RT Journal Article SR Electronic T1 A statistical framework for mapping risk genes from de novo mutations in whole-genome sequencing studies JF bioRxiv FD Cold Spring Harbor Laboratory SP 077578 DO 10.1101/077578 A1 Yuwen Liu A1 A. Ercument Cicek A1 Yanyu Liang A1 Jinchen Li A1 Rebecca Muhle A1 Martina Krenzer A1 Yue Mei A1 Yan Wang A1 Nicholas Knoblauch A1 Jean Morrison A1 Yi Jiang A1 Evan Geller A1 Zhongshan Li A1 Iuliana Ionita-Laza A1 Jinyu Wu A1 Kun Xia A1 James Noonan A1 Zhong Sheng Sun A1 Xin He YR 2017 UL http://biorxiv.org/content/early/2017/09/13/077578.abstract AB 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.