不完全病例对照研究中对照组部分基因信息缺失基因-环境交互作用的估计

目的探讨不完全病例对照研究中对照组基因信息部分缺失时基因一环境交互作用的估计。方法在Stata9．0软件上采用MonteCarlo方法模拟不同基因信息缺失比例数据，对缺失数据采用hotdeck多重填补程序后分析和删除缺失值分析结果进行比较。结果缺失数据〈50％时，hotdeck多重填补后分析和删除缺失值分析对环境主效应、基因主效应以及基因-环境交互作用的估计系数接近完全数据的系数，随缺失比例的增加，两种方法的估计方差均增加，但hotdeck多重填补估计方差小于删除缺失值分析。结论不完全病例对照研究中，对照组基因信息缺失比例〈50％时，可以用hotdeck填补方法充分利用已有的信息估计基因-环境的交互作用，提高估计精度。     

代谢酶基因多态性与环境暴露交互作用的分析方法及其应用

目的 以肿瘤易感基因谷胱苷肽-S转硫酶（GST）M1缺失基因型为例,说明基因与环境暴露交互作用的分析方法以及应用。方法 采用社区为基础的病例对照研究方法,代谢酶基因多态性的检测用PCR技术,资料分析用多因素logistic回归模型。研究对象为1997年1月至1998年12月经扬中市人民医院确诊,肠型胃癌病例112例,以同期该地无上消化道肿瘤“健康”人群为对照,共675例。结果 调整混杂因素后,GST M1缺失基因型与既往吸烟史的交互作用系数为3.38,OReg值达8.40,有极显著意义,为4型交互作用中的超相乘模型;GST M1缺失基因型与吸烟量的交互作用呈高暴露-基因效应,交互作用系数分别为0.995、2.085和2.157,即随着暴露剂量增加,交互作用强度也逐渐增加;与饮酒量呈低暴露-基因效应,交互作用系数分别为1.01和0.97,交互作用强度随暴露剂量增加而逐渐降低。结论 基于logistic模型的分析方法,可用于评价基因-环境之间的交互作用,以及剂量反应关系的暴露基因效应。     

对数线性模型在病例-父母对照研究中的应用

介绍应用对数线性模型分析病例-父母对照研究设计的方法.以亚甲基四氢叶酸还原酶基因(MTHFR)C677T与唇腭裂的关联研究为例,应用对数线性模型分析母亲、子代基因及其交互作用与唇腭裂的关系.结果显示变异型纯合子母亲的子代发生唇腭裂的风险低于野生型纯合子母亲的子代,S2=0.43 (95％CI:0.19 ～ 0.95),未发现唇腭裂与子代基因及母子交互作用相关.应用对数线性模型分析病例-父母对照研究设计的方法尤其适用于妊娠期疾病与源于胚胎时期疾病等的病因学研究.     

实现logistic与Cox回归相乘相加交互作用的临床实践宏程序

病例对照研究常采用条件或非条件logistic分析,生存资料分析常采用Cox比例模型,但多数文献仅纳入主效应模型,然而广义线性模型不同于一般线性模型,其交互作用分为相乘交互与相加交互作用,前者只有统计学意义而后者更符合生物学意义。笔者以SAS 9.4软件编写宏,在计算logistic与Cox相乘交互项同时计算交互对比度、归因比、交互作用指数指标及利用Wald、Delta、PL(profile likelihood) 3种方法的可信区间评价相加交互作用,便于临床流行病学与遗传学大数据分析相乘相加交互作用时参考。     

病例父母对照研究在遗传性疾病中的应用

现代分子生物技术和基因组信息学的发展,使进一步寻找疾病的易感基因成为可能,许多新的统计方法被引入多基因遗传病的研究,如群体关联分析(病例对照研究)、单纯病例研究、病例父母对照研究和患病亲属对研究,这些都是探索疾病的易感基因及研究基因与环境致病因素交互作用常用的研究方法.但是,病例对照研究在选择对照时要求很严格,该方法要求对照的遗传背景一致,而且由于群体的混合等容易造成虚假关联现象;单纯病例研究只能评估环境致病因素和易感基因都存在时的相乘模型交互作用,而不能单独评估易感基因的效应,以及它还存在不同亚人群暴露率和基因频率不一致所引起的偏倚.因此,Fack和Rubinstein提出了病例父母对照研究.     

系统性红斑狼疮HLA-DM基因与环境危险因素研究

目的探索HLA-DM基因及其与环境危险因素的交互作用对系统性红斑狼疮(SLE)发病的影响.方法采取病例对照研究,用PCR-RFLP的方法确定HLA-DM的基因型,用非条件logistic模型分析SLE的危险因素,用广义线性模型分析环境与基因的交互作用.结果共检测到3种DMA与4种DMB等位基因,其分布在SLE组与正常对照组分布一致.logistic模型分析显示SLE的环境危险因素有5个(潮湿、精神刺激、日晒、刀豆、扁桃体感染),女性个人婚育史因素有3个(出生时母亲的年龄、月经初潮年龄、流产次数).广义线性模型分析提示HLA-DMB*0102与扁桃体感染的交互项效应达到显著水平.结论有多个环境危险因素与SLE的发病有关,HLA-DM基因的遗传多态性对SLE发病及活动性没有独立作用,但HLA-DM基因与某些环境因素存在交互作用.     

叉生分析在基因-环境交互作用研究中的应用与意义

目的　探讨叉生分析在基因 环境交互作用研究中的作用和意义。方法　以口服避孕药和Leiden因子Ⅴ基因突变与静脉血栓栓塞的病例对照研究为例 ,分析 2× 4叉生分析表核心信息 ,并与分层分析、单纯病例研究相比较。结果　对基因 环境交互作用的分析依赖于相加或相乘模型的选择。基于相乘模型交互作用的叉生分析OR值为 1.35 (P >0 .0 5 ) ,其结果与分层分析和单纯病例研究一致 ;叉生分析还可计算相加模型的各项指标 :交互作用指数为 3.90、交互作用归因比为72 .2 4 %、交互作用超额相对危险度为 2 5 .0 8(P >0 .0 5 )。结论　叉生分析在基因 环境交互作用分析中可进一步应用。     

Logistic回归模型中连续变量交互作用的分析

Rothman提出生物学交互作用的评价应该基于相加尺度即是否有相加交互作用,而logistic回归模型的乘积项反映的是相乘交互作用.目前国内外文献讨论logistic回归模型中两因素的相加交互作用以两分类变量为主,本文介绍两连续变量或连续变量与分类变量相加交互作用可信区间估计的Bootstrap方法,文中以香港男性肺癌病例对照研究资料为例,辅以免费软件R的实现程序,为研究人员分析交互作用提供参考.     

Logistic回归模型中连续变量交互作用的分析

Rothman提出生物学交互作用的评价应该基于相加尺度即是否有相加交互作用,而logistic回归模型的乘积项反映的是相乘交互作用.目前国内外文献讨论logistic回归模型中两因素的相加交互作用以两分类变量为主,本文介绍两连续变量或连续变量与分类变量相加交互作用可信区间估计的Bootstrap方法,文中以香港男性肺癌病例对照研究资料为例,辅以免费软件R的实现程序,为研究人员分析交互作用提供参考.     

Logistic回归模型中连续变量交互作用的分析

Rothman提出生物学交互作用的评价应该基于相加尺度即是否有相加交互作用,而logistic回归模型的乘积项反映的是相乘交互作用.目前国内外文献讨论logistic回归模型中两因素的相加交互作用以两分类变量为主,本文介绍两连续变量或连续变量与分类变量相加交互作用可信区间估计的Bootstrap方法,文中以香港男性肺癌病例对照研究资料为例,辅以免费软件R的实现程序,为研究人员分析交互作用提供参考.     

Detecting Gene-Environment Interactions Using a Combined Case-Only and Case-Control Approach

The conventional method of detecting gene-environment interactions,the case-control analysis, suffers from low statistical power.In contrast, the case-only analysis/design can be powerful incertain scenarios, although violation of the assumption of independencebetween the genetic and environmental factors can greatly biasthe results. As an alternative, Bayes model averaging may beused to combine the case-control and case-only analyses. Thisapproach first frames the case-control and case-only analysesas variations of a log-linear model. The weighting between these2 models is then a function of the data and prior beliefs onthe independence of the 2 potentially interacting factors. Inthis paper, the authors demonstrate via simulations that whenthere is no prior information on the independence of the geneticand environmental factors, this approach tends to be more powerfulthan the case-control analysis. Additionally, when the geneticand environmental factors are not independent in the population,bias is substantially reduced, with a corresponding reductionin type I error in comparison with the case-only analysis. Increasedpower or increased robustness to violations of the independenceassumption may be obtained with more appropriate prior specification.The authors use an example data analysis to demonstrate theadvantages of this approach. Bayesian estimation; Bayesian model; case-control studies; epidemiologic methods; interaction     

Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs

To evaluate the risk of a disease associated with the joint effects of genetic susceptibility and environmental exposures, epidemiologic researchers often test for non-multiplicative gene-environment effects from case-control studies. In this article, we present a comparative study of four alternative tests for interactions: (i) the standard case-control method; (ii) the case-only method, which requires an assumption of gene-environment independence for the underlying population; (iii) a two-step method that decides between the case-only and case-control estimators depending on a statistical test for the gene-environment independence assumption and (iv) a novel empirical-Bayes (EB) method that combines the case-control and case-only estimators depending on the sample size and strength of the gene-environment association in the data. We evaluate the methods in terms of integrated Type I error and power, averaged with respect to varying scenarios for gene-environment association that are likely to appear in practice. These unique studies suggest that the novel EB procedure overall is a promising approach for detection of gene-environment interactions from case-control studies. In particular, the EB procedure, unlike the case-only or two-step methods, can closely maintain a desired Type I error under realistic scenarios of gene-environment dependence and yet can be substantially more powerful than the traditional case-control analysis when the gene-environment independence assumption is satisfied, exactly or approximately. Our studies also reveal potential utility of some non-traditional case-control designs that samples controls at a smaller rate than the cases. Apart from the simulation studies, we also illustrate the different methods by analyzing interactions of two commonly studied genes, N-acetyl transferase type 2 and glutathione s-transferase M1, with smoking and dietary exposures, in a large case-control study of colorectal cancer.     

Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias

BACKGROUND: The case-only study for investigating gene-environment interactions provides increased statistical efficiency over case-control analyses. This design has been criticized for being susceptible to bias arising from non-independence between the genetic and environmental factors in the population. Given that independence is critical to the validity of case-only estimates of interaction, researchers frequently use controls to evaluate whether the independence assumption is tenable, as advised in the literature. Our work investigates to what extent this approach is appropriate and how non-independence can be accounted for in case-only analyses. METHODS: We provide a formula in epidemiological terms that illustrates the relationship between the gene-environment association measured among controls and the gene-environment association in the source population. Using this formula, we conducted sensitivity analyses to describe the circumstances in which controls can be used as proxy for the source population when evaluating gene-environment independence. Lastly, we generated hypothetical cohort data to examine whether multivariable modelling approaches can be used to control for non-independence. RESULTS: Our sensitivity analyses show that controls should not be used to evaluate gene-environment independence in the population, even when the baseline risk of disease is low (i.e. 1%), and the interaction and independent effects are moderate (i.e. risk ratio = 2). When the factors are associated, it is possible to remove bias arising from non-independence using standard statistical multivariable techniques in case-only analyses. CONCLUSIONS: Even when the disease risk is low, evaluation of gene-environment independence in controls does not provide a consistent test for bias in the case-only study. Given that control for non-independence is possible when the source of the non-independence can be conceptualized, the case-only design may still be a useful epidemiological tool for examining gene-environment interactions.     

Designing and analysing case-control studies to exploit independence of genotype and exposure

Genetic susceptibility and environmental exposures play a synergistic role in the aetiology of many diseases. We consider a case-control study of a rare disease in relation to a categorical exposure and a genetic factor under the assumption that the genotype and the exposure occur independently in the population under study. Using a logistic model for risk, we describe maximum likelihood methods based on log-linear models that explicitly impose the independence assumption, something the usual logistic regression analyses cannot do. The estimator of the genotype–exposure interaction effect depends only on data from cases. Estimators for genotype and for exposure effects depend also on data from controls, but only through their respective marginal totals. All three estimators have smaller variance than they would were independence not enforced. These results have important implications for design: (i) Case-only studies can efficiently estimate gene-by-environment interactions. (ii) Studies where controls are genotyped anonymously can estimate genotype, exposure, and interaction effects as efficiently as designs where genotype and exposure data are linked. This feature addresses a growing concern of human subjects review boards. (iii) Exposure and interaction effects, but not genotype effects, can be estimated from studies where genetic information is only collected from cases (although one can recover the genotype effect if external gene prevalence data exist). Such designs have the compensatory benefit that the response rate (hence, validity) is higher when controls are not subjected to intrusive tissue sampling. However, the independence assumption can be checked only with linked genotype and exposure data for some controls. We illustrate the methods by applying them to recent study of cleft palate in relation to maternal cigarette smoking and to a variant of the transforming growth factor alpha gene in the child. © 1997 by John Wiley & Sons, Ltd.     

Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes

The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses' Health Study, 1976-2006, and the Health Professionals Follow-up Study, 1986-2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.     

A maximum likelihood method for studying gene-environment interactions under conditional independence of genotype and exposure

Given the biomedical interest in gene-environment interactions along with the difficulties inherent in gathering genetic data from controls, epidemiologists need methodologies that can increase precision of estimating interactions while minimizing the genotyping of controls. To achieve this purpose, many epidemiologists suggested that one can use case-only design. In this paper, we present a maximum likelihood method for making inference about gene-environment interactions using case-only data. The probability of disease development is described by a logistic risk model. Thus the interactions are model parameters measuring the departure of joint effects of exposure and genotype from multiplicative odds ratios. We extend the typical inference method derived under the assumption of independence between genotype and exposure to that under a more general assumption of conditional independence. Our maximum likelihood method can be applied to analyse both categorical and continuous environmental factors, and generalized to make inference about gene-gene-environment interactions. Moreover, the application of this method can be reduced to simply fitting a multinomial logistic model when we have case-only data. As a consequence, the maximum likelihood estimates of interactions and likelihood ratio tests for hypotheses concerning interactions can be easily computed. The methodology is illustrated through an example based on a study about the joint effects of XRCC1 polymorphisms and smoking on bladder cancer. We also give two simulation studies to show that the proposed method is reliable in finite sample situation.     

Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons

Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumption of gene-environment independence for the underlying population; and 3) a variety of hybrid methods, including empirical-Bayes, 2-step, and model averaging, that aim at gaining power by exploiting the assumption of gene-environment independence and yet can protect against false positives when the independence assumption is violated. These studies suggest that, although the case-only method generally has maximum power, it has the potential to create substantial false positives in large-scale studies even when a small fraction of markers are associated with the exposure under study in the underlying population. All the hybrid methods perform well in protecting against such false positives and yet can retain substantial power advantages over standard case-control tests. The authors conclude that, for future genome-wide scans for gene-environment interactions, major power gain is possible by using alternatives to standard case-control analysis. Whether a case-only type scan or one of the hybrid methods should be used depends on the strength and direction of gene-environment interaction and association, the level of tolerance for false positives, and the nature of replication strategies.     

Stratified case sampling and the use of family controls

We compare the asymptotic relative efficiency (ARE) of different study designs for estimating gene and gene-environment interaction effects using matched case-control data. In the sampling schemes considered, cases are selected differentially based on their family history of disease. Controls are selected either from unrelated subjects or from among the case's unaffected siblings and cousins. Parameters are estimated using weighted conditional logistic regression, where the likelihood contributions for each subject are weighted by the fraction of cases sampled sharing the same family history. Results showed that compared to random sampling, over-sampling cases with a positive family history increased the efficiency for estimating the main effect of a gene for sib-control designs (103-254% ARE) and decreased efficiency for cousin-control and population-control designs (68-94% ARE and 67-84% ARE, respectively). Population controls and random sampling of cases were most efficient for a recessive gene or a dominant gene with an relative risk less than 9. For estimating gene-environment interactions, over-sampling positive-family-history cases again led to increased efficiency using sib controls (111-180% ARE) and decreased efficiency using population controls (68-87% ARE). Using case-cousin pairs, the results differed based on the genetic model and the size of the interaction effect; biased sampling was only slightly more efficient than random sampling for large interaction effects under a dominant gene model (relative risk ratio = 8, 106% ARE). Overall, the most efficient study design for studying gene-environment interaction was the case-sib-control design with over-sampling of positive-family-history-cases.     

Reproductive factors and familial predisposition for breast cancer by age 50 years. A case-control-family study for assessing main effects and possible gene-environment interaction

BACKGROUND: The effect of environmental/lifestyle factors on breast cancer risk may be modified by genetic predisposition. METHODS: In a population-based case-control-family study performed in Germany including 706 cases by age 50 years, 1381 population, and 252 sister controls, we investigated main effects for environmental/lifestyle factors and genetic susceptibility and gene-environment interaction (G x E). Different surrogate measures for genetic predisposition using pedigree information were used: first-degree family history of breast or ovarian cancer; and gene carrier probability using a genetic model based on rare dominant genes. Possible G x E interaction was studied by (1) logistic regression using cases and population controls including an interaction term; (2) comparing results using sister controls and population controls; (3) case-only analysis with logistic regression and (4) a mixture logistic model. RESULTS: Familial predisposition showed the strongest main effect and the estimated gene carrier probability gave the best fit. High parity and longer duration of breastfeeding reduced breast cancer risk significantly, a history of abortions increased risk and age at menarche showed no significant effect. We found significant G x E interaction between parity and genetic susceptibility using different surrogate measures. In women most likely to have a high genetic susceptibility, high parity was less protective. Later age at menarche was protective in women with a positive family history. No evidence for G x E interaction was found for breastfeeding and abortion. CONCLUSIONS: These findings corroborate results from other studies and provide further evidence that the magnitude of protection from parity is reduced in women most likely to have a genetic risk in spite of the limitations of using surrogate genetic measures.