Mendelian imputation for family-based GWAS and association-by-proxy in diverse ancestries

项目摘要/摘要 对于这一应用程序，“孟德尔归罪于以家庭为基础的GWA和不同的代理关联 我们建议开发方法，以实现更强大的基于家族的基因组估计- 广泛的关联研究(GWAS)，并将这些方法应用于广泛的健康、疾病和老龄化 不同群体的表型。简而言之，我们建议： ·Meta分析基于家族的Gwas汇总统计数据，统计来自14个队列的30个表型 主要是欧洲血统。此外，通过与中国嘉道理生物库的合作， 和23andMe，我们将在一组不同的祖先中执行基于家庭的GWA。使用摘要 统计，我们将使用多基因指数(PGI，也称为 多基因得分)来自基于家庭的和标准的GWAS汇总统计，使我们能够 确定混淆在跨祖先的PGI预测准确性下降中的作用。我们会 研究结合标准GWAS汇总统计数据和基于家庭的GWAS的方法 汇总统计数据以改进跨祖先的多基因预测。 ·通过增加没有任何近亲的基因分型个人来增强以家庭为基础的GWAS的力量 估计样本。我们将推导出可用于量化效率的解析公式 在特定设置中的收益。我们将开发一种高效的线性混合模型算法，同时 执行基于标准和基于系列的GWA，最大限度地提高两者的能力。英国的初步结果 Biobank指出，这种方法导致了直接估计的有效样本量的增加 遗传效应在30%到40%之间。 ·通过输入亲属的基因类型，提高代理关联方法的效力。理论表明， 当未分型的亲属的基因型别时，发现关联的能力会增加 被认为是为了更准确地估计亲属的基因类型。我们将把这些方法应用于 英国生物库参与者的父母可以获得的表型包括阿尔茨海默病和长寿。 ·扩展算法，将父母的基因类型归因于不同的人群和其他亲属。 我们将开发一种算法，使用不同的单倍型参考小组作为系谱的基础- 基于责任的归责。除了消除不同样本中的偏倚外，我们的方法还将 将补偿算法推广到包括完全兄弟姐妹和父母以外的亲属，从而 提高下游基于家族的遗传关联分析的归因准确性和能力。 用于实施这些方法的软件将在GitHub储存库中公开提供。这个 汇总统计数据将在符合数据使用协议的最大程度上向公众公布。

Project Summary/Abstract For this application, “Mendelian imputation for family-based GWAS and association-by-proxy in diverse ancestries,” we propose to develop methods to enable more powerful estimation of family-based genome- wide association studies (GWASs) and apply these methods to a wide range of health, disease, and aging phenotypes in diverse populations. In brief, we propose to: · Meta-analyze family-based GWAS summary statistics on 30 phenotypes from 14 cohorts of predominantly European ancestry. In addition, through collaboration with the China Kadoorie Biobank and 23andMe, we will perform family-based GWAS in a set of diverse ancestries. Using the summary statistics, we will test within- and cross-ancestry prediction using polygenic indexes (PGIs, also called polygenic scores) derived from family-based and standard GWAS summary statistics, enabling us to determine the role of confounding in the drop in predictive accuracy of PGIs across ancestries. We will investigate methods that combine standard GWAS summary statistics and family-based GWAS summary statistics to improve polygenic prediction across ancestries. · Boost the power of family-based GWAS by adding genotyped individuals without any close relatives to the estimation sample. We will derive analytical formulas that can be used to quantify the efficiency gains in specific settings. We will develop an efficient linear mixed model algorithm that simultaneously performs standard- and family-based GWAS, maximizing power for both. Preliminary results from UK Biobank indicate this method results in an increase in effective sample size for estimation of direct genetic effects of between 30 and 40%. · Increase power for association-by-proxy methods by imputing relatives’ genotypes. Theory shows that power for discovery of associations could be increased when the genotype of the un-genotyped relative is imputed to give a more accurate estimate of the relative’s genotype. We will apply the methods to phenotypes available for UK Biobank participants’ parents, including Alzheimer’s disease and longevity. · Extend the algorithm for imputing parental genotypes to diverse populations and additional relatives. We will develop an algorithm that uses a diverse haplotype reference panel as the basis of pedigree- based imputation. In addition to removing bias from imputation in diverse samples, our approach will generalize the imputation algorithm to include relatives other than full-siblings and parents, thereby increasing imputation accuracy and power of downstream family-based genetic association analyses. The software for implementing the methods will be made publicly available on a GitHub repository. The summary statistics will be made publicly available to the maximum extent consistent with data use agreements.