Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics
Abstract Background Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human comp...
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doaj-769e0c256d3a469cb646a6d4634ed98e2020-11-24T21:42:11ZengBMCBMC Genomics1471-21642019-02-0120S1435410.1186/s12864-018-5373-7Investigation of multi-trait associations using pathway-based analysis of GWAS summary statisticsGuangsheng Pei0Hua Sun1Yulin Dai2Xiaoming Liu3Zhongming Zhao4Peilin Jia5Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at HoustonCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at HoustonCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at HoustonHuman Genetics Center, School of Public Health, The University of Texas Health Science Center at HoustonCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at HoustonCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at HoustonAbstract Background Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms. Results We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher’s Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted p FET < 1 × 10− 3 (p FET < 0.01) among the 25 traits. Our trait-trait association network revealed not only many relationships among the traits within the same group but also novel relationships among traits from different groups, which warrants further investigation in future. Conclusions Our study revealed that risk variants for 25 different traits aggregated in particular biological pathways and that these pathways were frequently shared among traits. Our results confirmed known mechanisms and also suggested several novel insights into the etiology of multi-traits.http://link.springer.com/article/10.1186/s12864-018-5373-7GWASPathway enrichment analysisMulti-dimensional scalingCross-trait associationSummary statisticsPleiotropy abbreviations |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guangsheng Pei Hua Sun Yulin Dai Xiaoming Liu Zhongming Zhao Peilin Jia |
spellingShingle |
Guangsheng Pei Hua Sun Yulin Dai Xiaoming Liu Zhongming Zhao Peilin Jia Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics BMC Genomics GWAS Pathway enrichment analysis Multi-dimensional scaling Cross-trait association Summary statistics Pleiotropy abbreviations |
author_facet |
Guangsheng Pei Hua Sun Yulin Dai Xiaoming Liu Zhongming Zhao Peilin Jia |
author_sort |
Guangsheng Pei |
title |
Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics |
title_short |
Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics |
title_full |
Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics |
title_fullStr |
Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics |
title_full_unstemmed |
Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics |
title_sort |
investigation of multi-trait associations using pathway-based analysis of gwas summary statistics |
publisher |
BMC |
series |
BMC Genomics |
issn |
1471-2164 |
publishDate |
2019-02-01 |
description |
Abstract Background Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms. Results We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher’s Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted p FET < 1 × 10− 3 (p FET < 0.01) among the 25 traits. Our trait-trait association network revealed not only many relationships among the traits within the same group but also novel relationships among traits from different groups, which warrants further investigation in future. Conclusions Our study revealed that risk variants for 25 different traits aggregated in particular biological pathways and that these pathways were frequently shared among traits. Our results confirmed known mechanisms and also suggested several novel insights into the etiology of multi-traits. |
topic |
GWAS Pathway enrichment analysis Multi-dimensional scaling Cross-trait association Summary statistics Pleiotropy abbreviations |
url |
http://link.springer.com/article/10.1186/s12864-018-5373-7 |
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