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Genes 2015, 6(1), 24-45; doi:10.3390/genes6010024

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

1
Department of Genetics, University of Alabama at Birmingham, 730 Hugh Kaul Human Genetics Building, 720 20th Street South, Birmingham, AL 35294, USA
2
Department of Statistics and Michael Smith Laboratories, University of British Columbia, 3182 Earth Sciences Building, 2207 Main Mall, Vancouver, BC V6T-1Z4, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Karen E. Nelson, John Burn, Nicholas J. Schork, James R. Lupski and Pabulo H. Rampelotto
Received: 20 August 2014 / Accepted: 12 January 2015 / Published: 6 February 2015
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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Abstract

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease. View Full-Text
Keywords: yeast phenomics; yeast models of human disease; cell proliferation phenotypes or cell proliferation parameters (CPPs); gene interaction networks; quantitative high throughput cell array phenotyping (Q-HTCP); genetic buffering; cystic fibrosis (CF); human-like (HL) yeast media; ammonium toxicity; recursive expectation-maximization clustering (REMc) yeast phenomics; yeast models of human disease; cell proliferation phenotypes or cell proliferation parameters (CPPs); gene interaction networks; quantitative high throughput cell array phenotyping (Q-HTCP); genetic buffering; cystic fibrosis (CF); human-like (HL) yeast media; ammonium toxicity; recursive expectation-maximization clustering (REMc)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hartman, J.L., IV; Stisher, C.; Outlaw, D.A.; Guo, J.; Shah, N.A.; Tian, D.; Santos, S.M.; Rodgers, J.W.; White, R.A. Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease. Genes 2015, 6, 24-45.

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