Dataset Information

Defining genetic interaction.

ABSTRACT: Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutation's individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, Additive, Log, and Min). Whether this choice holds practical consequences has not been clear, because the definitions yield identical results under some conditions. Here, we show that the choice among alternative definitions can have profound consequences. Although 52% of known synergistic genetic interactions in Saccharomyces cerevisiae were inferred according to the Min definition, we find that both Product and Log definitions (shown here to be practically equivalent) are better than Min for identifying functional relationships. Additionally, we show that the Additive and Log definitions, each commonly used in population genetics, lead to differing conclusions related to the selective advantages of sexual reproduction.

SUBMITTER: Mani R

PROVIDER: S-EPMC2265146 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Defining genetic interaction.

Mani Ramamurthy R St Onge Robert P RP Hartman John L JL Giaever Guri G Roth Frederick P FP

Proceedings of the National Academy of Sciences of the United States of America 20080227 9

Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutation's individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, A ...[more]

PMID: 18305163

Similar Datasets

Project description:Background Lung cancer is considered an archetypical environmentally induced disease because of the high risk from exposure to tobacco smoke. However, family studies clearly identified familial aggregation, beyond effects from familial correlations in smoking behavior. Family studies and genome wide association studies have identified selected variants influencing lung cancer risk but have been underpowered to provide a more comprehensive assessment of genetic architecture. Method We conducted a genome-wide association analysis using the Oncoarray, a genotyping platform that comprises over 530,000 genetic markers and designed to query variation influencing cancer risk. Here, we present the largest genome-wide scan of lung cancer susceptibility, in European-descent populations, comprising data derived from 29,863 patients and 55,586 controls queried for SNPs imputed from the 1000 Genomes Project. Analyses were adjusted for age, sex and the first three principal components for overall lung cancer and stratified by histology (adenocarcinoma, squamous carcinoma, and small cell carcinoma) and ever or never smoking. Results Results identified 24 loci influencing lung cancer risk for loci with minor allele frequencies of 0.5% or higher, of which 14 had not previously reached genome-wide significance levels. Odds ratio effect sizes ranged from 0.37 for the functional variants rs17879961 in CHEK2 to 2.12 for rs11571833 in BRCA2, [JF1] indicating the role of selected uncommon variants in strongly influencing lung cancer risk. Among the novel variants identified, 6 influenced overall lung cancer risk, 6 were specific to adenocarcinoma and 1 each [JF2] influenced never and ever smokers respectively. Aside from previously described variants in the CHRNA5 and CYP2A6 regions that influence cigarette consumption, all other loci showed significant heterogeneity among histologies. Array based heritability analysis also indicates no significant shared heritability between adenocarcinoma and squamous carcinoma, again pointing to striking etiological heterogeneity, other than that due to known smoking behavior related loci between these forms of lung cancer. Evaluation of eQTL results derived from normal lung tissue identified consistent cis-acting effects of the variant rs77468143 influencing expression of a little know gene SECISBP2L, rs6920364 influencing the extracellular ribonuclease RNASET2, and rs146729428 in the EPHX2, involved in inflammation. Several loci that influenced adenocarcinoma alone influenced telomere maintenance or cell cycle, while several of the squamous cancer-specific loci are involved in recombination repair. Conclusion These results indicate striking variation among histological subtypes of lung cancer. Further studies are ongoing to identify the impact that smoking has on lung cancer risk for these loci. These analyses provide a comprehensive assessment of genetic effects on lung cancer risk and will elucidate how these interact with smoking behavior. We would like to extend this study to include participants from Latin American countries because these were not yet well represented by our analyses.

Dataset Information

Defining genetic interaction.

Publications

Defining genetic interaction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets