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Multiple Testing (multiple + testing)
Terms modified by Multiple Testing Selected AbstractsCluster Formation as a Measure of Interpretability in Multiple TestingBIOMETRICAL JOURNAL, Issue 5 2008Juliet Popper Shaffer Abstract Multiple test procedures are usually compared on various aspects of error control and power. Power is measured as some function of the number of false hypotheses correctly identified as false. However, given equal numbers of rejected false hypotheses, the pattern of rejections, i.e. the particular set of false hypotheses identified, may be crucial in interpreting the results for potential application. In an important area of application, comparisons among a set of treatments based on random samples from populations, two different approaches, cluster analysis and model selection, deal implicitly with such patterns, while traditional multiple testing procedures generally focus on the outcomes of subset and pairwise equality hypothesis tests, without considering the overall pattern of results in comparing methods. An important feature involving the pattern of rejections is their relevance for dividing the treatments into distinct subsets based on some parameter of interest, for example their means. This paper introduces some new measures relating to the potential of methods for achieving such divisions. Following Hartley (1955), sets of treatments with equal parameter values will be called clusters. Because it is necessary to distinguish between clusters in the populations and clustering in sample outcomes, the population clusters will be referred to as P -clusters; any related concepts defined in terms of the sample outcome will be referred to with the prefix outcome. Outcomes of multiple comparison procedures will be studied in terms of their probabilities of leading to separation of treatments into outcome clusters, with various measures relating to the number of such outcome clusters and the proportion of true vs. false outcome clusters. The definitions of true and false outcome clusters and related concepts, and the approach taken here, is in the tradition of hypothesis testing with attention to overall error control and power, but with added consideration of cluster separation potential. The pattern approach will be illustrated by comparing two methods with apparent FDR control but with different ways of ordering outcomes for potential significance: The original Benjamini,Hochberg (1995) procedure (BH), and the Newman,Keuls (Newman, 1939; Keuls, 1952) procedure (NK). (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Semiparametric Bayes Multiple Testing: Applications to Tumor DataBIOMETRICS, Issue 2 2010Lianming Wang Summary In National Toxicology Program (NTP) studies, investigators want to assess whether a test agent is carcinogenic overall and specific to certain tumor types, while estimating the dose-response profiles. Because there are potentially correlations among the tumors, a joint inference is preferred to separate univariate analyses for each tumor type. In this regard, we propose a random effect logistic model with a matrix of coefficients representing log-odds ratios for the adjacent dose groups for tumors at different sites. We propose appropriate nonparametric priors for these coefficients to characterize the correlations and to allow borrowing of information across different dose groups and tumor types. Global and local hypotheses can be easily evaluated by summarizing the output of a single Monte Carlo Markov chain (MCMC). Two multiple testing procedures are applied for testing local hypotheses based on the posterior probabilities of local alternatives. Simulation studies are conducted and an NTP tumor data set is analyzed illustrating the proposed approach. [source] Multiple testing in the genomics era: Findings from Genetic Analysis Workshop 15, Group 15GENETIC EPIDEMIOLOGY, Issue S1 2007Lisa J. Martin Abstract Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies. Genet. Epidemiol. 31(Suppl. 1):S124,S131, 2007. © 2007 Wiley-Liss, Inc. [source] European Mathematical Genetics Meeting, Heidelberg, Germany, 12th,13th April 2007ANNALS OF HUMAN GENETICS, Issue 4 2007Article 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 http://www.esat.kuleuven.be/endeavour. 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. G.et 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. L.et 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, J.et 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] FDR Control by the BH Procedure for Two-Sided Correlated Tests with Implications to Gene Expression Data AnalysisBIOMETRICAL JOURNAL, Issue 1 2007Anat Reiner-Benaim Abstract The multiple testing problem attributed to gene expression analysis is challenging not only by its size, but also by possible dependence between the expression levels of different genes resulting from co-regulations of the genes. Furthermore, the measurement errors of these expression levels may be dependent as well since they are subjected to several technical factors. Multiple testing of such data faces the challenge of correlated test statistics. In such a case, the control of the False Discovery Rate (FDR) is not straightforward, and thus demands new approaches and solutions that will address multiplicity while accounting for this dependency. This paper investigates the effects of dependency between bormal test statistics on FDR control in two-sided testing, using the linear step-up procedure (BH) of Benjamini and Hochberg (1995). The case of two multiple hypotheses is examined first. A simulation study offers primary insight into the behavior of the FDR subjected to different levels of correlation and distance between null and alternative means. A theoretical analysis follows in order to obtain explicit upper bounds to the FDR. These results are then extended to more than two multiple tests, thereby offering a better perspective on the effect of the proportion of false null hypotheses, as well as the structure of the test statistics correlation matrix. An example from gene expression data analysis is presented. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Investigation of Adducin 2 (beta) DNA polymorphisms in genetic predisposition to diabetic nephropathy in Type 1 diabetesDIABETIC MEDICINE, Issue 8 2008D. Currie Abstract Aims Adducin 2 (beta) (ADD2) is a biological and positional candidate gene proposed to confer genetic risk for diabetic nephropathy. This study aimed to comprehensively investigate all common and putatively functional polymorphisms in the genomic region encompassing this gene. Methods Tag single nucleotide polymorphisms (n = 23) derived from phase II of the International HapMap Project and in silico functional variants (n = 2) were genotyped in 1467 White individuals from the British Isles (cases, n = 718; control subjects, n = 749) by a combination of Sequenom iPLEX and TaqMan technologies. Results ,2 analysis of genotype and allele frequencies in cases vs. control subjects revealed weak evidence for association of one variant at the 5% level of significance (rs10164951, P = 0.02). Adjusting for multiple testing in the present case,control collection negated this association. Conclusions We selected an appropriate subset of variants suitable for genetic investigations of the ADD2 gene and report the first investigation of polymorphisms in ADD2 with diabetic nephropathy. Our results suggest that common polymorphisms and putatively functional variants in the ADD2 gene do not strongly influence genetic susceptibility to diabetic nephropathy in this White population with Type 1 diabetes. [source] Dopamine transporter gene (DAT1) VNTR polymorphism in major psychiatric disorders: family-based association study in the Bulgarian populationACTA PSYCHIATRICA SCANDINAVICA, Issue 5 2002L. Georgieva Objective:,A 40-bp variable number tandem repeat in the 3,-UTR of dopamine transporter gene (DAT1) has been examined for association with major psychiatric disorders in several case,control studies. No significant results have been found. We used a new collection of parent,offspring trios to test for association with schizophrenia (SZ), bipolar 1 disorder (BPI) and schizoaffective (SA) disorder. Method:,We genotyped trios from Bulgarian origin where the proband had SZ (178 trios), BPI (77 trios) and SA (29 trios). Alleles ranging from 5 to 11 repeats were observed. The results were analysed with the extended TDT (ETDT). Results:,No preferential transmission of alleles was observed for any diagnostic group. The presence of allele DAT*10 was associated with the severity and frequency of auditory hallucinations, however, this result is not significant if corrected for multiple testing. Conclusion:,Our results are in agreement with previous reports of a lack of association between this polymorphism and major psychiatric disorders. [source] GENETIC STUDY: Interaction of SLC6A4 and DRD2 polymorphisms is associated with a history of delirium tremensADDICTION BIOLOGY, Issue 1 2010Victor M. Karpyak ABSTRACT Several genetic polymorphisms have been reported to be associated with alcohol withdrawal seizures (AWS) and delirium tremens (DT). To replicate and further explore these findings, we investigated the effects of 12 previously reported candidate genetic variations in two groups of alcohol-dependent European Americans with a history of withdrawal, which differed according to the presence (n = 112) or absence (n = 92) of AWS and/or DT. Associations of AWS and/or DT with the genomic and clinical characteristics and gene,gene interaction effects were investigated using logistic regression models. None of the polymorphisms were significantly associated with AWS/DT after correction for multiple testing. However, we found a significant interaction effect of the SLC6A4 promoter polymorphism (5-HTTLPR) and DRD2 exon 8 single nucleotide polymorphism rs6276 on AWS and/or DT history (P = 0.009), which became more significant after adjustment for lifetime maximum number of drinks consumed per 24 hours (P < 0.001). Subsequent analysis revealed an even stronger association of the SLC6A4,DRD2 interaction with DT (P < 0.0001), which remained significant after Bonferroni correction. Results reveal decreased likelihood of DT in alcoholics that carry the DRD2 rs6276 G allele and SLC6A4 LL genotype. This study provides the first evidence to implicate the interaction between serotonin and dopamine neurotransmission in the etiology of DT. Replication is necessary to verify this potentially important finding. [source] BRIEF REPORT: No association of alcohol dependence with SLC6A5 and SLC6A9 glycine transporter polymorphismsADDICTION BIOLOGY, Issue 4 2009Gabriele Koller ABSTRACT To determine whether glycine transporter polymorphisms are associated with alcoholism, three genetic variants of SLC6A5 and two polymorphisms of SLC6A9 were genotyped in 463 German non-alcoholic controls and 644 German alcohol-dependent subjects. Association was investigated employing chi-square statistics and haplotype analysis. There was a significant association between the SLC6A5 polymorphism (rs1443547) and alcohol dependence as alcoholic individuals had a lower rate of AG-allele (,2 = 6.048, P = 0.049, d.f. = 2), which did not remain significant after correction for multiple testing. There was no association between SLC6A9 glycine transporter polymorphisms and alcohol dependence, and also none in haplotype analysis. [source] SPR1 gene near HLA-C is unlikely to be a psoriasis susceptibility geneEXPERIMENTAL DERMATOLOGY, Issue 3 2003Y. T. Chang Abstract:, Although genetics analyses have identified the HLA-Cw6 allele to be the major risk allele for psoriasis vulgaris (PV) in many racial groups, it has been proposed that other putative genes near the HLA-C locus are involved in PV susceptibility and that the association of Cw6 is a result of linkage disequilibrium. The SPR1 gene, a predicted gene located 128 kb telomeric to the HLA-C locus, is considered to be one potential candidate gene of PV. Until now, no association study of the SPR1 gene has been conducted on psoriasis patients. We investigated the SPR1 gene for disease association by direct sequencing of the SPR1 gene in 116 Chinese patients with PV and 116 normal subjects. Genotyping for HLA-Cw6 was also carried out using polymerase chain reaction/restriction fragment length polymorphism. Significant increase of the HLA-Cw6 allele was found in psoriasis patients (32.8% vs. 13.8%, P = 0.001). We found that the SPR1 gene is a highly polymorphic gene containing 13 single nucleotide polymorphisms (SNPs), two of which have not been previously reported, and four SNPs cause amino acid change. No significantly different allelic distribution of 13 SPR1 SNPs could be found between the patients with PV and controls after correction for multiple testing. If the frequencies of SPR1 SNPs were compared between the early onset psoriatics and control subjects, early onset patients were more likely to have G allele at position 988 (60% vs. 35.3%, P = 0.001). However, the significance disappeared upon stratification for the Cw6 status. Haplotype-based association analysis showed two susceptibility haplotypes (types 8 and 19) in early onset psoriasis patients. Nonetheless, the significance also disappeared after stratification of the Cw6 status. Our results suggest that HLA-Cw6 remains the major risk allele in Chinese psoriatics, and that the SPR1 gene might not play an important role in the causation of PV. [source] Investigation of 17 candidate genes for personality traits confirms effects of the HTR2A gene on novelty seekingGENES, BRAIN AND BEHAVIOR, Issue 4 2009A. Heck Genes involved in serotonergic and dopaminergic neurotransmission have been hypothesized to affect different aspects of personality, but findings from genetic association studies did not provide conclusive results so far. In previous studies, however, only one or a few polymorphisms within single genes were investigated neglecting the possibility that the genetic associations might be more complex comprising several genes or gene regions. To overcome this limitation, we performed an extended genetic association study analyzing 17 serotonergic (SLC6A4, HTR1A, HTR1B, HTR2A, HTR2C, HTR3A, HTR6, MAOA, TPH1, TPH2) and dopaminergic genes (SLC6A3, DRD2, DRD3, DRD4, COMT, MAOA, TH, DBH), which have been previously reported to be implicated with personality traits. One hundred and ninety-five single nucleotide polymorphisms (SNPs) within these genes were genotyped with the Illumina BeadChip technology (HumanHap300, Human-1) in a sample of 366 mentally healthy Caucasians. Additionally, we tried to replicate our results in an independent sample of further 335 Caucasians. Personality traits in both samples were assessed with the German version of Cloninger's Tridimensional Personality Questionnaire. From 30 SNPs showing associations at a nominal level of significance, two intronic SNPs, rs2770296 and rs927544, both located in the HTR2A gene, withstood correction for multiple testing. These SNPs were associated with the personality trait novelty seeking. The effect of rs927544 could be replicated for the novelty seeking subscale extravagance, and the same SNP was also associated with extravagance inthe combined samples. Our results show that HTR2A polymorphisms modulate facets of novelty seeking behaviour in healthy adults suggesting that serotonergic neurotransmission is involved in this phenotype. [source] Multiple testing in the genomics era: Findings from Genetic Analysis Workshop 15, Group 15GENETIC EPIDEMIOLOGY, Issue S1 2007Lisa J. Martin Abstract Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies. Genet. Epidemiol. 31(Suppl. 1):S124,S131, 2007. © 2007 Wiley-Liss, Inc. [source] Haplotype interaction analysis of unlinked regionsGENETIC EPIDEMIOLOGY, Issue 4 2005Tim Becker Abstract Genetically complex diseases are caused by interacting environmental factors and genes. As a consequence, statistical methods that consider multiple unlinked genomic regions simultaneously are desirable. Such consideration, however, may lead to a vast number of different high-dimensional tests whose appropriate analysis pose a problem. Here, we present a method to analyze case-control studies with multiple SNP data without phase information that considers gene-gene interaction effects while correcting appropriately for multiple testing. In particular, we allow for interactions of haplotypes that belong to different unlinked regions, as haplotype analysis often proves to be more powerful than single marker analysis. In addition, we consider different marker combinations at each unlinked region. The multiple testing issue is settled via the minP approach; the P value of the "best" marker/region configuration is corrected via Monte-Carlo simulations. Thus, we do not explicitly test for a specific pre-defined interaction model, but test for the global hypothesis that none of the considered haplotype interactions shows association with the disease. We carry out a simulation study for case-control data that confirms the validity of our approach. When simulating two-locus disease models, our test proves to be more powerful than association methods that analyze each linked region separately. In addition, when one of the tested regions is not involved in the etiology of the disease, only a small amount of power is lost with interaction analysis as compared to analysis without interaction. We successfully applied our method to a real case-control data set with markers from two genes controlling a common pathway. While classical analysis failed to reach significance, we obtained a significant result even after correction for multiple testing with our proposed haplotype interaction analysis. The method described here has been implemented in FAMHAP. Genet. Epidemiol. 2005. © 2005 Wiley-Liss, Inc. [source] Analysis of single-locus tests to detect gene/disease associations,GENETIC EPIDEMIOLOGY, Issue 3 2005Kathryn Roeder Abstract A goal of association analysis is to determine whether variation in a particular candidate region or gene is associated with liability to complex disease. To evaluate such candidates, ubiquitous Single Nucleotide Polymorphisms (SNPs) are useful. It is critical, however, to select a set of SNPs that are in substantial linkage disequilibrium (LD) with all other polymorphisms in the region. Whether there is an ideal statistical framework to test such a set of ,tag SNPs' for association is unknown. Compared to tests for association based on frequencies of haplotypes, recent evidence suggests tests for association based on linear combinations of the tag SNPs (Hotelling T2 test) are more powerful. Following this logical progression, we wondered if single-locus tests would prove generally more powerful than the regression-based tests? We answer this question by investigating four inferential procedures: the maximum of a series of test statistics corrected for multiple testing by the Bonferroni procedure, TB, or by permutation of case-control status, TP; a procedure that tests the maximum of a smoothed curve fitted to the series of of test statistics, TS; and the Hotelling T2 procedure, which we call TR. These procedures are evaluated by simulating data like that from human populations, including realistic levels of LD and realistic effects of alleles conferring liability to disease. We find that power depends on the correlation structure of SNPs within a gene, the density of tag SNPs, and the placement of the liability allele. The clearest pattern emerges between power and the number of SNPs selected. When a large fraction of the SNPs within a gene are tested, and multiple SNPs are highly correlated with the liability allele, TS has better power. Using a SNP selection scheme that optimizes power but also requires a substantial number of SNPs to be genotyped (roughly 10,20 SNPs per gene), power of TP is generally superior to that for the other procedures, including TR. Finally, when a SNP selection procedure that targets a minimal number of SNPs per gene is applied, the average performances of TP and TR are indistinguishable. Genet. Epidemiol. © 2005 Wiley-Liss, Inc. [source] Expression of organic cation transporters OCT1 (SLC22A1) and OCT3 (SLC22A3) is affected by genetic factors and cholestasis in human liver,HEPATOLOGY, Issue 4 2009Anne T. Nies An important function of hepatocytes is the biotransformation and elimination of various drugs, many of which are organic cations and are taken up by organic cation transporters (OCTs) of the solute carrier family 22 (SLC22). Because interindividual variability of OCT expression may affect response to cationic drugs such as metformin, we systematically investigated genetic and nongenetic factors of OCT1/SLC22A1 and OCT3/SLC22A3 expression in human liver. OCT1 and OCT3 expression (messenger RNA [mRNA], protein) was analyzed in liver tissue samples from 150 Caucasian subjects. Hepatic OCTs were localized by way of immunofluorescence microscopy. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and genome-wide single-nucleotide polymorphism microarray technology served to genotype 92 variants in the SLC22A1-A3/OCT1-3 gene cluster. Transport of metformin by recombinant human OCT1 and OCT3 was compared using transfected cells. OCT1 mRNA and protein expression varied 113- and 83-fold, respectively; OCT3 mRNA expression varied 27-fold. OCT1 transcript levels were on average 15-fold higher compared with OCT3. We localized the OCT3 protein to the basolateral hepatocyte membrane and identified metformin as an OCT3 substrate. OCT1 and OCT3 expression are independent of age and sex but were significantly reduced in liver donors diagnosed as cholestatic (P , 0.01). Several haplotypes for OCT1 and OCT3 were identified. Multivariate analysis adjusted for multiple testing showed that only the OCT1-Arg61Cys variant (rs12208357) strongly correlated with decreased OCT1 protein expression (P < 0.0001), and four variants in OCT3 (rs2292334, rs2048327, rs1810126, rs3088442) were associated with reduced OCT3 mRNA levels (P = 0.03). Conclusion: We identified cholestasis and genetic variants as critical determinants for considerable interindividual variability of hepatic OCT1 and OCT3 expression. This indicates consequences for hepatic elimination of and response to OCT substrates such as metformin. (HEPATOLOGY 2009.) [source] Environmental factors in inflammatory bowel disease: A co-twin control study of a Swedish-Danish twin populationINFLAMMATORY BOWEL DISEASES, Issue 10 2006Jonas Halfvarson MD Abstract Background: Genetics and environmental factors are implicated in the etiology of inflammatory bowel disease (IBD). We studied environmental factors in a population-based Swedish-Danish twin cohort using the co-twin control method. Subjects and Methods: A questionnaire was sent to 317 twin pairs regarding markers of exposures in the following areas: infections/colonization and diet as well as smoking, appendectomy, and oral contraceptives. Odds ratios (OR) were calculated by conditional logistic regression. When confounding appeared plausible, multivariate conditional logistic regression was added. The questions were also divided into topic groups, and adjustment was made for multiple testing within each of the groups. Results: The response rate to the questionnaire was 83%. In consideration of the study design, only discordant pairs were included (Crohn's disease [CD], n = 102; ulcerative colitis [UC], n > = 125). Recurrent gastrointestinal infections were associated with both UC (OR, 8.0; 95% confidence interval [CI], 1.0,64) and CD (OR, 5.5; 95% CI, 1.2,25). Hospitalization for gastrointestinal infections was associated with CD (OR, 12; 95% CI, 1.6,92). Smoking was inversely associated with UC (OR, 0.4; 95% CI, 0.2,0.9) and associated with CD (OR, 2.9; 95% CI, 1.2,7.1). Conclusions: The observed associations indicate that markers of possible infectious events may influence the risk of IBD. Some of these effects might be mediated by long-term changes in gut flora or alterations in reactivity to the flora. The influence of smoking in IBD was confirmed. [source] Investigation of dopamine receptors in susceptibility to behavioural and psychological symptoms in Alzheimer's diseaseINTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 9 2009Antonia L. Pritchard Abstract Objective Alzheimer's disease (AD) patients commonly suffer from behavioural and psychological symptoms of dementia (BPSD). A genetic component to the development of BPSD in AD has been supported. Polymorphisms within dopamine receptors DRD1, DRD2, DRD3 and DRD4 have previously been investigated in a few interesting studies that are reviewed here and extended using our patient cohort. Methods Our large cohort of 395 probable AD patients had longitudinal information on the BPSD (Neuropsychiatric Inventory), which was used to dichotomise patients into whether they had ever suffered from a given symptom within the study period, or not. These measures were related to the DRD1 (A-48G), DRD2 (ser311cys; C-ins/del), DRD3 (ser9gly) and DRD4 (VNTR) genotype and allele frequencies. Results Associations were revealed between DRD3 and elation, and between DRD4 with agitation/aggression and with depression; however, these findings do not remain significant after correction for multiple testing. No associations were found with the other genetic variants and these symptoms and no associations were observed between any of the polymorphic variants examined and delusions, hallucinations, psychosis and aberrant motor behaviour. Conclusion Our data, in combination with a review of the literature, reveal a potential role for the VNTR variant of DRD4 in the development of depression in AD patients. The findings presented here need to be replicated in large, well characterised longitudinal cohorts. Copyright © 2009 John Wiley & Sons, Ltd. [source] Nonreplication in Genetic Studies of Complex Diseases,Lessons Learned From Studies of Osteoporosis and Tentative Remedies,JOURNAL OF BONE AND MINERAL RESEARCH, Issue 3 2005Hui Shen Abstract Inconsistent results have accumulated in genetic studies of complex diseases/traits over the past decade. Using osteoporosis as an example, we address major potential factors for the nonreplication results and propose some potential remedies. Over the past decade, numerous linkage and association studies have been performed to search for genes predisposing to complex human diseases. However, relatively little success has been achieved, and inconsistent results have accumulated. We argue that those nonreplication results are not unexpected, given the complicated nature of complex diseases and a number of confounding factors. In this article, based on our experience in genetic studies of osteoporosis, we discuss major potential factors for the inconsistent results and propose some potential remedies. We believe that one of the main reasons for this lack of reproducibility is overinterpretation of nominally significant results from studies with insufficient statistical power. We indicate that the power of a study is not only influenced by the sample size, but also by genetic heterogeneity, the extent and degree of linkage disequilibrium (LD) between the markers tested and the causal variants, and the allele frequency differences between them. We also discuss the effects of other confounding factors, including population stratification, phenotype difference, genotype and phenotype quality control, multiple testing, and genuine biological differences. In addition, we note that with low statistical power, even a "replicated" finding is still likely to be a false positive. We believe that with rigorous control of study design and interpretation of different outcomes, inconsistency will be largely reduced, and the chances of successfully revealing genetic components of complex diseases will be greatly improved. [source] Common single nucleotide polymorphisms in cyclooxygenase-2 and risk of severe chronic periodontitis in a Chinese populationJOURNAL OF CLINICAL PERIODONTOLOGY, Issue 3 2009Cheng-Jie Xie Abstract Aim: Several common single nucleotide polymorphisms (SNPs) of the cyclooxygenase-2 (COX-2) gene have been reported to be functional. The association between ,1195GA, ,765GC and 8473TC of COX-2, and severe chronic periodontitis (CP) in a Chinese population was investigated. Material and Methods: 148 cases of healthy controls (control group) and 146 cases of severe CP were recruited in this study. Genotypes of ,1195GA, ,765GC and 8473TC were determined by polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP). The distributions of genotypes and haplotypes were compared by ,2 test and the odds ratios (ORs) were calculated by logistic regression analysis. Results: The prevalence of the ,1195A was more prevalent in CP group (60.62%) than control group (51.35%), and the distributions of the ,765C and 8473C were higher in control group (6.76% and 21.96%) compared with CP group (3.08% and 15.07%). Only genotype distribution of ,1195GA was significant when p -value was corrected for multiple testing (pc=0.033). The adjusted ORs for the ,1195AA/GA, ,765GC and 8473CC/TC were 2.49 (95% CI=1.33,4.69, p=0.005), 0.45 (95% CI=0.20,1.04, p=0.061) and 0.67 (95% CI=0.41,1.11, p=0.118). Subjects with the haplotype AGT had a significantly higher risk of periodontitis than those with the most common haplotype GGT (OR=1.91, 95% CI=1.32,2.76, pc<0.001). Conclusions: It suggests the ,1195A variant is associated with an increased risk for severe CP. [source] Comparing three different methods to detect selective loci using dominant markersJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 10 2010A. PÉREZ-FIGUEROA Abstract We carried out a simulation study to compare the efficiency of three alternative programs (dfdist, detseld and bayescan) to detect loci under directional selection from genome-wide scans using dominant markers. We also evaluated the efficiency of correcting for multiple testing those methods that use a classical probability approach. Under a wide range of scenarios, we conclude that bayescan appears to be more efficient than the other methods, detecting a usually high percentage of true selective loci as well as less than 1% of outliers (false positives) under a fully neutral model. In addition, the percentage of outliers detected by this software is always correlated with the true percentage of selective loci in the genome. Our results show, nevertheless, that false positives are common even with a combination of methods and multitest correction, suggesting that conclusions obtained from this approach should be taken with extreme caution. [source] MHC-linked susceptibility to a bacterial infection, but no MHC-linked cryptic female choice in whitefishJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 1 2004C. Wedekind Abstract Non-random gamete fusion is one of several potential cryptic female choice mechanisms that have been postulated and that may enhance the survival probability of the offspring. Previous studies have found that gamete fusion in mice is influenced by genes of the major histocompatibility complex (MHC) region. Here we test (i) whether there is MHC-dependent gamete fusion in whitefish (Coregonus sp.) and (ii) whether there is a link between the MHC and embryo susceptibility to an infection by the bacterium Pseudomonas fluorescens. We experimentally bred whitefish and reared sibships in several batches that either experienced or did not experience strong selection by P. fluorescens. We then determined the MHC class II B1 genotype of 1016 surviving larvae of several full sibships. We found no evidence for MHC-linked gamete fusion. However, in one of seven sibships we found a strong connection between the MHC class II genotype and embryo susceptibility to P. fluorescens. This connection was still significant after correcting for multiple testing. Hence, the MHC class II genotype can considerably influence embryo survival in whitefish, but gamete fusion seems to be random with respect to the MHC. [source] Methodological and statistical issues in pharmacogenomicsJOURNAL OF PHARMACY AND PHARMACOLOGY: AN INTERNATI ONAL JOURNAL OF PHARMACEUTICAL SCIENCE, Issue 2 2010Bas J. M. Peters Abstract Pharmacogenomics strives to explain the interindividual variability in response to drugs due to genetic variation. Although technological advances have provided us with relatively easy and cheap methods for genotyping, promises about personalised medicine have not yet met our high expectations. Successful results that have been achieved within the field of pharmacogenomics so far are, to name a few, HLA-B*5701 screening to avoid hypersensitivity to the antiretroviral abacavir, thiopurine S-methyltransferase (TPMT) genotyping to avoid thiopurine toxicity, and CYP2C9 and VKORC1 genotyping for better dosing of the anticoagulant warfarin. However, few pharmacogenetic examples have made it into clinical practice in the treatment of complex diseases. Unfortunately, lack of reproducibility of results from observational studies involving many genes and diseases seems to be a common pattern in pharmacogenomic studies. In this article we address some of the methodological and statistical issues within study design, gene and single nucleotide polymorphism (SNP) selection and data analysis that should be considered in future pharmacogenomic research. First, we discuss some of the issues related to the design of epidemiological studies, specific to pharmacogenomic research. Second, we describe some of the pros and cons of a candidate gene approach (including gene and SNP selection) and a genome-wide scan approach. Finally, conventional as well as several innovative approaches to the analysis of large pharmacogenomic datasets are proposed that deal with the issues of multiple testing and systems biology in different ways. [source] Associations and Interactions Between SNPs in the Alcohol Metabolizing Genes and Alcoholism Phenotypes in European AmericansALCOHOLISM, Issue 5 2009Richard Sherva Background:, Alcohol dependence is a major cause of morbidity and mortality worldwide and has a strong familial component. Several linkage and association studies have identified chromosomal regions and/or genes that affect alcohol consumption, notably in genes involved in the 2-stage pathway of alcohol metabolism. Methods:, Here, we use multiple regression models to test for associations and interactions between 2 alcohol-related phenotypes and SNPs in 17 genes involved in alcohol metabolism in a sample of 1,588 European American subjects. Results:, The strongest evidence for association after correcting for multiple testing was between rs1229984, a nonsynonymous coding SNP in ADH1B, and DSM-IV symptom count (p = 0.0003). This SNP was also associated with maximum number of drinks in 24 hours (p = 0.0004). Each minor allele at this SNP predicts 45% fewer DSM-IV symptoms and 18% fewer max drinks. Another SNP in a splice site in ALDH1A1 (rs8187974) showed evidence for association with both phenotypes as well (p = 0.02 and 0.004, respectively), but neither association was significant after accounting for multiple testing. Minor alleles at this SNP predict greater alcohol consumption. In addition, pairwise interactions were observed between SNPs in several genes (p = 0.00002). Conclusions:, We replicated the large effect of rs1229984 on alcohol behavior, and although not common (MAF = 4%), this polymorphism may be highly relevant from a public health perspective in European Americans. Another SNP, rs8187974, may also affect alcohol behavior but requires replication. Also, interactions between polymorphisms in genes involved in alcohol metabolism are likely determinants of the parameters that ultimately affect alcohol consumption. [source] Coombs', haemoplasma and retrovirus testing in feline anaemiaJOURNAL OF SMALL ANIMAL PRACTICE, Issue 4 2010S. Tasker Objective: To investigate the associations between Coombs' testing, haemoplasma and retroviral infections, and feline anaemia. Methods: Haematology, Coombs' testing (including assessment of persistent autoagglutination) and selected infection testing (haemoplasma, feline leukaemia virus/feline immunodeficiency virus provirus) were performed in blood samples collected from 60 anaemic and 60 non-anaemic cats. Results: No association between infection and anaemia or Coombs' positivity existed. Anaemic cats (21.7%) were significantly more likely than non-anaemic cats (0%) to have cold autoagglutination (P<0.0001), but significance (set at ,0.0025 due to multiple testing) was not quite reached when Coombs' positivity was compared between anaemic (40.4% and 21.7% positive at 4°C and 37°C, respectively) and non-anaemic (20% and 3.3% positive, P=0.021 and P=0.004, at 4°C and 37°C, respectively) cats. Cats with immune-mediated haemolytic anaemia were significantly more likely to have persistent cold autoagglutination (P<0.0001) and be Coombs' positive at 37°C with polyvalent (P<0.0001), immunoglobulin (Ig)G (P<0.0001) or any antiserum (P<0.0001). Haemoplasmas and retroviruses were uncommonly detected. Clinical Significance: Cats suspected of having immune-mediated haemolytic anaemia should be evaluated for persistent autoagglutination at 4°C as well as performing Coombs' testing at 37°C, but positive results may occur in with other forms of anaemia. Testing for erythrocyte-bound antibodies should always be interpreted in parallel with documentation of haemolysis in anaemic cats. [source] Investigation of Quantitative Trait Loci in the CCKAR Gene With Susceptibility to AlcoholismALCOHOLISM, Issue 2002Takehito Okubo Background Cholecystokinin (CCK) plays an important role in the function of the central nervous system by interacting with dopamine and other neurotransmitters. We previously reported genetic variations in the promoter and coding regions of the CCKA receptor (CCKAR), CCKBR, and CCK genes and a possible association between polymorphisms of the CCKAR gene and alcoholism. In this study, association analyses were re-examined between the polymorphisms of the promoter region of the CCKAR gene and patients with alcohol withdrawal symptoms, in addition to patients with alcoholic liver injury. Methods A total of 131 Japanese male patients with alcohol withdrawal symptoms, 70 Japanese patients with alcoholic liver injury, and 98 age-matched Japanese male controls (nonhabitual drinkers) were examined using polymerase chain reaction-based single strand conformational polymorphism and sequencing analyses. Results Significant differences between patients with hallucination and controls were found in the allele frequencies at the ,388 and ,85 loci of the CCKAR gene (p= 0.0095, p= 0.0087, respectively), but these differences were not significant after Bonferroni correction for multiple testing. In contrast, the frequency of the homozygous genotype ,85 CC was significantly higher in hallucination-positive patients than in controls (p= 0.0031) and in patients with hallucination accompanying delirium tremens than in controls (p= 0.0022), and these differences were significant after Bonferroni correction. Conclusions The data from the case control suggest that polymorphisms of the promoter region of the CCKAR gene do not play a major role in the pathogenesis of alcohol withdrawal symptoms or alcoholic liver injury. However, a significant association was found between polymorphism at the ,85 locus of the CCKAR gene and patients with hallucination, and especially patients with hallucination accompanying delirium tremens. [source] ANALYZING CORRELATIONS BETWEEN STREAM AND WATERSHED ATTRIBUTES,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2003John Van Sickle ABSTRACT: Bivariate correlation analysis has been widely used to explore relationships between stream and watershed attributes that have all been measured on the same set of watersheds or sampling locations. Researchers routinely test H0: ,= 0 for each correlation in a large table and then go on to discuss only those that are declared "significant." Such test results are inaccurate because no allowance is made for multiple testing, and also because the tests are not mutually independent. This paper reviews the Bonferroni approach to controlling the overall error rate in multiple testing and shows how the approach becomes impractical for large correlation tables. The Hotelling/Williams test is introduced for comparing two dependent correlations that share a variable, and numerical constraints for two such correlations are illustrated. References are also given for testing other hypothesized patterns among dependent correlations, and links to dependent correlation software are provided. The methods are illustrated for watershed and stream variables sampled in 23 small agricultural watersheds of the Willamette Valley, Oregon. [source] A hierarchical modelling framework for identifying unusual performance in health care providersJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 4 2007David I. Ohlssen Summary. A wide variety of statistical methods have been proposed for detecting unusual performance in cross-sectional data on health care providers. We attempt to create a unified framework for comparing these methods, focusing on a clear distinction between estimation and hypothesis testing approaches, with the corresponding distinction between detecting ,extreme' and ,divergent' performance. When assuming a random-effects model the random-effects distribution forms the null hypothesis, and there appears little point in testing whether individual effects are greater or less than average. The hypothesis testing approach uses p -values as summaries and brings with it the standard problems of multiple testing, whether Bayesian or classical inference is adopted. A null random-effects formulation allows us to answer appropriate questions of the type: ,is a particular provider worse than we would expect the true worst provider (but still part of the null distribution) to be'? We outline a broad three-stage strategy of exploratory detection of unusual providers, detailed modelling robust to potential outliers and confirmation of unusual performance, illustrated by using two detailed examples. The concepts are most easily handled within a Bayesian analytic framework using Markov chain Monte Carlo methods, but the basic ideas should be generally applicable. [source] Large-scale multiple testing under dependenceJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2009Wenguang Sun Summary., The paper considers the problem of multiple testing under dependence in a compound decision theoretic framework. The observed data are assumed to be generated from an underlying two-state hidden Markov model. We propose oracle and asymptotically optimal data-driven procedures that aim to minimize the false non-discovery rate FNR subject to a constraint on the false discovery rate FDR. It is shown that the performance of a multiple-testing procedure can be substantially improved by adaptively exploiting the dependence structure among hypotheses, and hence conventional FDR procedures that ignore this structural information are inefficient. Both theoretical properties and numerical performances of the procedures proposed are investigated. It is shown that the procedures proposed control FDR at the desired level, enjoy certain optimality properties and are especially powerful in identifying clustered non-null cases. The new procedure is applied to an influenza-like illness surveillance study for detecting the timing of epidemic periods. [source] Proportion of non-zero normal means: universal oracle equivalences and uniformly consistent estimatorsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2008Jiashun Jin Summary., Since James and Stein's seminal work, the problem of estimating n normal means has received plenty of enthusiasm in the statistics community. Recently, driven by the fast expansion of the field of large-scale multiple testing, there has been a resurgence of research interest in the n normal means problem. The new interest, however, is more or less concentrated on testing n normal means: to determine simultaneously which means are 0 and which are not. In this setting, the proportion of the non-zero means plays a key role. Motivated by examples in genomics and astronomy, we are particularly interested in estimating the proportion of non-zero means, i.e. given n independent normal random variables with individual means Xj,N(,j,1), j=1,,,n, to estimate the proportion ,n=(1/n) #{j:,j /= 0}. We propose a general approach to construct the universal oracle equivalence of the proportion. The construction is based on the underlying characteristic function. The oracle equivalence reduces the problem of estimating the proportion to the problem of estimating the oracle, which is relatively easier to handle. In fact, the oracle equivalence naturally yields a family of estimators for the proportion, which are consistent under mild conditions, uniformly across a wide class of parameters. The approach compares favourably with recent works by Meinshausen and Rice, and Genovese and Wasserman. In particular, the consistency is proved for an unprecedentedly broad class of situations; the class is almost the largest that can be hoped for without further constraints on the model. We also discuss various extensions of the approach, report results on simulation experiments and make connections between the approach and several recent procedures in large-scale multiple testing, including the false discovery rate approach and the local false discovery rate approach. [source] Variation in 24 hemostatic genes and associations with non-fatal myocardial infarction and ischemic strokeJOURNAL OF THROMBOSIS AND HAEMOSTASIS, Issue 1 2008N. L. SMITH Summary., Background:, Arterial thrombosis involves platelet aggregation and clot formation, yet little is known about the contribution of genetic variation in fibrin-based hemostatic factors to arterial clotting risk. We hypothesized that common variation in 24 coagulation,fibrinolysis genes would contribute to risk of incident myocardial infarction (MI) or ischemic stroke (IS). Methods:, We conducted a population-based, case,control study. Subjects were hypertensive adults and postmenopausal women 30,79 years of age, who sustained a first MI (n = 856) or IS (n = 368) between 1995 and 2002, and controls matched on age, hypertension status, and calendar year (n = 2689). We investigated the risk of MI and IS associated with (i) global variation within each gene as measured by common haplotypes and (ii) individual haplotypes and single nucleotide polymorphisms (SNPs). Significance was assessed using a 0.2 threshold of the false discovery rate q -value, which accounts for multiple testing. Results:, After accounting for multiple testing, global genetic variation in factor (F) VIII was associated with IS risk. Two haplotypes in FVIII and one in FXIIIa1 were significantly associated with increased IS risk (all q -values < 0.2). A plasminogen gene SNP was associated with MI risk. All are new discoveries not previously reported. Another 24 tests had P -values < 0.05 and q -values > 0.2 in MI and IS analyses, 23 of which are new and hypothesis generating. Conclusions:, Apart from the association of FVIII variation with IS, we found little evidence that common variation in the 24 candidate fibrin-based hemostasis genes strongly influences arterial thrombotic risk, but our results cannot rule out small effects. [source] | |||||||||||||||||||||||||||||||