Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Nat Commun. 2021 Jul 20;12(1):4418. doi: 10.1038/s41467-021-24387-z.
Studies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a sample prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.
复杂性状的遗传基础研究表明，常见的、小效应变异多基因负担（PB）以及大效应变异（LEV，主要是罕见的）都起着重要作用。我们确定了充分的条件，在这些条件下，基于 GWAS 的 PB 可用于强大的罕见致病变异发现，或作为全基因组或外显子组测序的样本优先级工具。通过对遗传结构和疾病易感性生成模型进行广泛的模拟，并利用经验数据提供的参数进行信息告知，我们量化了在病例中检测到 LEV 携带者的 PB 低于非携带者的能力。此外，我们揭示了具有临床应用价值的条件，其中来自 PB 的风险与 LEV 衍生的风险相当。基于这种汇总统计数据的方法（具有公共可用软件，PB-LEV-SCAN）对基于 PB 的 36 种复杂性状的 LEV 筛查进行了预测，我们在英国生物库中具有可用 LEV 信息的几个疾病数据集中对其进行了确认，这对临床决策具有重要意义。
