几种基因—环境、基因—基因交互作用研究方法的样本量比较

基因 -环境和基因 -基因交互作用在疾病发病中的作用日益引起人们的注意 ,相应的流行病学研究也逐渐增多。本文对三种基因 -环境和基因 -基因交互作用研究方法的样本量进行了比较 ,这三种方法分别是配比病例 -对照研究、病例 -同胞研究和病例 -父母研究。1　研究方法简介1 .1　配比 -病例对照研究 ( matched case- controlstudy)　病例和对照来源于同一人群 ,对照是未患所研究疾病的个体且与病例在匹配因素上保持一致 ,每个样本单位包含一个病例和 m个对照 ,m=1时称为配对 ( pair matching)。这种方法可以排除匹配因素的干扰。在研究遗传因…     

单纯病例研究

复杂性疾病(complex disorder)致病基因的研究已经成为当代医学研究的新热点,其中遗传因素和环境危险因素暴露之间的交互作用对于疾病的病因研究具有重要的科学意义和公共卫生方面的应用价值.近年来,为了研究基因与环境的交互作用,流行病学方法中出现了许多新的研究设计,单纯病例研究(case-only study)就是其中的一种.本文就单纯病例研究的设计、统计分析方法及应用作了简要介绍.     

流行病学研究中的混杂与配比

流行病学研究中的混杂与配比宁波市卫生防疫站同济医科大学许国章王仁元陈衡平余松林在比较研究中（ｃｏｍｐａｒａｔｉｖｅｓｔｕｄｙ）人们通常采用配比（Ｍａｔｃｈｉｎｇ）的方法使研究组和比较组在研究因素之外的其他因素方面具有可比性，其目的是在特异的混杂层内分...     

病例对照方法在基因环境交互作用研究中的应用

现代流行病学研究越来越重视基因与环境交互作用，要研究基因环境交互作用，需要有基因型、表现型（患病与否）与环境暴露信息，由于基因型测定需要有尖端技术、设备，而且耗资巨大，不是首选方法，人们往往首先采用替代性方法对研究对象进行基因型分类。病例对照研究是流...     

特定情况下配比设计的分析方法

近年来流行病学研究中配比(Matchirlg)设计得到越来越多的应用，借以控制研究中的主要混杂因素，提高统计效率。有关配比设计的分析方法也有较多文献。国内对1：1、1：M配比设计的分析方法在教科书、参考书作了较详尽介绍；对不同配比的对照数同时存在的特定情况的分析方法亦有报道。但以下情况下配比设计的分析方法国内尚未做充分讨论，特简略辑于本文，就正于同道。     

单纯病例研究

复杂性疾病(complex disonder)致病基因的研究已经成为当代医学研究的新热点，其中遗传因素和环境危险因素暴露之间的交互作用对于疾病的病因研究具有重要的科学意义和公共卫生方面的应用价值。近年来，为了研究基因与环境的交互作用，流行病学方法中出现了许多新的研究设计，单纯病例研究(case-only study)就是其中的一种。本文就单纯病例研究的设计、统计分析方法及应用作了简要介绍。     

单纯病例研究方法在流行病学研究中的应用

流行病学在公共卫生决策与计划、预防疾病和促进健康方面正在发挥着愈来愈重要的作用。分子流行病学研究的兴起为揭开疾病的“黑匣子”之谜创造了条件 ,为阐明致病因素与发病机制提供了有利的工具 ,与此同时也促进了该领域方法学的研究 ,并取得了令人瞩目的进展。其中 ,单纯病例研究 (case onlystudy)方法在探索环境 基因交互作用、评价环境暴露和生物效应以及寻找突发病因的研究中已崭露头角 ,并在不断发展和完善之中。单纯病例研究的基本特征是研究对象仅用病例而不用对照。按其应用范围可分为四类 :病例分布研究 (case distributionstud…     

病因流行病学研究方法进展

流行病学是病因学研究的重要方法,病因学研究是流行病学研究的重要领域。由于面临的病因研究问题越来越复杂,促进了病因流行病学研究方法的快速发展。本文从病因概念与模型、混杂因素的控制、交互作用的研究、流行病学与组学研究的结合、分子病理学的概念与发展、病因判断标准这六个方面,对病因流行病学研究方法的进展做一全面总结。     

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.     

单纯病例研究

随着分子遗传学技术的不断发展,遗传与环境的交互作用(gene-environment interaction)在疾病病因研究中越来越具有重要的公共卫生学意义.传统的研究方法,如病例对照研究、队列研究等均可用于二者交互作用的评价,但这些方法的共同特点就是统计效率不高.1994年Piegorsch、Begg等流行病学家在病例对照研究基础上提出了一种新的研究方法--单纯病例研究,本文对该方法作一介绍.     

Counter-matching in studies of gene-environment interaction: efficiency and feasibility

The interest in studying gene-environment interaction is increasing for complex diseases. However, most methods of detecting gene-environment interactions may not be appropriate for the study of interactions involving rare genes (G:) or uncommon environmental exposures (E:), because of poor statistical power. To increase this power, the authors propose the counter-matching design. This design increases the number of subjects with the rare factor without increasing the number of measurements that must be performed. In this paper, the efficiency and feasibility (required sample sizes) of counter-matching designs are evaluated and discussed. Counter-matching on both G: and E: appears to be the most efficient design for detecting gene-environment interaction. The sensitivity and specificity of the surrogate measures, the frequencies of G: and E:, and, to a lesser extent, the value of the interaction effect are the most important parameters for determining efficiency. Feasibility is also more dependent on the exposure frequencies and the interaction effect than on the main effects of G: and E: Although the efficiency of counter-matching is greatest when the risk factors are very rare, the study of such rare factors is not realistic unless one is interested in very strong interaction effects. Nevertheless, counter-matching appears to be more appropriate than most traditional epidemiologic methods for the study of interactions involving rare factors.     

Case-parents design for gene-environment interaction

The scientific and public health implications of gene-environment interaction warrant that the most powerful study designs and methods of analysis be used. Because traditional case-control designs, which use nonrelated subjects, have demonstrated the need for large samples to detect interactions, alternative study designs may be worthwhile, such as sampling diseased cases and their parents. If the transmission of particular alleles from parents to their diseased child appears to be distorted from Mendelian expectation, then this suggests an etiologic association of the alleles with disease; if the frequency of transmission differs between exposed and nonexposed cases, then gene-environment interaction is suggested. We present likelihood-based methods to assess interaction, as well as an extension of the transmission/disequilibrium test (TDT). For these statistical tests, we also derive methods to compute sample size and power. Comparisons of sample size requirements between the case-parents design and the case-control design indicate that the case-parents design can be more powerful to detect gene-environment interactions, particularly when the disease susceptible allele is rare. Also, one of the derived likelihood methods, based on additive effects of alleles, tended to be the most robust in terms of power for a broad range of genetic mechanisms, and so may be useful for broad applications to assess gene-environment interactions.     

Limitations of the case-only design for identifying gene-environment interactions

The case-only design, which requires only diseased subjects, allows for estimation of multiplicative interactions between factors known to be independent in the study population. The design is being used as an alternative to the case-control design to study gene-environment interactions. Estimates of gene-environment interactions have been shown to be very efficient relative to estimates obtained with a case-control study under the assumption of independence between the genetic and environmental factors. In this paper, the authors explore the robustness of this procedure to uncertainty about the independence assumption. By using simulations, they demonstrate that inferences about the multiplicative interaction with the case-only design can be highly distorted when there is departure from the independence assumption. They illustrate their results with a recent study of gene-environment interactions and risk of lung cancer incidence in a cohort of miners from the Yunnan Tin Corporation in southern China. Investigators should be aware that the increased efficiency of the case-only design is a consequence of a strong assumption and that this design can perform poorly if the assumption is violated.     

Analysis of case-control studies of genetic and environmental factors with missing genetic information and haplotype-phase ambiguity

Case-control studies of unrelated subjects are now widely used to study the role of genetic susceptibility and gene-environment interactions in the etiology of complex diseases. Exploiting an assumption of gene-environment independence, and treating the distribution of environmental exposures as completely nonparametric, Chatterjee and Carroll recently developed an efficient retrospective maximum-likelihood method for analysis of case-control studies. In this article, we develop an extension of the retrospective maximum-likelihood approach to studies where genetic information may be missing on some study subjects. In particular, special emphasis is given to haplotype-based studies where missing data arise due to linkage-phase ambiguity of genotype data. We use a profile likelihood technique and an appropriate expectation-maximization (EM) algorithm to derive a relatively simple procedure for parameter estimation, with or without a rare disease assumption, and possibly incorporating information on the marginal probability of the disease for the underlying population. We also describe two alternative robust approaches that are less sensitive to the underlying gene-environment independence and Hardy-Weinberg-equilibrium assumptions. The performance of the proposed methods is studied using simulation studies in the context of haplotype-based studies of gene-environment interactions. An application of the proposed method is illustrated using a case-control study of ovarian cancer designed to investigate the interaction between BRCA1/2 mutations and reproductive risk factors in the etiology of ovarian cancer.     

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.     

A sibling-augmented case-only approach for assessing multiplicative gene-environment interactions

Family-based designs protect analyses of genetic effects from bias that is due to population stratification. Investigators have assumed that this robustness extends to assessments of gene-environment interaction. Unfortunately, this assumption fails for the common scenario in which the genotyped variant is related to risk through linkage with a causative allele. Bias also plagues other methods of assessment of gene-environment interaction. When testing against multiplicative joint effects, the case-only design offers excellent power, but it is invalid if genotype and exposure are correlated in the population. The authors describe 4 mechanisms that produce genotype-exposure dependence: exposure-related genetic population stratification, effects of family history on behavior, genotype effects on exposure, and selective attrition. They propose a sibling-augmented case-only (SACO) design that protects against the former 2 mechanisms and is therefore valid for studying young-onset disease in which genotype does not influence exposure. A SACO design allows the ascertainment of genotype and exposure for cases and exposure for 1 or more unaffected siblings selected randomly. Conditional logistic regression permits assessment of exposure effects and gene-environment interactions. Via simulations, the authors compare the likelihood-based inference on interactions using the SACO design with that based on other designs. They also show that robust analyses of interactions using tetrads or disease-discordant sibling pairs are equivalent to analyses using the SACO design.     

Dealing with high dimensionality for the identification of common and rare variants as main effects and for gene-environment interaction

In addition to genome-wide association studies, sequence data are now up and coming, increasing the need for even more effective methods of dealing with high dimensionality and the identification of variants beyond common variant main effects. The contributors to Genetic Analysis Workshop 17 Group 4 applied novel and recently proposed methods for handling population structure, high dimensionality, and gene-environment interactions in the context of mini-exome sequence data. For the collapsing of rare variants into gene summaries, some of the contributions considered the computationally fast, straightforward summing of all or particular subsets of rare variants. Other methods were comparatively time-consuming and complex but offered a data-driven approach, such as reduction in the subset of rare variants to be considered using a U statistic and semiparametric modeling of single-nucleotide polymorphism effects implementing kernel machines. Several approaches were applied using regression models, regularized regression, and kernels. Testing for gene-specific main effects and gene-environment interaction using least-squares kernel machines showed more flexibility and was supervised compared with a two-step approach that used a random effects model that incorporated an empirical Bayes estimate. However, the random effects model was the only method capable of treating family data, at least in their present form.     

Two-stage analysis for gene-environment interaction utilizing both case-only and family-based analysis

The case-only study and family-based study are two popular study designs for detecting gene-environment interactions. It is well known that the case-only analysis is efficient, but its validity relies crucially on the assumption of gene-environment independence in the study population. In contrast, the family-based analysis is robust to the violation of such an assumption, but is less efficient. We propose a two-stage study design for detecting gene-environment interactions, where a case-only study is performed at the first stage, and a case-parent/case-sibling study is performed at the second stage on a random subsample of the first-stage case sample as well as their parents/unaffected siblings. Statistical inference procedures are developed for the proposed two-stage study designs, which not only preserve the robustness property of the family-based analysis, but also utilize information from the case-only analysis to enhance estimation efficiency and testing power. Simulation results reveal both the robustness and efficiency of the proposed strategies.     

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.     

弹性匹配策略在分析基因与环境交互作用中的应用

以HER-2原癌基因Ile655Val多态性、吸烟与乳腺癌之间的关联研究为例,运用大样本近似原理计算检验效能,并通过逐步提高对照组中匹配因素的比例,探索弹性匹配策略在环境与基因交互作用分析中的应用价值及其效能计算方法 .HER-2基因多态和吸烟交互作用的检验效能在非匹配的病例对照研究中为30%,应用传统的频数匹配则提高为56%,进一步增加对照组的吸烟率,则能获得更高的效能值(power=74%).结论 :与非匹配或频数匹配的病例对照研究相比,应用弹性匹配的病例对照研究,能够显著增加环境与基因交互作用的检验效能和研究效率,此匹配策略尤其适用于人群中环境暴露率较低、环境暴露与基因易感性呈负向关联或匹配对照例数较少等情况.在研究设计时可充分权衡检验效能的提高与匹配成本的增加,从而选择最佳的匹配策略.