摘要
      了解复杂表型的遗传基础是遗传学的核心追求。全基因组关联研究（GWAS）是寻找与表型相关的遗传位点的有力方法。GWAS被广泛且成功地使用，但它们面临的挑战与这样一个事实有关，即变异是独立测试与表型的关联，而事实上，不同位点的变异是相关的，因为它们有共同的进化史。对这种共享历史进行建模的一种方法是通过祖先重组图（ARG），它对一系列局部合并树进行编码。最近的计算和方法突破使得从大规模样本中估计近似ARG变得可行。在这里，我们探索了基于ARG的定量性状位点（QTL）定位方法的潜力，与现有的方差分量方法相呼应。我们提出了一个框架，该框架依赖于给定ARG（局部eGRM）的局部遗传相关性矩阵的条件期望。模拟表明，我们的方法对于在存在等位基因异质性的情况下寻找QTL特别有益。通过根据估计的ARG构建QTL图谱，我们还可以促进对研究不足群体中QTL的检测。我们使用局部eGRM来识别夏威夷原住民样本中的一个大效应BMI基因座，即CREBRF基因，由于缺乏特定人群的插补资源，GWAS以前无法检测到该基因座。我们的研究可以提供关于在总体和统计遗传方法中使用估计ARGs的好处的直觉。
Abstract
Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the CREBRF gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

https://www.biorxiv.org/content/10.1101/2023.04.07.536093v1.abstract