Using individuals’ genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.

利用个人的遗传数据，研究人员可以生成多基因评分 (PS)，该评分能够预测疾病风险、不同行为的变异性以及拟人化措施。这是通过利用先前发表的大型全基因组关联研究 (GWAS) 中学到的模型来实现的，该模型将基因组中的位置与感兴趣的表型相关联。以前的 GWAS 主要在欧洲血统个体中进行。这是令人担忧的，因为与原始训练 GWAS 具有不同血统的样本中生成的 PS 已被证明具有较低的性能和有限的可移植性，并且现在正在进行许多努力来收集不同血统个体的遗传数据库。在本研究中，我们比较了多种生成 PS 的方法，包括剪枝和阈值处理以及贝叶斯连续收缩模型，以确定哪种方法最能克服这些限制。为此，我们使用 ABCD 研究，这是一项对不同血统个体进行深度表型分析的纵向队列。我们使用先前发布的 GWAS 摘要统计数据生成人体测量和精神表型的 PS，并检查其在 ABCD 的三个子样本中的表现：非洲血统个体 (n = 811)、欧洲血统个体 (n = 6703) 和混合血统个体 (n = 3664) ）。我们发现单祖先连续收缩方法 PRScs (CS) 和多祖先元方法 PRScsx Meta (CSx Meta) 显示出跨祖先和表型的最佳性能。