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

项目总结/摘要 对于这个应用程序，“孟德尔插补为家庭为基础的GWAS和关联的代理在不同的 祖先，”我们建议开发方法，使更强大的估计家庭为基础的基因组- 广泛的关联研究（GWAS），并将这些方法应用于广泛的健康，疾病和衰老 不同人群中的表型。简而言之，我们建议： ·荟萃分析来自14个队列的30种表型的基于家族的GWAS汇总统计量， 主要是欧洲血统。此外，通过与中国嘉道理生物库合作， 和23andMe，我们将在一组不同的祖先中执行基于家庭的GWAS。使用汇总 统计，我们将测试内和跨祖先预测使用多基因指数（PGIs，也称为 多基因评分）来源于基于家庭的和标准的GWAS汇总统计，使我们能够 确定混杂因素在各血统PGIs预测准确性下降中的作用。我们将 研究结合联合收割机标准GWAS汇总统计和基于家庭GWAS的方法 汇总统计，以提高跨祖先的多基因预测。 ·通过添加没有任何近亲的基因型个体来增强基于家庭的GWAS的力量， 估计样本。我们将推导出可用于量化效率的分析公式 在特定环境下的收益。我们将开发一个有效的线性混合模型算法，同时 执行基于标准和系列的GWAS，最大限度地提高两者的能力。英国的初步结果 Biobank表明，该方法增加了估计直接 遗传效应在30%到40%之间。 ·通过估算亲属的基因型来增加代理关联方法的效力。理论表明 当未分型亲属的基因型 是插补给一个更准确的估计相对的基因型。我们将把这些方法应用于 英国生物库参与者的父母可获得的表型，包括阿尔茨海默病和长寿。 ·扩展算法，将亲本基因型归因于不同的群体和其他亲属。 我们将开发一种算法，使用不同的单倍型参考面板作为谱系的基础- 基于估算。除了在不同样本中消除插补偏倚外，我们的方法还将 将插补算法推广到包括全同胞兄弟姐妹和父母以外的亲属，从而 增加下游基于家族的遗传关联分析的插补准确性和功效。 实现这些方法的软件将在GitHub存储库上公开提供。的 将根据数据使用协议，在最大程度上公布汇总统计数据。

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.