Accurate and robust inference of mutational bias across complex traits and diseases

项目摘要/摘要 人类遗传学中的一个长期存在的问题是，为什么常见疾病在人群中持续存在 尽管有潜在的健身后果。我们天真地认为，选择可以消除疾病 人口，这表明它必须由一种反补贴力量来维持。一个自然的零假设是 疾病在人群中的维持至少部分是由于对疾病状态的突变偏见。至 到目前为止，复杂疾病之间的突变程度仍有待研究。然而，在过去的十年里， 半个基因组范围的关联研究(GWAS)已经确定了与复合体相关的遗传位点 疾病提供了对复杂疾病的潜在遗传结构的洞察。随着时代的发展 在正确的工具中，这种大量涌入的Gwas数据将使我们能够测量特征之间的突变偏见。在这里，我 建议开发准确而有力地推断突变偏差的方法，并将其应用于GWAs 数据集。群体遗传学理论表明，因果风险等位基因的平均频率是一种敏感的 突变偏向的量度。在目标1中，我将首先演示计算 相关风险等位基因也是突变偏向的准确量度，如在GWAS中发现的那样。那我会的 对泛英国现有的7,221种表型进行广泛的突变偏见证据筛查 生物库。在目标2中，我将首先证明全球人口统计系统汇总统计中人口分层可以 潜在地模拟突变偏向的信号，然后开发一种方法来纠正分层的影响 使用等位基因的进化状态作为工具变量。最后，在目标3中，我将扩展尖端技术 基于LD的统计方法用于估计和划分遗传力以提供第一个全基因组范围的 对复杂性状和功能基因组类别内突变偏向程度的估计。 总而言之，我的结果将提供对推动复杂性状进化的进化力量的洞察，并产生 新方法的应用超出了我的直接研究问题。更好地理解进化 驱动疾病病因学的机制不仅有助于回答疾病持久性的问题，而且还将 揭示驱动疾病易感性的生物过程。

PROJECT SUMMARY/ABSTRACT A long-standing question in human genetics is why common diseases continue to persist in the population despite potential fitness consequences. Naively, we would expect selection to remove disease from the population, suggesting that it must be maintained by a countervailing force. A natural null hypothesis is that disease is maintained in the population at least partially by a mutational bias towards the disease state. To date, the degree of mutational across complex diseases remains unexplored. However, over the past decade and a half genome-wide association studies (GWAS) have identified genetic loci associated with complex diseases providing insight into the underlying genetic architecture of complex diseases. With the development of the right tools, this large influx of GWAS data will allow us to measure mutational bias across traits. Here I propose to develop methods that accurately and robustly infer mutational bias and apply them to GWAS datasets. Population genetic theory indicates that the average frequency of causal risk alleles is a sensitive measure of mutational bias. In Aim 1, I will first demonstrate that calculating the average frequency of associated risk alleles, as identified in GWAS, is also an accurate measure of mutational bias. I will then conduct a broad screen for evidence of mutational bias across the 7,221 phenotypes available in the Pan-UK Biobank. In Aim 2, I will first demonstrate that population stratification in GWAS summary statistics can potentially mimic signals of mutational bias and then develop a method to correct for the effects of stratification using the evolutionary status of an allele as an instrumental variable. Finally, in Aim 3 I will extend cutting-edge LD based statistical methods for estimating and partitioning heritability to provide the first genome-wide estimates of the degree of mutational bias across complex traits and within functional genomic categories. Together my results will provide insight into the evolutionary forces driving complex trait evolution and generate novel methods with application beyond my direct research question. A better understanding of the evolutionary mechanisms driving disease etiology will not only help answer the question of disease persistence but also uncover biological processes driving disease susceptibility.