Open AccessIntegrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease
Author(s) -
Li Mo,
Zeng Xue,
Jin Chentian,
Jin Sheng Chih,
Dong Weilai,
Brueckner Martina,
Lifton Richard,
Lu Qiongshi,
Zhao Hongyu
Publication year - 2021
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.15302/j-qb-021-0248
Subject(s) - proband , genetics , gene , biology , exome , exome sequencing , computational biology , population , disease , mutation , medicine , environmental health
Background Whole‐exome sequencing (WES) studies have identified multiple genes enriched for de novo mutations (DNMs) in congenital heart disease (CHD) probands. However, risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene. Methods In this manuscript, we introduce a hierarchical Bayesian framework for gene‐level association test which jointly analyzes de novo and rare transmitted variants. Through integrative modeling of multiple types of genetic variants, gene‐level annotations, and reference data from large population cohorts, our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only. Results Applied to WES data of 2,645 CHD proband‐parent trios, our method identified 15 significant genes, half of which are novel, leading to new insights into the genetic bases of CHD. Conclusion These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.