TAP1基因多态性与结核病易感关联性的家系病例对照研究

目的 分析结核病多发家系中TAP1基因rs1135216和rs1057141位点单核苷酸多态性和环境因素与结核病患病的关联,为结核病防治提供理论依据。方法 从广东省结核病防治单位纳入结核病多发家庭患者,同病例组有血缘关系的作为家庭密切接触者,另取同期体检的健康人群作为健康对照,共413人纳入本次研究。采用家系病例对照研究方法,通过自行设计的问卷,收集研究对象的一般人口学特征、行为危险因素以及环境因素。采用χ2检验进行单因素分析及Hardy - Weinberg遗传平衡检验,多因素条件logistic回归模型用于分析各因素与结核病患病的关联。应用Haploview软件对两位点进行连锁不平衡分析和单倍型构建。采用GMDR分析基因 - 基因、基因 - 环境交互作用对结核病患病的影响。结果 共纳入413名研究对象,其中结核病患者（TB）133人,健康对照者（HC）173人,家庭密切接触者（HHC）107人。显性模型分析结果显示,携带CT - CC基因型是结核病患病的危险因素,在HHC组和HC组中,其患病风险分别是携带TT基因型的2.409倍（95%CI:1.377～4.214）和2.014倍（95%CI:1.249～3.247）。单体型分析结果显示,C - T单体型可能是肺结核患病的保护因素。GMDR分析结果显示,基因 - 基因交互作用在TB组和HC组间的最优模型是rs1135216单因子模型（P＜0.05,CVC = 10/10,TA = 0.579）;基因 - 环境交互作用在TB组和HHC组中,最优模型为rs1135216位点、性别、年龄和文化程度四因子模型（P＜0.05,CVC = 10/10,TA = 0.651）;在TB组和HC组中,最优模型为rs1135216位点、文化程度、BMI、居住环境潮湿与否和环境卫生五因子模型（P＜0.05,CVC = 8/10,TA = 0.655）。结论 TAP1基因rs1135216位点与结核病患病风险相关,且宿主 - 环境因素对结核病的发生发展起着重要的作用。     

代谢酶基因多态性与慢性苯中毒关联的logistic回归与多因子降维法分析

目的 研究多种代谢酶基因多态性与慢性苯中毒的关联.方法 采用病例-对照研究,以152名苯中毒工人为病例组,152名接触苯而没有中毒表现的工人为对照组.应用聚合酶链反应-限制性片段长度多态性(PCR-RFLP)、测序等技术检测CYP2E1等13个基因的30个单核苷酸多态性(SNP).Logistic回归模型分析主效应和2阶交互作用,多因子降维法分析高阶交互作用.结果 logistic回归分析表明,控制了性别、吸烟、饮酒、苯接触强度的影响后,GSTP1 rs947894、CYP1A1rs4646903、CYP2D6 rs1065852、CYP2D6 rs1135840有主效应(P＜0.05).EPHX1 rs1051740可能有主效应(P=0.06).GSTP1 rs947894与饮酒有交互作用;CYP2E1 rs3813867和EPHX1 rs3738047无主效应,但有交互作用;EPHX1 rs3738047与饮酒也有交互作用.未发现其他SNP与慢性苯中毒的关联.多因子降维法模型发现了1个联合作用最强的3阶交互作用,即CYP1A1 rs4646903、CYP2D6 rs1065852、CYP2D6rs1135840的3因子组合.结论 基因-基因、基因-环境交互作用是影响个体慢性苯中毒遗传易感性的重要方式.

Abstract：

Objective To explore the association of polymorphisms of metabolizing enzyme genes with chronic benzene poisoning(CBP) comprehensively by case-control design. Methods 152 CBP patients and 152 workers occupationally exposed to benzene without poisoning manifestations were investigated. 30single nucleotide polymorphisms (SNPs) in 13 genes such as CYP2E1 were tested by PCR-RFLP, sequencing approaches. Logistic regression model was used to detect main effects and 2-order interaction effects of gene and/or environment. Multifactor dimensionality reduction (MDR) was used to detect high-order gene-gene or gene-environment interactions. Results Based on logistic regression, the main effects of G57P7rs947894,EPHX1 rs1051740, CYP1A1 rs4646903, CYP2D6 rs1065852 and rs1135840 were found to be significant (P＜0.05) while the confounding factors of sex, cigarette smoking, alcohol consumption and the intensity of benzene exposure were controlled. EPHX1 rs1051740 might be associated with CBP (P=0.06). There existed 3types of interactions were as followed: interactions of GSTP1 rs947894 with alcohol consumption, CYP2E1rs3813867 with EPHX1 rs3738047, EPHX1 rs3738047 with alcohol consumption(P＜0.05), while the main effects of CYP2E1 rs3813867 and EPHX1 rs3738047 were not significant (P＞0.05). The other SNPs did not show any significant associations with CBP. According to MDR, a 3-order interaction with the strongest combined effect was found, i.e. the 3-factor combination of CYP1A1 rs4646903, CYP2D6 rs1065852 and CYP2D6rs1135840. Conclusion Gene-gene, gene-environment interactions are important mechanism to genetic susceptibility of CBP.     

Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits

The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene‐environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene‐environment interaction effect to those of little or no gene‐environment interaction effect. To fill this demand, we have extended a kernel machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene‐environment interaction. The kernel‐based approach for joint testing is promising, because it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel machine approach typically outperforms the traditional joint test under strong gene‐environment interaction models and further outperforms the traditional main‐effect association test under models of weak or no gene‐environment interaction effects. We illustrate our test using genome‐wide association data from the Grady Trauma Project, a cohort of highly traumatized, at‐risk individuals, which has previously been investigated for interaction effects.     

Rapid testing of gene-gene interactions in genome-wide association studies of binary and quantitative phenotypes

Genome-wide association (GWA) studies have been extremely successful in identifying novel loci contributing effects to a wide range of complex human traits. However, despite this success, the joint marginal effects of these loci account for only a small proportion of the heritability of these traits. Interactions between variants in different loci are not typically modelled in traditional GWA analysis, but may account for some of the missing heritability in humans, as they do in other model organisms. One of the key challenges in performing gene-gene interaction studies is the computational burden of the analysis. We propose a two-stage interaction analysis strategy to address this challenge in the context of both quantitative traits and dichotomous phenotypes. We have performed simulations to demonstrate only a negligible loss in power of this two-stage strategy, while minimizing the computational burden. Application of this interaction strategy to GWA studies of T2D and obesity highlights potential novel signals of association, which warrant follow-up in larger cohorts.     

Interpreting Joint SNP Analysis Results: When Are Two Distinct Signals Really Two Distinct Signals?

In genetic association studies, much effort has focused on moving beyond the initial single‐nucleotide polymorphism (SNP)‐by‐SNP analysis. One approach is to reanalyze a chromosomal region where an association has been detected, jointly analyzing the SNP thought to best represent that association with each additional SNP in the region. Such joint analyses may help identify additional, statistically independent association signals. However, it is possible for a single genetic effect to produce joint SNP results that would typically be interpreted as two distinct effects (e.g., both SNPs are significant in the joint model). We present a general approach that can (1) identify conditions under which a single variant could produce a given joint SNP result, and (2) use these conditions to identify variants from a list of known SNPs (e.g., 1000 Genomes) as candidates that could produce the observed signal. We apply this method to our previously reported joint result for smoking involving rs16969968 and rs588765 in CHRNA5. We demonstrate that it is theoretically possible for a joint SNP result suggestive of two independent signals to be produced by a single causal variant. Furthermore, this variant need not be highly correlated with the two tested SNPs or have a large odds ratio. Our method aids in interpretation of joint SNP results by identifying new candidate variants for biological causation that would be missed by traditional approaches. Also, it can connect association findings that may seem disparate due to lack of high correlations among the associated SNPs.     

Methods for interaction analyses using family-based case-control data: conditional logistic regression versus generalized estimating equations

A complex web of gene-gene and gene-environment interactions likely underlies late-onset disease development. We compared conditional logistic regression (CLR) and generalized estimating equations (GEE) in modeling such interactions in pedigrees with missing parents. Using the simulation of linkage and association (SIMLA) program, disease genes, an environmental risk factor, gene-gene interaction, and gene-environment interaction were generated in family-based data sets. Four scenarios for the relationship between the marker and disease loci were examined: linkage and association, linkage without association, association without linkage, and absence of both linkage and association. Models for CLR and GEE (with exchangeable and independence correlation matrices) were built, and type I error, power, average odds ratio (OR), standard deviation, and 95% confidence intervals were estimated. CLR and GEE were valid tests of association in the presence of linkage, but type I error was inflated for association without linkage, particularly with GEE. CLR generated estimates of the OR with lower bias but often more variability than the OR estimates observed for GEE. Further, GEE was more powerful than CLR in detecting main and interactive effects. Although GEE with both matrices had similar power, use of the independence matrix resulted in lower type I error and less biased OR estimation as compared to the exchangeable matrix. Our findings support the use of GEE in maximizing power to detect gene-gene and gene-environment interactions but caution its use under potential association without linkage (e.g., population stratification) and the interpretation of its OR estimates.     

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction.     

A comparison of methods sensitive to interactions with small main effects

Numerous genetic variants have been successfully identified for complex traits, yet these genetic factors only account for a modest portion of the predicted variance due to genetic factors. This has led to increased interest in other approaches to account for the "missing" genetic contributions to phenotype, including joint gene-gene or gene-environment analysis. A variety of methods for such analysis have been advocated. However, they have seldom been compared systematically. To facilitate such comparisons, the developers of the multifactor dimensionality reduction (MDR) simulated 100 data replicates for each of 96 two-locus models displaying negligible marginal effects from either locus (16 variations on each of six basic genetic models). The genetic models, based on a dichotomous phenotype, had varying minor allele frequencies and from two to eight distinct risk levels associated with genotype. The basic models were modified to include "noise" from combinations of missing data, genotyping error, genetic heterogeneity, and phenocopies. This study compares the performance of three methods designed to be sensitive to joint effects (MDR, support vector machines (SVMs), and the restricted partition method (RPM)) on these simulated data. In these tests, the RPM consistently outperformed the other two methods for each of the six classes of genetic models. In contrast, the comparison between other two methods had mixed results. The MDR outperformed the SVM when the true model had only a few, well-separated risk classes; while the SVM outperformed the MDR on more complicated models. Of these methods, only MDR has a well-developed user interface.     

Trees Assembling Mann‐Whitney Approach for Detecting Genome‐Wide Joint Association Among Low‐Marginal‐Effect Loci

Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low marginal effects (LMEs) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and assessing their joint association on high‐dimensional data (e.g., genome‐wide data) remain a great challenge. To facilitate joint association analysis among a large ensemble of LME genetic variants, we proposed a computationally efficient and powerful approach, which we call Trees Assembling Mann‐Whitney (TAMW). Through simulation studies and an empirical data application, we found that TAMW outperformed multifactor dimensionality reduction (MDR) and the likelihood ratio‐based Mann‐Whitney approach (LRMW) when the underlying complex disease involves multiple LME loci and their interactions. For instance, in a simulation with 20 interacting LME loci, TAMW attained a higher power (power = 0.931) than both MDR (power = 0.599) and LRMW (power = 0.704). In an empirical study of 29 known Crohn's disease (CD) loci, TAMW also identified a stronger joint association with CD than those detected by MDR and LRMW. Finally, we applied TAMW to Wellcome Trust CD GWAS to conduct a genome‐wide analysis. The analysis of 459K single nucleotide polymorphisms was completed in 40 hrs using parallel computing, and revealed a joint association predisposing to CD (P‐value = 2.763 × 10^?19). Further analysis of the newly discovered association suggested that 13 genes, such as ATG16L1 and LACC1, may play an important role in CD pathophysiological and etiological processes.     

A Bivariate Mann‐Whitney Approach for Unraveling Genetic Variants and Interactions Contributing to Comorbidity

Although comorbidity among complex diseases (e.g., drug dependence syndromes) is well documented, genetic variants contributing to the comorbidity are still largely unknown. The discovery of genetic variants and their interactions contributing to comorbidity will likely shed light on underlying pathophysiological and etiological processes, and promote effective treatments for comorbid conditions. For this reason, studies to discover genetic variants that foster the development of comorbidity represent high‐priority research projects, as manifested in the behavioral genetics studies now underway. The yield from these studies can be enhanced by adopting novel statistical approaches, with the capacity of considering multiple genetic variants and possible interactions. For this purpose, we propose a bivariate Mann‐Whitney (BMW) approach to unravel genetic variants and interactions contributing to comorbidity, as well as those unique to each comorbid condition. Through simulations, we found BMW outperformed two commonly adopted approaches in a variety of underlying disease and comorbidity models. We further applied BMW to datasets from the Study of Addiction: Genetics and Environment, investigating the contribution of 184 known nicotine dependence (ND) and alcohol dependence (AD) single nucleotide polymorphisms (SNPs) to the comorbidity of ND and AD. The analysis revealed a candidate SNP from CHRNA5, rs16969968, associated with both ND and AD, and replicated the findings in an independent dataset with a P‐value of 1.06 × 10^–03.     

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits

The manifestation of complex traits is influenced by gene–gene and gene–environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low-density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi-Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model-based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.     

A Genome‐Wide Association Study for Serum Bilirubin Levels and Gene‐Environment Interaction in a Chinese Population

Bilirubin is an effective antioxidant and is influenced by both genetic and environmental factors. Recent genome‐wide association studies (GWAS) have identified multiple loci affecting serum total bilirubin levels. However, most of the studies were conducted in European populations and little attention has been devoted either to genetic variants associated with direct and indirect bilirubin levels or to the gene‐environment interactions on bilirubin levels. In this study, a two‐stage GWAS was performed to identify genetic variants associated with all types of bilirubin levels in 10,282 Han Chinese individuals. Gene‐environment interactions were further examined. Briefly, two previously reported loci, UGT1A1 on 2q37 (rs6742078 and rs4148323, combined P = 1.44 × 10^?89 and P = 5.05 × 10^?69, respectively) and SLCO1B3 on 12p12 (rs2417940, combined P = 6.93 × 10^?19) were successfully replicated. The two loci explained 9.2% and 0.9% of the total variations of total bilirubin levels, respectively. Ethnic genetic differences were observed between Chinese and European populations. More importantly, a significant interaction was found between rs2417940 in SLCO1B3 gene and smoking on total bilirubin levels (P = 1.99 × 10^?3). Single nucleotide polymorphism (SNP) rs2417940 had stronger effects on total bilirubin levels in nonsmokers than in smokers, suggesting that the effects of SLCO1B3 genotype on bilirubin levels were partly dependent on smoking status. Consistent associations and interactions were observed for serum direct and indirect bilirubin levels.     

基于模型的多因子降维方法在基因-基因/环境交互作用分析中的应用

介绍一种基于模型的多因子降维方法(MB-MDR),并通过实例说明其分析流程及其在基因-基因/环境交互作用分析中的应用。结果显示该方法可用于原始样本量较小的资料研究,同时也能解决许多经典MDR方法的不足;与其他MDR扩展方法相比,在探索交互作用方面具有更高的统计效能,并已成功应用于膀胱癌、湿疹等研究。MB-MDR能够处理二元性状和数量性状,并可在模型中调整因子的边际效应和混杂因子,与其他非参数方法相比具有一定优势。因此MB-MDR在基因-基因/环境交互作用分析中具有较好的应用前景。     

Genetic aspects of asthma

Over the last few years, a significant progress has been made in understanding of the genetic basis of asthma. This has led to the identification of several chromosomal regions and loci showing linkage to and association with asthma and asthma-linked phenotypes. Recent positional cloning approaches have also been informative in identifying several strong candidate genes for asthma. The next challenge will involve validation of these findings and, importantly, identification of the functional basis in the pathophysiology of asthma. This review will describe the power of positional cloning for the identification of asthma genes, highlight the functional importance of the genetic variants, and address the gene-gene and gene-environment interactions that are pertinent to this challenging field.     

A unified framework for transmission-disequilibrium test analysis of discrete and continuous traits.

This paper presents a unified framework for transmission-disequilibrium tests for discrete and continuous traits. A conditional score test is derived that maximizes power to detect small effects for any exponential family distribution, which includes binary and normal distributions, and distributions that are skewed or have non-normal kurtosis. The specific distributional form need not be specified, and the method applies to sibships of arbitrary size. Formulas for the distribution of the test statistic are given for models including complex genetic effects (additive, dominant, and recessive gene action), covariates, multiple gene models including gene-gene interactions or heterogeneity, and gene-environment interactions. We develop refinements of our method for trait-based sampling designs and multiple siblings that can have dramatic effects on power.     

EINVis: A Visualization Tool for Analyzing and Exploring Genetic Interactions in Large‐Scale Association Studies

Epistasis (gene‐gene interaction) detection in large‐scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user‐friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/ .     

Pathway‐Based Association Study of Multiple Candidate Genes and Multiple Traits Using Structural Equation Models

There is increasing interest in the joint analysis of multiple genetic variants from multiple genes and multiple correlated quantitative traits in association studies. The classical approach involves testing univariate associations between genotypes and phenotypes and correcting for multiple testing that results in loss of power to detect associations. In this paper, we propose modeling complex relationships between genetic variants in candidate genes and measured correlated traits using structural equation models (SEM), taking advantage of prior knowledge on clinical and genetic pathways. We adopt generalized structured component analysis (GSCA) as an approach to SEM and develop a single association test between multiple genetic variants in a gene and a set of correlated traits, taking into account all available data from other genes and other traits. The performance of this test is investigated by simulations. We apply the proposed method to the Quebec Child and Adolescent Health and Social Survey (1999) data to investigate genetic associations with cardiovascular disease‐related traits.     

Genetic Variants Influencing Joint Damage in Mexican Americans and European Americans With Rheumatoid Arthritis

Joint destruction in rheumatoid arthritis (RA) is heritable, but knowledge on specific genetic determinants of joint damage in RA is limited. We have used the Immunochip array to examine whether genetic variants influence variation in joint damage in a cohort of Mexican Americans (MA) and European Americans (EA) with RA. We studied 720 MA and 424 EA patients with RA. Joint damage was quantified using a radiograph of both hands and wrists, scored using Sharp's technique. We conducted association analyses with the transformed Sharp score and the Immunochip single nucleotide polymorphism (SNP) data using PLINK. In MAs, 15 SNPs from chromosomes 1, 5, 9, 17 and 22 associated with joint damage yielded strong p‐values (p < 1 × 10^?4). The strongest association with joint damage was observed with rs7216796, an intronic SNP located in the MAP3K14 gene, on chromosome 17 (β ± SE = ?0.25 ± 0.05, p = 6.23 × 10^?6). In EAs, 28 SNPs from chromosomes 1, 4, 6, 9, and 21 showed associations with joint damage (p‐value < 1 × 10^?4). The best association was observed on chromosome 9 with rs59902911 (β ± SE = 0.86 ± 0.17, p = 1.01 × 10^?6), a synonymous SNP within the CARD9 gene. We also observed suggestive evidence for some loci influencing joint damage in MAs and EAs. We identified two novel independent loci (MAP3K14 and CARD9) strongly associated with joint damage in MAs and EAs and a few shared loci showing suggestive evidence for association.     

Genome‐Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases

Genome‐wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome‐wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome‐wide genotype data up to high‐density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re‐sequencing experiments. By application of this approach to genome‐wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome‐wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome‐wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.     

Smoking and genetic risk variation across populations of European, Asian, and African American ancestry--a meta-analysis of chromosome 15q25

Recent meta-analyses of European ancestry subjects show strong evidence for association between smoking quantity and multiple genetic variants on chromosome 15q25. This meta-analysis extends the examination of association between distinct genes in the CHRNA5-CHRNA3-CHRNB4 region and smoking quantity to Asian and African American populations to confirm and refine specific reported associations. Association results for a dichotomized cigarettes smoked per day phenotype in 27 datasets (European ancestry (N = 14,786), Asian (N = 6,889), and African American (N = 10,912) for a total of 32,587 smokers) were meta-analyzed by population and results were compared across all three populations. We demonstrate association between smoking quantity and markers in the chromosome 15q25 region across all three populations, and narrow the region of association. Of the variants tested, only rs16969968 is associated with smoking (P < 0.01) in each of these three populations (odds ratio [OR] = 1.33, 95% CI = 1.25-1.42, P = 1.1 × 10(-17) in meta-analysis across all population samples). Additional variants displayed a consistent signal in both European ancestry and Asian datasets, but not in African Americans. The observed consistent association of rs16969968 with heavy smoking across multiple populations, combined with its known biological significance, suggests rs16969968 is most likely a functional variant that alters risk for heavy smoking. We interpret additional association results that differ across populations as providing evidence for additional functional variants, but we are unable to further localize the source of this association. Using the cross-population study paradigm provides valuable insights to narrow regions of interest and inform future biological experiments.