Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk
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 prom...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Published: |
Nature Publishing Group
2018
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| _version_ | 1797088476782395392 |
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| author | Reshef, YA Finucane, H Kelley, DR Gusev, A Kotliar, D Ulirsch, J Hormozdiari, F Nasser, J O’Connor, L van de Geijn, B Loh, PR Grossman, SR Bhatia, G Gazal, S Pinello, L Palamara, PF Patterson, N Adams, RP Price, AL |
| author_facet | Reshef, YA Finucane, H Kelley, DR Gusev, A Kotliar, D Ulirsch, J Hormozdiari, F Nasser, J O’Connor, L van de Geijn, B Loh, PR Grossman, SR Bhatia, G Gazal, S Pinello, L Palamara, PF Patterson, N Adams, RP Price, AL |
| author_sort | Reshef, YA |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T02:50:39Z |
| format | Journal article |
| id | oxford-uuid:ad924b53-5b3b-4389-ae19-5ef076afa4ff |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:50:39Z |
| publishDate | 2018 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | oxford-uuid:ad924b53-5b3b-4389-ae19-5ef076afa4ff2022-03-27T03:36:29ZDetecting genome-wide directional effects of transcription factor binding on polygenic disease riskJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ad924b53-5b3b-4389-ae19-5ef076afa4ffSymplectic Elements at OxfordNature Publishing Group2018Reshef, YAFinucane, HKelley, DRGusev, AKotliar, DUlirsch, JHormozdiari, FNasser, JO’Connor, Lvan de Geijn, BLoh, PRGrossman, SRBhatia, GGazal, SPinello, LPalamara, PFPatterson, NAdams, RPPrice, ALBiological 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. |
| spellingShingle | Reshef, YA Finucane, H Kelley, DR Gusev, A Kotliar, D Ulirsch, J Hormozdiari, F Nasser, J O’Connor, L van de Geijn, B Loh, PR Grossman, SR Bhatia, G Gazal, S Pinello, L Palamara, PF Patterson, N Adams, RP Price, AL Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk |
| title | Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk |
| title_full | Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk |
| title_fullStr | Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk |
| title_full_unstemmed | Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk |
| title_short | Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk |
| title_sort | detecting genome wide directional effects of transcription factor binding on polygenic disease risk |
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