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Am J Hum Genet. 2018 Jun 7;102(6):1031-1047. doi: 10.1016/j.ajhg.2018.03.023. Epub 2018 May 10.

A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies.

Author information

1
Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.
2
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15123, USA.
3
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15123, USA; Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.
4
Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
5
Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan 410078, China.
6
Child Study Center, Yale Medicine, New Haven, CT 06520, USA.
7
Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA.
8
Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100000, China.
9
Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL 60637, USA.
10
Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China.
11
Department of Genetics, Yale School of Medicine, New Haven, CT 06520, USA; Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA.
12
Department of Biostatistics, Columbia University, New York, NY 10027, USA.
13
Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100000, China; Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
14
Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China.
15
Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100000, China; Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China. Electronic address: sunzs@mail.biols.ac.cn.
16
Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA. Electronic address: xinhe@uchicago.edu.

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 (GWASs) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is challenging, however, because the functional significance of non-coding mutations is difficult to predict. We propose a 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, to learn from data which annotations are informative of pathogenic mutations, and to 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-affected family trios across five studies and discovered several autism risk genes. The software is freely available for all research uses.

KEYWORDS:

autism; de novo mutations; epigenomics; noncoding sequences; psychiatric disorders; statistical model

PMID:
29754769
PMCID:
PMC5992125
DOI:
10.1016/j.ajhg.2018.03.023
[Indexed for MEDLINE]
Free PMC Article

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