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Nat Biotechnol. 2018 Oct 22. doi: 10.1038/nbt.4246. [Epub ahead of print]

An integrative tissue-network approach to identify and test human disease genes.

Author information

1
Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
2
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
3
Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
4
Flatiron Institute, Simons Foundation, New York, New York, USA.

Abstract

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.

PMID:
30346941
DOI:
10.1038/nbt.4246

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