U‐Statistics‐based Tests for Multiple Genes in Genetic Association Studies

As our understanding of biological pathways and the genes that regulate these pathways increases, consideration of these biological pathways has become an increasingly important part of genetic and molecular epidemiology. Pathway‐based genetic association studies often involve genotyping of variants in genes acting in certain biological pathways. Such pathway‐based genetic association studies can potentially capture the highly heterogeneous nature of many complex traits, with multiple causative loci and multiple alleles at some of the causative loci. In this paper, we develop two nonparametric test statistics that consider simultaneously the effects of multiple markers. Our approach, which is based on data‐adaptive U‐statistics, can handle both qualitative data such as case‐control data and quantitative continuous phenotype data. Simulations demonstrate that our proposed methods are more powerful than standard methods, especially when there are multiple risk loci each with small genetic effects. When the number of disease‐predisposing genes is small, the data‐adaptive weighting of the U‐statistics over all the markers produces similar power to commonly used single marker tests. We further illustrate the potential merits of our proposed tests in the analysis of a data set from a pathway‐based candidate gene association study of breast cancer and hormone metabolism pathways. Finally, potential applications of the proposed tests to genome‐wide association studies are also discussed.     

A Three‐Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects

Congenital heart defects (CHDs) develop through a complex interplay between genetic variants, epigenetic modifications, and maternal environmental exposures. Genetic studies of CHDs have commonly tested single genetic variants for association with CHDs. Less attention has been given to complex gene‐by‐gene and gene‐by‐environment interactions. In this study, we applied a recently developed likelihood‐ratio Mann‐Whitney (LRMW) method to detect joint actions among maternal variants, fetal variants, and maternal environmental exposures, allowing for high‐order statistical interactions. All subjects are participants from the National Birth Defect Prevention Study, including 623 mother‐offspring pairs with CHD‐affected pregnancies and 875 mother‐offspring pairs with unaffected pregnancies. Each individual has 872 single nucleotide polymorphisms encoding for critical enzymes in the homocysteine, folate, and trans‐sulfuration pathways. By using the LRMW method, three variants (fetal rs625879, maternal rs2169650, and maternal rs8177441) were identified with a joint association to CHD risk (nominal P‐value = 1.13e‐07). These three variants are located within genes BHMT2, GSTP1, and GPX3, respectively. Further examination indicated that maternal SNP rs2169650 may interact with both fetal SNP rs625879 and maternal SNP rs8177441. Our findings suggest that the risk of CHD may be influenced by both the intragenerational interaction within the maternal genome and the intergenerational interaction between maternal and fetal genomes.     

The Role of Epistasis in the Etiology of Polycystic Ovary Syndrome among Indian Women: SNP‐SNP and SNP‐Environment Interactions

Polycystic Ovary Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age. It is a heterogeneous androgen excess disorder determined by the interaction of multiple genetic and environmental factors. Our earlier analysis on a panel of six candidate genes (Androgen receptor CAG repeats, Follistatin, Luteinizing hormone β subunit, Calpain10, Insulin receptor substrate‐1 and PPARγ) based on 250 PCOS cases and 299 controls revealed significant association patterns with PCOS among South‐Indian women. We report here for the first time, the SNP‐SNP and SNP‐environment interactions of these genes in the same cohort. Both multivariate logistic regression as well as epistasis analysis (using Multifactor dimensionality reduction software) yielded significant results (P < 0.05). All CAPN10 SNPs show association (either risk‐conferring or protective) in the obese group, highlighting the importance of this gene in the PCOS pathophysiology. LHP7(LHβ) and UCSNP44(CAPN10) emerged to be the prominent SNPs in the SNP‐SNP interaction analysis. The best SNP‐SNP interaction model was obtained between CAPN10 UCSNP‐44 and PPARγ His447His, implying a significant metabolic component in the PCOS pathology. Replicating our findings in BMI‐specific cohorts in different ethnic populations would be warranted in future to identify the physiological networks in PCOS.     

Sequential Sentinel SNP Regional Association Plots (SSS‐RAP): An Approach for Testing Independence of SNP Association Signals Using Meta‐Analysis Data

Genome‐Wide Association Studies (GWAS) frequently incorporate meta‐analysis within their framework. However, conditional analysis of individual‐level data, which is an established approach for fine mapping of causal sites, is often precluded where only group‐level summary data are available for analysis. Here, we present a numerical and graphical approach, “sequential sentinel SNP regional association plot” (SSS‐RAP), which estimates regression coefficients (beta) with their standard errors using the meta‐analysis summary results directly. Under an additive model, typical for genes with small effect, the effect for a sentinel SNP can be transformed to the predicted effect for a possibly dependent SNP through a 2×2 2‐SNP haplotypes table. The approach assumes Hardy–Weinberg equilibrium for test SNPs. SSS‐RAP is available as a Web‐tool ( http://apps.biocompute.org.uk/sssrap/sssrap.cgi ). To develop and illustrate SSS‐RAP we analyzed lipid and ECG traits data from the British Women's Heart and Health Study (BWHHS), evaluated a meta‐analysis for ECG trait and presented several simulations. We compared results with existing approaches such as model selection methods and conditional analysis. Generally findings were consistent. SSS‐RAP represents a tool for testing independence of SNP association signals using meta‐analysis data, and is also a convenient approach based on biological principles for fine mapping in group level summary data.     

Genetic Factors in Nonsmokers with Age‐Related Macular Degeneration Revealed Through Genome‐Wide Gene‐Environment Interaction Analysis

Relatively little is known about the interaction between genes and environment in the complex etiology of age‐related macular degeneration (AMD). This study aimed to identify novel factors associated with AMD by analyzing gene‐smoking interactions in a genome‐wide association study of 1207 AMD cases and 686 controls of Caucasian background with genotype data on 668,238 single nucleotide polymorphisms (SNPs) after quality control. Participants’ history of smoking at least 100 cigarettes lifetime was determined by a self‐administered questionnaire. SNP associations modeled the effect of the minor allele additively on AMD using logistic regression, with adjustment for age, sex, and ever/never smoking. Joint effects of SNPs and smoking were examined comparing a null model containing only age, sex, and smoking against an extended model including genotypic and interaction terms. Genome‐wide significant main effects were detected at three known AMD loci: CFH (P = 7.51×10^?30), ARMS2 (P = 1.94×10^?23), and RDBP/CFB/C2 (P = 4.37×10^?10), while joint effects analysis revealed three genomic regions with P < 10^?5. Analyses stratified by smoking found genetic associations largely restricted to nonsmokers, with one notable exception: the chromosome 18q22.1 intergenic SNP rs17073641 (between SERPINB8 and CDH7), more strongly associated in nonsmokers (OR = 0.57, P = 2.73 × 10^?5), with an inverse association among smokers (OR = 1.42, P = 0.00228), suggesting that smoking modifies the effect of some genetic polymorphisms on AMD risk.     

An efficient method for the isolation of interaction‐null/impaired mutants using the yeast two‐hybrid technique

Protein–protein interactions are one of the most basic and critical processes underlying biological functions. Thus, identification of the interacting proteins of a protein of interest and further elucidation of the roles of the interactions is critical for understanding the related biological processes. The yeast two‐hybrid (Y2H) method is a popular approach for identifying protein–protein interactions. Once interacting proteins are identified, a comparison of the phenotypes of mutants lacking the specific protein–protein interaction with those of the wild‐type strain is a powerful tool for uncovering the former interactions’ biological significance. However, isolation of such interaction‐defective mutants is often laborious. Here, I describe a novel and efficient approach for isolating such mutants that uses the Y2H technique with a modified Y2H vector, and provide an example of how this approach can be used to screen interaction‐null/impaired mutants. Because the strategy is simple and the modification of a pre‐existing Y2H vector is sufficient for the screening purpose, the same strategy can be applied to any existing two‐hybrid system.     

Genome scan in familial late‐onset Alzheimer's disease: A locus on chromosome 6 contributes to age‐at‐onset

Alzheimer's disease (AD) is a common, genetically complex, fatal neurodegenerative disorder of late life. Although several genes are known to play a role in early‐onset AD, identification of the genetic basis of late onset AD (LOAD) has been challenging, with only the APOE gene known to have a high contribution to both AD risk and age‐at‐onset. Here, we present the first genome‐scan analysis of the complete, well‐characterized University of Washington LOAD sample of 119 pedigrees, using age‐at‐onset as the trait of interest. The analysis approach used allows for a multilocus trait model while at the same time accommodating age censoring, effects of APOE as a known genetic covariate, and full pedigree and marker information. The results provide strong evidence for linkage of loci contributing to age‐at‐onset to genomic regions on chromosome 6q16.3, and to 19q13.42 in the region of the APOE locus. There was evidence for interaction between APOE and the locus on chromosome 6q and suggestive evidence for linkage to chromosomes 11p13, 15q12‐14, and 19p13.12. These results provide the first independent confirmation of an AD age‐at‐onset locus on chromosome 6 and suggest that further efforts towards identifying the underlying causal locus or loci are warranted. © 2013 Wiley Periodicals, Inc.     

GWAS and network analysis of co‐occurring nicotine and alcohol dependence identifies significantly associated alleles and network

Alcohol dependence (AD) and nicotine dependence (ND) co‐occur frequently (AD+ND). We integrated SNP‐based, gene‐based, and protein–protein interaction network analyses to identify shared risk genes or gene subnetworks for AD+ND in African Americans (AAs, N = 2,094) and European Americans (EAs, N = 1,207). The DSM‐IV criterion counts for AD and ND were modeled as two dependent variables in a multivariate linear mixed model, and analyzed separately for the two populations. The most significant SNP was rs6579845 in EAs (p < 1.29 × 10^?8) in GM2A, which encodes GM2 ganglioside activator, and is a cis‐expression quantitative locus that affects GM2A expression in blood and brain tissues. However, this SNP was not replicated in our another small sample (N = 678). We identified a subnetwork of 24 genes that contributed to the AD+ND criterion counts. In the gene‐set analysis for the subnetwork in an independent sample, the Study of Addiction: Genetics and Environment project (predominately EAs), these 24 genes as a set differed in AD+ND versus control subjects in EAs (p = .041). Functional enrichment analysis for this subnetwork revealed that the gene enrichment involved primarily nerve growth factor pathways, and cocaine and amphetamine addiction. In conclusion, we identified a genome‐wide significant variant at GM2A and a gene subnetwork underlying the genetic trait of shared AD+ND. These results increase our understanding of the shared (pleiotropic) genetic risk that underlies AD+ND.     

A functional SNP in the 3′‐UTR of TAP2 gene interacts with microRNA hsa‐miR‐1270 to suppress the gene expression

The transporter associated with antigen processing 2 (TAP2) is involved in the development of multidrug resistance and the etiology of immunological diseases. In this study, we investigated whether the expression of TAP2 can be perturbed by single nucleotide polymorphisms (SNPs) located in 3′‐untranslated region (3′‐UTR) of the gene via interactions with microRNAs. Using a series of in silico assays, we selected the candidate microRNAs (miRNAs) with the potential to interact with functional SNPs of TAP2. The SNP rs241456—located in the 3′‐UTR of TAP2—resides in a potential binding site for hsa‐miR‐1270 and hsa‐miR‐620. HEK 293 cells, from a human kidney cell line, were used to characterize the extent of binding of miRNAs to each polymorphic allele of the SNP by a luciferase reporter gene assay. RNA electrophoretic mobility shift assays were used to evaluate the interaction between the miRNAs and each allele sequence of the SNP. We found that hsa‐miR‐1270 inhibited luciferase activity by binding to the T allele of the SNP in an allele‐specific manner. A negative correlation was also found between the expression of hsa‐miR‐1270 and the T allele of the SNP in kidney tissues. Our findings support the hypothesis that hsa‐miR‐1270 suppresses the production of TAP2 by binding to this SNP in the 3′‐UTR of this gene. Environ. Mol. Mutagen. 59:134–143, 2018. © 2017 Wiley Periodicals, Inc.     

Genetic meta‐analysis of obsessive–compulsive disorder and self‐report compulsive symptoms

We investigated whether obsessive–compulsive (OC) symptoms from a population‐based sample could be analyzed to detect genetic variants influencing obsessive–compulsive disorder (OCD). We performed a genome‐wide association studies (GWAS) on the obsession (rumination and impulsions) and compulsion (checking, washing, and ordering/precision) subscales of an abbreviated version of the Padua Inventory (N = 8,267 with genome‐wide genotyping and phenotyping). The compulsion subscale showed a substantial and significant positive genetic correlation with an OCD case–control GWAS (r _G = 0.61, p = .017) previously published by the Psychiatric Genomics Consortium (PGC‐OCD). The obsession subscale and the total Padua score showed no significant genetic correlations (r _G = ?0.02 and r _G = 0.42, respectively). A meta‐analysis of the compulsive symptoms GWAS with the PGC‐OCD revealed no genome‐wide significant Single‐Nucleotide Polymorphisms (SNPs combined N = 17,992, indicating that the power is still low for individual SNP effects). A gene‐based association analysis, however, yielded two novel genes (WDR7 and ADCK1). The top 250 genes in the gene‐based test also showed a significant increase in enrichment for psychiatric and brain‐expressed genes. S‐Predixcan testing showed that for genes expressed in hippocampus, amygdala, and caudate nucleus significance increased in the meta‐analysis with compulsive symptoms compared to the original PGC‐OCD GWAS. Thus, the inclusion of dimensional symptom data in genome‐wide association on clinical case–control GWAS of OCD may be useful to find genes for OCD if the data are based on quantitative indices of compulsive behavior. SNP‐level power increases were limited, but aggregate, gene‐level analyses showed increased enrichment for brain‐expressed genes related to psychiatric disorders, and increased association with gene expression in brain tissues with known emotional, reward processing, memory, and fear‐formation functions.     

A Combinatorial Searching Method for Detecting a Set of Interacting Loci Associated with Complex Traits

Complex diseases are presumed to be the results of the interaction of several genes and environmental factors, with each gene only having a small effect on the disease. Mapping complex disease genes therefore becomes one of the greatest challenges facing geneticists. Most current approaches of association studies essentially evaluate one marker or one gene (haplotype approach) at a time. These approaches ignore the possibility that effects of multilocus functional genetic units may play a larger role than a single‐locus effect in determining trait variability. In this article, we propose a Combinatorial Searching Method (CSM) to detect a set of interacting loci (may be unlinked) that predicts the complex trait. In the application of the CSM, a simple filter is used to filter all the possible locus‐sets and retain the candidate locus‐sets, then a new objective function based on the cross‐validation and partitions of the multi‐locus genotypes is proposed to evaluate the retained locus‐sets. The locus‐set with the largest value of the objective function is the final locus‐set and a permutation procedure is performed to evaluate the overall p‐value of the test for association between the final locus‐set and the trait. The performance of the method is evaluated by simulation studies as well as by being applied to a real data set. The simulation studies show that the CSM has reasonable power to detect high‐order interactions. When the CSM is applied to a real data set to detect the locus‐set (among the 13 loci in the ACE gene) that predicts systolic blood pressure (SBP) or diastolic blood pressure (DBP), we found that a four‐locus gene‐gene interaction model best predicts SBP with an overall p‐value = 0.033, and similarly a two‐locus gene‐gene interaction model best predicts DBP with an overall p‐value = 0.045.     

A molecule‐based genetic association approach implicates a range of voltage‐gated calcium channels associated with schizophrenia

Traditional genome‐wide association studies (GWAS) have successfully detected genetic variants associated with schizophrenia. However, only a small fraction of heritability can be explained. Gene‐set/pathway‐based methods can overcome limitations arising from single nucleotide polymorphism (SNP)‐based analysis, but most of them place constraints on size which may exclude highly specific and functional sets, like macromolecules. Voltage‐gated calcium (Ca_v) channels, belonging to macromolecules, are composed of several subunits whose encoding genes are located far away or even on different chromosomes. We combined information about such molecules with GWAS data to investigate how functional channels associated with schizophrenia. We defined a biologically meaningful SNP‐set based on channel structure and performed an association study by using a validated method: SNP‐set (sequence) kernel association test. We identified eight subtypes of Ca_v channels significantly associated with schizophrenia from a subsample of published data (N = 56,605), including the L‐type channels (Ca_v1.1, Ca_v1.2, Ca_v1.3), P‐/Q‐type Ca_v2.1, N‐type Ca_v2.2, R‐type Ca_v2.3, T‐type Ca_v3.1, and Ca_v3.3. Only genes from Ca_v1.2 and Ca_v3.3 have been implicated by the largest GWAS (N = 82,315). Each subtype of Ca_v channels showed relatively high chip heritability, proportional to the size of its constituent gene regions. The results suggest that abnormalities of Ca_v channels may play an important role in the pathophysiology of schizophrenia and these channels may represent appropriate drug targets for therapeutics. Analyzing subunit‐encoding genes of a macromolecule in aggregate is a complementary way to identify more genetic variants of polygenic diseases. This study offers the potential of power for discovery the biological mechanisms of schizophrenia.     

Syndrome to gene (S2G): in‐silico identification of candidate genes for human diseases

The identification of genomic loci associated with human genetic syndromes has been significantly facilitated through the generation of high density SNP arrays. However, optimal selection of candidate genes from within such loci is still a tedious labor‐intensive bottleneck. Syndrome to Gene (S2G) is based on novel algorithms which allow an efficient search for candidate genes in a genomic locus, using known genes whose defects cause phenotypically similar syndromes. S2G ( http://fohs.bgu.ac.il/s2g/index.html ) includes two components: a phenotype Online Mendelian Inheritance in Man (OMIM)‐based search engine that alleviates many of the problems in the existing OMIM search engine (negation phrases, overlapping terms, etc.). The second component is a gene prioritizing engine that uses a novel algorithm to integrate information from 18 databases. When the detailed phenotype of a syndrome is inserted to the web‐based software, S2G offers a complete improved search of the OMIM database for similar syndromes. The software then prioritizes a list of genes from within a genomic locus, based on their association with genes whose defects are known to underlie similar clinical syndromes. We demonstrate that in all 30 cases of novel disease genes identified in the past year, the disease gene was within the top 20% of candidate genes predicted by S2G, and in most cases—within the top 10%. Thus, S2G provides clinicians with an efficient tool for diagnosis and researchers with a candidate gene prediction tool based on phenotypic data and a wide range of gene data resources. S2G can also serve in studies of polygenic diseases, and in finding interacting molecules for any gene of choice. Hum Mutat 30:1–8, 2010. © 2010 Wiley‐Liss, Inc.     

Measuring gene–gene interaction using Kullback–Leibler divergence

Genome‐wide association studies (GWAS) are used to investigate genetic variants contributing to complex traits. Despite discovering many loci, a large proportion of “missing” heritability remains unexplained. Gene–gene interactions may help explain some of this gap. Traditionally, gene–gene interactions have been evaluated using parametric statistical methods such as linear and logistic regression, with multifactor dimensionality reduction (MDR) used to address sparseness of data in high dimensions. We propose a method for the analysis of gene–gene interactions across independent single‐nucleotide polymorphisms (SNPs) in two genes. Typical methods for this problem use statistics based on an asymptotic chi‐squared mixture distribution, which is not easy to use. Here, we propose a Kullback–Leibler‐type statistic, which follows an asymptotic, positive, normal distribution under the null hypothesis of no relationship between SNPs in the two genes, and normally distributed under the alternative hypothesis. The performance of the proposed method is evaluated by simulation studies, which show promising results. The method is also used to analyze real data and identifies gene–gene interactions among RAB3A, MADD, and PTPRN on type 2 diabetes (T2D) status.     

Eicosanoid modulation by the short‐chain fatty acid n‐butyrate in human monocytes

n‐Butyrate deriving from bacterial fermentation in the mammalian intestine is a key determinant in gastrointestinal homeostasis. We examined the effects of this short‐chain fatty acid and Toll‐like receptor 2 (TLR) and TLR4 engagement on inflammatory/immunity‐associated genes, cyclo‐oxygenases (COXs), prostaglandins (PGs) and leukotrienes (LTs) in human monocytes. Before RNA isolation, freshly isolated human monocytes were co‐incubated for different time‐points with 1 mm n‐butyrate alone or in combination with bacterial stimuli. Based on a knowledge‐driven approach, a signature of 180 immunity/inflammation‐associated genes was picked and real‐time PCR analysis was performed. Pathway analysis was carried out using a web‐based database analysing program. Based on these gene expression studies the findings were evaluated at the protein/mediator level by Western blot analysis, FACS and ELISA. Following co‐incubation with n‐butyrate and lipopolysaccharide, key enzymes of the eicosanoid pathway, like PTGS2 (COX‐2), TXS, ALOX5, LTA4H and LTC4S, were significantly up‐regulated compared with stimulation with lipopolysaccharide alone. Furthermore, release of the lipid mediators PGE₂, 15d‐PGJ_2, LTB₄ and thromboxane B₂ was increased by n‐butyrate. Regarding signalling, n‐butyrate had no additional effect on mitogen‐activated protein kinase and interfered differently with early and late phases of nuclear factor‐κB signalling. Our results suggest that among many other mediators of eicosanoid signalling n‐butyrate massively induces PGE₂ production by increasing the expression of PTGS2 (COX‐2) in monocytes following TLR4 and TLR2 activation and induces secretion of LTB₄ and thromboxane B₂. This underscores the role of n‐butyrate as a crucial mediator of gut‐specific immunity.     

Analysis of pathway mutation profiles highlights collaboration between cancer‐associated superpathways

The biological interpretation of the complexity of cancer somatic mutation profiles is a major challenge in current cancer research. It has been suggested that mutations in multiple genes that participate in different pathways are collaborative in conferring growth advantage to tumor cells. Here, we propose a powerful pathway‐based approach to study the functional collaboration of gene mutations in carcinogenesis. We successfully identify many pairs of significantly comutated pathways for a large‐scale somatic mutation profile of lung adenocarcinoma. We find that the coordinated pathway pairs detected by comutations are also likely to be coaltered by other molecular changes, such as alterations in multifunctional genes in cancer. Then, we cluster comutated pathways into comutated superpathways and show that the derived superpathways also tend to be significantly coaltered by DNA copy number alterations. Our results support the hypothesis that comprehensive cooperation among a few basic functions is required for inducing cancer. The results also suggest biologically plausible models for understanding the heterogeneous mechanisms of cancers. Finally, we suggest an approach to identify candidate cancer genes from the derived comutated pathways. Together, our results provide guidelines to distill the pathway collaboration in carcinogenesis from the complexity of cancer somatic mutation profiles.Hum Mutat 32:1–8, 2011. © 2011 Wiley‐Liss, Inc.     

Glycopolymers by RAFT Polymerization as Functional Surfaces for Galectin‐3

Glycan‐protein interactions are essential biological processes with many disease‐related modulations and variations. One of the key proteins involved in tumor progression and metastasis is galectin‐3 (Gal‐3). A lot of effort is put into the development of Gal‐3 inhibitors as new therapeutic agents. The avidity of glycan‐protein interactions is strongly enhanced by multivalent ligand presentation. Multivalent presentation of glycans can be accomplished by utilizing glycopolymers, which are polymers with pendent glycan groups. For the production of glycopolymers, glycomonomers are synthesized by a regioselective, microwave‐assisted approach starting from lactose. The resulting methacrylamide derivatives are polymerized by RAFT and immobilized on gold surfaces using the trithiocarbonate group of the chain transfer agent. Surface plasmon resonance spectroscopy enables the label free kinetic characterization of Gal‐3 binding to these multivalent glycopolymers. The measurements indicate oligomerization of Gal‐3 upon exposure to multivalent environments and reveal strong specific interaction with the immobilized polymers.     

SNPs in axon guidance pathway genes and susceptibility for Parkinson's disease in the Korean population

Single-nucleotide polymorphisms (SNPs) in genes of the axon guidance pathway have been reported to be a possible susceptibility factor for Parkinson's disease (PD). This study investigated whether the genetic variability in the axon guidance pathway is a susceptibility factor in PD patients in the Korean population. A total of 373 patients and 384 healthy subjects were included. A set of 22 SNPs was analyzed, and the risk of PD was evaluated using odds ratios in an unconditional and conditional logistic regression models of age- and gender-matched subsets. A multidimensionality reduction (MDR) analysis was performed to explore potential gene-gene interactions. SNPs in the DCC, CHP, RRAS2 and EPHB1 genes of the axon guidance pathway showed significant associations with PD. The DCC rs17468382 and EPHB1 rs2030737 SNPs may be associated with increased PD risk, and the CHP rs6492998 and RRAS2 rs2970332 SNPs may be associated with reduced PD risk. However, no significant interactions for PD risk were found in the MDR analysis and logistic regression analysis using SNP interaction terms. This study supports that only four of the selected 22 SNPs are regulating factors associated with PD in the Korean population. However, no interactions were found among the SNPs, suggesting that the effect for the pathway as a whole is not greater than that for single genes in the Korean population. Further investigations involving populations of various ethnicities and other genetic markers and models are warranted.