Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease

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.

Author summary

Whole-exome sequencing (WES) studies have successfully identified multiple risk genes for congenital heart disease (CHD). However, it remains statistically challenging due to low mutation rate of de novo mutations (DNMs). In this paper, we present TADA-R, an innovative statistical test for identifying trait-associated genes through jointly analyzing de novo and rare transmitted variants. Applied to WES data of CHD proband-parent trios, our method identified novel risk genes and provided new insights into the genetic basis of CHD. Our method may benefit future sequencing-based studies in disease trios and accelerate findings of risk genes.

Graphical abstract

Keywords

rare variants / gene-level association test / congenital heart disease / de novo mutation

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SUPPORTING INFORMATION

TADA software is available as an R package at https://github.com/limo936/TADA-R. Statistical tests for genes can run independently and parallelly to speed up the algorithm. On average, each gene takes 40 seconds for computation.

ACKNOWLEDGEMENTS

This study was supported in part by the National Institutes of Health (NIH) grants R01 GM134005, and the National Science Foundation (NSF) grants DMS 1902903. Dr. Sheng Chih Jin's effort was supported by the Pathway to Independence Award (K99/R00) program, grants K99HL143036-01A1 and R00HL143036-02.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Mo Li, Xue Zeng, Chentian Jin , Sheng Chih Jin, Weilai Dong, Martina Brueckner, Richard Lifton, Qiongshi Lu, and Hongyu Zhao declare that they have no conflict of interests.

The article does not contain any human or animal subjects performed by any of the authors.

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