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Multi-trait analysis of genome-wide association summary statistics using MTAG.

ABSTRACT: We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff?=?354,862), neuroticism (N?=?168,105), and subjective well-being (N?=?388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

SUBMITTER: Turley P

PROVIDER: S-EPMC5805593 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Multi-trait analysis of genome-wide association summary statistics using MTAG.

Turley Patrick P Walters Raymond K RK Maghzian Omeed O Okbay Aysu A Lee James J JJ Fontana Mark Alan MA Nguyen-Viet Tuan Anh TA Wedow Robbee R Zacher Meghan M Furlotte Nicholas A NA Magnusson Patrik P Oskarsson Sven S Johannesson Magnus M Visscher Peter M PM Laibson David D Cesarini David D Neale Benjamin M BM Benjamin Daniel J DJ

Nature genetics 20180101 2

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N <sub>eff</sub> = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG i ...[more]

PMID: 29292387

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Project description: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 pFET?<?1?×?10-?3 (pFET?<?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.

Dataset Information

Multi-trait analysis of genome-wide association summary statistics using MTAG.

Publications

Multi-trait analysis of genome-wide association summary statistics using MTAG.

Similar Datasets

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