Combining SNP-to-gene Linking Strategies to Identify Disease Genes and Assess Disease Omnigenicity

Overview

Journal Nat Genet

Specialty Genetics

Date 2022 Jun 6

PMID 35668300

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 OConnor

Bogdan Pasaniuc

Jesse M Engreitz

Alkes L Price

Affiliations

Soon will be listed here.

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.

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References

de Leeuw C, Mooij J, Heskes T, Posthuma D . MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015; 11(4):e1004219. PMC: 4401657. DOI: 10.1371/journal.pcbi.1004219. View

Fachal L, Aschard H, Beesley J, Barnes D, Allen J, Kar S . Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. Nat Genet. 2020; 52(1):56-73. PMC: 6974400. DOI: 10.1038/s41588-019-0537-1. View

Stadhouders R, Aktuna S, Thongjuea S, Aghajanirefah A, Pourfarzad F, van IJcken W . HBS1L-MYB intergenic variants modulate fetal hemoglobin via long-range MYB enhancers. J Clin Invest. 2014; 124(4):1699-710. PMC: 3973089. DOI: 10.1172/JCI71520. View

. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020; 369(6509):1318-1330. PMC: 7737656. DOI: 10.1126/science.aaz1776. View

Hounkpe B, Chenou F, de Lima F, De Paula E . HRT Atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets. Nucleic Acids Res. 2020; 49(D1):D947-D955. PMC: 7778946. DOI: 10.1093/nar/gkaa609. View

Gallagher M, Chen-Plotkin A . The Post-GWAS Era: From Association to Function. Am J Hum Genet. 2018; 102(5):717-730. PMC: 5986732. DOI: 10.1016/j.ajhg.2018.04.002. View

Auton A, Brooks L, Durbin R, Garrison E, Kang H, Korbel J . A global reference for human genetic variation. Nature. 2015; 526(7571):68-74. PMC: 4750478. DOI: 10.1038/nature15393. View

Hormozdiari F, van de Bunt M, V Segre A, Li X, Joo J, Bilow M . Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet. 2016; 99(6):1245-1260. PMC: 5142122. DOI: 10.1016/j.ajhg.2016.10.003. View

Ulirsch J, Nandakumar S, Wang L, Giani F, Zhang X, Rogov P . Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits. Cell. 2016; 165(6):1530-1545. PMC: 4893171. DOI: 10.1016/j.cell.2016.04.048. View

10.

Fishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Iny Stein T . GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford). 2017; 2017. PMC: 5467550. DOI: 10.1093/database/bax028. View

11.

Pliner H, Packer J, McFaline-Figueroa J, Cusanovich D, Daza R, Aghamirzaie D . Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. Mol Cell. 2018; 71(5):858-871.e8. PMC: 6582963. DOI: 10.1016/j.molcel.2018.06.044. View

12.

Wang X, Goldstein D . Enhancer Domains Predict Gene Pathogenicity and Inform Gene Discovery in Complex Disease. Am J Hum Genet. 2020; 106(2):215-233. PMC: 7010980. DOI: 10.1016/j.ajhg.2020.01.012. View

13.

Wang X, Tucker N, Rizki G, Mills R, Krijger P, de Wit E . Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures. Elife. 2016; 5. PMC: 4862755. DOI: 10.7554/eLife.10557. View

14.

Benner C, Spencer C, Havulinna A, Salomaa V, Ripatti S, Pirinen M . FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016; 32(10):1493-501. PMC: 4866522. DOI: 10.1093/bioinformatics/btw018. View

15.

Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx B . Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016; 48(3):245-52. PMC: 4767558. DOI: 10.1038/ng.3506. View

16.

Spisak S, Lawrenson K, Fu Y, Csabai I, Cottman R, Seo J . CAUSEL: an epigenome- and genome-editing pipeline for establishing function of noncoding GWAS variants. Nat Med. 2015; 21(11):1357-63. PMC: 4746056. DOI: 10.1038/nm.3975. View

17.

Visscher P, Wray N, Zhang Q, Sklar P, McCarthy M, Brown M . 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017; 101(1):5-22. PMC: 5501872. DOI: 10.1016/j.ajhg.2017.06.005. View

18.

Freund M, Burch K, Shi H, Mancuso N, Kichaev G, Garske K . Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits. Am J Hum Genet. 2018; 103(4):535-552. PMC: 6174356. DOI: 10.1016/j.ajhg.2018.08.017. View

19.

Wray N, Wijmenga C, Sullivan P, Yang J, Visscher P . Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model. Cell. 2018; 173(7):1573-1580. DOI: 10.1016/j.cell.2018.05.051. View

20.

Bauer D, Kamran S, Lessard S, Xu J, Fujiwara Y, Lin C . An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. Science. 2013; 342(6155):253-7. PMC: 4018826. DOI: 10.1126/science.1242088. View