Heritability of complex traits via IBD and IBS in related and unrelated individua

描述（由申请人提供）： 全基因组关联研究（GWAS）已经确定了数百种与常见疾病和其他性状相关的风险变异，但在大多数情况下，只能解释一小部分遗传性。这种“缺失的遗传性”的来源是人类遗传学中的一个关键问题。解决这一问题的努力主要集中在寻找特定的疾病风险变异上，使得大部分遗传性仍然无法解释。我们将系统地解构遗传遗传力，通过研究遗传内容如何通过遗传的血统相同（IBD）或基因型等位基因相同的状态（IBS），无论是全基因组或跨基因组分区，对应于广泛的人类性状的表型相似性。我们将使用IBD来表征所有风险变体的集合，并使用IBS来表征由GWAS芯片进行基因分型或标记的风险变体的子集。我们将探讨的想法，“无关”的个人之间的遗传相似性可以是有用的，或更有用的，比近亲之间的相似性在量化和理解遗传遗传。通过基因组位置划分遗传性将使我们能够得出关于各种人类特征的遗传结构的结论，同时考虑所有风险变体的集合和GWAS芯片捕获的风险变体的子集。我们将通过模拟开发这些想法，并将其应用于由70，000个样本组成的真实的GWAS数据。我们将继续发布实用的、公开可用的软件包，以实现我们开发的方法。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have identified hundreds of risk variants associated to common diseases and other traits, yet in most cases have explained only a small fraction of genetic heritability. The source of this "missing heritability" is a critical question in human genetics. Efforts to address this question have largely focused on the search for specific disease risk variants, leaving the bulk of heritability still unexplained. We will systematically deconstruct genetic heritability by investigating how genetic content shared either via segments inherited identical-by-descent (IBD) or genotyped alleles identical-by-state (IBS), either genome-wide or partitioned across the genome, corresponds to phenotypic similarity across a broad range of human traits. We will use IBD to characterize the set of all risk variants, and IBS to characterize the subset of risk variants genotyped or tagged by GWAS chips. We will explore the idea that genetic similarity between "unrelated" individuals can be as useful, or more useful, than similarity between close relatives in quantifying and understanding genetic heritability. Partitioning heritability by genomic location will enable us to draw conclusions about the genetic architecture of various human traits, considering both the set of all risk variants and the subset of risk variants captured by GWAS chips. We will develop these ideas via simulation and apply them to real GWAS data comprising 70,000 samples. We will continue to release practical, publicly available software packages implementing the methods that we develop.