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Multi-trait analysis of genome-wide association summary statistics using MTAG
Patrick Turley1,2, Raymond K Walters3,4, Omeed Maghzian5
1Broad Institute, Cambridge, MA, USA. paturley@broadinstitute.org.
Nature Genetics
|January 3, 2018
Summary
Multi-trait Analysis of GWAS (MTAG) jointly analyzes genome-wide association study data for multiple traits. This novel method significantly increases the number of identified genetic loci for depressive symptoms, neuroticism, and well-being.
Area of Science:
- Genetics
- Psychiatry
- Statistical Genomics
Background:
- Genome-wide association studies (GWAS) identify genetic loci associated with specific traits.
- Analyzing multiple traits separately can limit the discovery of genetic associations, especially for complex traits with shared genetic underpinnings.
Purpose of the Study:
- To introduce and validate Multi-trait Analysis of GWAS (MTAG), a novel method for joint analysis of GWAS summary statistics.
- To increase the power for genetic locus discovery across multiple, potentially overlapping, traits.
Main Methods:
- MTAG integrates summary statistics from independent or partially overlapping GWAS datasets.
- The method was applied to large-scale GWAS data for depressive symptoms, neuroticism, and subjective well-being.
Main Results:
- MTAG identified significantly more genome-wide significant loci compared to single-trait GWAS for all three traits.
- For depressive symptoms, neuroticism, and subjective well-being, MTAG increased locus discovery from 32 to 64, 9 to 37, and 13 to 49, respectively.
- MTAG-derived association statistics improved polygenic score predictive power by approximately 25%.
Conclusions:
- MTAG is an effective method for enhancing genetic discovery by jointly analyzing GWAS data across multiple traits.
- The method provides more robust association statistics, leading to improved downstream analyses and polygenic prediction.
- MTAG offers a powerful approach to unraveling the genetic architecture of complex traits.
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