Comparison of two-phase analyses for case-control genetic association studies

To test for genetic association between a marker and a complex disease using a case-control design, Cochran-Armitage trend tests (CATTs) and Pearson's chi-square test are often employed. Both tests are genotype-based. Song and Elston (Statist. Med. 2006; 25:105-126) introduced the Hardy-Weinberg disequilibrium trend test and combined it with CATT to test for association. Compared to using a single statistic to test for case-control genetic association (referred to as single-phase analysis), two-phase analysis is a new strategy in that it employs two test statistics in one analysis framework, each statistic using all available case-control data. Two such two-phase analysis procedures were studied, in which Hardy-Weinberg equilibrium (HWE) in the population is a key assumption, although the procedures are robust to moderate departure from HWE. Our goal in this article is to study a new two-phase procedure and compare all three two-phase analyses and common single-phase procedures by extensive simulation studies. For illustration, the results are applied to real data from two case-control studies. On the basis of the results, we conclude that with an appropriate choice of significance level for the analysis in phase 1, some two-phase analyses could be more powerful than commonly used test statistics.     

Allelic association patterns for a dense SNP map

A dense set of 5,000 SNPs on a 10-Mb region of human chromosome 20 has been typed on samples of African Americans, East Asians, and United Kingdom Caucasians. There are departures from Hardy-Weinberg equilibrium beyond the level at which markers are often discarded because of possible genotyping errors. The observation that markers showing such departures are often close together on the chromosome confirms the result that Hardy-Weinberg tests at two loci are correlated to an extent that depends on the linkage disequilibrium between those two markers. Linkage disequilibrium can be described by the composite linkage disequilibrium coefficient, the parameter that determines the behavior of case-control allelic tests of association. A useful preliminary investigation of datasets of this type is provided by counting the numbers of distinct multi-locus genotypes in windows of a few markers.     

Assessing departure from Hardy-Weinberg equilibrium in the presence of disease association

Assessing Hardy-Weinberg equilibrium (HWE) is often employed as an important initial step for genotype data quality checking in genetics studies. Tests for HWE often assume that the genotypes are randomly sampled from the general population. However, in many human genetics studies, subjects are ascertained through their disease status, and affected individuals (and their relatives in family-based studies) are overly represented in the ascertained sample than in the general population. As a result, when a marker is associated with the disease, the type I error rate in the HWE tests can be inflated, leading to false exclusion of associated markers from future analysis. Here we develop a general likelihood framework that allows assessment of departure from HWE while taking into account potential association with the disease. Our method can differentiate HWE departure caused by disease association from departure caused by other reasons, such as genotyping errors. The framework can be used for various data structures, including unrelated cases and controls, nuclear families with one or more offspring, or a mixture of them. The type I error rate of our test is under control for a broad range of scenarios. For case-control data, compared to the traditional HWE test that uses only controls, our test is more powerful to detect HWE departure for common diseases and has comparable power for rare diseases. For case-parents trios, our test is more powerful than the traditional HWE test that uses parents only.     

The power of independent types of genetic information to detect association in a case-control study design

There have been many single nucleotide polymorphism-based tests suggested for association analysis in a case-control design. The possible evidence for association comprises three types of information: differences between cases and controls in allele frequencies, in parameters for Hardy Weinberg disequilibrium (HWD) and in parameters for linkage disequilibrium (LD). Here, first we find the pairwise covariances between statistics that measure these three types of information and show that the statistics are asymptotically trivariate normally distributed. Then we compare their power analytically to determine the most informative statistics according to the disease model. Our results show that differences in parameters for HWD are informative for dominant and recessive disease models, while differences in allele frequencies and in parameters for LD are generally informative except for rare recessive disease models. There is mutual independence of the statistics that detect these three differences under Hardy Weinberg equilibrium at the marker locus and linkage equilibrium between markers in the population. Knowing the pairwise covariances between the statistics makes it possible to define statistics that are mutually independent. This allows us to perform sequential analyses of the same data without the need to adjust significance levels for all the multiple analyses being performed on the same data set. As a result we can have improved flexible strategies to increase the power of genome-wide association studies without requiring the collection of a new, independent sample.     

Biased tests of association: comparisons of allele frequencies when departing from Hardy-Weinberg proportions

Association studies of genetic markers or candidate genes with disease are often conducted using the traditional case-control design. Cases and controls are sampled from genetically unrelated subjects, and allele frequencies compared between cases and controls using Pearson's chi-square statistic. An assumption of this analysis method is that the two alleles within each subject are statistically independent, at least when no association exists. This is equivalent to assuming that the frequencies of the genotypes in the general population comply with Hardy-Weinberg Equilibrium proportions, which may not always be the case. However, deviations from Hardy-Weinberg Equilibrium can inflate the chance of a false-positive association. These results demonstrate that when comparing the frequencies of two alleles between cases and controls, the chance of a false-positive association can be substantially increased if homozygotes for the putative high-risk allele are more common in the general population than predicted by Hardy-Weinberg Equilibrium. In contrast, Pearson's chi-square statistic can be conservative if the frequency of homozygotes for the high-risk allele is less than that predicted. A statistically valid method that corrects for deviations from Hardy-Weinberg Equilibrium is presented, so that the chance of a false-positive association is not greater than the acceptable level.     

Exploiting gene-environment independence in haplotype-based inferences for population-based case-control studies with complex sampling

The use of complex sampling in population-based case-control studies is becoming more common. Although most single nucleotide polymorphism-based association studies with complex sampling account for the design complications, many of haplotype-based genetic association studies with complex sampling tend to ignore them when estimating haplotype frequencies, regression coefficients, or both. In this article, we develop innovative one-step and two-step statistical methods that account for the design complications in haplotype-based association studies when cases and/or controls are sampled with complex sampling. Attracted by the efficiency advantage of the retrospective method, we explore the assumptions of Hardy-Weinberg equilibrium and gene-environment independence in the underlying population. Results of our simulation studies demonstrate superior performance of the proposed methods over selected existing methods under various complex sampling designs. An application of the proposed methods is illustrated using a population-based case-control study of kidney cancer.     

Sample size calculations for population- and family-based case-control association studies on marker genotypes

Most previous sample size calculations for case-control studies to detect genetic associations with disease assumed that the disease gene locus is known, whereas, in fact, markers are used. We calculated sample sizes for unmatched case-control and sibling case-control studies to detect an association between a biallelic marker and a disease governed by a putative biallelic disease locus. Required sample sizes increase with increasing discrepancy between the marker and disease allele frequencies, and with less-than-maximal linkage disequilibrium between the marker and disease alleles. Qualitatively similar results were found for studies of parent offspring triads based on the transmission disequilibrium test (Abel and Müller-Myhsok, 1998, Am. J. Hum. Genet. 63:664-667; Tu and Whittemore, 1999, Am. J. Hum. Genet. 64:641-649). We also studied other factors affecting required sample size, including attributable risk for the disease allele, inheritance mechanism, disease prevalence, and for sibling case-control designs, extragenetic familial aggregation of disease and recombination. The large sample-size requirements represent a formidable challenge to studies of this type.     

基因多态性与疾病关联研究中Hardy-Weinberg平衡的影响因素分析

目的 根据炎性基因多态性与胃癌易感性家系比较研究的数据,分析基因多态性与疾病关联研究中Hardy-Weinberg(H-W)平衡的影响因素,揭示群体中等位基因频率在逐代传递中平衡偏离的原因.方法 对胃癌风险因素的各种基因型的状态进行H-W平衡检验、连锁不平衡分析、Cochran-Armitage趋势检验和分析.结果 (1)单位点IL-1B-31、IL-1B-511、IL-1RN和TNF-A-308的病例组和对照组均显著背离H-W平衡(P<0.01),MIF-173趋于平衡.(2)双位点统计分析,各位点组合的H-W χT2检验均偏离表型组合概率的期望值(P<0.01).(3)Cochran-Armitage趋势检验不同组IL-1B-511和IL-1RN位点分布的差异有统计学意义(P<0.05),表明群体中可能存在群体分层.结论 (1)受连锁不平衡相结合的频率依赖性选择、突变及其共同作用的影响,IL-1B-31、IL-1B-511、IL-IRN和TNF-A-308位点的基因型频率在群体中分布偏离H-W平衡.(2)双位点遗传平衡检验模式更能反映表型组合适合度的差异.(3)群体分层是H-W平衡偏离的另一影响因素.     

Retrospective analysis of case-control studies when the population is in Hardy-Weinberg equilibrium

Association analysis of genetic polymorphisms has been mostly performed in a case-control setting in connection with the traditional logistic regression analysis. However, in a case-control study, subjects are recruited according to their disease status and their past exposures are determined. Thus the natural model for making inference is the retrospective model. In this paper, we discuss some retrospective models and give maximum likelihood estimators of exposure effects and estimators of asymptotic variances, when the frequency distribution of exposures in controls contains information about the parameters of interest. Two situations about the control population are considered in this paper: (a) the control population or its subpopulations are in Hardy-Weinberg equilibrium; and (b) genetic and environmental factors are independent in the control population. Using the concept of asymptotic relative efficiency, we shall show the precision advantages of such retrospective analysis over the traditional prospective analysis. Maximum likelihood estimates and variance estimates under retrospective models are simple in computation and thus can be applied in many practical applications. We present one real example to illustrate our methods.     

The exposure odds ratio in nested case-control studies with competing risks

A nested case-control study, also known as an ambidirectional study, is a case-control study within a cohort study. Although distortion by competing risks is well-recognized in follow-up studies, the problem has not been as widely appreciated in nested case-control studies. This paper extends previous work concerning the bias associated with competing risks for nested case-control studies. Specifically, the distorting effect of competing risks is illustrated for three methods of control selection. Assuming the proportional hazards model, the authors derived formulas for the bias of the odds ratio when competing risks cannot be ignored. Examples illustrate the magnitude of bias that occurs when the exposure of interest is associated with competing causes of death or withdrawal.     

维生素D受体基因与广西壮族骨质疏松相关性研究

目的 探讨维生素D受体（VDR）基因4个单核苷酸多态性（SNPs）位点与壮族中老年人骨质疏松的关系。方法 选取骨量正常的113例壮族中老年和骨量减少的74例、骨质疏松的196例壮族中老年人合并为骨量减少病例组进行病例对照研究。采用KASP基因分型技术对壮族中老人VDR基因的4个位点（rs731236、rs7975323、rs2228570、rs1544410）进行基因分型。结果 壮族中老年人骨密度随年龄增长逐年下降,logistic回归结果显示年龄（P＜0.001,OR = 1.053）,去脂体重（P＜0.001,OR = 0.911）是影响壮族中老年人骨密度的主要因素。VDR基因4个位点均符合Hardy-Weinberg遗传平衡定律（P＞0.05）,rs2228570位点GG基因型与骨量减少风险相关（OR = 3.360,95%CI = 1.565～7.215,P = 0.002）,等位基因A和G相比差异具有统计学意义（P＜0.05）。rs7975323与rs731236位点呈完全连锁不平衡（D’= 1.000）,rs1544410与rs731236呈强连锁不平衡（D’= 0.638,r2 = 0.367）,rs1544410与rs7975323呈强连锁不平衡（D’= 0.799）。 结论 年龄、去脂体重是壮族中老年人骨密度的主要影响因素,VDR基因rs2228570位点的GG基因型是骨量减少的危险因素,等位基因G可能是骨质疏松的遗传易感基因,增加中老年人骨量减少、骨质疏松的发病风险。     

Searching for disease-susceptibility loci by testing for Hardy-Weinberg disequilibrium in a gene bank of affected individuals

The future of genetic studies of complex human diseases will rely more and more on the epidemiologic association paradigm. The author proposes to scan the genome for disease-susceptibility gene(s) by testing for deviation from Hardy-Weinberg equilibrium in a gene bank of affected individuals. A power formula is presented, which is very accurate as revealed by Monte Carlo simulations. If the disease-susceptibility gene is recessive with an allele frequency of < or = 0.5 or dominant with an allele frequency of > or = 0.5, the number of subjects needed by the present method is smaller than that needed by using a case-parents design (using either the transmission/disequilibrium test or the 2-df likelihood ratio test). However, the method cannot detect genes with a multiplicative mode of inheritance, and the validity of the method relies on the assumption that the source population from which the cases arise is in Hardy-Weinberg equilibrium. Thus, it is prone to produce false positive and false negative results. Nevertheless, the method enables rapid gene hunting in an existing gene bank of affected individuals with no extra effort beyond simple calculations.     

Novel tests for marker-disease association using the Collaborative Study on the Genetics of Alcoholism data

We applied several novel tests for association and linkage in the presence of association to the Genetic Analysis Workshop 11 Problem 1 data set. Our analyses included a Hardy-Weinberg test for association between a marker and a disease susceptibility locus, a Bayesian transmission/disequilibrium test, and a Bayesian case-control test. Positive results for each of these methods require the presence of population association between the marker and a disease susceptibility locus.     

Relationship between case-control studies and the transmission/disequilibrium test

Case-control studies provide a powerful approach for detecting disease susceptibility loci that have only a weak to moderate impact on the risk of disease, or markers that are in linkage disequilibrium with such loci. However, since any association detected in a case-control study may result from uncontrolled confounding, evidence for disease-marker associations obtained from such studies must be confirmed by alternative methods. Since studies that use the transmission/disequilibrium test or TDT are frequently employed to confirm disease-marker associations detected in case-control studies, data are increasingly available from both case-control studies and "TDT studies" of the same disease-marker association. It would, therefore, be useful to have a single measure of the magnitude of the disease-marker association that would allow for comparison of results from these two study designs. Such a measure could also be used to estimate minimum sample size requirements for TDT studies of previously reported disease-marker associations. An obvious measure of the disease-marker association in TDT studies is the frequency (T) with which heterozygous parents transmit the putative, high-risk marker allele to affected offspring. In this paper, it is shown that T can also be estimated from case-control data with a minimum of assumptions, and that T is the critical parameter for determining power and estimating sample sizes for the TDT.     

Maximum likelihood estimation of haplotype effects and haplotype-environment interactions in association studies

The associations between haplotypes and disease phenotypes offer valuable clues about the genetic determinants of complex diseases. It is highly challenging to make statistical inferences about these associations because of the unknown gametic phase in genotype data. We describe a general likelihood-based approach to inferring haplotype-disease associations in studies of unrelated individuals. We consider all possible phenotypes (including disease indicator, quantitative trait, and potentially censored age at onset of disease) and all commonly used study designs (including cross-sectional, case-control, cohort, nested case-control, and case-cohort). The effects of haplotypes on phenotype are characterized by appropriate regression models, which allow various genetic mechanisms and gene-environment interactions. We present the likelihood functions for all study designs and disease phenotypes under Hardy-Weinberg disequilibrium. The corresponding maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We provide simple and efficient numerical algorithms to calculate the maximum likelihood estimators and their variances, and implement these algorithms in a freely available computer program. Extensive simulation studies demonstrate that the proposed methods perform well in realistic situations. An application to the Carolina Breast Cancer Study reveals significant haplotype effects and haplotype-smoking interactions in the development of breast cancer.     

TGFA基因多态性与NSCL/P的遗传易感性

目的:探讨中国汉族人TGFA基因的多态性对非综合征性唇腭裂的遗传易感性的作用。方法:76例核心家庭和60例正常对照儿童的TGFA基因型,采用病例对照研究和传递不平衡检验进行分析。结果:病例对照研究结果表示:(2χ=7.77,P<0.05),TDT(2χ=5.26,P<0.05))。结论:TGFA C2等位基因在NSCL/P中存在连锁不平衡,TGFA基因多态性可能是中国汉族人NSCL/P的遗传易感性因素。     

TAP1基因多态性与结核病易感关联性的家系病例对照研究

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

Power estimation of multiple SNP association test of case-control study and application

At the current stage, a large number of single nucleotide polymorphisms (SNPs) have been deployed in searching for genes underlying complex diseases. A powerful method is desirable for efficient analysis of SNP data. Recently, a novel method for multiple SNP association test using a combination of allelic association (AA) and Hardy-Weinberg disequilibrium (HWD) has been proposed. However, the power of this test has not been systematically examined. In this study, we conducted a simulation study to further evaluate the statistical power of the new procedure, as well as of the influence of the HWD on its performance. The simulation examined the scenarios of multiple disease SNPs among a candidate pool, assuming different parameters including allele frequencies and risk ratios, dominant, additive, and recessive genetic models, and the existence of gene-gene interactions and linkage disequilibrium (LD). We also evaluated the performance of this test in capturing real disease associated SNPs, when a significant global P value is detected. Our results suggest that this new procedure is more powerful than conventional single-point analyses with correction of multiple testing. However, inclusion of HWD reduces the power under most circumstances. We applied the novel association test procedure to a case-control study of preterm delivery (PTD), examining the effects of 96 candidate gene SNPs concurrently, and detected a global P value of 0.0250 by using Cochran-Armitage chi(2)s as "starting" statistics in the procedure. In the following single point analysis, SNPs on IL1RN, IL1R2, ESR1, Factor 5, and OPRM1 genes were identified as possible risk factors in PTD.     

Delimitation of the upstream region of NFKBIA gene associated with HTLV-1-associated myelopathy/tropical spastic paraparesis using candidate Tag-SNPs in Peruvian HTLV-1 infected individuals

In Peru, it is estimated that about 150 000-400 000 people carry the Human T-lymphotropic virus 1 (HTLV-1). Only 10% of HTLV-1 carries develop complications related to HTLV-1. HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a chronic disabling inflammatory disease affecting the spinal cord. HAM/TSP produces principally weakness in the lower limbs and bladder disturbances, among other complications. In a previous study, our group identified three SNPs (rs3138053, rs2233406, and rs3138045) located in the promoter region of the NFKBIA gene associated with HAM/TSP. This study aimed to analyze the association between four Tag-SNPs (rs10148482, rs17103274, rs17103282, and rs762009) located in the upstream region of the NFKBIA gene and HAM/TSP, and to delimit the linkage disequilibrium zone in the upstream region of the NFBKIA gene associated with HAM/TSP. The tetra-primers ARMS-PCR technique was used to genotype 4 Tag-SNPs on 140 HAM/TSP patients and 258 asymptomatic carriers. The SNP rs17103282 showed a deviation from Hardy-Weinberg equilibrium (p < .0001). Neither of three Tag-SNPs showed an association with HAM/TSP (P > .05). No linkage disequilibrium between four Tag-SNPs evaluated in this study and previous ones was observed. Here we show the region located in the upstream region of the NFKBIA gene highly associated with HAM/TSP disease in patients infected with HTLV-1 from Lima, Peru.     

Incorporation of covariates into multipoint linkage disequilibrium mapping in case-control studies

Case-control designs are commonly adopted in genetic epidemiological studies because they are cost effective and offer powerful tests for genetic and environmental risk factors, as well as their interactions. Previously, we proposed an association mapping approach to estimate the position of an unobserved disease locus as well as measuring its genetic effect on risk. The method provides a confidence interval for the estimated map position to help narrow the chromosomal region potentially harboring a disease locus. However, concerns often rise about case-control designs including possible false positives or bias due to confounders, heterogeneity or interactions among genes and between genes and environments. In the present work, we extended the multipoint linkage disequilibrium mapping approach for case-control studies to incorporate information about factors influencing the effect of causal genes to improve precision and efficiency of the estimated location. The efficiency, bias and coverage probability of this extended approach for locating a disease locus using case-control data with and without additional information on a covariate were compared through simulation. An example of a case-control study for type 2 diabetes was used to illustrate this extended method. In this study, a strong association between diabetes and a candidate gene, SCL2A10, was detected among nonobese subjects, whereas no evidence of association was found for either obese subjects or the whole sample when obesity was ignored. Simulation studies and these diabetes data both demonstrate how the efficiency of the estimated location of a disease gene can be improved substantially by incorporating information on covariates.