Gene–gene interactions of fatty acid synthase (FASN) using multifactor-dimensionality reduction method in Korean cattle

We examined the gene–gene interactions of five exonic single nucleotide polymorphisms (SNPs) in the gene encoding fatty acid synthase using 513 Korean cattle and using the model free and the non-parametrical multifactor dimensionality reduction method for the analysis. The five SNPs of g.12870 T>C, g.13126 T>C, g.15532 C>A, g.16907 T>C and g.17924 G>A associated with a variety of fatty acid compositions and marbling score were used in this study. The two-factor interaction between g.13126 T>C and g.15532 C>A had the highest training-balanced among the five-factor models and a testing-balanced accuracy at 70.18 % on C18:1 with a cross-validation consistency of 10 out of 10. Also, the two-factor interaction between g.13126 T>C and g.15532 C>A had the highest testing-balanced accuracy at 68.59 % with a 10 out of 10 cross-validation consistency, than any other models on MUFA. In MS, a single SNP g.15532 C>A had the best accuracy at 58.85 % and the two-factor interaction model g.12870 T>C and g.15532 C>A had the highest testing-balanced accuracy at 64.00 %. The three-factor interaction model g.12870 T>C, g.13126 T>C and g.15532 C>A was recorded as having a high testing-balanced accuracy of 63.24 %, but it was lower than the two-factor interaction model. We used likelihood ratio tests for interaction, and Chi square tests to validate our results, with all tests showing statistical significance. We also compared this with mean scores between the high-risk trait group and low-risk trait group. The genotypes of TTCA, TTAA and TCAA at g.15532 and g.13126 on C18:1, genotypes TTCC, TTCA, TTAA, TCAA CCAA at g.15532 and g.13126 on MUFA and genotypes CCCC, TCCA, CCCA, TTAA, TCAA and CCAA at g.15532 and g.12870 on MS were recommended for the genetic improvement of beef quality.     

A genome-wide screen of gene–gene interactions for rheumatoid arthritis susceptibility

The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach “random forest” applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene–gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls.     

Analysis of gene × environment interactions in sibships using mixed models

Background

Gene × environment models are widely used to assess genetic and environmental risks and their association with a phenotype of interest for many complex diseases. Mixed generalized linear models were used to assess gene × environment interactions with respect to systolic blood pressure on sibships adjusting for repeated measures and hierarchical nesting structures. A data set containing 410 sibships from the Framingham Heart Study offspring cohort (part of the Genetic Analysis Workshop 13 data) was used for all analyses. Three mixed gene × environment models, all adjusting for repeated measurement and varying levels of nesting, were compared for precision of estimates: 1) all sibships with adjustment for two levels of nesting (sibs within sibships and sibs within pedigrees), 2) all sibships with adjustment for one level of nesting (sibs within sibships), and 3) 100 data sets containing random draws of one sibship per extended pedigree adjusting for one level of nesting.

Results

The main effects were: gender, baseline age, body mass index (BMI), hypertensive treatment, cigarettes per day, grams of alcohol per day, and marker GATA48G07A. The interaction fixed effects were: baseline age by gender, baseline age by cigarettes per day, baseline age by hypertensive treatment, baseline age by BMI, hypertensive treatment by BMI, and baseline age by marker GATA48G07A. The estimates for all three nesting techniques were not widely discrepant, but precision of estimates and determination of significant effects did change with the change in adjustment for nesting.

Conclusion

Our results show the importance of the adjustment for all levels of hierarchical nesting of sibs in the presence of repeated measures.

     

Targeted and proteome-wide analysis of metabolite–protein interactions

Understanding the molecular mechanisms of endogenous and environmental metabolites is crucial for basic biology and drug discovery. With the genome, proteome, and metabolome of many organisms being readily available, researchers now have the opportunity to dissect how key metabolites regulate complex cellular pathways in vivo. Nonetheless, characterizing the specific and functional protein targets of key metabolites associated with specific cellular phenotypes remains a major challenge. Innovations in chemical biology are now poised to address this fundamental limitation in physiology and disease. In this review, we highlight recent advances in chemoproteomics for targeted and proteome-wide analysis of metabolite–protein interactions that have enabled the discovery of unpredicted metabolite–protein interactions and facilitated the development of new small molecule therapeutics.     

Gene–gene and gene–environment interactions in ulcerative colitis

Genome-wide association studies (GWAS) have identified at least 133 ulcerative colitis (UC) associated loci. The role of genetic factors in clinical practice is not clearly defined. The relevance of genetic variants to disease pathogenesis is still uncertain because of not characterized gene–gene and gene–environment interactions. We examined the predictive value of combining the 133 UC risk loci with genetic interactions in an ongoing inflammatory bowel disease (IBD) GWAS. The Wellcome Trust Case–Control Consortium (WTCCC) IBD GWAS was used as a replication cohort. We applied logic regression (LR), a novel adaptive regression methodology, to search for high-order interactions. Exploratory genotype correlations with UC sub-phenotypes [extent of disease, need of surgery, age of onset, extra-intestinal manifestations and primary sclerosing cholangitis (PSC)] were conducted. The combination of 133 UC loci yielded good UC risk predictability [area under the curve (AUC) of 0.86]. A higher cumulative allele score predicted higher UC risk. Through LR, several lines of evidence for genetic interactions were identified and successfully replicated in the WTCCC cohort. The genetic interactions combined with the gene-smoking interaction significantly improved predictability in the model (AUC, from 0.86 to 0.89, P = 3.26E?05). Explained UC variance increased from 37 to 42 % after adding the interaction terms. A within case analysis found suggested genetic association with PSC. Our study demonstrates that the LR methodology allows the identification and replication of high-order genetic interactions in UC GWAS datasets. UC risk can be predicted by a 133 loci and improved by adding gene–gene and gene–environment interactions.     

Genome-wide investigation of gene–environment interactions in colorectal cancer

Colorectal cancer (CRC), one of the most frequent neoplasias worldwide, has both genetic and environmental causes. As yet, however, gene–environment (G × E) interactions in CRC have been studied mostly for a small number of candidate genes only. Therefore, we investigated the possible interaction, in CRC etiology, between single-nucleotide polymorphisms (SNPs) on the one hand, and overweight, smoking and alcohol consumption on the other, at a genome-wide level. To this end, we adopted a two-tiered approach comprising a case-only screening stage I (314 cases) and a case–control validation stage II (259 cases, 1,002 controls). Interactions with the smallest p value in stage I were verified in stage II using multiple logistic regression analysis adjusted for sex and age. In addition, we specifically studied known CRC-associated SNPs for possible G × E interactions. Upon adjustment for sex and age, and after allowing for multiple testing, however, only a single SNP (rs1944511) was found to be involved in a statistically significant interaction, namely with overweight (multiplicity-corrected p = 0.042 in stage II). Several other G × E interactions were nominally significant but failed correction for multiple testing, including a previously reported interaction between rs9929218 and alcohol consumption that also emerged in our candidate SNP study (nominal p = 0.008). Notably, none of the interactions identified in our genome-wide analysis was with a previously reported CRC-associated SNP. Our study therefore highlights the potential of an “agnostic” genome-wide approach to G × E analysis.     

A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis

The widespread use of high-throughput methods of single nucleotide polymorphism (SNP) genotyping has created a number of computational and statistical challenges. The problem of identifying SNP–SNP interactions in case–control studies has been studied extensively, and a number of new techniques have been developed. Little progress has been made, however, in the analysis of SNP–SNP interactions in relation to time-to-event data, such as patient survival time or time to cancer relapse. We present an extension of the two class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP–SNP interactions in the context of survival analysis. The proposed Survival MDR (Surv-MDR) method handles survival data by modifying MDR’s constructive induction algorithm to use the log-rank test. Surv-MDR replaces balanced accuracy with log-rank test statistics as the score to determine the best models. We simulated datasets with a survival outcome related to two loci in the absence of any marginal effects. We compared Surv-MDR with Cox-regression for their ability to identify the true predictive loci in these simulated data. We also used this simulation to construct the empirical distribution of Surv-MDR’s testing score. We then applied Surv-MDR to genetic data from a population-based epidemiologic study to find prognostic markers of survival time following a bladder cancer diagnosis. We identified several two-loci SNP combinations that have strong associations with patients’ survival outcome. Surv-MDR is capable of detecting interaction models with weak main effects. These epistatic models tend to be dropped by traditional Cox regression approaches to evaluating interactions. With improved efficiency to handle genome wide datasets, Surv-MDR will play an important role in a research strategy that embraces the complexity of the genotype–phenotype mapping relationship since epistatic interactions are an important component of the genetic basis of disease.     

An angiosperm-wide analysis of the gynodioecy–dioecy pathway

Background and Aims

About 6 % of an estimated total of 240 000 species of angiosperms are dioecious. The main precursors of this sexual system are thought to be monoecy and gynodioecy. A previous angiosperm-wide study revealed that many dioecious species have evolved through the monoecy pathway; some case studies and a large body of theoretical research also provide evidence in support of the gynodioecy pathway. If plants have evolved through the gynodioecy pathway, gynodioecious and dioecious species should co-occur in the same genera. However, to date, no large-scale analysis has been conducted to determine the prevalence of the gynodioecy pathway in angiosperms. In this study, this gap in knowledge was addressed by performing an angiosperm-wide survey in order to test for co-occurrence as evidence of the gynodioecy pathway.

Methods

Data from different sources were compiled to obtain (to our knowledge) the largest dataset on gynodioecy available, with 275 genera that include at least one gynodioecious species. This dataset was combined with a dioecy dataset from the literature, and a study was made of how often dioecious and gynodioecious species could be found in the same genera using a contingency table framework.

Key Results

It was found that, overall, angiosperm genera with both gynodioecious and dioecious species occur more frequently than expected, in agreement with the gynodioecy pathway. Importantly, this trend holds when studying different classes separately (or sub-classes, orders and families), suggesting that the gynodioecy pathway is not restricted to a few taxa but may instead be widespread in angiosperms.

Conclusions

This work complements that previously carried out on the monoecy pathway and suggests that gynodioecy is also a common pathway in angiosperms. The results also identify angiosperm families where some (or all) dioecious species may have evolved from gynodioecious precursors. These families could be the targets of future small-scale studies on transitions to dioecy taking phylogeny explicitly into account.     

CYP1A1, GSTM1 and GSTT1 polymorphisms and lung cancer: a pooled analysis of gene–gene interactions

Gene–environment interactions have been extensively studied in lung cancer. It is likely that several genetic polymorphisms cooperate in increasing the individual risk. Therefore, the study of gene–gene interactions might be important to identify high-susceptibility subgroups. GSEC is an initiative aimed at collecting available data sets on metabolic polymorphisms and the risks of cancer at several sites and performing pooled analyses of the original data. Authors of published papers have provided original data sets. The present paper refers to gene–gene interactions in lung cancer and considers three polymorphisms in three metabolic genes: CYP1A1, GSTM1 and GSTT1. The present analyses compare the gene–gene interactions of the CYP1A1*2A, GSTM1 and GSTT1 polymorphisms from studies on lung cancer conducted in Europe and the USA between 1991 and 2000. Only Caucasians have been included. The data set includes 1466 cases and 1488 controls. The only clear-cut association was found with CYP1A1*2A. This association remained unchanged after stratification by polymorphisms in other genes (with an odds ratio [OR] of approximately 2.5), except when interaction with GSTM1 was considered. When the OR for CYP1A1*2A was stratified according to the GSTM1 genotype, the OR was increased only among the subjects who had the null (homozygous deletion) GSTM1 genotype (OR=2.8, 95% CI=0.9–8.4). The odds ratio for the interactive term (CYP1A1*2A by GSTM1) in logistic regression was 2.7 (95% CI=0.5–15.3). An association between lung cancer and the homozygous CYP1A1*2A genotype is confirmed. An apparent and biologically plausible interaction is suggested between this genotype and GSTM1.     

Systems for the detection and analysis of protein–protein interactions

The analysis of protein–protein interactions is important for developing a better understanding of the functional annotations of proteins that are involved in various biochemical reactions in vivo. The discovery that a protein with an unknown function binds to a protein with a known function could provide a significant clue to the cellular pathway concerning the unknown protein. Therefore, information on protein–protein interactions obtained by the comprehensive analysis of all gene products is available for the construction of interactive networks consisting of individual protein–protein interactions, which, in turn, permit elaborate biological phenomena to be understood. Systems for detecting protein–protein interactions in vitro and in vivo have been developed, and have been modified to compensate for limitations. Using these novel approaches, comprehensive and reliable information on protein–protein interactions can be determined. Systems that permit this to be achieved are described in this review.K. Kuroda, M. Kato and J. Mima contributed equally to this work.