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• W2897314609 abstract "Genome-wide association studies (GWAS) aim to identify genetic factors associated with phenotypes. Standard analyses test variants for associations individually. However, variant-level associations are hard to identify and can be difficult to interpret biologically. Enrichment analyses help address both problems by targeting sets of biologically related variants. Here we introduce a new model-based enrichment method that requires only GWAS summary statistics. Applying this method to interrogate 4,026 gene sets in 31 human phenotypes identifies many previously-unreported enrichments, including enrichments of endochondral ossification pathway for height, NFAT-dependent transcription pathway for rheumatoid arthritis, brain-related genes for coronary artery disease, and liver-related genes for Alzheimer's disease. A key feature of our method is that inferred enrichments automatically help identify new trait-associated genes. For example, accounting for enrichment in lipid transport genes highlights association between MTTP and low-density lipoprotein levels, whereas conventional analyses of the same data found no significant variants near this gene." @default.
• W2897314609 created "2018-10-26" @default.
• W2897314609 creator A5035361085 @default.
• W2897314609 creator A5036504609 @default.
• W2897314609 date "2018-10-19" @default.
• W2897314609 modified "2023-10-16" @default.
• W2897314609 title "Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes" @default.
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• W2897314609 doi "https://doi.org/10.1038/s41467-018-06805-x" @default.
• W2897314609 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6195536" @default.
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• W2897314609 hasPublicationYear "2018" @default.
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