Univariate and multivariate family-based association analysis of the IL-13 ARG130GLN polymorphism in the Childhood Asthma Management Program

Interleukin 13 (IL-13) has been demonstrated to have a crucial role in animal models of allergy and asthma. In human case-control genetic-association studies, the Arg130Gln polymorphism has been associated with elevated total serum IgE and an asthma diagnosis in atopic and nonatopic individuals (Graves et al. [2000] J. Allergy Clin. Immunol. 105:506-513; Heinzmann et al. [2000] Hum. Mol. Genet. 9:549-559). To apply family-based association methods, we obtained DNA samples from 685 asthmatic children from 640 sibships and their parents in the Childhood Asthma Management Program (CAMP). Six hundred and sixty-six asthmatic children had complete phenotypic information and were used for this analysis. We performed quantitative association analysis using the transmission disequilibrium test (TDT) on 22 individual phenotypes and 5 grouped phenotypes relating to allergy, airway responsiveness, pulmonary function, bronchodilator responsiveness, and asthma severity, using genotypes at the Arg130Gln polymorphism of the IL-13 gene. A positive association was obtained between Arg130Gln and a grouped phenotype of allergy (consisting of the individual phenotypes of eosinophils, IgE, and positive skin tests), using FBAT-GEE, a multivariate extension of the family-based association test (Lange et al. [2002] Biostatistics 1:1-15). The three phenotypes were then evaluated individually and revealed a significant association between total eosinophil count and the Arg130Gln locus; there was a trend for association between total IgE and the Arg130Gln polymorphism. The Arg130Gln polymorphism is associated with an elevated eosinophil count as well as with a grouped allergy phenotype, in children with mild to moderate asthma. No evidence for association was found between Arg130Gln and airway responsiveness, asthma diagnosis, or asthma severity.     

Exact family-based association tests for biallelic data

Family-based study designs have an important role in the search for association between disease phenotypes and genetic markers. Unlike traditional case-control methods, family-based tests use within-family data to avoid identification of spurious associations that may result from population admixture. Many family-based association tests have been proposed to accommodate a variety of ascertainment schemes and patterns of missing data. In this report, we describe exact family-based association tests for biallelic data. Specifically, we discuss test of the null hypotheses "no linkage and no association" and "linkage, but no association". These tests, which are valid under various models for inheritance and patterns of missingness, utilize the procedure proposed by Rabinowitz and Laird [2000: Hum Hered 50:211-223] that provides a unified framework for family based association testing (FBAT). The conditioning approach implemented in FBAT makes an exact test conceptually straightforward, but computationally difficult since the minimum sufficient statistics upon which we condition do not have a conventional form. An exact test may be especially critical when accurate computation of the extreme area of the FBAT statistic is needed, such as when the study design necessitates multiple comparisons adjustments. We describe the exact approach as a useful alternative to the asymptotic test and show that the exact tests for biallelic data may be most useful for the recessive disease model.     

Power calculations for likelihood ratio tests for offspring genotype risks, maternal effects, and parent-of-origin (POO) effects in the presence of missing parental genotypes when unaffected siblings are available

Genotype-based likelihood-ratio tests (LRT) of association that examine maternal and parent-of-origin effects have been previously developed in the framework of log-linear and conditional logistic regression models. In the situation where parental genotypes are missing, the expectation-maximization (EM) algorithm has been incorporated in the log-linear approach to allow incomplete triads to contribute to the LRT. We present an extension to this model which we call the Combined_LRT that incorporates additional information from the genotypes of unaffected siblings to improve assignment of incompletely typed families to mating type categories, thereby improving inference of missing parental data. Using simulations involving a realistic array of family structures, we demonstrate the validity of the Combined_LRT under the null hypothesis of no association and provide power comparisons under varying levels of missing data and using sibling genotype data. We demonstrate the improved power of the Combined_LRT compared with the family-based association test (FBAT), another widely used association test. Lastly, we apply the Combined_LRT to a candidate gene analysis in Autism families, some of which have missing parental genotypes. We conclude that the proposed log-linear model will be an important tool for future candidate gene studies, for many complex diseases where unaffected siblings can often be ascertained and where epigenetic factors such as imprinting may play a role in disease etiology.     

Weighted variance FBAT: a powerful method for including covariates in FBAT analyses

The family-based association test (FBAT), an extension of transmission/disequilibrium test, capitalizes on linkage disequilibrium to assess the association of genetic markers and traits in nuclear families. It does not permit a formal inclusion of covariates, although an offset under the FBAT -o option allows for an overall trait-intercept adjustment. The PBAT software provides additional features and permits the inclusion of covariates in the FBAT test statistic, but does not account for the parental genotype information when the traits are adjusted for the covariates. We propose the weighted variance FBAT (WVF) method to generate trait values adjusted for both parental genotypes and covariate values. WVF is expected to be more powerful, because the variance is minimized considering both of these factors simultaneously using a weighted Gauss-Newton algorithm. Two simulated parent/child trio data sets, both with a covariate and the second with a gene by covariate interaction, were simulated to compare WVF power with FBAT and PBAT for a quantitative trait. WVF is most powerful when levels of significance are greater and covariates have a larger influence, indicating WVF may be especially effective when multiple comparisons are an important consideration, such as with whole genome association studies. WVF will also improve the cost of an association study when environmental covariates are considered. A SAS program (www.SAS.com) for generating WVF residuals that can be input to the current versions of the FBAT (www.biostat.harvard.edu/fbat/fbat.html) and PBAT (www.biostat.harvard.edu/clange/default.htm) software is provided.     

Ordered-subset analysis (OSA) for family-based association mapping of complex traits

Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.     

IL10 gene polymorphisms are associated with asthma phenotypes in children

IL10 is an anti-inflammatory cytokine that has been found to have lower production in macrophages and mononuclear cells from asthmatics. Since reduced IL10 levels may influence the severity of asthma phenotypes, we examined IL10 single-nucleotide polymorphisms (SNPs) for association with asthma severity and allergy phenotypes as quantitative traits. Utilizing DNA samples from 518 Caucasian asthmatic children from the Childhood Asthma Management Program (CAMP) and their parents, we genotyped six IL10 SNPs: 3 in the promoter, 2 in introns, and one in the 3' UTR. Using family-based association tests, each SNP was tested for association with asthma and allergy phenotypes individually. Population-based association analysis was performed with each SNP locus, the promoter haplotypes and the 6-loci haplotypes. The 3' UTR SNP was significantly associated with FEV(1) as a percent of predicted (FEV(1)PP) (P=0.0002) in both the family and population analyses. The promoter haplotype GCC was positively associated with IgE levels and FEV(1)PP (P=0.007 and 0.012, respectively). The promoter haplotype ATA was negatively associated with lnPC(20) and FEV(1)PP (P=0.008 and 0.043, respectively). Polymorphisms in IL10 are associated with asthma phenotypes in this cohort. Further studies of variation in the IL10 gene may help elucidate the mechanism of asthma development in children.     

Testing association in the presence of linkage using the GRE and multiple markers

It has recently been shown that testing for association in the presence of linkage using a score test based on a gamma random effects (GRE) model is substantially more powerful than using the Family-Based Association Test. A reason for the increased power lies in better specification of the within family correlation structure, induced by linkage. The GRE, as presented in (Jonasdottir et al. 2007 Genet Epidemiol. 31:528-540), only considers one marker at a time and does not readily handle missing parental information. Here we extend the GRE to incorporate information from more than one marker. This extension leads to a haplotype GRE test and also to efficient handling of missing data on parental genotypes. We show that the haplotype GRE, the H-GRE, is substantially more powerful than the haplotype FBAT, the Haplotype-Based-Association Test. We demonstrate the usefulness of the extended GRE, by reanalyzing the collaborative study on the genetics of alcoholism data, allowing for missing parental information.     

On a general class of conditional tests for family-based association studies in genetics: the asymptotic distribution,the conditional power,and optimality considerations

Family-based association tests (FBATs) provide simple and powerful tests to detect association between a genetic marker and a disease-susceptibility locus, manifest in subjects by a phenotype or disease trait. Here we propose a new class of conditional tests for family-based association studies that includes most of the established tests and their generalizations. The class of tests is very general; it can be applied to longitudinal and multivariate traits or phenotypes, multiple genetic markers, and many other situations not yet discussed in the literature. For any test in this class, we derive the asymptotic distribution under the null hypothesis, the conditional power under any alternative hypothesis, and the optimal offset for single degree of freedom tests. The proposed methodology is illustrated with a genetic study of asthma.     

X-LRT: a likelihood approach to estimate genetic risks and test association with X-linked markers using a case-parents design

Recently, there has been interest in family-based tests of association to identify X-chromosome genes. However, none of the approaches allow for estimation of genetic risks. We propose a likelihood approach to estimate disease-related marker relative risks and test genotype association using a case-parents design. The test uses nuclear families with a single affected proband and allows additional siblings and missing parental genotypes. Extension to a haplotype test is based on assumptions of random mating and multiplicative penetrance. We investigate power and type I error rate of the likelihood-based test, using simulated data and apply our method to marker data from the monoamine oxidase A&B genes in families with Parkinson disease. We show how efficiency with missing parental information can be improved with additional sibling genotype information. Our likelihood approach offers great flexibility for testing different penetrance relationships within and between sexes. In addition, estimation of disease-related marker relative risks provides a measure of the magnitude of X-linked genetic effects on complex disorders.     

Inference on haplotype/disease association using parent-affected-child data: the projection conditional on parental haplotypes method

We develop a method that allows inference on parameters in log-linear models of the relative risk of disease given an individual's haplotypes, that can be used to analyze case-parent trio data. Our methods are robust to population stratification and can also be used for inference on the effect of interactions between haplotypes and environmental covariates. We compare our results with the family-based association test (FBAT) of Horvath et al. ([2004] Genet. Epidemiol. 26:61-69), and discuss when marginal tests, such as those available in FBAT, can be misleading. Our approach generalizes previous results of Allen et al. ([2005] Biometrika 92:559-571), allowing for missing genotype data and haplotype x environment interactions. Additional computational simplifications are also discussed.     

Validity, efficiency, and robustness of a family-based test of association

We propose a new test of linkage in the presence of allelic association that uses all available information in a sample of nuclear families, including parental phenotypes, genotypes from both affected and unaffected siblings, and families with homozygous parents. The test is based on the conditional framework developed by Rabinowitz and Laird [2000: Hum Hered 50:211-223] and is thus immune to population stratification and can be applied to families with any pattern of missing information. The test statistic is a conditional likelihood ratio based on a standard two-point linkage model with allelic association, where parameters are estimated from the sample. Through a simulation study, we determined that the proposed test has near optimal power for a wide range of scenarios, outperforming FBAT both when data were complete and when parental genotypes were missing, although differences between the two tests diminish as the genetic effect is reduced. To assess robustness, we also evaluated the performance of the tests under scenarios with population stratification and found that although there is a loss of efficiency, our proposed test remains a strong competitor to FBAT.     

TSC1、TSC2基因多态性和儿童孤独症的家系研究

目的 探讨结节性硬化症(tuberous sclerosis complex,TSC)相关基因TSC1、TSC2基因多态性与儿童孤独症之间的关联。 方法 利用SNaPshot基因分型技术,在97例孤独症核心家系中,对TSC1、TSC2基因上的8个标签SNP,即rs3761840、rs2809244、rs1050700、rs739441、rs2074968、rs2074969、rs2072314、rs8063461进行分型;通过FBAT软件及Haploview软件进行基于家系的单倍型分析。 结果 1)基于家系的关联分析发现8个SNPs等位基因中有2个SNPs的等位基因倾向于过传递(rs1050700 A:Z=2.708,P=0.006769;rs2074968 G:Z=3.244,P=0.001180),并且经过FDR校正后,2个SNPs仍显示出与孤独症之间存在显著关联性(校正P值分别为0.027,0.014)。2)rs3761840-rs2809244基因型的单体型A-C显示出显著的传递不平衡,双亲较少传递给子女(Z=-2.297,P=0.021629)。rs2074968-rs2072314基因型的2种单体型即 G-C及C-C均显示出显著的传递不平衡,单体型G-C能从双亲过传递给子女(Z=2.596,P=0.009444),单体型C-C则相反(Z=-3.657,P=0.000256)。 结论 TSC1、TSC2基因可能与儿童孤独症的发生存在关联。     

Tests for genetic association using family data.

We use likelihood-based score statistics to test for association between a disease and a diallelic polymorphism, based on data from arbitrary types of nuclear families. The Nonfounder statistic extends the transmission disequilibrium test (TDT) to accommodate affected and unaffected offspring, missing parental genotypes, phenotypes more general than qualitative traits, such as censored survival data and quantitative traits, and residual correlation of phenotypes within families. The Founder statistic compares observed or inferred parental genotypes to those expected in the general population. Here the genotypes of affected parents and those with many affected offspring are weighted more heavily than unaffected parents and those with few affected offspring. We illustrate the tests by applying them to data on a polymorphism of the SRD5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of these family-based statistics to that of the score statistic based on Cox's partial likelihood for censored survival data, and find that the family-based statistics have considerably more power when there are many untyped parents. The software program FGAP for computing test statistics is available at http://www.stanford.edu/dept/HRP/epidemiology/FGAP.     

An efficient family-based association test using multiple markers

In genetic association studies, multiple markers are usually employed to cover a genomic region of interest for localizing a trait locus. In this report, we propose a novel multi-marker family-based association test (T(LC)) that linearly combines the single-marker test statistics using data-driven weights. We examine the type-I error rate in a numerical study and compare its power to identify a common trait locus using tag single nucleotide polymorphisms (SNPs) within the same haplotype block that the trait locus resides with three competing tests including a global haplotype test (T(H)), a multi-marker test similar to the Hotelling-T(2) test for the population-based data (T(MM)), and a single-marker test with Bonferroni's correction for multiple testing (T(B)). The type-I error rate of T(LC) is well maintained in our numeric study. In all the scenarios we examined, T(LC) is the most powerful, followed by T(B). T(MM) and T(H) are the poorest. T(H) and T(MM) have essentially the same power when parents are available. However, when both parents are missing, T(MM) is substantially more powerful than T(H). We also apply this new test on a data set from a previous association study on nicotine dependence.     

Latent class model with familial dependence to address heterogeneity in complex diseases: adapting the approach to family-based association studies

Clinical diagnoses of complex diseases may often encompass multiple genetically heterogeneous disorders. One way of dissecting this heterogeneity is to apply latent class (LC) analysis to measurements related to the diagnosis, such as detailed symptoms, to define more homogeneous disease sub-types, influenced by a smaller number of genes that will thus be more easily detectable. We have previously developed a LC model allowing dependence between the latent disease class status of relatives within families. We have also proposed a strategy to incorporate the posterior probability of class membership of each subject in parametric linkage analysis, which is not directly transferable to genetic association methods. Under the framework of family-based association tests (FBAT), we now propose to make the contribution of an affected subject to the FBAT statistic proportional to his or her posterior class membership probability. Simulations showed a modest but robust power advantage compared to simply assigning each subject to his or her most probable class, and important power gains over the analysis of the disease diagnosis without LC modeling under certain scenarios. The use of LC analysis with FBAT is illustrated using autism spectrum disorder (ASD) symptoms on families from the Autism Genetics Research Exchange, where we examined eight regions previously associated to autism in this sample. The analysis using the posterior probability of membership to an LC detected an association in the JARID2 gene as significant as that for ASD (P = 3 × 10(-5)) but with a larger effect size (odds ratio = 2.17 vs. 1.55).     

On the analysis of copy-number variations in genome-wide association studies: a translation of the family-based association test

Though there is an increasing support for an important contribution of copy number variation (CNV) to the genetic architecture of complex disease, few methods have been developed for the analysis of such variation in the context of genetic association studies. In this paper, we propose a generalization of family-based association tests (FBATs) to allow for the analysis of CNVs at a genome-wide level. We translate the popular FBAT approach so that, instead of genotypes, raw intensity values that reflect copy number are used directly in the test statistic, thereby bypassing the need for a CNV genotyping algorithm. Moreover, both inherited and de novo CNVs can be analyzed without any prior knowledge about the type of CNV, making it easily applicable to large-scale association studies. All robustness properties of the genotype FBAT approach are maintained and all previously developed FBAT extensions, including FBATs for time-to-onset, multivariate FBATs, and FBAT-testing strategies, can be directly transferred to the analysis of CNVs. Using simulation studies, we evaluate the power and the robustness of the new approach. Furthermore, for those CNVs that can be genotyped, we compare FBATs based on genotype calls with FBATs that are directly based on the intensity data. An application to one of the first CNV genome-wide-association studies of asthma identifies a very plausible candidate gene. A software implementation of the approach is freely available at http://www.hsph.harvard.edu/research/iuliana-ionita/software. The approach has also been completely integrated in the PBAT software package.     

细胞色素P450 CYP1A1基因多态与鼻咽癌易感性的关联分析

目的 利用以核心家系为基础的关联研究探讨细胞色素P450酶系(cytochrome P450,CYP450)CYP1A1基因m1和m2多态性并分析其与鼻咽癌易感性的关联.方法 收集457个广东鼻咽癌核心家系(每个核心家系由患者和父母或同胞构成)共2134名成员作为研究对象,采用聚合酶链反应-限制性片段长度多态性(PCR-RFLP)方法对CYPlAl基因单核苷酸多态性(SNP)位点m1和m2(其参考编号分别为rs4646903和rs1048943)进行基因分型.挑选PCR产物测序验证.运用家系为基础的相关性检验(family-based association test,FBAT)软件分析这两个多态位点的基因型及其单体型与鼻咽癌易感性的关联.结果 FBAT软件分析结果 显示,位点m1和m2的微效等位基因频率(MAF)分别为0.442(C)和0.339(G).其中无论是否根据EB病毒壳抗原抗体(VCA-IgA)分层,m1位点与鼻咽癌的易感性之间关联均无统计学意义[未分层:X2=2.399,P=0.301;分层后:低滴度组(VCA-IgA<1:80),MAF=0.457(C),X2=1.221,P=0.543;高滴度组(VCA-IgA≥1:80),MAF=0.427(C),X2=2.832,P=0.243];对m2位点来说,末根据VCA-IgA分层时,该位点与鼻咽癌的易感性之间关联无统计学意义(X2=2.694,P=0.260).分层后,在低滴度组,累加和显性模式下,位点m2等位基因G显示了从亲代到子代传递减少[MAF=0.347(G);Z<,累加>=-2.120,P<,累加>=0.034;Z显性=-2.303,P显性=0.021],全局统计也提示了传递的改变(X2=5.394,P=0.067);由这两个位点构建的单体型TG(0.057)可能降低鼻咽癌的发病风险(Z=-2.002,P=0.045),全局统计也提示CYP1A1基因单体型可能与鼻咽癌易感性有关系(X2=7.067,P=0.070).结论 以家系为基础的相关性研究发现CYP1A1基因多态位点m1与鼻咽癌的易感性之间关联无统计学意义,位点m2基因多态性可能与鼻咽癌的发病风险降低有关系.     

A robust TDT-type association test under informative parental missingness

Many family-based association tests rely on the random transmission of alleles from parents to offspring. Among them, the transmission/disequilibrium test (TDT) may be considered to be the most popular statistical test. The TDT statistic and its variations were proposed to evaluate nonrandom transmission of alleles from parents to the diseased children. However, in family studies, parental genotypes may be missing due to parental death, loss, divorce, or other reasons. Under some missingness conditions, nonrandom transmission of alleles may still occur even when the gene and disease are not associated. As a consequence, the usual TDT-type tests would produce excessive false positive conclusions in association studies. In this paper, we propose a novel TDT-type association test which is not only simple in computation but also robust to the joint effect of population stratification and informative parental missingness. Our test is model-free and allows for different mechanisms of parental missingness across subpopulations. We use a simulation study to compare the performance of the new test with TDT and point out the advantage of the new method.     

转化生长因子α基因多态性和父亲吸烟的交互作用与唇腭裂发生的关联研究

目的探讨中国部分地区人群非综合征型唇裂伴或不伴腭裂（nsCL／P）与转化生长因子α基因（TGFα）TaqI位点多态性之间的关系,及其与父亲吸烟之间的交互作用。方法采用PCR-RFLP方法,对170个nsCL／P核心家庭成员DNA标本进行TGFα TaqI突变位点的基因型检测。利用TDT检验分析该突变与nsCL／P发生之间的关系,采用TDT检验的logistic回归模型分析TGFα基因突变与父亲吸烟之间的交互作用。结果未发现TGFα TaqI突变的致病C2等位基因在nsCL／P核心家庭成员中存在传递不平衡,但是父亲吸烟的nsCL／P核心家庭中C2C1基因型的父母将致病的C2等位基因传递给子代的频率是父亲不吸烟的nsCL／P核心家庭父母的约1／5（0．062～0．711）,控制其他环境因素后发现,父亲是否吸烟与TGFα TaqI突变位点C2等位基因传递之间是偏离乘法模型的负交互作用OR=0．102（0．017～0．619）。结论父亲是否吸烟与中国部分地区人群TGFα基因突变存在交互作用,但还有待于进一步研究加以验证。