Method for using complete and incomplete trios to identify genes related to a quantitative trait

A number of tests for linkage and association with qualitative traits have been developed, with the most well-known being the transmission/disequilibrium test (TDT). For quantitative traits, varying extensions of the TDT have been suggested. The quantitative trait approach we propose is based on extending the log-linear model for case-parent trio data (Weinberg et al. [1998] Am. J. Hum. Genet. 62:969-978). Like the log-linear approach for qualitative traits, our proposed polytomous logistic approach for quantitative traits allows for population admixture by conditioning on parental genotypes. Compared to other methods, simulations demonstrate good power and robustness of the proposed test under various scenarios of the genotype effect, distribution of the quantitative trait, and population stratification. In addition, missing parental genotype data can be accommodated through an expectation-maximization (EM) algorithm approach. The EM approach allows recovery of most of the lost power due to incomplete trios.     

A general test of association for complex diseases with variable age of onset

Analysis of age of onset is a key factor in linkage and association studies of some complex genetic traits. Recent methodological developments are mainly concentrated on binary or quantitative traits, in which age of onset information is ignored. We propose a linkage disequilibrium-based Cox model and a robust score test for testing association between a marker and a disease with variable age of onset. The proposed model is semi-parametric, with an unspecified baseline hazard function. Simulation results indicate that the proposed methods have correct error rates and good statistical power, even in the presence of population admixture. This approach offers a solution to the problem of testing for marker associations when there is a variable age of onset.     

Association and linkage analysis of ICD-10 diagnosis for alcoholism

We analyzed the GAW11 data on alcoholism provided by the Collaborative Study on the Genetics of Alcoholism (COGA) using an extension of a new test of linkage and association for quantitative traits developed by George et al. [1999]. This method determines linkage between marker loci and quantitative traits, when allelic association is present between the trait and marker loci, by regressing the disease trait on the parental transmission of the allele of interest. We found no strong evidence of linkage to any markers. However, we found several markers suggestive of possible linkage that may deserve further investigation.     

Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics

We provide a general purpose family-based testing strategy for associating disease phenotypes with haplotypes when phase may be ambiguous and parental genotype data may be missing. These tests for linkage and association can be used in candidate gene studies with tightly linked markers. Our proposed weighted conditional approach extends the method described in Rabinowitz and Laird to multiple markers. It is attractive because it provides haplotype tests for family-based studies that are efficient and robust to population admixture, phenotype distribution specification, and ascertainment based on phenotypes. It can handle missing parental genotypes and/or missing phase in both offspring and parents. It yields either haplotype-specific (univariate) tests or multi-haplotype (global) tests. This extension has been implemented in the freely available software haplotype FBAT. We used the haplotype FBAT program to test for associations between asthma phenotypes and single nucleotide polymorphisms (SNPs) in the beta-2 adrenergic receptor gene. Whereas no single SNP showed significant association with asthma diagnosis or bronchodilator responsiveness (quantitative trait), a haplotype-based global test found a highly significant association with asthma diagnosis (P value <0.00005) and the measure of bronchodilator responsiveness (P value =0.016).     

Quantitative trait transmission disequilibrium test: allowance for missing parents

The association of a quantitative trait with transmission disequilibrium of parental marker alleles can be a powerful method to discover the genetic basis of the trait. However, current methods require marker genotypes on all parents. We present a new method that allows the assessment of the association of a quantitative trait with transmission disequilibrium of marker alleles when parental marker genotypes are missing, but marker genotypes for sibships are available. Because our approach is based on regression methods, additional covariates, including multiple marker loci, can be included in our proposed method of analysis. Application of this methodology to the COGA data to assess the association of quantitative traits (ERPs) with marker loci on chromosome 7 failed to identify statistically significant transmission disequilibrium.     

A method for using incomplete triads to test maternally mediated genetic effects and parent-of-origin effects in relation to a quantitative trait

The authors recently developed a semiparametric family-based test for linkage and association between markers and quantitative traits. This quantitative polytomous logistic regression test allows for analysis of families with incomplete information on parental genotype. In addition, it is not necessary to assume normality of the quantitative trait. Previous simulations have shown that the new test is as powerful as the other widely used tests for linkage disequilibrium in relation to a quantitative trait. Here the authors propose an extension to quantitative polytomous logistic regression that allows testing for maternally mediated effects and parent-of-origin effects in the same framework. Missing data on parental genotype are accommodated through an expectation-maximization algorithm approach. Simulations show robustness of the new tests and good power for detecting effects in the presence or absence of offspring effects. Methods are illustrated with birth weight and gestational length, two quantitative outcomes for which data were collected in a Montreal, Canada, study of intrauterine growth restriction between May 1998 and June 2000.     

A Bayesian approach to the transmission/disequilibrium test for binary traits.

The transmission/disequilibrium test (TDT) for binary traits is a powerful method for detecting linkage between a marker locus and a trait locus in the presence of allelic association. The TDT uses information on the parent-to-offspring transmission status of the associated allele at the marker locus to assess linkage or association in the presence of the other, using one affected offspring from each set of parents. For testing for linkage in the presence of association, more than one offspring per family can be used. However, without incorporating the correlation structure among offspring, it is not possible to correctly assess the association in the presence of linkage. In this presentation, we propose a Bayesian TDT method as a complementary alternative to the classical approach. In the hypothesis testing setup, given two competing hypotheses, the Bayes factor can be used to weigh the evidence in favor of one of them, thus allowing us to decide between the two hypotheses using established criteria. We compare the proposed Bayesian TDT with a competing frequentist-testing method with respect to power and type I error validity. If we know the mode of inheritance of the disease, then the joint and marginal posterior distributions for the recombination fraction (theta) and disequilibrium coefficient (delta) can be obtained via standard MCMC methods, which lead naturally to Bayesian credible intervals for both parameters.     

Bayesian Association Mapping for Quantitative Traits in a Mixture of Two Populations

We introduce a novel Bayesian approach to estimate and account for population structure simultaneously with association mapping of multiple quantitative trait loci. The method is designed for an analysis of unrelated individuals from a mixture of two populations (no admixture), where the individual population memberships are unknown. In our approach, the population structure is estimated and accounted for by using data on additional “grouping” markers which are assumed to be in Hardy‐Weinberg equilibrium within the populations but have different allele frequencies between the populations. We use Bayesian hierarchical modeling and Markov chain Monte Carlo estimation, where we allow both population stratification and genetic heterogeneity. In our model the number of quantitative trait loci and their positions are treated as random variables, and we obtain their posterior distributions. Here we select the candidate and the grouping markers based on results from a preliminary SOLAR analysis. © 2001 Wiley‐Liss, Inc.     

Transmission/disequilibrium tests for quantitative traits

Spielman et al. [1993] proposed a transmission-disequilibrium test (TDT), based on marker data collected on affected offspring and their parents, to test for linkage between a genetic marker and a binary trait provided there is allelic association. It has been shown that this TDT is powerful and is not affected by allelic association due to population stratification in the absence of linkage. For quantitative traits, George and Elston [1987] proposed a likelihood method to detect the effect of a candidate gene in pedigree data when familial correlations are present. This test will detect allelic association but will do so in the absence of linkage. In this paper, we investigate two new likelihood-ratio test statistics for multi-generational quantitative traits to test either for linkage in the presence of allelic association or for allelic association in the presence of linkage, such as may be due to linkage disequilibrium. We compare these two tests analytically and by simulation with respect to 1) the sample size required for the asymptotic null distributions to be valid and 2) their power to detect association in those cases in which they are not sensitive to population stratification unless linkage is present. In general, 80 nuclear families with two children each and at least one heterozygous parent, or the equivalent number of children in large pedigrees, are enough for the asymptotic null distribution of the proposed conditional and TDT methods to be valid. The theoretical power is close to the simulated power except for the case of a recessive allele with low frequency. A sampling strategy is proposed that dramatically improves power.     

Testing association in the presence of linkage--a powerful score for binary traits

We present a score for testing association in the presence of linkage for binary traits. The score is robust to varying degrees of linkage, and it is valid under any ascertainment scheme based on trait values as well as under population stratification. The score test is derived from a mixed effects model where population level association is modeled using a fixed effect and where correlation among related individuals is allowed for by using log-gamma random effects. The score, as presented in this paper, does not assume full information about the inheritance pattern in families or parental genotypes. We compare the score to the semi-parametric family-based association test (FBAT), which has won ground because of its flexible and simple form. We show that a random effects formulation of co-inheritance can improve the power substantially. We apply the method to data from the Collaborative Study on the Genetics of Alcoholism. We compare our findings to previously published results.     

Combined Linkage and Association Analysis in Pedigrees

We applied a combined linkage and association model for quantitative traits in pedigrees to identify possible functional polymorphisms and to test for association resulting from population stratification and admixture. Functional polymorphisms are identified as variants that are significantly associated with a trait (high x² value) and showing no residual evidence of linkage (low lod score). Applying our model to the simulated data in the population isolate (replicate 1) we correctly identified the polymorphism in gene 6 (MG1) that affects Q1. Without modeling association the lod score for Q1 was 5.4. At the site of the functional variant (5782 bp) the association x² was 88.1 on 1 df (p < 0.001) and the lod score was 0.003. We estimated a 3.7‐unit increase in the average Q1 for each extra copy of the polymorphism (95% CI = 2.95–4.41) and there was no evidence of population stratification or admixture (x²= 0.08 on 2 df). For Q5 and gene 2, modeling the sequence variants at 11 loci simultaneously identified multiple functional variants. Including the main effect of 11 marker genotypes reduced the lod score at gene 2 from 8.7 to 0.9. Again, no evidence of population stratification or admixture was found (all x²< 4.9 on 2 df; p > 0.05). © 2001 Wiley‐Liss, Inc.     

Power comparison of regression methods to test quantitative traits for association and linkage

George et al. [1999 Am J Hum Genet 65:236-245] proposed a regression-based TDT method for quantitative traits consisting of regressing the trait on the parental transmission of a marker allele. Zhu and Elston [2000] also developed a TDT method for quantitative traits by defining a linear transformation to condition out founder information. Both methods test the null hypothesis of no linkage or association and can be applied to general pedigree structures. In this paper, we compare the power of these two methods through simulation, sampling those nuclear families with at least one heterozygous parent. Overall, we find that a variant of Zhu and Elston's method with 2 d.f. is more powerful. However, if the mode of inheritance is known, then a most powerful test with 1 d.f. can be found. All these regression TDT tests require linkage to detect association, but a test that does not require linkage will be more powerful.     

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.     

Linkage and association analyses of alcoholism using a regression-based transmission/disequilibrium test

Recently, George et al. proposed a regression-based transmission/disequilibrium test for linkage using information on the parent-to-offspring transmission status of an allele at a marker locus. We extended this test by simultaneously testing for any population association by incorporating the presence/absence status of the associated allele as a covariate in the model. We used this method to analyze markers on chromosomes 1 through 21 of the Collaborative Study on the Genetics of Alcoholism data on alcoholism for possible association and linkage. We found nominal significance (at the 0.02 level) at eight different regions for linkage, though statistical significance may not be concluded due to multiple testing. The strongest evidence of linkage was observed for markers D4S2639 and D12S397 with p-values less than 0.005. We also found strong association between the trait and alleles 149 of D7S691 and 131 of D21S1437.     

Power and robustness of linkage tests for quantitative traits in general pedigrees

There are numerous statistical methods for quantitative trait linkage analysis in human studies. An ideal such method would have high power to detect genetic loci contributing to the trait, would be robust to non-normality in the phenotype distribution, would be appropriate for general pedigrees, would allow the incorporation of environmental covariates, and would be appropriate in the presence of selective sampling. We recently described a general framework for quantitative trait linkage analysis, based on generalized estimating equations, for which many current methods are special cases. This procedure is appropriate for general pedigrees and easily accommodates environmental covariates. In this report, we use computer simulations to investigate the power and robustness of a variety of linkage test statistics built upon our general framework. We also propose two novel test statistics that take account of higher moments of the phenotype distribution, in order to accommodate non-normality. These new linkage tests are shown to have high power and to be robust to non-normality. While we have not yet examined the performance of our procedures in the context of selective sampling via computer simulations, the proposed tests satisfy all of the other qualities of an ideal quantitative trait linkage analysis method.     

Two tests of association for a susceptibility locus for families of variable size: an example using two sampling strategies

A two-stage approach was used to analyze Problem 2 simulated data from Genetic Analysis Workshop 11. In the first stage, we tested for linkage with the Haseman-Elston test in SIBPAL. Markers that were significant in the first stage were followed up with two types of association tests. These association tests differ in the type of family information used: 1) parental transmissions to affected children or 2) differences in marker allele frequencies between affected and unaffected siblings. We also explored how the conclusions changed when different sampling strategies were used. Of particular interest was whether the entire data set should be used to test for both linkage and association or whether the data set should be halved to allow for replication of the initial association results.     

以家庭为基础的关联检验在遗传流行病学中的应用

简述以家庭为基础的关联检验(FBAT)分析遗传标记等位基因与疾病表型之间关联的方法 在遗传流行病学研究中的应用.介绍FBAT的设计原理、基本步骤、应用原则,并结合实例说明如何利用相应的软件有效分析核心家庭数据.分析表明,相对于其他传统的遗传分析方法 ,FBAT可以充分利用双亲基因型、受累子代基因型及其表型的家系数据,同时还以其他性状,如环境暴露数据作为协变量,进行双等位基因或多等位基因与疾病表型的关联分析,其设计优势为可消除病例和不相关对照之间由于种族差异产生的虚假关联,有效控制由于群体分层引起的偏倚,较其他方法 有更高的检验效率.适用于核心家庭或家系资料的遗传分析,是目前为数不多的进行疾病与遗传标志物关联检验的家系遗传分析方法之一.     

2013年北京市首例人感染H7N9禽流感病例流行病学调查

2013年4月12日北京市某医院报告-例源自顺义区的人感染H7N9禽流感监测病例,次日确诊为北京市首例人感染H7N9禽流感病例.由于诊断、治疗和控制措施及时有效,该名患儿经救治于4月17日痊愈出院,疫情也未发生进-步传播.现将本次疫情流行病学调查资料分析如下. -、资料与方法 应用现场流行病学方法,按照2013年《人感染H7N9禽流感疫情防控方案(第1版)》,对病例进行回顾性个案调查.对病例的父母、老师等相关知情者进行访谈,求证病例发病前活动情况.查阅病例就诊的全部门诊和住院病历.     

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 semiparametric test to detect associations between quantitative traits and candidate genes using unrelated individuals

Although genetic association studies using unrelated individuals may be subject to bias caused by population stratification, alternative methods that are robust to population stratification such as family-based association designs may be less powerful. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate markers and traits of interest (both qualitative and quantitative). Here, we propose a semiparametric test for association (SPTA). SPTA controls for population stratification through a set of genomic markers by first deriving a genetic background variable for each sampled individual through his/her genotypes at a series of independent markers, and then modeling the relationship between trait values, genotypic scores at the candidate marker, and genetic background variables through a semiparametric model. We assume that the exact form of relationship between the trait value and the genetic background variable is unknown and estimated through smoothing techniques. We evaluate the performance of SPTA through simulations both with discrete subpopulation models and with continuous admixture population models. The simulation results suggest that our procedure has a correct type I error rate in the presence of population stratification and is more powerful than statistical association tests for family-based association designs in all the cases considered. Moreover, SPTA is more powerful than the Quantitative Similarity-Based Association Test (QSAT) developed by us under continuous admixture populations, and the number of independent markers needed by SPTA to control for population stratification is substantially fewer than that required by QSAT.