A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

Prediction of fetal hemoglobin in sickle cell anemia using an ensemble of genetic risk prediction models

BACKGROUND

Fetal hemoglobin (HbF) is the major modifier of the clinical course of sickle cell anemia. Its levels are highly heritable, and its interpersonal variability is modulated in part by 3 quantitative trait loci that affect HbF gene expression. Genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) in these quantitative trait loci that are highly associated with HbF but explain only 10% to 12% of the variance of HbF. Combining SNPs into a genetic risk score can help to explain a larger amount of the variability of HbF level, but the challenge of this approach is to select the optimal number of SNPs to be included in the genetic risk score.

METHODS AND RESULTS

We developed a collection of 14 models with genetic risk score composed of different numbers of SNPs and used the ensemble of these models to predict HbF in patients with sickle cell anemia. The models were trained in 841 patients with sickle cell anemia and were tested in 3 independent cohorts. The ensemble of 14 models explained 23.4% of the variability in HbF in the discovery cohort, whereas the correlation between predicted and observed HbF in the 3 independent cohorts ranged between 0.28 and 0.44. The models included SNPs in BCL11A, the HBS1L-MYB intergenic region, and the site of the HBB gene cluster, quantitative trait loci previously associated with HbF.

CONCLUSIONS

An ensemble of 14 genetic risk models can predict HbF levels with accuracy between 0.28 and 0.44, and the approach may also prove useful in other applications.

SNP-associations and phenotype predictions from hundreds of microbial genomes without genome alignments

SNP-association studies are a starting point for identifying genes that may be responsible for specific phenotypes, such as disease traits. The vast bulk of tools for SNP-association studies are directed toward SNPs in the human genome, and I am unaware of any tools designed specifically for such studies in bacterial or viral genomes. The PPFS (Predict Phenotypes From SNPs) package described here is an add-on to kSNP, a program that can identify SNPs in a data set of hundreds of microbial genomes. PPFS identifies those SNPs that are non-randomly associated with a phenotype based on the χ² probability, then uses those diagnostic SNPs for two distinct, but related, purposes: (1) to predict the phenotypes of strains whose phenotypes are unknown, and (2) to identify those diagnostic SNPs that are most likely to be causally related to the phenotype. In the example illustrated here, from a set of 68 E. coli genomes, for 67 of which the pathogenicity phenotype was known, there were 418,500 SNPs. Using the phenotypes of 36 of those strains, PPFS identified 207 diagnostic SNPs. The diagnostic SNPs predicted the phenotypes of all of the genomes with 97% accuracy. It then identified 97 SNPs whose probability of being causally related to the pathogenic phenotype was >0.999. In a second example, from a set of 116 E. coli genome sequences, using the phenotypes of 65 strains PPFS identified 101 SNPs that predicted the source host (human or non-human) with 90% accuracy.

A penalized robust method for identifying gene-environment interactions

In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model misspecification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example, with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications.

A system-level pathway-phenotype association analysis using synthetic feature random forest

As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations.

Asthma: Gln27Glu and Arg16Gly polymorphisms of the beta2-adrenergic receptor gene as risk factors

Background

Asthma is caused by both environmental and genetic factors. The ADRB2 gene, which encodes the beta 2-adrenergic receptor, is one of the most extensively studied genes with respect to asthma prevalence and severity. The Arg16Gly (+46A > G) and Gln27Glu (+79C > G) polymorphisms in the ADRB2 gene cause changes in the amino acids flanking the receptor ligand site, altering the response to bronchodilators and the risk of asthma through complex pathways. The ADRB2 polymorphisms affect beta-adrenergic bronchodilator action and are a tool to identify at-risk populations.

Objective

To determine the frequency of these two polymorphisms in allergic asthma patients and healthy subjects and to correlate these data with the occurrence and severity of asthma.

Methods

Eighty-eight allergic asthma patients and 141 healthy subjects were included in this study. The ADRB2 polymorphisms were analyzed using the amplification-refractory mutation system – polymerase chain reaction (ARMS-PCR) technique. The statistical analysis was performed with the SPSS 21.0 software using the Fisher’s Exact and χ² tests.

Results

The ADRB2 polymorphisms were associated with asthma occurrence. The Arg16Arg, Gln27Gln and Gln27Glu genotypes were risk factors; the odds ratios were 6.782 (CI = 3.07 to 16.03), 2.120 (CI = 1.22 to 3.71) and 8.096 (CI = 3.90 to 17.77), respectively. For the Gly16Gly and Glu27Glu genotypes, the odds ratios were 0.312 (CI = 0.17 to 0.56) and 0.084 (CI = 0.04 to 0.17), respectively. The haplotype analysis showed that there were associations between the following groups: Arg16Arg-Gln27Gln (OR = 5.108, CI = 1.82 to 16.37), Gly16Gly-Glu27Glu (OR = 2.816, CI = 1.25 to 6.54), Arg16Gly-Gln27Glu (OR = 0.048, CI = 0.01 to 0.14) and Gly16Gly-Gln27Glu (OR = 0.1036, CI = 0.02 to 0.39). The polymorphism Gln27Glu was associated with asthma severity, as the Gln27Gln genotype was a risk factor for severe asthma (OR = 2.798, CI = 1.099 to 6.674) and the Gln27Glu genotype was a protective factor for mild (OR = 3.063, CI = 1.037 to 9.041) and severe (OR = 0.182, CI = 0.048 to 0.691) asthma.

Conclusions

The Arg16Gly and Gln27Glu polymorphisms in the ADRB2 gene are associated with asthma presence and severity.

Association between genetic polymorphisms in apoptosis-related genes and risk of cutaneous melanoma in women and men

BACKGROUND

The P53 Arg72Pro, MDM2 c.+309T>G, BAX c.-248G>A, and BCL2 c.-717C>A polymorphisms have variable roles in the apoptosis pathways.

OBJECTIVE

To clarify the roles of these polymorphisms in the risk for cutaneous melanoma (CM).

METHODS

Genomic DNA of 200 CM patients and 215 controls was analyzed by PCR-RFLP.

RESULTS

In women, the frequencies of BAX GG (83.0% vs. 71.0%, P=0.04), BCL2 AA (32.0% vs. 15.0%, P=0.003), P53 ArgArg plus BAX GG (84.9% vs. 63.2%, P=0.01), P53 ArgArg plus BCL2 AA (37.0% vs. 13.1%, P=0.003), BAX GG plus BCL2 AA (70.3% vs. 33.3%, P=0.001), MDM2 GG plus BAX GG plus BCL2 AA (27.3% vs. 3.7%, P=0.03), and P53 ArgArg plus MDM2 GG plus BAX GG plus BCL2 AA (33.3% vs. 5.6%, P=0.04) genotypes were higher in patients than in controls. Female carriers of the respective genotypes were under 1.98 (95% CI: 1.01-3.91), 2.87 (95% CI: 1.43-5.77), 3.48 (95% CI: 1.34-9.04), 4.23 (95% CI: 1.63-10.96), 6.04 (95% CI: 2.10-17.37), 25.61 (95% CI: 1.29-507.24), and 25.69 (95% CI: 1.11-593.59)-fold increased risks for CM than others, respectively. In men, the frequencies of BCL2 CA+AA (83.0% vs. 67.6%, P=0.01) and MDM2 TG+GG plus BCL2 CA+AA (94.2% vs. 68.3%, P=0.003) genotypes were higher in patients than in controls. Male carriers of the respective genotypes were under 2.43 (95% CI: 1.23-4.82) and 9.22 (95% CI: 2.16-39.31)-fold increased CM risks than others, respectively.

CONCLUSION

The data suggest for the first time that P53 Arg72Pro, MDM2 c.+309T>G, BAX c.-248G>A, and BCL2 c.-717C>A polymorphisms, enrolled in apoptosis pathways, constitute distinct determinants of CM in women and men.

Systems analysis of high-throughput data

Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g.,  sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.

Genetic variations in radiation and chemotherapy drug action pathways and survival in locoregionally advanced nasopharyngeal carcinoma treated with chemoradiotherapy

Background and Purpose

Treatment outcomes vary greatly in patients with nasopharyngeal carcinoma (NPC). The purpose of this study is to evaluate the influence of radiation and chemotherapy drug action pathway gene polymorphisms on the survival of patients with locoregionally advanced NPC treated with cisplatin- and fluorouracil-based chemoradiotherapy.

Material and Methods

Four hundred twenty-one consecutive patients with locoregionally advanced NPC were prospectively recruited. We utilized a pathway approach and examined 18 polymorphisms in 13 major genes. Polymorphisms were detected using the LDR-PCR technique. Multifactor dimensionality reduction (MDR) analysis was performed to detect potential gene-gene interaction.

Results

After adjustment for clinicopathological characteristics, overall survival was significantly decreased in patients with the MPO rs2243828 CT/CC genotype (HR=2.453, 95% CI, 1.687-3.566, P<0.001). The ERCC1 rs3212986 CC (HR=1.711, 95% CI, 1.135-2.579, P=0.010), MDM2 rs2279744 GT/GG (HR=1.743, 95% CI, 1.086-2.798, P=0.021), MPO rs2243828 CT/CC (HR=3.184, 95% CI, 2.261-4.483, P<0.001) and ABCB1 rs2032582 AT/AA (HR=1.997, 95% CI, 1.086-3.670, P=0.026) genotypes were associated with poor progression-free survival. Prognostic score models based on independent prognostic factors successfully classified patients into low-, intermediate-, and high-risk groups. Furthermore, MDR analysis showed no significant interaction between polymorphisms.

Conclusions

Four single nucleotide polymorphisms were associated with survival in patients with locoregionally advanced NPC treated with cisplatin- and fluorouracil-based chemoradiotherapy. Combining clinical prognostic factors with genetic information was valuable in identifying patients with different risk.

Enabling personal genomics with an explicit test of epistasis

One goal of personal genomics is to use information about genomic variation to predict who is at risk for various common diseases. Technological advances in genotyping have spawned several personal genetic testing services that market genotyping services directly to the consumer. An important goal of consumer genetic testing is to provide health information along with the genotyping results. This has the potential to integrate detailed personal genetic and genomic information into healthcare decision making. Despite the potential importance of these advances, there are some important limitations. One concern is that much of the literature that is used to formulate personal genetics reports is based on genetic association studies that consider each genetic variant independently of the others. It is our working hypothesis that the true value of personal genomics will only be realized when the complexity of the genotype-to-phenotype mapping relationship is embraced, rather than ignored. We focus here on complexity in genetic architecture due to epistasis or nonlinear gene-gene interaction. We have previously developed a multifactor dimensionality reduction (MDR) algorithm and software package for detecting nonlinear interactions in genetic association studies. In most prior MDR analyses, the permutation testing strategy used to assess statistical significance was unable to differentiate MDR models that captured only interaction effects from those that also detected independent main effects. Statistical interpretation of MDR models required post-hoc analysis using entropy-based measures of interaction information. We introduce here a novel permutation test that allows the effects of nonlinear interactions between multiple genetic variants to be specifically tested in a manner that is not confounded by linear additive effects. We show using simulated nonlinear interactions that the power using the explicit test of epistasis is no different than a standard permutation test. We also show that the test has the appropriate size or type I error rate of approximately 0.05. We then apply MDR with the new explicit test of epistasis to a large genetic study of bladder cancer and show that a previously reported nonlinear interaction between is indeed significant, even after considering the strong additive effect of smoking in the model. Finally, we evaluated the power of the explicit test of epistasis to detect the nonlinear interaction between two XPD gene polymorphisms by simulating data from the MDR model of bladder cancer susceptibility. The results of this study provide for the first time a simple method for explicitly testing epistasis or gene-gene interaction effects in genetic association studies. Although we demonstrated the method with MDR, an important advantage is that it can be combined with any modeling approach. The explicit test of epistasis brings us a step closer to the type of routine gene-gene interaction analysis that is needed if we are to enable personal genomics.

Evaluation of leptin and leptin receptor gene 3' UTR polymorphisms in essential hypertension

INTRODUCTION

Leptin and leptin receptor gene polymorphisms have been associated with obesity; however, their association with blood pressure has not been fully elucidated. The aim of this study was to examine the effect of tetranucleotide repeat polymorphism in the 3' flanking region of the leptin and leptin receptor gene on blood pressure in hypertensives with obesity.

METHODS

Two hundred and eighty hypertensives and 200 healthy controls were analyzed for a tetranucleotide repeat polymorphism of leptin and leptin receptor genes. Genotyping was done by amplifying DNA and determining the allele sizes using gel documentation system. Odds ratios were computed to predict the risk for hypertension caused by specific genotypes of leptin and leptin receptor genes and the effect of interaction between them on the development of hypertension was determined by MDR test.

RESULTS

Significant preponderance in the incidence of male sex, obese individuals and those with positive family history was observed with significant elevation in the mean levels of SBP, DBP, BMI and reduction of HDL levels in hypertensives as compared to controls. Class I/I genotypes of leptin showed significantly high risk for developing hypertension irrespective of obesity. Genotypes of leptin receptor did not confer any risk for hypertension and cohorts studied.

CONCLUSION

Homozygotes I/I were at greater risk for developing hypertension irrespective of obesity. When leptin and leptin receptor genes were considered together, synergistic interaction was observed between the two genes leading to hypertension, while the polymorphism at leptin gene and obesity was correlated.

Using information interaction to discover epistatic effects in complex diseases

It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show stronger effects when considered together than when considered individually, a phenomenon known as epistasis or multilocus interaction. In this work, we explore the applicability of information interaction to discover pairwise epistatic effects related with complex diseases. We start by showing that traditional approaches such as classification methods or greedy feature selection methods (such as the Fleuret method) do not perform well on this problem. We then compare our information interaction method with BEAM and SNPHarvester in artificial datasets simulating epistatic interactions and show that our method is more powerful to detect pairwise epistatic interactions than its competitors. We show results of the application of information interaction method to the WTCCC breast cancer dataset. Our results are validated using permutation tests. We were able to find 89 statistically significant pairwise interactions with a p-value lower than . Even though many recent algorithms have been designed to find epistasis with low marginals, we observed that all (except one) of the SNPs involved in statistically significant interactions have moderate or high marginals. We also report that the interactions found in this work were not present in gene-gene interaction network STRING.

Rare single nucleotide polymorphisms in the regulatory regions of the superoxide dismutase genes in autism spectrum disorder

Oxidative stress is suspected to be one of the several contributing factors in the etiology of autism spectrum disorder (ASD). We analyzed genes of the superoxide dismutase family (SOD1, SOD2, and SOD3) that are part of a major antioxidative stress system in human in order to detect the genetic variants contributing to the development of ASD. Using the optimized high-resolution melting (HRM) analysis, we identified two rare single nucleotide polymorphisms (SNPs) associated with the etiology of ASD. Both are located in the superoxide dismutase 1 (SOD1) gene and have a minor allele frequency in healthy population ~5%. The SNP c.239 + 34A>C (rs2234694) and SNP g.3341C>G (rs36233090) were detected with an odds ratio of 2.65 and P < 0.01. Both are located in the noncoding potentially regulatory regions of the SOD1 gene. This adds to the importance of rare SNPs in the etiology of complex diseases as well as to the importance of noncoding genetic variants analysis with a potential influence on the regulation of gene expression.

Variants in TNFSF4, TNFAIP3, TNIP1, BLK, SLC15A4 and UBE2L3 interact to confer risk of systemic lupus erythematosus in Chinese population

Our previous genome-wide association studies on SLE have identified several susceptibility genes involved in NF-κB signaling pathway, including TNFSF4, TNFAIP3, TNIP1, BLK, SLC15A4 and UBE2L3. The aim of this study is to investigate the association model (additive, dominant, recessive) of these genes and search for possible gene-gene interactions between them. In this study, we explored the association model of these six genes and search for possible gene-gene interactions based on identified single-nucleotide polymorphisms (SNPs) among them by using logistic regression analysis in the combined sample of 4,199 cases and 8,255 controls. The most significant association evidence was observed under recessive model for all of these SNPs. Besides, significant interactions between these SNPs were observed in this study: the TNFSF4 and TNIP1 SNPs (P adjusted = 1.68E-10), the TNFSF4 and SLC15A4 SNPs (P adjusted = 3.55E-08), the TNFSF4 and UBE2L3 SNPs (P adjusted = 8.74E-13), the TNIP1 and BLK SNPs (P adjusted = 9.45E-10), the TNIP1 and UBE2L3 SNPs (P adjusted = 8.25E-11), the TNFAIP3 and UBE2L3 SNPs (P adjusted = 3.06E-14) and the BLK and SLC15A4 SNPs (P adjusted = 4.51E-12). These results may contribute to our understanding of SLE genetic interactions and account for the additional risk of certain patients to develop SLE.

Catecholaminergic gene variants: contribution in ADHD and associated comorbid attributes in the eastern Indian probands

Contribution of genes in attention deficit hyperactivity disorder (ADHD) has been explored in various populations, and several genes were speculated to contribute small but additive effects. We have assessed variants in four genes, DDC (rs3837091 and rs3735273), DRD2 (rs1800496, rs1801028, and rs1799732), DRD4 (rs4646984 and rs4646983), and COMT (rs165599 and rs740603) in Indian ADHD subjects with comorbid attributes. Cases were recruited following the Diagnostic and Statistical Manual for Mental Disorders-IV-TR after obtaining informed written consent. DNA isolated from peripheral blood leukocytes of ADHD probands (N = 170), their parents (N = 310), and ethnically matched controls (n = 180) was used for genotyping followed by population- and family-based analyses by the UNPHASED program. DRD4 sites showed significant difference in allelic frequencies by case-control analysis, while DDC and COMT exhibited bias in familial transmission (P < 0.05). rs3837091 “AGAG,” rs3735273 “A,” rs1799732 “C,” rs740603 “G,” rs165599 “G” and single repeat alleles of rs4646984/rs4646983 showed positive correlation with co-morbid characteristics (P < 0.05). Multi dimensionality reduction analysis of case-control data revealed significant interactive effects of all four genes (P < 0.001), while family-based data showed interaction between DDC and DRD2 (P = 0.04). This first study on these gene variants in Indo-Caucasoid ADHD probands and associated co-morbid conditions indicates altered dopaminergic neurotransmission in ADHD.

Interaction between γ-aminobutyric acid A receptor genes: new evidence in migraine susceptibility

Migraine is a common neurological episodic disorder with a female-to-male prevalence 3- to 4-fold higher, suggesting a possible X-linked genetic component. Our aims were to assess the role of common variants of gamma-aminobutyric acid A receptor (GABA_AR) genes, located in the X-chromosome, in migraine susceptibility and the possible interaction between them. An association study with 188 unrelated cases and 286 migraine-free controls age- and ethnic matched was performed. Twenty-three tagging SNPs were selected in three genes (GABRE, GABRA3 and GABRQ). Allelic, genotypic and haplotypic frequencies were compared between cases and controls. We also focused on gene-gene interactions. The AT genotype of rs3810651 of GABRQ gene was associated with an increased risk for migraine (OR: 4.07; 95% CI: 1.71-9.73, p=0.002), while the CT genotype of rs3902802 (OR: 0.41; 95% CI: 0.21-0.78, p=0.006) and GA genotype of rs2131190 of GABRA3 gene (OR: 0.53; 95% CI: 0.32-0.88, p=0.013) seem to be protective factors. All associations were found in the female group and maintained significance after Bonferroni correction. We also found three nominal associations in the allelic analyses although there were no significant results in the haplotypic analyses. Strikingly, we found strong interactions between six SNPs encoding for different subunits of GABA_AR, all significant after permutation correction. To our knowledge, we show for the first time, the putative involvement of polymorphisms in GABA_AR genes in migraine susceptibility and more importantly we unraveled a role for novel gene-gene interactions opening new perspectives for the development of more effective treatments.

Identification of gene-environment interactions in cancer studies using penalization

High-throughput cancer studies have been extensively conducted, searching for genetic markers associated with outcomes beyond clinical and environmental risk factors. Gene-environment interactions can have important implications beyond main effects. The commonly-adopted single-marker analysis cannot accommodate the joint effects of a large number of markers. The existing joint-effects methods also have limitations. Specifically, they may suffer from high computational cost, do not respect the "main effect, interaction" hierarchical structure, or use ineffective techniques. We develop a penalization method for the identification of important G × E interactions and main effects. It has an intuitive formulation, respects the hierarchical structure, accommodates the joint effects of multiple markers, and is computationally affordable. In numerical study, we analyze prognosis data under the AFT (accelerated failure time) model. Simulation shows satisfactory performance of the proposed method. Analysis of an NHL (non-Hodgkin lymphoma) study with SNP measurements shows that the proposed method identifies markers with important implications and satisfactory prediction performance.

Different genetic associations of the IgE production among fetus, infancy and childhood

Elevation of serum IgE levels has long been associated with allergic diseases. Many genes have been linked to IgE production, but few have been linked to the developmental aspects of genetic association with IgE production. To clarify developmental genetic association, we investigated what genes and gene-gene interactions affect IgE levels among fetus, infancy and childhood in Taiwan individuals. A birth cohort of 571 children with completion of IgE measurements from newborn to 1.5, 3, and 6 years of age was subject to genetic association analysis on the 384-customized SNPs of 159 allergy candidate genes. Fifty-three SNPs in 37 genes on innate and adaptive immunity, and stress and response were associated with IgE production. Polymorphisms of the IL13, and the HLA-DPA1 and HLA-DQA1 were, respectively, the most significantly associated with the IgE production at newborn and 6 years of age. Analyses of gene-gene interactions indentified that the combination of NPSR1, rs324981 TT with FGF1, rs2282797 CC had the highest risk (85.7%) of IgE elevation at 1.5 years of age (P = 1.46×10⁻⁴). The combination of IL13, CYFIP2 and PDE2A was significantly associated with IgE elevation at 3 years of age (P = 5.98×10⁻⁷), and the combination of CLEC2D, COLEC11 and CCL2 was significantly associated with IgE elevation at 6 years of age (P = 6.65×10⁻⁷). Our study showed that the genetic association profiles of the IgE production among fetus, infancy and childhood are different. Genetic markers for early prediction and prevention of allergic sensitization may rely on age-based genetic association profiles.

A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

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 a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR’s constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDR’s testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study.

Genetic interaction of GSH metabolic pathway genes in cystic fibrosis

Background

Cystic fibrosis (CF) is a monogenic disease caused by CFTR gene mutations, with clinical expression similar to complex disease, influenced by genetic and environmental factors. Among the possible modifier genes, those associated to metabolic pathways of glutathione (GSH) have been considered as potential modulators of CF clinical severity. In this way it is of pivotal importance investigate gene polymorphisms at Glutamate-Cysteine Ligase, Catalytic Subunit (GCLC), Glutathione S-transferase Mu 1 (GSTM1), Glutathione S-transferase Theta 1 (GSTT1), and Glutathione S-transferase P1 (GSTP1), which have been associated to the GSH metabolic pathway and CF clinical severity.

Method

A total of 180 CF’s patients were included in this study, which investigated polymorphisms in GCLC and GST genes (GCLC -129C>T and -3506A>G; GSTM1 and GSTT1 genes deletion, and GSTP1*+313A>G) by PCR and PCR-RFLP associating to clinical variables of CF severity, including variables of sex, clinical scores [Shwachman-Kulczycki, Kanga e Bhalla (BS)], body mass index, patient age, age for diagnosis, first clinical symptoms, first colonization by Pseudomonas aeruginosa, sputum’s microorganisms, hemoglobin oxygen saturation in the blood, spirometry and comorbidities. The CFTR genotype was investigated in all patients, and the genetic interaction was performed using MDR2.0 and MDRPT0.4.7 software.

Results

The analysis of multiple genes in metabolic pathways in diseases with variable clinical expression, as CF disease, enables understanding of phenotypic diversity. Our data show evidence of interaction between the GSTM1 and GSTT1 genes deletion, and GSTP1*+313A>G polymorphism with CFTR gene mutation classes, and BS (Balance testing accuracy= 0.6824, p= 0.008), which measures the commitment of bronchopulmonary segments by tomography.

Conclusion

Polymorphisms in genes associated with metabolism of GSH act on the CF’s severity.

GWIS--model-free, fast and exhaustive search for epistatic interactions in case-control GWAS

Background

It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci.

Results

We have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives.

Conclusions

We demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis.

Availability

A web-based version of the software used for this analysis is available at http://bioinformatics.research.nicta.com.au/gwis.

Toll-like receptor-associated sequence variants and prostate cancer risk among men of African descent

BACKGROUND

Recent advances demonstrate a relationship between chronic/recurrent inflammation and prostate cancer (PCA). Among inflammatory regulators, toll-like receptors (TLRs) play a critical role in innate immune responses. However, it remains unclear whether variant TLR genes influence PCA risk among men of African descent. Therefore, we evaluated the impact of 32 TLR-associated single nucleotide polymorphisms (SNPs) on PCA risk among African-Americans and Jamaicans.

METHODS

SNP profiles of 814 subjects were evaluated using Illumina’s Veracode genotyping platform. Single and combined effects of SNPs in relation to PCA risk were assessed using age-adjusted logistic regression and entropy-based multifactor dimensionality reduction (MDR) models.

RESULTS

Seven sequence variants detected in TLR6, TOLLIP, IRAK4, IRF3 were marginally related to PCA. However, none of these effects remained significant after adjusting for multiple hypothesis testing. Nevertheless, MDR modeling revealed a complex interaction between IRAK4 rs4251545 and TLR2 rs1898830 as a significant predictor of PCA risk among U.S. men (permutation testing p-value = 0.001).

CONCLUSIONS

MDR identified an interaction between IRAK4 and TLR2 as the best two factor model for predicting PCA risk among men of African descent. However, these findings require further assessment and validation.

Identification of multiple gene-gene interactions for ordinal phenotypes

Background

Multifactor dimensionality reduction (MDR) is a powerful method for analysis of gene-gene interactions and has been successfully applied to many genetic studies of complex diseases. However, the main application of MDR has been limited to binary traits, while traits having ordinal features are commonly observed in many genetic studies (e.g., obesity classification - normal, pre-obese, mild obese and severe obese).

Methods

We propose ordinal MDR (OMDR) to facilitate gene-gene interaction analysis for ordinal traits. As an alternative to balanced accuracy, the use of tau-b, a common ordinal association measure, was suggested to evaluate interactions. Also, we generalized cross-validation consistency (GCVC) to identify multiple best interactions. GCVC can be practically useful for analyzing complex traits, especially in large-scale genetic studies.

Results and conclusions

In simulations, OMDR showed fairly good performance in terms of power, predictability and selection stability and outperformed MDR. For demonstration, we used a real data of body mass index (BMI) and scanned 1~4-way interactions of obesity ordinal and binary traits of BMI via OMDR and MDR, respectively. In real data analysis, more interactions were identified for ordinal trait than binary traits. On average, the commonly identified interactions showed higher predictability for ordinal trait than binary traits. The proposed OMDR and GCVC were implemented in a C/C++ program, executables of which are freely available for Linux, Windows and MacOS upon request for non-commercial research institutions.

Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes

Although genome-wide association studies (GWAS) have identified a significant number of single-nucleotide polymorphisms (SNPs) associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene–gene interaction (G×G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU)-based GMDR program (named GWAS-GPU), which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D) from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes (FTO: rs9939973, rs9940128, rs9922047, rs1121980, rs9939609, rs9930506; TSPAN8: rs1495377; TCF7L2: rs4074720, rs7901695, rs4506565, rs4132670, rs10787472, rs11196205, rs10885409, rs11196208; L3MBTL3: rs10485400, rs4897366; CELF4: rs2852373, rs608489; RUNX1: rs445984, rs1040328, rs990074, rs2223046, rs2834970) that appear to be important for T2D. Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants further investigation with independent samples.

Genetics of eye colours in different rural populations on the Silk Road

Eye colour is a highly transmissible and discernible trait in humans. A genome-wide association scan for variants associated to eye pigmentation was carried out on a large group of individuals coming from the Silk Road. Significant associations were detected not only with HERC2 (P-value=4.99 × 10(-37)) and OCA2 (P-value=4.51 × 10(-9)) genes but also with CTNNA2 gene (P-value=4.06 × 10(-8)). Moreover, the multifactor dimensionality reduction analysis clearly showed the effect of HERC2 haplotype over OCA2 mostly associated with SNP, thus enabling a highly accurate eye-colour prediction. Finally, the regression tree analysis showed that individuals carrying a given combination of haplotypes have a significant probability to show a blue or green/grey iris colour as compared with brown, with a gradient from west to east.

ViSEN: methodology and software for visualization of statistical epistasis networks

The nonlinear interaction effect among multiple genetic factors, i.e. epistasis, has been recognized as a key component in understanding the underlying genetic basis of complex human diseases and phenotypic traits. Due to the statistical and computational complexity, most epistasis studies are limited to interactions with an order of two. We developed ViSEN to analyze and visualize epistatic interactions of both two-way and three-way. ViSEN not only identifies strong interactions among pairs or trios of genetic attributes, but also provides a global interaction map that shows neighborhood and clustering structures. This visualized information could be very helpful to infer the underlying genetic architecture of complex diseases and to generate plausible hypotheses for further biological validations. ViSEN is implemented in Java and freely available at https://sourceforge.net/projects/visen/.

The association between polymorphisms in the MRPL4 and TNF-α genes and susceptibility to allergic rhinitis

Background

Allergic rhinitis (AR) is a chronic inflammatory disease of the nasal mucosa, involving a complex interaction between genetic and environmental factors. Evidence suggests that polymorphisms in the gene coding for mitochondrial ribosomal protein L4 (MRPL4), located in close proximity to intercellular adhesion molecule-1 (ICAM-1) gene on chromosome location 19p13.2, may influence the risk factor for the development of AR.

Objective

The aim of our study was to investigate any association between AR susceptibility and polymorphisms in ICAM-1 gene, as well as associations between AR risk and polymorphisms in MRPL4, nuclear factor-kappaB (NF-κB) and tumor necrosis factor alpha(TNF-α) genes, associated with ICAM-1 expression.

Methods

A cohort of 414 patients with AR and 293 healthy controls was enrolled from the Han Chinese population in Beijing, China. Blood was drawn for DNA extraction and total serum immunoglobulin E (IgE). A total of 14 single nucleotide polymorphisms (SNPs) in ICAM-1, NF-κB, TNF-α, and MRPL4 genes were selected using the CHB genotyping data from the International Haplotype Mapping (HapMap) and assessed for differences in frequencies of the alleles and genotypes between the AR patients and control subjects.

Results

TNF-α SNP rs1799964 and MRPL4 SNP rs11668618 were found to occur in significantly greater frequencies in the AR group compared to control group. There were no significant associations between SNPs in NF-κB, ICAM-1 and AR. The SNP-SNP interaction information analysis further indicated that there were no synergistic effects among the selected sets of polymorphisms.

Conclusions

Our results suggest a strong association between AR risk and polymorphisms of MRPL4 and TNF-α genes in Han Chinese population.

SYMPHONY, an information-theoretic method for gene-gene and gene-environment interaction analysis of disease syndromes

We develop an information-theoretic method for gene-gene (GGI) and gene-environmental interactions (GEI) analysis of syndromes, defined as a phenotype vector comprising multiple quantitative traits (QTs). The K-way interaction information (KWII), an information-theoretic metric, was derived for multivariate normal distributed phenotype vectors. The utility of the method was challenged with three simulated data sets, the Genetic Association Workshop-15 (GAW15) rheumatoid arthritis data set, a high-density lipoprotein (HDL) and atherosclerosis data set from a mouse QT locus study, and the 1000 Genomes data. The dependence of the KWII on effect size, minor allele frequency, linkage disequilibrium, population stratification/admixture, as well as the power and computational time requirements of the novel method was systematically assessed in simulation studies. In these studies, phenotype vectors containing two and three constituent multivariate normally distributed QTs were used and the KWII was found to be effective at detecting GEI associated with the phenotype. High KWII values were observed for variables and variable combinations associated with the syndrome phenotype compared with uninformative variables not associated with the phenotype. The KWII values for the phenotype-associated combinations increased monotonically with increasing effect size values. The KWII also exhibited utility in simulations with non-linear dependence between the constituent QTs. Analysis of the HDL and atherosclerosis data set indicated that the simultaneous analysis of both phenotypes identified interactions not detected in the analysis of the individual traits. The information-theoretic approach may be useful for non-parametric analysis of GGI and GEI of complex syndromes.

Comprehensive SNP scan of DNA repair and DNA damage response genes reveal multiple susceptibility loci conferring risk to tobacco associated leukoplakia and oral cancer

Polymorphic variants of DNA repair and damage response genes play major role in carcinogenesis. These variants are suspected as predisposition factors to Oral Squamous Cell Carcinoma (OSCC). For identification of susceptible variants affecting OSCC development in Indian population, the "maximally informative" method of SNP selection from HapMap data to non-HapMap populations was applied. Three hundred twenty-five SNPs from 11 key genes involved in double strand break repair, mismatch repair and DNA damage response pathways were genotyped on a total of 373 OSCC, 253 leukoplakia and 535 unrelated control individuals. The significantly associated SNPs were validated in an additional cohort of 144 OSCC patients and 160 controls. The rs12515548 of MSH3 showed significant association with OSCC both in the discovery and validation phases (discovery P-value: 1.43E-05, replication P-value: 4.84E-03). Two SNPs (rs12360870 of MRE11A, P-value: 2.37E-07 and rs7003908 of PRKDC, P-value: 7.99E-05) were found to be significantly associated only with leukoplakia. Stratification of subjects based on amount of tobacco consumption identified SNPs that were associated with either high or low tobacco exposed group. The study reveals a synergism between associated SNPs and lifestyle factors in predisposition to OSCC and leukoplakia.

Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis

Background

Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB).

Results

The study used a previously published data set consisting of 19 candidate single-nucleotide polymorphisms (SNPs) in 321 pulmonary TB cases and 347 healthy controls from Guniea-Bissau in Africa. The ReliefF algorithm was applied first to generate a smaller set of the five most informative SNPs. MDR with 10-fold cross-validation was then applied to look at all possible combinations of two, three, four and five SNPs. The MDR model with the best testing accuracy (TA) consisted of SNPs rs2305619, rs187084, and rs11465421 (TA = 0.588) in PTX3, TLR9 and DC-Sign, respectively. A general 1000-fold permutation test of the null hypothesis of no association confirmed the statistical significance of the model (p = 0.008). An additional 1000-fold permutation test designed specifically to test the linear null hypothesis that the association effects are only additive confirmed the presence of non-additive (i.e. nonlinear) or epistatic effects (p = 0.013). An independent information-gain measure corroborated these results with a third-order epistatic interaction that was stronger than any lower-order associations.

Conclusions

We have identified statistically significant evidence for a three-way epistatic interaction that is associated with susceptibility to TB. This interaction is stronger than any previously described one-way or two-way associations. This study highlights the importance of using machine learning methods that are designed to embrace, rather than ignore, the complexity of common diseases such as TB. We recommend future studies of the genetics of TB take into account the possibility that high-order epistatic interactions might play an important role in disease susceptibility.

An information-gain approach to detecting three-way epistatic interactions in genetic association studies

Background

Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.

Objectives

In this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis.

Methods

Such a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis.

Results

Our method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations.

Conclusion

Our study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.

SVM-based generalized multifactor dimensionality reduction approaches for detecting gene-gene interactions in family studies

Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.

Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

BACKGROUND

Multifactor Dimensionality Reduction (MDR) has been widely applied to detect gene-gene (GxG) interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk) from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account.

RESULTS

We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX) that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy.

CONCLUSIONS

The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi) are proposed to detect multiple GxG interactions.

Epistasis, complexity, and multifactor dimensionality reduction

Genome-wide association studies (GWASs) and other high-throughput initiatives have led to an information explosion in human genetics and genetic epidemiology. Conversion of this wealth of new information about genomic variation to knowledge about public health and human biology will depend critically on the complexity of the genotype to phenotype mapping relationship. We review here computational approaches to genetic analysis that embrace, rather than ignore, the complexity of human health. We focus on multifactor dimensionality reduction (MDR) as an approach for modeling one of these complexities: epistasis or gene-gene interaction.

Applications of multifactor dimensionality reduction to genome-wide data using the R package 'MDR'

This chapter describes how to use the R package 'MDR' to search and identify gene-gene interactions in high-dimensional data and illustrates applications for exploratory analysis of multi-locus models by providing specific examples.

HLA variants rs9271366 and rs9275328 are associated with systemic lupus erythematosus susceptibility in Malays and Chinese

BACKGROUND

Human leukocyte antigen (HLA) antigens and genes have long been reported associated with systemic lupus erythematosus (SLE) susceptibility in many populations. With the advance in technologies such as genome-wide association studies, many newly discovered SLE-associated single-nucleotide polymorphisms (SNPs) have been reported in recent years. These include HLA-DRB1/HLA-DQA1 rs9271366 and HLA-DQB1/HLA-DQA2 rs9275328. Our aim was to investigate these SNPs in a Malaysian SLE cohort.

MATERIALS AND METHODS

SNPs rs9271366 and rs9275328 were screened across 790 Malaysian citizens from three ethnic groups (360 patients and 430 healthy volunteers) by Taqman SNP genotyping assays. Allele and genotyping frequencies, Hardy-Weinberg equilibrium, Fisher's exact test and odds ratio were calculated for each SNP and ethnic group. Linkage disequilibrium and interaction between the two SNPs were also evaluated.

RESULTS

The minor allele G and its homozygous genotype GG of HLA-DRB1/HLA-DQA1 rs9271366 significantly increased the SLE susceptibility in Malaysian patients, including those of Malay and Chinese ethnicity (odds ratio (OR) > 1, p < 0.05). As for HLA-DQB1/HLA-DQA2 rs9275328, the minor allele T and the heterozygous genotype CT conferred protective effect to SLE in Malaysians, as well as in Malays and Chinese, by having OR < 1 and p value <0.05. Both SNPs did not show associations to SLE in Indians. D' and r (2) values for the two SNPs in LD analysis were 0.941 and 0.065, respectively, with haplotype GC and AT being significantly associated with SLE (p < 5.0 × 10(-4)) after 10,000 permutations were performed. The MDR test clustered the genotype combinations of GG and CC, and AG and CC of rs9271366 and rs9275328, accordingly, as high-risk group, and the two SNPs interacted redundantly by removing 1.96% of the entropy.

CONCLUSIONS

Our findings suggest that in addition to some classical HLA variants, rs9271366 and rs9275328 are additional polymorphisms worth considering in the Malaysian and possibly in a larger Asian SLE scenario.

Use of entropy in the analysis of nominal traits in sheep

In the analysis of dependencies between nominal traits entropy and its function, mutual information seems to be a proper descriptive statistic. This is shown by characterizing the relationships between the prolificacy of dams and selected genetic attributes: the genotype of transferrin, the genotype of hemoglobin, and the type of birth, as well as the environmental attribute, i.e., year of birth. The entropy method may improve the exactitude of investigations concerning the influence of different factors on production trait. The index of relative uniformity, introduced in this study, proved to be an adequate tool for the determination of similarity in the examined flocks. The application of mutual information in the determination of values of the dependence measures in the analyzed experiment was justified.

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data.

Genetic association studies: an information content perspective

The availability of high-density single nucleotide polymorphisms (SNPs) data has made the human genetic association studies possible to identify common and rare variants underlying complex diseases in a genome-wide scale. A handful of novel genetic variants have been identified, which gives much hope and prospects for the future of genetic association studies. In this process, statistical and computational methods play key roles, among which information-based association tests have gained large popularity. This paper is intended to give a comprehensive review of the current literature in genetic association analysis casted in the framework of information theory. We focus our review on the following topics: (1) information theoretic approaches in genetic linkage and association studies; (2) entropy-based strategies for optimal SNP subset selection; and (3) the usage of theoretic information criteria in gene clustering and gene regulatory network construction.

Epistasis analysis for estrogen metabolic and signaling pathway genes on young ischemic stroke patients

Background

Endogenous estrogens play an important role in the overall cardiocirculatory system. However, there are no studies exploring the hormone metabolism and signaling pathway genes together on ischemic stroke, including sulfotransferase family 1E (SULT1E1), catechol-O-methyl-transferase (COMT), and estrogen receptor α (ESR1).

Methods

A case-control study was conducted on 305 young ischemic stroke subjects aged ≦ 50 years and 309 age-matched healthy controls. SULT1E1 -64G/A, COMT Val158Met, ESR1 c.454−397 T/C and c.454−351 A/G genes were genotyped and compared between cases and controls to identify single nucleotide polymorphisms associated with ischemic stroke susceptibility. Gene-gene interaction effects were analyzed using entropy-based multifactor dimensionality reduction (MDR), classification and regression tree (CART), and traditional multiple regression models.

Results

COMT Val158Met polymorphism showed a significant association with susceptibility of young ischemic stroke among females. There was a two-way interaction between SULT1E1 -64G/A and COMT Val158Met in both MDR and CART analysis. The logistic regression model also showed there was a significant interaction effect between SULT1E1 -64G/A and COMT Val158Met on ischemic stroke of the young (P for interaction = 0.0171). We further found that lower estradiol level could increase the risk of young ischemic stroke for those who carry either SULT1E1 or COMT risk genotypes, showing a significant interaction effect (P for interaction = 0.0174).

Conclusions

Our findings support that a significant epistasis effect exists among estrogen metabolic and signaling pathway genes and gene-environment interactions on young ischemic stroke subjects.

Investigation of homocysteine-pathway-related variants in essential hypertension

Hyperhomocysteinemia (hHcy) has been associated with an increased risk of cardiovascular disease and stroke. Essential hypertension (EH), a polygenic condition, has also been associated with increased risk of cardiovascular related disorders. To investigate the role of the homocysteine (Hcy) metabolism pathway in hypertension we conducted a case-control association study of Hcy pathway gene variants in a cohort of Caucasian hypertensives and age- and sex-matched normotensives. We genotyped two polymorphisms in the methylenetetrahydrofolate reductase gene (MTHFR C677T and MTHFR A1298C), one polymorphism in the methionine synthase reductase gene (MTRR A66G), and one polymorphism in the methylenetetrahydrofolate dehydrogenase 1 gene (MTHFD1 G1958A) and assessed their association with hypertension using chi-square analysis. We also performed a multifactor dimensionality reduction (MDR) analysis to investigate any potential epistatic interactions among the four polymorphisms and EH. None of the four polymorphisms was significantly associated with EH and although we found a moderate synergistic interaction between MTHFR A1298C and MTRR A66G, the association of the interaction model with EH was not statistically significant (P = 0.2367). Our findings therefore suggest no individual or interactive association between four prominent Hcy pathway markers and EH.

Gene-gene interaction and functional impact of polymorphisms on innate immune genes in controlling Plasmodium falciparum blood infection level

Genetic variations in toll-like receptors and cytokine genes of the innate immune pathways have been implicated in controlling parasite growth and the pathogenesis of Plasmodium falciparum mediated malaria. We previously published genetic association of TLR4 non-synonymous and TNF-α promoter polymorphisms with P.falciparum blood infection level and here we extend the study considerably by (i) investigating genetic dependence of parasite-load on interleukin-12B polymorphisms, (ii) reconstructing gene-gene interactions among candidate TLRs and cytokine loci, (iii) exploring genetic and functional impact of epistatic models and (iv) providing mechanistic insights into functionality of disease-associated regulatory polymorphisms. Our data revealed that carriage of AA (P = 0.0001) and AC (P = 0.01) genotypes of IL12B 3′UTR polymorphism was associated with a significant increase of mean log-parasitemia relative to rare homozygous genotype CC. Presence of IL12B+1188 polymorphism in five of six multifactor models reinforced its strong genetic impact on malaria phenotype. Elevation of genetic risk in two-component models compared to the corresponding single locus and reduction of IL12B (2.2 fold) and lymphotoxin-α (1.7 fold) expressions in patients'peripheral-blood-mononuclear-cells under TLR4Thr399Ile risk genotype background substantiated the role of Multifactor Dimensionality Reduction derived models. Marked reduction of promoter activity of TNF-α risk haplotype (C-C-G-G) compared to wild-type haplotype (T-C-G-G) with (84%) and without (78%) LPS stimulation and the loss of binding of transcription factors detected in-silico supported a causal role of TNF-1031. Significantly lower expression of IL12B+1188 AA (5 fold) and AC (9 fold) genotypes compared to CC and under-representation (P = 0.0048) of allele A in transcripts of patients' PBMCs suggested an Allele-Expression-Imbalance. Allele (A+1188C) dependent differential stability (2 fold) of IL12B-transcripts upon actinomycin-D treatment and observed structural modulation (P = 0.013) of RNA-ensemble were the plausible explanations for AEI. In conclusion, our data provides functional support to the hypothesis that de-regulated receptor-cytokine axis of innate immune pathway influences blood infection level in P. falciparum malaria.

Gene-gene interactions in folate and adenosine biosynthesis pathways affect methotrexate efficacy and tolerability in rheumatoid arthritis

OBJECTIVE

As no single nucleotide polymorphism has emerged as pivotal to predict the lack of efficacy and dose-limiting toxicities to methotrexate (MTX), we evaluated the contribution of gene-gene interactions to the effects of this prodrug in rheumatoid arthritis.

METHODS

A total of 255 patients treated with MTX for at least 3 months were evaluated with efficacy assessed using the European League Against Rheumatism response criteria or a physician's assessment of patient's response to MTX visual analog scale. Gastrointestinal and neurological idiosyncrasies were recorded in 158 patients. Fourteen single nucleotide polymorphisms in folate and adenosine biosynthesis pathways were measured and detection of gene-gene interactions was performed using multifactor-dimensionality reduction, a method that reduces high-dimensional genetic data into a single dimension of predisposing or risk-genotype combinations.

RESULTS

Efficacy to MTX (53% responders) was associated with high-order epistasis among variants in inosine-triphosphate pyrophosphatase, aminoimidazole-carboxamide ribonucleotide transformylase, and reduced folate carrier genes. In the absence of predisposing genotype combinations, a 3.8-fold lower likelihood of efficacy was observed (vs. in their presence, 95% confidence interval: 2.2-6.4; P<0.001). Increasing MTX polyglutamate concentrations tended to partially overcome this selective disadvantage. Idiosyncrasies occurred in 29% of patients. In the presence of risk-genotype combinations among variants in methylene tetrahydrofolate reductase, γ-glutamyl-hydrolase, thymidylate synthase, serine hydroxymethyltransferase, and inosine-triphosphate pyrophosphatase genes, an 8.9-fold higher likelihood to exhibit toxicities was observed (vs. in their absence, 95% confidence interval: 3.6-21.9; P<0.001). False-positive report probabilities were below 0.2, thereby indicating that true signals were likely detected in this cohort.

CONCLUSION

These data indicate that gene-gene interactions impact MTX efficacy and tolerability in rheumatoid arthritis.

Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest

Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm ‘Random Forest’ to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits—autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.

Unveiling case-control relationships in designing a simple and powerful method for detecting gene-gene interactions

The detection of gene-gene interactions (i.e., epistasis) in the human genome is becoming decisive for the complete characterization of the genetic factors associated with complex binary traits. Despite the fact that many methods have been developed to address this challenging issue, their performance still remains insufficient. We will show how case and control groups store complementary information regarding interactions, and the use of this fundamental property in the design of a new, rapid, and highly powerful epistasis analysis method. Unlike previous approaches where statistical methods are tested over a very limited range of situations, we have performed an exhaustive evaluation of the power of our new method. To this end, we also propose a more comprehensive interpretation of epistasis in which genotype interactions may be of risk, protective, or neutral. In this extended view of genetic interactions, we demonstrate that our method has superior performance than existing approaches, thus, providing a highly powerful tool for the identification of gene-gene interactions associated with binary traits.

Genetic epistasis in female suicide attempters

BACKGROUND

Complex behaviors such as suicidal behavior likely exhibit gene-gene interactions. The main aim of this study is to explore potential single nucleotide polymorphisms combinations with epistatic effect in suicidal behavior using a data mining tool (Multifactor Dimensionality Reduction).

METHODS

Genomic DNA from peripheral blood samples was analyzed using SNPlex Technology. Multifactor Dimensionality Reduction was used to detect epistatic interactions between single nucleotide polymorphisms from the main central nervous system (CNS) neurotransmitters (dopamine: 9; noradrenaline: 19; serotonin: 23; inhibitory neurotransmitters: 60) in 889 individuals (417 men and 472 women) aged 18 years or older (585 psychiatric controls without a history of suicide attempts, and 304 patients with a history of suicide attempts). Individual analysis of association between single nucleotide polymorphisms and suicide attempts was estimated using logistic regression models.

RESULTS

Multifactor Dimensionality Reduction showed significant epistatic interactions involving four single nucleotide polymorphisms in female suicide attempters with a classification test accuracy of 60.7% (59.1%-62.4%, 95% CI): rs1522296, phenylalanine hydroxylase gene (PAH); rs7655090, dopamine receptor D5 gene (DRD5); rs11888528, chromosome 2 open reading frame 76, close to diazepam binding inhibitor gene (DBI); and rs2376481, GABA-A receptor subunit γ3 gene (GABRG3). The multivariate logistic regression model confirmed the relevance of the epistatic interaction [OR(95% CI)=7.74(4.60-13.37)] in females.

CONCLUSIONS

Our results suggest an epistatic interaction between genes of all monoamines and GABA in female suicide attempters.