High order gene-gene interactions in eight single nucleotide polymorphisms of renin-angiotensin system genes for hypertension association study

Dimensionality reduction approach for many-objective epistasis analysis

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

Relationship of angiotensin converting enzyme (I/D) polymorphism (rs4646994) and coronary heart disease among a male Iraqi population with type 2 diabetes mellitus

BACKGROUND

Insertion deletion (I/D) polymorphism (rs4646994) in the angiotensin-converting enzyme (ACE) has a substantial effect on coronary heart disease (CHD). The amplification of an Alu repetitive element in an intron of the ACE has shown three potential genotypes of I/I and D/D as homozygous, and I/D as heterozygous.

OBJECTIVE

The objective of this study was to investigate the association between the ACE gene polymorphism and CHD among male Iraqi patients with and without type2 diabetes mellitus (T2DM).

METHODS

A case-control study of total 217 male subjects participated in this study, divided into three groups; Group 1 including 86 CHD patients with T2DM, group 2 including 78 CHD patients without T2DM, and group 3 including 53 age and sex-matched healthy individuals (as a control group). Genotyping of ACE (I/D) gene was performed using polymerase chain reaction (PCR) technique.

RESULTS

The II allele was significantly more frequent in CHD patients without T2DM compared to the control population, but not from those patients with T2DM (p < 0.05). Nonetheless, the ID allele was significantly more frequent in each of CHD with T2DM and control populations compared to the CHD without T2DM. The DD allele was significantly more frequent in CHD patients with T2DM compared to each of CHD patients without T2DM and control populations (p < 0.05).

CONCLUSION

We conclude that the D/D and I/D genotypes are implicated as risk factors for development of CHD with T2DM, but not CHD without T2DM among the male Iraqi population. However, larger sample sizes are needed to monitor the CHD patients and to validate this study.

Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions

Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.

Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions

Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene-gene interactions

Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.

Risk given by AGT polymorphisms in inducing susceptibility to essential hypertension among isolated populations from a remote region of China: A case-control study among the isolated populations

INTRODUCTION

Hypertension is a serious risk factor affecting up to 30% of the world's population with a heritability of more than 30-50%. The aim of this study was to investigate the contribution of the polymorphisms localized in the angiotensinogen (AGT) gene, a main component of the renin-angiotensin-aldosterone system, in inducing the susceptibility to essential hypertension (EH) among isolated populations (Yi and Hani minorities) with low prevalence rate from the remote region of Yunnan in China.

METHODS

A case-control association study was performed, and all subjects were genotyped for the seven single nucleotide polymorphisms localized in the AGT region by polymerase chain reaction-restriction fragment length polymorphism analysis.

RESULTS

Three polymorphisms, i.e. rs5046, rs5049, and rs2478544, were significantly associated with EH among the Hani minority. The associations, found in the Yi minority, did not reach a conclusive level of statistical significance. The polymorphisms of rs2478544 and rs5046 caused the transformations of exonic splicing enhancer sites and transcription factor binding sites, respectively, in the bioinformatic analyses. The haplotype-rs5046T, rs5049A, rs11568020G, rs3789679C, rs2478544C was susceptible for EH among the Hani minority.

CONCLUSION

Our findings suggested that the AGT polymorphisms have played a vital role in determining an individual's susceptibility to EH among the isolated population, which would be helpful for EH management in the remote mountainous region of Yunnan in China.