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

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
doi: https://doi.org/10.1101/077578
Yuwen Liu
Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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Yanyu Liang
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15123, USA
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A. Ercument Cicek
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15123, USAComputer Engineering Department, Bilkent University, Ankara 06800, Turkey
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Zhongshan Li
Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
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Jinchen Li
Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, ChinaNational Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410078, China
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Rebecca Muhle
Department of Genetics, Yale School of Medicine, New Haven, Connecticut 06520, USAChild Study Center, Yale Medicine, New Haven, Connecticut 06520, USAKavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut 06520, USA
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Martina Krenzer
Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut 06520, USA
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Yue Mei
Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100000, China
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Yan Wang
Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100000, China
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Nicholas Knoblauch
Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL 60637, USA
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Jean Morrison
Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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Siming Zhao
Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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Yi Jiang
Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, ChinaCenter for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China
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Evan Geller
Department of Genetics, Yale School of Medicine, New Haven, Connecticut 06520, USAKavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut 06520, USA
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Iuliana Ionita-Laza
Department of Biostatistics, Columbia University, New York, New York 10027, USA
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Jinyu Wu
Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100000, ChinaInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
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Kun Xia
Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China
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James Noonan
Department of Genetics, Yale School of Medicine, New Haven, Connecticut 06520, USAKavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut 06520, USA
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Zhong Sheng Sun
Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100000, ChinaInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
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  • For correspondence: sunzs@mail.biols.ac.cn xinhe@uchicago.edu
Xin He
Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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  • For correspondence: sunzs@mail.biols.ac.cn xinhe@uchicago.edu
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Abstract

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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Posted March 18, 2018.
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A statistical framework for mapping risk genes from de novo mutations in whole-genome sequencing studies
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
bioRxiv 077578; doi: https://doi.org/10.1101/077578
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A statistical framework for mapping risk genes from de novo mutations in whole-genome sequencing studies
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
bioRxiv 077578; doi: https://doi.org/10.1101/077578

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