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

描述（由申请人提供）：大规模平行（“下一代”）鸟枪DNA测序项目将提供迄今为止人类群体遗传变异的最高分辨率。这项新技术为探究复杂疾病的遗传病因提供了巨大的希望。然而，这一承诺带来了挑战。这些新的测序方法容易出现非平凡的错误率和映射读数的稀疏覆盖，混淆多态性发现和基因分型。拷贝数的变化往往必须间接推断。这些数据集的庞大规模需要快速和可扩展的分析方法。在这项提案中，我们提出了统计方法来直接解决这些挑战，使用计算易处理的模型，人口遗传变异。我们的方法考虑到附近的等位基因（连锁不平衡）与单倍型变异的基于聚类的模型之间的依赖性，并利用这些信息来帮助推断样本的潜在遗传结构。具体而言，我们建议调用基因型和检测新的多态性基因座从下一代鸟枪测序数据，检测罕见疾病的风险等位基因的后续测序研究，并同时建模的单核苷酸和拷贝数多态性的人口数据，以促进研究表型和基因型之间的关联。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业知识和数据集。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.