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- W2897771749 abstract "An integrative computational approach leverages data from model systems and human genome-wide studies to predict genes that have a functional role in disease. Effective discovery of causal disease genes must overcome the statistical challenges of quantitative genetics studies and the practical limitations of human biology experiments. Here we developed diseaseQUEST, an integrative approach that combines data from human genome-wide disease studies with in silico network models of tissue- and cell-type-specific function in model organisms to prioritize candidates within functionally conserved processes and pathways. We used diseaseQUEST to predict candidate genes for 25 different diseases and traits, including cancer, longevity, and neurodegenerative diseases. Focusing on Parkinson's disease (PD), a diseaseQUEST-directed Caenhorhabditis elegans behavioral screen identified several candidate genes, which we experimentally verified and found to be associated with age-dependent motility defects mirroring PD clinical symptoms. Furthermore, knockdown of the top candidate gene, bcat-1, encoding a branched chain amino acid transferase, caused spasm-like 'curling' and neurodegeneration in C. elegans, paralleling decreased BCAT1 expression in PD patient brains. diseaseQUEST is modular and generalizable to other model organisms and human diseases of interest." @default.
- W2897771749 created "2018-10-26" @default.
- W2897771749 creator A5006247664 @default.
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- W2897771749 date "2018-10-22" @default.
- W2897771749 modified "2023-09-27" @default.
- W2897771749 title "An integrative tissue-network approach to identify and test human disease genes" @default.
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- W2897771749 doi "https://doi.org/10.1038/nbt.4246" @default.
- W2897771749 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7021177" @default.
- W2897771749 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30346941" @default.
- W2897771749 hasPublicationYear "2018" @default.
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