Leveraging biobank-scale whole-genome sequencing for polygenic risk prediction

项目摘要/摘要 种群生物库队列的全基因组测序为实现准确的预测提供了巨大的希望 可遗传的人类疾病和特征的遗传介导性风险。这样的信息有可能成为 精准医学的强大资源，为预防和治疗决策提供信息。要更全面地 认识到这一潜力，需要新的统计方法来纳入所有遗传变异--包括 结构变异、血源性体细胞突变以及罕见的SNPs和Indels--纳入遗传风险模型。 这些类别的遗传变异，已知包括许多对疾病有重大影响的变异 风险，可以在现在生物库规模产生的高覆盖率全基因组测序数据中检测到。 然而，这些变异还不能从以前的遗传数据集(依赖于SNP)中获得。 阵列基因分型和归因法)。因此，现有的多基因预测方法通常 仅考虑常见的遗传SNP和INDELs。 我们建议开发一套统计方法，使这些额外类别的遗传变异能够 纳入遗传风险模型，从而提高预测能力。对于符合以下条件的变量类型 目前甚至很难从全基因组测序数据中确定-包括体细胞突变和 一些类型的结构变异-我们将开发新的基因分型算法，以改进统计推断 通过利用大量有序列的队列中的信息。我们将高效整合所有领域的信息 使用快速贝叶斯回归方法将变异类型转化为遗传风险模型。我们将应用这些方法 使用来自超大型生物库测序项目的数据训练常见疾病的遗传风险模型。 这个项目将有三个具体目标。首先，我们将开发和应用结合结构的方法 变异转化为多基因得分。众所周知，许多结构变异会带来很大的疾病风险，但在 被现有的多基因得分模拟得不完美，因此直接包括这样的变种将增加 预测准确性和跨世系可转移性。第二，我们将开发和应用方法来 将血液中可检测到的体细胞突变纳入遗传风险模型。这样获得的 突变，通常表明血细胞的克隆性扩张，提供了一个相互关联的风险来源 标准多基因评分所考虑的遗传变异。三是开发应用高效 基于生物库规模测序数据训练多基因得分模型的计算方法。这些方法 将允许对个体水平的遗传数据执行模型拟合，从而优化预测精度。我们 预计这些努力将显著提高针对当前和 未来人口规模的全基因组测序数据集。

Project Summary/Abstract Whole-genome sequencing of population biobank cohorts holds great promise for enabling accurate prediction of genetically-mediated risk for heritable human diseases and traits. Such information has the potential to be a powerful resource for precision medicine, informing preventative and therapeutic decisions. To more fully realize this potential, new statistical methods are needed to incorporate all genetic variants – including structural variants, blood-derived somatic mutations, and rare SNPs and indels – into genetic risk models. These classes of genetic variation, which are known to include many variants with large effects on disease risk, can be detected in high-coverage whole-genome sequencing data now being generated at biobank scale. However, such variants have not been accessible from previous genetic data sets (which have relied on SNP- array genotyping and imputation). Consequently, existing methods for polygenic prediction have typically considered only common inherited SNPs and indels. We propose to develop a suite of statistical methods to enable these additional classes of genetic variants to be incorporated into models of genetic risk, thereby improving predictive power. For variant types that are currently difficult to ascertain even from whole-genome sequencing data – including somatic mutations and some types of structural variants – we will develop new genotyping algorithms that improve statistical inference by harnessing information across large sequenced cohorts. We will efficiently integrate information across all variant types into genetic risk models using fast Bayesian regression methods. We will apply these approaches to train genetic risk models for common diseases using data from very large biobank sequencing projects. This project will have three specific aims. First, we will develop and apply methods for incorporating structural variants into polygenic scores. Many structural variants are known to confer substantial disease risk but are at imperfectly modeled by existing polygenic scores, such that directly including such variants will increase prediction accuracy and cross-ancestry transferability. Second, we will develop and apply methods for incorporating somatic mutations detectable in blood-derived DNA into genetic risk models. Such acquired mutations, often indicative of clonal expansions of blood cells, provide an orthogonal source of risk compared to the inherited variants considered by standard polygenic scores. Third, we will develop and apply efficient computational methods for training polygenic score models on biobank-scale sequencing data. These methods will allow model-fitting to be performed on individual-level genetic data, optimizing prediction accuracy. We anticipate that these efforts will significantly improve performance of genetic risk models trained on current and future population-scale whole-genome sequencing data sets.