Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks.

PLoS Genetics
Abhirami RatnakumarNadeem Riaz

Abstract

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent w...Continue Reading

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Citations

May 7, 2021·Briefings in Bioinformatics·Bingbo WangLin Gao
Jul 6, 2021·Computational and Mathematical Methods in Medicine·Zhiqiang WangJie Yang

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Methods Mentioned

BETA
exome sequencing
genotyping

Software Mentioned

custom Perl scripts
ONCOKB
Ensembl
COSMIC
Clinvar
bedtools
custom Perl script
custom R scripts
Gephi
STRING

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