A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids
A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory...
| Үндсэн зохиолчид: | , , , , , , |
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| Формат: | Journal article |
| Хэл сонгох: | English |
| Хэвлэсэн: |
Cell Press
2022
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| _version_ | 1826310486465970176 |
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| author | Ramdas, S Judd, J Graham, SE Chen, Z Walters, R Millwood, I Lin, K |
| author_facet | Ramdas, S Judd, J Graham, SE Chen, Z Walters, R Millwood, I Lin, K |
| author_sort | Ramdas, S |
| collection | OXFORD |
| description | A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology. |
| first_indexed | 2024-03-07T07:52:42Z |
| format | Journal article |
| id | oxford-uuid:3b20d254-b65c-4d5e-b05e-e29e2b7ad5c6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:52:42Z |
| publishDate | 2022 |
| publisher | Cell Press |
| record_format | dspace |
| spelling | oxford-uuid:3b20d254-b65c-4d5e-b05e-e29e2b7ad5c62023-08-04T09:26:59ZA multi-layer functional genomic analysis to understand noncoding genetic variation in lipidsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3b20d254-b65c-4d5e-b05e-e29e2b7ad5c6EnglishSymplectic ElementsCell Press2022Ramdas, SJudd, JGraham, SEChen, ZWalters, RMillwood, ILin, KA major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology. |
| spellingShingle | Ramdas, S Judd, J Graham, SE Chen, Z Walters, R Millwood, I Lin, K A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids |
| title | A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids |
| title_full | A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids |
| title_fullStr | A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids |
| title_full_unstemmed | A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids |
| title_short | A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids |
| title_sort | multi layer functional genomic analysis to understand noncoding genetic variation in lipids |
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