Pharmacogenomics of multiple sclerosis: association of immune response genes polymorphism with copaxone treatment efficacy

Complex association analysis of copaxone (glatiramer acetate) immunotherapy efficacy with allelic polymorphism in the number of immune response genes, which encode interferone beta (IFNB1), transforming growth factor beta1 (TGFB1), interferone gamma (IFNG), tumor necrosis factor (TNF), interferon alpha/beta receptor 1 (IFNAR1), CC chemokine receptor 5 (CCR5), interleukin 7 receptor alpha subunit (IL7RA), cytotoxic T-lymphocyte antigen 4 (CTLA4) and HLA class II histocompatibility antigen beta chain (DRB1) was performed with APSampler algorithm for 285 multiple sclerosis patients of Russian ethnicity. The results show evidence for the contribution of polymorphic variants in CCRS, DRB1, IFNG, TGFB1, IFNAR1, IL7RA and, probably, TNF and CTLA4 genes to copaxone treatment response. Single alleles of CCR5 and DRB1 genes are reliably associated with treatment efficacy. Carriage of allelic variants of other above mentioned genes contribute with reliable effect to copaxone treatment response as part of bi- and three-allelic combinations only. Present investigation may support basis toward the future possibility of prognostic test realization, which can provide a personal choice of immunomodulatory treatment for a patient with multiple sclerosis.     

Pharmacogenomics of multiple sclerosis: Association of immune response gene polymorphisms with copaxone treatment efficacy

A complex association analysis of copaxone (glatiramer acetate) immunotherapy efficacy with allelic polymorphism of the number of immune response genes, including the genes for interferon β (IFNB1), transforming growth factor β1 (TGFB1), interferon γ (IFNG), tumor necrosis factor (TNF), interferon α/β receptor 1 (IFNAR1), CC chemokine receptor 5 (CCR5), interleukin 7 receptor subunit α (IL7RA), cytotoxic T-lymphocyte antigen 4 (CTLA4), and HLA class II histocompatibility antigen β chain (DRB1), was performed using the APSampler algorithm for 285 multiple sclerosis patients of Russian ethnicity. The results demonstrate that the polymorphic variants of CCR5, DRB1, IFNG, TGFB1, IFNAR1, IL7RA, and, possibly, TNF and CTLA4 contribute to the copaxone treatment response. Single alleles of CCR5 and DRB1 genes were reliably associated with treatment efficacy. Allelic variants of the other genes exerted a weaker, though still reliable, effect on the copaxone treatment response, but as part of bi- and triallelic combinations only. The study may provide a basis for a prognostic test allowing an individual choice of immune-modulating treatment for a patient with multiple sclerosis.     

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.     

CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0700-7) contains supplementary material, which is available to authorized users.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

From QTL to QTN: Candidate Gene Set Approach and a Case Study in Porcine IGF1-FoxO Pathway

Unraveling the genetic background of economic traits is a major goal in modern animal genetics and breeding. Both candidate gene analysis and QTL mapping have previously been used for identifying genes and chromosome regions related to studied traits. However, most of these studies may be limited in their ability to fully consider how multiple genetic factors may influence a particular phenotype of interest. If possible, taking advantage of the combined effect of multiple genetic factors is expected to be more powerful than analyzing single sites, as the joint action of multiple loci within a gene or across multiple genes acting in the same gene set will likely have a greater influence on phenotypic variation. Thus, we proposed a pipeline of gene set analysis that utilized information from multiple loci to improve statistical power. We assessed the performance of this approach by both simulated and a real IGF1-FoxO pathway data set. The results showed that our new method can identify the association between genetic variation and phenotypic variation with higher statistical power and unravel the mechanisms of complex traits in a point of gene set. Additionally, the proposed pipeline is flexible to be extended to model complex genetic structures that include the interactions between different gene sets and between gene sets and environments.     

Revealing the genetic basis of multiple sclerosis: are we there yet?

For more than 30 years the only genetic factor associated with susceptibility to multiple sclerosis (MS) was the human leukocyte antigen (HLA) region. Recent advancements in genotyping platforms and the development of more effective statistical methods resulted in the identification of 16 more genes by genome-wide association studies (GWAS) in the last three years alone. While the effect of each of these genes is modest compared to that of HLA, this list is expected to grow significantly in the near future, thus defining a complex landscape in which susceptibility may be determined by a combination of allelic variants in different pathways according to ethnic background, disease sub-type, and specific environmental triggers. A considerable overlap of susceptibility genes among multiple autoimmune diseases is becoming evident and integration of these genetic variants with our current knowledge of affected biological pathways will greatly improve our understanding of mechanisms of general autoimmunity and of tissue specificity.     

Multiple sclerosis as a polygenic disease: An update

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. The observed type of heredity associated with MS is characteristic of polygenic diseases, which arises from a joint contribution of a number of independently acting or interacting polymorphic genes. Recently to identify the genes responsible for genetic predisposition to MS two main approaches have been applied: (1) analysis of association of individual “candidate genes” with the disease and (2) analysis of the wide spectrum of chromosomal loci (whole genome screen) linkage with the disease in families with several MS patients. In the last two years, a new method, which borrowed the best approaches of the previous studies, genome-wide association screening (GWAS), which is based on the modern high-throughput DNA analysis, has been developed. This review describes replicated (validated) results for individual genes and DNA loci located on the majority of chromosomes obtained using these three strategies as well as data on association of MS with allelic combinations of various genes.     

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning

Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.     

Common single-nucleotide polymorphisms act in concert to affect plasma levels of high-density lipoprotein cholesterol

The identification of DNA sequence variants underlying human complex phenotypes remains a significant challenge for several reasons: individual variants can have small phenotypic effects or low population frequencies, and multiple allelic variants may act in concert to affect a trait. We evaluated the combined effect of allelic variants in seven genes involved in high-density lipoprotein (HDL) metabolism, using forward stepwise regression. Analysis of all known common single-nucleotide polymorphisms (SNPs) in the seven candidate genes revealed four variants that were associated with incremental changes in HDL cholesterol levels in three independent samples. Conversely, analysis of 660 polymorphisms in eight genes that do not appear to be involved in HDL metabolism did not identify any associations with plasma HDL-cholesterol levels. These data indicate that several common SNPs act in concert to influence plasma levels of HDL cholesterol.     

A Bayesian network classification methodology for gene expression data.

We present new techniques for the application of a Bayesian network learning framework to the problem of classifying gene expression data. The focus on classification permits us to develop techniques that address in several ways the complexities of learning Bayesian nets. Our classification model reduces the Bayesian network learning problem to the problem of learning multiple subnetworks, each consisting of a class label node and its set of parent genes. We argue that this classification model is more appropriate for the gene expression domain than are other structurally similar Bayesian network classification models, such as Naive Bayes and Tree Augmented Naive Bayes (TAN), because our model is consistent with prior domain experience suggesting that a relatively small number of genes, taken in different combinations, is required to predict most clinical classes of interest. Within this framework, we consider two different approaches to identifying parent sets which are supported by the gene expression observations and any other currently available evidence. One approach employs a simple greedy algorithm to search the universe of all genes; the second approach develops and applies a gene selection algorithm whose results are incorporated as a prior to enable an exhaustive search for parent sets over a restricted universe of genes. Two other significant contributions are the construction of classifiers from multiple, competing Bayesian network hypotheses and algorithmic methods for normalizing and binning gene expression data in the absence of prior expert knowledge. Our classifiers are developed under a cross validation regimen and then validated on corresponding out-of-sample test sets. The classifiers attain a classification rate in excess of 90% on out-of-sample test sets for two publicly available datasets. We present an extensive compilation of results reported in the literature for other classification methods run against these same two datasets. Our results are comparable to, or better than, any we have found reported for these two sets, when a train-test protocol as stringent as ours is followed.     

Accuracy, efficiency and robustness of four algorithms allowing full sibship reconstruction from DNA marker data

In the problem of reconstructing full sib pedigrees from DNA marker data, three existing algorithms and one new algorithm are compared in terms of accuracy, efficiency and robustness using real and simulated data sets. An algorithm based on the exclusion principle and another based on a maximization of the Simpson index were very accurate at reconstructing data sets comprising a few large families but had problems with data sets with limited family structure, while a Markov Chain Monte Carlo (MCMC) algorithm based on the maximization of a partition score had the opposite behaviour. An MCMC algorithm based on maximizing the full joint likelihood performed best in small data sets comprising several medium-sized families but did not work well under most other conditions. It appears that the likelihood surface may be rough and presents challenges for the MCMC algorithm to find the global maximum. This likelihood algorithm also exhibited problems in reconstructing large family groups, due possibly to limits in computational precision. The accuracy of each algorithm improved with an increasing amount of information in the data set, and was very high with eight loci with eight alleles each. All four algorithms were quite robust to deviation from an idealized uniform allelic distribution, to departures from idealized Mendelian inheritance in simulated data sets and to the presence of null alleles. In contrast, none of the algorithms were very robust to the probable presence of error/mutation in the data. Depending upon the type of mutation or errors and the algorithm used, between 70 and 98% of the affected individuals were classified improperly on average.     

High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.     

Identifying differentially expressed genes in cancer patients using a non-parameter Ising model

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.     

Complexity and power in case-control association studies

A general method is described for estimation of the power and sample size of studies relating a dichotomous phenotype to multiple interacting loci and environmental covariates. Either a simple case-control design or more complex stratified sampling may be used. The method can be used to design individual studies, to evaluate the power of alternative test statistics for complex traits, and to examine general questions of study design through explicit scenarios. The method is used here to study how the power of association tests is affected by problems of allelic heterogeneity and to investigate the potential role for collective testing of sets of related candidate genes in the presence of locus heterogeneity. The results indicate that allele-discovery efforts are crucial and that omnibus tests or collective testing of alleles can be substantially more powerful than separate testing of individual allelic variants. Joint testing of multiple candidate loci can also dramatically improve power, despite model misspecification and inclusion of irrelevant loci, but requires an a priori hypothesis defining the set of loci to investigate.     

Exome-Based Mapping and Variant Prioritization for Inherited Mendelian Disorders

Exome sequencing in families affected by rare genetic disorders has the potential to rapidly identify new disease genes (genes in which mutations cause disease), but the identification of a single causal mutation among thousands of variants remains a significant challenge. We developed a scoring algorithm to prioritize potential causal variants within a family according to segregation with the phenotype, population frequency, predicted effect, and gene expression in the tissue(s) of interest. To narrow the search space in families with multiple affected individuals, we also developed two complementary approaches to exome-based mapping of autosomal-dominant disorders. One approach identifies segments of maximum identity by descent among affected individuals; the other nominates regions on the basis of shared rare variants and the absence of homozygous differences between affected individuals. We showcase our methods by using exome sequence data from families affected by autosomal-dominant retinitis pigmentosa (adRP), a rare disorder characterized by night blindness and progressive vision loss. We performed exome capture and sequencing on 91 samples representing 24 families affected by probable adRP but lacking common disease-causing mutations. Eight of 24 families (33%) were revealed to harbor high-scoring, most likely pathogenic (by clinical assessment) mutations affecting known RP genes. Analysis of the remaining 17 families identified candidate variants in a number of interesting genes, some of which have withstood further segregation testing in extended pedigrees. To empower the search for Mendelian-disease genes in family-based sequencing studies, we implemented them in a cross-platform-compatible software package, MendelScan, which is freely available to the research community.     

Automated construction and testing of multi-locus gene-gene associations

It has been argued that the missing heritability in common diseases may be in part due to rare variants and gene-gene effects. Haplotype analyses provide more power for rare variants and joint analyses across genes can address multi-gene effects. Currently, methods are lacking to perform joint multi-locus association analyses across more than one gene/region. Here, we present a haplotype-mining gene-gene analysis method, which considers multi-locus data for two genes/regions simultaneously. This approach extends our single region haplotype-mining algorithm, hapConstructor, to two genes/regions. It allows construction of multi-locus SNP sets at both genes and tests joint gene-gene effects and interactions between single variants or haplotype combinations. A Monte Carlo framework is used to provide statistical significance assessment of the joint and interaction statistics, thus the method can also be used with related individuals. This tool provides a flexible data-mining approach to identifying gene-gene effects that otherwise is currently unavailable. AVAILABILITY: http://bioinformatics.med.utah.edu/Genie/hapConstructor.html.     

A Maximum-Likelihood Method to Correct for Allelic Dropout in Microsatellite Data with No Replicate Genotypes

Allelic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one or both allelic copies at a locus fail to be amplified by the polymerase chain reaction. Especially for samples with poor DNA quality, this problem causes a downward bias in estimates of observed heterozygosity and an upward bias in estimates of inbreeding, owing to mistaken classifications of heterozygotes as homozygotes when one of the two copies drops out. One general approach for avoiding allelic dropout involves repeated genotyping of homozygous loci to minimize the effects of experimental error. Existing computational alternatives often require replicate genotyping as well. These approaches, however, are costly and are suitable only when enough DNA is available for repeated genotyping. In this study, we propose a maximum-likelihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropout rates and allele frequencies when only one set of nonreplicated genotypes is available. Our method considers estimates of allelic dropout caused by both sample-specific factors and locus-specific factors, and it allows for deviation from Hardy–Weinberg equilibrium owing to inbreeding. Using the estimated parameters, we correct the bias in the estimation of observed heterozygosity through the use of multiple imputations of alleles in cases where dropout might have occurred. With simulated data, we show that our method can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (2) correctly estimate model parameters, including sample-specific dropout rates, locus-specific dropout rates, and the inbreeding coefficient; and (3) successfully correct the downward bias in estimating the observed heterozygosity. We find that our method is fairly robust to violations of model assumptions caused by population structure and by genotyping errors from sources other than allelic dropout. Because the data sets imputed under our model can be investigated in additional subsequent analyses, our method will be useful for preparing data for applications in diverse contexts in population genetics and molecular ecology.     

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.