Mo Li1, Xue Zeng2, Chentian Jin3, Sheng Chih Jin4, Weilai Dong2, Martina Brueckner2,5, Richard Lifton2,6, Qiongshi Lu7, Hongyu Zhao1,2,8. 1. Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA. 2. Department of Genetics, Yale University, New Haven, CT 06510, USA. 3. Department of Molecular, Cellular & Developmental Biology, Yale University, CT 06510, USA. 4. Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA. 5. Department of Pediatrics, Yale University, New Haven, CT 06510, USA. 6. Laboratory of Human Genetics and Genomics, Rockefeller University, New York, NY 10065, USA. 7. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA. 8. Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA.
Abstract
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.
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.
Entities:
Keywords:
congenital heart disease; de novo mutation; gene-level association test; rare variants
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