从基因组数据推断人类起源的人口统计中的两个盲区
Two blind spots in the demographic inference of human origins from genomic data
浏览论文内容
中文总结 AI 辅助
本文指出从基因组数据推断人类起源的人口统计时存在两个盲区，即数据摘要的限制和候选模型空间的问题，并提出了缩小第二个盲区的方法。
中文摘要 AI 辅助
古DNA和新的推断方法已改变了人类起源研究，但尚未达成共识。越来越多证据表明人科种群普遍存在结构和基因交流，因此复杂性而非简单性才是恰当的先验。本文强调了阻碍解决该复杂性的两个盲区：其一，每次推断都要经过数据摘要，而这些摘要限制了可恢复的内容；其二，候选模型空间庞大，但竞争模型类很少拟合同一数据，因此报告的最佳模型对未测试的模型类几乎无证据。第二个盲区源于实践而非数据，可通过用预留摘要测试竞争模型、报告尝试过且被拒绝的模型而非仅报告获胜模型来缩小盲区。
英文摘要
Ancient DNA and new inference methods have transformed the study of human origins, but consensus has not followed. Evidence increasingly indicates that hominin populations were pervasively structured and admixed, so complexity rather than simplicity is the appropriate prior. Here I highlight two blind spots that impede resolving that complexity. First, every inference passes through summaries of the data, and those summaries bound what can be recovered. Second, the space of candidate models is vast, yet competing model classes are rarely fit to common data, so a reported best model carries little evidence about untested model classes. This second blind spot reflects practice rather than data. It can be narrowed by testing competing models against withheld summaries and by reporting the models that were tried and rejected rather than only the winner.
