A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence

Effects of MCF2L2, ADIPOQ and SOX2 genetic polymorphisms on the development of nephropathy in type 1 Diabetes Mellitus

Background

MCF2L2, ADIPOQ and SOX2 genes are located in chromosome 3q26-27, which is linked to diabetic nephropathy (DN). ADIPOQ and SOX2 genetic polymorphisms are found to be associated with DN. In the present study, we first investigated the association between MCF2L2 and DN, and then evaluated effects of these three genes on the development of DN.

Methods

A total of 1177 type 1 diabetes patients with and without DN from the GoKinD study were genotyped with TaqMan allelic discrimination. All subjects were of European descent.

Results

Leu359Ile T/G variant in the MCF2L2 gene was found to be associated with DN in female subjects (P = 0.017, OR = 0.701, 95%CI 0.524-0.938) but not in males. The GG genotype carriers among female patients with DN had tendency decreased creatinine and cystatin levels compared to the carriers with either TT or TG genotypes. This polymorphism MCF2L2-rs7639705 together with SNPs of ADIPOQ-rs266729 and SOX2-rs11915160 had combined effects on decreased risk of DN in females (P = 0.001).

Conclusion

The present study provides evidence that MCF2L2, ADIPOQ and SOX2 genetic polymorphisms have effects on the resistance of DN in female T1D patients, and suggests that the linkage with DN in chromosome 3q may be explained by the cumulated genetic effects.

A comparison of internal validation techniques for multifactor dimensionality reduction

BACKGROUND

It is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data.

RESULTS

MDR with 3WS is computationally approximately five times faster than 5-fold cross-validation. The power to find the exact true disease loci without detecting false positive loci is higher with 5-fold cross-validation than with 3WS before pruning. However, the power to find the true disease causing loci in addition to false positive loci is equivalent to the 3WS. With the incorporation of a pruning procedure after the 3WS, the power of the 3WS approach to detect only the exact disease loci is equivalent to that of MDR with cross-validation. In the real data application, the cross-validation and 3WS analyses indicate the same two-locus model.

CONCLUSIONS

Our results reveal that the performance of the two internal validation methods is equivalent with the use of pruning procedures. The specific pruning procedure should be chosen understanding the trade-off between identifying all relevant genetic effects but including false positives and missing important genetic factors. This implies 3WS may be a powerful and computationally efficient approach to screen for epistatic effects, and could be used to identify candidate interactions in large-scale genetic studies.

mbmdr: an R package for exploring gene-gene interactions associated with binary or quantitative traits

SUMMARY

We describe mbmdr, an R package for implementing the model-based multifactor dimensionality reduction (MB-MDR) method. MB-MDR has been proposed by Calle et al. as a dimension reduction method for exploring gene-gene interactions in case-control association studies. It is an extension of the popular multifactor dimensionality reduction (MDR) method of Ritchie et al. allowing a more flexible definition of risk cells. In MB-MDR, risk categories are defined using a regression model which allows adjustment for covariates and main effects and, in addition to the classical low risk and high risk categories, MB-MDR considers a third category of indeterminate or not informative cells. An important improvement added to the current mbmdr algorithm with respect to the original MB-MDR formulation in Calle et al. and also to the classical MDR approach, is the extension of the methodology to different outcome types. While MB-MDR was initially proposed for binary traits in the context of case-control studies, the mbmdr package provides options to analyze both binary or quantitative traits for unrelated individuals.

AVAILABILITY

http://cran.r-project.org/.

Exploring epistatic relationships of NO biosynthesis pathway genes in susceptibility to CHD

AIM

To assess the epistatic relationships of nitric oxide (NO) biosynthesis pathway genes in susceptibility to coronary heart disease (CHD).

METHODS

A total of 2142 subjects enrolled in two case-control studies was genotyped for 7 single-nucleotide polymorphisms (SNP) within NO biosynthesis pathway genes using TaqMan assays. The association analyses were performed at both SNP and haplotype levels. Two-way SNP-SNP interactions and high-order interactions were tested using multiple unconditional logistic regression analyses and generalized multifactor dimensionality reduction (GMDR) analyses, respectively.

RESULTS

Two alleles (rs1049255*C and rs841*A) were identified that were significantly associated with increased risk of CHD after adjusting for all confounders (OR=1.21, 95% CI: 1.06-1.39, combined P=0.001, P(corr)=0.007 and OR=1.30, 95% CI 1.12-1.50, combined P<0.001, P(corr)<0.001, respectively). Significant two-way SNP-SNP interactions were found between SNP rs2297518 and these two significant polymorphisms, affecting the risk of CHD (P<0.001 for both). No significant high-order interactions were identified.

CONCLUSION

The results suggested that two-way SNP-SNP interactions of polymorphisms within NO biosynthesis pathway genes contribute to CHD risk.

Testing multiple gene interactions by the ordered combinatorial partitioning method in case-control studies

MOTIVATION

The multifactor-dimensionality reduction (MDR) method has been widely used in multi-locus interaction analysis. It reduces dimensionality by partitioning the multi-locus genotypes into a high-risk group and a low-risk group according to whether the genotype-specific risk ratio exceeds a fixed threshold or not. Alternatively, one can maximize the chi(2) value exhaustively over all possible ways of partitioning the multi-locus genotypes into two groups, and we aim to show that this is computationally feasible.

METHODS

We advocate finding the optimal MDR (OMDR) that would have resulted from an exhaustive search over all possible ways of partitioning the multi-locus genotypes into two groups. It is shown that this optimal MDR can be obtained efficiently using an ordered combinatorial partitioning (OCP) method, which differs from the existing MDR method in the use of a data-driven rather than fixed threshold. The generalized extreme value distribution (GEVD) theory is applied to find the optimal order of gene combination and assess statistical significance of interactions.

RESULTS

The computational complexity of OCP strategy is linear in the number of multi-locus genotypes in contrast with an exponential order for the naive exhaustive search strategy. Simulation studies show that OMDR can be more powerful than MDR with substantial power gain possible when the partitioning of OMDR is different from that of MDR. The analysis results of a breast cancer dataset show that the use of GEVD accelerates the determination of interaction order and reduces the time cost for P-value calculation by more than 10-fold.

AVAILABILITY

C++ program is available at http://home.ustc.edu.cn/~zhanghan/ocp/ocp.html

FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.

Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population

Background

Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.

Methods/Principal Findings

Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7–5.3]; P≤0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7–3.4; P≤0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4–2.8; P≤0.0001] and 2.3 [95% CI = 1.2-4.4; P≤0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.

Conclusions

These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P≤0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.

Investigation of an epistastic effect between a set of TAAR6 and HSP-70 genes variations and major mood disorders

Epistasis, the interaction between genes, is a topic of current interest in molecular and quantitative genetics. We have further studied a previously investigated sample of 187 major depressive disorder (MDD) patients, 171 bipolar disorder (BD) patients, and 288 controls, and tried to analyze the interaction between a set of variations of independent genes: the trace amine receptor 6 (rs4305745, rs8192625, rs7452939, rs6903874, and rs6937506) and the heat shock protein 70 (rs562047, rs1061581, rs2227956). The multifactor dimensionality reduction (MDR) method was applied and the covariates associated with diagnosis were also controlled. A significant predictive value of specific interactions between genotypes located in the investigated genes was found. We then report preliminary evidence that the epistasis between trace amine receptor 6 and heat shock protein 70 variations may compose a risk profile for major mood disorders. The level of statistical significance (P < 0.001) and the testing balancing accuracy over 0.62 suggest a cautious optimism toward this result, although the possibility of false positivity warrants further analyses in independent samples.

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

Contact:jason.h.moore@dartmouth.edu

Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction

Human health is a complex process that is dependent on many genes, many environmental factors and chance events that are perhaps not measurable with current technology or are simply unknowable. Success in the design and execution of population-based association studies to identify those genetic and environmental factors that play an important role in human disease will depend on our ability to embrace, rather that ignore, complexity in the genotype to phenotype mapping relationship for any given human ecology. We review here three general computational challenges that must be addressed. First, data mining and machine learning methods are needed to model nonlinear interactions between multiple genetic and environmental factors. Second, filter and wrapper methods are needed to identify attribute interactions in large and complex solution landscapes. Third, visualization methods are needed to help interpret computational models and results. We provide here an overview of the multifactor dimensionality reduction (MDR) method that was developed for addressing each of these challenges.

Evaluating epistatic interaction signals in complex traits using quantitative traits

Rheumatoid arthritis (RA) is a complex, chronic inflammatory disease implicated to have several plausible candidate loci; however, these may not account for all the genetic variations underlying RA. Common disorders are hypothesized to be highly complex with interaction among genes and other risk factors playing a major role in the disease process. This complexity is further magnified because such interactions may be with or without a strong independent effect and are thus difficult to detect using traditional statistical methodologies. The main challenge to analyze such gene x gene and gene x environment interaction is attributed to a phenomenon referred to as the "curse of dimensionality." Several combinatorial methodologies have been proposed to tackle this analytical challenge. Because quantitative traits underlie complex phenotypes and contain more information on the trait variation within genotypes than qualitative dichotomy, analyzing quantitative traits correlated with the affection status is a more powerful tool for mapping such trait genes. Recently, a generalized multifactor dimensionality reduction method was proposed that allows for adjustment for discrete and quantitative traits and can be used to analyze qualitative and quantitative phenotypes in a population based study design.In this report, we evaluate the efficiency of the generalized multifactor dimensionality reduction statistical suite to decipher small interacting factors that contribute to RA disease pathogenesis.

Statistical tests of genetic association in the presence of gene-gene and gene-environment interactions

BACKGROUND

While its importance is well recognized, it remains challenging to test genetic association in the presence of gene-gene (or gene-environment) interactions. A major technical difficulty lies in the fact that a general model of gene-gene interactions calls for the use of often a large number of parameters, leading to possibly reduced statistical power. An emerging theme of some recent work is to reduce the number of such parameters through dimension reduction. Wang et al. [2009] proposed such an approach based on the partial least squares (PLS) for dimension reduction. They compared their method with several others using simulated data, establishing that their PLS test performed best. Unfortunately, Wang et al. did not include in their evaluations several powerful tests just recently discovered for analyzing multiple SNPs in a candidate gene or region.

METHODS

In this paper, we first extend these tests to the current context to detect gene-gene interactions in the presence of nuisance parameters, then compare these tests with the PLS test using the simulated data of Wang et al. [2009].

RESULTS

It is confirmed that some other tests can be more powerful than the PLS test, though there is no uniform winner. Some interesting, albeit not new, observations are also made: some of the new tests are more robust to the large number of parameters in a model and may thus perform well; on the other hand, even for a purely epistatic genetic model, some of the tests applied to a logistic main-effects model without any interaction terms may be superior to that based on a full model that explicitly accounts for gene-gene interactions.

CONCLUSION

The proposed statistical tests are potentially useful in practice.

Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits

Background

The purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The method is built on two information-theoretic metrics, the k-way interaction information (KWII) and phenotype-associated information (PAI). The PAI is a novel information theoretic metric that is obtained from the total information correlation (TCI) information theoretic metric by removing the contributions for inter-variable dependencies (resulting from factors such as linkage disequilibrium and common sources of environmental pollutants).

Results

The KWII and the PAI were critically evaluated and incorporated within an algorithm called CHORUS for analyzing QT. The combinations with the highest values of KWII and PAI identified each known GEI associated with the QT in the simulated data sets. The CHORUS algorithm was tested using the simulated GAW15 data set and two real GGI data sets from QTL mapping studies of high-density lipoprotein levels/atherosclerotic lesion size and ultra-violet light-induced immunosuppression. The KWII and PAI were found to have excellent sensitivity for identifying the key GEI simulated to affect the two quantitative trait variables in the GAW15 data set. In addition, both metrics showed strong concordance with the results of the two different QTL mapping data sets.

Conclusion

The KWII and PAI are promising metrics for analyzing the GEI of QT.

Epistasis between a set of variations located in the TAAR6 and HSP-70 genes toward schizophrenia and response to antipsychotic treatment

Suggestive associations have been reported between trace amines and heat shock proteins, and a disrupted pathophysiology that enhances the risk of psychosis and that modifies responses to antipsychotic treatments. Our group previously reported genetic studies on TAAR6 and HSP-70 separately in patients with schizophrenia. In the current study, we investigated possible epistasis between the same set of variations in a sample of 281 patients diagnosed with schizophrenia and 288 healthy controls. We applied the generalized multifactor dimensionality reduction (MDR) method and controlled covariates significantly associated with both diagnosis and treatment efficacy. To the best of our knowledge, epistasis between the present set of variations in schizophrenia has not been tested before. We found significant associations with both the risk of disease and response to treatment. However, the insufficiently balanced accuracy of the applied tests suggests that, despite significantly different genetic variations between cases and controls, these factors have a poor predictive value. Explanations for these findings and possible future directions are also discussed.

Interaction of serotonin-related genes affects short-term antidepressant response in major depressive disorder

BACKGROUND

Four serotonin-related genes including guanine nucleotide binding protein beta polypeptide 3 (GNB3), 5-hydroxytryptamine receptor 1A (HTR1A; serotonin receptor 1A), 5-hydroxytryptamine receptor 2A (HTR2A; serotonin receptor 2A), and solute carrier family 6 member 4 (SLC6A4; serotonin neurotransmitter transporter) have been suggested to be candidate genes for influencing antidepressant treatment outcome. The aim of this study was to explore whether interaction among these genes could contribute to the pharmacogenomics of short-term antidepressant response in a Taiwanese population with major depressive disorder (MDD).

METHODS

Included in this study were 101 MDD patients who were treated with antidepressants, 35 of whom were rapid responders and 66 non-responders after 2weeks of treatment. We genotyped four single nucleotide polymorphisms (SNPs), including GNB3 rs5443 (C825T), HTR1A rs6295 (C-1019G), HTR2A rs6311 (T102C), and SLC6A4 rs25533, and employed the generalized multifactor dimensionality reduction (GMDR) method to investigate gene-gene interactions.

RESULTS

Single-locus analyses showed the GNB3 rs5443 polymorphism to be associated with short-term antidepressant treatment outcome (P-value=0.029). We did not correct for multiple testing in these multiple exploratory analyses. Finally, the GMDR approach identified a significant gene-gene interaction (P-value=0.025) involving GNB3 and HTR2A, as well as a significant 3-locus model (P-value=0.015) among GNB3, HTR2A, and SLC6A4.

CONCLUSIONS

These results support the hypothesis that GNB3, HTR2A, and SLC6A4 may play a role in the outcome of short-term antidepressant treatment for MDD in an interactive manner. Future research with independent replication using large sample sizes is needed to confirm the functions of the candidate genes identified in this study as being involved in short-term antidepressant treatment response.

Single and multigenic analysis of the association between variants in 12 steroid hormone metabolism genes and risk of prostate cancer

To estimate the prostate cancer risk conferred by individual single nucleotide polymorphisms (SNPs), SNP-SNP interactions, and/or cumulative SNP effects, we evaluated the association between prostate cancer risk and the genetic variants of 12 key genes within the steroid hormone pathway (CYP17, HSD17B3, ESR1, SRD5A2, HSD3B1, HSD3B2, CYP19, CYP1A1, CYP1B1, CYP3A4, CYP27B1, and CYP24A1). A total of 116 tagged SNPs covering the group of genes were analyzed in 2,452 samples (886 cases and 1,566 controls) in three ethnic/racial groups. Several SNPs within CYP19 were significantly associated with prostate cancer in all three ethnicities (P = 0.001-0.009). Genetic variants within HSD3B2 and CYP24A1 conferred increased risk of prostate cancer in non-Hispanic or Hispanic Caucasians. A significant gene-dosage effect for increasing numbers of potential high-risk genotypes was found in non-Hispanic and Hispanic Caucasians. Higher-order interactions showed a seven-SNP interaction involving HSD17B3, CYP19, and CYP24A1 in Hispanic Caucasians (P = 0.001). In African Americans, a 10-locus model, with SNPs located within SRD5A2, HSD17B3, CYP17, CYP27B1, CYP19, and CYP24A1, showed a significant interaction (P = 0.014). In non-Hispanic Caucasians, an interaction of four SNPs in HSD3B2, HSD17B3, and CYP19 was found (P < 0.001). These data are consistent with a polygenic model of prostate cancer, indicating that multiple interacting genes of the steroid hormone pathway confer increased risk of prostate cancer.

Methods for optimizing statistical analyses in pharmacogenomics research

Pharmacogenomics is a rapidly developing sector of human genetics research with arguably the highest potential for immediate benefit. There is a considerable body of evidence demonstrating that variability in drug-treatment response can be explained in part by genetic variation. Subsequently, much research has ensued and is ongoing to identify genetic variants associated with drug-response phenotypes. To reap the full benefits of the data we collect we must give careful consideration to the study population under investigation, the phenotype being examined and the statistical methodology used in data analysis. Here, we discuss principles of study design and optimizing statistical methods for pharmacogenomic studies when the outcome of interest is a continuous measure. We review traditional hypothesis testing procedures, as well as novel approaches that may be capable of accounting for more variance in a quantitative pharmacogenomic trait. We give examples of studies that have employed the analytical methodologies discussed here, as well as resources for acquiring software to run the analyses.

Genetic risk profiling and prediction of disease course in Crohn's disease patients

BACKGROUND & AIMS

Clinical presentation at diagnosis and disease course of Crohn's disease (CD) are heterogeneous and variable over time. Early introduction of immunomodulators and/or biologicals might be justified in patients at risk for disease progression, so it is important to identify these patients as soon as possible. We examined the influence of recently discovered CD-associated susceptibility loci on changes in disease behavior and evaluated whether a genetic risk model for disease progression could be generated.

METHODS

Complete medical data were available for 875 CD patients (median follow-up time, 14 years; interquartile range, 7-22). Fifty CD-associated polymorphisms were genotyped. Kaplan-Meier survival analyses, multiple logistic regression, and generalized multifactor dimensionality reduction analyses (GMDR) were performed, correcting for follow-up time.

RESULTS

Homozygosity for the rs1363670 G-allele in a gene encoding a hypothetical protein near the IL12B gene was independently associated with stricturing disease behavior (odds ratio [OR], 5.48; 95% confidence interval [CI], 1.60-18.83; P = .007) and with shorter time to strictures (P = .01), especially in patients with ileal involvement (P = .0002). Male patients carrying at least one rs12704036 T-allele in a gene desert had the shortest time to non-perianal fistula (P < .0001). The presence of a C-allele at the CDKAL1 single nucleotide polymorphism rs6908425 and the absence of NOD2 variants were independently associated with development of perianal fistula (OR, 8.86; 95% CI, 1.13-69.78; P = .04 and OR, 0.56; 95% CI, 0.38-0.83; P = .004, respectively), particularly when colonic involvement and active smoking were present.

CONCLUSIONS

CD-associated polymorphisms play a role in disease progression and might be useful in identifying patients who could benefit from an early top-down treatment approach.

Dopamine genes and nicotine dependence in treatment-seeking and community smokers

We utilized a cohort of 828 treatment-seeking self-identified white cigarette smokers (50% female) to rank candidate gene single nucleotide polymorphisms (SNPs) associated with the Fagerström Test for Nicotine Dependence (FTND), a measure of nicotine dependence which assesses quantity of cigarettes smoked and time- and place-dependent characteristics of the respondent's smoking behavior. A total of 1123 SNPs at 55 autosomal candidate genes, nicotinic acetylcholine receptors and genes involved in dopaminergic function, were tested for association to baseline FTND scores adjusted for age, depression, education, sex, and study site. SNP P-values were adjusted for the number of transmission models, the number of SNPs tested per candidate gene, and their intragenic correlation. DRD2, SLC6A3, and NR4A2 SNPs with adjusted P-values <0.10 were considered sufficiently noteworthy to justify further genetic, bioinformatic, and literature analyses. Each independent signal among the top-ranked SNPs accounted for approximately 1% of the FTND variance in this sample. The DRD2 SNP appears to represent a novel association with nicotine dependence. The SLC6A3 SNPs have previously been shown to be associated with SLC6A3 transcription or dopamine transporter density in vitro, in vivo, and ex vivo. Analysis of SLC6A3 and NR4A2 SNPs identified a statistically significant gene-gene interaction (P=0.001), consistent with in vitro evidence that the NR4A2 protein product (NURR1) regulates SLC6A3 transcription. A community cohort of N=175 multiplex ever-smoking pedigrees (N=423 ever smokers) provided nominal evidence for association with the FTND at these top ranked SNPs, uncorrected for multiple comparisons.

Epistasis and its implications for personal genetics

The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.

Epistasis between IL1A, IL1B, TNF, HTR2A, 5-HTTLPR and TPH2 variations does not impact alcohol dependence disorder features

We assessed a set of biological (HDL, LDL, SGOT, SGPT, GGT, HTc, Hb and T levels) and psychometric variables (investigated through HAM-D, HAM-A, GAS, Liebowitz Social Anxiety Scale, Mark & Mathews Scale, Leyton scale, and Pilowski scale) in a sample of 64 alcohol dependent patients, at baseline and after a detoxification treatment. Moreover, we recruited 47 non-consanguineous relatives who did not suffer alcohol related disorders and underwent the same tests. In both groups we genotyped 11 genetic variations (rs1800587; rs3087258; rs1799724; 5-HTTLPR; rs1386493; rs1386494; rs1487275; rs1843809; rs4570625; rs2129575; rs6313) located in genes whose impact on alcohol related behaviors and disorders has been hypothesized (IL1A, IL1B, TNF, 5-HTTLPR, TPH2 and HTR2A). We analyzed the epistasis of these genetic variations upon the biological and psychological dimensions in the cases and their relatives. Further on, we analyzed the effects of the combined genetic variations on the short - term detoxification treatment efficacy. Finally, being the only not yet investigated variation within this sample, we analyzed the impact of the rs6313 alone on baseline assessment and treatment efficacy. We detected the following results: the couple rs6313 + rs2129575 affected the Leyton -Trait at admission (p = 0.01) (obsessive-compulsive trait), whilst rs1800587 + 5-HTTLPR impacted the Pilowski test at admission (p = 0.01) (hypochondriac symptoms). These results did not survive Bonferroni correction (p < or = 0.004). This lack of association may depend on the incomplete gene coverage or on the small sample size which limited the power of the study. On the other hand, it may reflect a substantial absence of relevance of the genotype variants toward the alcohol related investigated dimensions. Nonetheless, the marginal significance we detected could witness an informative correlation worth investigating in larger samples.

Multiple distinct risk loci for nicotine dependence identified by dense coverage of the complete family of nicotinic receptor subunit (CHRN) genes

Tobacco smoking continues to be a leading cause of preventable death. Recent research has underscored the important role of specific cholinergic nicotinic receptor subunit (CHRN) genes in risk for nicotine dependence and smoking. To detect and characterize the influence of genetic variation on vulnerability to nicotine dependence, we analyzed 226 SNPs covering the complete family of 16 CHRN genes, which encode the nicotinic acetylcholine receptor (nAChR) subunits, in a sample of 1,050 nicotine-dependent cases and 879 non-dependent controls of European descent. This expanded SNP coverage has extended and refined the findings of our previous large-scale genome-wide association and candidate gene study. After correcting for the multiple tests across this gene family, we found significant association for two distinct loci in the CHRNA5-CHRNA3-CHRNB4 gene cluster, one locus in the CHRNB3-CHRNA6 gene cluster, and a fourth, novel locus in the CHRND-CHRNG gene cluster. The two distinct loci in CHRNA5-CHRNA3-CHRNB4 are represented by the non-synonymous SNP rs16969968 in CHRNA5 and by rs578776 in CHRNA3, respectively, and joint analyses show that the associations at these two SNPs are statistically independent. Nominally significant single-SNP association was detected in CHRNA4 and CHRNB1. In summary, this is the most comprehensive study of the CHRN genes for involvement with nicotine dependence to date. Our analysis reveals significant evidence for at least four distinct loci in the nicotinic receptor subunit genes that each influence the transition from smoking to nicotine dependence and may inform the development of improved smoking cessation treatments and prevention initiatives.

Detecting gene-gene interactions that underlie human diseases

Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

Association of three-gene interaction among MTHFR, ALOX5AP and NOTCH3 with thrombotic stroke: a multicenter case-control study

Stroke is a common complex trait and does not follow Mendelian pattern of inheritance. Gene-gene or gene-environment interactions may be responsible for the complex trait. How the interactions contribute to stroke is still under research. This study aimed to explore the association between gene-gene interactions and stroke in Chinese in a large case-control study. Nearly 4,000 participants were recruited from seven clinical centers. Eight variants in five candidate genes were examined for stroke risk. Gene-gene interactions were explored by using Generalized Multifactor Dimensionality Reduction (GMDR). A significant gene-gene interaction was found by GMDR. The best model including MTHFR C677T, ALOX5AP T2354A and NOTCH3 C381T scored 10 for Cross-Validation Consistency and 9 for Sign Test (P = 0.0107). The individuals with combination of MTHFR 677TT, ALOX5AP 2354AA and NOTCH3 381TT/TC had a significantly higher risk of thrombotic stroke (OR 3.165, 95% CI 1.461-6.858, P = 0.003). Our results show that combination of these alleles conferred higher risk for stroke than single risk allele. The gene-gene interaction may serve as a novel area for stroke research. The three-locus combination may change the susceptibility of particular subjects to the disease.

New insights into the genetics of addiction

Drug addiction is a common brain disorder that is extremely costly to the individual and to society. Genetics contributes significantly to vulnerability to this disorder, but identification of susceptibility genes has been slow. Recent genome-wide linkage and association studies have implicated several regions and genes in addiction to various substances, including alcohol and, more recently, tobacco. Current efforts aim not only to replicate these findings in independent samples but also to determine the functional mechanisms of these genes and variants.

Improving strategies for detecting genetic patterns of disease susceptibility in association studies

The analysis of gene interactions and epistatic patterns of susceptibility is especially important for investigating complex diseases such as cancer characterized by the joint action of several genes. This work is motivated by a case-control study of bladder cancer, aimed at evaluating the role of both genetic and environmental factors in bladder carcinogenesis. In particular, the analysis of the inflammation pathway is of interest, for which information on a total of 282 SNPs in 108 genes involved in the inflammatory response is available. Detecting and interpreting interactions with such a large number of polymorphisms is a great challenge from both the statistical and the computational perspectives. In this paper we propose a two-stage strategy for identifying relevant interactions: (1) the use of a synergy measure among interacting genes and (2) the use of the model-based multifactor dimensionality reduction method (MB-MDR), a model-based version of the MDR method, which allows adjustment for confounders.

Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models

BACKGROUND/AIMS

In genetic studies of complex disease a consideration for the investigator is detection of joint effects. The Multifactor Dimensionality Reduction (MDR) algorithm searches for these effects with an exhaustive approach. Previously unknown aspects of MDR performance were the power to detect interactive effects given large numbers of non-model loci or varying degrees of heterogeneity among multiple epistatic disease models.

METHODS

To address the performance with many non-model loci, datasets of 500 cases and 500 controls with 100 to 10,000 SNPs were simulated for two-locus models, and one hundred 500-case/500-control datasets with 100 and 500 SNPs were simulated for three-locus models. Multiple levels of locus heterogeneity were simulated in several sample sizes.

RESULTS

These results show MDR is robust to locus heterogeneity when the definition of power is not as conservative as in previous simulation studies where all model loci were required to be found by the method. The results also indicate that MDR performance is related more strongly to broad-sense heritability than sample size and is not greatly affected by non-model loci.

CONCLUSIONS

A study in which a population with high heritability estimates is sampled predisposes the MDR study to success more than a larger ascertainment in a population with smaller estimates.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence

BACKGROUND

Epidemiological data indicate that nicotine dependence (ND) are influenced by genes, environmental factors, and their interactions. Although it has been documented from molecular experiments that brain-derived neurotrophic factor (BDNF) exerts its functions via neurotrophic tyrosine kinase receptor 2 (NTRK2) and both alpha 4 (CHRNA4) and beta 2 (CHRNB2) subunits are required to form functional alpha 4 beta 2-containing nicotinic receptors (nAChRs), no study is reported demonstrating that there exist gene-gene interactions among the four genes in affecting ND.

METHODS

To determine if gene-gene interactions exist among the four genes, we genotyped six single nucleotide polymorphisms (SNPs) for CHRNA4 and BDNF, nine SNPs for NTRK2, and four SNPs for CHRNB2 in a case-control sample containing 275 unrelated smokers with a Fagerström Test for Nicotine Dependence score of 4.0 or more and 348 unrelated nonsmokers.

RESULTS

By using a generalized multifactor dimensionality reduction algorithm recently developed by us, we found highly significant gene-gene interactions for the gene pairs of CHRNA4 and CHRNB2, CHRNA4 and NTRK2, CHRNB2 and NTRK2, and BDNF and NTRK2 (p < .01 for all four gene pairs) and significant gene-gene interaction between CHRNA4 and BDNF (p = .031) on ND. No significant interaction was detected for CHRNB2 and BDNF (p = .068).

CONCLUSIONS

Our study provides first evidence on the presence of gene-gene interaction among the four genes in affecting ND. Although CHRNB2 alone was not significantly associated with ND in several previously reported association studies on ND, we found it affects ND through interactions with CHRNA4 and NTRK2.

Characterization of a functional polymorphism in the 3' UTR of SLC6A4 and its association with drinking intensity

BACKGROUND

The propensity for severe drinking is hypothesized to be regulated by differential expression of serotonin transporter gene (SLC6A4) in the human brain. The SLC6A4 promoter region 5-HTTLPR has been examined previously as a candidate polymorphic variant associated with severe drinking. In this study, we investigated whether other SLC6A4 single nucleotide polymorphisms (SNPs) are associated with drinking intensity among treatment-seeking alcoholics and whether these polymorphic variants result in differential SLC6A4 expression levels.

METHODS

We analyzed associations of drinking intensity in 275 (78.5% male) treatment-seeking alcoholics of Caucasian and Hispanic origin, with 6 SLC6A4 polymorphisms. Next, to examine the functionality of the SNP that showed a significant association with drinking intensity, we transfected the 2 alleles of rs1042173 into HeLa cell cultures and measured serotonin transporter mRNA and protein expression levels by using qRT-PCR and western blotting techniques.

RESULTS

One of the 6 polymorphisms we examined, rs1042173 in the 3' untranslated region (3'-UTR) of SLC6A4, showed a significant association with drinking intensity. The G allele carriers for rs1042173 were associated with significantly lower drinking intensity (p = 0.0034) compared to T-allele homozygotes. In HeLa cell cultures, the cells transfected with G allele showed a significantly higher mRNA and protein levels than the T allele-transfected cells.

CONCLUSION

These findings suggest that the allelic variations of rs1042173 affect drinking intensity in alcoholics possibly by altering serotonin transporter expression levels. This provides additional support to the hypothesis that SLC6A4 polymorphisms play an important role in regulating propensity for severe drinking.

A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G x G) and gene-environment (G x E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G x G and G x E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G x G and G x E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence.

Nicotinic acetylcholine receptor beta2 subunit gene implicated in a systems-based candidate gene study of smoking cessation

Although the efficacy of pharmacotherapy for tobacco dependence has been previously demonstrated, there is substantial variability among individuals in treatment response. We performed a systems-based candidate gene study of 1295 single nucleotide polymorphisms (SNPs) in 58 genes within the neuronal nicotinic receptor and dopamine systems to investigate their role in smoking cessation in a bupropion placebo-controlled randomized clinical trial. Putative functional variants were supplemented with tagSNPs within each gene. We used global tests of main effects and treatment interactions, adjusting the P-values for multiple correlated tests. An SNP (rs2072661) in the 3' UTR region of the beta2 nicotinic acetylcholine receptor subunit (CHRNB2) has an impact on abstinence rates at the end of treatment (adjusted P = 0.01) and after a 6-month follow-up period (adjusted P = 0.0002). This latter P-value is also significant with adjustment for the number of genes tested. Independent of treatment at 6-month follow-up, individuals carrying the minor allele have substantially decreased the odds of quitting (OR = 0.31; 95% CI 0.18-0.55). Effect of estimates indicate that the treatment is more effective for individuals with the wild-type (OR = 2.14, 95% CI 1.20-3.81) compared with individuals carrying the minor allele (OR = 0.83, 95% CI 0.32-2.19), although this difference is only suggestive (P = 0.10). Furthermore, this SNP demonstrated a role in the time to relapse (P = 0.0002) and an impact on withdrawal symptoms at target quit date (TQD) (P = 0.0009). Overall, while our results indicate strong evidence for CHRNB2 in ability to quit smoking, these results require replication in an independent sample.

AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes

We developed a computationally efficient algorithm AMBIENCE, for identifying the informative variables involved in gene-gene (GGI) and gene-environment interactions (GEI) that are associated with disease phenotypes. The AMBIENCE algorithm uses a novel information theoretic metric called phenotype-associated information (PAI) to search for combinations of genetic variants and environmental variables associated with the disease phenotype. The PAI-based AMBIENCE algorithm effectively and efficiently detected GEI in simulated data sets of varying size and complexity, including the 10K simulated rheumatoid arthritis data set from Genetic Analysis Workshop 15. The method was also successfully used to detect GGI in a Crohn's disease data set. The performance of the AMBIENCE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized MDR (GMDR), and pedigree disequilibrium test (PDT) methods. Furthermore, we assessed the computational speed of AMBIENCE for detecting GGI and GEI for data sets varying in size from 100 to 10(5) variables. Our results demonstrate that the AMBIENCE information theoretic algorithm is useful for analyzing a diverse range of epidemiologic data sets containing evidence for GGI and GEI.

The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies

The candidate gene approach to pharmacogenetics is hypothesis driven, and anchored in biological plausibility. Whole genome scanning is hypothesis generating, and it may lead to new biology. While both approaches are important, the scientific community is rapidly reallocating resources toward the latter. We propose a step-wise approach to large-scale pharmacogenetic association studies that begins with candidate genes, then uses a pathway-based intermediate step, to inform subsequent analyses of data generated through whole genome scanning. Novel computational strategies are explored in the context of two clinically relevant examples, cholesterol synthesis and lipid signaling.

Genetic interactions model among Eotaxin gene polymorphisms in asthma

Eotaxin family (Eotaxin 1,2 and 3) recruits and activates CCR3-bearing cells such as eosinophil, mast cells, and Th2 lymphocytes that play a major role in allergic disorders. We examined the polygenetic effects of the Eotaxin gene family in a Korean population. Gene-gene interactions were tested using a multistep approach with multifactor dimensionality reduction (MDR) method between asthmatics and normal controls. The overall best MDR model of the main effect single nucleotide polymorphisms (SNPs) included EOT2 + 1272A > G and EOT3 + 77C > T (model 1) [testing accuracy 0.597, cross-validation consistency (CVC) 10/10, P < 0.001]. The overall best MDR model of the SNPs with no main effects included EOT2 + 304C > A, EOT3 + 716A > G, and EOT3 + 1579G > A (model 2) (testing accuracy 0.616, CVC 10/10, P < 0.001). Model 3 was obtained by including the MDR variables for models 1 and 2. This new composite model predicted asthma with better accuracy than either model 1 or model 2 (testing accuracy 0.643, CVC 10/10, P < 0.001). The detection of statistical interaction models is one evidence of gene-gene interactions among Eotaxin genes, and this interaction is thought to influence the development of asthma. Although the models are limited to determining statistical interactions within a population, they may be useful for identifying groups at high risk of developing asthma.

Study of gene-gene interactions for endophenotypic quantitative traits in Chinese asthmatic children

BACKGROUND

Asthma is a complex disease resulting from interactions between multiple genes and environmental factors. Study of gene-gene interactions could provide insight into the pathophysiology of asthma.

METHODS

We investigated the interactions among 18 single-nucleotide polymorphisms in eight candidate genes for plasma total immunoglobulin E (IgE) concentration and peripheral blood (PB) eosinophil count in 298 Chinese asthmatic children and 175 controls. Generalized multifactor dimensionality reduction and generalized linear model were used to analyze gene-gene interactions for the quantitative traits.

RESULTS

A significant interaction was found between R130Q in IL13 and I50V in IL4RA for plasma total IgE concentration, with a cross-validation (CV) consistency of nine of 10 and a prediction error of 41.1% (P = 0.013). Plasma total IgE concentration was significantly higher in the high-risk than the low-risk groups (P < 0.0001). For PB eosinophil count, significant interaction was found between C-431T in TARC and RsaI_in2 in FCERIB, with a CV consistency of nine of 10 and a prediction error of 40.2% (P = 0.009). PB eosinophil count was significantly higher in the high-risk group than the low-risk groups (P < 0.0001). Generalized linear model also revealed significant gene-gene interaction for the above two endophenotypes with P = 0.013 for plasma total IgE concentration and P = 0.029 for PB eosinophil count respectively.

CONCLUSIONS

Our data suggest significant interactions between IL13 and IL4RA for plasma total IgE concentration, and this is the first report to show significant interaction between TARC and FCERIB for PB eosinophil count in Chinese asthmatic children.

Analysis of heterogeneity and epistasis in physiological mixed populations by combined structural equation modelling and latent class analysis

BACKGROUND

Biological systems are interacting, molecular networks in which genetic variation contributes to phenotypic heterogeneity. This heterogeneity is traditionally modelled as a dichotomous trait (e.g. affected vs. non-affected). This is far too simplistic considering the complexity and genetic variations of such networks.

METHODS

In this study on type 2 diabetes mellitus, heterogeneity was resolved in a latent class framework combined with structural equation modelling using phenotypic indicators of distinct physiological processes. We modelled the clinical condition "the metabolic syndrome", which is known to be a heterogeneous and polygenic condition with a clinical endpoint (type 2 diabetes mellitus). In the model presented here, genetic factors were not included and no genetic model is assumed except that genes operate in networks. The impact of stratification of the study population on genetic interaction was demonstrated by analysis of several genes previously associated with the metabolic syndrome and type 2 diabetes mellitus.

RESULTS

The analysis revealed the existence of 19 distinct subpopulations with a different propensity to develop diabetes mellitus within a large healthy study population. The allocation of subjects into subpopulations was highly accurate with an entropy measure of nearly 0.9. Although very few gene variants were directly associated with metabolic syndrome in the total study sample, almost one third of all possible epistatic interactions were highly significant. In particular, the number of interactions increased after stratifying the study population, suggesting that interactions are masked in heterogenous populations. In addition, the genetic variance increased by an average of 35-fold when analysed in the subpopulations.

CONCLUSION

The major conclusions from this study are that the likelihood of detecting true association between genetic variants and complex traits increases tremendously when studied in physiological homogenous subpopulations and on inclusion of epistasis in the analysis, whereas epistasis (i.e. genetic networks) is ubiquitous and should be the basis in modelling any biological process.

C2 and CFB genes in age-related maculopathy and joint action with CFH and LOC387715 genes

Background

Age-related maculopathy (ARM) is a common cause of visual impairment in the elderly populations of industrialized countries and significantly affects the quality of life of those suffering from the disease. Variants within two genes, the complement factor H (CFH) and the poorly characterized LOC387715 (ARMS2), are widely recognized as ARM risk factors. CFH is important in regulation of the alternative complement pathway suggesting this pathway is involved in ARM pathogenesis. Two other complement pathway genes, the closely linked complement component receptor (C2) and complement factor B (CFB), were recently shown to harbor variants associated with ARM.

Methods/Principal Findings

We investigated two SNPs in C2 and two in CFB in independent case-control and family cohorts of white subjects and found rs547154, an intronic SNP in C2, to be significantly associated with ARM in both our case-control (P-value 0.00007) and family data (P-value 0.00001). Logistic regression analysis suggested that accounting for the effect at this locus significantly (P-value 0.002) improves the fit of a genetic risk model of CFH and LOC387715 effects only. Modeling with the generalized multifactor dimensionality reduction method showed that adding C2 to the two-factor model of CFH and LOC387715 increases the sensitivity (from 63% to 73%). However, the balanced accuracy increases only from 71% to 72%, and the specificity decreases from 80% to 72%.

Conclusions/Significance

C2/CFB significantly influences AMD susceptibility and although accounting for effects at this locus does not dramatically increase the overall accuracy of the genetic risk model, the improvement over the CFH-LOC387715 model is statistically significant.

Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction

Background

Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.

Results

In this study, we compare the detection and power of MDR using a variety of measures for two-way contingency table analysis. We simulated 40 genetic models, varying the number of disease loci in the model (2 – 5), allele frequencies of the disease loci (.2/.8 or .4/.6) and the broad-sense heritability of the model (.05 – .3). Overall, detection using NMI was 65.36% across all models, and specific detection was 59.4% versus detection using classification error at 62% and specific detection was 52.2%.

Conclusion

Of the 10 measures evaluated, the likelihood ratio and normalized mutual information (NMI) are measures that consistently improve the detection and power of MDR in simulated data over using classification error. These measures also reduce the inclusion of spurious variables in a multi-locus model. Thus, MDR, which has already been demonstrated as a powerful tool for detecting gene-gene interactions, can be improved with the use of alternative fitness functions.

Genetic variation and antipsychotic drug effects on serum lipids

Evaluation of: Smith RC, Segman RH, Golcer Dubner T, Pavlov V, Lerer B: Allelic variation in ApoC3, ApoA5, and LPL genes and first and second generation antipsychotic effects on serum lipids in patients with schizophrenia. Pharmacogenomics J. DOI: 10.1038/sj.tpj.6500474 (2007) (Epub ahead of print) [1] . Some newer antipsychotic drugs may raise serum lipid levels, but it is not known whether specific genetic variants affect an individual’s susceptibility to this. In 189 schizophrenia patients treated with either first- or second-generation antipsychotic drugs, Smith and colleagues analyzed drug-by-genotype interaction effects on serum cholesterol and triglyceride levels using five polymorphisms in three genes that affect serum lipids: APOC3, APOA5 and LPL. Three interactions involving the APOA5 -1131 T/C polymorphism remained significant after adjustment for multiple testing; the rarer C allele was associated with higher serum cholesterol levels in patients treated with first-generation antipsychotics, and lower levels in those treated with clozapine or olanzapine. This article emphasizes aspects of their study which illustrate points that may be useful in planning future pharmacogenetic studies.

Novel methods for detecting epistasis in pharmacogenomics studies

The importance of gene–gene and gene–environment interactions in the underlying genetic architecture of common, complex phenotypes is gaining wide recognition in the field of pharmacogenomics. In epidemiological approaches to mapping genetic variants that predict drug response, it is important that researchers investigate potential epistatic interactions. In the current review, we discuss data-mining tools available in genetic epidemiology to detect such interactions and appropriate applications. We survey several classes of novel methods available and present an organized collection of successful applications in the literature. Finally, we provide guidance as to how to incorporate these novel methods into a genetic analysis. The overall goal of this paper is to aid researchers in developing an analysis plan that accounts for gene–gene and gene–environment in their own work.