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”）霰弹枪DNA测序项目将提供迄今为止人类种群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，这一承诺也带来了挑战。这些新的测序方法容易出现高错误率和稀疏的图谱覆盖，混淆多态性发现和基因分型。拷贝数的变化通常必须间接推断。这些数据集的庞大规模需要快速和可扩展的分析方法。在本提案中，我们提出了统计方法来直接解决这些挑战，使用计算易于处理的群体遗传变异模型。我们的方法利用基于聚类的单倍型变异模型考虑了附近等位基因之间的依赖性（连锁不平衡），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，我们建议从下一代霰弹枪序列数据中调用基因型并检测新的多态性位点，为后续测序研究检测罕见疾病风险等位基因，同时在群体数据中模拟单核苷酸和拷贝数多态性，以促进表型和基因型之间的关联研究。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.