Citation
Jiang, Min-Zhi; Gaynor, Sheila M.; Li, Xihao; Van Buren, Eric; Stilp, Adrienne M.; Buth, Erin; Wang, Fei Fei; Manansala, Regina; Gogarten, Stephanie M.; & Li, Zilin, et al. (Preprint). Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium. bioRxiv. PMCID: PMC10515765Abstract
Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.URL
http://dx.doi.org/10.1101/2023.09.10.555215Reference Type
Journal ArticleYear Published
PreprintJournal Title
bioRxivAuthor(s)
Jiang, Min-ZhiGaynor, Sheila M.
Li, Xihao
Van Buren, Eric
Stilp, Adrienne M.
Buth, Erin
Wang, Fei Fei
Manansala, Regina
Gogarten, Stephanie M.
Li, Zilin
Polfus, Linda M.
Salimi, Shabnam
Bis, Joshua C.
Pankratz, Nathan
Yanek, Lisa R.
Durda, Peter
Tracy, Russell P.
Rich, Stephen S.
Rotter, Jerome I.
Mitchell, Braxton D.
Lewis, Joshua P.
Psaty, Bruce M.
Pratte, Katherine A.
Silverman, Edwin K.
Kaplan, Robert C.
Avery, Christy L.
North, Kari
Mathias, Rasika A.
Faraday, Nauder
Lin, Honghuang
Wang, Biqi
Carson, April P.
Norwood, Arnita F.
Gibbs, Richard A.
Kooperberg, Charles
Lundin, Jessica
Peters, Ulrike
Dupuis, Josée
Hou, Lifang
Fornage, Myriam
Benjamin, Emelia J.
Reiner, Alexander P.
Bowler, Russell P.
Lin, Xihong
Auer, Paul L.
Raffield, Laura M.