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Population Stratification (population + stratification)

Distribution by Scientific Domains

Life Sciences	80%
Medical Sciences	19%

Selected Abstracts

Attributing Hardy-Weinberg Disequilibrium to Population Stratification and Genetic Association in Case-Control Studies

ANNALS OF HUMAN GENETICS, Issue 1 2010
Vaneeta K. Grover
Summary Loci exhibiting Hardy-Weinberg disequilibrium (HWD) are often excluded from association studies, because HWD may indicate genotyping error, population stratification or selection bias. For case-control studies, HWD can result from a genetic effect at the locus. We extend the modelling to accommodate both stratification and genetic effects. Theoretical genotype frequencies and HWD coefficients are derived under a general genetic model for a population with two strata. Maximum likelihood is used to estimate model parameters and a test for lack of fit identifies the models most consistent with the data. Simulations were used to assess the method. The technique was applied to a group of ethnically and clinically heterogeneous kidney stone formers and controls, both exhibiting HWD for the R990G SNP of the CASR gene. Results indicate the best fitting model incorporates both stratification and genetic association. The ability of our method to apportion HWD to stratification and genetic effects may well be a significant advance in dealing with heterogeneity in case-control genetic association studies. [source]

Tests of Association for Quantitative Traits in Nuclear Families Using Principal Components to Correct for Population Stratification

ANNALS OF HUMAN GENETICS, Issue 6 2009
Lei Zhang
SUMMARY Traditional transmission disequilibrium test (TDT) based methods for genetic association analyses are robust to population stratification at the cost of a substantial loss of power. We here describe a novel method for family-based association studies that corrects for population stratification with the use of an extension of principal component analysis (PCA). Specifically, we adopt PCA on unrelated parents in each family. We then infer principal components for children from those for their parents through a TDT-like strategy. Two test statistics within the variance-components model are proposed for association tests. Simulation results show that the proposed tests have correct type I error rates regardless of population stratification, and have greatly improved power over two popular TDT-based methods: QTDT and FBAT. The application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility of the proposed method. [source]

A critical evaluation of genomic control methods for genetic association studies

GENETIC EPIDEMIOLOGY, Issue 4 2009
Tony Dadd
Abstract Population stratification is an important potential confounder of genetic case-control association studies. For replication studies, limited availability of samples may lead to imbalanced sampling from heterogeneous populations. Genomic control (GC) can be used to correct ,2 test statistics which are presumed to be inflated by a factor ,; this may be estimated by a summary ,2 value (,median or ,mean) from a set of unlinked markers. Many studies applying GC methods have used fewer than 50 unlinked markers and an important question is whether this can adequately correct for population stratification. We assess the behavior of GC methods in imbalanced case-control studies using simulation. SNPs are sampled from two subpopulations with intra-continental levels of FST (,0.005) and sampling schemata ranging from balanced to completely imbalanced between subpopulations. The sampling properties of ,median and ,mean are explored using 6,1,600 unlinked markers to estimate Type 1 error and power empirically. GC corrections based on the ,2 -distribution (GCmedian or GCmean) can be anti-conservative even when more than 100 single nucleotide polymorphisms (SNPs) are genotyped and realistic levels of population stratification exist. The GCF procedure performs well over a wider range of conditions, only becoming anti-conservative at low levels of , and with fewer than 25 SNPs genotyped. A substantial loss of power can arise when population stratification is present, but this is largely independent of the number of SNPs used. A literature survey shows that most studies applying GC have used GCmedian or GCmean, rather than GCF, which is the most appropriate GC correction method. Genet. Epidemiol. 2009. © 2008 Wiley Liss, Inc. [source]

Variation in GABRA2 Predicts Drinking Behavior in Project MATCH Subjects

ALCOHOLISM, Issue 11 2007
Lance O. Bauer
Background:, Previous studies demonstrated, and replicated, an association between single nucleotide polymorphisms (SNPs) within the GABRA2 gene and risk for alcohol dependence. The present study examines the association of a GABRA2 SNP with another definition of alcohol involvement and with the effects of psychosocial treatment. Methods:, European-American subjects (n = 812, 73.4% male) provided DNA samples for the analysis. All were participants in Project Matching Alcoholism Treatment to Client Heterogeneity (MATCH), a multi-center randomized clinical trial evaluating the efficacy of 3 types of psychosocial treatment for alcoholism: Cognitive Behavioral Therapy (CBT), Motivational Enhancement Therapy (MET), or twelve-step facilitation (TSF). The daily probabilities of drinking and heavy drinking were estimated during the 12-week treatment and 12-month post-treatment periods. Results:, Subjects homozygous for the allele associated with low risk for alcohol dependence in previous studies had lower daily probabilities of drinking and heavy drinking in the present study. This low-risk allele was also associated with a greater difference in drinking outcomes between the treatments. In addition, it enhanced the relative superiority of TSF over CBT and MET. Population stratification was excluded as a confound using ancestry informative marker analysis. Conclusions:, The assessment of genetic vulnerability may be relevant to studies of the efficacy of psychosocial treatment: GABRA2 genotype modifies the variance in drinking and can therefore moderate power for resolving differences between treatments. [source]

Analysis of Genetically Complex Epilepsies

EPILEPSIA, Issue 2005
Ruth Ottman
Summary:, 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. [source]

STrengthening the REporting of Genetic Association studies (STREGA) , an extension of the STROBE statement

EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, Issue 4 2009
Julian Little
Abstract Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy,Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis. [source]

Additive effect of BDNF and REST polymorphisms is associated with improved general cognitive ability

GENES, BRAIN AND BEHAVIOR, Issue 7 2008
F. Miyajima
Brain-derived neurotrophic factor (BDNF) is a pleiotropic protein involved in neuronal proliferation, differentiation, synaptic plasticity and survival. Independent studies investigating association between the functional BDNF Val66Met polymorphism and cognitive abilities have reported some conflicting findings, which may reflect inadequate sample size, variation in testing methods, population stratification or the confounding effects of other genes. To test the latter hypothesis, we screened and genotyped polymorphisms in the RE1-silencing transcription factor (REST) gene whose function includes the downregulation of BDNF expression. We identified an exon 4 hexadecapeptide variable number tandem repeat (VNTR) with either four or five copies that was located within a proline-rich domain and investigated a further five single nucleotide polymorphisms (SNPs). Using a cohort of 746 community-dwelling older volunteers, we analysed REST genotype data both independently and in combination with the BDNF Val66Met polymorphism. A haplotype within the REST gene containing the four copy VNTR and a non-synonymous SNP showed a weak but significant association with a higher score of general intelligence (P = 0.05). Analysis of this haplotype and the BDNF Val66Met polymorphism in combination showed a significant interaction (global P -value = 0.0003) with an additive increase in cognitive performance for those possessing the BDNF Val66 allele and the REST haplotype containing the four copy repeat (P = 0.004). The REST haplotypes in combination with the BDNF Met66 polymorphism did not reduce cognitive performance more than the independent influence of the Met66 allele. Our results suggest that investigation of a common REST polymorphism may be necessary to help reduce contrasting reports based around BDNF Val66Met and cognition. [source]

Association between the CHRM2 gene and intelligence in a sample of 304 Dutch families

GENES, BRAIN AND BEHAVIOR, Issue 8 2006
M. F. Gosso
The CHRM2 gene is thought to be involved in neuronal excitability, synaptic plasticity and feedback regulation of acetylcholine release and has previously been implicated in higher cognitive processing. In a sample of 667 individuals from 304 families, we genotyped three single-nucleotide polymorphisms (SNPs) in the CHRM2 gene on 7q31,35. From all individuals, standardized intelligence measures were available. Using a test of within-family association, which controls for the possible effects of population stratification, a highly significant association was found between the CHRM2 gene and intelligence. The strongest association was between rs324650 and performance IQ (PIQ), where the T allele was associated with an increase of 4.6 PIQ points. In parallel with a large family-based association, we observed an attenuated , although still significant , population-based association, illustrating that population stratification may decrease our chances of detecting allele,trait associations. Such a mechanism has been predicted earlier, and this article is one of the first to empirically show that family-based association methods are not only needed to guard against false positives, but are also invaluable in guarding against false negatives. [source]

Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic density

GENETIC EPIDEMIOLOGY, Issue 4 2010
J. Stone
Abstract Haplotype-based association studies have been proposed as a powerful comprehensive approach to identify causal genetic variation underlying complex diseases. Data comparisons within families offer the additional advantage of dealing naturally with complex sources of noise, confounding and population stratification. Two problems encountered when investigating associations between haplotypes and a continuous trait using data from sibships are (i) the need to define within-sibship comparisons for sibships of size greater than two and (ii) the difficulty of resolving the joint distribution of haplotype pairs within sibships in the absence of parental genotypes. We therefore propose first a method of orthogonal transformation of both outcomes and exposures that allow the decomposition of between- and within-sibship regression effects when sibship size is greater than two. We conducted a simulation study, which confirmed analysis using all members of a sibship is statistically more powerful than methods based on cross-sectional analysis or using subsets of sib-pairs. Second, we propose a simple permutation approach to avoid errors of inference due to the within-sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the VDR gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4-SNP VDR haplotype. In conclusion, our proposed method retains the benefits of the between- and within-pair analysis for pairs of siblings and can be implemented in standard software. Genet. Epidemiol. 34: 309,318, 2010. © 2009 Wiley-Liss, Inc. [source]

A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors

GENETIC EPIDEMIOLOGY, Issue 8 2009
Huaqing Zhao
Abstract Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=,0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors. Genet. Epidemiol. 33:679,690, 2009. © 2009 Wiley-Liss, Inc. [source]

Genome-wide association studies for discrete traits

GENETIC EPIDEMIOLOGY, Issue S1 2009
Duncan C. Thomas
Abstract Genome-wide association studies of discrete traits generally use simple methods of analysis based on ,2 tests for contingency tables or logistic regression, at least for an initial scan of the entire genome. Nevertheless, more power might be obtained by using various methods that analyze multiple markers in combination. Methods based on sliding windows, wavelets, Bayesian shrinkage, or penalized likelihood methods, among others, were explored by various participants of Genetic Analysis Workshop 16 Group 1 to combine information across multiple markers within a region, while others used Bayesian variable selection methods for genome-wide multivariate analyses of all markers simultaneously. Imputation can be used to fill in missing markers on individual subjects within a study or in a meta-analysis of studies using different panels. Although multiple imputation theoretically should give more robust tests of association, one participant contribution found little difference between results of single and multiple imputation. Careful control of population stratification is essential, and two contributions found that previously reported associations with two genes disappeared after more precise control. Other issues considered by this group included subgroup analysis, gene-gene interactions, and the use of biomarkers. Genet. Epidemiol. 33 (Suppl. 1):S8,S12, 2009. © 2009 Wiley-Liss, Inc. [source]

STrengthening the REporting of Genetic Association Studies (STREGA),an extension of the STROBE statement,

GENETIC EPIDEMIOLOGY, Issue 7 2009
Julian Little
Abstract Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis. Genet. Epidemiol. 33:581,598, 2009. © 2009 Wiley-Liss, Inc. [source]

SNP selection and multidimensional scaling to quantify population structure

GENETIC EPIDEMIOLOGY, Issue 6 2009
Kelci Miclaus
Abstract In the new era of large-scale collaborative Genome Wide Association Studies (GWAS), population stratification has become a critical issue that must be addressed. In order to build upon the methods developed to control the confounding effect of a structured population, it is extremely important to visualize and quantify that effect. In this work, we develop methodology for single nucleotide polymorphism (SNP) selection and subsequent population stratification visualization based on deviation from Hardy-Weinberg equilibrium in conjunction with non-metric multidimensional scaling (MDS); a distance-based multivariate technique. Through simulation, it is shown that SNP selection based on Hardy-Weinberg disequilibrium (HWD) is robust against confounding linkage disequilibrium patterns that have been problematic in past studies and methods as well as producing a differentiated SNP set. Non-metric MDS is shown to be a multivariate visualization tool preferable to principal components in conjunction with HWD SNP selection through theoretical and empirical study from HapMap samples. The proposed selection tool offers a simple and effective way to select appropriate substructure-informative markers for use in exploring the effect that population stratification may have in association studies. Genet. Epidemiol. 33:488,496, 2009. © 2009 Wiley-Liss, Inc. [source]

A critical evaluation of genomic control methods for genetic association studies

Improved correction for population stratification in genome-wide association studies by identifying hidden population structures,

GENETIC EPIDEMIOLOGY, Issue 3 2008
Qizhai Li
Abstract Hidden population substructure can cause population stratification and lead to false-positive findings in population-based genome-wide association (GWA) studies. Given a large panel of markers scanned in a GWA study, it becomes increasingly feasible to uncover the hidden population substructure within the study sample based on measured genotypes across the genome. Recognizing that population substructure can be displayed as clustered and/or continuous patterns of genetic variation, we propose a method that aims at the detection and correction of the confounding effect resulting from both patterns of population substructure. The proposed method is an extension of the EIGENSTRAT method (Price et al. [2006] Nat Genet 38:904,909). This approach is computationally feasible and easily applied to large-scale GWA studies. We show through simulation studies that, compared with the EIGENSTRAT method, the new method requires a smaller number of markers and yields a more appropriate correction for population stratification. Genet. Epidemiol. 2007. Published 2007 Wiley-Liss, Inc. [source]

Quantitative trait association in parent offspring trios: Extension of case/pseudocontrol method and comparison of prospective and retrospective approaches

GENETIC EPIDEMIOLOGY, Issue 8 2007
Eleanor Wheeler
Abstract The case/pseudocontrol method provides a convenient framework for family-based association analysis of case-parent trios, incorporating several previously proposed methods such as the transmission/disequilibrium test and log-linear modelling of parent-of-origin effects. The method allows genotype and haplotype analysis at an arbitrary number of linked and unlinked multiallelic loci, as well as modelling of more complex effects such as epistasis, parent-of-origin effects, maternal genotype and mother-child interaction effects, and gene-environment interactions. Here we extend the method for analysis of quantitative as opposed to dichotomous (e.g. disease) traits. The resulting method can be thought of as a retrospective approach, modelling genotype given trait value, in contrast to prospective approaches that model trait given genotype. Through simulations and analytical derivations, we examine the power and properties of our proposed approach, and compare it to several previously proposed single-locus methods for quantitative trait association analysis. We investigate the performance of the different methods when extended to allow analysis of haplotype, maternal genotype and parent-of-origin effects. With randomly ascertained families, with or without population stratification, the prospective approach (modeling trait value given genotype) is found to be generally most effective, although the retrospective approach has some advantages with regard to estimation and interpretability of parameter estimates when applied to selected samples. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source]

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

GENETIC EPIDEMIOLOGY, Issue 6 2007
Gudrun Jonasdottir
Abstract 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. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source]

Centralizing the non-central chi-square: a new method to correct for population stratification in genetic case-control association studies

GENETIC EPIDEMIOLOGY, Issue 4 2006
Prakash Gorroochurn
Abstract We present a new method, the ,-centralization (DC) method, to correct for population stratification (PS) in case-control association studies. DC works well even when there is a lot of confounding due to PS. The latter causes overdispersion in the usual chi-square statistics which then have non-central chi-square distributions. Other methods approach the non-centrality indirectly, but we deal with it directly, by estimating the non-centrality parameter , itself. Specifically: (1) We define a quantity ,, a function of the relevant subpopulation parameters. We show that, for relatively large samples, , exactly predicts the elevation of the false positive rate due to PS, when there is no true association between marker genotype and disease. (This quantity , is quite different from Wright's FST and can be large even when FST is small.) (2) We show how to estimate ,, using a panel of unlinked "neutral" loci. (3) We then show that ,2 corresponds to , the non-centrality parameter of the chi-square distribution. Thus, we can centralize the chi-square using our estimate of ,; this is the DC method. (4) We demonstrate, via computer simulations, that DC works well with as few as 25,30 unlinked markers, where the markers are chosen to have allele frequencies reasonably close (within ±.1) to those at the test locus. (5) We compare DC with genomic control and show that where as the latter becomes overconservative when there is considerable confounding due to PS (i.e. when , is large), DC performs well for all values of ,. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source]

Evaluating bias due to population stratification in case-control association studies of admixed populations,

GENETIC EPIDEMIOLOGY, Issue 1 2004
Yiting Wang
Abstract The potential for bias from population stratification (PS) has raised concerns about case-control studies involving admixed ethnicities. We evaluated the potential bias due to PS in relating a binary outcome with a candidate gene under simulated settings where study populations consist of multiple ethnicities. Disease risks were assigned within the range of prostate cancer rates of African Americans reported in SEER registries assuming k=2, 5, or 10 admixed ethnicities. Genotype frequencies were considered in the range of 5,95%. Under a model assuming no genotype effect on disease (odds ratio (OR)=1), the range of observed OR estimates ignoring ethnicity was 0.64,1.55 for k=2, 0.72,1.33 for k=5, and 0.81,1.22 for k=10. When genotype effect on disease was modeled to be OR=2, the ranges of observed OR estimates were 1.28,3.09, 1.43,2.65, and 1.62,2.42 for k=2, 5, and 10 ethnicities, respectively. Our results indicate that the magnitude of bias is small unless extreme differences exist in genotype frequency. Bias due to PS decreases as the number of admixed ethnicities increases. The biases are bounded by the minimum and maximum of all pairwise baseline disease odds ratios across ethnicities. Therefore, bias due to PS alone may be small when baseline risk differences are small within major categories of admixed ethnicity, such as African Americans. © 2004 Wiley-Liss, Inc. [source]

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

GENETIC EPIDEMIOLOGY, Issue 1 2004
Emily O. Kistner
Abstract 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. Published 2004 Wiley-Liss, Inc. [source]

Properties of the transmission-disequilibrium test in the presence of inbreeding

GENETIC EPIDEMIOLOGY, Issue 2 2002
Emmanuelle Génin
Abstract Family-based association tests such as the transmission-disequilibrium test (TDT), which compare alleles transmitted and non-transmitted from parents to affected offspring, are widely used to detect the role of genetic risk factors in diseases. These methods have the advantage of being robust to population stratification and are thus believed to be valid whatever the population context. In different studies of the statistical properties of the TDT, parents of affected offspring are typically assumed to be neither inbred nor related. In many human populations, however, this assumption is false and parental alleles are then no longer independent. It is thus of interest to determine whether the TDT is a valid test of linkage and association in the presence of inbreeding. We present a method to derive the expected value of the TDT statistic under different disease models and for any relationship between the parents of affected offspring. Using this method, we show that in the presence of inbreeding, the TDT is still a valid test for linkage but not for association. The power of the test to detect linkage may, however, be increased in the presence of inbreeding under different modes of inheritance. Genet. Epidemiol. 22:116,127, 2002. © 2002 Wiley-Liss, Inc. [source]

Genetic association analysis: a primer on how it works, its strengths and its weaknesses

INTERNATIONAL JOURNAL OF ANDROLOGY, Issue 6 2008
Laura Rodriguez-Murillo
Summary Currently, the most used approach to mapping disease genes is the genome wide association study, using large samples of cases and controls and hundreds of thousands of markers spread throughout the genome. This review focuses in explaining how an association study works, its strengths and its weaknesses, and the methods available to analyse the data. Issues related to sample size, genetic effect sizes, epistasis, replication and population stratification are specifically addressed, issues that an investigator must take into account when planning an association study of any complex disease. Finally, we include some special features concerning association studies in the Y chromosome, and we contrast the analysis characteristics of linkage and association. [source]

Nonreplication in Genetic Studies of Complex Diseases,Lessons Learned From Studies of Osteoporosis and Tentative Remedies,

JOURNAL OF BONE AND MINERAL RESEARCH, Issue 3 2005
Hui Shen
Abstract Inconsistent results have accumulated in genetic studies of complex diseases/traits over the past decade. Using osteoporosis as an example, we address major potential factors for the nonreplication results and propose some potential remedies. Over the past decade, numerous linkage and association studies have been performed to search for genes predisposing to complex human diseases. However, relatively little success has been achieved, and inconsistent results have accumulated. We argue that those nonreplication results are not unexpected, given the complicated nature of complex diseases and a number of confounding factors. In this article, based on our experience in genetic studies of osteoporosis, we discuss major potential factors for the inconsistent results and propose some potential remedies. We believe that one of the main reasons for this lack of reproducibility is overinterpretation of nominally significant results from studies with insufficient statistical power. We indicate that the power of a study is not only influenced by the sample size, but also by genetic heterogeneity, the extent and degree of linkage disequilibrium (LD) between the markers tested and the causal variants, and the allele frequency differences between them. We also discuss the effects of other confounding factors, including population stratification, phenotype difference, genotype and phenotype quality control, multiple testing, and genuine biological differences. In addition, we note that with low statistical power, even a "replicated" finding is still likely to be a false positive. We believe that with rigorous control of study design and interpretation of different outcomes, inconsistency will be largely reduced, and the chances of successfully revealing genetic components of complex diseases will be greatly improved. [source]

Role of cytokine gene polymorphisms in acute rejection and renal impairment after liver transplantation

LIVER TRANSPLANTATION, Issue 3 2001
Julie R. Jonsson
Although immunosuppressive regimens are effective, rejection occurs in up to 50% of patients after orthotopic liver transplantation (OLT), and there is concern about side effects from long-term therapy. Knowledge of clinical and immunogenetic variables may allow tailoring of immunosuppressive therapy to patients according to their potential risks. We studied the association between transforming growth factor-,, interleukin-10, and tumor necrosis factor , (TNF-,) gene polymorphisms and graft rejection and renal impairment in 121 white liver transplant recipients. Clinical variables were collected retrospectively, and creatinine clearance was estimated using the formula of Cockcroft and Gault. Biallelic polymorphisms were detected using polymerase chain reaction-based methods. Thirty-seven of 121 patients (30.6%) developed at least 1 episode of rejection. Multivariate analysis showed that Child-Pugh score (P = .001), immune-mediated liver disease (P = .018), normal pre-OLT creatinine clearance (P = .037), and fewer HLA class 1 mismatches (P = .038) were independently associated with rejection. Renal impairment occurred in 80% of patients and was moderate or severe in 39%. Clinical variables independently associated with renal impairment were female sex (P = .001), pre-OLT renal dysfunction (P = .0001), and a diagnosis of viral hepatitis (P = .0008). There was a significant difference in the frequency of TNF-,-308 alleles among the primary liver diseases. After adjustment for potential confounders and a Bonferroni correction, the association between the TNF-,-308 polymorphism and graft rejection approached significance (P = .06). Recipient cytokine genotypes do not have a major independent role in graft rejection or renal impairment after OLT. Additional studies of immunogenetic factors require analysis of large numbers of patients with appropriate phenotypic information to avoid population stratification, which may lead to inappropriate conclusions. [source]

Functional and geographical differentiation of candidate balanced polymorphisms in Arabidopsis thaliana

MOLECULAR ECOLOGY, Issue 13 2009
JENNIFER M. REININGA
Abstract Molecular population genetic analysis of three chromosomal regions in Arabidopsis thaliana suggested that balancing selection might operate to maintain variation at three novel candidate adaptive trait genes, including SOLUBLE STARCH SYNTHASE I (SSI), PLASTID TRANSCRIPTIONALLY ACTIVE 7(PTAC7), and BELL-LIKE HOMEODOMAIN 10 (BLH10). If balanced polymorphisms are indeed maintained at these loci, then we would expect to observe functional variation underlying the previously detected signatures of selection. We observe multiple replacement polymorphisms within and in the 32 amino acids just upstream of the protein,protein interacting BELL domain at the BLH10 locus. While no clear protein sequence differences are found between allele types in SSI and PTAC7, these two genes show evidence for allele-specific variation in expression levels. Geographical patterns of allelic differentiation seem consistent with population stratification in this species and a significant longitudinal cline was observed at all three candidate loci. These data support a hypothesis of balancing selection at all three candidate loci and provide a basis for more detailed functional work by identifying possible functional differences that might be selectively maintained. [source]

Association of polymorphisms in the dopamine D4 receptor gene and the activity-impulsivity endophenotype in dogs

ANIMAL GENETICS, Issue 6 2007
K. Hejjas
Summary A variable number of tandem repeats (VNTR) polymorphism in exon 3 of the human dopamine D4 receptor gene (DRD4) has been associated with attention deficit hyperactivity disorder (ADHD). Rodents possess no analogous repeat sequence, whereas a similar tandem repeat polymorphism of the DRD4 gene was identified in dogs, horses and chimpanzees. Here, we present a genetic association study of the DRD4 VNTR and the activity-impulsivity dimension of the recently validated dog-ADHD Rating Scale. To avoid false positives arising from population stratification, a single breed of dogs (German shepherd) was studied. Two DRD4 alleles (referred to as 2 and 3a) were detected in this breed, and genotype frequencies were in Hardy,Weinberg equilibrium. For modelling distinct environmental conditions, ,pet' and ,police' German shepherds were characterized. Police German shepherds possessing at least one 3a allele showed significantly higher scores in the activity-impulsivity dimension of the dog-ADHD Rating Scale than dogs without this allele (P = 0.0180). This difference was not significant in pet German shepherds. To the best of our knowledge, this is the first report of an association between a candidate gene and a behaviour trait in dogs, and it reinforces the functional role of DRD4 exon 3 polymorphism. [source]

On the Use of Allelic Transmission Rates for Assessing Gene-by-Environment Interaction in Case-Parent Trios

ANNALS OF HUMAN GENETICS, Issue 5 2010
Ji-Hyung Shin
Summary Allelic transmission rates from parents to cases are frequently stratified by an environmental risk factor E and compared, with heterogeneity interpreted as gene-environment interaction or G×E. Though generally invalid, such analyses continue to appear. We revisit why heterogeneity is not equivalent to G×E in a range of settings not considered previously. The objective is a fuller understanding of the bias in transmission rates and what is driving it. Extending previously published findings, we derive parental mating-type probabilities in cases and use them to obtain transmission rates, which we then compare to G×E. Through simulation, we investigate the practical implications of the bias for a transmission-based test of G×E. We find that general population characteristics distort the picture of G×E obtained from transmission rates: the stratum-specific mating-type probabilities under G , E dependence and the allele frequency under independence. Furthermore, the transmission-based test has inflated error rates relative to a likelihood-based test. Our investigation provides further insight into how and why transmission-based tests and descriptive summaries can mislead about G×E. For exploring G×E, we suggest graphical displays of the transmission rates within parental mating types, as they are robust to population stratification and the penetrance model. [source]

Influence of population stratification on population-based marker-disease association analysis

ANNALS OF HUMAN GENETICS, Issue 4 2010
Tengfei Li
Summary Population-based genetic association analysis may suffer from the failure to control for confounders such as population stratification (PS). There has been extensive study on the influence of PS on candidate gene-disease association analysis, but much less attention has been paid to its influence on marker-disease association analysis. In this paper, we focus on the Pearson ,2 test and the trend test for marker-disease association analysis. The mean and variance of the test statistics are derived under presence of PS, so that the power and inflated type I error rate can be evaluated. It is shown that the bias and the variance distortion are not zero in the presence of both PS and penetrance heterogeneity (PH). Unlike candidate gene-disease association analysis, when PS is present, the bias is not zero no matter whether PH is present or not. This work generalises the published results, where only the fully recessive penetrance model is considered and only the bias is calculated. It is shown that candidate gene-disease association analysis can be treated as a special case of marker-disease association analysis. Consequently, our results extend previous studies on candidate gene-disease association analysis. A simulation study confirms the theoretical findings. [source]