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Tejaas: reverse regression increases power for detecting trans-eQTLs

MPS-Authors
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Banerjee,  S.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Simonetti,  F.L.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Detrois,  K. E.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Kaphle,  A.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Söding,  J.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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3346729.pdf
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Citation

Banerjee, S., Simonetti, F., Detrois, K. E., Kaphle, A., Mitra, R., Nagial, R., et al. (2021). Tejaas: reverse regression increases power for detecting trans-eQTLs. Genome Biology, 22: 142. doi:10.1186/s13059-021-02361-8.


Cite as: http://hdl.handle.net/21.11116/0000-0009-5D65-E
Abstract
Trans-acting expression quantitative trait loci (trans-eQTLs) account for ≥70% expression heritability and could therefore facilitate uncovering mechanisms underlying the origination of complex diseases. Identifying trans-eQTLs is challenging because of small effect sizes, tissue specificity, and a severe multiple-testing burden. Tejaas predicts trans-eQTLs by performing L2-regularized “reverse” multiple regression of each SNP on all genes, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Combined with a novel unsupervised k-nearest neighbor method to remove confounders, Tejaas predicts 18851 unique trans-eQTLs across 49 tissues from GTEx. They are enriched in open chromatin, enhancers, and other regulatory regions. Many overlap with disease-associated SNPs, pointing to tissue-specific transcriptional regulation mechanisms.