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Gene-based and single-nucleotide polymorphism (SNP) set association studies provide an important complement to SNP analysis. Kernel-based nonparametric regression has recently emerged as a powerful and flexible tool for this purpose. Our goal is to explore whether this approach can be extended to incorporate and test for interaction effects, especially for genes containing rare variant SNPs. Here, we construct nonparametric regression models that can be used to include a gene-environment interaction effect under the framework of the least-squares kernel machine and examine the performance of the proposed method on the Genetic Analysis Workshop 17 unrelated individuals data set. Two hundred simulated replicates were used to explore the power for detecting interaction. We demonstrate through a genome scan of the quantitative phenotype Q1 that the simulated gene-environment interaction effect in the data can be detected with reasonable power by using the least-squares kernel machine method.

Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main effects and gene-age interactions with longitudinal family data. The simulated Genetic Analysis Workshop 16 Problem 3 data sets were used to evaluate the method. Genome-wide association analyses were conducted based on cross-sectional data, i.e., each of the three single-visit data sets separately, and also on the longitudinal data, i.e., using data from all three visits simultaneously. Results from the analysis of coronary artery calcification phenotype showed that the longitudinal association tests were much more powerful than those based on single-visit data only. Gene-age interactions were evaluated under the same framework for detecting genetic effects that are modulated by age.

The Framingham Heart Study is a well known longitudinal cohort study. In recent years, the community-based Framingham Heart Study has embarked on genome-wide association studies. In this paper, we present a Framingham Heart Study genome-wide analysis for fasting triglycerides trait in the Genetic Analysis Workshop16 Problem 2 using multivariate adaptive splines for the analysis of longitudinal data (MASAL). With MASAL, we are able to perform analysis of genome-wide data with longitudinal phenotypes and covariates, making it possible to identify genes, gene-gene, and gene-environment (including time) interactions associated with the trait of interest. We conducted a permutation test to assess the associations between MASAL selected markers and triglycerides trait and report significant gene-gene and gene-environment interaction effects on the trait of interest.

Identifying gene-environment (G × E) interactions has become a crucial issue in the past decades. Different methods have been proposed to test for G × E interactions in the framework of linkage or association testing. However, their respective performances have rarely been compared. Using Genetic Analysis Workshop 15 simulated data, we compared the power of four methods: one based on affected sib pairs that tests for linkage and interaction (the mean interaction test) and three methods that test for association and/or interaction: a case-control test, a case-only test, and a log-linear approach based on case-parent trios. Results show that for the particular model of interaction between tobacco use and Locus B simulated here, the mean interaction test has poor power to detect either the genetic effect or the interaction. The association studies, i.e., the log-linear-modeling approach and the case-control method, are more powerful to detect the genetic effect (power of 78% and 95%, respectively) and taking into account interaction moderately increases the power (increase of 9% and 3%, respectively). The case-only design exhibits a 95% power to detect G × E interaction but the type I error rate is increased.

It is believed that epistatic interactions among loci contribute to variations in quantitative traits. Several methods are available to detect epistasis using population-based data. However, methods to characterize epistasis for quantitative traits in family-based association analysis are not well developed, especially for studying thousands of gene expression traits. Here, we proposed a linear mixed-model approach to detect epistasis for quantitative traits using family data. The proposed method was implemented in a widely used software program SOLAR. We evaluated the power of the method by simulation studies and applied this method to the analysis of the Centre d'Etude du Polymorphisme Humain family gene expression data provided by Genetics Analysis Workshop 15 (GAW15).

Accounting for interactions with environmental factors in association studies may improve the power to detect genetic effects and may help identifying important environmental effect modifiers. The power of unphased genotype-versus haplotype-based methods in regions with high linkage disequilibrium (LD), as measured by D', for analyzing gene × environment (gene × sex) interactions was compared using the Genetic Analysis Workshop 15 (GAW15) simulated data on rheumatoid arthritis with prior knowledge of the answers. Stepwise and regular conditional logistic regression (CLR) was performed using a matched case-control sample for a HLA region interacting with sex. Haplotype-based analyses were performed using a haplotype-sharing-based Mantel statistic and a test for haplotype-trait association in a general linear model framework. A step-down minP algorithm was applied to derive adjusted p-values and to allow for power comparisons. These methods were also applied to the GAW15 real data set for PTPN22.

For markers in strong LD, stepwise CLR performed poorly because of the correlation/collinearity between the predictors in the model. The power was high for detecting genetic main effects using simple CLR models and haplotype-based methods and for detecting joint effects using CLR and Mantel statistics. Only the haplotype-trait association test had high power to detect the gene × sex interaction.

In the PTPN22 region with markers characterized by strong LD, all methods indicated a significant genotype × sex interaction in a sample of about 1000 subjects. The previously reported R620W single-nucleotide polymorphism was identified using logistic regression, but the haplotype-based methods did not provide any precise location information.

After more than 200 genome-wide association studies, there have been some successful identifications of a single novel locus. Thus, the identification of single-nucleotide polymorphisms (SNP) with interaction effects is of interest. Using the Genetic Analysis Workshop 16 data from the North American Rheumatoid Arthritis Consortium, we propose an approach to screen for SNP-SNP interaction using a two-stage method and an approach for detecting gene-gene interactions using principal components. We selected a set of 17 rheumatoid arthritis candidate genes to assess both approaches. Our approach using principal components holds promise in detecting gene-gene interactions. However, further study is needed to evaluate the power and the feasibility for a whole genome-wide association analysis using the principal components approach.

Complex diseases are multifactorial in nature and can involve multiple loci with gene × gene and gene × environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene × gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem.

Summary

Complex phenotypes are known to be associated with interactions among genetic factors. A growing body of evidence suggests that gene–gene interactions contribute to many common human diseases. Identifying potential interactions of multiple polymorphisms thus may be important to understand the biology and biochemical processes of the disease etiology. However, despite the great success of genome-wide association studies that mostly focus on single locus analysis, it is challenging to detect these interactions, especially when the marginal effects of the susceptible loci are weak and/or they involve several genetic factors. Here we describe a Bayesian classification tree model to detect such interactions in case-control association studies. We show that this method has the potential to uncover interactions involving polymorphisms showing weak to moderate marginal effects as well as multi-factorial interactions involving more than two loci.

Genome-wide association studies for complex traits are based on the common disease/common variant (CDCV) and common disease/rare variant (CDRV) assumptions. Under the CDCV hypothesis, classical genome-wide association studies using single-marker tests are powerful in detecting common susceptibility variants, but under the CDRV hypothesis they are not as powerful. Several methods have been recently proposed to detect association with multiple rare variants collectively in a functional unit such as a gene. In this paper, we compare the relative performance of several of these methods on the Genetic Analysis Workshop 17 data. We evaluate these methods using the unrelated individual and family data sets. Association was tested using 200 replicates for the quantitative trait Q1. Although in these data the power to detect association is often low, our results show that collapsing methods are promising tools. However, we faced the challenge of assessing the proper type I error to validate our power comparisons. We observed that the type I error rate was not well controlled; however, we did not find a general trend specific to each method. Each method can be conservative or nonconservative depending on the studied gene. Our results also suggest that collapsing and the single-locus association approaches may not be affected to the same extent by population stratification. This deserves further investigation.

The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level contributing to RA using real data sets (Problem 1) of North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Analysis Workshop 16 (GAW16). We applied our novel method that can detect the interaction by a definition of nonrandom association of alleles that occurs when the contribution to RA of a particular allele inherited in one gene depends on a particular allele inherited at other unlinked genes. Starting with 639 single-nucleotide polymorphisms (SNPs) from 26 candidate genes, we identified ten two-way interacting genes and one case of three-way interacting genes. SNP rs2476601 on PTPN22 interacts with rs2306772 on SLC22A4, which interacts with rs881372 on TRAF1 and rs2900180 on C5, respectively. SNP rs2900180 on C5 interacts with rs2242720 on RUNX1, which interacts with rs881375 on TRAF1. Furthermore, rs2476601 on PTPN22 also interacts with three SNPs (rs2905325, rs1476482, and rs2106549) in linkage disequilibrium (LD) on IL6. The other three SNPs (rs2961280, rs2961283, and rs2905308) in LD on IL6 interact with two SNPs (rs477515 and rs2516049) on HLA-DRB1. SNPs rs660895 and rs532098 on HLA-DRB1 interact with rs2834779 and four SNPs in LD on RUNX1. Three-way interacting genes of rs10229203 on IL6, rs4816502 on RUNX1, and rs10818500 on C5 were also detected.

Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets.

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.

Rare variants are believed to play an important role in disease etiology. Recent advances in high-throughput sequencing technology enable investigators to systematically characterize the genetic effects of both common and rare variants. We introduce several approaches that simultaneously test the effects of common and rare variants within a single-nucleotide polymorphism (SNP) set based on logistic regression models and logistic kernel machine models. Gene-environment interactions and SNP-SNP interactions are also considered in some of these models. We illustrate the performance of these methods using the unrelated individuals data from Genetic Analysis Workshop 17. Three true disease genes (FLT1, PIK3C3, and KDR) were consistently selected using the proposed methods. In addition, compared to logistic regression models, the logistic kernel machine models were more powerful, presumably because they reduced the effective number of parameters through regularization. Our results also suggest that a screening step is effective in decreasing the number of false-positive findings, which is often a big concern for association studies.

For complex diseases, the relationship between genotypes, environment factors and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene-gene and gene-environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy-based approach of information theory. In this paper we first develop entropy-based test statistics to identify 2-way and higher order gene-gene and gene-environment interactions. We then apply these methods to a bladder cancer data set and thereby test their power and identify strengths and weaknesses. For two-way interactions, we propose an information-gain approach based on mutual information. For three-ways and higher order interactions, an interaction-information-gain approach is used. In both case we develop one-dimensional test statistics to analyze sparse data. Compared to the naive chi-square test, the test statistics we develop have similar or higher power and is robust. Applying it to the bladder cancer data set allowed to investigate the complex interactions between DNA repair gene SNPs, smoking status, and bladder cancer susceptibility. Although not yet widely applied, entropy-based approaches appear as a useful tool for detecting gene-gene and gene-environment interactions. The test statistics we develop add to a growing body methodologies that will gradually shed light on the complex architecture of common diseases.

As genome-wide association studies grow in popularity for the identification of genetic factors for common and rare diseases, analytical methods to comb through large numbers of genetic variants efficiently to identify disease association are increasingly in demand. We have developed a pattern-based data-mining approach to discover unlinked multilocus genetic effects for complex disease and to detect genotype × phenotype/genotype × environment interactions. On a densely mapped chromosome 18 data set for rheumatoid arthritis that was made available by Genetic Analysis Workshop 15, this method detected two potential two-locus associations as well as a putative two-locus gene × gender interaction.

Each year, the Pharmacogenetics Research Network (PGRN) holds an analysis workshop for the members of the PGRN to share new methodologies, study design approaches and to discuss real data applications. This event is closed to members of the PGRN, but the methods presented are relevant to others conducting pharmacogenomics research. This special report describes many of the novel approaches discussed at the workshop and provides a resource for investigators in the field performing pharmacogenomics data analysis. While the focus is pharmacogenomics, the methods discussed are far ranging and have relevance to all types of genetic association studies: identifying noncoding variants and tag-SNPs, haplotype analysis, multivariate techniques, quantitative trait analysis, gene–gene and gene–environment interactions, and genome-wide association studies. The goal is to introduce readers to the topics discussed at the workshop and provide a direction for future development of analysis tools and methods for analysis of pharmacogenomic data.

Recent genome-wide association studies on several complex diseases have focused on individual single-nucleotide polymorphism (SNP) analysis; however, not many studies have reported interactions among genes perhaps because the gene-gene and gene-environment interaction analysis could be infeasible due to heavy computing requirements. In this study we propose a new strategy for exploring the interactions among haplotypes. The proposed method consists of two steps. Step 1 tests the single-SNP association of whole genome with multiple testing corrections and finds the haplotype blocks of the significant SNPs. Step 2 performs interaction analysis of haplotypes within blocks. Our proposed method is applied to the rheumatoid arthritis data for Genetic Analysis Workshop 16.

Genome-wide association studies are helping to dissect the etiology of complex diseases. Although case-control association tests are generally more powerful than family-based association tests, population stratification can lead to spurious disease-marker association or mask a true association. Several methods have been proposed to match cases and controls prior to genotyping, using family information or epidemiological data, or using genotype data for a modest number of genetic markers. Here, we describe a genetic similarity score matching (GSM) method for efficient matched analysis of cases and controls in a genome-wide or large-scale candidate gene association study. GSM is comprised of three steps: 1) calculating similarity scores for pairs of individuals using the genotype data; 2) matching sets of cases and controls based on the similarity scores so that matched cases and controls have similar genetic background; and 3) using conditional logistic regression to perform association tests. Through computer simulation we show that GSM correctly controls false positive rates and improves power to detect true disease predisposing variants. We compare GSM to genomic control using computer simulations, and find improved power using GSM. We suggest that initial matching of cases and controls prior to genotyping combined with careful re-matching after genotyping is a method of choice for genome-wide association studies.

Background

Identifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.

Results

RF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.

Conclusions

While RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

Background

There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power.

Methods

We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to the meta-analysis of marginal test of genetic association and the meta-analysis of standard 1 d.f. interaction tests across a broad range of genetic main effects and gene-environment interaction effects.

Results

We show that the joint meta-analysis is valid and can be more powerful than classical meta-analytic approaches, with a potential gain of power over 50% compared to the marginal test. The standard interaction test had less than 1% power in almost all the situations we considered. We also show that regardless of the test used, sample sizes far exceeding those of a typical individual GWAS will be needed to reliably detect genes with subtle gene-environment interaction patterns.

Conclusion

The joint meta-analysis is an attractive approach to discover markers which may have been missed by initial GWASs focusing on marginal marker-trait associations.

We present an evaluation of discovery power for two association tests that work well with common alleles but are applied to the Genetic Analysis Workshop 17 simulations with rare causative single-nucleotide polymorphisms (SNPs) (minor allele frequency [MAF] < 1%). The methods used were genome-wide single-SNP association tests based on a linear mixed-effects model for discovery and applied to the familial sample and sliding windows haplotype association tests for replication, implemented within causative genes in the unrelated individuals sample. Both methods are evaluated with respect to the simulated trait Q2. The linear mixed-effects model and haplotype association tests failed to detect the rare alleles of the simulated associations. In contrast, the linear mixed-effects model and haplotype association tests detected effects for the most important simulated SNPs with MAF > 1%. We conclude that these findings reflect inadequate statistical power (the result of small simulated samples) for the complex genetic model that underlies these data.

We propose a nonparametric association analysis combining both family and unrelated case-control genotype data. Under the assumption of Hardy-Weinberg equilibrium, we formed an affected group to compare with a group of unaffecteds.

Comparison with traditional case-control chi-square test and transmission-disequilibrium test shows that this new approach has noticeably improved power. All analysis was based on the simulated rheumatoid arthritis data provided by Genetic Analysis Workshop 15. In the situation of population stratification, we also suggest an approach to update the genotype data using principal components. However, the Genetic Analysis Workshop 15 simulation data does not simulate population stratification. All analysis was done without knowledge of the answers.

Human genome resequencing technologies are becoming ever more affordable and provide a valuable source of data about rare genetic variants in the human genome. Such rare variation may play an important role in explaining the missing heritability of complex human traits. We implement an existing method for analyzing rare variants by testing for association with the mutational load across genes. In this study, we make use of simulated data from the Genetic Analysis Workshop 17 to assess the power of this approach to detect association with simulated quantitative and dichotomous phenotypes and to evaluate the impact of missing genotypes on the power of the analysis. According to our results, the mutational load based rare variant analysis method is relatively robust to call-rate and is adequately powered for genome-wide association analysis.

Many common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene-gene and gene-environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships among these methods are also discussed. I conclude this review by highlighting some areas of future research.