ArticleHGG advances2024
Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability.
Article in HGG advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Research on multi-trait genome association study method based on Shannon information entropy.BMC bioinformatics · 2026Article
- Optimized phenotype definitions boost GWAS power.PLoS computational biology · 2026Article
- Multivariate genetic analysis reveals three distinct pathological dimensions in musculoskeletal disorders.Nature communications · 2026Article
- Dissecting pleiotropy to gain mechanistic insights into human disease.Nature reviews. Genetics · 2026Review
- Diversity and longitudinal records: Genetic architecture of disease associations and polygenic risk in the Taiwanese Han population.Science advances · 2025Article
- Review
- Multi-trait GWAS for diverse ancestries: mapping the knowledge gap.BMC genomics · 2024Article
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15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10
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