A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies

Sister haplotypes and recombination disequilibrium: a new approach to identify associations of haplotypes with complex diseases

Haplotype-based association analysis has several advantages over single-SNP association analysis. However, to date all haplotype-disease associations have not excluded recombination interference among multiple loci and hence some results might be confounded by recombination interference. Association of sister haplotypes with a complex disease, based on recombination disequilibrium (RD) was presented. Sister haplotypes can be determined by translating notation of DNA base haplotypes to notation of genetic genotypes. Sister haplotypes provide haplotype pairs available for haplotype-disease association analysis. After performing RD tests in control and case cohorts, a two-by-two contingency table can be constructed using sister haplotype pair and case-control pair. With this standard two-by-two table, one can perform classical Chi-square test to find statistical haplotype-disease association. Applying this method to a haplotype dataset of Alzheimer disease (AD), association of sister haplotypes containing ApoE3/4 with risk for AD was identified under no RD. Haplotypes within gene IL-13 were not associated with risk for breast cancer in the case of no RD and no association of haplotypes in gene IL-17A with risk for coronary artery disease were detected without RD. The previously reported associations of haplotypes within these genes with risk for these diseases might be due to strong RD and/or inappropriate haplotype pairs.

A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator

Evaluating gene by environment (G × E) interaction under an additive risk model (i.e., additive interaction) has gained wider attention. Recently, statistical tests have been proposed for detecting additive interaction, utilizing an assumption on gene-environment (G-E) independence to boost power, that do not rely on restrictive genetic models such as dominant or recessive models. However, a major limitation of these methods is a sharp increase in type I error when this assumption is violated. Our goal was to develop a robust test for additive G × E interaction under the trend effect of genotype, applying an empirical Bayes-type shrinkage estimator of the relative excess risk due to interaction. The proposed method uses a set of constraints to impose the trend effect of genotype and builds an estimator that data-adaptively shrinks an estimator of relative excess risk due to interaction obtained under a general model for G-E dependence using a retrospective likelihood framework. Numerical study under varying levels of departures from G-E independence shows that the proposed method is robust against the violation of the independence assumption while providing an adequate balance between bias and efficiency compared with existing methods. We applied the proposed method to the genetic data of Alzheimer disease and lung cancer.

Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies

Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors. We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically. We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.

Multi-SNP Haplotype Analysis Methods for Association Analysis

Haplotype analysis forms the basis of much of genetic association analysis using both related and unrelated individuals (we concentrate on unrelated). For example, haplotype analysis indirectly underlies the SNP imputation methods that are used for testing trait associations with known but unmeasured variants and for performing collaborative post-GWAS meta-analysis. This chapter is focused on the direct use of haplotypes in association testing. It reviews the rationale for haplotype-based association testing, discusses statistical issues related to haplotype uncertainty that affect the analysis, then gives practical guidance for testing haplotype-based associations with phenotype or outcome trait, first of candidate gene regions and then for the genome as a whole. Haplotypes are interesting for two reasons, first they may be in closer LD with a causal variant than any single measured SNP, and therefore may enhance the coverage value of the genotypes over single SNP analysis. Second, haplotypes may themselves be the causal variants of interest and some solid examples of this have appeared in the literature.This chapter discusses three possible approaches to incorporation of SNP haplotype analysis into generalized linear regression models: (1) a simple substitution method involving imputed haplotypes, (2) simultaneous maximum likelihood (ML) estimation of all parameters, including haplotype frequencies and regression parameters, and (3) a simplified approximation to full ML for case-control data.Examples of the various approaches for a haplotype analysis of a candidate gene are provided. We compare the behavior of the approximation-based methods and argue that in most instances the simpler methods hold up well in practice. We also describe the practical implementation of haplotype risk estimation genome-wide and discuss several shortcuts that can be used to speed up otherwise potentially very intensive computational requirements.

Recent advance in investigation of gene polymorphisms in Japanese patients with aspirin-exacerbated respiratory disease

Aspirin-exacerbated respiratory disease (AERD) is a complex clinical syndrome characterised by severe asthmatic attack upon treatment with aspirin and/or non-steroidal anti-inflammatory drugs (NSAIDs). Genetic predisposition has been considered as a crucial determinant and candidate genes have concentrated especially on cysteinyl leukotrienes (LTs)-related genes as the inhibitory action of aspirin and NSAIDs on cyclooxygenase activity may cause overproduction of cysteinyl LTs. However, conflicting results have been reported, in parallel with replication studies in different ethnic groups. Thus, future areas of investigations need to focus on comprehensive approaches towards the discovery of other genetic biomarkers. Unfortunately, few papers have been reported about gene polymorphisms in Japanese patients with AERD. Here, we described on our recent genetic investigations on B2ADR, IL-13, IL-17A, CYP2C19, TBXA2R, CRTH2 and HSP70. This review indicates potential genetic biomarkers contributing to the early diagnosis of AERD, which may include CYP2C19 and HSP70 gene polymorphisms, and future validation studies in independent population are required to provide reassurance about our findings.

Powerful haplotype-based Hardy-Weinberg equilibrium tests for tightly linked loci

Recently, there have been many case-control studies proposed to test for association between haplotypes and disease, which require the Hardy-Weinberg equilibrium (HWE) assumption of haplotype frequencies. As such, haplotype inference of unphased genotypes and development of haplotype-based HWE tests are crucial prior to fine mapping. The goodness-of-fit test is a frequently-used method to test for HWE for multiple tightly-linked loci. However, its degrees of freedom dramatically increase with the increase of the number of loci, which may lack the test power. Therefore, in this paper, to improve the test power for haplotype-based HWE, we first write out two likelihood functions of the observed data based on the Niu's model (NM) and inbreeding model (IM), respectively, which can cause the departure from HWE. Then, we use two expectation-maximization algorithms and one expectation-conditional-maximization algorithm to estimate the model parameters under the HWE, IM and NM models, respectively. Finally, we propose the likelihood ratio tests LRT and LRT for haplotype-based HWE under the NM and IM models, respectively. We simulate the HWE, Niu's, inbreeding and population stratification models to assess the validity and compare the performance of these two LRT tests. The simulation results show that both of the tests control the type I error rates well in testing for haplotype-based HWE. If the NM model is true, then LRT is more powerful. While, if the true model is the IM model, then LRT has better performance in power. Under the population stratification model, LRT is still more powerful. To this end, LRT is generally recommended. Application of the proposed methods to a rheumatoid arthritis data set further illustrates their utility for real data analysis.

Association of PON1 genotype and haplotype with susceptibility to coronary artery disease and clinical outcomes in dual antiplatelet-treated Han Chinese patients

PURPOSE

The aim of this study was to evaluate the association of PON1 genetic variants with the susceptibility to coronary artery disease (CAD) and with the clinical endpoints in aspirin and clopidogrel (dual antiplatelet therapy)-treated Han Chinese patients with CAD after percutaneous coronary intervention (PCI).

METHODS

A total of 538 Han Chinese patients undergoing PCI and receiving dual-antiplatelet therapy were sequentially recruited to the study and followed for up to 1 year. Healthy controls (n = 539) were enrolled during the same period. All study participants were genotyped for five genetic variants in PON1 and the cytochrome P450 2C19*2 mutation (CYP2C19*2). The effect of genetic variants on disease risk and clinical outcome of major adverse cardiac events (MACE) within 1 year or bleeding within 6 months was assessed.

RESULTS

CYP2C19*2 was associated with a higher risk of MACE (adjusted P = 0.0098), but a lower risk of bleeding events (adjusted P = 0.0016). The PON1 Q192R polymorphism was significantly associated with a lower risk of bleeding events [odds ratio (OR) 0.61, 95% confidence interval (CI) 0.43-0.87, adjusted P = 0.0066). The haplotype bearing the PON1 -126C allele was associated with a higher risk to CAD (OR 1.48, 95% CI 1.04-2.09, P = 0.029) and a higher risk of bleeding events (OR 1.68, 95% CI 1.10-2.56, P = 0.017) compared to the most frequent haplotype. The transcription activity of haplotype p-162A-126C-108C in the PON1 promoter was 2.6-fold higher than that of the most frequent haplotype (p-162G-126G-108T).

CONCLUSIONS

Based on these results, we suggest that the haplotype-bearing PON1 -126C allele contributes to the disease risk and the risk of bleeding events in dual antiplatelet-treated CAD patients after PCI.

Recursive organizer (ROR): an analytic framework for sequence-based association analysis

The advent of next-generation sequencing technologies affords the ability to sequence thousands of subjects cost-effectively, and is revolutionizing the landscape of genetic research. With the evolving genotyping/sequencing technologies, it is not unrealistic to expect that we will soon obtain a pair of diploidic fully phased genome sequences from each subject in the near future. Here, in light of this potential, we propose an analytic framework called, recursive organizer (ROR), which recursively groups sequence variants based upon sequence similarities and their empirical disease associations, into fewer and potentially more interpretable super sequence variants (SSV). As an illustration, we applied ROR to assess an association between HLA-DRB1 and type 1 diabetes (T1D), discovering SSVs of HLA-DRB1 with sequence data from the Wellcome Trust Case Control Consortium. Specifically, ROR reduces 36 observed unique HLA-DRB1 sequences into 8 SSVs that empirically associate with T1D, a fourfold reduction of sequence complexity. Using HLA-DRB1 data from Type 1 Diabetes Genetics Consortium as cases and data from Fred Hutchinson Cancer Research Center as controls, we are able to validate associations of these SSVs with T1D. Further, SSVs consist of nine nucleotides, and each associates with its corresponding amino acids. Detailed examination of these selected amino acids reveals their potential functional roles in protein structures and possible implication to the mechanism of T1D.

HapBoost: a fast approach to boosting haplotype association analyses in genome-wide association studies

Genome-wide association study (GWAS) has been successful in identifying genetic variants that are associated with complex human diseases. In GWAS, multilocus association analyses through linkage disequilibrium (LD), named haplotype-based analyses, may have greater power than single-locus analyses for detecting disease susceptibility loci. However, the large number of SNPs genotyped in GWAS poses great computational challenges in the detection of haplotype associations. We present a fast method named HapBoost for finding haplotype associations, which can be applied to quickly screen the whole genome. The effectiveness of HapBoost is demonstrated by using both synthetic and real data sets. The experimental results show that the proposed approach can achieve comparably accurate results while it performs much faster than existing methods.

Robust estimation for homoscedastic regression in the secondary analysis of case-control data

Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

Androgen metabolism and JAK/STAT pathway genes and prostate cancer risk

BACKGROUND

Prostate cancer (PC) is the most frequently diagnosed solid tumor in U.S. men. Genome-wide association studies (GWAS) have identified over 40 risk-associated single nucleotide polymorphisms (SNPs), including variants in androgen pathway genes (e.g., KLK3 and AR). Androgens are important in PC and genes involved in this pathway are therefore candidates for conferring susceptibility to PC.

METHODS

In this hypothesis-testing study, we evaluated PC risk in association with SNPs in 22 candidate genes involved in androgen metabolism or interactions with the androgen receptor (AR). A total of 187 SNPs were genotyped in 1458 cases and 1351 age-matched controls from a population-based study. PC risk was estimated using adjusted unconditional logistic regression and multinomial regression models.

RESULTS

Single SNP analyses showed evidence (p < 0.05) for associations with 14 SNPs in 9 genes: NKX3.1, HSD17B3, AKR1C3, SULT2A1, CYP17A1, KLK3, JAK2, NCOA4 and STAT3. The most significant result was observed for rs2253502 in HSD17B3 (odds ratio, OR = 0.57, 95% CI: 0.39-0.84). In addition, five SNPs in four genes (CYP17A1, HSD17B4, NCOA4, and SULT2A1) were associated with more aggressive disease (p < 0.01).

CONCLUSIONS

Our results replicate previously reported associations for SNPs in CYP17A1, HSD17B3, ARK1C3, NKX3.1, NCOA4 and KLK3. In addition, novel associations were observed for SNPs in JAK2, HSD17B4, and SULT2A1. These results will require replication in larger studies.

Dual effect of the macrophage migration inhibitory factor gene on the development and severity of human systemic lupus erythematosus

OBJECTIVE

To study the effect of the innate cytokine macrophage migration inhibitory factor (MIF) on the susceptibility and severity of systemic lupus erythematosus (SLE) in a multinational population of 1,369 Caucasian and African American patients.

METHODS

Two functional polymorphisms in the MIF gene, a -794 CATT(5-8) microsatellite repeat (rs5844572) and a -173 G/C single-nucleotide polymorphism (rs755622), were assessed for association with SLE in 3,195 patients and healthy controls. We also measured MIF plasma levels in relation to genotypes and clinical phenotypes, and assessed Toll-like receptor 7 (TLR-7)-stimulated MIF production in vitro.

RESULTS

Both Caucasians and African Americans with the high-expression MIF haplotype -794 CATT(7)/-173*C had a lower incidence of SLE (in Caucasians, odds ratio [OR] 0.63, 95% confidence interval [95% CI] 0.53-0.89, P = 0.001; in African Americans, OR 0.46, 95% CI 0.23-0.95, P = 0.012). In contrast, among patients with established SLE, reduced frequencies of low-expression MIF genotypes (-794 CATT(5)) were observed in those with nephritis, those with serositis, and those with central nervous system (CNS) involvement when compared to patients without end-organ involvement (P = 0.023, P = 0.005, and P = 0.04, respectively). Plasma MIF levels and TLR-7-stimulated MIF production in vitro reflected the underlying MIF genotype of the studied groups.

CONCLUSION

These findings suggest that MIF, which has both proinflammatory properties and macrophage and B cell survival functions, exerts a dual influence on the immunopathogenesis of SLE. High-expression MIF genotypes are associated with a reduced susceptibility to SLE and may contribute to an enhanced clearance of infectious pathogens. Once SLE develops, however, low-expression MIF genotypes may protect from ensuing inflammatory end-organ damage.

Multi-SNP haplotype analysis methods for association analysis

This chapter reviews the rationale for the use of haplotypes in association-based testing, discusses statistical issues related to haplotype uncertainty that complicate the analysis, then gives practical guidance for testing haplotype-based associations with phenotype or outcome trait, first of candidate gene regions and then for the genome as a whole. Haplotypes are interesting for two reasons: First, they may be in closer LD with a causal variant than any single measured SNP, and therefore may enhance the coverage value of the genotypes over single SNP analysis. Second, haplotypes may themselves be the causal variants of interest and some solid examples of this have appeared in the literature. This chapter discusses three possible approaches to incorporation of SNP haplotype analysis into generalized linear regression models: (1) a simple substitution method involving imputed haplotypes; (2) simultaneous maximum likelihood (ML) estimation of all parameters, including haplotype frequencies and regression parameters; and (3) a simplified approximation to full ML for case-control data. Examples of the various approaches for a haplotype analysis of a candidate gene are provided. We compare the behavior of the approximation-based methods and show that in most instances the simpler methods hold up well in practice. We also describe the practical implementation of genome-wide haplotype risk estimation and discuss several shortcuts that can be used to speed up otherwise potentially very intensive computational requirements.

The additive risk model for estimation of effect of haplotype match in BMT studies

In this paper we consider a problem from bone marrow transplant (BMT) studies where there is interest on assessing the effect of haplotype match for donor and patient on the overall survival. The BMT study we consider is based on donors and patients that are genotype matched, and this therefore leads to a missing data problem. We show how Aalen's additive risk model can be applied in this setting with the benefit that the time-varying haplo-match effect can be easily studied. This problem has not been considered before, and the standard approach where one would use the EM-algorithm cannot be applied for this model because the likelihood is hard to evaluate without additional assumptions. We suggest an approach based on multivariate estimating equations that are solved using a recursive structure. This approach leads to an estimator where the large sample properties can be developed using product-integration theory. Small sample properties are investigated using simulations in a setting that mimics the motivating haplo-match problem.

A Bayesian hierarchical model for detecting haplotype-haplotype and haplotype-environment interactions in genetic association studies

OBJECTIVE

Genetic association studies based on haplotypes are powerful in the discovery and characterization of the genetic basis of complex human diseases. However, statistical methods for detecting haplotype-haplotype and haplotype-environment interactions have not yet been fully developed owing to the difficulties encountered: large numbers of potential haplotypes and unknown haplotype pairs. Furthermore, methods for detecting the association between rare haplotypes and disease have not kept pace with their counterpart of common haplotypes.

METHODS/RESULTS

We herein propose an efficient and robust method to tackle these problems based on a Bayesian hierarchical generalized linear model. Our model simultaneously fits environmental effects, main effects of numerous common and rare haplotypes, and haplotype-haplotype and haplotype-environment interactions. The key to the approach is the use of a continuous prior distribution on coefficients that favors sparseness in the fitted model and facilitates computation. We develop a fast expectation-maximization algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares for classical generalized linear models as implemented in the R package glm. We evaluate the proposed method and compare its performance to existing methods on extensive simulated data.

CONCLUSION

The results show that the proposed method performs well under all situations and is more powerful than existing approaches.

Polymorphisms in nucleotide excision repair genes and endometrial cancer risk

BACKGROUND

Exposure to estrogens increases the risk of endometrial cancer. Certain estrogen metabolites can form bulky DNA adducts, which are removed via nucleotide excision repair (NER), and the ability to carry out this repair might be related to endometrial cancer risk.

METHODS

We examined 64 tag and functional single-nucleotide polymorphisms (SNPs) in the NER genes ERCC1, ERCC2 (XPD), ERCC3 (XPB), ERCC4 (XPF), ERCC5 (XPG), LIG1, XPA, and XPC in a population-based case-control study in Washington state, with 783 endometrial cancer cases and 795 controls.

RESULTS

The presence of ERCC5 rs4150386 C, LIG1 rs3730865 C, XPA rs2808667 T, or XPC rs3731127 T alleles was associated with risk of endometrial cancer, with respective age-, county-, and reference year-adjusted per-allele ORs and 95% CIs of 0.68 (0.53-0.87, P = 0.002), 1.46 (1.02-2.10, P = 0.04), 0.71 (0.52-0.97, P = 0.03), and 1.57 (1.13-2.17, P = 0.007), respectively.

CONCLUSIONS

Certain ERCC5, LIG1, XPA, and XPC genotypes might influence endometrial cancer risk.

IMPACT

Because of multiple redundancies in DNA repair pathways (and therefore a low prior probability) and the large number of associations examined, false-positive findings are likely. Further characterization of the relation between variation in NER genes and endometrial cancer risk is warranted.

Powerful multi-marker association tests: unifying genomic distance-based regression and logistic regression

To detect genetic association with common and complex diseases, many statistical tests have been proposed for candidate gene or genome-wide association studies with the case-control design. Due to linkage disequilibrium (LD), multi-marker association tests can gain power over single-marker tests with a Bonferroni multiple testing adjustment. Among many existing multi-marker association tests, most target to detect only one of many possible aspects in distributional differences between the genotypes of cases and controls, such as allele frequency differences, while a few new ones aim to target two or three aspects, all of which can be implemented in logistic regression. In contrast to logistic regression, a genomic distance-based regression (GDBR) approach aims to detect some high-order genotypic differences between cases and controls. A recent study has confirmed the high power of GDBR tests. At this moment, the popular logistic regression and the emerging GDBR approaches are completely unrelated; for example, one has to choose between the two. In this article, we reformulate GDBR as logistic regression, opening a venue to constructing other powerful tests while overcoming some limitations of GDBR. For example, asymptotic distributions can replace time-consuming permutations for deriving P-values and covariates, including gene-gene interactions, can be easily incorporated. Importantly, this reformulation facilitates combining GDBR with other existing methods in a unified framework of logistic regression. In particular, we show that Fisher's P-value combining method can boost statistical power by incorporating information from allele frequencies, Hardy-Weinberg disequilibrium, LD patterns, and other higher-order interactions among multi-markers as captured by GDBR.

Genome-wide association studies using haplotype clustering with a new haplotype similarity

Association analysis, with the aim of investigating genetic variations, is designed to detect genetic associations with observable traits, which has played an increasing part in understanding the genetic basis of diseases. Among these methods, haplotype-based association studies are believed to possess prominent advantages, especially for the rare diseases in case-control studies. However, when modeling these haplotypes, they are subjected to statistical problems caused by rare haplotypes. Fortunately, haplotype clustering offers an appealing solution. In this research, we have developed a new befitting haplotype similarity for "affinity propagation" clustering algorithm, which can account for the rare haplotypes primely, so as to control for the issue on degrees of freedom. The new similarity can incorporate haplotype structure information, which is believed to enhance the power and provide high resolution for identifying associations between genetic variants and disease. Our simulation studies show that the proposed approach offers merits in detecting disease-marker associations in comparison with the cladistic haplotype clustering method CLADHC. We also illustrate an application of our method to cystic fibrosis, which shows quite accurate estimates during fine mapping.

Estimating haplotype effects for survival data

Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.

Haplotype misclassification resulting from statistical reconstruction and genotype error, and its impact on association estimates

Haplotypes are an important concept for genetic association studies, but involve uncertainty due to statistical reconstruction from single nucleotide polymorphism (SNP) genotypes and genotype error. We developed a re-sampling approach to quantify haplotype misclassification probabilities and implemented the MC-SIMEX approach to tackle this as a 3 x 3 misclassification problem. Using a previously published approach as a benchmark for comparison, we evaluated the performance of our approach by simulations and exemplified it on real data from 15 SNPs of the APM1 gene. Misclassification due to reconstruction error was small for most, but notable for some, especially rarer haplotypes. Genotype error added misclassification to all haplotypes resulting in a non-negligible drop in sensitivity. In our real data example, the bias of association estimates due to reconstruction error alone reached -48.2% for a 1% genotype error, indicating that haplotype misclassification should not be ignored if high genotype error can be expected. Our 3 x 3 misclassification view of haplotype error adds a novel perspective to currently used methods based on genotype intensities and expected number of haplotype copies. Our findings give a sense of the impact of haplotype error under realistic scenarios and underscore the importance of high-quality genotyping, in which case the bias in haplotype association estimates is negligible.

Associations between NBS1 polymorphisms, haplotypes and smoking-related cancers

Constituents of tobacco smoke can cause DNA double-strand breaks (DSBs), leading to tumorigenesis. The NBS1 gene product is a vital component in DSB detection and repair, thus genetic variations may influence cancer development. We examined the associations between NBS1 polymorphisms and haplotypes and newly incident smoking-related cancers in three case-control studies (Los Angeles: 611 lung and 601 upper aero-digestive tract (UADT) cancer cases and 1040 controls; Memorial Sloan-Kettering Cancer Center: 227 bladder cancer cases and 211 controls and Taixing, China: 218 esophagus, 206 stomach, 204 liver cancer cases and 415 controls). rs1061302 was associated with cancers of the lung [adjusted odds ratio (OR(adj)) = 1.6, 95% confidence interval (CI): 1.2, 2.4], larynx (OR(adj) = 0.56, 95% CI: 0.32, 0.97) and liver (OR(adj) = 1.7, 95% CI: 1.0, 2.9). Additionally, positive associations were found for rs709816 with bladder cancer (OR(adj) = 4.2, 95% CI: 1.4, 12) and rs1063054 with lung cancer (OR(adj) = 1.6, 95% CI: 1.0, 2.3). Some associations in lung and stomach cancers varied with smoking status. CAC haplotype was positively associated with smoking-related cancers: lung (OR(adj) = 1.7, 95% CI: 1.1, 2.9) and UADT (OR(adj) = 2.0, 95% CI: 1.1, 3.7), specifically, oropharynx (OR(adj) = 2.1, 95% CI: 1.0, 4.2) and larynx (OR(adj) = 4.8, 95% CI: 1.7, 14). Bayesian false-discovery probabilities were calculated to assess Type I error. It appears that NBS1 polymorphisms and haplotypes may be associated with smoking-related cancers and that these associations may differ by smoking status. Our findings also suggest that single-nucleotide polymorphisms located in the binding region of the MRE-RAD50-NBS1 complex or microRNA targeted pathways may influence tumor development. These hypotheses should be further examined in functional studies.

A general framework for studying genetic effects and gene-environment interactions with missing data

Missing data arise in genetic association studies when genotypes are unknown or when haplotypes are of direct interest. We provide a general likelihood-based framework for making inference on genetic effects and gene-environment interactions with such missing data. We allow genetic and environmental variables to be correlated while leaving the distribution of environmental variables completely unspecified. We consider 3 major study designs-cross-sectional, case-control, and cohort designs-and construct appropriate likelihood functions for all common phenotypes (e.g. case-control status, quantitative traits, and potentially censored ages at onset of disease). The likelihood functions involve both finite- and infinite-dimensional parameters. The maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Expectation-Maximization (EM) algorithms are developed to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed inferential and numerical methods perform well in practical settings. Illustration with a genome-wide association study of lung cancer is provided.

STrengthening the REporting of Genetic Association Studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Genetic polymorphisms and human sensitivity to opioid analgesics

Opioid analgesics are commonly used for the treatment of acute as well as chronic, moderate to severe pain. Well-known, however, is the wide interindividual variability in sensitivity to opioids that exists, which has often been a critical problem in pain treatment. To date, only a limited number of studies have addressed the relationship between human genetic variations and sensitivity to opioids, and such studies are still in their early stages. Therefore, revealing the relationship between genetic variations in many candidate genes and individual differences in sensitivity to opioids will provide valuable information for appropriate individualization of opioid doses required for adequate pain control. Although the methodologies for such association studies can be diverse, here we summarize protocols for investigating the association between genetic polymorphisms and sensitivity to opioids in human volunteers and patients undergoing painful surgery.

Genetic variation of the human urinary tract innate immune response and asymptomatic bacteriuria in women

Background

Although several studies suggest that genetic factors are associated with human UTI susceptibility, the role of DNA variation in regulating early in vivo urine inflammatory responses has not been fully examined. We examined whether candidate gene polymorphisms were associated with altered urine inflammatory profiles in asymptomatic women with or without bacteriuria.

Methodology

We conducted a cross-sectional analysis of asymptomatic bacteriuria (ASB) in 1,261 asymptomatic women ages 18-49 years originally enrolled as participants in a population-based case-control study of recurrent UTI and pyelonephritis. We genotyped polymorphisms in CXCR1, CXCR2, TLR1, TLR2, TLR4, TLR5, and TIRAP in women with and without ASB. We collected urine samples and measured levels of uropathogenic bacteria, neutrophils, and chemokines.

Principal Findings

Polymorphism TLR2_G2258A, a variant associated with decreased lipopeptide-induced signaling, was associated with increased ASB risk (odds ratio 3.44, 95%CI; 1.65–7.17). Three CXCR1 polymorphisms were associated with ASB caused by gram-positive organisms. ASB was associated with urinary CXCL-8 levels, but not CXCL-5, CXCL-6, or sICAM-1 (P≤0.0001). Urinary levels of CXCL-8 and CXCL-6, but not ICAM-1, were associated with higher neutrophil levels (P≤0.0001). In addition, polymorphism CXCR1_G827C was associated with increased CXCL-8 levels in women with ASB (P = 0.004).

Conclusions

TLR2 and CXCR1 polymorphisms were associated with ASB and a CXCR1 variant was associated with urine CXCL-8 levels. These results suggest that genetic factors are associated with early in vivo human bladder immune responses prior to the development of symptomatic UTIs.

Test selection with application to detecting disease association with multiple SNPs

We consider the motivating problem of testing for association between a phenotype and multiple single nucleotide polymorphisms (SNPs) within a candidate gene or region. Various statistical approaches have been proposed, including those based on either (combining univariate) single-locus analyses or (multivariate) multilocus analyses. However, it is known in theory that there is no single uniformly most powerful test to detect association with multiple SNPs. On the other hand, several tests have been shown to be among frequent winners across a range of practical situations, but the identity of the most powerful one changes with the situation in an unknown way. Here we propose a novel test selection procedure to select from five such tests: a so-called UminP test that combines multiple univariate/single-locus score tests by taking the minimum of their p values as its test statistic, a multivariate score test and its two modifications, and a so-called sum test. We also illustrate its application to selecting genotype codings for the sum test since the performance of the sum test depends on its genotype coding in an unknown way. Our major contributions include the methodology of estimating the power of a given test with a given dataset and the idea of using the estimated power as the criterion for test selection. We also propose a fast simulation-based method to calculate p values for the test selection procedure and for any method of combining p values. Our numerical results indicated that the proposed test selection procedure always yielded power close to the most powerful test among the candidate tests at any given situation, and in particular, our proposed test selection performed either better than or as well as the popular combining method of taking the minimum p value of the candidate tests.

Lack of association of endothelial nitric oxide synthase polymorphisms and migraine

OBJECTIVE

The aim of this study was to evaluate if 2 functional endothelial nitric oxide synthase (eNOS) gene polymorphisms might be risk factors for migraine.

BACKGROUND

Nitric oxide synthase promotes the synthesis of nitric oxide (NO). NO is a potent vasodilator and mediates several processes involved in migraine pathophysiology. Only one study has suggested an association with migraine with aura.

METHODS

We performed a sex- and age-matched case-control study using 2 eNOS polymorphisms (rs1800779 and rs1799983), which are in linkage disequilibrium. Genotypes were obtained with allele-specific probes in a real-time polymerase chain reaction assay. Genotypic and allelic distributions were compared with chi(2) method. We also estimated the reconstructed haplotypes and calculated ORs for individual haplotypes.

RESULTS

A total of 337 migraine patients (188 with aura) and 341 healthy controls were recruited. We found no significant differences in the distribution of genotypes and alleles for either polymorphism among clinical subgroups. Neither rs1800779 nor rs1799983 polymorphisms increased the risk for suffering from migraine aura.

CONCLUSIONS

As others have previously reported, we failed to demonstrate genetic association of the eNOS gene with migraine.

Polymorphisms in toll-like receptor 4 and toll-like receptor 9 influence viral load in a seroincident cohort of HIV-1-infected individuals

OBJECTIVES

Toll-like receptors (TLRs) are innate immune sensors that are integral to resisting chronic and opportunistic infections. Mounting evidence implicates TLR polymorphisms in susceptibilities to various infectious diseases, including HIV-1. We investigated the impact of TLR single nucleotide polymorphisms (SNPs) on clinical outcome in a seroincident cohort of HIV-1-infected volunteers.

DESIGN

We analyzed TLR SNPs in 201 antiretroviral treatment-naive HIV-1-infected volunteers from a longitudinal seroincident cohort with regular follow-up intervals (median follow-up 4.2 years, interquartile range 4.4). Participants were stratified into two groups according to either disease progression, defined as peripheral blood CD4(+) T-cell decline over time, or peak and setpoint viral load.

METHODS

Haplotype tagging SNPs from TLR2, TLR3, TLR4, and TLR9 were detected by mass array genotyping, and CD4(+) T-cell counts and viral load measurements were determined prior to antiretroviral therapy initiation. The association of TLR haplotypes with viral load and rapid progression was assessed by multivariate regression models using age and sex as covariates.

RESULTS

Two TLR4 SNPs in strong linkage disequilibrium [1063 A/G (D299G) and 1363 C/T (T399I)] were more frequent among individuals with high peak viral load compared with low/moderate peak viral load (odds ratio 6.65, 95% confidence interval 2.19-20.46, P < 0.001; adjusted P = 0.002 for 1063 A/G). In addition, a TLR9 SNP previously associated with slow progression was found less frequently among individuals with high viral setpoint compared with low/moderate setpoint (odds ratio 0.29, 95% confidence interval 0.13-0.65, P = 0.003, adjusted P = 0.04).

CONCLUSION

This study suggests a potentially new role for TLR4 polymorphisms in HIV-1 peak viral load and confirms a role for TLR9 polymorphisms in disease progression.

Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes

Although prospective logistic regression is the standard method of analysis for case-control data, it has been recently noted that in genetic epidemiologic studies one can use the "retrospective" likelihood to gain major power by incorporating various population genetics model assumptions such as Hardy-Weinberg-Equilibrium (HWE), gene-gene and gene-environment independence. In this article, we review these modern methods and contrast them with the more classical approaches through two types of applications (i) association tests for typed and untyped single nucleotide polymorphisms (SNPs) and (ii) estimation of haplotype effects and haplotype-environment interactions in the presence of haplotype-phase ambiguity. We provide novel insights to existing methods by construction of various score-tests and pseudo-likelihoods. In addition, we describe a novel two-stage method for analysis of untyped SNPs that can use any flexible external algorithm for genotype imputation followed by a powerful association test based on the retrospective likelihood. We illustrate applications of the methods using simulated and real data.

Asymptotic tests of association with multiple SNPs in linkage disequilibrium

We consider detecting associations between a trait and multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD). To maximize the use of information contained in multiple SNPs while minimizing the cost of large degrees of freedom (DF) in testing multiple parameters, we first theoretically explore the sum test derived under a working assumption of a common association strength between the trait and each SNP, testing on the corresponding parameter with only one DF. Under the scenarios that the association strengths between the trait and the SNPs are close to each other (and in the same direction), as considered by Wang and Elston [Am. J. Hum. Genet. [2007] 80:353-360], we show with simulated data that the sum test was powerful as compared to several existing tests; otherwise, the sum test might have much reduced power. To overcome the limitation of the sum test, based on our theoretical analysis of the sum test, we propose five new tests that are closely related to each other and are shown to consistently perform similarly well across a wide range of scenarios. We point out the close connection of the proposed tests to the Goeman test. Furthermore, we derive the asymptotic distributions of the proposed tests so that P-values can be easily calculated, in contrast to the use of computationally demanding permutations or simulations for the Goeman test. A distinguishing feature of the five new tests is their use of a diagonal working covariance matrix, rather than a full covariance matrix as used in the usual Wald or score test. We recommend the routine use of two of the new tests, along with several other tests, to detect disease associations with multiple linked SNPs.

Association analysis of Wnt pathway genes on prostate-specific antigen recurrence after radical prostatectomy

BACKGROUND

Approximately one-third of prostate cancer (PCa) patients show biochemical failure after radical prostatectomy (RP) and are prone to develop metastasis with significant mortality. Although aberrant Wnt/beta-catenin (CTNNB1) signaling has been observed in numerous types of human cancers, including PCa, to our knowledge there is currently no information on the role of Wnt signaling gene polymorphisms in PCa.

METHODS

We comprehensively studied the contribution of genetic variations in CTNNB1 and adenomatous polyposis coli (APC), one of the key genes encoding the CTNNB1 destruction complex, to PCa risk and prognosis after RP using a hospital-based case-control study. We selected and genotyped 13 tagged single-nucleotide polymorphisms (tSNP) to predict common variants across entire APC and CTNNB1 genes in 307 patients with clinically localized PCa who received RP and 371 unaffected controls.

RESULTS

Four tSNPs (rs3846716, rs2431238, rs41115, and rs565453) and a specific haplotype (GTAAGA) in the APC tumor suppressor gene were associated with a 0.57- to 0.71-fold lower risk of localized PCa. The association of tSNPs with prostate-specific antigen (PSA) recurrence in PCa patients was then analyzed by Kaplan-Meier analysis and Cox regression model. Interestingly, we found that the APC rs3846716 GA/AA genotypes were also significantly associated with poorer PSA-free survival (log-rank test, P = 0.037) compared with the GG genotype.

CONCLUSIONS

This is the first report documenting the potential prognostic role of the APC rs3846716 GA/AA genotype on PSA recurrence after RP.

Modeling Informatively Missing Genotypes in Haplotype Analysis

It is common to have missing genotypes in practical genetic studies. The majority of the existing statistical methods, including those on haplotype analysis, assume that genotypes are missing at random-that is, at a given marker, different genotypes and different alleles are missing with the same probability. In our previous work, we have demonstrated that the violation of this assumption may lead to serious bias in haplotype frequency estimates and haplotype association analysis. We have proposed a general missing data model to simultaneously characterize missing data patterns across a set of two or more biallelic markers. We have proved that haplotype frequencies and missing data probabilities are identifiable if and only if there is linkage disequilibrium between these markers under the general missing data model. In this study, we extend our work to multi-allelic markers and observe a similar finding. Simulation studies on the analysis of haplotypes consisting of two markers illustrate that our proposed model can reduce the bias for haplotype frequency estimates due to incorrect assumptions on the missing data mechanism. Finally, we illustrate the utilities of our method through its application to a real data set from a study of scleroderma.

Genetic variation in immune regulation and DNA repair pathways and stomach cancer in China

The incidence of stomach cancer is high in certain parts of the world, particularly in China. Chronic Helicobacter pylori infection is the main risk factor, yet the vast majority of infected individuals remain unaffected with cancer, suggesting an important role of other risk factors. We conducted a population-based case-control study including 196 incident stomach cancer cases and 397 matched controls to test the hypothesis that adverse single nucleotide polymorphism (SNP) genotypes and haplotypes within genes of the DNA repair and immune regulatory pathways are associated with increased stomach cancer risk. Genomic DNA isolated from blood samples was used for genotyping, and results were obtained for 57 putatively functional SNPs in 28 genes. Odds ratios (OR) and 95% confidence intervals (95% CI) were obtained from adjusted logistic regression models. For PTGS2, a gene involved in the inflammatory response, associations with stomach cancer risk were observed for TC genotype carriers of rs5279 (OR, 0.24; 95% CI, 0.08-0.73), CT genotype carriers of the 3'-untranslated region SNP rs689470 (OR, 7.49; 95% CI, 1.21-46.20), and CTTC haplotype carriers of rs5277, rs5278, rs5279, and rs689470 (OR, 0.41; 95% CI, 0.18-0.95). For ERCC5, a gene involved in nucleotide excision repair, TC genotype carriers of rs1047768 (OR, 0.65; 95% CI, 0.41-1.03), GC genotype carriers of the nonsynonymous SNP rs2227869 (OR, 0.30; 95% CI, 0.13-0.67), and CCG haplotype carriers of rs1047768, rs17655, and rs2227869 (OR, 0.45; 95% CI, 0.20-1.04) were associated with reduced stomach cancer risk. In conclusion, PTGS2 and ERCC5 were associated with stomach cancer risk in a Chinese population.

Haplotype associations with quantitative traits in the presence of complex multilocus and heterogeneous effects

In genetic mapping of complex traits, scored haplotypes are likely to represent only a subset of all causal polymorphisms. At the extreme of this scenario, observed polymorphisms are not themselves functional, and only linked to causal ones via linkage disequilibrium (LD). We will demonstrate that due to such incomplete knowledge regarding the underlying genetic mechanism, the variance of a trait may become different between the scored haplotypes. Thus, unequal variances between haplotypes may be indicative of additional functional polymorphisms affecting the trait. Methods accounting for such haplotype-specific variance may also provide an increased power to detect complex associations. We suggest ways to estimate and test these haplotypic variance contrasts, and incorporate them into the haplotypic tests for association. We further extend this approach to data with unknown gametic phase via likelihood-based simultaneous estimation of haplotypic effects and their frequencies. We find our approach to provide additional power, especially under the following types of models: (a) where scored and unobserved variants are epistatically interacting with each other; and (b) under heterogeneity models, where multiple unobserved mutations are linked to non-functional observed polymorphisms via LD. An illustrative example of usefulness of the method is discussed, utilizing analysis of multilocus effects within the catechol-O-methyltransferase gene.

Toll-like receptor polymorphisms and susceptibility to urinary tract infections in adult women

Background

Although behavioral risk factors are strongly associated with urinary tract infection (UTI) risk, the role of genetics in acquiring this disease is poorly understood.

Methodology/Principal Findings

To test the hypothesis that polymorphisms in Toll-like receptor (TLR) pathway genes are associated with susceptibility to UTIs, we conducted a population-based case-control study of women ages 18–49 years. We examined DNA variants in 9 TLR pathway genes in 431 recurrent cystitis (rUTI) cases, 400 pyelonephritis cases, and 430 controls with no history of UTIs. In the Caucasian subgroup of 987 women, polymorphism TLR4_A896G was associated with protection from rUTI, but not pyelonephritis, with an odds ratio (OR) of 0.54 and a 95% confidence interval (CI) of 0.31 to 0.96. Polymorphism TLR5_C1174T, which encodes a variant that abrogates flagellin-induced signaling, was associated with an increased risk of rUTI (OR(95%CI): 1.81 (1.00–3.08)), but not pyelonephritis. Polymorphism TLR1_G1805T was associated with protection from pyelonephritis (OR(95%CI): 0.53 (0.29–0.96)).

Conclusions

These results provide the first evidence of associations of TLR5 and TLR1 variants with altered risks of acquiring rUTI and pyelonephritis, respectively. Although these data suggest that TLR polymorphisms are associated with adult susceptibility to UTIs, the statistical significance was modest and will require further study including validation with independent cohorts.

STrengthening the REporting of Genetic Association studies (STREGA)--an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies

Case-control association studies often aim to investigate the role of genes and gene-environment interactions in terms of the underlying haplotypes (i.e., the combinations of alleles at multiple genetic loci along chromosomal regions). The goal of this article is to develop robust but efficient approaches to the estimation of disease odds-ratio parameters associated with haplotypes and haplotype-environment interactions. We consider "shrinkage" estimation techniques that can adaptively relax the model assumptions of Hardy-Weinberg-Equilibrium and gene-environment independence required by recently proposed efficient "retrospective" methods. Our proposal involves first development of a novel retrospective approach to the analysis of case-control data, one that is robust to the nature of the gene-environment distribution in the underlying population. Next, it involves shrinkage of the robust retrospective estimator toward a more precise, but model-dependent, retrospective estimator using novel empirical Bayes and penalized regression techniques. Methods for variance estimation are proposed based on asymptotic theories. Simulations and two data examples illustrate both the robustness and efficiency of the proposed methods.

STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

A systematic search for SNPs/haplotypes associated with disease phenotypes using a haplotype-based stepwise procedure

Background

Genotyping technologies enable us to genotype multiple Single Nucleotide Polymorphisms (SNPs) within selected genes/regions, providing data for haplotype association analysis. While haplotype-based association analysis is powerful for detecting untyped causal alleles in linkage-disequilibrium (LD) with neighboring SNPs/haplotypes, the inclusion of extraneous SNPs could reduce its power by increasing the number of haplotypes with each additional SNP.

Methods

Here, we propose a haplotype-based stepwise procedure (HBSP) to eliminate extraneous SNPs. To evaluate its properties, we applied HBSP to both simulated and real data, generated from a study of genetic associations of the bactericidal/permeability-increasing (BPI) gene with pulmonary function in a cohort of patients following bone marrow transplantation.

Results

Under the null hypothesis, use of the HBSP gave results that retained the desired false positive error rates when multiple comparisons were considered. Under various alternative hypotheses, HBSP had adequate power to detect modest genetic associations in case-control studies with 500, 1,000 or 2,000 subjects. In the current application, HBSP led to the identification of two specific SNPs with a positive validation.

Conclusion

These results demonstrate that HBSP retains the essence of haplotype-based association analysis while improving analytic power by excluding extraneous SNPs. Minimizing the number of SNPs also enables simpler interpretation and more cost-effective applications.

On combining triads and unrelated subjects data in candidate gene studies: an application to data on testicular cancer

Combining data collected from different sources is a cost-effective and time-efficient approach for enhancing the statistical efficiency in estimating weak-to-modest genetic effects or gene-gene or gene-environment interactions. However, combining data across studies becomes complicated when data are collected under different study designs, such as family-based and unrelated individual-based (e.g., population-based case-control design). In this paper, we describe a general method that permits the joint estimation of effects on disease risk of genes, environmental factors, and gene-gene/gene-environment interactions under a hybrid design that includes cases, parents of cases, and unrelated individuals. We provide both asymptotic theory and statistical inference. Extensive simulation experiments demonstrate that the proposed estimation and inferential methods perform well in realistic settings. We illustrate the method by an application to a study of testicular cancer.

Mixed modeling and multiple imputation for unobservable genotype clusters

Understanding the genetic contributions to complex diseases will require consideration of interaction across multiple genes and environmental factors. At the same time, capturing information on allelic phase, that is, whether alleles within a gene are in cis (on the same chromosome) or in trans (on different chromosomes), is critical when using haplotypic approaches in disease association studies. This paper proposes a combination of mixed modeling and multiple imputation for assessing high-order genotype-phenotype associations while accounting for the uncertainty in phase inherent in population-based association studies. This method provides a flexible statistical framework for controlling for potential confounders and assessing gene-environment and gene-gene interactions in studies of unrelated individuals where the haplotypic phase is generally unobservable. The proposed method is applied to a cohort of 626 subjects with human immunodeficiency virus (HIV) to assess the potential contribution of four genes, apolipoprotein-C-III, apolipoprotein-E, endothelial lipase and hepatic lipase in predicting lipid abnormalities. A simulation study is also presented to describe the method performance.

Analgesic requirements after major abdominal surgery are associated with OPRM1 gene polymorphism genotype and haplotype

Aims: The association between SNPs of the human OPRM1 gene encoding the µ-opioid receptor and postoperative analgesic requirements in surgical patients remains controversial. Here, we evaluate whether any of the five tag SNPs (A118G, IVS2+G691C, IVS3+G5953A, IVS3+A8449G and TAA+A2109G) representing the four linkage disequilibrium blocks of the OPRM1 gene influences postoperative analgesic requirements. Materials & methods: We studied 138 adult Japanese patients who underwent major open abdominal surgery under combined general and epidural anesthesia and received continuous postoperative epidural analgesia with opioids. Results: The 118G homozygous (GG) patients required 24-h postoperative analgesics more than 118A homozygous (AA) and heterozygous (AG) patients. Tag SNP haplotypes also were associated with 24-h postoperative analgesic requirements. Conclusions: These results suggest that OPRM1 gene tag SNP genotypes and haplotypes can primarily contribute to prediction of postoperative analgesic requirements in individual patients undergoing major open abdominal surgery.

Resequencing of the human major histocompatibility complex in patients with rheumatoid arthritis and healthy controls in Alaska Natives of Southeast Alaska

High prevalence and severity of rheumatoid arthritis (RA) with an early age of onset have previously been described in Alaska Native and American Indian (AN/AI) populations. The contribution of HLA-DRB1 alleles encoding a similar amino acid sequence, referred to as the shared epitope (SE), to RA risk is well recognized in multiple populations worldwide. DRB1*1402 allele is the major SE-encoding allele in AN/AI populations. However, DRB1*1402 is highly prevalent in healthy Alaska Natives of Southeast Alaska (AN), with no significant difference from RA patients, indicating this allele alone is not informative for defining genetic risk and non-human leukocyte antigen (non-HLA) genes are likely important in AN. We sought to deep resequence the human major histocompatibility complex (MHC) to characterize the single-nucleotide polymorphism (SNP) haplotypes within this region in RA cases and controls in AN. Approximately 99 kb of the MHC was resequenced with 95 amplicons throughout this region. Thirty-four cases and 74 controls were examined. A total of 696 SNPs were discovered from 85 of the selected 95 amplicons. Disease association signals were detected for nine of the 95 amplicons analyzed. Increased risk of RA was associated with five amplicons in the class I, class II or class III region and resistance to disease with four amplicons in the class I region. Our results indicate that non-HLA MHC genes and/or unknown exogenous factors likely modulate risk of RA in the AN population.