			Home About us Contact

Tag SNPs (tag + snp)

Distribution by Scientific Domains

Life Sciences	57%
Medical Sciences	42%

Selected Abstracts

Population differences in the International Multi-Centre ADHD Gene Project

GENETIC EPIDEMIOLOGY, Issue 2 2008
Benjamin M. Neale
Abstract The International Multi-Centre ADHD Gene sample consists of 674 families from eight countries (Belgium, England, Germany, Holland, Ireland, Israel, Spain, and Switzerland) ascertained from clinics for combined-type attention definity hyperactivity disorder in an offspring. 863 SNPs were successfully genotyped across 47 autosomal genes implicated in psychiatric disorders yielding a single nucleotide polymorphism (SNP) density of approximately one SNP per 2.5,kb. A global test of heterogeneity showed 269 SNPs nominally significant (expected 43). Inclusion of the Israeli population accounted for approximately 70% of these nominally significant tests. Hardy-Weinberg equilibrium tests suggest that combining all these populations would induce stratification, but that the Northern European populations (Belgium, England, Germany, Holland, and Ireland) could be appropriate. Tag SNPs were generated using pair-wise and aggressive tagging from Carlson et al. [2004] and de Bakker et al. [2005], respectively, in each population and applied to the other populations. Cross-population performance across Northern Europe was consistent with within population comparisons. Smaller sample size for each population tended to yield more problems for the generation of aggressive tags and the application of pair-wise tags. Any case-control sample employing an Israeli sample with Northern Europeans must consider stratification. A Northern European tag set, however, appears to be appropriate for capturing the variation across populations. Genet. Epidemiol. 2008. © 2007 Wiley-Liss, Inc. [source]

Tag SNPs chosen from HapMap perform well in several population isolates

GENETIC EPIDEMIOLOGY, Issue 3 2007
Susan Service
Abstract Population isolates may be particularly useful for association studies of complex traits. This utility, however, largely depends on the transferability of tag SNPs chosen from reference samples, such as HapMap, to samples from such populations. Factors that characterize population isolates, such as widespread genetic drift, could impede such transferability. In this report, we show that tag SNPs chosen from HapMap perform well in several population isolates; this is true even for populations that differ substantially from the HapMap sample either in levels of linkage disequilibrium or in SNP allele frequency distributions. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source]

Association of FcGRIIa with Graves' disease: a potential role for dysregulated autoantibody clearance in disease onset/progression

CLINICAL ENDOCRINOLOGY, Issue 1 2010
Kadija Yesmin
Summary Objective, Although autoantibody production is a key feature of autoimmunity, it is not known whether variation in autoantibody production and clearance pathways is involved in disease susceptibility. The Fc Gamma Receptor IIa (FcGRIIa) molecule is involved in the clearance of autoantibodies and a functional single nucleotide polymorphism (SNP), rs1801274, which has been shown to alter autoantibody clearance, has been associated with a number of autoimmune diseases (AIDs) including systemic lupus erythematosus and type 1 diabetes. This study aimed to determine whether FcGRIIa is associated with Graves' disease (GD) in the UK Caucasian population by Tag SNP screening common polymorphisms within the FcGRIIa region. Design, A case control association study investigating nine Tag SNPs within FcGRIIa, which captured the majority of known common variation within this gene region. Patients, A dataset comprising 2504 UK Caucasian GD patients and 2784 geographically matched controls taken from the 1958 British Birth cohort. Measurements, We used the ,2 -test to investigate association between the Tag SNPs and GD. Results, Association between the rs1801274 (P,= 0·003, OR = 1·12 [95% CI = 1·03,1·22] and rs6427598 (P = 0·012, OR = 0·90 [95% CI = 0·83-0·98]) SNPs and GD was observed. No other SNPs showed association with GD. No associations were seen between any of the SNPs investigated and specific GD clinical phenotypes. Conclusions, This study suggests that variation in FcGRIIa predisposes to GD and further supports the role of FcGRIIa as a susceptibility locus for AIDs in general. [source]

Polymorphisms in innate immunity genes and lung cancer risk in Xuanwei, China,

ENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Issue 4 2009
Min Shen
Abstract The high incidence of lung cancer in Xuanwei County, China has been attributed to exposure to indoor smoky coal emissions that contain polycyclic aromatic hydrocarbons (PAHs). The inflammatory response induced by coal smoke components may promote lung tumor development. We studied the association between single nucleotide polymorphisms (SNPs) in genes involved in innate immunity and lung cancer risk in a population-based case,control study (122 cases and 122 controls) in Xuanwei. A total of 1,360 tag SNPs in 149 gene regions were included in the analysis. FCER2 rs7249320 was the most significant SNP (OR: 0.30; 95% CI: 0.16,0.55; P: 0.0001; false discovery rate value, 0.13) for variant carriers. The gene regions ALOX12B/ALOX15B and KLK2 were associated with increased lung cancer risk globally (false discovery rate value <0.15). In addition, there were positive interactions between KLK15 rs3745523 and smoky coal use (OR: 9.40; Pinteraction = 0.07) and between FCER2 rs7249320 and KLK2 rs2739476 (OR: 10.77; Pinteraction = 0.003). Our results suggest that genetic polymorphisms in innate immunity genes may play a role in the genesis of lung cancer caused by PAH-containing coal smoke. Integrin/receptor and complement pathways as well as IgE regulation are particularly noteworthy. Environ. Mol. Mutagen., 2009. Published 2009 Wiley-Liss, Inc. [source]

GENETIC STUDY: Association between the nociceptin receptor gene (OPRL1) single nucleotide polymorphisms and alcohol dependence

ADDICTION BIOLOGY, Issue 1 2008
Jia Huang
ABSTRACT OPRL1 encodes the nociceptin receptor, which has been shown to be involved in alcohol dependence in previous studies. In the present study, we investigated the association between genetic polymorphisms of OPRL1 and alcohol dependence in a Scandinavian population. We genotyped 15 single nucleotide polymorphisms (SNPs) spanning the OPRL1 locus and found that SNP rs6010718 was significantly associated with both Type I and Type II alcoholics (P < 0.05). Linkage disequilibrium and haplotype analysis identified two haplotype blocks in this region. Furthermore, two haplotypes composed of five tag SNPs showed significant association with alcohol dependence. These findings suggest that genetic variants of the OPRL1 gene play a role in alcohol dependence in the Scandinavian population, warranting further investigation at the OPRL1 locus. [source]

Tag SNPs chosen from HapMap perform well in several population isolates

A new multimarker test for family-based association studies

GENETIC EPIDEMIOLOGY, Issue 1 2007
Cyril S. Rakovski
Abstract We propose a new multimarker test for family-based studies in candidate genes. We use simulations under different genetic models to assess the performance of competing testing strategies, characterized in this study as combinations of the following important factors: genes, statistical tests, tag single nucleotide polymorphisms (SNP) methods, number of tag SNPs and family designs. An ANOVA model is employed to provide descriptive summaries of the effects on power of the above-mentioned factors. We find that tag SNP methods, gene characteristics and family designs have minimal impact on the best testing strategy. The familywise error rate (FWER) controlling multiple comparison procedure and the new multimarker test offer the highest power followed by the asymptotic global haplotype test. Both the FWER and the multimarker test are invariant to family designs and gain power as we increase the number of tag SNPs. However, the performance of the global haplotype test is slightly degraded when analyzing larger numbers of tag SNPs. Within the framework of our study, the best strategy for family-based studies in candidate genes that emerged from our analysis is to use the FWER or the multimarker test and select 6,10 tag SNPs using any of the tag SNP methods considered. We confirm the conclusions of our study with an application to Alzheimer's disease data. Genet. Epidemiol. © 2006 Wiley-Liss, Inc. [source]

Analysis of single-locus tests to detect gene/disease associations,

GENETIC EPIDEMIOLOGY, Issue 3 2005
Kathryn Roeder
Abstract A goal of association analysis is to determine whether variation in a particular candidate region or gene is associated with liability to complex disease. To evaluate such candidates, ubiquitous Single Nucleotide Polymorphisms (SNPs) are useful. It is critical, however, to select a set of SNPs that are in substantial linkage disequilibrium (LD) with all other polymorphisms in the region. Whether there is an ideal statistical framework to test such a set of ,tag SNPs' for association is unknown. Compared to tests for association based on frequencies of haplotypes, recent evidence suggests tests for association based on linear combinations of the tag SNPs (Hotelling T2 test) are more powerful. Following this logical progression, we wondered if single-locus tests would prove generally more powerful than the regression-based tests? We answer this question by investigating four inferential procedures: the maximum of a series of test statistics corrected for multiple testing by the Bonferroni procedure, TB, or by permutation of case-control status, TP; a procedure that tests the maximum of a smoothed curve fitted to the series of of test statistics, TS; and the Hotelling T2 procedure, which we call TR. These procedures are evaluated by simulating data like that from human populations, including realistic levels of LD and realistic effects of alleles conferring liability to disease. We find that power depends on the correlation structure of SNPs within a gene, the density of tag SNPs, and the placement of the liability allele. The clearest pattern emerges between power and the number of SNPs selected. When a large fraction of the SNPs within a gene are tested, and multiple SNPs are highly correlated with the liability allele, TS has better power. Using a SNP selection scheme that optimizes power but also requires a substantial number of SNPs to be genotyped (roughly 10,20 SNPs per gene), power of TP is generally superior to that for the other procedures, including TR. Finally, when a SNP selection procedure that targets a minimal number of SNPs per gene is applied, the average performances of TP and TR are indistinguishable. Genet. Epidemiol. © 2005 Wiley-Liss, Inc. [source]

Genetic variation in the RANKL/RANK/OPG signaling pathway is associated with bone turnover and bone mineral density in men

JOURNAL OF BONE AND MINERAL RESEARCH, Issue 8 2010
Delnaz Roshandel
Abstract The aim of this study was to determine if single-nucleotide polymorphisms (SNPs) in RANKL, RANK, and OPG influence bone turnover and bone mineral density (BMD) in men. Pairwise tag SNPs (r2,,,0.8) were selected for RANKL, RANK, and OPG and their 10-kb flanking regions. Selected tag SNPs plus five SNPs near RANKL and OPG, associated with BMD in published genome-wide association studies (GWAS), were genotyped in 2653 men aged 40 to 79 years of age recruited for participation in a population-based study of male aging, the European Male Ageing Study (EMAS). N-terminal propeptide of type I procollagen (PINP) and C-terminal cross-linked telopeptide of type I collagen (CTX-I) serum levels were measured in all men. BMD at the calcaneus was estimated by quantitative ultrasound (QUS) in all men. Lumbar spine and total-hip areal BMD (BMDa) was measured by dual-energy X-ray absorptiometry (DXA) in a subsample of 620 men. Multiple OPG, RANK, and RANKL SNPs were associated with bone turnover markers. We also identified a number of SNPs associated with BMD, including rs2073618 in OPG and rs9594759 near RANKL. The minor allele of rs2073618 (C) was associated with higher levels of both PINP (,,=,1.83, p,=,.004) and CTX-I (,,=,17.59, p,=,4.74,×,10,4), and lower lumbar spine BMDa (,,=,,0.02, p,=,.026). The minor allele of rs9594759 (C) was associated with lower PINP (,,=,,1.84, p,=,.003) and CTX-I (,,=,,27.02, p,=,6.06,×,10,8) and higher ultrasound BMD at the calcaneus (,,=,0.01, p,=,.037). Our findings suggest that genetic variation in the RANKL/RANK/OPG signaling pathway influences bone turnover and BMD in European men. © 2010 American Society for Bone and Mineral Research [source]

Genetic Variation of the Ghrelin Signaling System in Females With Severe Alcohol Dependence

ALCOHOLISM, Issue 9 2010
Sara Landgren
Introduction:, Central ghrelin signaling is required for the rewarding effects of alcohol in mice. Because ghrelin is implied in other addictive behaviors such as eating disorders and smoking, and because there is co-morbidity between these disorders and alcohol dependence, the ghrelin signaling system could be involved in mediating reward in general. Furthermore, in humans, single nucleotide polymorphisms (SNPs) and haplotypes of the pro-ghrelin gene (GHRL) and the ghrelin receptor gene (GHSR) have previously been associated with increased alcohol consumption and increased body weight. Known gender differences in plasma ghrelin levels prompted us to investigate genetic variation of the ghrelin signaling system in females with severe alcohol dependence (n = 113) and in a selected control sample of female low-consumers of alcohol from a large cohort study in southwest Sweden (n = 212). Methods:, Six tag SNPs in the GHRL (rs696217, rs3491141, rs4684677, rs35680, rs42451, and rs26802) and four tag SNPs in the GHSR (rs495225, rs2232165, rs572169, and rs2948694) were genotyped in all individuals. Results:, We found that one GHRL haplotype was associated with reports of paternal alcohol dependence as well as with reports of withdrawal symptoms in the female alcohol-dependent group. Associations with 2 GHSR haplotypes and smoking were also shown. One of these haplotypes was also negatively associated with BMI in controls, while another haplotype was associated with having the early-onset, more heredity-driven, type 2 form of alcohol dependence in the patient group. Conclusion:, Taken together, the genes encoding the ghrelin signaling system cannot be regarded as major susceptibility genes for female alcohol dependence, but is, however, involved in paternal heritability and may affect other reward- and energy-related factors such as smoking and BMI. [source]

Common single nucleotide polymorphisms in immunoregulatory genes and multiple myeloma risk among women in Connecticut,

AMERICAN JOURNAL OF HEMATOLOGY, Issue 8 2010
Kyoung-Mu Lee
In light of the relationship between immune system dysregulation and multiple myeloma (MM) risk, we investigated whether genetic variation in 92 immune function genes among 77 gene regions are associated with MM susceptibility in a population-based case-control study (108 cases and 482 controls) conducted among Caucasian women in Connecticut. Tagging single-nucleotide polymorphisms (SNPs; N = 870) were selected using a pairwise linkage-disequilibrium based algorithm. Odds ratios (ORs) and 95% confidence intervals (CIs) for SNP genotypes were estimated using unconditional logistic regression. Tests of association for gene regions were conducted using the minP test. We applied the false discovery rate (FDR) method to the minP test results as a means of controlling for multiple comparisons. The CD4 gene region located on 12p13-q13 (minP = 0.0009), had an FDR value <0.1. In this region, a total of six tag SNPs in two genes (CD4 and LAG3) were significantly associated with MM risk (Ptrend<0.05), with the strongest association observed for the CD4 variant rs11064392 (ORAG/GG = 2.53, 95% CI = 1.59,4.02). Our findings suggest that genetic variation in CD4 may influence susceptibility to MM. Additional studies are needed to replicate these findings and, more generally, to explore the manner in which genes and receptors may influence the pathogenesis of this poorly understood malignancy. Am. J. Hematol., 2010. © 2010 Wiley-Liss, Inc. [source]

Polymorphisms in the STAT6 gene and their association with carcass traits in feedlot cattle

ANIMAL GENETICS, Issue 6 2009
G. Rincon
Summary Identification of the genes and polymorphisms underlying quantitative traits, and understanding how these genes and polymorphisms affect economic traits, are important for successful marker-assisted selection and more efficient management strategies in commercial cattle populations. Signal transducer and activator of transcription 6 (STAT6) gene is tightly connected to IL-4 and IL-13 signalling and plays a key role in TH2 polarization of the immune system. In addition, STAT6 acts as a mediator of leptin signalling and has been associated with body weight regulation. The objective of this study was to determine if SNPs within the bovine STAT6 gene are associated with economically important traits in feedlot cattle. The approach consisted of resequencing STAT6 using a panel of DNA from unrelated animals of different beef breeds. Specifically, 16 kb of STAT6 was resequenced in 47 animals and the process revealed 39 SNPs. From the 39 SNPs, a panel of 15 tag SNPs was genotyped in 1500 beef cattle samples with phenotypes to perform a marker-trait association analysis. Among the 15 tag SNPs, five and six were polymorphic in Bos taurus and Bos indicus respectively. An association analysis was performed between the 15 tag SNPs and 14 performance and production traits. SNP ss115492459:C > A, ss115492461:A > G and ss115492458:G > C were significantly associated with back fat, calculated yield grade, cutability, hot carcass weight, dry matter intake, days on feed, back fat rate and average daily gain. These three SNPs were present in all Bos taurus beef breeds examined. Our results provide evidence that polymorphisms in STAT6 are associated with carcass and growth efficiency traits, and may be used for marker-assisted selection and management in feedlot cattle. [source]

Nucleotide variability and linkage disequilibrium patterns at the porcine FABP5 gene

ANIMAL GENETICS, Issue 5 2008
A. Ojeda
Summary Fatty acid binding protein 5 (FABP5) is a major positional and physiological candidate gene for the porcine FAT1 QTL on SSC4. Here we characterize the nucleotide polymorphism and haplotype variability of FABP5 and we compare it with that of FABP4, given their close physical location and similar metabolic roles. DNA resequencing of the FABP5 gene region in 29 pigs from 14 breeds and in European and Japanese wild boars revealed 36 polymorphisms in 5.2 kb, and a nucleotide diversity of 0.19%, comparable to values reported in other domestic species but sixfold lower than that previously found for FABP4. Remarkably, both the nucleotide variability and the haplotype structure of FABP5 and FABP4 were dramatically different, and the Hudson,Kreitman,Aguadé test was highly significant. Nevertheless, both genes also had similarities. The neighbour-joining trees of their haplotypes did not show a geographical arrangement for any of the genes. Besides, both genes presented a similar extent and pattern of linkage disequilibrium. Haplotype blocks did not extend for large stretches (,1 kb in both genes), and the number of tag SNPs required to capture all variability was higher than previously expected. Our findings indicate that FABP4 and FABP5 have undergone different selective or evolutive processes. The fact that haplotype blocks were so small may require us to increase the number of SNPs in prospective whole-genome association studies in the pig. [source]

Candidate Molecular Pathway Genes Related to Appetite Regulatory Neural Network, Adipocyte Homeostasis and Obesity: Results from the CARDIA Study

ANNALS OF HUMAN GENETICS, Issue 5 2010
Yechiel Friedlander
Summary Appetite regulatory neural network and adipocyte homeostasis molecular pathways are critical to long-term weight maintenance. Associations between obesity-related phenotypes and four genes in these pathways , leptin (LEP), leptin receptor (LEPR), neuropeptide Y2 receptor (NPY2R) and peptide YY (PYY) were examined in CARDIA Study participants (aged 18,30 at recruitment in 1985,6). Weight, BMI and waist circumference were measured at baseline and at years 2, 5, 7, 10, 15, and 20. Genotyping was conducted using tag SNPs characterising common genetic variations in these genes. Generalized estimating equation (GEE) models estimated associations between SNPs and repeated anthropometric measurements, controlling for sex and age. False discovery rate was used to adjust for multiple testing. In African-Americans, SNPs across the LEP gene demonstrated significant overall associations with all obesity-related phenotypes. The associations between LEP rs17151919 with weight tended to strengthen with time , the difference in weight associated with each additional minor allele increased from 2.6 kg at baseline to 4.8 kg at year 20 (SNP*time interaction p = 0.0193). NPY2R gene SNPs were associated with waist circumference among African-American men (p = 0.0462). In Caucasians, LEP SNPs also tended to be associated with weight (p = 0.0471), and PYY rs11684664 was associated with obesity-related phenotypes in women only (p = 0.010,0.026). Several LEP, and NPY2R and PYY SNPs were associated with obesity-related phenotypes in young adults, particularly among African-Americans. [source]

Efficient Association Study Design Via Power-Optimized Tag SNP Selection

ANNALS OF HUMAN GENETICS, Issue 6 2008
B. Han
Summary Discovering statistical correlation between causal genetic variation and clinical traits through association studies is an important method for identifying the genetic basis of human diseases. Since fully resequencing a cohort is prohibitively costly, genetic association studies take advantage of local correlation structure (or linkage disequilibrium) between single nucleotide polymorphisms (SNPs) by selecting a subset of SNPs to be genotyped (tag SNPs). While many current association studies are performed using commercially available high-throughput genotyping products that define a set of tag SNPs, choosing tag SNPs remains an important problem for both custom follow-up studies as well as designing the high-throughput genotyping products themselves. The most widely used tag SNP selection method optimizes the correlation between SNPs (r2). However, tag SNPs chosen based on an r2 criterion do not necessarily maximize the statistical power of an association study. We propose a study design framework that chooses SNPs to maximize power and efficiently measures the power through empirical simulation. Empirical results based on the HapMap data show that our method gains considerable power over a widely used r2 -based method, or equivalently reduces the number of tag SNPs required to attain the desired power of a study. Our power-optimized 100k whole genome tag set provides equivalent power to the Affymetrix 500k chip for the CEU population. For the design of custom follow-up studies, our method provides up to twice the power increase using the same number of tag SNPs as r2 -based methods. Our method is publicly available via web server at http://design.cs.ucla.edu. [source]

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

ANNALS OF HUMAN GENETICS, Issue 4 2007
Article first published online: 28 MAY 200
Saurabh Ghosh 11 Indian Statistical Institute, Kolkata, India High correlations between two quantitative traits may be either due to common genetic factors or common environmental factors or a combination of both. In this study, we develop statistical methods to extract the contribution of a common QTL to the total correlation between the components of a bivariate phenotype. Using data on bivariate phenotypes and marker genotypes for sib-pairs, we propose a test for linkage between a common QTL and a marker locus based on the conditional cross-sib trait correlations (trait 1 of sib 1 , trait 2 of sib 2 and conversely) given the identity-by-descent sharing at the marker locus. The null hypothesis cannot be rejected unless there exists a common QTL. We use Monte-Carlo simulations to evaluate the performance of the proposed test under different trait parameters and quantitative trait distributions. An application of the method is illustrated using data on two alcohol-related phenotypes from the Collaborative Study On The Genetics Of Alcoholism project. Rémi Kazma 1 , Catherine Bonaïti-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 naïve 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]

Association of the FAM167A,BLK region with systemic sclerosis

ARTHRITIS & RHEUMATISM, Issue 3 2010
Ikue Ito
Objective An association of single-nucleotide polymorphisms (SNPs) in the FAM167A (previously referred to as C8orf13),BLK region with systemic lupus erythematosus (SLE) has been demonstrated in Caucasians and in Asians. Recent studies have shown that many genes, including IRF5, STAT4, and PTPN22, are shared susceptibility genes in multiple autoimmune diseases. We undertook the current study to examine whether the FAM167A,BLK region is also associated with susceptibility to systemic sclerosis (SSc). Methods Japanese patients with SSc (n = 309) and healthy controls (n = 769) were enrolled in a 2-tiered case,control association study. In tier 1, 124 patients and 412 controls were tested to determine association of 16 tag SNPs encompassing the FAM167A,BLK region with SSc. In tier 2, an additional 185 patients and 357 controls were analyzed for SNP rs13277113. Results Two haplotype blocks that correspond approximately to FAM167A and BLK were observed. In tier 1 of the study, the rs13277113A allele in the BLK block exhibited the most significant association with SSc after correction for multiple testing (permutated P = 0.024). Two SNP haplotypes formed by rs13277113 and the most significant SNP in the FAM167A block did not exhibit stronger association. When samples from tier 1 and tier 2 were combined, the rs13277113A allele was significantly associated with SSc (odds ratio 1.45 [95% confidence interval 1.17,1.79], P = 6.1 × 10,4). Association or a tendency toward association of rs13277113A with SSc was observed regardless of a patient's autoantibody profile or whether a patient had diffuse cutaneous or limited cutaneous SSc. Conclusion Our findings indicate that the rs13277113A allele is associated not only with SLE but also with SSc and that the FAM167A,BLK region is a common genetic risk factor for both SLE and SSc. [source]

Association of ERAP1, but not IL23R, with ankylosing spondylitis in a Han Chinese population

ARTHRITIS & RHEUMATISM, Issue 11 2009
Stuart I. Davidson
Objective The results of a recent genome-wide association study have shown that ERAP1 and IL23R are associated with ankylosing spondylitis (AS) in Caucasian populations from North America and the UK. Based on these findings, we undertook the current study to investigate whether single-nucleotide polymorphisms (SNPs) covering the genes ERAP1 and IL23R are associated with AS in a Han Chinese population. Methods A case,control study was performed in Han Chinese patients with AS (n = 527) and controls (n = 945) from Shanghai and Nanjing. All patients met the modified New York criteria for AS. The Sequenom iPlex platform was used to genotype cases and controls for 21 tag SNPs covering IL23R and 38 tag SNPs covering ERAP1. Statistical analysis was performed using the Cochran-Armitage test for trend. Results Multiple SNPs in ERAP1 were significantly associated with AS (for rs27980, P = 0.0048; for rs7711564, P = 0.0081). However, no association was observed between IL23R and AS (for all SNPs, P > 0.1). The nonsynonymous SNP in IL23R, rs11209026, widely thought to be the primary AS-associated SNP in IL23R in Europeans, was found not to be polymorphic in Chinese. Conclusion Our results demonstrate that genetic polymorphisms in ERAP1 are associated with AS in Han Chinese, suggesting a common pathogenic mechanism for the disease in Chinese and Caucasian populations, and that IL23R is not associated with AS in Chinese, indicating a difference in the mechanism of disease pathogenesis between Chinese and Caucasian populations. This may result from the fact that rs11209026, the nonsynonymous SNP in IL23R, is not polymorphic in Chinese patients, providing further evidence that rs11209026 is the key polymorphism associated with AS (and likely inflammatory bowel disease and psoriasis) in this gene. [source]

TRAF1 polymorphisms associated with rheumatoid arthritis susceptibility in Asians and in Caucasians

ARTHRITIS & RHEUMATISM, Issue 9 2009
Tae-Un Han
Objective Recent genome-wide association scans and replication studies of European populations have disclosed several single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA) susceptibility. The aim of this study was to evaluate the RA-associated loci by genotyping previously reported SNPs and additional tag SNPs in a Korean population. Methods A total of 1,316 unrelated RA patients and 1,006 controls were genotyped for 12 SNPs identified in genome-wide scans and for 12 additional tag SNPs in IL2RB, OLIG3,TNFAIP3, PTPN22, and TRAF1,C5, and the findings were statistically compared. Results None of the SNPs tested was associated with RA susceptibility, except rs7021206 in TRAF1 intron 3 (P = 0.0032) and, among the SNPs previously reported, rs6457617 in HLA (P = 4.6 × 10,35). The association of rs7021206 was positive in patients who were seropositive for rheumatoid factor (P = 0.0051) or for anti,cyclic citrullinated peptide autoantibodies (P = 0.0062). However, Korean patients were negative for the association of rs3761847 in the TRAF1,C5 intergenic region previously reported in Caucasians. Linkage disequilibrium between rs3761847 and rs7021206 was not as high in Koreans (r2 = 0.37) as in Caucasians (r2 = 0.67), which explains the lack of association of rs3761847 in Koreans. Accordingly, RA susceptibility was localized to an extended haplotype marked by rs7021206 rather than rs3761847, and SNPs highly correlated with rs7021206 (r2 , 0.81) extended from rs1953126 in the PHF19,TRAF1 intergenic region to rs2900180 in the TRAF1,C5 intergenic region, spanning 66 kb. Conclusion Our results demonstrate that within and around TRAF1, excluding PHF19 and C5, SNPs highly correlated with rs7021206, but not those correlated with rs3761847, are associated with RA in both Asians and Caucasians and are possibly correlated with causative variations. [source]

Genetic risk factors for rheumatoid arthritis differ in caucasian and Korean populations

ARTHRITIS & RHEUMATISM, Issue 2 2009
Hye-Soon Lee
Objective Recent studies have identified a number of novel rheumatoid arthritis (RA) susceptibility loci in Caucasian populations. The aim of this study was to determine whether the genetic variants at 4q27, 6q23, CCL21, TRAF1/C5, and CD40 identified in Caucasians are also associated with RA in a Korean case,control collection. We also comprehensively evaluated the genetic variation within PTPN22, a well-established autoimmune disease,associated gene. Methods We designed an experiment to thoroughly evaluate the PTPN22 linkage disequilibrium region, using tag single-nucleotide polymorphisms (SNPs) and disease-associated SNPs at 5 RA-associated loci recently identified in Caucasians, in 1,128 Korean patients with RA and 1,022 ethnically matched control subjects. We also resequenced the PTPN22 gene to seek novel coding variants that might be contributing to disease in this population. Results None of the susceptibility loci identified in Caucasian patients with RA contributed significantly to disease in Koreans. Although tag SNPs covering the PTPN22 linkage disequilibrium block were polymorphic, they did not reveal any disease association, and resequencing did not identify any new common coding region variants in this population. The 6q23 and 4q27 SNPs assayed were nonpolymorphic in this population, and the TRAF1/C5, CD40, and CCL21 SNPs did not show any evidence for association with RA in this population of Korean patients. Conclusion The genetic risk factors for RA are different in Caucasian and Korean patients. Although patients of different ethnic groups share the HLA region as a major genetic risk locus, most other genes shown to be significantly associated with disease in Caucasians appear not to play a role in Korean patients with RA. [source]

Replication of the association between the C8orf13,BLK region and systemic lupus erythematosus in a Japanese population

ARTHRITIS & RHEUMATISM, Issue 2 2009
Ikue Ito
Objective Recent genome-wide association studies identified an association between single-nucleotide polymorphisms (SNPs) in the C8orf13 region of BLK, the B lymphoid tyrosine kinase gene, with systemic lupus erythematosus (SLE) in Caucasians. The purpose of this study was to evaluate the significance of this region in the genetic background of Japanese patients with SLE. Methods Fourteen tag SNPs in the C8orf13,BLK region were genotyped in 327 Japanese patients with SLE and 322 healthy Japanese controls. The population-attributable risk percentage (PAR%) of rs13277113 in Japanese was compared with that in Caucasians as well as with that of other SLE susceptibility genes in Japanese. Results As in Caucasians, rs13277113A demonstrated the strongest association in Japanese (P = 1.73 × 10,6 for the genotype frequency, P = 4.75 × 10,7 for the allele frequency, odds ratio [OR] 2.44 [95% confidence interval (95% CI) 1.43,4.16]). The association in Japanese was consistent with a recessive model (P = 2.74 × 10,7, OR 2.27 [95% CI 1.66,3.11]). In contrast to the Caucasian population, this risk allele was the major allele in the Japanese population. Because both the risk allele frequency and the OR were higher in Japanese than in Caucasians, the PAR% of rs13277113 was estimated to be much higher in Japanese (35.4%) than in Caucasians (16.2%), and the second highest among the 6 confirmed SLE susceptibility genes in Japanese. Conclusion The association of the C8orf13,BLK region with SLE was replicated in a Japanese population. Contribution of this region to the genetic predisposition to SLE appeared to be greater in Japanese than in Caucasians. [source]

The chromosome 7q region association with rheumatoid arthritis in females in a british population is not replicated in a North American case,control series

ARTHRITIS & RHEUMATISM, Issue 1 2009
Benjamin D. Korman
Objective The single-nucleotide polymorphism (SNP) rs11761231 on chromosome 7q has been reported to be sexually dimorphic marker for rheumatoid arthritis (RA) susceptibility in a British population. We sought to replicate this finding and to better characterize susceptibility alleles in the region in a North American population. Methods DNA from 2 North American collections of RA patients and controls (1,605 cases and 2,640 controls) was genotyped for rs11761231 and 16 additional chromosome 7q tag SNPs using Sequenom iPlex assays. Association tests were performed for each collection and also separately, contrasting male cases with male controls and female cases with female controls. Principal components analysis (EigenStrat) was used to determine association with RA before and after adjusting for population stratification in the subset of the samples for which there were whole-genome SNP data (772 cases and 1,213 controls). Results We failed to replicate an association of the 7q region with RA. Initially, rs11761231 showed evidence for association with RA in the North American Rheumatoid Arthritis Consortium (NARAC) collection (P = 0.0073), and rs11765576 showed association with RA in both the NARAC (P = 0.038) and RA replication (P = 0.0013) collections. These markers also exhibited sex differentiation. However, in the whole-genome subset, neither SNP showed significant association with RA after correction for population stratification. Conclusion While 2 SNPs on chromosome 7q appeared to be associated with RA in a North American cohort, the significance of this finding did not withstand correction for population substructure. Our results emphasize the need to carefully account for population structure to avoid false-positive disease associations. [source]

Tag SNP selection using particle swarm optimization

BIOTECHNOLOGY PROGRESS, Issue 2 2010
Li-Yeh Chuang
Abstract Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations amongst species. With the genome-wide SNP discovery, many genome-wide association studies are likely to identify multiple genetic variants that are associated with complex diseases. However, genotyping all existing SNPs for a large number of samples is still challenging even though SNP arrays have been developed to facilitate the task. Therefore, it is essential to select only informative SNPs representing the original SNP distributions in the genome (tag SNP selection) for genome-wide association studies. These SNPs are usually chosen from haplotypes and called haplotype tag SNPs (htSNPs). Accordingly, the scale and cost of genotyping are expected to be largely reduced. We introduce binary particle swarm optimization (BPSO) with local search capability to improve the prediction accuracy of STAMPA. The proposed method does not rely on block partitioning of the genomic region, and consistently identified tag SNPs with higher prediction accuracy than either STAMPA or SVM/STSA. We compared the prediction accuracy and time complexity of BPSO to STAMPA and an SVM-based (SVM/STSA) method using publicly available data sets. For STAMPA and SVM/STSA, BPSO effective improved prediction accuracy for smaller and larger scale data sets. These results demonstrate that the BPSO method selects tag SNP with higher accuracy no matter the scale of data sets is used. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010 [source]

The autoimmune regulator gene (AIRE) is strongly associated with vitiligo

BRITISH JOURNAL OF DERMATOLOGY, Issue 3 2008
R. Tazi-Ahnini
Summary Background, Vitiligo is an autoimmune disorder that occurs with greatly increased frequency in the rare recessive autoimmune polyendocrinopathy,candidiasis,ectodermal dystrophy syndrome (APECED) caused by mutations of the autoimmune regulator (AIRE) gene on chromosome 21q22·3. We have previously detected an association between alopecia areata and single nucleotide polymorphisms (SNPs) in the AIRE gene. Objectives, To report the findings of an extended study including haplotype analysis on six AIRE polymorphisms (AIRE C,103T, C4144G, T5238C, G6528A, T7215C and T11787C) in vitiligo, another APECED-associated disease. Methods, A case,control analysis was performed. Results, Results showed a strong association between AIRE 7215C and vitiligo [P = 1·36 × 10,5, odds ratio (OR) 3·12, 95% confidence interval (CI) 1·87,5·46]. We found no significant association with the other polymorphisms individually. However, haplotype analysis revealed that the AIRE haplotype CCTGCC showed a highly significant association with vitiligo (P = 4·14 × 10,4, OR 3·00, 95% CI 1·70,5·28). To select the most informative minimal haplotypes, we tagged the polymorphisms using SNP tag software. Using AIRE C,103T, G6528A, T7215C and T11787C as tag SNPs, the haplotype AIRE CGCC was associated with vitiligo (P = 0·003, OR 2·49, 95% CI 1·45,4·26). Conclusions, The link between vitiligo and AIRE raises the possibility that defective skin peripheral antigen selection in the thymus is involved in the changes that result in melanocyte destruction in this disorder. [source]