Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China; Center for Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China; Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, China.
Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Am J Hum Genet. 2023 May 4;110(5):762-773. doi: 10.1016/j.ajhg.2023.03.010. Epub 2023 Apr 4.
The ongoing release of large-scale sequencing data in the UK Biobank allows for the identification of associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a scalable and accurate method for rare-variant association tests, POLMM-GENE, in which we used a proportional odds logistic mixed model to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole-exome-sequencing data for five ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.
英国生物库中不断释放的大规模测序数据允许鉴定罕见变异与复杂特征之间的关联。SAIGE-GENE+ 是一种有效的方法，可用于对定量和二项特征进行基于集合的关联测试。然而，对于有序分类表型，将 SAIGE-GENE+应用于将特征视为定量或对特征进行二值化会导致Ⅰ型错误率或功效损失增加。在这项研究中，我们提出了一种用于罕见变异关联测试的可扩展且准确的方法 POLMM-GENE，其中我们使用比例优势逻辑混合模型来描述有序分类表型，同时调整样本相关性。POLMM-GENE 充分利用了表型的分类性质，因此可以很好地控制Ⅰ型错误率，同时保持强大的功效。在对英国生物库 450k 全外显子组测序数据的五个有序分类表型的分析中，POLMM-GENE 确定了 54 个基因-表型关联。
