Quantifying concordant genetic effects of de novo mutations on multiple disorders

Abstract

Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.

Roles of statistical modeling in characterizing the genetic basis of human diseases and traits

“…Although the random effects model is useful for quantifying genetic correlations for the effect sizes for common variants, it is more challenging for rare variants due to the large errors in estimating their effect sizes. Alternative methods are needed to quantify concordance of association signals for rare and de novo variants between traits [24]. With shared genetics between traits, we can leverage this to better identify disease genes [25,26].…”

VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants

“…Several recently published methods attempt to increase power using additional information. EncoreDNM 18 , m-TADA 19 and M-DATA 20 are statistical models that improve power by leveraging pleiotropic effect across conditions. DECO 21 integrates pathways and gene sets information to prioritize risk genes.…”

Decomposing heritability and genetic covariance by direct and indirect effect paths

“…Several methods have been developed to improve genetic correlation estimation using individual-level GWAS data [3,4], GWAS summary statistics [5,6], or both [7]. Recent studies have also expanded this concept to quantify genetic correlations in local genomic regions [8][9][10][11], between human ancestral populations [12,13], and using other types of genetic variations [14]. Overall, these methods have become a routine component of complex trait genetic studies and provided insights into the genetic basis of numerous human traits.…”