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Collaborative Study (collaborative + study)
Kinds of Collaborative Study Selected AbstractsEvaluation of the rodent micronucleus assay by a 28-day treatment protocol: Summary of the 13th Collaborative Study by the Collaborative Study Group for the Micronucleus Test (CSGMT)/Environmental Mutagen Society of Japan (JEMS),Mammalian Mutagenicity Study Group (MMS)ENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Issue 2 2001Shuichi Hamada Abstract To examine whether micronucleus tests can be incorporated into general toxicology assays, we performed micronucleus tests applying the treatment protocols typically used in such assays. In this 13th Collaborative Study of the CSGMT, both rats and mice were tested, although rats were used in the majority of the studies. Fifteen mutagens were tested in rats, mainly by oral (p.o.) administration. Micronucleus induction was evaluated 2, 3, and 4 days, and 1, 2, 3, and 28 days after the beginning of the treatment in the peripheral blood, and at 28 days in the bone marrow. Of the 15 chemicals that induced micronuclei in rats in short-term assays, two chemicals (1,2-dimethylhydrazine·2HCl and mitomycin C) were negative in all our experiments, possibly because of insufficient dose levels. The remaining 13 were positive within the estimated dose range of a general toxicology assay, suggesting the possibility of integrating the micronucleus assay into general toxicology assays. Three patterns were observed in micronucleus induction during the period of repeated treatment: (1) gradual increases in micronucleus frequency with sequential doses, (2) a peak at 3,5 days followed by gradual decreases in micronucleus frequency with sequential doses, and (3) a rapid increase in micronucleus frequency followed by a plateau. We evaluated factors that might have been involved in those patterns, such as the spleen function, target organ exposure, extramedullary hematopoiesis, hypothermia, and hypoxia. Another factor we considered was dosage. Because the dosages employed in a general toxicity assay are usually lower than those used in short-term micronucleus assays, this discrepancy was considered the greatest potential problem for integrating the micronucleus assay into general toxicology assays. Our results indicate that the integration of the micronucleus assay into a 28-day toxicological assay is feasible. To serve this purpose, blood samples collected 4 days after the beginning of treatment and blood and bone marrow samples collected at autopsy should be examined. Furthermore, although it is recognized that mice may be suitable for performing independent micronucleus assays, we propose that rats can provide biologically important and relevant information regarding potential chemical mutagens that can be evaluated under conditions used in the conduct of general toxicology studies. Environ. Mol. Mutagen. 37:93,110, 2001 © 2001 Wiley-Liss, Inc. [source] Oral status indicators DMFT and FS-T: reflections on index selectionEUROPEAN JOURNAL OF ORAL SCIENCES, Issue 3 2001Annemarie A. Schuller Oral status in a population has traditionally been described by the DMFT index (decayed, filled, and missing teeth). There seems to be contradicting and confusing evidence in the literature with regard to the usefulness of different indices. Limitations of the DMFT are recognised, and attempts have been made to develop other indices. Two indices, DMFT and FS-T (filled and sound teeth) have been selected for analysis in the present paper. The purpose of this paper is to examine the relationship between DMFT and FS-T in different populations, and to show consequences of choice of index exemplified in analytical analysis. Data stem from the Trřndelag-83 and -94 studies that were follow-up studies of the Norwegian portion of the 1973 International Collaborative Study. Sunflower scatter plots and regression analyses were used to describe the variation in DMFT and FS-T in different populations. DMFT was more suitable for describing variation in populations with low levels of disease than FS-T, while FS-T was more suitable for describing variation in populations with high levels of disease. It may be concluded that both DMFT and FS-T should be presented when describing oral status in a population. However, choice of index depends first of all on the purpose of the investigation. If there are theoretical reasons to prefer one index instead of the other, the superiority of the alternative index in terms of variation must be disregarded. [source] Testing association in the presence of linkage , a powerful score for binary traitsGENETIC EPIDEMIOLOGY, Issue 6 2007Gudrun Jonasdottir Abstract We present a score for testing association in the presence of linkage for binary traits. The score is robust to varying degrees of linkage, and it is valid under any ascertainment scheme based on trait values as well as under population stratification. The score test is derived from a mixed effects model where population level association is modeled using a fixed effect and where correlation among related individuals is allowed for by using log-gamma random effects. The score, as presented in this paper, does not assume full information about the inheritance pattern in families or parental genotypes. We compare the score to the semi-parametric family-based association test (FBAT), which has won ground because of its flexible and simple form. We show that a random effects formulation of co-inheritance can improve the power substantially. We apply the method to data from the Collaborative Study on the Genetics of Alcoholism. We compare our findings to previously published results. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source] Semiparametric variance-component models for linkage and association analyses of censored trait dataGENETIC EPIDEMIOLOGY, Issue 7 2006G. Diao Abstract Variance-component (VC) models are widely used for linkage and association mapping of quantitative trait loci in general human pedigrees. Traditional VC methods assume that the trait values within a family follow a multivariate normal distribution and are fully observed. These assumptions are violated if the trait data contain censored observations. When the trait pertains to age at onset of disease, censoring is inevitable because of loss to follow-up and limited study duration. Censoring also arises when the trait assay cannot detect values below (or above) certain thresholds. The latent trait values tend to have a complex distribution. Applying traditional VC methods to censored trait data would inflate type I error and reduce power. We present valid and powerful methods for the linkage and association analyses of censored trait data. Our methods are based on a novel class of semiparametric VC models, which allows an arbitrary distribution for the latent trait values. We construct appropriate likelihood for the observed data, which may contain left or right censored observations. The maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We develop stable and efficient numerical algorithms to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed methods outperform the existing ones in practical situations. We provide an application to the age at onset of alcohol dependence data from the Collaborative Study on the Genetics of Alcoholism. A computer program is freely available. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source] Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: Presentation Groups 1, 2, and 3GENETIC EPIDEMIOLOGY, Issue S1 2005Marsha A. Wilcox Abstract The papers in presentation groups 1,3 of Genetic Analysis Workshop 14 (GAW14) compared microsatellite (MS) markers and single-nucleotide polymorphism (SNP) markers for a variety of factors, using multiple methods in both data sets provided to GAW participants. Group 1 focused on data provided from the Collaborative Study on the Genetics of Alcoholism (COGA). Group 2 focused on data simulated for the workshop. Group 3 contained analyses of both data sets. Issues examined included: information content, signal strength, localization of the signal, use of haplotype blocks, population structure, power, type I error, control of type I error, the effect of linkage disequilibrium, and computational challenges. There were several broad resulting observations. 1) Information content was higher for dense SNP marker panels than for MS panels, and dense SNP markers sets appeared to provide slightly higher linkage scores and slightly higher power to detect linkage than MS markers. 2) Dense SNP panels also gave higher type I errors, suggesting that increased test thresholds may be needed to maintain the correct error rate. 3) Dense SNP panels provided better trait localization, but only in the COGA data, in which the MS markers were relatively loosely spaced. 4) The strength of linkage signals did not vary with the density of SNP panels, once the marker density was ,1 SNP/cM. 5) Analyses with SNPs were computationally challenging, and identified areas where improvements in analysis tools will be necessary to make analysis practical for widespread use. Genet. Epidemiol. 29:(Suppl. 1): S7,S28, 2005. © 2005 Wiley-Liss, Inc. [source] Linkage mapping methods applied to the COGA data set: Presentation Group 4 of Genetic Analysis Workshop 14GENETIC EPIDEMIOLOGY, Issue S1 2005E. Warwick Daw Abstract Presentation Group 4 participants analyzed the Collaborative Study on the Genetics of Alcoholism data provided for Genetic Analysis Workshop 14. This group examined various aspects of linkage analysis and related issues. Seven papers included linkage analyses, while the eighth calculated identity-by-descent (IBD) probabilities. Six papers analyzed linkage to an alcoholism phenotype: ALDX1 (four papers), ALDX2 (one paper), or a combination both (one paper). Methods used included Bayesian variable selection coupled with Haseman-Elston regression, recursive partitioning to identify phenotype and covariate groupings that interact with evidence for linkage, nonparametric linkage regression modeling, affected sib-pair linkage analysis with discordant sib-pair controls, simulation-based homozygosity mapping in a single pedigree, and application of a propensity score to collapse covariates in a general conditional logistic model. Alcoholism linkage was found with ,2 of these approaches on chromosomes 2, 4, 6, 7, 9, 14, and 21. The remaining linkage paper compared the utility of several single-nucleotide polymorphism (SNP) and microsatellite marker maps for Monte Carlo Markov chain combined oligogenic segregation and linkage analysis, and analyzed one of the electrophysiological endophenotypes, ttth1, on chromosome 7. Linkage was found with all marker sets. The last paper compared the multipoint IBD information content of several SNP sets and the microsatellite set, and found that while all SNP sets examined contained more information than the microsatellite set, most of the information contained in the SNP sets was captured by a subset of the SNP markers with ,1-cM marker spacing. From these papers, we highlight three points: a 1-cM SNP map seems to capture most of the linkage information, so denser maps do not appear necessary; careful and appropriate use of covariates can aid linkage analysis; and sources of increased gene-sharing between relatives should be accounted for in analyses. Genet. Epidemiol. 29(Suppl. 1):S29,S34, 2005. © 2005 Wiley-Liss, Inc. [source] Approaches to detecting gene × gene interaction in Genetic Analysis Workshop 14 pedigreesGENETIC EPIDEMIOLOGY, Issue S1 2005Brion S. Maher Abstract Whether driven by the general lack of success in finding single-gene contributions to complex disease, by increased knowledge about the potential involvement of specific biological interactions in complex disease, or by recent dramatic increases in computational power, a large number of approaches to detect locus × locus interactions were recently proposed and implemented. The six Genetic Analysis Workshop 14 (GAW14) papers summarized here each applied either existing or refined approaches with the goal of detecting gene × gene, or locus × locus, interactions in the GAW14 data. Five of six papers analyzed the simulated data; the other analyzed the Collaborative Study on the Genetics of Alcoholism data. The analytic strategies implemented for detecting interactions included multifactor dimensionality reduction, conditional linkage analysis, nonparametric linkage correlation, two-locus parametric linkage analysis, and a joint test of linkage and association. Overall, most of the groups found limited success in consistently detecting all of the simulated interactions due, in large part, to the nature of the generating model. Genet. Epidemiol. 29(Suppl. 1):S116,S119, 2005. © 2005 Wiley-Liss, Inc. [source] Genotyping errors, pedigree errors, and missing dataGENETIC EPIDEMIOLOGY, Issue S1 2005Anthony L. Hinrichs Abstract Our group studied the effects of genotyping errors, pedigree errors, and missing data on a wide range of techniques, with a focus on the role of single-nucleotide polymorphisms (SNPs). Half of our group used simulated data, and half of our group used data from the Collaborative Study on the Genetics of Alcoholism (COGA). The simulated data had no missing genotypes and no genotyping errors, so our group, as a whole, removed data and introduced artificial errors to study the robustness of various techniques. Our teams showed that genotyping errors are less detectable and may have a greater impact on SNPs than on microsatellites, but recently developed methods that account for genotyping errors help reduce false positives, and the assumptions of these methods appear to be supported by observations from repeated genotyping. The ability to detect linkage disequilibrium (LD) was also substantially reduced by missing data; this in turn could affect tagging SNPs chosen to generate haplotypes. In the COGA sample, genotyping measurements were repeated in three ways. First, full-genome screens were performed on three sets of markers: 328 microsatellites, 11,560 SNPs from the Affymetrix GeneChip Mapping 10,K Array marker set, and 4,720 SNPs from the Illumina Linkage III panel. Second, the entire Affymetrix marker set was typed on the same 184 individuals by two different laboratories. Finally, the Affymetrix and Illumina marker panels had 94 SNPs in common. Our teams showed that both SNPs and microsatellites can be readily used to identify pedigree errors, and that SNPs have fewer genotyping errors and a low inconsistency rate. However, a fairly high rate of no-calls, especially for the Affymetrix platform, suggests that the inconsistency rate may be higher than observed. Genet. Epidemiol. 29(Suppl. 1):S120,S124, 2005. © 2005 Wiley-Liss, Inc. [source] Parent-of-origin, imprinting, mitochondrial, and X-linked effects in traits related to alcohol dependence: Presentation Group 18 of Genetic Analysis Workshop 14GENETIC EPIDEMIOLOGY, Issue S1 2005Konstantin Strauch Abstract The participants of Presentation Group 18 of Genetic Analysis Workshop 14 analyzed the Collaborative Study on the Genetics of Alcoholism data set to investigate sex-specific effects for phenotypes related to alcohol dependence. In particular, the participants looked at imprinting (which is also known as parent-of-origin effect), differences between recombination fractions for the two sexes, and mitochondrial and X-chromosomal effects. Five of the seven groups employed newly developed or existing methods that take imprinting into account when testing for linkage, or test for imprinting itself. Single-marker and multipoint analyses were performed for microsatellite as well as single-nucleotide polymorphism markers, and several groups used a sex-specific genetic map in addition to a sex-averaged map. Evidence for paternal imprinting (i.e., maternal expression) was consistently obtained by at least two groups at genetic regions on chromosomes 10, 12, and 21 that possibly harbor genes responsible for alcoholism. Evidence for maternal imprinting (which is equivalent to paternal expression) was consistently found at a locus on chromosome 11. Two groups applied extensions of variance components analysis that model a mitochondrial or X-chromosomal effect to latent class variables and electrophysiological traits employed in the diagnosis of alcoholism. The analysis, without using genetic markers, revealed mitochondrial or X-chromosomal effects for several of these traits. Accounting for sex-specific environmental variances appeared to be crucial for the identification of an X-chromosomal factor. In linkage analysis using marker data, modeling a mitochondrial variance component increased the linkage signals obtained for autosomal loci. Genet. Epidemiol. 29(Suppl. 1):S125,S132, 2005. © 2005 Wiley-Liss, Inc. [source] Complications of craniofacial resection for malignant tumors of the skull base: Report of an International Collaborative Study,HEAD & NECK: JOURNAL FOR THE SCIENCES & SPECIALTIES OF THE HEAD AND NECK, Issue 6 2005Ian Ganly MD Abstract Background. Advances in imaging, surgical technique, and perioperative care have made craniofacial resection (CFR) an effective and safe option for treating malignant tumors involving the skull base. The procedure does, however, have complications. Because of the relative rarity of these tumors, most existing data on postoperative complications come from individual reports of relatively small series of patients. This international collaborative report examines a large cohort of patients accumulated from multiple institutions with the aim of identifying patient-related and tumor-related predictors of postoperative morbidity and mortality and set a benchmark for future studies. Methods. One thousand one hundred ninety-three patients from 17 institutions were analyzed for postoperative mortality and complications. Postoperative complications were classified into systemic, wound, central nervous system (CNS), and orbit. Statistical analyses were carried out in relation to patient characteristics, extent of disease, prior radiation treatment, and type of reconstruction to determine factors that predicted mortality or complications. Results. Postoperative mortality occurred in 56 patients (4.7%). The presence of medical comorbidity was the only independent predictor of mortality. Postoperative complications occurred in 433 patients (36.3%). Wound complications occurred in 237 (19.8%), CNS-related complications in 193 (16.2%), orbital complications in 20 (1.7%), and systemic complications in 57 (4.8%) patients. Medical comorbidity, prior radiation therapy, and the extent of intracranial tumour involvement were independent predictors of postoperative complications. Conclusions. CFR is a safe surgical treatment for malignant tumors of the skull base, with an overall mortality of 4.7% and complication rate of 36.3%. The impact of medical comorbidity and intracranial tumor extent should be carefully considered when planning therapy for patients whose tumors are amenable to CFR. © 2005 Wiley Periodicals, Inc. Head Neck27: XXX,XXX, 2005 [source] Hepatitis B or hepatitis C coinfection in HIV-infected pregnant women in EuropeHIV MEDICINE, Issue 7 2008M Landes Objectives The aim of the study was to investigate the prevalence of and risk factors for hepatitis C or B virus (HCV or HBV) coinfection among HIV-infected pregnant women, and to investigate their immunological and virological characteristics and antiretroviral therapy use. Methods Information on HBV surface antigen (HBsAg) positivity and HCV antibody (anti-HCV) was collected retrospectively from the antenatal records of HIV-infected women enrolled in the European Collaborative Study and linked to prospectively collected data. Results Of 1050 women, 4.9% [95% confidence interval (CI) 3.6,6.3] were HBsAg positive and 12.3% (95% CI 10.4,14.4) had anti-HCV antibody. Women with an injecting drug use(r) (IDU) history had the highest HCV-seropositivity prevalence (28%; 95% CI 22.8,35.7). Risk factors for HCV seropositivity included IDU history [adjusted odds ratio (AOR) 2.92; 95% CI 1.86,4.58], age (for ,35 years vs. <25 years, AOR 3.45; 95% CI 1.66,7.20) and HBsAg carriage (AOR 5.80; 95% CI 2.78,12.1). HBsAg positivity was associated with African origin (AOR 2.74; 95% CI 1.20,6.26) and HCV seropositivity (AOR 6.44; 95% CI 3.08,13.5). Highly active antiretroviral therapy (HAART) use was less likely in HIV/HCV-seropositive than in HIV-monoinfected women (AOR 0.34; 95% CI 0.20,0.58). HCV seropositivity was associated with a higher adjusted HIV RNA level (+0.28log10 HIV-1 RNA copies/mL vs. HIV-monoinfected women; P=0.03). HIV/HCV-seropositive women were twice as likely to have detectable HIV in the third trimester/delivery as HIV-monoinfected women (AOR 1.95; P=0.049). Conclusions Although HCV serostatus impacted on HAART use, the association between HCV seropositivity and uncontrolled HIV viraemia in late pregnancy was independent of HAART. [source] Interlaboratory Collaborative Study on the Kjeldahl Reference Method for Nitrogen Determination in Sheep and Goat Milk according to ISO 8968-1/2,IDF 20-1/2INTERNATIONAL JOURNAL OF DAIRY TECHNOLOGY, Issue 3 2010Anna Polychroniadou No abstract is available for this article. [source] Are there subgroups of bulimia nervosa based on comorbid psychiatric disorders?INTERNATIONAL JOURNAL OF EATING DISORDERS, Issue 1 2005Alexis E. Duncan MPH Abstract Objective The current study sought to determine whether there are subtypes of bulimia nervosa (BN) differentiated by comorbid psychiatric disorders. Method Data on comorbid psychiatric diagnoses in female relatives of probands and controls in the Collaborative Study of the Genetics of Alcoholism (COGA) who met criteria for BN (as outlined in the 3rd Rev. ed. of the Diagnostic and Statistical Manual of Mental Disorders) were analyzed using latent class analysis. Resulting latent classes were compared on a variety of variables related to impulsive behaviors and psychological functioning. Results The best-fitting solution, a two-class model, yielded one class (72%) characterized by substance dependence, depression, antisocial personality disorder (ASPD), and anxiety disorders, and another characterized by depression. The highly comorbid class had more suicidality, more daily smokers, sought help for emotional problems, and had lower Global Assessment of Functioning (GAF) scores compared with those in the comorbid depression only class. Discussion Latent class findings suggest the existence of two classes of BN differentiated by substance dependence, impulsive behaviors, and poorer psychological functioning. © 2004 by Wiley Periodicals, Inc. [source] Validation of Analytical Measurements by Single-trial and Collaborative StudyJOURNAL OF FOOD SCIENCE EDUCATION, Issue 1 2003C.E. Carpenter ABSTRACT: Validation of analytical measurements is necessary to report scientifically justifiable results. In this exercise, students validate the accuracy and precision of analytical instruments in single trials and in collaborative study, and validation results are used to report measurements in a scientifically justifiable manner. Tables are provided to guide data collection and to clarify reporting of results. A set of allied questions explores validation theory to develop student understanding, stimulate discussion, and provide feedback to the instructor regarding student comprehension. References are given that reinforce the validation concepts. [source] Single-Nucleotide Polymorphisms in Corticotropin Releasing Hormone Receptor 1 Gene (CRHR1) Are Associated With Quantitative Trait of Event-Related Potential and Alcohol DependenceALCOHOLISM, Issue 6 2010Andrew C. H. Chen Background:, Endophenotypes reflect more proximal effects of genes than diagnostic categories, hence providing a more powerful strategy in searching for genes involved in complex psychiatric disorders. There is strong evidence suggesting the P3 amplitude of the event-related potential (ERP) as an endophenotype for the risk of alcoholism and other disinhibitory disorders. Recent studies demonstrated a crucial role of corticotropin releasing hormone receptor 1 (CRHR1) in the environmental stress response and ethanol self-administration in animal models. The aim of the present study was to test the potential associations between single-nucleotide polymorphisms (SNPs) in the CRHR1 gene and the quantitative trait, P3 amplitude during the processing of visual target signals in an oddball paradigm, as well as alcohol dependence diagnosis. Methods:, We analyzed a sample from the Collaborative Study on the Genetics of Alcoholism (COGA) comprising 1049 Caucasian subjects from 209 families (including 472 alcohol-dependent individuals). Quantitative transmission disequilibrium test (QTDT) and family-based association test (FBAT) were used to test the association, and false discovery rate (FDR) was applied to correct for multiple comparisons. Results:, Significant associations (p < 0.05) were found between the P3 amplitude and alcohol dependence with multiple SNPs in the CRHR1 gene. Conclusions:, Our results suggest that CRHR1 may be involved in modulating the P3 component of the ERP during information processing and in vulnerability to alcoholism. These findings underscore the utility of electrophysiology and the endophenotype approach in the genetic study of psychiatric disorders. [source] The Relationship Between Self-Reported Drinking and BAC Level in Emergency Room Injury Cases: Is it a Straight Line?ALCOHOLISM, Issue 6 2010Jason Bond Background:, While the validity of self-reported consumption based on blood alcohol concentration (BAC) has been found to be high in emergency room (ER) samples, little research exists on the estimated number of drinks consumed given a BAC level. Such data would be useful in establishing a dose,response relationship between drinking and risk (e.g., of injury) in those studies for which the number of drinks consumed is not available but BAC is. Methods:, Several methods were used to estimate the number of drinks consumed in the 6 hours prior to injury based on BAC obtained at the time of ER admission of n = 1,953 patients who self-reported any drinking 6 hours prior to their injury and who arrived to the ER within 6 hours of the event, from the merged Emergency Room Collaborative Alcohol Analysis Project (ERCAAP) and the World Health Organization Collaborative Study on Alcohol and Injury across 16 countries. Results:, The relationship between self-reported consumption and averaged BAC within each consumption level appeared to be fairly linear up to about 7 drinks and a BAC of approximately 100 mg/dl. Above about 7 reported drinks, BAC appeared to have no relationship with drinking, possibly representing longer consumption periods than only the 6 hours before injury for those reporting higher quantities consumed. Both the volume estimate from the bivariate BAC to self-report relationship as well as from a Widmark calculation using BAC and time from last drink to arrival to the ER indicated a somewhat weak relationship to actual number of self-reported drinks. Conclusions:, Future studies may benefit from investigating the factors suspected to be driving the weak relationships between these measures, including the actual time over which the reported alcohol was consumed and pattern of drinking over the consumption period. [source] Genome-Wide Association Study of Alcohol Dependence Implicates a Region on Chromosome 11ALCOHOLISM, Issue 5 2010Howard J. Edenberg Background:, Alcohol dependence is a complex disease, and although linkage and candidate gene studies have identified several genes associated with the risk for alcoholism, these explain only a portion of the risk. Methods:, We carried out a genome-wide association study (GWAS) on a case,control sample drawn from the families in the Collaborative Study on the Genetics of Alcoholism. The cases all met diagnostic criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders,Fourth Edition; controls all consumed alcohol but were not dependent on alcohol or illicit drugs. To prioritize among the strongest candidates, we genotyped most of the top 199 single nucleotide polymorphisms (SNPs) (p , 2.1 × 10,4) in a sample of alcohol-dependent families and performed pedigree-based association analysis. We also examined whether the genes harboring the top SNPs were expressed in human brain or were differentially expressed in the presence of ethanol in lymphoblastoid cells. Results:, Although no single SNP met genome-wide criteria for significance, there were several clusters of SNPs that provided mutual support. Combining evidence from the case,control study, the follow-up in families, and gene expression provided strongest support for the association of a cluster of genes on chromosome 11 (SLC22A18, PHLDA2, NAP1L4, SNORA54, CARS, and OSBPL5) with alcohol dependence. Several SNPs nominated as candidates in earlier GWAS studies replicated in ours, including CPE, DNASE2B, SLC10A2, ARL6IP5, ID4, GATA4, SYNE1, and ADCY3. Conclusions:, We have identified several promising associations that warrant further examination in independent samples. [source] Demonstrating Successful Aging Using the International Collaborative Study for Oral Health OutcomesJOURNAL OF PUBLIC HEALTH DENTISTRY, Issue 4 2000Kathryn A. Atchison DDS Abstract As the lifespan increases and people are faced with 15 to 20 years of "old age," we ask what one considers successful aging with respect to oral health. We propose a comprehensive combination of outcome variables, maintenance of teeth, manageable periodontal condition, positive perceived oral health, satisfaction with their access to and receipt of dental services, and minimal functional problems, that together comprise a definition of successful aging. The International Collaborative Study for Oral Health Outcomes provides a data set for exploring the oral health of a diverse sample of older adults in US and international sites using the modified Andersen Behavioral Model. The percent of adults who report no natural teeth ranged from 16 percent in San Antonio to 59 percent in New Zealand. Seventy percent or more of the adults from each site rated their oral health as good/fair or better except in Poland. The current cohort of older adults is faring better on some indicators than others; nevertheless, ethnic minorities and poorer countries still demonstrate inequities. Dentistry must attempt to educate individuals early in their lifespan that a combination of personal oral health practices and current dental techniques offers the potential for successful oral health throughout one's lifetime. [source] Lack of Association of Alcohol Dependence and Habitual Smoking With Catechol-O-methyltransferaseALCOHOLISM, Issue 11 2007Tatiana Foroud Objective:, To test whether variation in the gene encoding the enzyme catechol-O-methyltransferase (COMT), which catalyzes the breakdown of dopamine and other catecholamine neurotransmitters, is associated with the risk for alcohol dependence and habitual smoking. Methods:, Single nucleotide polymophisms (SNPs) were genotyped in a sample of 219 multiplex alcohol-dependent families of European American descent from the Collaborative Study on the Genetics of Alcoholism (COGA). Family-based tests of association were performed to evaluate the evidence of association between the 18 SNPs distributed throughout COMT, including the functional Val158Met polymorphism, and the phenotypes of alcohol dependence, early onset alcohol dependence, habitual smoking, and comorbid alcohol dependence and habitual smoking. Results:, No significant, consistent evidence of association was found with alcohol dependence, early onset alcohol dependence, habitual smoking or the comorbid phenotype. There was no evidence that the functional Val158Met polymorphism, previously reported to be associated with these phenotypes, was associated with any of them. Conclusion:, Despite the substantial size of this study, we did not find evidence to support an association between alcohol dependence or habitual smoking and variation in COMT. [source] Association Between GABRA1 and Drinking Behaviors in the Collaborative Study on the Genetics of Alcoholism SampleALCOHOLISM, Issue 7 2006Danielle M. Dick Background: A wealth of literature supports the role of , -aminobutyric acid (GABA) in neurobiological pathways contributing to alcohol dependence and related phenotypes. Animal studies have consistently tied rodent homologs of the GABAA receptor genes on human chromosome 5q to alcohol-related behaviors; however, human studies have produced mixed results. Family-based association analyses previously conducted in the Collaborative Study on the Genetics of Alcoholism (COGA) sample yielded no evidence of association with Diagnostic and Statistical Manual of Mental Disorder,fourth edition (DSM-IV) alcohol dependence and these genes. As a follow-up to that study, we examined several alcohol-related behaviors in the COGA sample as follows: (1) a broader definition of alcohol dependence, including DSM-III-R symptoms and Feighner criteria (referred to as COGA alcohol dependence); (2) withdrawal; (3) history of alcohol-induced blackouts; (4) level of response to alcohol; (5) age of onset of regular drinking; and (6) age at first drunkenness. Methods: Family-based association tests were conducted, using multiple single-nucleotide polymorphisms (SNPs) in each of the 4 GABAA receptor genes on chromosome 5q. Results: In GABRA1, we found evidence of association with several of the drinking behavior phenotypes, including COGA alcohol dependence, history of blackouts, age at first drunkenness, and level of response to alcohol. We did not find consistent evidence of association with the remaining genes and any of the phenotypes. Conclusions: We found evidence for association between GABRA1 and COGA alcohol dependence, history of blackouts, age at first drunkenness, and level of response to alcohol. These analyses suggest that efforts to characterize genetic contributions to alcohol dependence may benefit by examining alcohol-related behaviors in addition to clinical alcohol dependence diagnoses. [source] Suggestive Linkage on Chromosome 1 for a Quantitative Alcohol-Related PhenotypeALCOHOLISM, Issue 10 2002Danielle M. Dick Background Alcohol dependence is a clinically and etiologically heterogeneous disorder. Accordingly, a variety of subtypes of alcohol-dependent individuals have been proposed, and multiple operational definitions of alcohol use, abuse, and dependence have been used in linkage analyses directed toward detecting genes involved in alcohol use and problems. Here, we develop quantitative phenotypes that characterize drinking patterns among both alcoholic and nonalcoholic subjects, and use these phenotypes in subsequent linkage analyses. Methods More than 9000 individuals from alcoholic and control families were administered a semistructured interview and personality questionnaire as part of the initial stage of the Collaborative Study on the Genetics of Alcoholism (COGA). A principal component analysis was conducted on items that captured many of the dimensions of drinking and related behaviors, including aspects of alcohol use, antisocial behavior and affective disturbance when drinking, and personality. Factor scores were computed for all individuals. Nonparametric linkage analyses were conducted on these factor scores, in the initial COGA sample consisting of 987 individuals from 105 extended families, and in a replication sample consisting of 1295 individuals from 157 extended families. Results Three factors were identified, accounting for 68% of the total variance. The most promising regions of linkage appeared for factor 2, on which higher scores indicate a later age of onset of regular drinking and higher harm avoidance. Chromosome 1 yielded consistent evidence of linkage in both samples, with a maximum lod score of 3.3 when the samples were combined for analysis. Consistent suggestion of linkage also was found to chromosome 15. Conclusions Developing novel phenotypes that more accurately model the effect of influential genes may help efforts to detect genes involved in complex disorders. Applying principal component analysis in the COGA sample provided support for some regions of linkage previously reported in COGA, and identified other new, promising regions of linkage. [source] Habitual snoring in an outpatient population in JapanPSYCHIATRY AND CLINICAL NEUROSCIENCES, Issue 4 2000Yuhei Kayukawa MD Abstract In order to investigate the occurrence and history of sleep problems in Japan, the 11-Centre Collaborative Study on Sleep Problems (COSP) project was carried out. Complaints of snoring are examined, and its prevalence, risk factors and screening reliability are discussed. The subjects who participated in the study were 6445 new outpatients from a general hospital. They were asked to answer a sleep questionnaire that consisted of 34 items with seven demographic items; each item was composed of four grades of frequency. In order to offset possible seasonal variations in sleep habits, data were collected across four seasons. Sleep patterns, insomnia, hypersomnia, parasomnia and circadian rhythm sleep disorders were covered. Habitual snoring was seen in 16.0% of males and 6.5% of females. Male predominance was noted. From these data, the relationship between habitual snoring and sleep complaints was statistically analyzed. Habitual snorers (HS) were observed to wake up more frequently during sleep (17.8% of males, 21.5% of females) than were non-habitual snorers (NHS; 6.6% of males, 9.7% of females). Mid-sleep awakening of HS was also more frequent than it was for NHS; however, there were no differences in difficulty in falling asleep and early morning awakening. Body mass index, cigarette smoking and alcohol consumption were also correlated with habitual snoring. [source] Validation Study of Genetic Associations with Coronary Artery Disease on Chromosome 3q13-21 and Potential Effect Modification by SmokingANNALS OF HUMAN GENETICS, Issue 6 2009Benjamin D. Horne Summary The CATHGEN study reported associations of chromosome 3q13-21 genes (KALRN, MYLK, CDGAP, and GATA2) with early-onset coronary artery disease (CAD). This study attempted to independently validate those associations. Eleven single nucleotide polymorphisms (SNPs) were examined (rs10934490, rs16834817, rs6810298, rs9289231, rs12637456, rs1444768, rs1444754, rs4234218, rs2335052, rs3803, rs2713604) in patients (N = 1618) from the Intermountain Heart Collaborative Study (IHCS). Given the higher smoking prevalence in CATHGEN than IHCS (41% vs. 11% in controls, 74% vs. 29% in cases), smoking stratification and genotype-smoking interactions were evaluated. Suggestive association was found for GATA2 (rs2713604, p = 0.057, OR = 1.2). Among smokers, associations were found in CDGAP (rs10934490, p = 0.019, OR = 1.6) and KALRN (rs12637456, p = 0.011, OR = 2.0) and suggestive association was found in MYLK (rs16834871, p = 0.051, OR = 1.8, adjusting for gender). No SNP association was found among non-smokers, but smoking/SNP interactions were detected for CDGAP (rs10934491, p = 0.017) and KALRN (rs12637456, p = 0.010). Similar differences in SNP effects by smoking status were observed on re-analysis of CATHGEN. CAD associations were suggestive for GATA2 and among smokers significant post hoc associations were found in KALRN, MYLK, and CDGAP. Genetic risk conferred by some of these genes may be modified by smoking. Future CAD association studies of these and other genes should evaluate effect modification by smoking. [source] 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] Familial aggregation of postpartum mood symptoms in bipolar disorder pedigreesBIPOLAR DISORDERS, Issue 1 2008Jennifer L Payne Objectives:, We sought to determine if postpartum mood symptoms and depressive episodes exhibit familial aggregation in bipolar I pedigrees. Methods:, A total of 1,130 women were interviewed with the Diagnostic Interview for Genetic Studies as part of the National Institute of Mental Health (NIMH) Genetics Initiative Bipolar Disorder Collaborative Study and were asked whether they had ever experienced mood symptoms within four weeks postpartum. Women were also asked whether either of two major depressive episodes described in detail occurred postpartum. We examined the odds of postpartum mood symptoms in female siblings, who had previously been pregnant and had a diagnosis of bipolar I, bipolar II, or schizoaffective (bipolar type) disorders (n = 303), given one or more relatives with postpartum mood symptoms. Results:, The odds ratio for familial aggregation of postpartum mood symptoms was 2.31 (p = 0.011) in an Any Mood Symptoms analysis (n = 304) and increased to 2.71 (p = 0.005) when manic symptoms were excluded, though this was not significantly different from the Any Mood Symptoms analysis. We also examined familial aggregation of postpartum major depressive episodes; however, the number of subjects was small. Conclusions:, Limitations of the study include the retrospective interview, the fact that the data were collected for other purposes and the inability to control for such factors as medication use. Taken together with previous studies, these data provide support for the hypothesis that there may be a genetic basis for the trait of postpartum mood symptoms generally and postpartum depressive symptoms in particular in women with bipolar disorder. Genetic linkage and association studies incorporating this trait are warranted. [source] Clusters of sirenomelia in South AmericaBIRTH DEFECTS RESEARCH, Issue 2 2009Ięda M. Orioli Abstract BACKGROUND One hospital in the city of Cali, Colombia, of the ECLAMC (Latin-American Collaborative Study of Congenital Malformations) network, reported the unusual occurrence of four cases of sirenomelia within a 55-day period. METHODS An ECLAMC routine for cluster evaluation (RUMOR) was followed that included: calculations of observed/expected ratios, site visits, comparison with comprehensively collected local, South American, and worldwide data, cluster analysis, and search for risk factors. RESULTS All four Cali sirenomelia cases were born to mothers living in a 2 km2 area, in neighboring communes, within the municipality of Cali. Considering the total births of the city of Cali as the denominator, and based on ECLAMC baseline birth prevalence rates (per 100,000) for sirenomelia (2.25, 95% CI: 2.66, 3.80), the cluster for this congenital abnormality was unlikely to have occurred by chance (observed/expected ratio = 5.77; 95% CI: 1.57,14.78; p = .002). No consistent common factor was identified, but vicinity to an open landfill as the cause could not be rejected. Another ECLAMC hospital in San Justo, Buenos Aires, Argentina, reported three further cases but these did not seem to constitute a nonrandom cluster. CONCLUSIONS The methodology used to evaluate the two possible clusters of sirenomelia determined that the Cali sirenomelia cluster was unlikely to have occurred by chance whereas the sirenomelia cluster from San Justo seemed to be random. Birth Defects Research (Part A) 2009. © 2008 Wiley-Liss, Inc. [source] Secular decreasing trend of the frequency of hypospadias among newborn male infants in SpainBIRTH DEFECTS RESEARCH, Issue 2 2004María Luisa Martínez-Frías Abstract BACKGROUND The frequency of hypospadias is not uniform worldwide. Several countries have reported an increase in its frequency at birth. Although a better ascertainment of the minor forms has been considered as an explanation, the potential effect of environmental endocrine disrupters has also been proposed. We studied the secular trend of hypospadias in Spain over the past 22 years, separating the minor and major forms. METHODS We used data from the Spanish Collaborative Study of Congenital Malformations (ECEMC) registry, analyzing the frequency in two different periods: from 1978 to 1995, and from 1996 to 2002. To evaluate the "step" of the frequency between the two periods, we applied a parametric Student's t -test, and the nonparametric Mann-Whitney rank test. RESULTS The birth frequency of isolated and total hypospadias was quite stable between 1978 and 1995. In 1996, it decreased dramatically in a step, essentially due to isolated cases with minor forms of hypospadias. Study of the geographical distribution by the 17 Spanish regions showed that the frequency step occurred in nearly all of them. CONCLUSIONS It is difficult to consider that the observed decrease of the frequency of minor forms of hypospadias could be due to less accurate reporting of these minor forms in all 87 hospitals in the same year without any previous physician agreement. This decrease rather suggests a change in some product or exposure affecting the whole country. We think that the observed change in the frequency cannot be attributable to a lower exposure to endocrine disrupters or the voluntary interruption of gestation. Birth Defects Research (Part A) 67:000,000, 2003. © 2004 Wiley-Liss, Inc. [source] Antiretroviral therapy and preterm delivery,a pooled analysis of data from the United States and EuropeBJOG : AN INTERNATIONAL JOURNAL OF OBSTETRICS & GYNAECOLOGY, Issue 11 2010CL Townsend Please cite this paper as: Townsend C, Schulte J, Thorne C, Dominguez K, Tookey P, Cortina-Borja M, Peckham C, Bohannon B, Newell M, for the Pediatric Spectrum of HIV Disease Consortium, the European Collaborative Study and the National Study of HIV in Pregnancy and Childhood. Antiretroviral therapy and preterm delivery,a pooled analysis of data from the United States and Europe. BJOG 2010;117:1399,1410. Objective, To investigate reported differences in the association between highly active antiretroviral therapy (HAART) in pregnancy and the risk of preterm delivery among HIV-infected women. Design, Combined analysis of data from three observational studies. Setting, USA and Europe. Population, A total of 19 585 singleton infants born to HIV-infected women, 1990,2006. Methods, Data from the Pediatric Spectrum of HIV Disease project (PSD), a US monitoring study, the European Collaborative Study (ECS), a consented cohort study, and the National Study of HIV in Pregnancy and Childhood (NSHPC), the United Kingdom and Ireland surveillance study. Main outcome measure, Preterm delivery rate (<37 weeks of gestation). Results, Compared with monotherapy, HAART was associated with increased preterm delivery risk in the ECS (adjusted odds ratio [AOR] 2.40, 95% CI 1.49,3.86) and NSHPC (AOR 1.43, 95% CI 1.10,1.86), but not in the PSD (AOR 0.92, 95% CI 0.67,1.26), after adjusting for relevant covariates. Because of heterogeneity, data were not pooled for this comparison, but heterogeneity disappeared when HAART was compared with dual therapy (P = 0.26). In a pooled analysis, HAART was associated with 1.5-fold increased odds of preterm delivery compared with dual therapy (95% CI 1.19,1.87, P = 0.001), after adjusting for covariates. Conclusions, Heterogeneity in the association between HAART and preterm delivery was not explained by study design, adjustment for confounders or a standard analytical approach, but may have been the result of substantial differences in populations and data collected. The pooled analysis comparing HAART with dual therapy showed an increased risk of preterm delivery associated with HAART. [source] Increasing likelihood of further live births in HIV-infected women in recent yearsBJOG : AN INTERNATIONAL JOURNAL OF OBSTETRICS & GYNAECOLOGY, Issue 7 2005European Collaborative Study Objective To examine how the subsequent childbearing of HIV-infected mothers enrolled in the European Collaborative Study (ECS) has changed over time and identify factors predictive of further childbearing. Design Prospective cohort study. Setting Centres in nine European countries included in the ECS, enrolled between end 1986 and November 2003. Population HIV-infected women (3911): 3693 with only one and 218 with subsequent live births. Methods Univariable and multivariable logistic regression analyses to obtain odds ratios (OR) and 95% confidence intervals (CI). Kaplan,Meier (KM) analyses to estimate cumulative proportions of women having a subsequent live birth. Main outcome measures Subsequent live birth. Results In multivariable analysis adjusting for time period, ethnicity, maternal age and parity, black women were more likely [adjusted odds ratio (AOR) 2.45; 95% CI, 1.75,3.43], and women >30 years less likely (AOR 0.54, 0.37,0.80), to have a subsequent live birth. Time to subsequent live birth significantly shortened over time, with an estimated 2% of women having a subsequent live birth within 24 months of enrolment pre-1989 versus 14% in 2000,2003 (P < 0.001). Estimated time to subsequent live birth was shorter for black than for white women (P < 0.001). Conclusions The likelihood of subsequent live births substantially increased after 1995 and birth intervals were shorter in women on HAART and among black women. Numbers are currently too small to address the issue of advantages and disadvantages in the management of subsequent deliveries. [source] Risk factors for suicide attempts in patients with alcohol dependence or abuse and a history of depressive symptoms: A subgroup analysis from the WHO/ISBRA studyDRUG AND ALCOHOL REVIEW, Issue 1 2010ÖZGÜR YALDIZLI Abstract Introduction and Aims. Alcoholism, depression and suicide attempts (SA) are strongly interrelated. The aims were to determine risk factors and develop a prognostic predictor model for SA in a subgroup of patients with a history of alcohol dependence or abuse and depressive symptoms. Design and Methods. A subgroup analysis from the data of the World Health Organisation (WHO)/the International Society for Biomedical Research on Alcoholism (ISBRA)-collaborative study on biological state and trait marker of alcohol use and dependence, an international multi-centre study with a cross-sectional design, based on a standardised questionnaire. We analysed from 1314 variables 43 factors,including demographic characteristics, dependence variables, comorbid disorders, personality trait markers and family history,that were supposed to be most predictive for SA according to the literature. Correlation analyses by the ,2 -test and Mann,Whitney U -test were performed to obtain statistical meaningful parameters for logistic regression analysis. Results. Of the 1863 persons included in the WHO/ISBRA study, 292 had both a history of depressive symptoms and alcohol dependence or abuse and were included in the subgroup analysis. In the logistic regression analysis, drinking status, depressive symptoms, adverse drinking experiences during alcohol consumption, bad experiences from drug abuse and antidepressant therapy were found to be independent risk factors for SA. Positive family history of alcoholism was a model-improving co-factor. This predictive model explains approximately 60% of the variance (Nagelkerkes' square). Discussion and Conclusions. This prognostic model derived from data of the WHO/ISBRA collaborative study shows important risk factors for SA in patients with history of alcohol abuse or dependence and depressive symptoms. [ Yaldizli Ö, Kuhl HC, Graf M, Wiesbeck GA, Wurst FM. Risk factors for suicide attempts in patients with alcohol dependence or abuse and a history of depressive symptoms: A subgroup analysis from the WHO/ISBRA study. Drug Alcohol Rev 2009] [source] | |||||||||||||||||||||||||