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- W2909830512 abstract "The genetic contribution to obesity has been widely studied, yet the functional mechanisms underlying metabolic states remain elusive. This has prompted analysis of endophenotypes via quantitative trait locus studies, which assess how genetic variants affect intermediate gene (eQTL) or protein (pQTL) expression phenotypes. However, most such studies rely on univariate screening, which entails a strong multiplicity burden and does not leverage shared regulatory patterns. We present the first multivariate pQTL analysis with our highly scalable Bayesian framework LOCUS, on plasma protein levels from a dual mass-spectrometry and SomaLogic assay, and show that it is more powerful than a standard univariate procedure on this data. We identify 136 pQTL associations in the Ottawa obesity cohort, of which > 80% replicate in the independent DiOGenes cohort and have significant functional enrichments; 15% of the hits would be missed by univariate analysis. By exploiting clinical data, we reveal the implication of proteins under genetic control in low-grade inflammation, insulin resistance, and dyslipidemia, opening new perspectives for diagnosing and treating metabolic disorders. All results are freely accessible online from our searchable database." @default.
- W2909830512 created "2019-01-25" @default.
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- W2909830512 date "2019-07-03" @default.
- W2909830512 modified "2023-10-01" @default.
- W2909830512 title "A Bayesian joint pQTL study sheds light on the genetic architecture of obesity" @default.
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