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

Victoria Yao, Rachel Kaletsky, William Keyes, Danielle Emille Mor, Aaron K Wong, Salman Sohrabi, Coleen T. Murphy, Olga Troyanskaya

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


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.
Original languageEnglish (US)
JournalNature Biotechnology
StatePublished - Oct 22 2018
Externally publishedYes


Dive into the research topics of 'An integrative tissue-network approach to identify and test human disease genes.'. Together they form a unique fingerprint.

Cite this