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Differential analysis of high-throughput quantitative genetic interaction data.

ABSTRACT: Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.

SUBMITTER: Bean GJ

PROVIDER: S-EPMC4056373 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Differential analysis of high-throughput quantitative genetic interaction data.

Bean Gordon J GJ Ideker Trey T

Genome biology 20121226 12

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also ...[more]

PMID: 23268787

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Project description:DNA from buccal brush samples is being used for high-throughput analyses in a variety of applications, but the impact of sample type on genotyping success and downstream statistical analysis remains unclear. The objective of the current study was to determine laboratory predictors of genotyping failure among buccal DNA samples, and to evaluate the successfully genotyped results with respect to analytic quality control metrics. Sample and genotyping characteristics were compared between buccal and blood samples collected in the population-based Genetic and Environmental Risk Factors for Hemorrhagic Stroke (GERFHS) study (https://gerfhs.phs.wfubmc.edu/public/index.cfm).Seven-hundred eight (708) buccal and 142 blood DNA samples were analyzed for laboratory-based and analysis metrics. Overall genotyping failure rates were not statistically different between buccal (11.3%) and blood (7.0%, p?=?0.18) samples; however, both the Contrast Quality Control (cQC) rate and the dynamic model (DM) call rates were lower among buccal DNA samples (p?<?0.0001). The ratio of double-stranded to total DNA (ds/total ratio) in the buccal samples was the only laboratory characteristic predicting sample success (p?<?0.0001). A threshold of at least 34% ds/total DNA provided specificity of 98.7% with a 90.5% negative predictive value for eliminating probable failures. After genotyping, median sample call rates (99.1% vs. 99.4%, p?<?0.0001) and heterozygosity rates (25.6% vs. 25.7%, p?=?0.006) were lower for buccal versus blood DNA samples, respectively, but absolute differences were small. Minor allele frequency differences from HapMap were smaller for buccal than blood samples, and both sample types demonstrated tight genotyping clusters, even for rare alleles.We identified a buccal sample characteristic, a ratio of ds/total DNA <34%, which distinguished buccal DNA samples likely to fail high-throughput genotyping. Applying this threshold, the quality of final genotyping resulting from buccal samples is somewhat lower, but compares favorably to blood. Caution is warranted if cases and controls have different sample types, but buccal samples provide comparable results to blood samples in large-scale genotyping analyses.

Dataset Information

Differential analysis of high-throughput quantitative genetic interaction data.

Publications

Differential analysis of high-throughput quantitative genetic interaction data.

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