M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits

• DOI: 10.1371/journal.pgen.1009849
• Publication Date: 2021-11-04T17:25:42Z
• Authors: Yuhan Xie; Mo Li; Weilai Dong; Wei Jiang; Hongyu Zhao
• Keywords: Heart Defects, Congenital; 0301 basic medicine; 0303 health sciences; QH426-470; 3. Good health; 03 medical and health sciences; Data Interpretation, Statistical; Mutation; Genetics; Humans; Genetic Predisposition to Disease; Autistic Disorder; Algorithms; Research Article

Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings fromde novomutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework forDe novomutationAssociationTest withAnnotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.