Abstract
We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.
| Original language | English |
|---|---|
| Journal | Nature Biotechnology |
| Volume | 25 |
| Issue number | 3 |
| Pages (from-to) | 309-16 |
| ISSN | 1087-0156 |
| DOIs | |
| Publication status | Published - 2007 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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