Imputation-based approach to identify low frequency variants in prostate cancer

描述（由申请人提供）：全基因组关联研究（GWAS）已经非常成功地识别了与复杂性状相关的位点，包括前列腺癌（PrCa）。尽管这些新发现的基因座揭示了一些潜在的生物学，但在预防或治疗PrCa方面的临床影响有限。此外，已知的变异并不能解释大多数的PrCa。GWAS主要关注常见的遗传变异（MAF占5%），然而，经验证据表明，包括PrCa在内的低频变异在癌症中发挥作用。尽管低频变异可以解释PrCa部分缺失的遗传性，并可能导致预防或治疗的可行目标，但目前的发现策略并不可行。对于充分评估PrCa的低频变异所需的样本量来说，全基因组测序研究过于昂贵。需要创新的战略。一些研究表明，低频变异的输入精度随着参考面板尺寸的增加而提高。在这里，我们提出以下建议：AIM 1 -组装一个超过10,000个全基因组的增强参考面板，并对从可用的GWAS数据组装的83,991例PrCa病例和58,430例无病对照进行代入（AIM 1a）。使用上述数据，定义使用现有GWAS数据的不常见和罕见变体的归算操作特性。澄清GWAS标记频谱、目标市场频谱、参考面板大小和通过代入R2测量的代入成功之间的统计关系，为上述数据确定可代入的常见、不常见和罕见变体的目标集（Aim 1b）；AIM 2 -对无病对照中与总体和侵袭性PrCa易感性和PSA水平相关的可归因的常见、不常见和罕见变异进行全基因组扫描；AIM 3 -开发新的统计方法，通过结合基因组注释（例如外显子，调控区域，途径等）来改进遗传力的估计和划分，同时考虑代入误差以改进风险预测模型。最近，我们利用1000基因组计划的GWAS数据对31,663例PrCa病例和35,870例无病对照进行了PrCa荟萃分析，确定了30多个目前正在验证的常见新型易感位点。此外，我们将使用带有GWAS主干的定制阵列对另外90,000名欧洲血统的个体（2/3的PrCa病例和1/3的对照）进行基因分型，作为NCI“后GWAS”肿瘤遗传关联和机制计划（GAME-ON）的一部分。我们最近的经验表明，我们有能力在大型数据资源中实施归算，并通过强有力的合作，将世界上最大的PrCa病例对照系列与可用的GWAS数据组合在一起。我们正在与几个小组合作，协调工作，组建一个加强的参考小组，以进行归因，并将提供进入研究界的途径。这一建议不仅解决了PrCa的一个基本问题，而且将极大地帮助量化遗传学在其他复杂表型中的作用。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have been highly successful identifying loci associated with complex traits, including prostate cancer (PrCa). Although these newly discovered loci have shed some light into the underlying biology, the clinical impact for preventing or treating PrCa has been limited. Furthermore, the known variation fails to explain a majority of PrCa. GWAS primarily focus on common genetic variation (MAF>5%), however, empirical evidence suggests low-frequency variants play a role in cancer, including PrCa. Although low-frequency variants will explain a portion of the missing heritability for PrCa and could lead to actionable targets for prevention or treatment, current discovery strategies are not feasible. Whole-genome sequencing studies are too costly for the sample sizes needed to adequately evaluate low-frequency variants in PrCa. Innovative strategies are needed. Several studies have demonstrated that the accuracy of imputation for low-frequency variants improves with increasing size of the reference panel. Here, we propose the following: AIM 1 - Assemble an enhanced reference panel of over 10,000 whole-genomes and perform imputation for 83,991 PrCa cases and 58,430 disease-free controls assembled from available GWAS data (Aim 1a). Using the above data, define operating characteristics of imputation for less common and rare variants using existing GWAS data. Clarify statistical relationship between GWAS marker frequency spectrum, target market frequency spectrum, reference panel size, and imputation success as measured by imputation R2, determine for above data a target set of imputable common, less common, and rare variants (Aim 1b); AIM 2 - Perform a whole-genome scan of imputable common, less common, and rare variants associated with overall and aggressive PrCa susceptibility and PSA levels among disease-free controls; AIM 3 - Develop novel statistical approaches to improve estimation and partition of heritability by incorporating genomic annotation (e.g. exonic, regulatory regions, pathways, etc.) while accounting for imputation error to improve risk prediction models. Recently, we performed a meta-analysis for PrCa utilizing GWAS data imputed to the 1000 Genomes Project for 31,663 PrCa cases and 35,870 disease-free controls identifying over 30 common novel susceptibility loci that are currently being validated. Furthermore, we will be genotyping an additional 90,000 individuals of European ancestry (2/3 PrCa cases and 1/3 controls) using a customized array with a GWAS backbone as part of the NCI Genetic Associations and Mechanisms in Oncology "post-GWAS" initiative (GAME-ON). Our recent experience demonstrates our ability to implement imputation in a large data resource and our strong collaborations to assemble the world's largest PrCa case-control series with available GWAS data. We are collaborating with several groups to coordinate efforts assembling an enhanced reference panel for imputation and will provide access to the research community. This proposal not only addresses a fundamental question in PrCa but will greatly aid quantifying the role of genetics in other complex phenotypes.