| Title: | Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity |
| Journal: | Nature Genetics |
| Published: | 6 Jun 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35668300/ |
| DOI: | https://doi.org/10.1038/s41588-022-01087-y |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894581 |
Publication 8163
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Abstract
Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.13 Authors
- Steven Gazal
- Omer Weissbrod
- Farhad Hormozdiari
- Kushal K. Dey
- Joseph Nasser
- Karthik A. Jagadeesh
- Daniel J. Weiner
- Huwenbo Shi
- Charles P. Fulco
- Luke J. O'Connor
- Bogdan Pasaniuc
- Jesse M. Engreitz
- Alkes L. Price
1 Application
| Application ID | Title |
|---|---|
| 16549 | Components of heritability in a UK Biobank cohort |
Enabling scientific discoveries that improve human health