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  Tejaas: reverse regression increases power for detecting trans-eQTLs

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.

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 Creators:
Banerjee, S.1, Author              
Simonetti, F.L.1, Author              
Detrois, K. E.1, Author              
Kaphle, A.1, Author              
Mitra, R., Author
Nagial, R., Author
Söding, J.1, Author              
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1Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society, ou_1933286              

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 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.

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Language(s): eng - English
 Dates: 2021-05-06
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1186/s13059-021-02361-8
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Title: Genome Biology
Source Genre: Journal
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Pages: 16 Volume / Issue: 22 Sequence Number: 142 Start / End Page: - Identifier: -