M-DATA: A Statistical Approach to Jointly Analyzing De Novo Mutations for Multiple Traits

Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (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 for De novo mutation Association Test with Annotations) 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. Author SummaryCongenital heart disease (CHD) is the most common birth defect. With the development of new generation sequencing technology, germline mutations such as de novo mutations (DNMs) with deleterious effects can be identified to aid in discovering the genetic causes for early on-set diseases such as CHD. However, the statistical power is still limited by the small sample size of DNM studies due to the high cost of recruiting and sequencing samples, and the low occurrence of DNMs given its rarity. Compared to DNM analysis for other diseases, it is even more challenging for CHD given its genetic heterogeneity. Recent research has suggested shared disease mechanisms between early-onset neurodevelopmental diseases and CHD based on findings from DNMs. Currently, there are few methods that can jointly analyze DNM data on multiple traits. Therefore, we develop a framework to identify risk genes for multiple traits simultaneously for DNM data. The new method is applied to CHD and autism as a case study to demonstrate its improved power in identifying risk genes compared with single-trait analysis. Our results lead to new insights on the disease etiology of CHD, and the shared etiological mechanisms between CHD and autism.