» Articles » PMID: 35666111

Quantifying Concordant Genetic Effects of De Novo Mutations on Multiple Disorders

Overview
Journal Elife
Specialty Biology
Date 2022 Jun 6
PMID 35666111
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Statistical methods for assessing the effects of de novo variants on birth defects.

Xie Y, Wu R, Li H, Dong W, Zhou G, Zhao H Hum Genomics. 2024; 18(1):25.

PMID: 38486307 PMC: 10938830. DOI: 10.1186/s40246-024-00590-z.


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

Zhong G, Choi Y, Shen Y Commun Biol. 2023; 6(1):774.

PMID: 37491581 PMC: 10368729. DOI: 10.1038/s42003-023-05155-9.


Decomposing heritability and genetic covariance by direct and indirect effect paths.

Song J, Zou Y, Wu Y, Miao J, Yu Z, Fletcher J PLoS Genet. 2023; 19(1):e1010620.

PMID: 36689559 PMC: 9894552. DOI: 10.1371/journal.pgen.1010620.

References
1.
Kielinen M, Rantala H, Timonen E, Linna S, Moilanen I . Associated medical disorders and disabilities in children with autistic disorder: a population-based study. Autism. 2004; 8(1):49-60. DOI: 10.1177/1362361304040638. View

2.
Jin S, Lewis S, Bakhtiari S, Zeng X, Sierant M, Shetty S . Mutations disrupting neuritogenesis genes confer risk for cerebral palsy. Nat Genet. 2020; 52(10):1046-1056. PMC: 9148538. DOI: 10.1038/s41588-020-0695-1. View

3.
Lu Q, Li B, Ou D, Erlendsdottir M, Powles R, Jiang T . A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics. Am J Hum Genet. 2017; 101(6):939-964. PMC: 5812911. DOI: 10.1016/j.ajhg.2017.11.001. View

4.
Wang Q, Yang C, Gelernter J, Zhao H . Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. Hum Genet. 2015; 134(11-12):1195-209. PMC: 4630076. DOI: 10.1007/s00439-015-1596-8. View

5.
Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K . Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015; 24(8):2125-37. PMC: 4375422. DOI: 10.1093/hmg/ddu733. View