Year: 2022
Type: article
Abstract: Expression quantitative trait locus (eQTL) analysis associates SNPs with gene expression; these relationships can be represented as a bipartite network with association strength as "edge weights" between SNPs and genes. However, most eQTL networks use binary edge weights based on thresholded FDR estimates: definitions that influence reproducibility and downstream analyses. We constructed twenty-nine tissue-specific eQTL networks using GTEx data and evaluated a comprehensive set of network specifications based on false discovery rates, test statistics, and p values, focusing on the degree centrality-a metric of an SNP or gene node's potential network influence. We found a thresholded Benjamini-Hochberg q value weighted by the Z-statistic balances metric reproducibility and computational efficiency. Our estimated gene degrees positively correlate with gene degrees in gene regulatory networks, demonstrating that these networks are complementary in understanding regulation. Gene degrees also correlate with genetic diversity, and heritability analyses show that highly connected nodes are enriched for tissue-relevant traits.
Source: Cell Reports Methods
Institutions: Harvard University, Dana-Farber Cancer Institute, Centre National de la Recherche Scientifique, AgroParisTech, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Génétique Quantitative et Évolution Le Moulon, Brigham and Women's Hospital
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,200
Funders: National Science Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, National Cancer Institute, National Institute of Neurological Disorders and Stroke
OpenAlex ID: W4281381734