Family-based association studies

Case-Parent Trio Studies in Cleft Lip and Palate

Cleft lip with or without cleft palate (CL/P) is one of the most common congenital malformations in humans involving various genetic and environmental risk factors. The prevalence of CL/P varies according to geographical location, ethnicity, race, gender, and socioeconomic status, affecting approximately 1 in 800 live births worldwide. Genetic studies aim to understand the mechanisms contributory to a phenotype by measuring the association between genetic variants and also between genetic variants and phenotype population. Genome-wide association studies are standard tools used to discover genetic loci related to a trait of interest. Genetic association studies are generally divided into two main design types: population-based studies and family-based studies. The epidemiological population-based studies comprise unrelated individuals that directly compare the frequency of genetic variants between (usually independent) cases and controls. The alternative to population-based studies (case-control designs) includes various family-based study designs that comprise related individuals. An example of such a study is a case-parent trio design study, which is commonly employed in genetics to identify the variants underlying complex human disease where transmission of alleles from parents to offspring is studied. This article describes the fundamentals of case-parent trio study, trio design and its significances, statistical methods, and limitations of the trio studies.

Family-based exome-wide association study of childhood acute lymphoblastic leukemia among Hispanics confirms role of ARID5B in susceptibility

We conducted an exome-wide association study of childhood acute lymphoblastic leukemia (ALL) among Hispanics to confirm and identify novel variants associated with disease risk in this population. We used a case-parent trio study design; unlike more commonly used case-control studies, this study design is ideal for avoiding issues with population stratification bias among this at-risk ethnic group. Using 710 individuals from 323 Guatemalan and US Hispanic families, two inherited SNPs in ARID5B reached genome-wide level significance: rs10821936, RR = 2.31, 95% CI = 1.70–3.14, p = 1.7×10⁻⁸ and rs7089424, RR = 2.22, 95% CI = 1.64–3.01, p = 5.2×10⁻⁸. Similar results were observed when restricting our analyses to those with the B-ALL subtype: ARID5B rs10821936 RR = 2.22, 95% CI = 1.63–3.02, p = 9.63×10⁻⁸ and ARID5B rs7089424 RR = 2.13, 95% CI = 1.57–2.88, p = 2.81×10⁻⁷. Notably, effect sizes observed for rs7089424 and rs10821936 in our study were >20% higher than those reported among non-Hispanic white populations in previous genetic association studies. Our results confirmed the role of ARID5B in childhood ALL susceptibility among Hispanics; however, our assessment did not reveal any strong novel inherited genetic risks for acute lymphoblastic leukemia among this ethnic group.

Generalized estimating equations: A hybrid approach for mean parameters in multivariate regression models

We propose an extension of the generalized estimating equation approach to multivariate regression models (Liang and Zeger, 1986) which allows the estimation of dispersion and association parameters in the covariance matrix partly using estimating equations as in Prentice and Zhao (1991), and partly by the direct use of consistent estimators. The advantages of this hybrid approach over that of Prentice and Zhao (1991) are a reduction in the number of fourth moment assumptions that must be made, and the consequent reduction in numerical complexity. We show that the type of estimation used for covariance parameters does not affect the asymptotic efficiency of the mean parameter estimates. The advantages of the hybrid model are illustrated by a simulation study. This work was motivated by problems in statistical genetics, and we illustrate our approach using a twin study examining association between the osteocalcin receptor and various osteoporisis-related traits.

Association of TMEM106B gene polymorphism with age at onset in granulin mutation carriers and plasma granulin protein levels

OBJECTIVE

To test whether rs1990622 (TMEM106B) is associated with age at onset (AAO) in granulin (GRN) mutation carriers and with plasma GRN levels in mutation carriers and healthy, elderly individuals. Rs1990622 (TMEM106B) was identified as a risk factor for frontotemporal lobar degeneration with TAR DNA-binding protein inclusions (FTLD-TDP) in a recent genome-wide association.

DESIGN

Rs1990622 was genotyped in GRN mutation carriers and tested for association with AAO using the Kaplan-Meier method and a Cox proportional hazards model.

SETTING

Alzheimer's Disease Research Center. Subjects  We analyzed 50 affected and unaffected GRN mutation carriers from 4 previously reported FTLD-TDP families (HDDD1, FD1, HDDD2, and the Karolinska family). The GRN plasma levels were also measured in 73 healthy, elderly individuals.

MAIN OUTCOME MEASURES

Age at onset and GRN plasma levels.

RESULTS

The risk allele of rs1990622 was associated with a mean decrease of the AAO of 13 years (P = 9.9 × 10(-7)) and with lower plasma GRN levels in both healthy older adults (P = 4 × 10(-4)) and GRN mutation carriers (P = .0027). Analysis of the HapMap database identified a nonsynonymous single-nucleotide polymorphism rs3173615 (T185S) in perfect linkage disequilibrium with rs1990622.

CONCLUSIONS

The association of rs1990622 with AAO explains, in part, the wide range in the AAO of disease among GRN mutation carriers. We hypothesize that rs1990622 or another variant in linkage disequilibrium could act in a manner similar to APOE in Alzheimer disease, increasing risk for disease in the general population and modifying AAO in mutation carriers. Our results also suggest that genetic variation in TMEM106B may influence risk for FTLD-TDP by modulating secreted levels of GRN.

Blood lead levels of children and its trend in China

To evaluate Chinese children's blood lead levels (BLLs) and identify its distribution features, we collected articles on children's BLLs published from Jan, 2004 to Aug, 2007 with sampling time since 2001, by searching Chinese Biomedical Disk (CBMDisk), Chinese Journal Full-test Database (CJFD) and other ways. After a rigorous investigation, 35 articles eligible for inclusion criteria were reviewed. Meanwhile, to reveal the trend of Chinese children's BLLs, the data was compared with the results from our former study carried out in 2004, which reviewed the articles published since 1995 to 2003. The results showed that the mean BLLs of Chinese children from 2001 to 2007 was 80.7 microg/L (45.5-165.3 microg/L), and 23.9% (3.2%-80.7%) of children's BLLs was higher than 100 microg/L. Both BLLs and prevalence of BLLs higher than 100 microg/L were lower than that of 1995 to 2003. Of 24 provinces or cities, only 4 showed higher BLLs and higher prevalence rates of lead poisoning (BLLs > or =100 microg/L) than that of 1995 to 2003. The mean BLLs of children living in industrial areas were higher than those in urban and suburban areas, and suburban higher than urban areas. Boys' BLLs were 79.3 microg/L, significantly higher than girls 76.9 microg/L (P<0.001). The results also showed that children's BLLs increased with their ages, and the decreased BLLs for all age were observed comparing with the results of our former study in 2004. Overall, our study revealed that the BLLs of children in China showed a decreasing trend after gasoline with lead was banned in China in 1st July 2000. Our study also showed that the BLLs of children in China were still higher than that in developed countries, which suggested that children's lead poisoning prevention and controlling would be a long-term mission in China.

Correcting for relatedness in Bayesian models for genomic data association analysis

For small pedigrees, the issue of correcting for known or estimated relatedness structure in population-based Bayesian multilocus association analysis is considered. Two such relatedness corrections: [1] a random term arising from the infinite polygenic model and [2] a fixed covariate following the class D model of Bonney, are compared with the case of no correction using both simulated and real marker and gene-expression data from lymphoblastoid cell lines from four CEPH families. This comparison is performed with clinical quantitative trait locus (cQTL) models-multilocus association models where marker data and expression levels of gene transcripts as well as possible genotype x expression interaction terms are jointly used to explain quantitative trait variation. We found out that regardless of having a correction term in the model, the cQTL-models fit a few extra small-effect components (similar to finite polygenic models) which itself serves as a relatedness correction. For small data and small heritability one may use the covariate model, which clearly outperforms the infinite polygenic model in small data examples.

Semiparametric Bayesian modeling of random genetic effects in family-based association studies

We consider the inference problem of estimating covariate and genetic effects in a family-based case-control study where families are ascertained on the basis of the number of cases within the family. However, our interest lies not only in estimating the fixed covariate effects but also in estimating the random effects parameters that account for varying correlations among family members. These random effects parameters, though weakly identifiable in a strict theoretical sense, are often hard to estimate due to the small number of observations per family. A hierarchical Bayesian paradigm is a very natural route in this context with multiple advantages compared with a classical mixed effects estimation strategy based on the integrated likelihood. We propose a fully flexible Bayesian approach allowing nonparametric modeling of the random effects distribution using a Dirichlet process prior and provide estimation of both fixed effect and random effects parameters using a Markov chain Monte Carlo numerical integration scheme. The nonparametric Bayesian approach not only provides inference that is less sensitive to parametric specification of the random effects distribution but also allows possible uncertainty around a specific genetic correlation structure. The Bayesian approach has certain computational advantages over its mixed-model counterparts. Data from the Prostate Cancer Genetics Project, a family-based study at the University of Michigan Comprehensive Cancer Center including families having one or more members with prostate cancer, are used to illustrate the proposed methods. A small-scale simulation study is carried out to compare the proposed nonparametric Bayes methodology with a parametric Bayesian alternative.

CD83 gene polymorphisms increase susceptibility to human invasive cervical cancer

We previously mapped a nonrandom frequent loss of heterozygosity (LOH) region in cervical cancers to 1 Mb of 6p23. Here, we describe the identification of a novel cervical cancer susceptibility gene, CD83. The gene was identified by several complementary approaches, including a family-based association study, comparison of transcript expression in normal and cancerous tissue, and genomic sequencing of candidate. CD83 encodes an inducible glycoprotein in the immunoglobulin superfamily and is a marker for mature dendritic cells. The association study that includes 377 family trios showed that five single nucleotide polymorphisms (SNP) within 8 kb of its 3'-end showed significant allelic association that was strengthened in a subgroup of women with invasive cancers infected by high-risk human papillomavirus type 16 and 18 (rs9296925, P = 0.0193; rs853360, P = 0.0035; rs9230, P = 0.0011; rs9370729, P = 0.0012; rs750749, P = 0.0133). Investigation of CD83 uncovered three alternative transcripts in cervical tissue and cell lines, with variant 3 (lacking exons 3 and 4) being more frequent in cervical cancer than in normal cervical epithelium (P = 0.0181). Genomic sequencing on 36 paired normal and cervical tumors revealed several somatic mutations and novel SNPs in the promoter, exons, and introns of CD83. LOH was confirmed in >90% of cervical cancer specimens. Immunofluorescence colocalized CD83 protein to the Golgi apparatus and cell membrane of cervical cancer cell lines. None of seven nearby genes was differentially expressed in cervical cancer. The importance of CD83 in epithelial versus dendritic cells needs to be determined, as does its role in promoting cervical cancer.

The effect of assortative mating upon genetic association studies: spurious associations and population substructure in the absence of admixture

Spurious associations due to confounding factors are an often cited and intensely debated concern for genetic association studies. Great attention has been focused upon the specific threat of confounding due to population stratification. This emphasis has spurred the development of many statistical genetic methods to detect and correct for the potentially confounding effects of admixture. Unfortunately, this emphasis on admixture has led some authors to suggest that if ethnically homogenous populations are used, spurious associations are unlikely to occur. We show that under small and realistic degrees of assortative mating over time, spurious associations arise even in ethnically homogeneous populations. We demonstrate that structured association and genomic control tests can, under certain conditions, correct for these spurious associations. We conclude that investigators should not assume spurious associations will not occur in association studies using ethnically homogenous populations and recommend the use of genomic control methods and/or family-based association tests within genetic association studies.

A fast, unbiased and exact allelic test for case-control association studies

Association studies are traditionally performed in the case-control framework. As a first step in the analysis process, comparing allele frequencies using the Pearson's chi-square statistic is often invoked. However such an approach assumes the independence of alleles under the hypothesis of no association, which may not always be the case. Consequently this method introduces a bias that deviates the expected type I error-rate. In this article we first propose an unbiased and exact test as an alternative to the biased allelic test. Available data require to perform thousands of such tests so we focused on its fast execution. Since the biased allelic test is still widely used in the community, we illustrate its pitfalls in the context of genome-wide association studies and particularly in the case of low-level tests. Finally, we compare the unbiased and exact test with the Cochran-Armitage test for trend and show it perfoms similarly in terms of power. The fast, unbiased and exact allelic test code is available in R, C++ and Perl at: http://stat.genopole.cnrs.fr/software/fueatest.

Family-based versus unrelated case-control designs for genetic associations

The most simple and commonly used approach for genetic associations is the case-control study design of unrelated people. This design is susceptible to population stratification. This problem is obviated in family-based studies, but it is usually difficult to accumulate large enough samples of well-characterized families. We addressed empirically whether the two designs give similar estimates of association in 93 investigations where both unrelated case-control and family-based designs had been employed. Estimated odds ratios differed beyond chance between the two designs in only four instances (4%). The summary relative odds ratio (ROR) (the ratio of odds ratios obtained from unrelated case-control and family-based studies) was close to unity (0.96 [95% confidence interval, 0.91–1.01]). There was no heterogeneity in the ROR across studies (amount of heterogeneity beyond chance I² = 0%). Differences on whether results were nominally statistically significant (p < 0.05) or not with the two designs were common (opposite classification rates 14% and 17%); this reflected largely differences in power. Conclusions were largely similar in diverse subgroup analyses. Unrelated case-control and family-based designs give overall similar estimates of association. We cannot rule out rare large biases or common small biases.

Different types of designs are used for the assessment of genetic associations for complex diseases. Case-control studies of unrelated people and family-based designs are the most widely used. Each has its advantages and disadvantages. This paper compares the estimates of the two types of design using a meta-analytic approach, i.e. a systematic selection of data and quantitative synthesis of results across many studies. The authors examined 93 associations where both unrelated case-control and family-based designs had been employed. Both designs gave overall similar estimates of association and the conclusions were very similar in subgroup analyses that considered various design features that might affect in theory the degree of agreement between the two designs. No heterogeneity between studies was observed. Hence, there was no consistent pattern of over-estimation or under-estimation of the probed association with one or the other design. However, one cannot exclude the possibility that rare large differences or common small differences may occur between the two designs.

Case-parent triads: estimating single- and double-dose effects of fetal and maternal disease gene haplotypes

Case-parent triad data are considered a robust basis for studying association between variants of a gene and a disease. Methods evaluating statistical significance of association, like the TDT-test and its extensions, are frequently used. When there are prior hypotheses of a causal effect of the gene under study, however, methods measuring penetrance of alleles or haplotypes as relative risks will be more informative. Log-linear models have been proposed as a flexible tool for such relative risk estimation. We demonstrate an extension of the log-linear model to a natural framework for also estimating effects of multiple alleles or haplotypes, incorporating both single- and double-dose effects. The model also incorporates effects of single- and double-dose maternal haplotypes on a fetus during pregnancy. Unknown phase of haplotypes as well as missing parents are accounted for by the EM algorithm. A number of numerical improvements to maximum likelihood estimation are also implemented to facilitate a larger number of haplotypes. Software for these analyses, HAPLIN, is publicly available through our web site. As an illustration we have re-analyzed data on the MSX1 homeobox-gene on chromosome 4 to show how haplotypes may influence the risk of oral clefts.

Detecting linkage disequilibrium in the presence of locus heterogeneity

Locus heterogeneity is a common phenomenon in complex diseases and is one of the most important factors that affect the power of either linkage or linkage disequilibrium (LD) analysis. In linkage analysis, the heterogeneity LOD score (HLOD) rather than LOD itself is often used. However, the existing methods for detecting linkage disequilibrium, such as the TDT and many of its variants, do not take into account locus heterogeneity. We propose two novel likelihood-based methods, an LD-Het likelihood and an LD-multinomial likelihood, to test linkage disequilibrium (LD) that explicitly incorporate locus heterogeneity in the analysis. The LD-Het is applicable to general nuclear family data but requires a working penetrance model. The LD-multinomial is only applicable to affected sib-pair data but does not require specification of a trait model. For affected sib-pair data, both methods have similar power to detect LD under the recessive model, but the LD-multinomial model has greater power when the underlying model is dominant or additive.

Family-based association test for time-to-onset data with time-dependent differences between the hazard functions

In genetic association studies, the differences between the hazard functions for the individual genotypes are often time-dependent. We address the non-proportional hazards data by using the weighted logrank approach by Fleming and Harrington [1981]:Commun Stat-Theor M 10:763-794. We introduce a weighted FBAT-Logrank whose weights are based on a non-parametric estimator for the genetic marker distribution function under the alternative hypothesis. We show that the computation of the marker distribution under the alternative does not bias the significance level of any subsequently computed FBAT-statistic. Hence, we use the estimated marker distribution to select the Fleming-Harrington weights so that the power of the weighted FBAT-Logrank test is maximized. In simulation studies and applications to an asthma study, we illustrate the practical relevance of the new methodology. In addition to power increases of 100% over the original FBAT-Logrank test, we also gain insight into the age at which a genotype exerts the greatest influence on disease risk.

Analysis of genetically complex epilepsies

During the last decade, great progress has been made in the discovery of genes that influence risk for epilepsy. However, these gene discoveries have been in epilepsies with Mendelian modes of inheritance, which comprise only a tiny fraction of all epilepsy. Most people with epilepsy have no affected relatives, suggesting that the great majority of all epilepsies are genetically complex: multiple genes contribute to their etiology, none of which has a major effect on disease risk. Gene discovery in the genetically complex epilepsies is a formidable task. It is unclear which epilepsy phenotypes are most advantageous to study, and chromosomal localization and mutation detection are much more difficult than in Mendelian epilepsies. Association studies are very promising for the identification of complex epilepsy genes, but we are still in the earliest stages of their application in the epilepsies. Future studies should employ very large sample sizes to ensure adequate statistical power, clinical phenotyping methods of the highest quality, designs and analytic techniques that control for population stratification, and state-of-the-art molecular methods. Collaborative studies are essential to achieve these goals.

Power of QTL detection using association tests with family controls

The power of testing for a population-wide association between a biallelic quantitative trait locus and a linked biallelic marker locus is predicted both empirically and deterministically for several tests. The tests were based on the analysis of variance (ANOVA) and on a number of transmission disequilibrium tests (TDT). Deterministic power predictions made use of family information, and were functions of population parameters including linkage disequilibrium, allele frequencies, and recombination rate. Deterministic power predictions were very close to the empirical power from simulations in all scenarios considered in this study. The different TDTs had very similar power, intermediate between one-way and nested ANOVAs. One-way ANOVA was the only test that was not robust against spurious disequilibrium. Our general framework for predicting power deterministically can be used to predict power in other association tests. Deterministic power calculations are a powerful tool for researchers to plan and evaluate experiments and obviate the need for elaborate simulation studies.

Genes and gene polymorphisms associated with periodontal disease

The scientific literature during the last ten years has seen an exponential increase in the number of reports claiming links for genetic polymorphisms with a variety of medical diseases, particularly chronic immune and inflammatory conditions. Recently, periodontal research has contributed to this growth area. This new research has coincided with an increased understanding of the genome which, in turn, has permitted the functional interrelationships of gene products with each other and with environmental agents to be understood. As a result of this knowledge explosion, it is evident that there is a genetic basis for most diseases, including periodontitis. This realization has fostered the idea that if we can understand the genetic basis of diseases, genetic tests to assess disease risk and to develop etiology-based treatments will soon be reality. Consequently, there has been great interest in identifying allelic variants of genes that can be used to assess disease risk for periodontal diseases. Reports of genetic polymorphisms associated with periodontal disease are increasing, but the limitations of such studies are not widely appreciated. While there have been dramatic successes in the identification of mutations responsible for rare genetic conditions, few genetic polymorphisms reported for complex genetic diseases have been demonstrated to be clinically valid, and fewer have been shown to have clinical utility. Although geneticists warn clinicians on the over-enthusiastic use and interpretation of their studies, there continues to be a disparity between the geneticists and the clinicians in the emphasis placed on genes and genetic polymorphism associations. This review critically reviews genetic associations claimed for periodontal disease. It reveals that, despite major advances in the awareness of genetic risk factors for periodontal disease (with the exception of periodontitis associated with certain monogenetic conditions), we are still some way from determining the genetic basis of both aggressive and chronic periodontitis. We have, however, gained considerable insight into the hereditary pattern for aggressive periodontitis. Related to our understanding that it is autosomal-dominant with reduced penetrance comes a major clinically relevant insight into the risk assessment and screening for this disease, in that we appreciate that parents, offspring, and siblings of patients affected with aggressive periodontitis have a 50% risk of this disease also. Nevertheless, we must exercise caution and proper scientific method in the pursuit of clinically valid and useful genetic diagnostic tests for chronic and aggressive periodontitis. We must plan our research using plausible biological arguments and carefully avoid the numerous bias and misinterpretation pitfalls inherent in researching genetic associations with disease.

Estimating recombination rates from population-genetic data

Obtaining an accurate measure of how recombination rates vary across the genome has implications for understanding the molecular basis of recombination, its evolutionary significance and the distribution of linkage disequilibrium in natural populations. Although measuring the recombination rate is experimentally challenging, good estimates can be obtained by applying population-genetic methods to DNA sequences taken from natural populations. Statistical methods are now providing insights into the nature and scale of variation in the recombination rate, particularly in humans. Such knowledge will become increasingly important owing to the growing use of population-genetic methods in biomedical research.

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Using the noninformative families in family-based association tests: a powerful new testing strategy

For genetic association studies with multiple phenotypes, we propose a new strategy for multiple testing with family-based association tests (FBATs). The strategy increases the power by both using all available family data and reducing the number of hypotheses tested while being robust against population admixture and stratification. By use of conditional power calculations, the approach screens all possible null hypotheses without biasing the nominal significance level, and it identifies the subset of phenotypes that has optimal power when tested for association by either univariate or multivariate FBATs. An application of our strategy to an asthma study shows the practical relevance of the proposed methodology. In simulation studies, we compare our testing strategy with standard methodology for family studies. Furthermore, the proposed principle of using all data without biasing the nominal significance in an analysis prior to the computation of the test statistic has broad and powerful applications in many areas of family-based association studies.

Gene finding strategies

Both linkage and association methods have been used to localise and identify genes related to behaviour and other complex traits. The linkage approach (parametric or non-parametric) can be used for whole genome screens to localise genes of unknown function. The parametric linkage approach is very effective for locating single-gene disorders and is usually based on large family pedigrees. The non-parametric method is useful to detect quantitative trait loci (QTLs) for complex traits and was originally developed for sib pair analyses. Genetic association studies are most often used to test the association of alleles at a candidate gene with a disease or with levels of a quantitative trait. Allelic association between a trait and a marker can be studied in a case-control design, but because of possible problems due to population stratification, within-family designs have been proposed as the optimal test for association.

Power calculations for a general class of family-based association tests: dichotomous traits

Using large-sample theory, we present a unified approach to power calculations for family-based association tests. Currently available methods for power calculations are restricted to special designs or require approximations or simulations. Our analytical approach to power calculations is broadly applicable in many settings. We discuss power calculations for two scenarios that have high practical relevance and in which power previously could only be assessed by simulation studies or by approximations: (1) studies using both affected and unaffected offspring and (2) studies with missing parental information. When the population prevalence is high, it can be worthwhile to genotype unaffected offspring. For many scenarios, high power can be achieved with reasonable sample sizes, even when no parental information is available.

Test of association for quantitative traits in general pedigrees: the quantitative pedigree disequilibrium test

Many statistical methods have been proposed in recent years to test for genetic linkage and association between genetic markers and traits of interest through unrelated nuclear families. However, most of these methods are not valid tests of association in the presence of linkage when some of the nuclear families are related. As a result, related nuclear families in large pedigrees cannot be included in a single analysis to test for linkage disequilibrium. Recently, Martin et al. [Am J Hum Genet 67:146-54, 2000] proposed the pedigree disequilibrium test (PDT) to test for linkage and association in general pedigrees for qualitative traits. In this article, we develop a similar quantitative pedigree disequilibrium test (QPDT) to test for linkage and association in general pedigrees for quantitative traits. We apply both the PDT and the QPDT to analyze the sequence data from the seven candidate genes in the simulated data sets in the Genetic Analysis Workshop 12.

Ethical perspectives on pharmacogenomic profiling in the drug development process

Pharmacogenomics, which is a field that encompasses the study of genetic polymorphisms that underlie individual differences in drug response, is rapidly advancing. The potential for the widespread use of pharmacogenomics in the drug development process merits an examination of its fundamental impact on clinical-trial design and practice. This article provides a critical analysis of some of the issues that pertain to pharmacogenomics in the drug development process. In particular, four areas will be discussed: clinical-trial design; subject stratification; some new social risks; and economic concerns. Recommendations are offered for addressing the issues that are discussed and anticipating the regulatory needs for pharmacogenomics-based trials.