M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.

Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can...

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Autores principales:	Yuhan Xie, Mo Li, Weilai Dong, Wei Jiang, Hongyu Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/9f7c4776473c43619a25895923adf4b3
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id	oai:doaj.org-article:9f7c4776473c43619a25895923adf4b3
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spelling	oai:doaj.org-article:9f7c4776473c43619a25895923adf4b32021-12-02T20:03:16ZM-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.1553-73901553-740410.1371/journal.pgen.1009849https://doaj.org/article/9f7c4776473c43619a25895923adf4b32021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009849https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.Yuhan XieMo LiWeilai DongWei JiangHongyu ZhaoPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 11, p e1009849 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Yuhan Xie Mo Li Weilai Dong Wei Jiang Hongyu Zhao M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
description	Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.
format	article
author	Yuhan Xie Mo Li Weilai Dong Wei Jiang Hongyu Zhao
author_facet	Yuhan Xie Mo Li Weilai Dong Wei Jiang Hongyu Zhao
author_sort	Yuhan Xie
title	M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
title_short	M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
title_full	M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
title_fullStr	M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
title_full_unstemmed	M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.
title_sort	m-data: a statistical approach to jointly analyzing de novo mutations for multiple traits.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9f7c4776473c43619a25895923adf4b3
work_keys_str_mv	AT yuhanxie mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT moli mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT weilaidong mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT weijiang mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT hongyuzhao mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits
_version_	1718375675559149568

M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.

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