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...

Descripción completa

Guardado en:

Detalles Bibliográficos
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/d29dcddfbd6d4497a13588ad6a03a51c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d29dcddfbd6d4497a13588ad6a03a51c
record_format	dspace
spelling	oai:doaj.org-article:d29dcddfbd6d4497a13588ad6a03a51c2021-11-11T06:17:54ZM-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits1553-73901553-7404https://doaj.org/article/d29dcddfbd6d4497a13588ad6a03a51c2021-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568192/?tool=EBIhttps://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. Author summary With the development of new generation sequencing technology, germline mutations such as de novo mutations (DNMs) with deleterious effects can be identified to aid in discovering the genetic causes for early on-set diseases such as congenital heart disease (CHD). However, the statistical power is still limited by the small sample size of DNM studies due to the high cost of recruiting and sequencing samples, and the low occurrence of DNMs given its rarity. Compared to DNM analyses for other diseases, it is even more challenging for CHD given its genetic heterogeneity. Recent research has suggested shared disease mechanisms between early-onset neurodevelopmental diseases and CHD based on findings from DNMs. Currently, there are few methods that can jointly analyze DNM data on multiple traits. Therefore, we develop a framework to identify risk genes for multiple traits simultaneously for DNM data. The new method is applied to CHD and autism as a case study to demonstrate its improved power in identifying risk genes compared with single-trait analyses. Our results lead to new insights on the disease etiology of CHD, and the shared etiological mechanisms between CHD and autism.Yuhan XieMo LiWeilai DongWei JiangHongyu ZhaoPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 11 (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. Author summary With the development of new generation sequencing technology, germline mutations such as de novo mutations (DNMs) with deleterious effects can be identified to aid in discovering the genetic causes for early on-set diseases such as congenital heart disease (CHD). However, the statistical power is still limited by the small sample size of DNM studies due to the high cost of recruiting and sequencing samples, and the low occurrence of DNMs given its rarity. Compared to DNM analyses for other diseases, it is even more challenging for CHD given its genetic heterogeneity. Recent research has suggested shared disease mechanisms between early-onset neurodevelopmental diseases and CHD based on findings from DNMs. Currently, there are few methods that can jointly analyze DNM data on multiple traits. Therefore, we develop a framework to identify risk genes for multiple traits simultaneously for DNM data. The new method is applied to CHD and autism as a case study to demonstrate its improved power in identifying risk genes compared with single-trait analyses. Our results lead to new insights on the disease etiology of CHD, and the shared etiological mechanisms between CHD and autism.
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/d29dcddfbd6d4497a13588ad6a03a51c
work_keys_str_mv	AT yuhanxie mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT moli mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT weilaidong mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT weijiang mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits AT hongyuzhao mdataastatisticalapproachtojointlyanalyzingdenovomutationsformultipletraits
_version_	1718439541972402176

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

Ejemplares similares