Leveraging identity-by-descent information in large-scale population sequencing

描述（由申请人提供）：该项目旨在开发一种方法，通过这种方法，可以在看似无关的个体之间推断出特定染色体片段的共同祖先基因流模式，并用于分析通过测序发现的罕见突变，以及与精神分裂症等疾病的关联。血统同一性（IBD）意味着两个或两个以上的个体各自携带一段单倍体序列的延伸片段，这段单倍体序列是存在于（或曾经存在于）这些个体最近的共同祖先中的单个祖先单倍体的直接拷贝或后代。在大量的样本中，不难发现成千上万的例子，在这些例子中，看似不相关的个体，在他们基因组的某些部分上，完全像父母和子女一样密切相关。在对罕见和常见遗传变异进行大规模、基于人群的研究的背景下，我们提出，在罕见突变和多态性测序数据集的基础上，分层绘制个体内IBD共享图谱，可以帮助解决将遗传变异与常见疾病风险联系起来的艰巨挑战。具体来说，我们建议在测序研究中使用IBD共享信息，以1)确定可能的新生和最近的（私人）突变，2)确定优先级

DESCRIPTION (provided by applicant): This project aims to develop ways in which the patterns of shared ancestral gene-flow for specific chromosomal segments can be inferred between seemingly-unrelated individuals and used to empower analyses of rare mutations discovered by sequencing, with respect to association with diseases such as schizophrenia. Identity-by-descent (IBD) implies that two or more individuals each carry an extended stretch of haploid sequence that is a direct copy, or descendant, of a single, ancestral haplotype that resides (or once resided) in a recent common ancestor of those individuals. In large samples it is not unusual to find many thousands of instances in which seemingly unrelated individuals are, for some fraction of their genome, related exactly as closely as are parent and offspring. In the context of large, population-based studies of rare and common genetic variation, we propose that layering a map of intra-individual IBD sharing on top of datasets of rare mutation and polymorphism from sequencing can help in the daunting challenge of relating genetic variation to risk for common disease. Specifically, we propose to use IBD sharing information in sequencing studies to 1) identify likely de novo and very recent (private) mutations, 2) prioritize rare variants for likely functional impact and 3) allow additional un-sequenced samples to prioritize rare alleles according to the likelihood they are causal given their IBD sharing with sequenced individuals. We will apply the methods developed here to two large schizophrenia sequencing studies, with whole-exome data on over 6,000 individuals and genome-wide SNP data on over 14,000. The statistical approaches developed here will be implemented and distributed as part of the PLINK/Seq software package.