the papers you sent me beforehand, Malik 2016, found that there are not fixed differences between modern and archaic humans 50,000 years ago. And of course, we know this is the period in which the so-called cognitive revolution happened and modernity started and people are making art or whatever. Does this suggest that nothing biological changed to make modern humans modern and the thing that happened was some cultural change? How do we understand what this data tells us? Right 50 years ago or so or maybe 100 to 50 years ago there a quickening of the pace of change in culture So people you see the first extensive representational art and like bead necklaces and drawings on the wall and so on and so forth and also a rapid increasing pace of innovation the types of tools that people use And so the thought might be that there was going to have been some kind of genetic switch a kind of important genetic change that occurred in the population and that swept to high frequency that everybody suddenly had, soon had, and that made it possible to do these things, maybe some genes that allowed people to have complex language, representational language, for example. And so one thing that we did in 2016 in this paper by Shat Malik and colleagues is we looked across the DNA for places that might be expected to look like this, that where all people living today or nearly all people living today share a common ancestor, maybe 100,000 or 200,000 years ago. And we looked really hard, and right across all the DNA we could look at, we couldn't find anything more than 400 or 500,000, more recent than 400 or 500,000 years ago. This is like a crazy result because it looks like there's no key selective sweeps that have occurred in this period that is ancestral to everyone living today. We talked before about no selective sweeps between Europeans and East Asians, but there don't even seem to be any selective sweeps shared between all humans in this really important period when a lot of evidence in the material culture record appears. [65:00] And so it could be that there's biological adaptation in this period, but it's polygenic. There's lots of mutations that all shift in the same direction to help the population to move to a new set point. But there's no key biological change that rises to high frequency in this time. And this group, 50,000 years ago, they are the ancestors of everybody out of Africa or also some Africans? So this is 100 to 50,000 years ago, and this is the population that's ancestral to West Africans, to most East Africans, to all non-Africans. And there's a couple of populations in Africa that have substantial ancestry that comes from more divergent groups. For example, Khoisan from southern Africa or central African rainforest hunter-gatherers have substantial fractions of their ancestry from groups that diverged maybe 200,000 years ago from the other lineages. But all of these groups today are able to go to college, do everything everybody else does. And so there is no evidence that there is any key mutation lacking in some groups that are not present in the others. So the differences we see between different groups of people especially if this group of people 50 to 100 people years ago had a very small population size I think last time we were discussing on the order of 10 people Yeah. So basically, everybody in the world or almost everybody in the world or the variance we see between different humans today was latent in this group. And I get your point that, well, if you just stack up different things across the genome, then stacking them up really has a big effect. But it's interesting that we have so many different groups in the world today, and all that diversity comes from a very small population size. I think a lot of us in human genetics think that our population contains within it the clay that's needed to make almost any trait. And that depending on environmental conditions or selection conditions, the mean value of these traits will move in different directions. There's an empirical question, a real question about how much selection there's been in different human populations over time. One of the things this new work that we involved in is doing is showing that at least in the last 18 years 10 years 5 years in this part of the world there actually has been significant movement at least for a handful of important traits We looked at more than 500 traits about 100 of them Complex traits showed significant movement in systematic direction over this time period. So it really does seem that there is a response to the environments people are living in that has occurred over this period and is potentially stronger than in previous periods. CRUSO has an amazing MLInfo team that keeps finding clever ways to squeeze more performance out of their hardware. For example, tokenization has become a real bottleneck for Argentic workloads. Argentic prompts are often extremely long. They tend to have high KV cache rate rates, which shrinks the GPU's pre-fill work. This means that the tokenization step, which is traditionally sequential, is a much larger fraction of time-to-first token. To solve this, Crusoe built Fast Tokens, an open-source Rust-based tokenizer which paralyzes things in order to take advantage of all the cores on modern CPUs. Crusoe had to get creative here because the naive approach doesn't work. For example, for pre-tokenization, you can't just split your text into chunks and run regex because you'd end up with issues whenever a word straddled the split. Crusoe solved this by giving each thread an authority zone plus the ability to read 1KB past its own edges This 1kB buffer guarantees that you won misprocess a token and the Authority Zone guarantees that you won end up with duplicates No cross coordination required Crusoe combined this optimization with a handful of other smart tweaks in order to get up to 40% faster time-to-first token on real production workloads. To learn more, go to crusoe.ai slash thorkesh. We were talking earlier how there's no fixed differences between humans 50,000 years ago and humans today. So if there's no genetic basis for the kind of thing that allowed humans to have more symbolic representation, have farming, etc., I think I asked you this question last time we talked, but especially with this context, why no farming before the Ice Age? Genetically, we're there. That is such an interesting question. Genetically, we're there. The common ancestral population has all of the ingredients for farming 50,000 years ago. And these people are distributed into different parts of the world. The Americas, 15,000 years ago, or whatever it is. New Guinea, 40,000 years ago. East Asia, Europe, West Africa. No farming develops before 12,000 or 11,000 years ago. It only develops in the last 12,000 years, the period known as the Holocene, which is sort of the end of the ice [70:00] And if you talk to climate scientists and archaeologists, you know, I keep asking people this question every time I meet someone who's an expert in this. It's like, how can this be that farming develops in all these places? Are we really living in such an unusual time? And people tell me, indeed, we're living in an very unusual time on a scale of 2 million years. That is, 12,000 years ago, we switched into this period of not just warmth, but climate stability. And that actually, this is true. And it's sort of hard to believe that we're living in such a special time. But if you look at, for example, data from the bottoms of ponds, where you can measure the fluctuations of temperatures using isotopic signatures, apparently we're in a period where it's just fluctuating a lot less year to year, and 10 years to 10 years, and 100 years to 100 years, and it's just a period of relative stability. that we are miraculously living in, and that when this period of relative stability happens, somehow it follows that multiple groups independently turn to agriculture, even though the genetic complement, all of whom have the same genetic complement that arises 50,000, 100 200 300 years ago It kind of a crazy observation that people just accept but it like unbelievable Oh so you increased the range there So you said 100 200 300 years ago And we based on the genetic differences between modern people and people from even 300,000 years ago, you think basically there's, they're modern 300,000 years ago? I don't know. Like, I'm thinking about this all the time right now. This is actually like actively what I'm thinking about right now. And there's a big transformation in terms of the culture of humans 300, 400,000 years ago, this invention of level technology, the ability to make stone tools out of cores, the Middle Stone Age revolution or the Middle Paleolithic revolution, depending on what you call it in Africa or Eurasia. And this is a revolution, a new way of making stone tools that's shared by Neanderthals and by modern humans, but is not shared in East or South Asia. And it's a big change, and it involves a cognitive change, presumably, in order to make this sort of technology. And then there's a further change to the Upper Paleolithic later Stone Age, maybe 100,000 to 50,000 years ago, when there a second transition with a new type of toolmaking but not as revolutionary as the earlier one So when the cognitive leap happens is unclear The diversification of the lineages leading to people living today like Khoisan Southern Africa and rainforest hunter and that all occurs more on the timescale of 300 or 200 years and all of these people are capable of going to college and doing everything. And so it's not obvious that all the toolkit, the cognitive toolkit, the behavioral toolkit, the genetic abilities were not all in place two or three hundred thousand years ago and that even Neanderthals had them, right? So it's not obvious that this was not the case. And so I just don't know. You sort of distribute these people descended from this diversification that happens 200, 300,000 years ago to different parts of the world. And then, bing, after 12,000 years ago, you start having agriculture popping up in different places. It's kind of an outstanding mystery of human history. And I find it unbelievable that we live in a time period that climate orologically is so unique on a scale of 2 million years. But my colleagues tell me it's true. The climate thing seems surprising given there are so many different environments in which agriculture was independently developed now i understand that across environments the variants could have gone down but just like if it only had happened in one place at one time I could have bought that explanation But the fact that they making maize in the new world and they got you know cereals in the old world and so forth and just in very different environments makes it surprising. It's very, very surprising. And I think we accept it, but it's just like a crazy observation that most normal people don't realize. The thing that basically everybody accepts is that the common ancestral population of almost everybody in the world except for rainforest hunter-gatherers in Khoisan is around 70,000 years ago. Everybody accepts that these people all have in place the cognitive, behavioral, intellectual ingredients that are necessary for the farming revolution and building state societies. Because when these descendants of these people get distributed to to West Africa, to East Africa, to the Americas, to Europe, to South Asia, to East Asia, to New Guinea, and so on. Their descendants all do this, like independently or semi-independently or completely independently, or demonstrably completely independently in all these different parts of the world. So the cognitive resources for doing this must have all been in place, but it's a very long fuse. Like it delays for 40,000 years, for 60,000 years in all these different places after the [75:00] An ancestral population splits up and then ignites into agriculture and all these other things after that point. It's kind of a crazy claim. And then you could argue about whether the actual fuse is 300,000 years from when Neanderthals separate and from when different lineages of extant modern humans separate, and that's also plausible. So it's kind of a crazy sort of set of things that we're being asked to believe. Is it possible that agriculture existed, but you didn't have modern metallurgy or whatever it was that allowed populations to explode starting in 5000 BC with the Bronze Age? Because population-wise, it doesn't seem like 10,000 BC to 5000 BC, the early Neolithic, much is happening. It's possible that they had farming, but they didn't have copper, they didn't have tin, which you needed to go to, I guess, the Middle East for, to develop a civilization that could make use of bronze at a large scale, and so they just disappeared from the historical record? I think we would see their archaeology, and the extraordinary developments in the Americas, which are entirely Stone Age. You would see them today if they had gone completely vanished Oh yeah I mean there like you know we should go for a trip to Teotihuacan in Mexico And it like so impressive Like, you know, when I went there, when I was 20, you know, it's just like, it's totally as impressive as ancient Egypt. You know, it's like huge. It's massive. It's without metal. And it's even more impressive because it's not only without metal, but it's without animals and without wheels, which is crazy. Like the Marvel is just like, Right. Halls without wheels. Right. Like, take any person who has, like, an old world superiority and, like, take them to these places, and they will not have it anymore. It's just extraordinary what's in these places. And these are people who separated 20,000 years ago, at least, from the ancestors of East Asians and 40,000 years ago from the ancestors of West Eurasians. And, you know, just had the same biological, you know, cultural shared toolkit from then. but there's just a fuse, a long fuse delay until all this stuff happens. It's kind of like an amazing thing and we don't question it. What are other questions you have that you either are investigating right now or want to investigate these kinds of big picture questions of human history I think that I perplexed I don know if we talked about it before but I remain very very confused about the relationships between archaic and modern humans We have genome sequences now from archaic humans who lived in Europe and the West Eurasia and Central Eurasia and the Neanderthals We have archaic sequences from these enigmatic Denisovans who we now have a skeleton for since we last talked. There's now a skull from a Denisovan that's been shown to be a Denisovan. And we have data from lots of modern humans. And there's really big mysteries about the relationships amongst these groups. So genetically, the Denisovans and the Neanderthals are sisters. They descend from a common ancestral population five or 600,000 years ago. And that group descends a couple hundred thousand years before, seven or 800,000 years ago, from the common ancestors of modern humans. and so genetically the whole genome data says that Neanderthals and Denisovans are archaic humans from a common ancestral archaic population but there are so many things shared between Neanderthals and modern humans that don't seem to be shared with East Asians they both share middle stone age stone tools level technology this cognitively unique type of way of making stone tools that wasn used in East Asia They both have the same mitochondrial DNA and Y chromosome sequence So the Y chromosome sequence of Neanderthals, the mitochondrial DNA of Neanderthals is actually modern human that came through interbreeding two or 300,000 years ago and then shot up to 100% frequency. And then Neanderthals and modern humans are both the product of mixture events that happened between archaic and modern humans 300,000 or 200,000 years ago, demonstrably through patterns of variation in ancient and modern DNA. And so it feels that there's something shared between Neanderthals and modern humans that's not shared with Denisovans, even though the vote of the whole genome says that Denisovans and Neanderthals are related. So one wonders whether there's something connecting kind of Neanderthals and modern humans that's different from Denisovans, even though genome-wide denisovans and neanderthals cluster so i'm thinking about that all the time now and then connecting them would be interbreeding events or being in the same place at the same time that we missed there's a known interbreeding event from the lineage leading to uh modern humans into neanderthals but it's supposed to be only five percent yeah so i'm interested in that that that five percent is actually a sign of something much more impactful that is that [80:00] Somehow, Neanderthals are, in some sense, deeply modern in some ways. And even though they get swamped by archaic genes, that somehow they actually have more of a modern impact than one would think. And that the Middle Stone Age and Middle Paleolithic revolution that they share with modern humans is actually more fundamentally a part of who they are, in some sense, than we think. Interesting. So when was this an interbreeding event? 300,000 to 200,000 years ago. And so the common ancestor between Neanderthals and most humans alive today is potentially more recent than the common ancestor between all humans alive today. Oh, for sure. Yeah. Which is crazy. Yeah. Well, the divergence to all the archaic humans, including Dinesovins, is within human variation. Wait, what? Yes. So the average time to the common ancestor of any two human genes is one or two million years ago. So, like, if you look at any bit of your DNA that you get from your mother and a bit of your, the same bit of your DNA on the same chromosome, the copy of chromosome three you get from your mother and the copy of chromosome three you get from your father, typical time they share common ancestor is one or two million years ago. That before the split from Neanderthals and Denisovans So there many places in your DNA where you more closely related to a Neanderthal on your mother side than you are to your father and um i sure that there a simple explanation but how this is it the same reason that like if you have like a sister you know you're in some places in your dna more closely related to her than you are to me because you share a parent but in other places you're more closely related to me than you are to your sister because you happen not to share the same dna from your parents it's a process it's just that the DNA that we get from our common ancestral population was already quite variable 500,000 years ago, 700,000 years ago, a million years ago. And some of us descend from some of those ancestors and others of us descend from other of those ancestors. And Neanderthals split from our lineage really close in time on human evolutionary timescale, such that in some places in our DNA, we're more closely related to Neanderthals than to each other. Interesting. What are the other big questions? I think that's the main thing that I'm thinking about a lot these days. I think that I really continue to be very obsessed with questions about the spread of human populations around the world and trying to reconstruct that with ancient DNA. After the recording ended David started spontaneously explaining a new theory he working on about Neanderthal genetics on a whiteboard in the room which I ended up capturing on my iPhone Because it a whiteboard I think it might be helpful to switch over to a video platform like YouTube or Spotify But if you can it totally okay to listen on audio The thing I'm thinking about a lot recently is the possibility that maybe we're not thinking in the right way about the relationship between archaic and modern humans. So the standard model is one like this, where Denisovans, these archaic humans that were found from ancient DNA, and Neanderthals descend from common ancestral populations 500,000 or 600,000 years ago, and that these two separate earlier, maybe 700,000 to 800,000 years ago, from the ancestors of modern humans, people like us. So that's the big result of a lot of studies since 2010. But there's also evidence of an interbreeding event that happened maybe 200,000 to 300,000 years ago, and that actually resulted in modern humans contributing DNA to the ancestors of Neanderthals So this is maybe 5 of the DNA of Neanderthals comes from this interbreeding event and a lot of studies have shown this. And so I'm very interested in this because actually from the archaeological record, Neanderthals and modern humans sort of look actually quite similar to each other, much more similar to each other than a lot of them do to Denisovans, these archaic humans in East Asia. So a lot of the history, people have thought that Neanderthals are our sister, but in 2010, the sequencing of the Denisovan genome made it very clear that on average, Denisovans are closer to Neanderthals than to modern humans. So this was like a very confusing result. And most people now think that Neanderthals and Denisovans descend from a common ancestral population. separated earlier from the ancestors of modern humans. So, I'm interested in the possibility that actually the right way to think about Neanderthals is actually as somehow culturally modern humans, even though genetically they're mostly Denisovans. And the model I'm thinking about is motivated by this archaeological [85:00] phenomenon known as the Middle Stone Age Revolution. So if this is Africa and this is, I don't know, Europe, we know that the new way of making stone tools with these cores that were very carefully mined far away from the locations they were used, made out of high-quality stone like flint, start being used three or four hundred thousand years ago, first in the Caucasus, places like Georgia today or East Africa, and that this way of making stone tools, which is quite revolutionary and is known in Europe as the Middle Paleolithic and Africa as the Middle Stone Age, and is associated with much more widespread use of fire and also moving stone around at much further distances than before. I'm interested in the idea that this is something that's shared between modern humans and Neanderthals, as somehow some shared cultural feature that's absent in East Asia. And that might have a relationship in the genetic data and is somehow related to this 5% DNA. So the idea I'm interested in is the possibility that there is a population here that invents the Middle Stone Age and the Middle Paleolithic sometimes called level technology and that people from this population expand into Europe and they mix with the local archaic humans who are there And that is what this 5 interbreeding event is. It happens 200,000 to 300,000 years ago, and it produces a group that, as it expands across this landscape in Europe, mostly picks up the local DNA and becomes mostly archaic genetically, but retained its modern human culture, the way of making stone tools and some of its traditions. And so one of the things that's super interesting about this is that if you actually look at the genetics, the whole genome, the Neanderthals and Denisovans cluster, but if you look at the mitochondrial DNA, which people get from their moms and they get from their moms, Neanderthals and modern humans cluster. So if you look at the mitochondrial DNA, Denisovans and modern humans share an ancestor well more than 700,000 or 800,000 years, as you expect from the history. And if you look at the Y chromosome that you get from your dad, Denisovans and modern humans share an ancestor more than 700,000 or 800,000 years ago, which is consistent with this history. But if you look at the Neanderthal mitochondrial DNA, it's only 3,000 to 450,000 years. If you look at the Y chromosome it only 3 to 450 years So what the current genetic work is asking us to believe is that even though this is only 5 of the whole genome it introduces mitochondrial DNA and Y chromosomes and they jump up to 100 frequency It's kind of a crazy claim, because the probability of this occurring by chance is low, maybe 5% times 5%, so a very small number. And so it's sort of what we actually all believe, but it's sort of a very sort of surprising event. And somehow it's accreted all the findings in the literature so that we make ourselves believe this, but it seems sort of unlikely on first principles that somehow only 5% will introduce both the Y chromosome and mitochondrial DNA. And it really looks like this. So there's this amazing data from this site in Spain that's like two to 400,000 years old. It's three to 400,000 years old at a site called Sema de los Huesos. And they have a nuclear genome that looks Neanderthal-like, most of the genome, but their mitochondrial DNA and Y chromosome is Denisovan-like. So it really looks like there was a population related to modern humans that pushed into this Sema de la Suesis-like population, displaced its mitochondrial DNA in Y chromosome, but kept the rest of its genome. So it really looked like something like this happened. So the idea that I'm sort of playing with, and probably it's wrong, who knows, but is that there a landscape this is maybe Europe and you can break it up into a hundred or so deems little areas and modern humans get introduced at the bottom right corner in the Middle East or something and they spread into Europe. And as this population spreads, there's a wavefront of expansion, and they're interacting with the local archaic humans. And even if there's a small amount of interbreeding, the theory from lots of studies, simulations, and lots of studies of all these different species, like mammals and birds and so on, shows that there is, when there's even a little amount of interbreeding, as there's an invasion or a movement of expansion of one group into the territory occupied by the other, there's massive intergression of local genes. There's that these pioneers at the wave front, they'll sometimes interbreed with the local population. There's so many of them around that their DNA will get swamped by the local group. So by the time they make it to the other side, they're largely local. And so maybe what we're seeing is that this is what's happened. You have like a modern human population that's matrilineal, for example, where transmission of making stone tools this way is happening from your mother to the kid. And that's why they're retaining their mitochondrial DNA. But by the time they get to the other end of Europe, they're mostly archaic. They're mostly local archaic. So you end up... [90:00] the 95% population, replacement. So this would explain why the mitochondrial DNA is shared between Neanderthals and modern humans, and it would also explain why the mixture proportion is only 5%. But the really interesting thing is that actually there's other evidence from studies of modern humans that actually modern humans are also admixed, and that the right way to think about this is that modern humans are a mixture of two groups maybe like 1.5 million years ago, and that they come together 200-300,000 years ago with like 20%, 200-300,000 years ago, with maybe 20% ancestry from this archaic African group, and 80% ancestry from this early modern lineage, and that the same group then mixes with with Neanderthals, and it's 5% modern here and 95% local here. And so you actually have this key population that makes the Middle Stone Age or Levawatt Technology this one that appears here And it expands in all directions into Europe here into Africa here two to three hundred thousand years ago bringing this technology bringing these new ideas, bringing perhaps some genetic adaptations. It expands into archaic humans in Europe. It mixes with the local population. It gets 95% replaced, but still retains its cultural features and maybe some genetic features. And it expands in Africa, too. And here it's not 95% replaced. It's only 20% replaced. And probably the reason that happens is that this group is much, much more diverged. It's much more archaic. It's 1.5 million years diverged rather than 700,000 to 800,000 years diverged. And as a result, there's many more incompatibilities genetically, and there's much more barriers to gene flow. But there's still a lot, maybe 20%. And we have evidence that this is a big mixture that happens. And so what you're actually seeing is a modern human expansion, both into Europe and into Africa. In one place is it forms Neanderthals, in one place is it forms the ancestors of everybody living today. But all of these groups are descended from this key sort of revolutionary event that happens here. So we often talk about the revolutionary events 50 to 100 years ago the more symbolic behavior and so on and so forth that sort of first appear in Africa and the Middle East and spread beyond But there's also this earlier event. And this event is sort of contemporaneous with the breakup of all the different groups also in Africa today, you know, the Khoisan Southern Africans and the Central African Rainforest Hunter-Gatherers. So one wonders whether this is an equally important formative event. And it also, if that's true, makes you think of Neanderthals as actually somehow our cousins, that they actually share our Y chromosome, they share our mitochondrial DNA, they share formation of this 200,000 or 300,000-year-old event, their shared toolkit. So even though the genome is telling us that they're cousins of Neanderthal, Denisovans, the actual correct way to think about them may be in an important sense somehow the relations or the close cousins of modern humans. I have so many questions. Do you have 15 more minutes? Yeah. Okay. First of all, what is going on with this group of archaic Africans 1.5 million years ago? Where in Africa are they? And what happens to the portion of them that don form modern humans Do they survive So the genetic data suggests this is analysis not of any ancient DNA, but only an analysis of modern DNA from different people, mostly in Africa, but also non-Africans. And multiple studies, there's at least three, maybe four or five studies that I know about, have looked at the patterns of variation in people today and say the data in modern people today, including in Africans, is not consistent with the homogeneous populations. It looks like a population that split well more than a million years ago into multiple, at least two, but maybe many groups, and then came together with an important coming together a few hundred thousand years ago. The papers have different models that they fit, but they all have this feature of more than a million years ago, there's a split up. And then on the order of a few hundred thousand years ago, there's a coming together and a remixture event forming the ancestors of anatomically modern humans. And this includes the Khoisan and whatever other groups? Okay, great. All of these groups have this. Maybe it's in slightly different proportions. So you ask, where are these people living? Who knows? Right? Like, you know, in this scenario, the 80% is coming from the Caucasus or Northeast Africa, where this Middle Stone Age form. It's from this population that forms the Middle Stone Age. And they mix with, like, local groups. And who knows where they are? Southern Africa, Western Africa, Central Africa. [95:00] Eastern Africa, we don't have any ancient DNA. But this is a very rich environment. People have been living there for seven million years at least. And there would have been different groups of people everywhere. Probably it's not just two groups. It's probably more groups. I think the important theme here is there's evidence of substructure that's well more than a million years ago. And this place would have been a landscape full of archaic humans that would have been differently related to these expanding people and would have admixed with them when they came through. Okay, so the Neanderthals, first time around 300,000 years ago, our ancestors share culture with them. They share the Middle East and the Stone Age technology, but they don't replace the population. The technology spreads through culture, basically. Well, it spreads through genes, too. If you look at Yamnaya in India, there's almost no Yamnaya ancestry in India. Huh? I mean, it's just diluted, diluted, diluted down. as modern as yamnaya expanded into central asia you know like expands into europe it makes the corded wear there's a 25 dilution it expands back across central asia it goes through the hindu kush you know it gets into northern south asia it mixes more with local people by the time you know today the most yamnaya ancestry you see in india is 20 or 10 you know most people have less than 10 or 5 It you know there just been a lot of mixture on the way, but it is the tracer dye, right? Like it tracks Indo-European languages and important aspects of Indo-European culture are coming through Yamnaya. So if you know where to look, that tracer dye is only 10%, it's only 5%, it's only 2% in some groups, but it's the languages people speak and it's important cultural shared elements that connect them to people on the other side of the Indo-European-speaking world. So this 5%, you shouldn't sneeze at it, right? Like, that's tracing something important in this model. And then I understand that if things are transmitted more through women that—actually, sorry, let me back up. I don't understand why the maternal DNA and mitochondrial DNA and the Y chromosome would be especially privileged as the spreading is happening. Can you explain that? So the reason I'm talking about these matrilineal or patrilineal expansions is I'm really troubled and have been troubled for many years, actually 15 years, but especially in the last three or four years, by the fact that the mitochondrial DNA and Y chromosome cluster Neanderthals in modern humans, but the rest of the genome clusters Neanderthals and Denisovans This is like a crazy result that is not seen in any other species where you see this pattern So I very interested in patterns that would explain this If you invoke and assume that there was like a matrilineal or a patrilineal expansion it could be either where modern humans when they were expanded across the landscape of Europe retained their identity along one of the lines Like if you incorporate a local, if it's matrilineal, when they incorporate a male from the local community, they're brought into the community and the kids are raised based on the culture of the mothers or something. Or if it's a patrilineal expansion, they incorporate a female from the community. It's incorporated, sort of raised with the culture of the fathers. So if that happens, it guarantees one of these two parts of the genome to look like it does. Because if it's a modern human expansion, if it's patrilineal, it will retain the Y chromosome. If it's matrilineal, it will retain the mitochondrial DNA. So it will solve one of your two problems. But not both. It won't solve the other one, so you need to solve the other one. So the other one you can solve either by natural selection or you can solve it by social selection. So by the way, patrilineality and matrilineality are the rule, not the exception in human communities. Usually communities sort of have continuity along the male or the female line And usually it patrilineality sometimes it matrilineality So you can also have phenomena like social selection So it could be that once you have kids of someone whose father, for example, is from the outside community, that usually in most communities, females all reproduce. That's typical today. Usually women have kids if they can. But men in traditional societies are actually very variable in their reproductive success. A large fraction of men never have kids. And then there's a relatively smaller number. There's a subset of men have many kids with many women. And so there's competition among men for kids. So in this context where males are competing for access to females, then female mate choice begins to be an important process. And you have a phenomenon where it could be the case that if your dad is an archaic male, then you're not going to be as successful in the competition for local females as if your dad is a non-archaic male. So some simple social phenomenon like that could explain the data. And we actually see this in human societies. So for example, if I remember right, like in Central African rainforest hunter-gatherers, there's different tree... [100:00] of boys and girls depending on whether their dad or mom is one group or the other i guess i don't understand how the maternal like okay um you know the group spreads and it gets to the next front yeah and uh they have kids and some of those kids are um okay the the from the group from the humans that have just entered, the kids will have the maternal DNA, the mitochondrial DNA from the humans. But from the existing people, they will have the mitochondrial DNA of the archaic humans. And why are the people with the archaic mitochondrial DNA not surviving? So it's a question. So there's multiple possible explanations, but it's much easier to explain that than both the mitochondrial DNA and the Y chromosome. One possibility is that the mitochondrial DNA was less biologically fit. Another possibility is that there's social discrimination against people based on whether their parents are archaic or modern. Which is, I think, not at all surprising in a human context. It's the weakest link in this argument. This argument probably wrong but I just telling you what I thinking about Okay the 000 years ago we um our lineage interacts with them but mostly their lineage survives and there cultural diffusion etc and genetic diffusion and then is it 70 000 years ago that we interact again yes and they don't survive the the genetic ancestry doesn't survive the genetic ancestry doesn't survive so um presumably there was also other contact in between 300,000 years ago and 70,000 years ago? Probably, yeah. But these are the ones we were detecting currently. Is it just sort of like, there's not really an answer or just contingent why one time there's this kind of diffusion where most of the archaic genome survives and the other time it's total replacement? I think that this is not at all surprising given the context. So if you think about this model, this is 700,000 or 800,000 years ago. This is 300,000 years ago, right? So this is like 400,000 years separated. You talked about the Batia paper with me earlier. That's two populations 70,000 years separated. There's no biological incompatibilities between West Africans and Europeans. There's no natural selection against biological incompatibilities. So we know when Neanderthals and modern humans met and mixed there were biological incompatibilities That was at 700 years ago And so as populations become more apart there begins to be biological incompatibilities rapidly developing probably as the square of the distance separation because you need pairs of interacting genes and therefore it the square of the separation So here, it would have been maybe only 400,000 years separated between this lineage and this lineage. Here, it's like 1.2 million years. It's a lot. So these are at the edge of not being able to produce children. So this is quite different humans. These are actually three times closer than these. You know, and like, if you look at mixtures of humans today, there are mixtures in Southern Africa today or people who are half this distance, right? If you look at Khoisan and Bantu people mixing in Southern Africa, like the Xhosa, which is the population of, for example, Nelson Mandela, this is groups that are separated by almost 200,000 years, which is half of this. Totally compatible. And so what you're seeing is this is a group that's actually completely permeable genetically or nearly completely permeable. This one almost certainly has substantial biological incompatibilities, because 300,000 years later, two or 300,000 years later, we see the interbreeding between Neanderthals and modern humans or between Denisovans and modern humans There clear evidence of incompatibility at that point But this would be even bigger So what you would expect to see is that as this group spread they would be moving into a territory full of archaic humans and there would be some interbreeding, but the kids would be not very fit, they would die off, there would be a lot of infertility. And so the barriers to gene flow and to interbreeding would be greater. So to me, it's not at all surprising that as this group moves into Eurasia, here's Eurasian archaics, you know, the ancestors of Denisovans, you know, and these are only 400,000 years diverged from these people over here. And here's African archaics. And these are like 1.2 million years diverged. So, you know, they just don't interbreed as much. And so you don't get as much, much, much, much gene flow. But the key thing is at the same time, it's the same time. So like, it really feels like the signature of an explosion of people from one place, interacting with people here, interacting with people here. It's the same sort of cultural revolution or technological revolution impacting this place, impacting this place, and creating populations that are kind of both impacted by this cultural revolution, which we know is the case because they share the same toolkit. And so some people argue that level-wide technology is independent of the world. [105:00] invented, but this would be a sort of, but, you know, it's very similar. And this would be a way that it would have the same origin. And sort of, so there's a cultural shared thread, this shared toolkit. There's a mitochondrial DNA and Y chromosome thread, which is, and then there is a timing sort of shared thread, which is they both form by mixture. Because otherwise you'd have to believe that Neanderthals independently develop Stone Age. Yes, which is not inconceivable, but it's a little bit like believing that farming independently developed in multiple parts of the world. Right. But it did. It did? Yeah. So as I said, this is probably wrong. I'm trying to tell you that we don't really know the world we live in. And this is not obviously wrong. In fact, to me, this is much more plausible than the model we currently write down. It's probably wrong, but it's just much more plausible. It explains many more things. It's no more complicated. Interesting. Do you want to recapitulate the thing you were saying about the analogy to the Ptolemy and the epicycles? It was quite interesting. Yeah. I mean, you know, like, I think that, you know, the model that we've put together collectively about the relationships between archaic and modern humans is sort of accreted over time There was this idea that modern humans are distinct and that Neanderthals and Denisovans are like sisters of each other And then over time, we detected additional mixture events like this modern human into Neanderthal and then other ones I didn't even talk about, like super divergent lineage going into Denisovans and like all this other stuff. And we still say, oh, the whole genome says Neanderthals and Denisovans are sisters, so that's the truth. And we've patched it all together and gotten it all to work. And oh, you look at the mitochondrial DNA in the Y chromosome, and they have this odd pattern, and it's improbable, but we can get that to work if we invoke natural selection, you know, things like this. So you patch it all together, you make these, it's a little reminds one of sort of what happened in the ancient world, where there was this idea that the sun revolves around the Earth, but it doesn't quite explain the movements of the planets properly. And so, in order to get the movements of the planets to work right, the Ptolemian astronomers would have made up these epicycles these special extra rotations and movements to make everything work about right And it was such a convoluted model And then when Copernicus and colleagues suggested instead that actually what happening is everything's revolving around the sun, that simplified things and made things every so much simpler. So the situation that was happening is that as astronomical information accumulated, it kept being contradictory to the standard model, but it could be made to work by proposing another complication and another complication and another complication. But, you know, this is not like as like fantastic as, you know, proposing that everything revolves around the sun rather than the earth. But it is much simpler. And actually, it explains many, many things. What is counterintuitive or unexpected or hard to accept about this alternative model? Like, what is the hesitation that people have for adopting this as the... I don't know. I mean, nobody's thinking about this model right now. So, I don't know. It's just, I think that I don't know, it seems like obviously a very natural model to me. So Aristarchus, ancient Greek, had the heliocentric theory because he had done a bunch of observations about how far the Earth is from from the sun had noticed other things but it was not um adopted because his fellow Athenians were like look if we believe that the earth revolves around the sun for it to be the case that we don't see relative movement of the stars to the earth the only possible explanation is that the stars are so far away that it is just incomprehensible and implausible. And so the heliocentric theory was dismissed. And the reason what I'm trying to ask is, what is the equivalent of like, oh, for this to work, the stars have to be so far away that it's inconceivable, where actually the stars are so far away. And maybe we should adopt the implausible implication that this theory gives us. That's a great question. I think that we have to assume that there's a linkage between the cultural transformations in Africa and Eurasia at this time. And that's sort of not something that the community has really put together with the genetic data. So I think that there's this thread in the genetics about substructure in Africans. And then there's this whole world based on ancient DNA, and they've never been put together. So nobody's put together the now extensive work on modern human substructure with, [110:00] and now extensive work based on ancient DNA of archaic human relationships to modern humans. And if you put them together, you realize they line up in terms of their time of substructuring. So I think that, I don't know if that's improbable, it seems actually parsimonious to me. But yeah. Yeah. And it also seems significant that different groups of humans at this time were capable of adopting Stone Age technology. that once one group had figured it out, the genetic difference between different human lineages was not so big that you could not show people how to use the system. Well, who knows? I mean, it could be that actually this was genetically driven, right? Like, you know, the time to, and we talked before about the time to the common ancestor of human genes, you know, there's nothing at, you know, 100,000 years or 150,000 years, but there's a lot at 400 or 500,000 years. So if that's what happens and you have a mutation that occurs in the Caucasus or somewhere in the Middle East or Northeast Africa, and there's key genetic mutations that make people able to do this. And then this population expands. When it moves into Europe, it's swamped by local genes, but there could be retention of those genes through selection as it expands So maybe what you actually seeing is that actually there are genetic developments Most of the discussion on this I point has been focused on the 50 to 100 year event And this is like anatomically modern human behavior But this is like a lot of my archaeologists think this is an equally, if not more profoundly significant event in many ways. And why is that not the event that we should be talking about? And then we know you're talking about how there's no fixed differences between modern humans and the humans 50,000 years ago. So do we know if there's any fixed differences between the people 50,000 years ago and the people 300,000 years ago, other than obviously these interbreedings? I think that this is what we're talking about, which is like if you look at the genetic variation going back 300,000 or 400,000 years, then there do begin to be places where all modern humans share common ancestry 300,000 or 400,000 years ago. And that's another way of saying there begin to be fixed differences at that time depth. So that is where you start seeing evidence for possible fixed differences. What's basically happening if everybody shares a common ancestor 400,000, 500,000 years ago, is there's a single ancestor at that time, and if you compared it to another population, like these guys, they would descend from a different lineage, so any mutation that occurred ancestral to that single ancestor would be a fixed difference So this is the time at which you can begin to see fixed differences But anatomically modern cognitively modern humans exist by the beginning of the Middle Stone Age and before were breeding with this ancient group of Africans or breeding with Neanderthals. Anatomically modern humans occur exactly here. It's the same moment. This is when they occur. that people who like have skeletal features like ours and neanderthals appear exactly then this is when it all happens so like this is when we there is this disconnect between anatomically modern humans in the skeletal record and between you know behaviorally modern humans which is 50 to 100 thousand years ago but anatomically modern humans appear at this time and actually recognizable neanderthals appear roughly around this time too interesting interesting but we don't know what exactly happens if anything between 200 000 years ago and 50 000 years ago that goes from just anatomical modernity to behavioral modernity? No, my understanding is no. There begins to be, they're busy making level lost stone tools like Neanderthals for 200,000 years and not more impressive than Neanderthals in any way. Interesting. And in any obvious way, as I understand. And then there begins to be in the archeological record a quickening of you know which could be not genetic at all or it could be genetic Like you know there was there lots of arguments about this. But, you know, people are obsessed with, you know, like we were obsessed with intelligence earlier in our conversation, but people are obsessed with art and, you know, these things that seem important to us, but like, who knows what's important? Yeah. And, yeah. Interesting. Cool. Thanks for the digression. The work that I've been involved in has consistently shown that I was wrong in my biases coming into the work. And I've really been almost traumatized by this. Like, again and again, I've come into a project with some kind of guess about what the data was showing, and then the data doesn't show that. So, for example, when I got involved in the Neanderthal Genome Project and helping to analyze data looking at how archaic Neanderthals were related to modern humans, I was part of a group of scientists who had established that non-Africans were a simple subset of African variation and that there was no evidence at all of Neanderthal interbreeding into the ancestors of modern humans or other archaic interbreeding. different analyses that I and very much more other people had done made it look like non-African variations [115:00] It was just a subset, a small sample of that in Africa, and that could have fully explained the data. And so that when I was involved in analyzing the Neanderthal DNA sequences, what happened was I found this very strong evidence of Neanderthals being more closely related to non-Africans than to Africans. And it was very surprising, and I thought it must be a mistake. I thought it was – I was quite incredulous. I thought it was unlikely to be true because other evidence that had been found before seemed to point in the other direction. And so I spent several years trying to make these results go away, as did my colleagues, and we just couldn't make the results go away. They just kept getting stronger. And this experience working on natural selection was the same. So what we had felt here was that what we were convinced of was that natural selection had been pretty quiescent in our species over the last several hundred thousand years. Therefore, if we look at patterns of variation in non-African people today, or in any people today, we should see not a lot of selection going on. And indeed, the first ancient DNA studies, beginning in 2015 with this paper that we were involved in with Ian Matheson and colleagues indeed these papers seem to show relatively small numbers of genetic positions associated with natural selection So in 2015 we analyzed data from about 200 Europeans and Middle Easterners to try to understand frequency changes over time. And we compared those ancient people who were the sources of modern Europeans to people in Europe today. And we looked at frequency differences that were too extreme to be due to chance. And we were very excited to find 12 positions that we were convinced were highly different in frequency between Europeans today and what we would expect based on the history that we and others had identified was the history relating modern to ancient Europeans. And so some of these were known, and some of these were not known, and this was very exciting. And we hoped that as the numbers of samples would increase and we would get higher resolution to be able to appreciate differences in frequencies over time, we hoped that this would make it possible to detect far more. And what was quite disappointing over the subsequent decade is that that didn't happen. So, for example, the largest study of that type in 2024 by a group in Copenhagen analyzed the data much better data than we had in 2015 and found only 21 positions that were highly different in frequency across time And while that was exciting it was almost twice as many as we had found in 2015 In a lot of ways it was disappointing because the sample size and data quality had gone up so much and yet this is all that was found. And so what that suggested is that we might be hitting an asymptote, and we might not be able to get beyond where we currently were, and that this approach to learning about biology, which was very promising in theory, might actually not produce a high yield. that maybe, in fact, natural selection was quiescent. And in fact, the reason we're seeing so few changes is that actually there's not been a lot of adaptive directional selection. So that was the situation we found ourselves in until just a few years ago when we carried out this study in our research group led by Ali Akbar. So what we did is we deployed a few innovations to try to improve our power to detect natural selection. One of them is we just pumped a lot of data into the system. And so we increased the amount of data by about 14-fold. And the main thing that we do in this study is we report data in this study from about 10,000 individuals with new data. So this is like a very big increase in the amount of data in the literature and the total dataset size of ancient individuals distributed over the last 18 years is about 16 people So this is a large data set It much larger than was previously possible. And when you have more data, you can estimate frequency changes with much more subtlety. And the data comes from only one part of the world, which is Europe and the Middle East. It's not a more important part of the world than other places, but it's the place where maybe 70 or 80% of the data in the ancient DNA literature so far comes from due to historical reasons. And it provides us with a natural laboratory where we can see what happens over one place over time as environments change to the genome. It's really interesting to imagine doing this type of analysis in other parts of the world. And the comparative analyses are super important and interesting. But this study right now is about this one place in the world where we have particularly fantastic data. The other thing we did is we developed an entirely new methodology that hadn't been used in this area before. And the methodology is based on a technique that had been developed for finding risk factors for disease in medical studies. And a simple way to explain it is we ask how to predict the genetic type a person has based on its pattern of relatedness to other people. So we'll have a data set of about 16,000. [120:00] ancient people and 22,000 people, if we include the ancient and modern people. And then we look at how closely related each of these 22,000 people are to each other. And we predict the genetic type at each position in the DNA at 10 million positions, based on the pattern of relatedness to all of the other 22,000 people. And then we ask if natural selection blowing the frequency of the mutation in the same direction in all the geographic places and at all times, predicts the data a little bit better than just knowing the relatedness to all the other samples in the database. So we're simply asking, the alternative hypothesis is that selection has been blowing in the same direction at all times, and we simply ask if that explains the data better. And that's a dumb assumption, because, of course, the truth is that natural selection is going to have changed in frequency over time. But we're just asking the simplest of questions, whether assuming a constant rate of selection explains the data more than not doing so. And just to summarize, to make sure I've understood, you're trying to make a model that predicts allele frequency changes over time. Right. And you have two different parts. Right. One part is this genetic relatedness matrix which captures how similar different genomes are to each other And that should capture the impact of different bottlenecks and of drift and of population admixtures and all those things which affect the entire genome correct and uh then you have the separate thing which is like okay if we look at specific locations can we just say that oh this location has been selected at whatever coefficient over time um and if we add some coefficient, does it become easier to predict the allele frequency changes than you would have just seen from this other artifact, which is only predicted, which is just looking at like, oh, if you look at the whole genome, are these guys in the same, you know, have they gone through the same bottlenecks? Have they gone through the same drift, etc.? That's precisely right. Okay. Okay, so what have you learned? So when we analyzed the data this way, we looked at 10 million positions in the DNA that in these 22,000 people, 16,000 of them were ancient. And we looked to see if there was more change in this consistent direction over time than you would expect by chance. And when we analyzed the data, we found many, many hundreds of places in the DNA that were changing too much over time in too consistent a way to be explained by chance. Now, there's a bit of a statistical problem in figuring out how many there are because they so densely packed that they close to each other and they interfering with each other But when you try to piece them out and say let count them only one in each place in the DNA and blank out the others we find at least about 479 positions that are all independently pushing in the same way. Those positions are 99% confident that they're real. By another criteria of more than 50% confident that they're real, We think that about 3,800 positions are all pushing in the same direction. So this is like a crazy number of results, given that in our work previously and other people's work, there were at most a couple of dozen discoveries coming from a single scan. So when we got this result, we were very surprised. We thought it must be wrong. And we spent the next couple of years trying to make the results go away, but they just kept getting stronger. And so what we were trying to do is to look for some kind of independent type of evidence to tell us whether these positions were real. And we stumbled on something really powerful for this purpose that had not been used in this way before. And it relied on the fact that we had very large numbers of discoveries, like many hundreds of discoveries or even thousands. And so what we did is we took a completely independent data set, which was the corpus of genome association studies So these are studies that people have carried out in hundreds of thousands of people looking for whether particular genetic mutations are more common in people with high blood pressure than with low blood pressure or something like this So we took the UK Biobank, which is about 500,000 people from Great Britain, who have been measured for hundreds and hundreds of traits. The whole genomes of all these peoples have been sequenced. And for each of these traits, we could look whether each of these 10 million positions are connected to this trait in some way, in a convincing way. So in 10 million positions, about 15%, about 1.5 million positions in the DNA are predictive of at least one of these several hundred traits. So then we could ask a question, is our natural selection signal, our statistic, is it related to whether a mutation causes a high blood pressure or some other trait? So we slid our statistic for natural selection from upward to a value of one, a value of two, a value of three, a value of four, a value of five. And as we did that, the enrichment for genetic mutations that affect traits got higher and higher. So whereas it was only 15% when we didn't use our selection statistic, when we required the selection statistic to be above about five, there was about a five-fold enrichment for mutations that cause traits. Oh, sorry, what is it? [125:00] This is the statistic we use to measure whether a mutation is changing over time significantly in a non-zero way. So it can be approximately thought of as a normally distributed statistic, a Gaussian statistic, which is the number of standard deviations the statistical value is away from zero, where zero is no natural selection. It's not exactly that, but it's close to that. And so if this statistic is above five, we see about a five-fold enrichment in mutations that affect a trait. And so instead of 15% of the mutations that are random affecting the trait, it's like 60 or 70 that are affecting the trait when we slide our statistic upward. And this is providing completely independent evidence that these sites are real. And as you slide above five, there's no more enrichment. So our interpretation of these results, that we were able to validate and show that these interpretations made sense using computer simulations of our process, our interpretation of this result is that once you slide the statistic above five essentially all the signals of natural selection are real okay and so just to make sure I understood you saying look in order to figure out what alleles have been under selection your model assigns a statistic saying oh in order to explain why this allel has a specific frequency, we're going to give it a selection statistic. And independently, you know, we run these studies on modern populations where we say, if you look at height or eye color, intelligence, whatever trait, what are the parts of the genome that are correlated with that trait? And the higher statistic you give it in your study in order to explain the ill frequency changes over time as a result of selection, the more probable it is that that region in the genome is associated with traits that have like some functional thing that we can measure. That's exactly right. And this is like a brilliant idea that Ali had. And it really abandons the traditional approach of assigning statistical significance to mutations that cause a trait because we just using an external piece of information the correlation to traits measured in a completely different way to read off the probability mutations are real So we can ask how much enrichment for real signal is there given a particular selection statistic And if it halfway enriched to the plateau the correct interpretation of that we able to show is that 50 of the mutations are really selected If it three quarters of the way toward the plateau there a three quarters probability that the mutation is real If there a 99 of the way to the plateau there a 99 probability that real So that gives us a calibrated estimate of the probability that a particular position is really under natural selection. A major concern here is that actually what we're seeing is not that these mutations are really under selection, but rather that both association to a disease and our selection signal are due to some third thing that's causing both of them, which is a type of selection, which is not what we're after, not selection to adapt to new environments, but what's called background selection, selection against newly arising bad mutations that are removed from the population that tend to be concentrated in genes. Genes are also the parts of the genome that tend to be associated to traits. And so this common process is causing both the enrichment for trait signals and is also causing the enrichment for selection signals that we observing That the concern We were super concerned about this So what we did is we repeated this enrichment analysis in slices of the DNA that all were affected to the same extent by background selection, by this rain of slightly bad mutations, and we get exactly the same pattern. We also repeated this experiment in just using mutations of the same frequencies, because there's different statistical power to detect these signals at different frequencies. And we see the same pattern where above a value of the selection statistic of around five, we get this plateau. So the thing that changed and allowed you to increase the amount of sequences you're generating by two inches of magnitude is just the statistical method you're using to identify which part is human? Or what exactly changed in 2014 and since then? So there's been a whole series of improvements. I think that the big ones have been the huge drop in sequencing cost, which made it possible to generate ancient DNA in the first place. So the drop in cost has been a million fold since the late 2000s. And another maybe one to two orders of magnitude from 2010 to today. So that's one big change. Another change has been in solution enrichment. So it's been this way of taking a sample that has very small percentages [130:00] human DNA, but then suddenly creating a process that will mean that the great majority of the sequences that one's analyzing will be useful for analyses. And so the approach that we used was we took the DNA samples that we had, most of which were very low percentages of human DNA, less than 10%, often less than 1%, which is such a low proportion that it's prohibitively expensive to sequence them into just brute force sequencing them, given the technology that we had available at the time. And so we took these samples and washed them over an artificially synthesized set of short DNA fragments that targeted positions of the DNA that we were interested in analyzing. So this is more than a million positions that are highly variable in people. And we picked many of these to be biologically interesting. We had a whole set of known biological targets that affected traits in genome-wide association studies, which is the way that people look to see if there's particular genetic variants in modern people that have particular impacts and phenotypes and traits. And so what we did is we had this artificially synthesized set of DNA fragments that we washed our ancient sample over, and it bound the parts of the DNA that we targeted. And the resulting sequence that we generated was very enriched for the parts of the genome that were informative about history. And even though only 10% or 1% of the DNA was human, it ended up that a very large fraction was from the parts of the genome that we were interested in, and it became economically efficient to do it. And sorry, what was the other 99% of the DNA? It mostly microbial So it from bacteria and fungi that colonize a person body after they die Depending on how they die there be more or less of these bacteria and fungi And so when you sequence DNA from a person, it'll just be full of microbial sequence. Sometimes the microbial sequence is very interesting. So it might be pathogens that a person died of. So there's, For example, amazing work about different plagues of malaria and black death and hepatitis B and so on that have been obtained from the sequences of these pathogens in people's teeth and other parts of their body when they died. But we're focusing here on the human DNA. And so what we did is we – this changed the amount of data that was possible to produce from tens per year to hundreds per year. And then we further roboticized and industrialized the process so that there were many hundreds or even thousands per year. And so just in our laboratory, we've been generating genome scale data from more than 5,000 individuals per year. I know this is true also of several other laboratories in the world now. And this huge jump in data, this sort of semi-exponential or even super-exponential jump in some cases, has made it possible to ask and answer questions. And so while they were only on the order of 10 genome sequences from humans in 2010, this year it was passed more than 20,000 reported sequences. So there's several orders of magnitude increase. And the questions we were able to ask in 2014 are just not the same as the ones we can ask today. Yeah. Awesome. Excellent. David, thanks for your time. Thank you. Thank you, Dorkesh.