Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity

Explaining the genetics of many diseases is challenging because most associations localize to incompletely characterized regulatory regions. Using new computational methods, we show that transcription factors (TFs) occupy multiple loci associated with individual complex genetic disorders. Applicatio...

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Veröffentlicht in:Nature genetics 2018-05, Vol.50 (5), p.699-707
Hauptverfasser: Harley, John B., Chen, Xiaoting, Pujato, Mario, Miller, Daniel, Maddox, Avery, Forney, Carmy, Magnusen, Albert F., Lynch, Arthur, Chetal, Kashish, Yukawa, Masashi, Barski, Artem, Salomonis, Nathan, Kaufman, Kenneth M., Kottyan, Leah C., Weirauch, Matthew T.
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Sprache:eng
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Zusammenfassung:Explaining the genetics of many diseases is challenging because most associations localize to incompletely characterized regulatory regions. Using new computational methods, we show that transcription factors (TFs) occupy multiple loci associated with individual complex genetic disorders. Application to 213 phenotypes and 1,544 TF binding datasets identified 2,264 relationships between hundreds of TFs and 94 phenotypes, including androgen receptor in prostate cancer and GATA3 in breast cancer. Strikingly, nearly half of systemic lupus erythematosus risk loci are occupied by the Epstein–Barr virus EBNA2 protein and many coclustering human TFs, showing gene–environment interaction. Similar EBNA2-anchored associations exist in multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, juvenile idiopathic arthritis and celiac disease. Instances of allele-dependent DNA binding and downstream effects on gene expression at plausibly causal variants support genetic mechanisms dependent on EBNA2. Our results nominate mechanisms that operate across risk loci within disease phenotypes, suggesting new models for disease origins. Linking transcription factors with disease loci implicates EBNA2, encoded by Epstein–Barr virus, in autoimmune diseases. Applying the method more widely identifies associations for hundreds of transcription factors, illuminating disease mechanisms.
ISSN:1061-4036
1546-1718
DOI:10.1038/s41588-018-0102-3