Challenge In Genome-wide Association Studies Research Articles

Application of Deep Learning for the Detection of Genetic Variations: Its Implementation in Classifying Alzheimer's Disease

Deep learning emerges as a promising technique, utilizing nonlinear transformations for feature extraction from high-dimensional datasets. However, its application encounters challenges in genome-wide association studies (GWAS) dealing with high-dimensional genomic data. This study introduces an innovative three-step method termed SWAT-CNN for the identification of genetic variants. This approach employs deep learning to pinpoint phenotype-related single nucleotide polymorphisms (SNPs), facilitating the development of precise disease classification models. In the first step, the entire genome undergoes division into non overlapping fragments of an optimal size. Subsequently, convolutional neural network (CNN) analysis is conducted on each fragment to identify phenotype-associated segments. The second step, employs a Sliding Window Association Test (SWAT), where CNN is utilized on the selected fragments to compute phenotype influence scores (PIS) and detect phenotype-associated SNPs based on these scores. The third step involves running CNN on all identified SNPs to construct a comprehensive classification model. Validation of the proposed approach utilized GWAS data from the Alzheimer’s disease Neuroimaging Initiative (ADNI), encompassing 981 subjects, including cognitively normal older adults (CN) and individuals with Alzheimer's disease (AD). Notably, the method successfully identified the widely recognized APOE region as the most significant genetic locus for AD. The resulting classification model exhibited an area under the curve (AUC) of 0.82, demonstrating compatibility with traditional machine learning approaches such as random forest and XGBoost. SWAT-CNN, as a groundbreaking deep learning-based genome-wide methodology, not only identified AD-associated SNPs but also presented a robust classification model for Alzheimer's disease, suggesting potential applications across diverse biomedical domains.

Association rule mining for genome-wide association studies through Gibbs sampling

AbstractFinding associations between genetic markers and a phenotypic trait such as coronary artery disease (CAD) is of primary interest in genome-wide association studies (GWAS). A major challenge in GWAS is the involved genomic data often contain large number of genetic markers and the underlying genotype-phenotype relationship is mostly complex. Current statistical and machine learning methods lack the power to tackle this challenge with effectiveness and efficiency. In this paper, we develop a stochastic search method to mine the genotype-phenotype associations from GWAS data. The new method generalizes the well-established association rule mining (ARM) framework for searching for the most important genotype-phenotype association rules, where we develop a multinomial Gibbs sampling algorithm and use it together with the Apriori algorithm to overcome the overwhelming computing complexity in ARM in GWAS. Three simulation studies based on synthetic data are used to assess the performance of our developed method, delivering the anticipated results. Finally, we illustrate the use of the developed method through a case study of CAD GWAS.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Science, Medicine, and the Anesthesiologist

Science, Medicine, and the Anesthesiologist

KMeans-NM-SalpEpi: Genetic Interactions Detection through K-Means Clustering with Nelder-Mead and Salp Optimization Techniques in Genome-Wide Association Studies

Complex diseases identification through Gene-Gene Interactions (GGIs) plays a significant challenge in Genome-Wide Association Studies (GWAS). A typical indicator of genetic variations in many human diseases is Single Nucleotide Polymorphisms (SNPs). SNPs are the most prevalent sort of genetic variation seen in human beings. The interactions between various SNPs are called Epistasis or genetic interactions. This research paper proposes a two-stage epistasis detection approach based on K-Means clustering and optimization techniques to detect epistasis effects responsible for complex human diseases. In the screening stage, K-Means clustering is adapted to partition the genotype dataset into various clusters. Traditional K-Means clustering algorithms have the flaw of arbitrary selection of the initial k centroid, which leads to inconsistent solutions and traps in the local optimum. We present a hybridized technique based on the K-Means algorithm and Nelder-Mead (NM) optimization (KMeans-NM) to avoid local optima, and all the genotype data falls into a unique collection of clusters for different runs. In the search stage, Salp Optimization with single objective functions (Salp-SO) and Salp Optimization with multi-objective functions (Salp-MO) are employed over the clusters obtained from the screening stage to find disease correlated SNP combinations. The performance of the various proposed algorithms is tested over the simulated datasets. Experimental findings indicated that the KMeans-NM-SalpEpi-SO and KMeans-NM-SalpEpi-MO method is superior to other techniques.

Fast and efficient correction for population stratification in multi-locus genome-wide association studies

Reducing false discoveries caused by population stratification (PS) has always been a challenge in genome-wide association studies (GWAS). The current literature established several single marker approaches including genomic control (GC), EIGENSTRAT and generalized linear mixed model association test (GMMAT) and multi-marker methods such as LASSO mixed model (LASSOMM). However, the single-marker methods require prespecifying an arbitrary p value threshold in the selection process, likely resulting in suboptimal precision or recall. On the other hand, it appears that LASSOMM is extremely computationally intensive and may not suitable for large-scale GWAS. In this paper, we proposed a simple multi-marker approach (PCA-LASSO) combining principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). We utilize PCA to correct for the confounding effects of PS and LASSO with built-in cross-validation for a data-driven selection. Compared to the current single-marker approaches, the proposed PCA-LASSO provides optimal balance between precision and recall, and consequently superior F1 scores. Similarly, compared to LASSOMM, PCA-LASSO markedly increases the precision while minimizing the loss of recall, and therefore improves the overall F1 score in presence of PS. More importantly, PCA-LASSO drastically reduces the computational time by > 1000 times when compared to LASSOMM. We applied PCA-LASSO to a real dataset of Alzheimer's disease and successfully identified SNP rs429358 (Gene APOE4) which has been widely reported to be associated with the onset and elevated risk of Alzheimer's disease. In conclusion, PCA-LASSO is a simple, fast, but accurate approach for GWAS in presence of latent PS.

Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.

MotivationIdentification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals.ResultsWe develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases.Availability and implementationThe R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS.Supplementary information Supplementary data are available at Bioinformatics online.

Two-Stage Model with Rough Cluster and Salp Optimization Technique for Epistasis Detection

The discovery of gene-gene interactions to identify complex diseases is one of the primary challenges in genome-wide association studies (GWAS). Genetic interactions (Epistasis) are typically seen as interactions between various single nucleotide polymorphisms (SNPs). Genetic interactions discovery can assist the researchers in identifying gene pathways, recognizing gene activity, and discovering potential drug targets. Rough Cluster based Salp Optimization for Epistasis detection (RCSalp-Epi) is a two-stage epistasis model that has been evaluated on a variety of simulated disease models. In the screening stage, the rough clustering algorithm is employed to partition the genotype data into different clusters. The selection stage presents Salp optimization with a single objective function (SalpEpi-SO) and multiple objective functions (SalpEpi-MO) over the clusters to find disease-related SNP combinations. RCSalp-performance Epi's is evaluated in comparison with SalpEpi-SO and SalpEpi-MO. The outcome of the experimental analysis revealed that RCSalp-Epi-MO is superior to SalpEpi-SO and SalpEpi-MO in terms of power and execution time.

Exploiting Homoplasy in Genome-Wide Association Studies to Enhance Identification of Antibiotic-Resistance Mutations in Bacterial Genomes.

Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional GWAS methods, such as those based on allele counting or linear regression, are not designed to take homoplasy into account. In this article, we present a new method, called ECAT (for Evolutionary Cluster-based Association Test), that extends traditional regression-based GWAS methods with the ability to take advantage of homoplasy. This is achieved through a preprocessing step which identifies hypervariable regions in the genome exhibiting statistically significant clusters of distinct evolutionary changes, to which association testing by a linear mixed model (LMM) is applied using GEMMA (a well-established LMM-based GWAS tool). Thus, the approach can be viewed as extending GEMMA from the usual site- or gene-level analysis to focusing on clustered regions of mutations. This approach was evaluated on a large collection of more than 600 clinical isolates of multidrug-resistant (MDR) Mycobacterium tuberculosis from Lima, Peru. We show that ECAT does a better job of detecting known resistance mutations for several antitubercular drugs (including less prevalent mutations with weaker associations), compared with (site- or gene-based) GEMMA, as representative of existing GWAS methods. The power of the multiphase approach in ECAT comes from focusing association testing on the hypervariable regions of the genome, which reduces complexity in the model and increases statistical power.

17 - FUMA: FUNCTIONAL MAPPING AND ANNOTATION OF GENETIC ASSOCIATIONS

Background A main challenge in genome-wide association studies (GWAS) is to prioritize genetic variants and identify potential causal mechanisms of human diseases. Although a variety of bioinformatics tools are now available for downstream analyses of GWAS results, a standard, integrative approach is lacking. We developed FUMA: a web-based platform to facilitate functional annotation of GWAS results, prioritization of potential causal genetic variants and genes, and interactive visualization by incorporating 14 biological data repositories and tools. FUMA is available as an online tool at http://fuma.ctglab.nl. Methods FUMA contains two core functions to annotate input summary statistics to prioritize potential causal genetic variants and genes; SNP2GENE and GENE2FUNC. In SNP2GENE, SNPs are annotated with their biological functionality and mapped to genes based on positional and functional information of SNPs. In this process, significantly associated SNPs from GWAS are characterized as genomic risk loci by incorporating the linkage disequilibrium structure. Functionally annotated SNPs are mapped to genes based on functional consequences on genes (positional mapping), expression quantitative trait loci (eQTLs) and chromatin interactions of phenotype relevant tissue types (eQTL and chromatin interaction mappings). By combining these three mapping strategies, FUMA enables to prioritize genes that are highly likely involved in the trait of interest. To obtain insight into putative causal mechanisms, the GENE2FUNC process annotates the prioritized genes in biological context, such as tissue specific gene expression pattern, enrichment of gene sets and direct links to well defined external biological databases such as disease associated genes and drug targets. Results We have applied FUMA to the most recent Body Mass Index (BMI)[1]. We successfully prioritized previously reported genes from the original GWAS study, and also novel candidates by combining positional mapping of deleterious coding SNPs and eQTL mapping. For example, IRX3 was prioritized by eQTLs from FTO locus which is the most significantly associated locus with BMI. Although, FTO is the only gene reported in the original study since the genomic risk locus is located within the FTO gene and not overlapped with any other genes, IRX3 has been recently validated as whose expression is affected by variants in the FTO locus[2]. Thus, by incorporating multiple biological information, we could prioritise genes that are located outside of genomic risk loci. Chromatin interaction mapping identified additional novel candidates, including genes located outside of the risk loci, which showed share biological functions with the previously reported genes. Discussion In summary, FUMA provides an easy-to-use tool to interpret and functionally annotate results from genetic association studies. It allows to quickly gain insight into the directional biological implications of significant genetic associations. FUMA combines information of state-of-the-art biological data sources in a single platform to facilitate the generation of hypotheses for functional follow-up analysis aimed at proving causal relations between genetic variants and diseases.

Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies.

In recent years, the detection of epistatic interactions of multiple genetic variants on the causes of complex diseases brings a significant challenge in genome-wide association studies (GWAS). However, most of the existing methods still suffer from algorithmic limitations such as single-objective optimization, intensive computational requirement, and premature convergence. In this paper, we propose and formulate an epistatic interaction multi-objective artificial bee colony algorithm based on decomposition (EIMOABC/D) to address those problems for genetic interaction detection in genome-wide association studies. First, to direct the genetic interaction detection, two objective functions are formulated to characterize various epistatic models; rank probability model is proposed to sort each population into different nondomination levels based on the fast nondominated sorting approach. After that, the mutual information based local search algorithm is proposed to guide the population search for disease model evaluations in an unbiased manner. To validate the effectiveness of EIMOABC/D, we compare EIMOABC/D against seven state-of-the-art methods on 77 epistatic models including eight small-scale epistatic models with marginal effects, eight large-scale epistatic models with marginal effects, 60 large-scale epistatic models without any marginal effect, and one case study. The experimental results indicate that our proposed algorithm EIMOABC/D outperforms seven state-of-the-art methods on those epistatic models. Furthermore, time complexity analysis and parameter analysis are conducted to demonstrate various properties of our proposed algorithm.

Functional mapping and annotation of genetic associations with FUMA

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown. We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study. We identified 2,350 trans-eQTLs (at p < 10-7); more than 80% of them were found to have cis-associated eGenes. Mediation testing suggested that for 35% of trans-eQTL-trans-eGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the trans-eGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

Stories and Challenges of Genome Wide Association Studies in Livestock - A Review.

Undoubtedly livestock is one of the major contributors to the economy of any country. The economic value of livestock includes meat, dairy products, fiber, fertilizer etc. Understanding and identifying the associations of quantitative trait loci (QTL) with the economically important traits is believed to substantially benefit the livestock industry. The past two decades have seen a flurry of interest in mapping the QTL associated with traits of economic importance on the genome. With the availability of single nucleotide polymorphism chip of various densities it is possible to identify regions, QTL and genes on the genome that explain the association and its effect on the phenotype under consideration. Remarkable advancement has been seen in genome wide association studies (GWAS) since its inception till the present day. In this review we describe the progress and challenges of GWAS in various livestock species.

Molecular polymorphism of β-fructosidase SUC genes in the Saccharomyces yeasts

Genome-wide association study (GWAS), which is a powerful tool for investigating the genetic architecture of polygenic disease in humans, is generally applied for identification of genetic factors of disease susceptibility, clinical phenotypes and treatment response. The differences in allele frequencies of genome-wide distributedsingle nucleotide polymorphisms (SNPs) are analyzed with microarray technique or other technologies, that allow simultaneous genotyping from tens'of thousands to several millions SNPs per sample. The power toexplore highly-reliable differences between compared groups of patients aind controls allowed GWAS to become a common approach for identification of genetic susceptibility for complex diseases with polygenic nature. The main achievements and challenges of GWAS in multiple sclerosis (MS), which is a prototype example of complex disease, are reviewed here for identification of MS causative genes, which expand the knowledge on molecular mechanisms of MS pathogenesis and genetic risk factors.

Urinary bladder cancer risk variants: recent findings and new challenges of GWAS and confirmatory studies

polymorphisms (SNPs) associated with a moderate UBC risk at sixteen chromosomal locations, besides GSTM1 (Table 1; reviews, see Golka et al. 2011; Dudek et al. 2013). While most of them could be confirmed in independent follow-up case–control series, novel UBC risk SNPs are still requiring validation. It can be assumed that the most relevant loci are discovered now as the size of the GWAS is considerable large with more than 6,900 cases and 45,000 controls (Kiemeney et al. 2010; rothman et al. 2010; rafnar et al. 2011; Figueroa et al. 2014a, b), and the density of the SNP chips combined with highly accurate imputation algorithms results in a sufficient representation of the genetic variation. Subsequent fine-mapping studies aim to identify the functional relevant genetic variants that are in linkage disequilibrium with the initial findings that are often located in non-coding regions more than 20 kb and several haploblocks apart of the next genes (review: Dudek et al. 2013; see also Selinski 2013). Tang et al. (2012), and Fu et al. (2012) used this approach to localize the genetic variants explaining the bladder cancer risk associated with SNPs in the UGTA1 and PSCA gene, respectively (Wu et al. 2009; rothman et al. 2010). however, further finemapping and functional studies are required to elucidate the functional relationship between polymorphism and disease for most of the SNPs. Currently, subgroup analyses, e.g., restricted to smokers or high-grade tumors, are becoming more and more relevant. First results of a GWAS on SNPs that interact with smoking habits suggest at least two novel loci that modify UBC risk in non-smokers or in smokers (Figueroa et al. 2014a). Besides, discovery of novel loci in subgroups stratification and interaction analyses of known risk factors indicate how and to which extend genetic risk factors modify bladder cancer risk in presence or absence of exogenous risk factors, e.g., smoking (Garcia-Closas et al. 2013) and Urinary bladder cancer (UBC) is the fourth most common cancer in men and the fourteenth most common cancer in women in western europe (Ferlay et al. 2013). Approximately 50 % of the UBC cases are caused by cigarette smoking (Schwender et al. 2012; Burger et al. 2013). Further, 7.1–20 % of the bladder cancer cases can be attributed to occupational exposure to aromatic amines and polycyclic aromatic hydrocarbons (Golka et al. 2004, 2009, 2012; Delclos and Lerner 2008; rushton et al. 2012; Burger et al. 2013; Carreon et al. 2014). Genetic risk factors explain about 31 % of the bladder cancer cases (Lichtenstein et al. 2000). As segregation analysis in 1,193 families failed to identify a major gene (Aben et al. 2006), a polygenetic basis of bladder cancer can be assumed. This is consistent with recent findings of a number of polymorphisms each conferring a small to moderate UBC risk (Golka et al. 2011; Selinski 2012). Polymorphisms of phase II metabolizing enzymes are well known to increase bladder cancer risk and to modulate the effect of bladder carcinogen exposure via smoking and occupation (Garcia-Closas et al. 2005; Golka et al. 2009; Moore et al. 2011). Most relevant is the deletion variant of glutathione S-transferase M1 (GSTM1) (Arand et al. 1996; Golka et al. 1997, 2009; Garcia-Closas et al. 2005; Ovsiannikov et al. 2012) and polymorphisms in the N-acetyltransferase 2 (NAT2) gene resulting in a reduced acetylation capacity (Golka et al. 1996, 2002; Moore et al. 2011; Selinski et al. 2011, 2013). Meanwhile, genome-wide association studies (GWAS) have identified a number of novel single nucleotide

Genome-wide Association Studies for Osteoporosis: A 2013 Update.

In the past few years, the bone field has witnessed great advances in genome-wide association studies (GWASs) of osteoporosis, with a number of promising genes identified. In particular, meta-analysis of GWASs, aimed at increasing the power of studies by combining the results from different study populations, have led to the identification of novel associations that would not otherwise have been identified in individual GWASs. Recently, the first whole genome sequencing study for osteoporosis and fractures was published, reporting a novel rare nonsense mutation. This review summarizes the important and representative findings published by December 2013. Comments are made on the notable findings and representative studies for their potential influence and implications on our present understanding of the genetics of osteoporosis. Potential limitations of GWASs and their meta-analyses are evaluated, with an emphasis on understanding the reasons for inconsistent results between different studies and clarification of misinterpretation of GWAS meta-analysis results. Implications and challenges of GWAS are also discussed, including the need for multi- and inter-disciplinary studies.

Полногеномный поиск ассоциаций как метод анализа генетической архитектуры полигенных заболеваний (на примере рассеянного склероза)

Genome-wide association study (GWAS), which is a powerful tool for investigating the genetic architecture of polygenic disease in humans, is generally applied for identification of genetic factors of disease susceptibility, clinical phenotypes and treatment response. The differences in allele frequencies of genome-wide distributedsingle nucleotide polymorphisms (SNPs) are analyzed with microarray technique or other technologies, that allow simultaneous genotyping from tens'of thousands to several millions SNPs per sample. The power toexplore highly-reliable differences between compared groups of patients aind controls allowed GWAS to become a common approach for identification of genetic susceptibility for complex diseases with polygenic nature. The main achievements and challenges of GWAS in multiple sclerosis (MS), which is a prototype example of complex disease, are reviewed here for identification of MS causative genes, which expand the knowledge on molecular mechanisms of MS pathogenesis and genetic risk factors.

Large-scale multiple testing in genome-wide association studies via region-specific hidden Markov models.

BackgroundIdentifying genetic variants associated with complex human diseases is a great challenge in genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) arising from genetic background are often dependent. The existing methods, i.e., local index of significance (LIS) and pooled local index of significance (PLIS), were both proposed for modeling SNP dependence and assumed that the whole chromosome follows a hidden Markov model (HMM). However, the fact that SNP data are often collected from separate heterogeneous regions of a single chromosome encourages different chromosomal regions to follow different HMMs. In this research, we developed a data-driven penalized criterion combined with a dynamic programming algorithm to find change points that divide the whole chromosome into more homogeneous regions. Furthermore, we extended PLIS to analyze the dependent tests obtained from multiple chromosomes with different regions for GWAS.ResultsThe simulation results show that our new criterion can improve the performance of the model selection procedure and that our region-specific PLIS (RSPLIS) method is better than PLIS at detecting disease-associated SNPs when there are multiple change points along a chromosome. Our method has been used to analyze the Daly study, and compared with PLIS, RSPLIS yielded results that more accurately detected disease-associated SNPs.ConclusionsThe genomic rankings based on our method differ from the rankings based on PLIS. Specifically, for the detection of genetic variants with weak effect sizes, the RSPLIS method was able to rank them more efficiently and with greater power.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

BackgroundRecently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls.MethodsIn this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point.ResultsResults from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations.ConclusionsIn this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests.