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Nat Genet DOI:10.1038/s41588-018-0196-7

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk.

Publication TypeJournal Article
Year of Publication2018
AuthorsReshef, YA, Finucane, HK, Kelley, DR, Gusev, A, Kotliar, D, Ulirsch, JC, Hormozdiari, F, Nasser, J, O'Connor, L, van de Geijn, B, Loh, P-R, Grossman, SR, Bhatia, G, Gazal, S, Palamara, PFrancesco, Pinello, L, Patterson, N, Adams, RP, Price, AL
JournalNat Genet
Volume50
Issue10
Pages1483-1493
Date Published2018 10
ISSN1546-1718
KeywordsBinding Sites, Blood Cells, Blood Chemical Analysis, Disease, Gene Expression Regulation, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Protein Binding, Quantitative Trait Loci, Risk Factors, Transcription Factors
Abstract

Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed linkage disequilibrium profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular quantitative trait loci in blood, recovering known transcriptional regulators. We apply the method to expression quantitative trait loci in 48 Genotype-Tissue Expression tissues, identifying 651 transcription factor-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average n = 290 K), identifying 77 annotation-trait associations representing 12 independent transcription factor-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms.

DOI10.1038/s41588-018-0196-7
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/30177862?dopt=Abstract

Alternate JournalNat. Genet.
PubMed ID30177862
PubMed Central IDPMC6202062
Grant ListS10 RR028832 / RR / NCRR NIH HHS / United States
P50 HD028138 / HD / NICHD NIH HHS / United States
T32 GM007753 / GM / NIGMS NIH HHS / United States
T32 DK110919 / DK / NIDDK NIH HHS / United States
R00 HG008399 / HG / NHGRI NIH HHS / United States
U01 HG009379 / HG / NHGRI NIH HHS / United States
R01 MH107649 / MH / NIMH NIH HHS / United States
R01 MH101244 / MH / NIMH NIH HHS / United States
R01 MH109978 / MH / NIMH NIH HHS / United States