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A statistical framework for mapping risk genes from de novo mutations in whole-genome sequencing studies

By Yuwen Liu, Yanyu Liang, A. Ercument Cicek, Zhongshan Li, Jinchen Li, Rebecca Muhle, Martina Krenzer, Yue Mei, Yan Wang, Nicholas Knoblauch, Jean Morrison, Siming Zhao, Yi Jiang, Evan Geller, Iuliana Ionita-Laza, Jinyu Wu, Kun Xia, James Noonan, Zhong Sheng Sun, Xin He

Posted 26 Sep 2016
bioRxiv DOI: 10.1101/077578 (published DOI: 10.1016/j.ajhg.2018.03.023)

Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWAS) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is, however, challenging because the functional significance of non-coding mutations is difficult to predict. We propose a new statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, learn from data which annotations are informative of pathogenic mutations and combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ~300 autism family trios across five studies, and discovered several new autism risk genes. The software is freely available for all research uses.

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