| Title |
Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants
|
|---|---|
| Published in |
Human Genetics, May 2017
|
| DOI | 10.1007/s00439-017-1815-6 |
| Pubmed ID | |
| Authors |
Christina A. Markunas, Eric O. Johnson, Dana B. Hancock |
| Abstract |
Genome-wide association study (GWAS)-identified variants are enriched for functional elements. However, we have limited knowledge of how functional enrichment may differ by disease/trait and tissue type. We tested a broad set of eight functional elements for enrichment among GWAS-identified SNPs (p < 5×10(-8)) from the NHGRI-EBI Catalog across seven disease/trait categories: cancer, cardiovascular disease, diabetes, autoimmune disease, psychiatric disease, neurological disease, and anthropometric traits. SNPs were annotated using HaploReg for the eight functional elements across any tissue: DNase sites, expression quantitative trait loci (eQTL), sequence conservation, enhancers, promoters, missense variants, sequence motifs, and protein binding sites. In addition, tissue-specific annotations were considered for brain vs. blood. Disease/trait SNPs were compared to a control set of 4809 SNPs matched to the GWAS SNPs (N = 1639) on allele frequency, gene density, distance to nearest gene, and linkage disequilibrium at ~3:1 ratio. Enrichment analyses were conducted using logistic regression, with Bonferroni correction. Overall, a significant enrichment was observed for all functional elements, except sequence motifs. Missense SNPs showed the strongest magnitude of enrichment. eQTLs were the only functional element significantly enriched across all diseases/traits. Magnitudes of enrichment were generally similar across diseases/traits, where enrichment was statistically significant. Blood vs. brain tissue effects on enrichment were dependent on disease/trait and functional element (e.g., cardiovascular disease: eQTLs P TissueDifference = 1.28 × 10(-6) vs. enhancers P TissueDifference = 0.94). Identifying disease/trait-relevant functional elements and tissue types could provide new insight into the underlying biology, by guiding a priori GWAS analyses (e.g., brain enhancer elements for psychiatric disease) or facilitating post hoc interpretation. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 23% |
| Researcher | 4 | 13% |
| Student > Postgraduate | 3 | 10% |
| Student > Bachelor | 2 | 7% |
| Professor | 2 | 7% |
| Other | 6 | 20% |
| Unknown | 6 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 37% |
| Agricultural and Biological Sciences | 5 | 17% |
| Medicine and Dentistry | 4 | 13% |
| Neuroscience | 1 | 3% |
| Materials Science | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 8 | 27% |
Attention Score in Context
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