Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood

Diptavo Dutta, Yuan He, Ashis Saha, Marios Arvanitis, Alexis Battle () and Nilanjan Chatterjee ()
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Diptavo Dutta: Johns Hopkins University
Yuan He: Johns Hopkins University
Ashis Saha: Johns Hopkins University
Marios Arvanitis: Johns Hopkins University
Alexis Battle: Johns Hopkins University
Nilanjan Chatterjee: Johns Hopkins University

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Large scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in the downstream regulation of gene-expressions can uncover important mediating biological mechanisms. Here we propose ARCHIE, a summary statistic based sparse canonical correlation analysis method to identify sets of gene-expressions trans-regulated by sets of known trait-related genetic variants. Simulation studies show that compared to standard methods, ARCHIE is better suited to identify “core”-like genes through which effects of many other genes may be mediated and can capture disease-specific patterns of genetic associations. By applying ARCHIE to publicly available summary statistics from the eQTLGen consortium, we identify gene sets which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. Around half (50.7%) of the selected genes do not have any strong trans-associations and are not detected by standard methods. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans-regulation may be related to specific complex traits.

Date: 2022
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DOI: 10.1038/s41467-022-31845-9

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