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Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk.
- Source :
-
Nature genetics [Nat Genet] 2018 Oct; Vol. 50 (10), pp. 1483-1493. Date of Electronic Publication: 2018 Sep 03. - Publication Year :
- 2018
-
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 nā=ā290āK), 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.
- Subjects :
- Binding Sites genetics
Blood Cells metabolism
Blood Cells pathology
Blood Chemical Analysis
Gene Expression Regulation
Genetic Predisposition to Disease
Humans
Linkage Disequilibrium
Phenotype
Polymorphism, Single Nucleotide
Protein Binding
Risk Factors
Disease genetics
Genome-Wide Association Study
Multifactorial Inheritance genetics
Quantitative Trait Loci
Transcription Factors metabolism
Subjects
Details
- Language :
- English
- ISSN :
- 1546-1718
- Volume :
- 50
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- Nature genetics
- Publication Type :
- Academic Journal
- Accession number :
- 30177862
- Full Text :
- https://doi.org/10.1038/s41588-018-0196-7