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

描述(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包括前列腺癌(PrCa)在内的复杂特征相关的基因座。尽管这些新发现的基因座揭示了潜在的生物学基础，但对预防或治疗PrCa的临床影响有限。此外，已知的变异不能解释PrCa的大部分。Gwas主要关注常见的遗传变异(MAF&GT；5%)，然而，经验证据表明，低频变异在癌症中发挥了作用，包括PrCa。虽然低频变异将解释部分PrCa缺失的遗传性，并可能导致预防或治疗的可操作靶点，但目前的发现策略是不可行的。对于充分评估PrCa低频变异所需的样本量来说，全基因组测序研究成本太高。我们需要创新的战略。几项研究表明，随着参考面板大小的增加，对低频变量的推算精度会提高。在这里，我们提出如下建议：目标1-组装一个由10,000多个全基因组组成的增强型参考小组，并对83,991个PrCa病例和58,430个无疾病对照进行推算，这些参考小组是根据现有的Gwas数据组装的(目标1a)。利用上述数据，利用现有的GWAS数据，定义不太常见和罕见的变种的分配的操作特征。澄清Gwas标记频谱、目标市场频谱、参考面板大小和通过归因R2测量的归因成功率之间的统计关系，为上述数据确定可归因于常见、不太常见和稀有变异的目标集合(目标1b)；目标2-对与无疾病对照中总体和侵袭性PrCa易感性和PSA水平相关的可归因性常见、不太常见和罕见变异进行全基因组扫描；目的3-开发新的统计方法，通过纳入基因组注释(例如外显子、调节区、途径等)来改进遗传性的估计和划分。同时考虑归责误差，完善风险预测模型。最近，我们利用1000基因组计划中的31,663例PrCa病例和35,870例无疾病对照的Gwas数据对PrCa进行了荟萃分析，确定了目前正在验证的30多个常见的新易感基因。此外，作为NCI肿瘤遗传协会和机制倡议的一部分，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 PrCa病例和1/3对照)进行基因分型。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责，并与我们进行了强有力的合作，利用现有的Gwas数据汇编了世界上最大的PrCa病例对照系列。我们正在与几个小组合作，协调努力，组建一个加强的参考小组，以便进行指责，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.