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Meta-Analysis
. 2024 Sep 5;111(9):1899-1913.
doi: 10.1016/j.ajhg.2024.07.017. Epub 2024 Aug 21.

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits

K Alaine Broadaway  1 , Sarah M Brotman  1 , Jonathan D Rosen  1 , Kevin W Currin  1 , Abdalla A Alkhawaja  1 , Amy S Etheridge  1 , Fred Wright  2 , Paul Gallins  3 , Dereje Jima  3 , Yi-Hui Zhou  4 , Michael I Love  5 , Federico Innocenti  6 , Karen L Mohlke  7
Affiliations
Meta-Analysis

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits

K Alaine Broadaway et al. Am J Hum Genet. .

Abstract

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

Keywords: GWAS; allelic heterogeneity; colocalization; complex traits; eQTL meta-analysis; liver; signal identification.

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Conflict of interest statement

Declaration of interests F.I. is an employee of BeiGene and received stocks from the company. F.I. was also an AbbVie employee and received stocks from that company.

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References

    1. Trefts E., Gannon M., Wasserman D.H. The liver. Curr. Biol. 2017;27:R1147–R1151. - PMC - PubMed
    1. Gallagher M.D., Chen-Plotkin A.S. The Post-GWAS Era: From Association to Function. Am. J. Hum. Genet. 2018;102:717–730. - PMC - PubMed
    1. Giambartolomei C., Vukcevic D., Schadt E.E., Franke L., Hingorani A.D., Wallace C., Plagnol V. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10 - PMC - PubMed
    1. Strunz T., Grassmann F., Gayán J., Nahkuri S., Souza-Costa D., Maugeais C., Fauser S., Nogoceke E., Weber B.H.F. A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. Sci. Rep. 2018;8:5865. - PMC - PubMed
    1. Zhou Y.H., Gallins P.J., Etheridge A.S., Jima D., Scholl E., Wright F.A., Innocenti F. A resource for integrated genomic analysis of the human liver. Sci. Rep. 2022;12 - PMC - PubMed

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