Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity

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

doi:10.1038/s41588-022-01087-y

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity

Nat Genet. 2022 Jun;54(6):827-836. doi: 10.1038/s41588-022-01087-y. Epub 2022 Jun 6.

Authors

Steven Gazal^{1

2

3

4}, Omer Weissbrod^{5

6}, Farhad Hormozdiari^{5

6}, Kushal K Dey^{5

6}, Joseph Nasser⁶, Karthik A Jagadeesh^{5

6}, Daniel J Weiner⁶, Huwenbo Shi^{5

6}, Charles P Fulco^{6

7

8}, Luke J O'Connor⁶, Bogdan Pasaniuc⁹, Jesse M Engreitz^{6

10

11}, Alkes L Price^{12

13

14}

Affiliations

¹ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. gazal@usc.edu.
² Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. gazal@usc.edu.
³ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. gazal@usc.edu.
⁴ Broad Institute of MIT and Harvard, Cambridge, MA, USA. gazal@usc.edu.
⁵ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁶ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁷ Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
⁸ Bristol Myers Squibb, Cambridge, MA, USA.
⁹ Departments of Computational Medicine, Human Genetics, Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁰ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
¹¹ BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA.
¹² Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.
¹³ Broad Institute of MIT and Harvard, Cambridge, MA, USA. aprice@hsph.harvard.edu.
¹⁴ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.

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.

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity

Authors

Affiliations

Abstract

Publication types

MeSH terms

Grants and funding