Genetic Association (genetic + association)

Distribution by Scientific Domains
Distribution within Medical Sciences

Terms modified by Genetic Association

  • genetic association analysis
  • genetic association studies
  • genetic association study

  • Selected Abstracts

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

    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]

    The Cost Effectiveness of Duplicate Genotyping for Testing Genetic Association

    Nathan Tintle
    Summary We consider a modification to the traditional genome wide association (GWA) study design: duplicate genotyping. Duplicate genotyping (re-genotyping some of the samples) has long been suggested for quality control reasons; however, it has not been evaluated for its statistical cost-effectiveness. We demonstrate that when genotyping error rates are at least m%, duplicate genotyping provides a cost-effective (more statistical power for the same price) design alternative when relative genotype to phenotype/sample acquisition costs are no more than m%. In addition to cost and error rate, duplicate genotyping is most cost-effective for SNPs with low minor allele frequency. In general, relative genotype to phenotype/sample acquisition costs will be low when following up a limited number of SNPs in the second stage of a two-stage GWA study design, and, thus, duplicate genotyping may be useful in these situations. In cases where many SNPs are being followed up at the second stage, duplicate genotyping only low-quality SNPs with low minor allele frequency may be cost-effective. We also find that in almost all cases where duplicate genotyping is cost-effective, the most cost-effective design strategy involves duplicate genotyping all samples. Free software is provided which evaluates the cost-effectiveness of duplicate genotyping based on user inputs. [source]

    Testing for Genetic Association With Constrained Models Using Triads

    J. F. Troendle
    Summary It has been shown that it is preferable to use a robust model that incorporated constraints on the genotype relative risk rather than rely on a model that assumes the disease operates in a recessive or dominant fashion. Previous methods are applicable to case-control studies, but not to family based studies of case children along with their parents (triads). We show here how to implement analogous constraints while analyzing triad data. The likelihood, conditional on the parents genotype, is maximized over the appropriately constrained parameter space. The asymptotic distribution for the maximized likelihood ratio statistic is found and used to estimate the null distribution of the test statistics. The properties of several methods of testing for association are compared by simulation. The constrained method provides higher power across a wide range of genetic models with little cost when compared to methods that restrict to a dominant, recessive, or multiplicative model, or make no modeling restriction. The methods are applied to two SNPs on the methylenetetrahydrofolate reductase (MTHFR) gene with neural tube defect (NTD) triads. [source]

    Testing for Genetic Association in the Presence of Linkage and Gene,Covariate Interactions

    Andrea Callegaro
    Abstract In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity64, 5,15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene,covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene,covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus. [source]

    Improving Power for Testing Genetic Association in Case,Control Studies by Reducing the Alternative Space

    BIOMETRICS, Issue 1 2010
    Jungnam Joo
    Summary To detect association between a genetic marker and a disease in case,control studies, the Cochran,Armitage trend test is typically used. The trend test is locally optimal when the genetic model is correctly specified. However, in practice, the underlying genetic model, and hence the optimal trend test, are usually unknown. In this case, Pearson's chi-squared test, the maximum of three trend test statistics (optimal for the recessive, additive, and dominant models), and the test based on genetic model selection (GMS) are useful. In this article, we first modify the existing GMS method so that it can be used when the risk allele is unknown. Then we propose a new approach by excluding a genetic model that is not supported by the data. Using either the model selection or exclusion, the alternative space is reduced conditional on the observed data, and hence the power to detect a true association can be increased. Simulation results are reported and the proposed methods are applied to the genetic markers identified from the genome-wide association studies conducted by the Wellcome Trust Case,Control Consortium. The results demonstrate that the genetic model exclusion approach usually performs better than existing methods under its worst situation across scientifically plausible genetic models we considered. [source]

    Validation Study of Genetic Associations with Coronary Artery Disease on Chromosome 3q13-21 and Potential Effect Modification by Smoking

    Benjamin D. Horne
    Summary The CATHGEN study reported associations of chromosome 3q13-21 genes (KALRN, MYLK, CDGAP, and GATA2) with early-onset coronary artery disease (CAD). This study attempted to independently validate those associations. Eleven single nucleotide polymorphisms (SNPs) were examined (rs10934490, rs16834817, rs6810298, rs9289231, rs12637456, rs1444768, rs1444754, rs4234218, rs2335052, rs3803, rs2713604) in patients (N = 1618) from the Intermountain Heart Collaborative Study (IHCS). Given the higher smoking prevalence in CATHGEN than IHCS (41% vs. 11% in controls, 74% vs. 29% in cases), smoking stratification and genotype-smoking interactions were evaluated. Suggestive association was found for GATA2 (rs2713604, p = 0.057, OR = 1.2). Among smokers, associations were found in CDGAP (rs10934490, p = 0.019, OR = 1.6) and KALRN (rs12637456, p = 0.011, OR = 2.0) and suggestive association was found in MYLK (rs16834871, p = 0.051, OR = 1.8, adjusting for gender). No SNP association was found among non-smokers, but smoking/SNP interactions were detected for CDGAP (rs10934491, p = 0.017) and KALRN (rs12637456, p = 0.010). Similar differences in SNP effects by smoking status were observed on re-analysis of CATHGEN. CAD associations were suggestive for GATA2 and among smokers significant post hoc associations were found in KALRN, MYLK, and CDGAP. Genetic risk conferred by some of these genes may be modified by smoking. Future CAD association studies of these and other genes should evaluate effect modification by smoking. [source]

    Interleukin-10 is associated with resistance to febrile seizures: Genetic association and experimental animal studies

    EPILEPSIA, Issue 4 2009
    Yoshito Ishizaki
    Summary Purpose:, Febrile seizures (FS) are the most common form of childhood convulsions. Many reports have shown that a proinflammatory cytokine, interleukin-1 (IL-1) ,, may have a facilitatory effect on the development of FS. We have previously shown that the IL1B -511C/T single nucleotide polymorphism (SNP) is associated with simple FS of sporadic occurrence. The balance between pro- and antiinflammatory cytokines influences the regulation of infections and could, therefore, play a role in the pathogenesis of FS. Here, to determine whether pro- and antiinflammatory cytokine genes are responsible for the susceptibility to FS, we have performed an association study on functional SNPs of cytokine genes in FS patients and controls. Methods:, The promoter SNPs of four inflammatory cytokine genes (IL6 -572C/G, IL8 -251A/T, IL10 -592A/C and TNFA -1037C/T) were examined in 249 patients with FS (186 simple and 63 complex FS) and 225 controls. Because the IL10 -592 SNP showed a positive association with FS, two additional SNPs (IL10 -1082A/G and -819T/C) were subjected to haplotype analysis. Furthermore, we examined the in vivo role of IL-10 in hyperthermia-induced seizures using immature animal models. Results:, The frequencies of the IL10 -592C allele and -1082A/-819C/-592C haplotype were significantly decreased in FS as compared with in controls (p = 0.014 and 0.013, respectively). The seizure threshold temperature in the IL-10,administered rats was significantly higher than that in the saline-treated control ones (p = 0.027). Conclusions:, The present study suggests that IL-10 is genetically associated with FS and, contrary to IL-1,, confers resistance to FS. [source]

    A prevalence-based association test for case-control studies

    Kelli K. Ryckman
    Abstract Genetic association is often determined in case-control studies by the differential distribution of alleles or genotypes. Recent work has demonstrated that association can also be assessed by deviations from the expected distributions of alleles or genotypes. Specifically, multiple methods motivated by the principles of Hardy-Weinberg equilibrium (HWE) have been developed. However, these methods do not take into account many of the assumptions of HWE. Therefore, we have developed a prevalence-based association test (PRAT) as an alternative method for detecting association in case-control studies. This method, also motivated by the principles of HWE, uses an estimated population allele frequency to generate expected genotype frequencies instead of using the case and control frequencies separately. Our method often has greater power, under a wide variety of genetic models, to detect association than genotypic, allelic or Cochran-Armitage trend association tests. Therefore, we propose PRAT as a powerful alternative method of testing for association. Genet. Epidemiol. 2008. © 2008 Wiley-Liss, Inc. [source]

    Genetic association with rheumatoid arthritis,Genetic Analysis Workshop 15: summary of contributions from Group 2

    Marsha A. Wilcox
    Abstract The papers in presentation group 2 of Genetic Analysis Workshop 15 (GAW15) conducted association analyses of rheumatoid arthritis data. The analyses were carried out primarily in the data provided by the North American Rheumatoid Arthritis Consortium (NARAC). One group conducted analyses in the data provided by the Canadian Rheumatoid Arthritis Genetics Study (CRAGS). Analysis strategies included genome-wide scans, the examination of candidate genes, and investigations of a region of interest on chromosome 18q21. Most authors employed relatively new methods, proposed extensions of existing methods, or introduced completely novel methods for aspects of association analysis. There were several common observations; a group of papers using a variety of methods found stronger association, on chromosomes 6 and 18 and in candidate gene PTPN22 among women with early onset. Generally, models that considered haplotypes or multiple markers showed stronger evidence for association than did single marker analyses. Genet. Epidemiol. 31 (Suppl. 1):S12,S21, 2007. © 2007 Wiley-Liss, Inc. [source]

    Genetic association of vitamin D receptor polymorphisms with primary biliary cirrhosis and autoimmune hepatitis

    HEPATOLOGY, Issue 1 2002
    Arndt Vogel
    Autoimmune hepatitis (AIH) and primary biliary cirrhosis (PBC) are immune-mediated chronic inflammatory diseases of the liver of unknown etiology. Genetic factors appear to be involved in the pathogenesis of both diseases. 1,25-Dihydroxyvitamin D3 has been implicated as an immunomodulator, which acts through its own receptor (VDR). Polymorphisms of the VDR have been linked to a variety of autoimmune diseases. In this study VDR polymorphisms were analyzed in 123 patients with AIH, 74 patients with PBC, and 214 controls. VDR polymorphisms were assessed by BsmI, TaqI, ApaI, and Fok endonuclease digestion after specific polymerase chain reaction (PCR) amplification. We found a significant association between the BsmI polymorphisms in PBC patients in comparison with controls (,2 = 9.49, P = .009). Furthermore we detected a significant association of the Fok polymorphims in AIH patients in comparison to controls (,2 = 9.71, P = .008) indicating a genetic link of VDR polymorphisms to autoimmune liver diseases such as PBC and AIH in German patients. These findings contribute to the knowledge of the complex events determining immunologic tolerance in the liver. Further studies are needed to elucidate the mechanisms by which the vitamin D receptor contributes to the development of autoimmune diseases. [source]

    Genetic association between TNF-, ,308 G>A polymorphism and longitudinal weight change during clozapine treatment

    Ying-Chieh Wang
    Abstract Objective The aim of the study was to investigate the association between genetic variation in the tumor necrosis factor-alpha (TNF-,) gene and longitudinal weight change during long-term clozapine treatment. Methods Fifty-five patients with refractory schizophrenia treated with clozapine for 8 years were recruited. Gender, age, treatment response to clozapine in the first 14 months, baseline BMI, clozapine dose, concomitant use of mood stabilizers and other antipsychotics, and ,308 G,>,A polymorphism in the human TNF-, gene were analyzed using generalized estimating equations. Results In addition to having a lower baseline BMI (p,=,0.0013) and a longer treatment time (p,=,0.050), the ,308 GG carriers gained significantly more weight than the ,308 A allele carriers (p,=,0.0084) during 8 years of clozapine treatment, after controlling for other non-genetic factors. Conclusions The ,308 G,>,A genetic variant of the TNF-, gene is associated with longitudinal weight change during clozapine treatment. Follow-up duration is an important factor to consider when performing pharmacogenetic study of clozapine-induced weight gain. Copyright © 2010 John Wiley & Sons, Ltd. [source]

    Gene,gene and gene,environment interactions on IgE production in prenatal stage

    ALLERGY, Issue 6 2010
    K. D. Yang
    To cite this article: Yang KD, Chang J-C, Chuang H, Liang H-M, Kuo H-C, Lee Y-S, Hsu T-Y, Ou C-Y. Gene,gene and gene,environment interactions on IgE production in prenatal stage. Allergy 2010; 65: 731,739. Abstract Background:, Prevalence of allergic diseases in children has increased worldwide over the past decades. Allergy sensitization may occur in fetal life. This study investigated whether gene,gene and gene,environment interactions affected cord blood IgE (CBIgE) levels. Methods:, A total of 575 cord blood DNA samples were subjected to a multiplex microarray for 384 single nucleotide polymorphisms (SNPs) in 159 allergy candidate genes. Genetic association was initially assessed by univariate and multivariate analyses. Multifactor dimensionality reduction (MDR) was used to identify gene,gene and gene,environment interactions. Environmental factors for analysis included maternal atopy, paternal atopy, parental smoking, gender, and prematurity. Results:, Twenty-one SNPs in 14 genes were associated with CBIgE elevation (,0.5 KU/l) in univariate analysis. Multivariate analysis identified eleven genes (IL13, IL17A, IL2RA, CCL17, CXCL1, PDGFRA, FGF1, HAVCR1, GNAQ, C11orf72, and ADAM33) which were significantly associated with CBIgE elevation. MDR analyses of gene,gene interactions identified IL13 interacted with IL17A and/or redox genes on CBIgE elevation with the prediction accuracy of 62.52%. Analyses of gene,environment interactions identified that maternal atopy combined with IL13, rs1800925 and CCL22, rs170359 SNPs had the highest prediction accuracy of 67.15%. All the high and low risk classifications on gene,gene and gene,environment interactions by MDR analyses could be validated by Chi-square test. Conclusions:, Gene,gene (e.g. immune and redox genes) and gene,environment (e.g. maternal atopy and FGF1or redox genes) interactions on IgE production begin in prenatal stage, suggesting that prevention of IgE-mediated diseases may be made possible by control of maternal atopy and redox responses in prenatal stage. [source]

    Polymorphisms in IL13 pathway genes in asthma and chronic obstructive pulmonary disease

    ALLERGY, Issue 4 2010
    B. Beghé
    To cite this article: Beghé B, Hall IP, Parker SG, Moffatt MF, Wardlaw A, Connolly MJ, Fabbri LM, Ruse C, Sayers I. Polymorphisms in IL13 pathway genes in asthma and chronic obstructive pulmonary disease. Allergy 2010; 65: 474,481. Abstract Background:, Asthma and chronic obstructive pulmonary disease (COPD) are chronic respiratory diseases involving an interaction between genetic and environmental factors. Interleukin-13 (IL13) has been suggested to have a role in both asthma and COPD. We investigated whether single nucleotide polymorphisms (SNPs) in the IL13 pathway may contribute to the susceptibility and severity of asthma and COPD in adults. Methods:, Twelve SNPs in IL13 pathway genes ,IL4, IL13, IL4RA, IL13RA1, IL13RA2 and STAT6, were genotyped in subjects with asthma (n = 299) and in subjects with COPD or healthy smokers (n = 992). Genetic association was evaluated using genotype and allele models for asthma severity, atopy phenotypes and COPD susceptibility. Linear regression was used to determine the effects of polymorphism on baseline lung function (FEV1, FEV1/FVC). Results:, In asthmatics, three IL13 SNPs , rs1881457(,1512), rs1800925(,1111) and rs20541(R130Q) , were associated with atopy risk. One SNP in IL4RA1 [rs1805010(I75V)] was associated with asthma severity, and several IL13 SNPs showed borderline significance. IL13 SNPs rs1881457(,1512) and rs1800925(,1111) were associated with better FEV1 and FEV1/FVC in asthmatics. IL13 SNPs rs2066960(intron 1), rs20541(R130Q) and rs1295685(exon 4) were associated with COPD risk and lower baseline lung function in the recessive model. In females, but not in males, rs2250747 of the IL13RA1 gene was associated with COPD and lower FEV1. Conclusion:, These data suggest that IL13 SNPs (promoter and coding region) and, to a lesser extent, IL4RA SNPs may contribute to atopy and asthma. We also provide tentative evidence that IL13 SNPs in the coding region may be of significance in COPD susceptibility. [source]

    European Mathematical Genetics Meeting, Heidelberg, Germany, 12th,13th April 2007

    Article first published online: 28 MAY 200
    Saurabh Ghosh 11 Indian Statistical Institute, Kolkata, India High correlations between two quantitative traits may be either due to common genetic factors or common environmental factors or a combination of both. In this study, we develop statistical methods to extract the contribution of a common QTL to the total correlation between the components of a bivariate phenotype. Using data on bivariate phenotypes and marker genotypes for sib-pairs, we propose a test for linkage between a common QTL and a marker locus based on the conditional cross-sib trait correlations (trait 1 of sib 1 , trait 2 of sib 2 and conversely) given the identity-by-descent sharing at the marker locus. The null hypothesis cannot be rejected unless there exists a common QTL. We use Monte-Carlo simulations to evaluate the performance of the proposed test under different trait parameters and quantitative trait distributions. An application of the method is illustrated using data on two alcohol-related phenotypes from the Collaborative Study On The Genetics Of Alcoholism project. Rémi Kazma 1 , Catherine Bonaďti-Pellié 1 , Emmanuelle Génin 12 INSERM UMR-S535 and Université Paris Sud, Villejuif, 94817, France Keywords: Gene-environment interaction, sibling recurrence risk, exposure correlation Gene-environment interactions may play important roles in complex disease susceptibility but their detection is often difficult. Here we show how gene-environment interactions can be detected by investigating the degree of familial aggregation according to the exposure of the probands. In case of gene-environment interaction, the distribution of genotypes of affected individuals, and consequently the risk in relatives, depends on their exposure. We developed a test comparing the risks in sibs according to the proband exposure. To evaluate the properties of this new test, we derived the formulas for calculating the expected risks in sibs according to the exposure of probands for various values of exposure frequency, relative risk due to exposure alone, frequencies of latent susceptibility genotypes, genetic relative risks and interaction coefficients. We find that the ratio of risks when the proband is exposed and not exposed is a good indicator of the interaction effect. We evaluate the power of the test for various sample sizes of affected individuals. We conclude that this test is valuable for diseases with moderate familial aggregation, only when the role of the exposure has been clearly evidenced. Since a correlation for exposure among sibs might lead to a difference in risks among sibs in the different proband exposure strata, we also add an exposure correlation coefficient in the model. Interestingly, we find that when this correlation is correctly accounted for, the power of the test is not decreased and might even be significantly increased. Andrea Callegaro 1 , Hans J.C. Van Houwelingen 1 , Jeanine Houwing-Duistermaat 13 Dept. of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands Keywords: Survival analysis, age at onset, score test, linkage analysis Non parametric linkage (NPL) analysis compares the identical by descent (IBD) sharing in sibling pairs to the expected IBD sharing under the hypothesis of no linkage. Often information is available on the marginal cumulative hazards (for example breast cancer incidence curves). Our aim is to extend the NPL methods by taking into account the age at onset of selected sibling pairs using these known marginal hazards. Li and Zhong (2002) proposed a (retrospective) likelihood ratio test based on an additive frailty model for genetic linkage analysis. From their model we derive a score statistic for selected samples which turns out to be a weighed NPL method. The weights depend on the marginal cumulative hazards and on the frailty parameter. A second approach is based on a simple gamma shared frailty model. Here, we simply test whether the score function of the frailty parameter depends on the excess IBD. We compare the performance of these methods using simulated data. Céline Bellenguez 1 , Carole Ober 2 , Catherine Bourgain 14 INSERM U535 and University Paris Sud, Villejuif, France 5 Department of Human Genetics, The University of Chicago, USA Keywords: Linkage analysis, linkage disequilibrium, high density SNP data Compared with microsatellite markers, high density SNP maps should be more informative for linkage analyses. However, because they are much closer, SNPs present important linkage disequilibrium (LD), which biases classical nonparametric multipoint analyses. This problem is even stronger in population isolates where LD extends over larger regions with a more stochastic pattern. We investigate the issue of linkage analysis with a 500K SNP map in a large and inbred 1840-member Hutterite pedigree, phenotyped for asthma. Using an efficient pedigree breaking strategy, we first identified linked regions with a 5cM microsatellite map, on which we focused to evaluate the SNP map. The only method that models LD in the NPL analysis is limited in both the pedigree size and the number of markers (Abecasis and Wigginton, 2005) and therefore could not be used. Instead, we studied methods that identify sets of SNPs with maximum linkage information content in our pedigree and no LD-driven bias. Both algorithms that directly remove pairs of SNPs in high LD and clustering methods were evaluated. Null simulations were performed to control that Zlr calculated with the SNP sets were not falsely inflated. Preliminary results suggest that although LD is strong in such populations, linkage information content slightly better than that of microsatellite maps can be extracted from dense SNP maps, provided that a careful marker selection is conducted. In particular, we show that the specific LD pattern requires considering LD between a wide range of marker pairs rather than only in predefined blocks. Peter Van Loo 1,2,3 , Stein Aerts 1,2 , Diether Lambrechts 4,5 , Bernard Thienpont 2 , Sunit Maity 4,5 , Bert Coessens 3 , Frederik De Smet 4,5 , Leon-Charles Tranchevent 3 , Bart De Moor 2 , Koen Devriendt 3 , Peter Marynen 1,2 , Bassem Hassan 1,2 , Peter Carmeliet 4,5 , Yves Moreau 36 Department of Molecular and Developmental Genetics, VIB, Belgium 7 Department of Human Genetics, University of Leuven, Belgium 8 Bioinformatics group, Department of Electrical Engineering, University of Leuven, Belgium 9 Department of Transgene Technology and Gene Therapy, VIB, Belgium 10 Center for Transgene Technology and Gene Therapy, University of Leuven, Belgium Keywords: Bioinformatics, gene prioritization, data fusion The identification of genes involved in health and disease remains a formidable challenge. Here, we describe a novel bioinformatics method to prioritize candidate genes underlying pathways or diseases, based on their similarity to genes known to be involved in these processes. It is freely accessible as an interactive software tool, ENDEAVOUR, at Unlike previous methods, ENDEAVOUR generates distinct prioritizations from multiple heterogeneous data sources, which are then integrated, or fused, into one global ranking using order statistics. ENDEAVOUR prioritizes candidate genes in a three-step process. First, information about a disease or pathway is gathered from a set of known "training" genes by consulting multiple data sources. Next, the candidate genes are ranked based on similarity with the training properties obtained in the first step, resulting in one prioritized list for each data source. Finally, ENDEAVOUR fuses each of these rankings into a single global ranking, providing an overall prioritization of the candidate genes. Validation of ENDEAVOUR revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified YPEL1 as a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. Finally, we are currently evaluating a pipeline combining array-CGH, ENDEAVOUR and in vivo validation in zebrafish to identify novel genes involved in congenital heart defects. Mark Broom 1 , Graeme Ruxton 2 , Rebecca Kilner 311 Mathematics Dept., University of Sussex, UK 12 Division of Environmental and Evolutionary Biology, University of Glasgow, UK 13 Department of Zoology, University of Cambridge, UK Keywords: Evolutionarily stable strategy, parasitism, asymmetric game Brood parasites chicks vary in the harm that they do to their companions in the nest. In this presentation we use game-theoretic methods to model this variation. Our model considers hosts which potentially abandon single nestlings and instead choose to re-allocate their reproductive effort to future breeding, irrespective of whether the abandoned chick is the host's young or a brood parasite's. The parasite chick must decide whether or not to kill host young by balancing the benefits from reduced competition in the nest against the risk of desertion by host parents. The model predicts that three different types of evolutionarily stable strategies can exist. (1) Hosts routinely rear depleted broods, the brood parasite always kills host young and the host never then abandons the nest. (2) When adult survival after deserting single offspring is very high, hosts always abandon broods of a single nestling and the parasite never kills host offspring, effectively holding them as hostages to prevent nest desertion. (3) Intermediate strategies, in which parasites sometimes kill their nest-mates and host parents sometimes desert nests that contain only a single chick, can also be evolutionarily stable. We provide quantitative descriptions of how the values given to ecological and behavioral parameters of the host-parasite system influence the likelihood of each strategy and compare our results with real host-brood parasite associations in nature. Martin Harrison 114 Mathematics Dept, University of Sussex, UK Keywords: Brood parasitism, games, host, parasite The interaction between hosts and parasites in bird populations has been studied extensively. Game theoretical methods have been used to model this interaction previously, but this has not been studied extensively taking into account the sequential nature of this game. We consider a model allowing the host and parasite to make a number of decisions, which depend on a number of natural factors. The host lays an egg, a parasite bird will arrive at the nest with a certain probability and then chooses to destroy a number of the host eggs and lay one of it's own. With some destruction occurring, either natural or through the actions of the parasite, the host chooses to continue, eject an egg (hoping to eject the parasite) or abandon the nest. Once the eggs have hatched the game then falls to the parasite chick versus the host. The chick chooses to destroy or eject a number of eggs. The final decision is made by the host, choosing whether to raise or abandon the chicks that are in the nest. We consider various natural parameters and probabilities which influence these decisions. We then use this model to look at real-world situations of the interactions of the Reed Warbler and two different parasites, the Common Cuckoo and the Brown-Headed Cowbird. These two parasites have different methods in the way that they parasitize the nests of their hosts. The hosts in turn have a different reaction to these parasites. Arne Jochens 1 , Amke Caliebe 2 , Uwe Roesler 1 , Michael Krawczak 215 Mathematical Seminar, University of Kiel, Germany 16 Institute of Medical Informatics and Statistics, University of Kiel, Germany Keywords: Stepwise mutation model, microsatellite, recursion equation, temporal behaviour We consider the stepwise mutation model which occurs, e.g., in microsatellite loci. Let X(t,i) denote the allelic state of individual i at time t. We compute expectation, variance and covariance of X(t,i), i=1,,,N, and provide a recursion equation for P(X(t,i)=z). Because the variance of X(t,i) goes to infinity as t grows, for the description of the temporal behaviour, we regard the scaled process X(t,i)-X(t,1). The results furnish a better understanding of the behaviour of the stepwise mutation model and may in future be used to derive tests for neutrality under this model. Paul O'Reilly 1 , Ewan Birney 2 , David Balding 117 Statistical Genetics, Department of Epidemiology and Public Health, Imperial, College London, UK 18 European Bioinformatics Institute, EMBL, Cambridge, UK Keywords: Positive selection, Recombination rate, LD, Genome-wide, Natural Selection In recent years efforts to develop population genetics methods that estimate rates of recombination and levels of natural selection in the human genome have intensified. However, since the two processes have an intimately related impact on genetic variation their inference is vulnerable to confounding. Genomic regions subject to recent selection are likely to have a relatively recent common ancestor and consequently less opportunity for historical recombinations that are detectable in contemporary populations. Here we show that selection can reduce the population-based recombination rate estimate substantially. In genome-wide studies for detecting selection we observe a tendency to highlight loci that are subject to low levels of recombination. We find that the outlier approach commonly adopted in such studies may have low power unless variable recombination is accounted for. We introduce a new genome-wide method for detecting selection that exploits the sensitivity to recent selection of methods for estimating recombination rates, while accounting for variable recombination using pedigree data. Through simulations we demonstrate the high power of the Ped/Pop approach to discriminate between neutral and adaptive evolution, particularly in the context of choosing outliers from a genome-wide distribution. Although methods have been developed showing good power to detect selection ,in action', the corresponding window of opportunity is small. In contrast, the power of the Ped/Pop method is maintained for many generations after the fixation of an advantageous variant Sarah Griffiths 1 , Frank Dudbridge 120 MRC Biostatistics Unit, Cambridge, UK Keywords: Genetic association, multimarker tag, haplotype, likelihood analysis In association studies it is generally too expensive to genotype all variants in all subjects. We can exploit linkage disequilibrium between SNPs to select a subset that captures the variation in a training data set obtained either through direct resequencing or a public resource such as the HapMap. These ,tag SNPs' are then genotyped in the whole sample. Multimarker tagging is a more aggressive adaptation of pairwise tagging that allows for combinations of two or more tag SNPs to predict an untyped SNP. Here we describe a new method for directly testing the association of an untyped SNP using a multimarker tag. Previously, other investigators have suggested testing a specific tag haplotype, or performing a weighted analysis using weights derived from the training data. However these approaches do not properly account for the imperfect correlation between the tag haplotype and the untyped SNP. Here we describe a straightforward approach to testing untyped SNPs using a missing-data likelihood analysis, including the tag markers as nuisance parameters. The training data is stacked on top of the main body of genotype data so there is information on how the tag markers predict the genotype of the untyped SNP. The uncertainty in this prediction is automatically taken into account in the likelihood analysis. This approach yields more power and also a more accurate prediction of the odds ratio of the untyped SNP. Anke Schulz 1 , Christine Fischer 2 , Jenny Chang-Claude 1 , Lars Beckmann 121 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany 22 Institute of Human Genetics, University of Heidelberg, Germany Keywords: Haplotype, haplotype sharing, entropy, Mantel statistics, marker selection We previously introduced a new method to map genes involved in complex diseases, using haplotype sharing-based Mantel statistics to correlate genetic and phenotypic similarity. Although the Mantel statistic is powerful in narrowing down candidate regions, the precise localization of a gene is hampered in genomic regions where linkage disequilibrium is so high that neighboring markers are found to be significant at similar magnitude and we are not able to discriminate between them. Here, we present a new approach to localize susceptibility genes by combining haplotype sharing-based Mantel statistics with an iterative entropy-based marker selection algorithm. For each marker at which the Mantel statistic is evaluated, the algorithm selects a subset of surrounding markers. The subset is chosen to maximize multilocus linkage disequilibrium, which is measured by the normalized entropy difference introduced by Nothnagel et al. (2002). We evaluated the algorithm with respect to type I error and power. Its ability to localize the disease variant was compared to the localization (i) without marker selection and (ii) considering haplotype block structure. Case-control samples were simulated from a set of 18 haplotypes, consisting of 15 SNPs in two haplotype blocks. The new algorithm gave correct type I error and yielded similar power to detect the disease locus compared to the alternative approaches. The neighboring markers were clearly less often significant than the causal locus, and also less often significant compared to the alternative approaches. Thus the new algorithm improved the precision of the localization of susceptibility genes. Mark M. Iles 123 Section of Epidemiology and Biostatistics, LIMM, University of Leeds, UK Keywords: tSNP, tagging, association, HapMap Tagging SNPs (tSNPs) are commonly used to capture genetic diversity cost-effectively. However, it is important that the efficacy of tSNPs is correctly estimated, otherwise coverage may be insufficient. If the pilot sample from which tSNPs are chosen is too small or the initial marker map too sparse, tSNP efficacy may be overestimated. An existing estimation method based on bootstrapping goes some way to correct for insufficient sample size and overfitting, but does not completely solve the problem. We describe a novel method, based on exclusion of haplotypes, that improves on the bootstrap approach. Using simulated data, the extent of the sample size problem is investigated and the performance of the bootstrap and the novel method are compared. We incorporate an existing method adjusting for marker density by ,SNP-dropping'. We find that insufficient sample size can cause large overestimates in tSNP efficacy, even with as many as 100 individuals, and the problem worsens as the region studied increases in size. Both the bootstrap and novel method correct much of this overestimate, with our novel method consistently outperforming the bootstrap method. We conclude that a combination of insufficient sample size and overfitting may lead to overestimation of tSNP efficacy and underpowering of studies based on tSNPs. Our novel approach corrects for much of this bias and is superior to the previous method. Sample sizes larger than previously suggested may still be required for accurate estimation of tSNP efficacy. This has obvious ramifications for the selection of tSNPs from HapMap data. Claudio Verzilli 1 , Juliet Chapman 1 , Aroon Hingorani 2 , Juan Pablo-Casas 1 , Tina Shah 2 , Liam Smeeth 1 , John Whittaker 124 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK 25 Division of Medicine, University College London, UK Keywords: Meta-analysis, Genetic association studies We present a Bayesian hierarchical model for the meta-analysis of candidate gene studies with a continuous outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping markers (typically SNPs) in the same genetic region. Meta analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequlibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian hierarchical linear regression that models the observed genotype group means and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach is applied to the meta analysis of 24 studies assessing the effect of 7 variants in the C-reactive protein (CRP) gene region on plasma CRP levels, an inflammatory biomarker shown in observational studies to be positively associated with cardiovascular disease. Cathryn M. Lewis 1 , Christopher G. Mathew 1 , Theresa M. Marteau 226 Dept. of Medical and Molecular Genetics, King's College London, UK 27 Department of Psychology, King's College London, UK Keywords: Risk, genetics, CARD15, smoking, model Recently progress has been made in identifying mutations that confer susceptibility to complex diseases, with the potential to use these mutations in determining disease risk. We developed methods to estimate disease risk based on genotype relative risks (for a gene G), exposure to an environmental factor (E), and family history (with recurrence risk ,R for a relative of type R). ,R must be partitioned into the risk due to G (which is modelled independently) and the residual risk. The risk model was then applied to Crohn's disease (CD), a severe gastrointestinal disease for which smoking increases disease risk approximately 2-fold, and mutations in CARD15 confer increased risks of 2.25 (for carriers of a single mutation) and 9.3 (for carriers of two mutations). CARD15 accounts for only a small proportion of the genetic component of CD, with a gene-specific ,S, CARD15 of 1.16, from a total sibling relative risk of ,S= 27. CD risks were estimated for high-risk individuals who are siblings of a CD case, and who also smoke. The CD risk to such individuals who carry two CARD15 mutations is approximately 0.34, and for those carrying a single CARD15 mutation the risk is 0.08, compared to a population prevalence of approximately 0.001. These results imply that complex disease genes may be valuable in estimating with greater precision than has hitherto been possible disease risks in specific, easily identified subgroups of the population with a view to prevention. Yurii Aulchenko 128 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Compression, information, bzip2, genome-wide SNP data, statistical genetics With advances in molecular technology, studies accessing millions of genetic polymorphisms in thousands of study subjects will soon become common. Such studies generate large amounts of data, whose effective storage and management is a challenge to the modern statistical genetics. Standard file compression utilities, such as Zip, Gzip and Bzip2, may be helpful to minimise the storage requirements. Less obvious is the fact that the data compression techniques may be also used in the analysis of genetic data. It is known that the efficiency of a particular compression algorithm depends on the probability structure of the data. In this work, we compared different standard and customised tools using the data from human HapMap project. Secondly, we investigate the potential uses of data compression techniques for the analysis of linkage, association and linkage disequilibrium Suzanne Leal 1 , Bingshan Li 129 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, USA Keywords: Consanguineous pedigrees, missing genotype data Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al (2005) that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. The false-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. Which family members will aid in the reduction of false-positive evidence of linkage is highly dependent on which other family members are genotyped. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband's sibling-grandparents. When parental genotypes are not available, false-positive evidence for linkage can be reduced by including in the analysis genotype data from either unaffected siblings of the proband or the proband's married-in-grandparents. Najaf Amin 1 , Yurii Aulchenko 130 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Genomic Control, pedigree structure, quantitative traits The Genomic Control (GC) method was originally developed to control for population stratification and cryptic relatedness in association studies. This method assumes that the effect of population substructure on the test statistics is essentially constant across the genome, and therefore unassociated markers can be used to estimate the effect of confounding onto the test statistic. The properties of GC method were extensively investigated for different stratification scenarios, and compared to alternative methods, such as the transmission-disequilibrium test. The potential of this method to correct not for occasional cryptic relations, but for regular pedigree structure, however, was not investigated before. In this work we investigate the potential of the GC method for pedigree-based association analysis of quantitative traits. The power and type one error of the method was compared to standard methods, such as the measured genotype (MG) approach and quantitative trait transmission-disequilibrium test. In human pedigrees, with trait heritability varying from 30 to 80%, the power of MG and GC approach was always higher than that of TDT. GC had correct type 1 error and its power was close to that of MG under moderate heritability (30%), but decreased with higher heritability. William Astle 1 , Chris Holmes 2 , David Balding 131 Department of Epidemiology and Public Health, Imperial College London, UK 32 Department of Statistics, University of Oxford, UK Keywords: Population structure, association studies, genetic epidemiology, statistical genetics In the analysis of population association studies, Genomic Control (Devlin & Roeder, 1999) (GC) adjusts the Armitage test statistic to correct the type I error for the effects of population substructure, but its power is often sub-optimal. Turbo Genomic Control (TGC) generalises GC to incorporate co-variation of relatedness and phenotype, retaining control over type I error while improving power. TGC is similar to the method of Yu et al. (2006), but we extend it to binary (case-control) in addition to quantitative phenotypes, we implement improved estimation of relatedness coefficients, and we derive an explicit statistic that generalizes the Armitage test statistic and is fast to compute. TGC also has similarities to EIGENSTRAT (Price et al., 2006) which is a new method based on principle components analysis. The problems of population structure(Clayton et al., 2005) and cryptic relatedness (Voight & Pritchard, 2005) are essentially the same: if patterns of shared ancestry differ between cases and controls, whether distant (coancestry) or recent (cryptic relatedness), false positives can arise and power can be diminished. With large numbers of widely-spaced genetic markers, coancestry can now be measured accurately for each pair of individuals via patterns of allele-sharing. Instead of modelling subpopulations, we work instead with a coancestry coefficient for each pair of individuals in the study. We explain the relationships between TGC, GC and EIGENSTRAT. We present simulation studies and real data analyses to illustrate the power advantage of TGC in a range of scenarios incorporating both substructure and cryptic relatedness. References Clayton, D. al. (2005) Population structure, differential bias and genomic control in a large-scale case-control association study. Nature Genetics37(11) November 2005. Devlin, B. & Roeder, K. (1999) Genomic control for association studies. Biometics55(4) December 1999. Price, A. al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics38(8) (August 2006). Voight, B. J. & Pritchard, J. K. (2005) Confounding from cryptic relatedness in case-control association studies. Public Library of Science Genetics1(3) September 2005. Yu, al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics38(2) February 2006. Hervé Perdry 1 , Marie-Claude Babron 1 , Françoise Clerget-Darpoux 133 INSERM U535 and Univ. Paris Sud, UMR-S 535, Villejuif, France Keywords: Modifier genes, case-parents trios, ordered transmission disequilibrium test A modifying locus is a polymorphic locus, distinct from the disease locus, which leads to differences in the disease phenotype, either by modifying the penetrance of the disease allele, or by modifying the expression of the disease. The effect of such a locus is a clinical heterogeneity that can be reflected by the values of an appropriate covariate, such as the age of onset, or the severity of the disease. We designed the Ordered Transmission Disequilibrium Test (OTDT) to test for a relation between the clinical heterogeneity, expressed by the covariate, and marker genotypes of a candidate gene. The method applies to trio families with one affected child and his parents. Each family member is genotyped at a bi-allelic marker M of a candidate gene. To each of the families is associated a covariate value; the families are ordered on the values of this covariate. As the TDT (Spielman et al. 1993), the OTDT is based on the observation of the transmission rate T of a given allele at M. The OTDT aims to find a critical value of the covariate which separates the sample of families in two subsamples in which the transmission rates are significantly different. We investigate the power of the method by simulations under various genetic models and covariate distributions. Acknowledgments H Perdry is funded by ARSEP. Pascal Croiseau 1 , Heather Cordell 2 , Emmanuelle Génin 134 INSERM U535 and University Paris Sud, UMR-S535, Villejuif, France 35 Institute of Human Genetics, Newcastle University, UK Keywords: Association, missing data, conditionnal logistic regression Missing data is an important problem in association studies. Several methods used to test for association need that individuals be genotyped at the full set of markers. Individuals with missing data need to be excluded from the analysis. This could involve an important decrease in sample size and a loss of information. If the disease susceptibility locus (DSL) is poorly typed, it is also possible that a marker in linkage disequilibrium gives a stronger association signal than the DSL. One may then falsely conclude that the marker is more likely to be the DSL. We recently developed a Multiple Imputation method to infer missing data on case-parent trios Starting from the observed data, a few number of complete data sets are generated by Markov-Chain Monte Carlo approach. These complete datasets are analysed using standard statistical package and the results are combined as described in Little & Rubin (2002). Here we report the results of simulations performed to examine, for different patterns of missing data, how often the true DSL gives the highest association score among different loci in LD. We found that multiple imputation usually correctly detect the DSL site even if the percentage of missing data is high. This is not the case for the naďve approach that consists in discarding trios with missing data. In conclusion, Multiple imputation presents the advantage of being easy to use and flexible and is therefore a promising tool in the search for DSL involved in complex diseases. Salma Kotti 1 , Heike Bickeböller 2 , Françoise Clerget-Darpoux 136 University Paris Sud, UMR-S535, Villejuif, France 37 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany Keywords: Genotype relative risk, internal controls, Family based analyses Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRRs. We will analytically derive the GRR estimators for the 1:1 and 1:3 matching and will present the results at the meeting. Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRR. We will analytically derive the GRR estimator for the 1:1 and 1:3 matching and will present the results at the meeting. Luigi Palla 1 , David Siegmund 239 Department of Mathematics,Free University Amsterdam, The Netherlands 40 Department of Statistics, Stanford University, California, USA Keywords: TDT, assortative mating, inbreeding, statistical power A substantial amount of Assortative Mating (AM) is often recorded on physical and psychological, dichotomous as well as quantitative traits that are supposed to have a multifactorial genetic component. In particular AM has the effect of increasing the genetic variance, even more than inbreeding because it acts across loci beside within loci, when the trait has a multifactorial origin. Under the assumption of a polygenic model for AM dating back to Wright (1921) and refined by Crow and Felsenstein (1968,1982), the effect of assortative mating on the power to detect genetic association in the Transmission Disequilibrium Test (TDT) is explored as parameters, such as the effective number of genes and the allelic frequency vary. The power is reflected by the non centrality parameter of the TDT and is expressed as a function of the number of trios, the relative risk of the heterozygous genotype and the allele frequency (Siegmund and Yakir, 2007). The noncentrality parameter of the relevant score statistic is updated considering the effect of AM which is expressed in terms of an ,effective' inbreeding coefficient. In particular, for dichotomous traits it is apparent that the higher the number of genes involved in the trait, the lower the loss in power due to AM. Finally an attempt is made to extend this relation to the Q-TDT (Rabinowitz, 1997), which involves considering the effect of AM also on the phenotypic variance of the trait of interest, under the assumption that AM affects only its additive genetic component. References Crow, & Felsenstein, (1968). The effect of assortative mating on the genetic composition of a population. Eugen.Quart.15, 87,97. Rabinowitz,, 1997. A Transmission Disequilibrium Test for Quantitative Trait Loci. Human Heredity47, 342,350. Siegmund, & Yakir, (2007) Statistics of gene mapping, Springer. Wright, (1921). System of mating.III. Assortative mating based on somatic resemblance. Genetics6, 144,161. Jérémie Nsengimana 1 , Ben D Brown 2 , Alistair S Hall 2 , Jenny H Barrett 141 Leeds Institute of Molecular Medicine, University of Leeds, UK 42 Leeds Institute for Genetics, Health and Therapeutics, University of Leeds, UK Keywords: Inflammatory genes, haplotype, coronary artery disease Genetic Risk of Acute Coronary Events (GRACE) is an initiative to collect cases of coronary artery disease (CAD) and their unaffected siblings in the UK and to use them to map genetic variants increasing disease risk. The aim of the present study was to test the association between CAD and 51 single nucleotide polymorphisms (SNPs) and their haplotypes from 35 inflammatory genes. Genotype data were available for 1154 persons affected before age 66 (including 48% before age 50) and their 1545 unaffected siblings (891 discordant families). Each SNP was tested for association to CAD, and haplotypes within genes or gene clusters were tested using FBAT (Rabinowitz & Laird, 2000). For the most significant results, genetic effect size was estimated using conditional logistic regression (CLR) within STATA adjusting for other risk factors. Haplotypes were assigned using HAPLORE (Zhang et al., 2005), which considers all parental mating types consistent with offspring genotypes and assigns them a probability of occurence. This probability was used in CLR to weight the haplotypes. In the single SNP analysis, several SNPs showed some evidence for association, including one SNP in the interleukin-1A gene. Analysing haplotypes in the interleukin-1 gene cluster, a common 3-SNP haplotype was found to increase the risk of CAD (P = 0.009). In an additive genetic model adjusting for covariates the odds ratio (OR) for this haplotype is 1.56 (95% CI: 1.16-2.10, p = 0.004) for early-onset CAD (before age 50). This study illustrates the utility of haplotype analysis in family-based association studies to investigate candidate genes. References Rabinowitz, D. & Laird, N. M. (2000) Hum Hered50, 211,223. Zhang, K., Sun, F. & Zhao, H. (2005) Bioinformatics21, 90,103. Andrea Foulkes 1 , Recai Yucel 1 , Xiaohong Li 143 Division of Biostatistics, University of Massachusetts, USA Keywords: Haploytpe, high-dimensional, mixed modeling The explosion of molecular level information coupled with large epidemiological studies presents an exciting opportunity to uncover the genetic underpinnings of complex diseases; however, several analytical challenges remain to be addressed. Characterizing the components to complex diseases inevitably requires consideration of synergies across multiple genetic loci and environmental and demographic factors. In addition, it is critical to capture information on allelic phase, that is whether alleles within a gene are in cis (on the same chromosome) or in trans (on different chromosomes.) In associations studies of unrelated individuals, this alignment of alleles within a chromosomal copy is generally not observed. We address the potential ambiguity in allelic phase in this high dimensional data setting using mixed effects models. Both a semi-parametric and fully likelihood-based approach to estimation are considered to account for missingness in cluster identifiers. In the first case, we apply a multiple imputation procedure coupled with a first stage expectation maximization algorithm for parameter estimation. A bootstrap approach is employed to assess sensitivity to variability induced by parameter estimation. Secondly, a fully likelihood-based approach using an expectation conditional maximization algorithm is described. Notably, these models allow for characterizing high-order gene-gene interactions while providing a flexible statistical framework to account for the confounding or mediating role of person specific covariates. The proposed method is applied to data arising from a cohort of human immunodeficiency virus type-1 (HIV-1) infected individuals at risk for therapy associated dyslipidemia. Simulation studies demonstrate reasonable power and control of family-wise type 1 error rates. Vivien Marquard 1 , Lars Beckmann 1 , Jenny Chang-Claude 144 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Genotyping errors, type I error, haplotype-based association methods It has been shown in several simulation studies that genotyping errors may have a great impact on the type I error of statistical methods used in genetic association analysis of complex diseases. Our aim was to investigate type I error rates in a case-control study, when differential and non-differential genotyping errors were introduced in realistic scenarios. We simulated case-control data sets, where individual genotypes were drawn from a haplotype distribution of 18 haplotypes with 15 markers in the APM1 gene. Genotyping errors were introduced following the unrestricted and symmetric with 0 edges error models described by Heid et al. (2006). In six scenarios, errors resulted from changes of one allele to another with predefined probabilities of 1%, 2.5% or 10%, respectively. A multiple number of errors per haplotype was possible and could vary between 0 and 15, the number of markers investigated. We examined three association methods: Mantel statistics using haplotype-sharing; a haplotype-specific score test; and Armitage trend test for single markers. The type I error rates were not influenced for any of all the three methods for a genotyping error rate of less than 1%. For higher error rates and differential errors, the type I error of the Mantel statistic was only slightly and of the Armitage trend test moderately increased. The type I error rates of the score test were highly increased. The type I error rates were correct for all three methods for non-differential errors. Further investigations will be carried out with different frequencies of differential error rates and focus on power. Arne Neumann 1 , Dörthe Malzahn 1 , Martina Müller 2 , Heike Bickeböller 145 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany 46 GSF-National Research Center for Environment and Health, Neuherberg & IBE-Institute of Epidemiology, Ludwig-Maximilians University München, Germany Keywords: Interaction, longitudinal, nonparametric Longitudinal data show the time dependent course of phenotypic traits. In this contribution, we consider longitudinal cohort studies and investigate the association between two candidate genes and a dependent quantitative longitudinal phenotype. The set-up defines a factorial design which allows us to test simultaneously for the overall gene effect of the loci as well as for possible gene-gene and gene time interaction. The latter would induce genetically based time-profile differences in the longitudinal phenotype. We adopt a non-parametric statistical test to genetic epidemiological cohort studies and investigate its performance by simulation studies. The statistical test was originally developed for longitudinal clinical studies (Brunner, Munzel, Puri, 1999 J Multivariate Anal 70:286-317). It is non-parametric in the sense that no assumptions are made about the underlying distribution of the quantitative phenotype. Longitudinal observations belonging to the same individual can be arbitrarily dependent on one another for the different time points whereas trait observations of different individuals are independent. The two loci are assumed to be statistically independent. Our simulations show that the nonparametric test is comparable with ANOVA in terms of power of detecting gene-gene and gene-time interaction in an ANOVA favourable setting. Rebecca Hein 1 , Lars Beckmann 1 , Jenny Chang-Claude 147 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Indirect association studies, interaction effects, linkage disequilibrium, marker allele frequency Association studies accounting for gene-environment interactions (GxE) may be useful for detecting genetic effects and identifying important environmental effect modifiers. Current technology facilitates very dense marker spacing in genetic association studies; however, the true disease variant(s) may not be genotyped. In this situation, an association between a gene and a phenotype may still be detectable, using genetic markers associated with the true disease variant(s) (indirect association). Zondervan and Cardon [2004] showed that the odds ratios (OR) of markers which are associated with the disease variant depend highly on the linkage disequilibrium (LD) between the variant and the markers, and whether the allele frequencies match and thereby influence the sample size needed to detect genetic association. We examined the influence of LD and allele frequencies on the sample size needed to detect GxE in indirect association studies, and provide tables for sample size estimation. For discordant allele frequencies and incomplete LD, sample sizes can be unfeasibly large. The influence of both factors is stronger for disease loci with small rather than moderate to high disease allele frequencies. A decline in D' of e.g. 5% has less impact on sample size than increasing the difference in allele frequencies by the same percentage. Assuming 80% power, large interaction effects can be detected using smaller sample sizes than those needed for the detection of main effects. The detection of interaction effects involving rare alleles may not be possible. Focussing only on marker density can be a limited strategy in indirect association studies for GxE. Cyril Dalmasso 1 , Emmanuelle Génin 2 , Catherine Bourgain 2 , Philippe Broët 148 JE 2492 , Univ. Paris-Sud, France 49 INSERM UMR-S 535 and University Paris Sud, Villejuif, France Keywords: Linkage analysis, Multiple testing, False Discovery Rate, Mixture model In the context of genome-wide linkage analyses, where a large number of statistical tests are simultaneously performed, the False Discovery Rate (FDR) that is defined as the expected proportion of false discoveries among all discoveries is nowadays widely used for taking into account the multiple testing problem. Other related criteria have been considered such as the local False Discovery Rate (lFDR) that is a variant of the FDR giving to each test its own measure of significance. The lFDR is defined as the posterior probability that a null hypothesis is true. Most of the proposed methods for estimating the lFDR or the FDR rely on distributional assumption under the null hypothesis. However, in observational studies, the empirical null distribution may be very different from the theoretical one. In this work, we propose a mixture model based approach that provides estimates of the lFDR and the FDR in the context of large-scale variance component linkage analyses. In particular, this approach allows estimating the empirical null distribution, this latter being a key quantity for any simultaneous inference procedure. The proposed method is applied on a real dataset. Arief Gusnanto 1 , Frank Dudbridge 150 MRC Biostatistics Unit, Cambridge UK Keywords: Significance, genome-wide, association, permutation, multiplicity Genome-wide association scans have introduced statistical challenges, mainly in the multiplicity of thousands of tests. The question of what constitutes a significant finding remains somewhat unresolved. Permutation testing is very time-consuming, whereas Bayesian arguments struggle to distinguish direct from indirect association. It seems attractive to summarise the multiplicity in a simple form that allows users to avoid time-consuming permutations. A standard significance level would facilitate reporting of results and reduce the need for permutation tests. This is potentially important because current scans do not have full coverage of the whole genome, and yet, the implicit multiplicity is genome-wide. We discuss some proposed summaries, with reference to the empirical null distribution of the multiple tests, approximated through a large number of random permutations. Using genome-wide data from the Wellcome Trust Case-Control Consortium, we use a sub-sampling approach with increasing density to estimate the nominal p-value to obtain family-wise significance of 5%. The results indicate that the significance level is converging to about 1e-7 as the marker spacing becomes infinitely dense. We considered the concept of an effective number of independent tests, and showed that when used in a Bonferroni correction, the number varies with the overall significance level, but is roughly constant in the region of interest. We compared several estimators of the effective number of tests, and showed that in the region of significance of interest, Patterson's eigenvalue based estimator gives approximately the right family-wise error rate. Michael Nothnagel 1 , Amke Caliebe 1 , Michael Krawczak 151 Institute of Medical Informatics and Statistics, University Clinic Schleswig-Holstein, University of Kiel, Germany Keywords: Association scans, Bayesian framework, posterior odds, genetic risk, multiplicative model Whole-genome association scans have been suggested to be a cost-efficient way to survey genetic variation and to map genetic disease factors. We used a Bayesian framework to investigate the posterior odds of a genuine association under multiplicative disease models. We demonstrate that the p value alone is not a sufficient means to evaluate the findings in association studies. We suggest that likelihood ratios should accompany p values in association reports. We argue, that, given the reported results of whole-genome scans, more associations should have been successfully replicated if the consistently made assumptions about considerable genetic risks were correct. We conclude that it is very likely that the vast majority of relative genetic risks are only of the order of 1.2 or lower. Clive Hoggart 1 , Maria De Iorio 1 , John Whittakker 2 , David Balding 152 Department of Epidemiology and Public Health, Imperial College London, UK 53 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: Genome-wide association analyses, shrinkage priors, Lasso Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants of small effect, which is a plausible scenario for many complex diseases. Moreover, many simulation studies assume a single causal variant and so more complex realities are ignored. Analysing large numbers of variants simultaneously is now becoming feasible, thanks to developments in Bayesian stochastic search methods. We pose the problem of SNP selection as variable selection in a regression model. In contrast to single SNP tests this approach simultaneously models the effect of all SNPs. SNPs are selected by a Bayesian interpretation of the lasso (Tibshirani, 1996); the maximum a posterior (MAP) estimate of the regression coefficients, which have been given independent, double exponential prior distributions. The double exponential distribution is an example of a shrinkage prior, MAP estimates with shrinkage priors can be zero, thus all SNPs with non zero regression coefficients are selected. In addition to the commonly-used double exponential (Laplace) prior, we also implement the normal exponential gamma prior distribution. We show that use of the Laplace prior improves SNP selection in comparison with single -SNP tests, and that the normal exponential gamma prior leads to a further improvement. Our method is fast and can handle very large numbers of SNPs: we demonstrate its performance using both simulated and real genome-wide data sets with 500 K SNPs, which can be analysed in 2 hours on a desktop workstation. Mickael Guedj 1,2 , Jerome Wojcik 2 , Gregory Nuel 154 Laboratoire Statistique et Génome, Université d'Evry, Evry France 55 Serono Pharmaceutical Research Institute, Plan-les-Ouates, Switzerland Keywords: Local Replication, Local Score, Association In gene-mapping, replication of initial findings has been put forwards as the approach of choice for filtering false-positives from true signals for underlying loci. In practice, such replications are however too poorly observed. Besides the statistical and technical-related factors (lack of power, multiple-testing, stratification, quality control,) inconsistent conclusions obtained from independent populations might result from real biological differences. In particular, the high degree of variation in the strength of LD among populations of different origins is a major challenge to the discovery of genes. Seeking for Local Replications (defined as the presence of a signal of association in a same genomic region among populations) instead of strict replications (same locus, same risk allele) may lead to more reliable results. Recently, a multi-markers approach based on the Local Score statistic has been proposed as a simple and efficient way to select candidate genomic regions at the first stage of genome-wide association studies. Here we propose an extension of this approach adapted to replicated association studies. Based on simulations, this method appears promising. In particular it outperforms classical simple-marker strategies to detect modest-effect genes. Additionally it constitutes, to our knowledge, a first framework dedicated to the detection of such Local Replications. Juliet Chapman 1 , Claudio Verzilli 1 , John Whittaker 156 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: FDR, Association studies, Bayesian model selection As genomewide association studies become commonplace there is debate as to how such studies might be analysed and what we might hope to gain from the data. It is clear that standard single locus approaches are limited in that they do not adjust for the effects of other loci and problematic since it is not obvious how to adjust for multiple comparisons. False discovery rates have been suggested, but it is unclear how well these will cope with highly correlated genetic data. We consider the validity of standard false discovery rates in large scale association studies. We also show that a Bayesian procedure has advantages in detecting causal loci amongst a large number of dependant SNPs and investigate properties of a Bayesian FDR. Peter Kraft 157 Harvard School of Public Health, Boston USA Keywords: Gene-environment interaction, genome-wide association scans Appropriately analyzed two-stage designs,where a subset of available subjects are genotyped on a genome-wide panel of markers at the first stage and then a much smaller subset of the most promising markers are genotyped on the remaining subjects,can have nearly as much power as a single-stage study where all subjects are genotyped on the genome-wide panel yet can be much less expensive. Typically, the "most promising" markers are selected based on evidence for a marginal association between genotypes and disease. Subsequently, the few markers found to be associated with disease at the end of the second stage are interrogated for evidence of gene-environment interaction, mainly to understand their impact on disease etiology and public health impact. However, this approach may miss variants which have a sizeable effect restricted to one exposure stratum and therefore only a modest marginal effect. We have proposed to use information on the joint effects of genes and a discrete list of environmental exposures at the initial screening stage to select promising markers for the second stage [Kraft et al Hum Hered 2007]. This approach optimizes power to detect variants that have a sizeable marginal effect and variants that have a small marginal effect but a sizeable effect in a stratum defined by an environmental exposure. As an example, I discuss a proposed genome-wide association scan for Type II diabetes susceptibility variants based in several large nested case-control studies. Beate Glaser 1 , Peter Holmans 158 Biostatistics and Bioinformatics Unit, Cardiff University, School of Medicine, Heath Park, Cardiff, UK Keywords: Combined case-control and trios analysis, Power, False-positive rate, Simulation, Association studies The statistical power of genetic association studies can be enhanced by combining the analysis of case-control with parent-offspring trio samples. Various combined analysis techniques have been recently developed; as yet, there have been no comparisons of their power. This work was performed with the aim of identifying the most powerful method among available combined techniques including test statistics developed by Kazeem and Farrall (2005), Nagelkerke and colleagues (2004) and Dudbridge (2006), as well as a simple combination of ,2-statistics from single samples. Simulation studies were performed to investigate their power under different additive, multiplicative, dominant and recessive disease models. False-positive rates were determined by studying the type I error rates under null models including models with unequal allele frequencies between the single case-control and trios samples. We identified three techniques with equivalent power and false-positive rates, which included modifications of the three main approaches: 1) the unmodified combined Odds ratio estimate by Kazeem & Farrall (2005), 2) a modified approach of the combined risk ratio estimate by Nagelkerke & colleagues (2004) and 3) a modified technique for a combined risk ratio estimate by Dudbridge (2006). Our work highlights the importance of studies investigating test performance criteria of novel methods, as they will help users to select the optimal approach within a range of available analysis techniques. David Almorza 1 , M.V. Kandus 2 , Juan Carlos Salerno 2 , Rafael Boggio 359 Facultad de Ciencias del Trabajo, University of Cádiz, Spain 60 Instituto de Genética IGEAF, Buenos Aires, Argentina 61 Universidad Nacional de La Plata, Buenos Aires, Argentina Keywords: Principal component analysis, maize, ear weight, inbred lines The objective of this work was to evaluate the relationship among different traits of the ear of maize inbred lines and to group genotypes according to its performance. Ten inbred lines developed at IGEAF (INTA Castelar) and five public inbred lines as checks were used. A field trial was carried out in Castelar, Buenos Aires (34° 36' S , 58° 39' W) using a complete randomize design with three replications. At harvest, individual weight (P.E.), diameter (D.E.), row number (N.H.) and length (L.E.) of the ear were assessed. A principal component analysis, PCA, (Infostat 2005) was used, and the variability of the data was depicted with a biplot. Principal components 1 and 2 (CP1 and CP2) explained 90% of the data variability. CP1 was correlated with P.E., L.E. and D.E., meanwhile CP2 was correlated with N.H. We found that individual weight (P.E.) was more correlated with diameter of the ear (D.E.) than with length (L.E). Five groups of inbred lines were distinguished: with high P.E. and mean N.H. (04-70, 04-73, 04-101 and MO17), with high P.E. but less N.H. (04-61 and B14), with mean P.E. and N.H. (B73, 04-123 and 04-96), with high N.H. but less P.E. (LP109, 04-8, 04-91 and 04-76) and with low P.E. and low N.H. (LP521 and 04-104). The use of PCA showed which variables had more incidence in ear weight and how is the correlation among them. Moreover, the different groups found with this analysis allow the evaluation of inbred lines by several traits simultaneously. Sven Knüppel 1 , Anja Bauerfeind 1 , Klaus Rohde 162 Department of Bioinformatics, MDC Berlin, Germany Keywords: Haplotypes, association studies, case-control, nuclear families The area of gene chip technology provides a plethora of phase-unknown SNP genotypes in order to find significant association to some genetic trait. To circumvent possibly low information content of a single SNP one groups successive SNPs and estimates haplotypes. Haplotype estimation, however, may reveal ambiguous haplotype pairs and bias the application of statistical methods. Zaykin et al. (Hum Hered, 53:79-91, 2002) proposed the construction of a design matrix to take this ambiguity into account. Here we present a set of functions written for the Statistical package R, which carries out haplotype estimation on the basis of the EM-algorithm for individuals (case-control) or nuclear families. The construction of a design matrix on basis of estimated haplotypes or haplotype pairs allows application of standard methods for association studies (linear, logistic regression), as well as statistical methods as haplotype sharing statistics and TDT-Test. Applications of these methods to genome-wide association screens will be demonstrated. Manuela Zucknick 1 , Chris Holmes 2 , Sylvia Richardson 163 Department of Epidemiology and Public Health, Imperial College London, UK 64 Department of Statistics, Oxford Center for Gene Function, University of Oxford, UK Keywords: Bayesian, variable selection, MCMC, large p, small n, structured dependence In large-scale genomic applications vast numbers of markers or genes are scanned to find a few candidates which are linked to a particular phenotype. Statistically, this is a variable selection problem in the "large p, small n" situation where many more variables than samples are available. An additional feature is the complex dependence structure which is often observed among the markers/genes due to linkage disequilibrium or their joint involvement in biological processes. Bayesian variable selection methods using indicator variables are well suited to the problem. Binary phenotypes like disease status are common and both Bayesian probit and logistic regression can be applied in this context. We argue that logistic regression models are both easier to tune and to interpret than probit models and implement the approach by Holmes & Held (2006). Because the model space is vast, MCMC methods are used as stochastic search algorithms with the aim to quickly find regions of high posterior probability. In a trade-off between fast-updating but slow-moving single-gene Metropolis-Hastings samplers and computationally expensive full Gibbs sampling, we propose to employ the dependence structure among the genes/markers to help decide which variables to update together. Also, parallel tempering methods are used to aid bold moves and help avoid getting trapped in local optima. Mixing and convergence of the resulting Markov chains are evaluated and compared to standard samplers in both a simulation study and in an application to a gene expression data set. Reference Holmes, C. C. & Held, L. (2006) Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis1, 145,168. Dawn Teare 165 MMGE, University of Sheffield, UK Keywords: CNP, family-based analysis, MCMC Evidence is accumulating that segmental copy number polymorphisms (CNPs) may represent a significant portion of human genetic variation. These highly polymorphic systems require handling as phenotypes rather than co-dominant markers, placing new demands on family-based analyses. We present an integrated approach to meet these challenges in the form of a graphical model, where the underlying discrete CNP phenotype is inferred from the (single or replicate) quantitative measure within the analysis, whilst assuming an allele based system segregating through the pedigree. [source]

    Genetic association of the major histocompatibility complex with rheumatoid arthritis implicates two non-DRB1 loci

    ARTHRITIS & RHEUMATISM, Issue 1 2009
    Charlotte Vignal
    Objective The HLA,DRB1 locus within the major histocompatibility complex (MHC) at 6p21.3 has been identified as a susceptibility gene for rheumatoid arthritis (RA); however, there is increasing evidence of additional susceptibility genes in the MHC region. The aim of this study was to estimate their number and location. Methods A case,control study was performed involving 977 control subjects and 855 RA patients. The HLA,DRB1 locus was genotyped together with 2,360 single-nucleotide polymorphisms in the MHC region. Logistic regression was used to detect DRB1-independent effects. Results After adjusting for the effect of HLA,DRB1, 18 markers in 14 genes were strongly associated with RA (P < 10,4). Multivariate logistic regression analysis of these markers and DRB1 led to a model containing DRB1 plus the following 3 markers: rs4678, a nonsynonymous change in the VARS2L locus, ,1.7 Mb telomeric of DRB1; rs2442728, upstream of HLA,B, ,1.2 Mb telomeric of DRB1; and rs17499655, located in the 5,-untranslated region of DQA2, only 0.1 Mb centromeric of DRB1. In-depth investigation of the DQA2 association, however, suggested that it arose through cryptic linkage disequilibrium with an allele of DRB1. Two non,shared epitope alleles were also strongly associated with RA (P < 10,4): *0301 with anti, cyclic citrullinated peptide,negative RA and *0701 independently of autoantibody status. Conclusion These results confirm the polygenic contribution of the MHC to RA and implicate 2 additional non-DRB1 susceptibility loci. The role of the HLA,DQ locus in RA has been a subject of controversy, but in our data, it appears to be spurious. [source]

    Genetic association of vasoactive intestinal peptide receptor with rheumatoid arthritis: Altered expression and signal in immune cells

    ARTHRITIS & RHEUMATISM, Issue 4 2008
    Mario Delgado
    Objective Vasoactive intestinal peptide (VIP) has been shown to be one of the endogenous factors involved in the maintenance of immune tolerance. Administration of VIP ameliorates clinical signs in various experimental autoimmune disorders. This study was undertaken to investigate whether the exacerbated inflammatory autoimmune response in rheumatoid arthritis (RA) might result directly from altered expression and/or signaling of VIP receptors in immune cells. Methods The effect of specific agonists of different VIP receptors on collagen-induced arthritis in mice was investigated by clinical and histologic assessment and measurement of cytokine and chemokine production. Expression of VIP receptor type 1 (VPAC1) in synovial cells and monocytes from RA patients was determined by flow cytometry. Potential associations of VPAC1 genetic polymorphisms with RA susceptibility were investigated. Results A VPAC1 agonist was very efficient in the treatment of experimental arthritis, and deficient expression of VPAC1 in immune cells of RA patients was associated with the predominant proinflammatory Th1 milieu found in this disease. Immune cells derived from RA patients were less responsive to VIP signaling than were cells from healthy individuals and showed reduced VIP-mediated immunosuppressive activity, rendering leukocytes and synovial cells more proinflammatory in RA. A significant association between multiple-marker haplotypes of VPAC1 and susceptibility to RA was found, suggesting that the reduced VPAC1 expression in RA-derived immune cells is associated with the described VPAC1 genetic polymorphism. Conclusion These findings are highly relevant to the understanding of RA pathogenesis. They suggest that VIP signaling through VPAC1 is critical to maintaining immune tolerance in RA. In addition, the results indicate that VPAC1 may be a novel therapeutic target in RA. [source]


    EVOLUTION, Issue 5 2010
    Sarah R. Pryke
    Assortative mating is a key aspect in the speciation process because it is important for both initial divergence and maintenance of distinct species. However, it remains a challenge to explain how assortative mating evolves when diverging populations are undergoing gene flow (e.g., during hybridization). Here I experimentally test how assortative mating is maintained with frequent gene flow between diverged head-color morphs of the Gouldian finch (Erythrura gouldiae). Contrary to the predominant view on the development of sexual preferences in birds, cross-fostered offspring did not imprint on the phenotype of their conspecific (red or black morphs) or heterospecific (Bengalese finch) foster parents. Instead, the mating preferences of F1 and F2 intermorph-hybrids are consistent with inheritance on the Z chromosomes, which are also the location for genes controlling color expression and the genes causing low fitness of intermorph-hybrids. Genetic associations between color signal and preference loci on the sex chromosomes may prevent recombination from breaking down these associations when the morphs interbreed, helping to maintain assortative mating in the face of gene flow. Although sex linkage of reproductively isolating traits is theoretically expected to promote speciation, social and ecological constraints may enforce frequent interbreeding between the morphs, thus preventing complete reproductive isolation. [source]

    Abnormal CTLA-4 function in T cells from patients with systemic lupus erythematosus

    Elizabeth C. Jury
    Abstract CTLA-4 is a critical gatekeeper of T-cell activation and immunological tolerance and has been implicated in patients with a variety of autoimmune diseases through genetic association. Since T cells from patients with the autoimmune disease systemic lupus erythematosus (SLE) display a characteristic hyperactive phenotype, we investigated the function of CTLA-4 in SLE. Our results reveal increased CTLA-4 expression in FOXP3, responder T cells from patients with SLE compared with other autoimmune rheumatic diseases and healthy controls. However, CTLA-4 was unable to regulate T-cell proliferation, lipid microdomain formation and phosphorylation of TCR-, following CD3/CD28 co-stimulation, in contrast to healthy T cells. Although lupus T cells responded in vitro to CD3/CD28 co-stimulation, there was no parallel increase in CTLA-4 expression, which would normally provide a break on T-cell proliferation. These defects were associated with exclusion of CTLA-4 from lipid microdomains providing an anatomical basis for its loss of function. Collectively our data identify CTLA-4 dysfunction as a potential cause for abnormal T-cell activation in patients with SLE, which could be targeted for therapy. [source]

    Identification of brain neurons expressing the dopamine D4 receptor gene using BAC transgenic mice

    Daniela Noaín
    Abstract The dopamine D4 receptor (D4R) has received considerable interest because of its higher affinity for atypical antipsychotics, the extremely polymorphic nature of the human gene and the genetic association with attention deficit and hyperactivity disorder (ADHD). Several efforts have been undertaken to determine the D4R expression pattern in the brain using immunohistochemistry, binding autoradiography and in situ hybridization, but the overall published results present large discrepancies. Here, we have explored an alternative genetic approach by studying bacterial artificial chromosome (BAC) transgenic mice that express enhanced green fluorescent protein (EGFP) under the transcriptional control of the mouse dopamine D4 receptor gene (Drd4). Immunohistochemical analysis performed in brain sections of Drd4 -EGFP transgenic mice using an anti-EGFP polyclonal antibody showed that transgenic expression was predominant in deep layer neurons of the prefrontal cortex, particularly in the orbital, prelimbic, cingulate and rostral agranular portions. In addition, discrete groups of Drd4 -EGFP labelled neurons were observed in the anterior olfactory nucleus, ventral pallidum, and lateral parabrachial nucleus. EGFP was not detected in the striatum, hippocampus or midbrain as described using other techniques. Given the fine specificity of EGFP expression in BAC transgenic mice and the high sensitivity of the EGFP antibody used in this study, our results indicate that Drd4 expression in the adult mouse brain is limited to a more restricted number of areas than previously reported. Its leading expression in the prefrontal cortex supports the importance of the D4R in complex behaviours depending on cortical dopamine (DA) transmission and its possible role in the etiopathophysiology of ADHD. [source]

    Tests for genetic association using family data

    Mei-Chiung Shih
    Abstract We use likelihood-based score statistics to test for association between a disease and a diallelic polymorphism, based on data from arbitrary types of nuclear families. The Nonfounder statistic extends the transmission disequilibrium test (TDT) to accommodate affected and unaffected offspring, missing parental genotypes, phenotypes more general than qualitative traits, such as censored survival data and quantitative traits, and residual correlation of phenotypes within families. The Founder statistic compares observed or inferred parental genotypes to those expected in the general population. Here the genotypes of affected parents and those with many affected offspring are weighted more heavily than unaffected parents and those with few affected offspring. We illustrate the tests by applying them to data on a polymorphism of the SRD5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of these family-based statistics to that of the score statistic based on Cox's partial likelihood for censored survival data, and find that the family-based statistics have considerably more power when there are many untyped parents. The software program FGAP for computing test statistics is available at Genet. Epidemiol. 22:128,145, 2002. © 2002 Wiley-Liss, Inc. [source]

    Organic geochemistry indicates Gebel El Zeit, Gulf of Suez, is a source of bitumen used in some Egyptian mummies

    A.O. Barakat
    Molecular geochemical properties of crude oils and surface petroleum seeps from the southern part of the Gulf of Suez were evaluated. The characterizations of individual aliphatic, aromatic, and biomarker compounds were based on gas chromatography (GC) and gas chromatography/mass spectrometry (GC/MS) analyses. The results provided strong evidence for a close genetic association of these samples. The geochemical characteristics suggest an origin from Tertiary source rocks deposited in a normal marine environment that received continental runoff. The molecular signatures of the investigated samples were very similar to those of the Lower Miocene Rudeis Formation source rock in the southern Gulf of Suez. Further, biomarker fingerprints of the investigated oil seeps were compared with those of the Dead Sea asphalt, as well as the bitumen from some Egyptian mummies reported in the literature. The results demonstrate that oil seeps from the southern end of Gebel El Zeit were used by ancient Egyptians for embalming. © 2005 Wiley Periodicals, Inc. [source]

    Genetic background of primary biliary cirrhosis

    Atsushi Tanaka
    The clustering of patients in a representative family as well as relatively high concordance rate in monozygotic twins strongly indicate that genetic factors play a crucial role in modulating primary biliary cirrhosis (PBC) by conferring susceptibility to, or providing protection from, the disease. Therefore, much like other autoimmune diseases, intensive investigations have attempted to elucidate which genes are incriminated in the etiology of PBC. So far, a number of genes, including major histocompatibility complex (MHC) class I and II, cytokines and cell surface molecules, have been examined to seek the possibility of whether single nucleotide polymorphisms (SNP) of the gene might be associated with susceptibility to PBC. Nevertheless, it appears that methodologicaldifficulties, mainly the limitation of the number of individuals tested in each study, hamper the detection of a convincing and reproducible link between genetic polymorphisms and the etiology of PBC. Also, the difference in genetic background among several ethnic groups may play a role in concealing the association. In this review, I will highlight the genetic association in PBC, and review the association of genetic polymorphisms with the etiology of PBC, which have been reported in various ethnicities. [source]

    Runt-related transcription factor 3 is associated with ulcerative colitis and shows epistasis with solute carrier family 22, members 4 and 5

    Rinse K. Weersma MD
    Abstract Background: Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), are intestinal inflammatory disorders with a complex genetic background. Mice deficient for the runt-domain-transcription-factor3 (Runx3) develop spontaneous colitis. Human RUNX3 resides in an IBD-susceptibility locus. We studied the association of RUNX3 in a cohort of IBD patients and analyzed the interaction with SLC22A4/5. RUNX3 and OCTN1 mRNA expression was assessed in inflamed and noninflamed mucosa from patients and controls. Methods: 543 IBD patients (309 CD / 234 UC) and 296 controls were included. Four single nucleotide polymorphisms (SNPs) and 4 microsatellite markers were studied for RUNX3. Five SNPs (including SNP-207G,C and SNP1672C,T) were analyzed for SLC22A4/5. RUNX3, and OCTN1 expression in mucosal tissue from 30 patients (14 UC / 16 CD) and 6 controls were determined by quantitative polymerase chain reaction. Results: A significant association between RUNX3 -SNP rs2236851 and UC (OR 1.61; 95% confidence interval [CI] 1.11,2.32, P = 0.020) was found. Carriership is associated with pancolitis (odds ratio [OR] 1.86; 95% CI 1.08,3.21). SLC22A4/5 -SNPs rs272893 and rs273900 are associated with CD (OR 2.16; 95% CI 1.21,3.59 and OR 2.40; 95% CI 1.43,4.05). We found epistasis for carriership of a risk-associated allele in RUNX3 and SLC22A4/5 for UC patients versus CD patients (OR 3.83; 95% CI 1.26,11.67). RUNX3 mRNA expression is increased (P = 0.01) in inflamed colonic mucosa of UC patients compared to noninflamed mucosa and controls. Conclusions: We provide evidence for the genetic association of RUNX3 with UC and for CD with the IBD5 locus including SLC22A4/5. An epistatic effect of RUNX3 and SLC22A4 was associated with an increased risk for UC. Our data suggest a role for RUNX3 in UC susceptibility. (Inflamm Bowel Dis 2008) [source]

    HLA haplotypes in recurrent aphthous stomatitis: a mode­ of inheritance?

    E. Albanidou-Farmaki
    Summary The aim of this study was to investigate the genetic association between recurrent aphthous stomatitis (RAS) and human leucocyte antigen (HLA) class I and II alleles and HLA haplotypes. Families selected had at least one child suffering from recurrent aphthous stomatitis in addition to one or both of the parents. HLA-A, -B and -DR alleles were typed in 29 families, 27 nuclear and two extended (121 subjects). HLA haplotypes of all family members with RAS were compared with those who were RAS negative. Although major histocompatibility complex class I and II gene analysis failed to demonstrate any significant association between RAS and HLA antigens, the study of HLA haplotypes revealed a significant association between HLA haplotypes and susceptibility to RAS. The results indicate that susceptibility to RAS segregates in families in association with HLA haplotypes. [source]

    Human MHC architecture and evolution: implications for disease association studies

    J. A. Traherne
    Summary Major histocompatibility complex (MHC) variation is a key determinant of susceptibility and resistance to a large number of infectious, autoimmune and other diseases. Identification of the MHC variants conferring susceptibility to disease is problematic, due to high levels of variation and linkage disequilibrium. Recent cataloguing and analysis of variation over the complete MHC has facilitated localization of susceptibility loci for autoimmune diseases, and provided insight into the MHC's evolution. This review considers how the unusual genetic characteristics of the MHC impact on strategies to identify variants causing, or contributing to, disease phenotypes. It also considers the MHC in relation to novel mechanisms influencing gene function and regulation, such as epistasis, epigenetics and microRNAs. These developments, along with recent technological advances, shed light on genetic association in complex disease. [source]

    Single nucleotide polymorphisms and haplotype of MD-1 gene associated with high serum IgE phenotype with mite-sensitive allergy in Taiwanese children

    J. Y. Wang
    Summary MD-1 (myeloid differentiation 1; also known as Ly86, lymphocyte antigen 86), interacting with RP105, plays an important role in Toll-like receptor 4 (TLR4) signalling pathway. It has been suggested to be involved in the pathological mechanism of inflammation and atopic diseases. The purpose of this study was to investigate the genetic association between single nucleotide polymorphisms (SNPs) of MD-1 promoter and coding region and mite-sensitive allergy in Taiwanese children. We conducted a case-control study on 237 controls and 281 allergic patients sensitive to Dermatophagoides pteronyssinus (Der p) and Dermatophagoides farinae (Der f) by genotyping 35 SNPs in MD-1 gene region. In the promoter region we identified three SNPs, rs1334710, rs4959389, and rs977785 that are associated with mite-sensitive allergy in Taiwanese children. The P -values ranged from 0.0150 to 0.009. The haplotypes including promoter region were also associated with mite-sensitive allergy. Our results suggested that MD-1 could be a susceptible gene for mite-sensitive allergy in Taiwanese children. [source]

    Spatial genetic structure links between soil seed banks and above-ground populations of Primula modesta in subalpine grassland

    JOURNAL OF ECOLOGY, Issue 1 2006
    Summary 1The spatial genetic structure of soil seed banks establishes the initial template for development of spatial genetic structure in above-ground plants, but is rarely evaluated. 2We used kinship coefficients to analyse the fine-scale spatial genetic autocorrelation of plants and of seed banks from different soil depths for Primula modesta at a subalpine fen site on Mt Asama, central Japan. 3The spatial genetic structure of surface seeds (0,1 cm depth) was significant, while deeper seeds (1,5 cm depth) had no significant genetic structure. We also detected a more pronounced spatial genetic association between the surface seeds and flowering genets than between the deeper seeds and flowering genets. 4These results suggest that the surface seed bank accounts for a large proportion of the previous season's seed dispersal and therefore represents the transient seed bank, whereas the deeper (persistent) seed bank pools the reproductive output of multiple generations. 5Directional analysis indicated that secondary dispersal by running water modifies the spatial genetic structure and extends dispersal distances. Over time, this may impact on the spatial pattern of soil seeds, possibly accounting for the absence of spatial genetic structure in deeper seeds. 6Emerging seedlings and flowering ramets were strongly clustered together at distances up to 20 cm. Surviving seedlings were aggregated at short distances because of the patchy spatial distribution of safe sites for establishment, allowing development and strengthening of the marked fine-scale spatial genetic structure. [source]

    A free radical-generating system induces the cholesterol biosynthesis pathway: a role in Alzheimer's disease

    AGING CELL, Issue 2 2009
    María Recuero
    Summary Oxidative stress, which plays a critical role in the pathogenesis of neurodegenerative diseases such as Alzheimer's disease (AD), is intimately linked to aging , the best established risk factor for AD. Studies in neuronal cells subjected to oxidative stress, mimicking the situation in AD brains, are therefore of great interest. This paper reports that, in human neuronal cells, oxidative stress induced by the free radical-generating xanthine/xanthine oxidase (X-XOD) system leads to apoptotic cell death. Microarray analyses showed a potent activation of the cholesterol biosynthesis pathway following reductions in the cell cholesterol synthesis caused by the X-XOD treatment; furthermore, the apoptosis was reduced by inhibiting 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) expression with an interfering RNA. The potential importance of this mechanism in AD was investigated by genetic association, and it was found that HMGCR, a key gene in cholesterol metabolism and among those most strongly upregulated, was associated with AD risk. In summary, this work presents a human cell model prepared to mimic the effect of oxidative stress in neurons that might be useful in clarifying the mechanism involved in free radical-induced neurodegeneration. Gene expression analysis followed by genetic association studies indicates a possible link among oxidative stress, cholesterol metabolism and AD. [source]

    E-selectin and L-selectin polymorphisms in patients with periodontitis

    B. Houshmand
    Background amd Objective:, Periodontitis is a multifactorial disease in which environmental and genetic determinant factors contribute to individual subject's susceptibility. A DNA polymorphism in the regulating region of adhesion molecule genes is suggested to modulate the molecule's physiological effects. The aim of this study was to investigate the genetic association between the E-selectin Ser128Arg and L-selectin Phe206Leu polymorphisms and periodontitis. Material and Methods:, DNA was isolated from the whole blood of 88 patients with periodontitis and 139 healthy individuals. All samples were genotyped for the E-selectin Ser128Arg and L-selectin Phe206Leu polymorphisms using the polymerase chain reaction with sequence specific primers. Results:, Our findings revealed a significant difference in the Ser128Arg polymorphism of E-selectin, but not in the L-selectin polymorphism, between periodontal patients and controls. The 128Arg allele was present more frequently in patients than in healthy individuals (31.25% vs. 12.2%, p < 0.0001). In addition, there was an association between the presence of the 128Arg allele and periodontitis (odds ratio 2.9; 95% confidence interval: 1.75,4.4, p < 0.0001). No significant association was found between the polymorphisms tested and the subgroups of periodontal disease (i.e. chronic periodontitis and aggressive periodontitis). Conclusion:, The findings of this study showed that the Ser128Arg polymorphism of E-selectin might contribute to the susceptibility of Iranian individuals to periodontitis. [source]

    Pharmacologic Dissociation Between Impulsivity and Alcohol Drinking in High Alcohol Preferring Mice

    ALCOHOLISM, Issue 8 2010
    Brandon G. Oberlin
    Background:, Impulsivity is genetically correlated with, and precedes, addictive behaviors and alcoholism. If impulsivity or attention is causally related to addiction, certain pharmacological manipulations of impulsivity and/or attention may affect alcohol drinking, and vice versa. The current studies were designed to explore the relationship among impulsivity, drinking, and vigilance in selectively bred High Alcohol Preferring (HAP) mice, a line that has previously demonstrated both high impulsivity and high alcohol consumption. Amphetamine, naltrexone, and memantine were tested in a delay discounting (DD) task for their effects on impulsivity and vigilance. The same drugs and doses were also assessed for effects on alcohol drinking in a 2-bottle choice test. Methods:, HAP mice were subjected to a modified version of adjusting amount DD using 0.5-second and 10-second delays to detect decreases and increases, respectively, in impulsive responding. In 2 experiments, mice were given amphetamine (0.4, 0.8, or 1.2 mg/kg), naltrexone (3 and 10 mg/kg), and memantine (1 and 5 mg/kg) before DD testing. Another pair of studies used scheduled access, 2-bottle choice drinking to assess effects of amphetamine (0.4, 1.2, or 3.0 mg/kg), naltrexone (3 and 10 mg/kg), and memantine (1 and 5 mg/kg) on alcohol consumption. Results:, Amphetamine dose-dependently reduced impulsivity and vigilance decrement in DD, but similar doses left alcohol drinking unaffected. Naltrexone and memantine decreased alcohol intake at doses that did not affect water drinking but had no effects on impulsivity or vigilance decrement in the DD task. Conclusions:, Contrary to our hypothesis, none of the drugs tested here, while effective on either alcohol drinking or impulsivity, decreased both behaviors. These findings suggest that the genetic association between drinking and impulsivity observed in this population is mediated by mechanisms other than those targeted by the drugs tested in these studies. [source]