Specificity, length, and luck: How genes are prioritized by rare and common variant association studies
Spence, J. P.; Mostafavi, H.; Ota, M.; Milind, N.; Gjorgjieva, T.; Smith, C. J.; Simons, Y. B.; Sella, G.; Pritchard, J. K.
Show abstract
Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank 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 -- a genes importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, while burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies 95%
- SparsePro: an efficient fine-mapping method integrating summary statistics and functional annotations 95%
- Negative linkage disequilibrium between amino acid changing variants reveals interference among deleterious mutations in the human genome 95%
Similar papers in this journal
- Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model 96%
- Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. 96%
- Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.