Specificity, length and luck drive gene rankings in association studies

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes<sup>1</sup>. Although these methods are conceptually similar, by analysing association studies of 209 quantitative traits in the UK Biobank<sup>2-4</sup>, we show that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: (1) trait importance - how much a gene quantitatively affects a trait; and (2) trait specificity - the importance of a gene for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, whereas burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, whereas burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

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Publication Details

Journal
Nature
Published
2025-11-05
DOI
https://doi.org/10.1038/s41586-025-09703-7
Citations
14
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
25.44
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article

Specificity, length and luck drive gene rankings in association studies

Yuval B. Simons, Guy Sella, Hakhamanesh Mostafavi, Courtney J. Smith et al.
14 citations
Nature
Genetic Associations and Epidemiology
25.44
article

Specificity, length and luck drive gene rankings in association studies

Yuval B. Simons, Guy Sella, Hakhamanesh Mostafavi, Courtney J. Smith, Jonathan K. Pritchard, Tamara Gjorgjieva, Jeffrey P. Spence, Nikhil Milind, Mineto Ota
article en
14 citations

Abstract

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes<sup>1</sup>. Although these methods are conceptually similar, by analysing association studies of 209 quantitative traits in the UK Biobank<sup>2-4</sup>, we show that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: (1) trait importance - how much a gene quantitatively affects a trait; and (2) trait specificity - the importance of a gene for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, whereas burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, whereas burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

NatureVol. 649(8098)
University of California, San Francisco (US), University of Chicago (US), NYU Langone Health (US), Stanford Medicine (US), The University of Tokyo (JP), Columbia University (US), Stanford University (US)
Openalex Percentile: Top 0%
Genetic Associations and Epidemiology
25.44
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