Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Department of Statistics, Dongguk University, Seoul, Republic of Korea.
Nat Genet. 2018 Sep;50(9):1318-1326. doi: 10.1038/s41588-018-0193-x. Epub 2018 Aug 13.
We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.
我们开发了一种基于似然的方法，用于分析汇总统计数据和外部连锁不平衡信息，以估计常见变体的效应大小分布，其特征是潜在易感性 SNP 的比例和用于其效应的灵活正态混合模型。对跨越 32 项全基因组关联研究的结果进行分析表明，虽然所有特征都是高度多基因的，但多基因的程度和性质存在很大差异。精神疾病和与心理健康及能力相关的特征似乎是最多的多基因，涉及一系列小的影响。大多数其他特征，包括主要的慢性疾病，涉及具有不同效应大小的 SNP 簇。我们预测，识别解释全基因组关联研究中发现的大多数遗传率的 SNP 需要的样本量将根据特征的潜在效应大小分布从几十万到几百万不等。因此，我们预测了多种疾病的多基因风险评分的风险预测能力。
