| Title: | Bayesian Effect Size Ranking to Prioritise Genetic Risk Variants in Common Diseases for Follow-Up Studies |
| Journal: | Genetic Epidemiology |
| Published: | 3 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39749473/ |
| DOI: | https://doi.org/10.1002/gepi.22608 |
Publication 14572
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Abstract
Biological datasets often consist of thousands or millions of variables, e.g. genetic variants or biomarkers, and when sample sizes are large it is common to find many associated with an outcome of interest, for example, disease risk in a GWAS, at high levels of statistical significance, but with very small effects. The False Discovery Rate (FDR) is used to identify effects of interest based on ranking variables according to their statistical significance. Here, we develop a complementary measure to the FDR, the priorityFDR, that ranks variables by a combination of effect size and significance, allowing further prioritisation among a set of variables that pass a significance or FDR threshold. Applying to the largest GWAS of type 1 diabetes to date (15,573 cases and 158,408 controls), we identified 26 independent genetic associations, including two newly-reported loci, with qualitatively lower priorityFDRs than the remaining 175 signals. We detected putatively causal type 1 diabetes risk genes using Mendelian Randomisation, and found that these were located disproportionately close to low priorityFDR signals (p = 0.005), as were genes in the IL-2 pathway (p = 0.003). Selecting variables on both effect size and significance can lead to improved prioritisation for mechanistic follow-up studies from genetic and other large biological datasets.</p>
9 Keywords
- Bayes Theorem
- Diabetes Mellitus, Type 1
- Follow-Up Studies
- Genetic Predisposition to Disease
- Genetic Variation
- Genome-Wide Association Study
- Humans
- Mendelian Randomization Analysis
- Polymorphism, Single Nucleotide
14 Authors
- Daniel J. M. Crouch
- Jamie R. J. Inshaw
- Catherine C. Robertson
- Esther Ng
- Jia-Yuan Zhang
- Wei-Min Chen
- Suna Onengut-Gumuscu
- Antony J. Cutler
- Carlo Sidore
- Francesco Cucca
- Flemming Pociot
- Patrick Concannon
- Stephen S. Rich
- John A. Todd
1 Application
| Application ID | Title |
|---|---|
| 31295 | Type 1 diabetes fine-mapping using related traits and meta-analyses with existing T1D data |
Enabling scientific discoveries that improve human health