Statistical Methods for Population Genomics and "Next-gen" Sequencing Data

描述(由申请人提供)：大规模并行(“下一代”)鸟枪式DNA测序项目将为人类群体的遗传变异提供迄今最高的分辨率。这项新技术为询问复杂疾病的遗传病因学提供了巨大的希望。然而，伴随着这一承诺而来的是挑战。这些新的测序方法容易出现不平凡的错误率和稀疏的图谱读数覆盖率，混淆了多态发现和基因分型。拷贝数变异通常必须是间接推断的。这些数据集的巨大规模需要快速且可扩展的分析方法。在这项提案中，我们提出了直接解决这些挑战的统计方法，使用了易于计算的种群遗传变异模型。我们的方法考虑了附近等位基因之间的依赖(连锁不平衡)和基于集群的单倍型变异模型，并利用这些信息来帮助推断样本的潜在遗传结构。具体地说，我们建议从下一代鸟枪序列数据中调用基因类型并检测新的多态基因座，检测罕见的疾病风险等位基因用于后续测序研究，并同时在群体数据中建立单核苷酸和拷贝数多态模型，以促进表型和基因之间的关联研究。我们经验丰富的医学和统计遗传学家团队拥有实现这些目标所需的技术专长和数据集。我们将在我们广泛使用的软件包FastPhase中实现我们的方法。

DESCRIPTION (provided by applicant): Massively-parallel ("next-generation") shotgun DNA sequencing projects will provide the highest resolution to date for genetic variation of human populations. This new technology offers great promise for interrogating the genetic etiology of complex disease. However, with this promise come challenges. These new sequencing methods are prone to nontrivial error rates and sparse coverage of mapped reads, confounding polymorphism discovery and genotyping. Copy number variation must often be inferred indirectly. The massive size of these data sets requires rapid and scaleable analytic approaches. In this proposal, we present statistical methods to address these challenges directly, using computationally tractable models for population genetic variation. Our methods take account of the dependence among nearby alleles (linkage disequilibrium) with a clusterbased model for haplotype variation, and utilize this information to aid inferences about the underlying genetic architecture of the samples. Specifically, we propose to call genotypes and detect novel polymorphic loci from next- generation shotgun sequence data, detect rare disease risk alleles for follow-up sequencing studies, and simultaneously model single nucleotide and copy number polymorphism in population data to facilitate studies of association between phenotype and genotype. Our experienced team of medical and statistical geneticists have the technical expertise and access to data sets necessary for achieving these aims. We will implement our methods in our widely-used software package fastPHASE.