An R package implementation of multifactor dimensionality reduction

Association of Genetic Polymorphisms with Complications of Implanted LVAD Devices in Patients with Congestive Heart Failure: A Kazakhstani Study

The left ventricular assist device (LVAD) is one of the alternative treatments for heart failure (HF) patients. However, LVAD support is followed by thrombosis, and bleeding complications which are caused by high non-physiologic shear stress and antithrombotic/anticoagulant therapy. A high risk of complications occurs in the presence of the genotype polymorphisms which are involved in the coagulation system, hemostasis function and in the metabolism of the therapy. The aim of the study was to investigate the influence of single-nucleotide polymorphisms (SNP) in HF patients with LVAD complications. We analyzed 21 SNPs in HF patients (n = 98) with/without complications, and healthy controls (n = 95). SNPs rs9934438; rs9923231 in VKORC1, rs5918 in ITGB3 and rs2070959 in UGT1A6 demonstrated significant association with HF patients’ complications (OR (95% CI): 3.96 (1.42–11.02), p = 0.0057), (OR (95% CI): 3.55 (1.28–9.86), p = 0.011), (OR (95% CI): 5.37 (1.79–16.16), p = 0.0056) and OR (95% CI): 4.40 (1.06–18.20), p = 0.044]. Genotype polymorphisms could help to predict complications at pre- and post-LVAD implantation period, which will reduce mortality rate. Our research showed that patients can receive treatment with warfarin and aspirin with a personalized dosage and LVAD complications can be predicted by reference to their genotype polymorphisms in VKORC1, ITGB3 and UGT1A6 genes.

Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug development failure and drug withdrawal from the market after approval. The identification of human risk factors associated with susceptibility to DILI is of paramount importance. Increasing evidence suggests that genetic variants may lead to inter-individual differences in drug response; however, individual single-nucleotide polymorphisms (SNPs) usually have limited power to predict human phenotypes such as DILI. In this study, we aim to identify appropriate statistical methods to investigate gene-gene and/or gene-environment interactions that impact DILI susceptibility. Three machine learning approaches, including Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression, were used. The simulation study suggested that all three methods were robust and could identify the known SNP-SNP interaction when up to 4% of genotypes were randomly permutated. When applied to a real-life DILI chronicity dataset, both MARS and MDR, but not logistic regression, identified combined genetic variants having better associations with DILI chronicity in comparison to the use of individual SNPs. Furthermore, a simple decision tree model using the SNPs identified by MARS and MDR was developed to predict DILI chronicity, with fair performance. Our study suggests that machine learning approaches may help identify gene-gene interactions as potential risk factors for better assessing complicated diseases such as DILI chronicity.

Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Some genetic diseases ("digenic traits") are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

Association Mapping and Development of Marker-Assisted Selection Tools for the Resistance to White Pine Blister Rust in the Alberta Limber Pine Populations

Since its introduction to North America in the early 1900s, white pine blister rust (WPBR) caused by the fungal pathogen Cronartium ribicola has resulted in substantial economic losses and ecological damage to native North American five-needle pine species. The high susceptibility and mortality of these species, including limber pine (Pinus flexilis), creates an urgent need for the development and deployment of resistant germplasm to support recovery of impacted populations. Extensive screening for genetic resistance to WPBR has been underway for decades in some species but has only started recently in limber pine using seed families collected from wild parental trees in the USA and Canada. This study was conducted to characterize Alberta limber pine seed families for WPBR resistance and to develop reliable molecular tools for marker-assisted selection (MAS). Open-pollinated seed families were evaluated for host reaction following controlled infection using C. ribicola basidiospores. Phenotypic segregation for presence/absence of stem symptoms was observed in four seed families. The segregation ratios of these families were consistent with expression of major gene resistance (MGR) controlled by a dominant R locus. Based on linkage disequilibrium (LD)-based association mapping used to detect single nucleotide polymorphism (SNP) markers associated with MGR against C. ribicola, MGR in these seed families appears to be controlled by Cr4 or other R genes in very close proximity to Cr4. These associated SNPs were located in genes involved in multiple molecular mechanisms potentially underlying limber pine MGR to C. ribicola, including NBS-LRR genes for recognition of C. ribicola effectors, signaling components, and a large set of defense-responsive genes with potential functions in plant effector-triggered immunity (ETI). Interactions of associated loci were identified for MGR selection in trees with complex genetic backgrounds. SNPs with tight Cr4-linkage were further converted to TaqMan assays to confirm their effectiveness as MAS tools. This work demonstrates the successful translation and deployment of molecular genetic knowledge into specific MAS tools that can be easily applied in a selection or breeding program to efficiently screen MGR against WPBR in Alberta limber pine populations.

Robust genetic interaction analysis

For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.

Application of computational methods in genetic study of inflammatory bowel disease

Genetic factors play an important role in the etiology of inflammatory bowel disease (IBD). The launch of genome-wide association study (GWAS) represents a landmark in the genetic study of human complex disease. Concurrently, computational methods have undergone rapid development during the past a few years, which led to the identification of numerous disease susceptibility loci. IBD is one of the successful examples of GWAS and related analyses. A total of 163 genetic loci and multiple signaling pathways have been identified to be associated with IBD. Pleiotropic effects were found for many of these loci; and risk prediction models were built based on a broad spectrum of genetic variants. Important gene-gene, gene-environment interactions and key contributions of gut microbiome are being discovered. Here we will review the different types of analyses that have been applied to IBD genetic study, discuss the computational methods for each type of analysis, and summarize the discoveries made in IBD research with the application of these methods.

A roadmap to multifactor dimensionality reduction methods

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

Polymorphisms in key pulmonary inflammatory pathways and the development of acute respiratory distress syndrome

PURPOSE/AIM

Acute Respiratory Distress Syndrome (ARDS) is an important clinical and public health problem. Why some at-risk individuals develop ARDS and others do not is unclear but may be related to differences in inflammatory and cell signaling systems. The Receptor for Advanced Glycation Endproducts (RAGE) and Granulocyte-Monocyte Stimulating Factor (GM-CSF) pathways have recently been implicated in pulmonary pathophysiology; whether genetic variation within these pathways contributes to ARDS risk or outcome is unknown.

MATERIALS AND METHODS

We studied 842 patients from three centers in Utah and 14 non-Utah ARDS Network centers. We studied patients at risk for ARDS and patients with ARDS to determine whether Single Nucleotide Polymorphisms (SNPs) in the RAGE and GM-CSF pathways were associated with development of ARDS. We studied 29 SNPs in 5 genes within the two pathways and controlled for age, sepsis as ARDS risk factor, and severity of illness, while targeting a false discovery rate of ≤ 5%. In a secondary analysis we evaluated associations with mortality.

RESULTS

Of 842 patients, 690 had ARDS, and 152 were at-risk. Sepsis was the risk factor for ARDS in 250 (30%) patients. When controlling for age, APACHE III score, sepsis as risk factor, and multiple comparisons, no SNPs were significantly associated with ARDS. In a secondary analysis, only rs743564 in CSF2 approached significance with regard to mortality (OR 2.17, unadjusted p = 0.005, adjusted p = 0.15).

CONCLUSIONS

Candidate SNPs within 5 genes in the RAGE and GM-CSF pathways were not significantly associated with development of ARDS in this multi-centric cohort.

Detecting epistasis in human complex traits

Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

Differential association of RANTES-403 and IL-1B-1464 polymorphisms on histological subtypes in male Korean patients with gastric cancer

The aims of this study were to elucidate the association between RANTES-403 and an increased risk of gastric cancer in Korean males and to investigate the gene-gene interaction between IL-1B and RANTES. In total, 218 male patients with gastric cancer (114 diffuse types, 97 intestinal types, and 7 mixed types) and 377 male controls were included. RANTES-403 was genotyped, and age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were estimated by logistic regression. A multifactor dimensionality reduction (MDR) test with three-way split interval validation confirmed by likelihood ratio and permutation analysis was carried out. A significant increase in the risk of gastric cancer for the intestinal-type group was observed for IL-1B-1464G carriers (OR = 2.535; 95% CI = 1.121-5.732; P = 0.02) as well as for those with IL-1B-1464 CG (OR = 2.342; 95% CI = 0.998-5.500; P = 0.05) or IL-1B-1464 GG (OR = 2.819; 95% CI = 1.170-6.793; P = 0.02). For the RANTES-403 genotype, there was no significant difference in the risk of gastric cancer between the overall gastric cancer and the control groups. When further stratified according to histological types, RANTES-403A carriers (OR = 1.743; 95% CI = 1.086-2.798; P = 0.021) or heterozygotes (OR = 1.791; 95% CI = 1.092-2.935; P = 0.021) showed increased risk for developing diffuse-type gastric cancer. MDR revealed a three-way locus-locus interaction between RANTES-403AA, IL-1B-1464GG, and IL-1B-511CT for diffuse-type gastric cancer in Korean males. We demonstrated that RANTES-403 was significantly associated with the risk of developing diffuse-type gastric cancer in men and found a possible gene-gene interaction between RANTES and IL-1B polymorphisms in gastric cancer carcinogenesis.

Gene-environment interactions in genome-wide association studies: current approaches and new directions

BACKGROUND

Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge.

METHODS

We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually.

RESULTS

Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research.

CONCLUSIONS

Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

High throughput sequencing of the Angiostrongylus cantonensis genome: a parasite spreading worldwide

Angiostrongylus cantonensis is a parasitic nematode of rodents and a leading aetiological agent of eosinophilic meningitis in humans. Definitive diagnosis is difficult, often relying on immunodiagnostic methods which utilize crude antigens. New immunodiagnostic methods based on recombinant proteins are being developed, and ideally these methods would be made available worldwide. Identification of diagnostic targets, as well as studies on the biology of the parasite, are limited by a lack of molecular information on Angiostrongylus spp. available in databases. In this study we present data collected from DNA random high-throughput sequencing together with proteomic analyses and a cDNA walking methodology to identify and obtain the nucleotide or amino acid sequences of unknown immunoreactive proteins. 28 080 putative ORFs were obtained, of which 3371 had homology to other deposited protein sequences. Using the A. cantonensis genomic sequences, 156 putative ORFs, matching peptide sequences obtained from previous proteomic studies, were considered novel, with no homology to existing sequences. Full-length coding sequences of eight antigenic target proteins were obtained. In this study we generated not only the complete nucleotide sequences of the antigenic protein targets but also a large amount of genomic data which may help facilitate future genomic, proteomic, transcriptomic or metabolomic studies on Angiostrongylus.

Evaluation of polymorphisms in the sulfonamide detoxification genes NAT2, CYB5A, and CYB5R3 in patients with sulfonamide hypersensitivity

OBJECTIVE

To determine whether polymorphisms in the sulfonamide detoxification genes, CYB5A (encoding cytochrome b(5)), CYB5R3 (encoding cytochrome b(5) reductase), or NAT2 (encoding N-acetyltransferase 2) were over-represented in patients with delayed sulfonamide drug hypersensitivity, compared with control patients who tolerated a therapeutic course of trimethoprim-sulfamethoxazole without adverse event.

METHODS

DNA from 99 nonimmunocompromised patients with sulfonamide hypersensitivity who were identified from the Personalized Medicine Research Project at the Marshfield Clinic, and from 99 age-matched, race-matched, and sex-matched drug-tolerant controls, were genotyped for four CYB5A and five CYB5R3 polymorphisms, and for all coding NAT2 SNPs.

RESULTS

CYB5A and CYB5R3 SNPs were found at low allele frequencies (<3-4%), which did not differ between hypersensitive and tolerant patients. NAT2 allele and haplotype frequencies, as well as inferred NAT2 phenotypes, also did not differ between groups (60 vs. 59% slow acetylators). Finally, no difference in NAT2 status was found in a subset of patients with more severe hypersensitivity signs (drug reaction with eosinophilia and systemic symptoms) compared with tolerant patients.

CONCLUSION

We found no evidence of a substantial involvement of these nine CYB5A or CYB5R3 polymorphisms in sulfonamide hypersensitivity risk, although minor effects cannot be completely ruled out. Despite careful medical record review and full resequencing of the NAT2 coding region, we found no association of NAT2 coding alleles with sulfonamide hypersensitivity (predominantly cutaneous eruptions) in this adult Caucasian population.

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.

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.

A comparison of internal model validation methods for multifactor dimensionality reduction in the case of genetic heterogeneity

BACKGROUND

Determining the genes responsible for certain human traits can be challenging when the underlying genetic model takes a complicated form such as heterogeneity (in which different genetic models can result in the same trait) or epistasis (in which genes interact with other genes and the environment). Multifactor Dimensionality Reduction (MDR) is a widely used method that effectively detects epistasis; however, it does not perform well in the presence of heterogeneity partly due to its reliance on cross-validation for internal model validation. Cross-validation allows for only one "best" model and is therefore inadequate when more than one model could cause the same trait. We hypothesize that another internal model validation method known as a three-way split will be better at detecting heterogeneity models.

RESULTS

In this study, we test this hypothesis by performing a simulation study to compare the performance of MDR to detect models of heterogeneity with the two different internal model validation techniques. We simulated a range of disease models with both main effects and gene-gene interactions with a range of effect sizes. We assessed the performance of each method using a range of definitions of power.

CONCLUSIONS

Overall, the power of MDR to detect heterogeneity models was relatively poor, especially under more conservative (strict) definitions of power. While the overall power was low, our results show that the cross-validation approach greatly outperformed the three-way split approach in detecting heterogeneity. This would motivate using cross-validation with MDR in studies where heterogeneity might be present. These results also emphasize the challenge of detecting heterogeneity models and the need for further methods development.

Global tests of P-values for multifactor dimensionality reduction models in selection of optimal number of target genes

Background

Multifactor Dimensionality Reduction (MDR) is a popular and successful data mining method developed to characterize and detect nonlinear complex gene-gene interactions (epistasis) that are associated with disease susceptibility. Because MDR uses a combinatorial search strategy to detect interaction, several filtration techniques have been developed to remove genes (SNPs) that have no interactive effects prior to analysis. However, the cutoff values implemented for these filtration methods are arbitrary, therefore different choices of cutoff values will lead to different selections of genes (SNPs).

Methods

We suggest incorporating a global test of p-values to filtration procedures to identify the optimal number of genes/SNPs for further MDR analysis and demonstrate this approach using a ReliefF filter technique. We compare the performance of different global testing procedures in this context, including the Kolmogorov-Smirnov test, the inverse chi-square test, the inverse normal test, the logit test, the Wilcoxon test and Tippett’s test. Additionally we demonstrate the approach on a real data application with a candidate gene study of drug response in Juvenile Idiopathic Arthritis.

Results

Extensive simulation of correlated p-values show that the inverse chi-square test is the most appropriate approach to be incorporated with the screening approach to determine the optimal number of SNPs for the final MDR analysis. The Kolmogorov-Smirnov test has high inflation of Type I errors when p-values are highly correlated or when p-values peak near the center of histogram. Tippett’s test has very low power when the effect size of GxG interactions is small.

Conclusions

The proposed global tests can serve as a screening approach prior to individual tests to prevent false discovery. Strong power in small sample sizes and well controlled Type I error in absence of GxG interactions make global tests highly recommended in epistasis studies.