EUROPEAN JOURNAL OF IMMUNOLOGY, Issue 9 2004
Donna
Abstract Humoral immunity is effected through the rearrangement of immunoglobulin (Ig) genes in individual somatic cells committed to the B,lymphocyte lineage. Haplotype or allelic exclusion restricts B,lymphocytes to the expression of a single Ig receptor that can sustain further somatic modification. In most species, a specific Ig chain is encoded at a single genetic locus. However, in cartilaginous fish, hundreds of independent Ig heavy- (IgH) and Ig light-chain (IgL) gene loci are present, many of which are joined in the germ line. Ig gene transcripts have been amplified from single peripheral blood lymphocytes isolated from the clearnose skate (Raja eglanteria) using reverse-transcription PCR, and a single productive IgH transcript was detected in the majority of cells analyzed. Similarly, only a single IgL transcript was detected in over half of the individual cells. Taken together, these findings suggest that a mechanism for haplotype exclusion arose early in the evolution of antibody diversity and is independent of a single genetic locus. [source]

SPR1 gene near HLA-C is unlikely to be a psoriasis susceptibility gene

EXPERIMENTAL DERMATOLOGY, Issue 3 2003
Y. T. Chang
Abstract:, Although genetics analyses have identified the HLA-Cw6 allele to be the major risk allele for psoriasis vulgaris (PV) in many racial groups, it has been proposed that other putative genes near the HLA-C locus are involved in PV susceptibility and that the association of Cw6 is a result of linkage disequilibrium. The SPR1 gene, a predicted gene located 128 kb telomeric to the HLA-C locus, is considered to be one potential candidate gene of PV. Until now, no association study of the SPR1 gene has been conducted on psoriasis patients. We investigated the SPR1 gene for disease association by direct sequencing of the SPR1 gene in 116 Chinese patients with PV and 116 normal subjects. Genotyping for HLA-Cw6 was also carried out using polymerase chain reaction/restriction fragment length polymorphism. Significant increase of the HLA-Cw6 allele was found in psoriasis patients (32.8% vs. 13.8%, P = 0.001). We found that the SPR1 gene is a highly polymorphic gene containing 13 single nucleotide polymorphisms (SNPs), two of which have not been previously reported, and four SNPs cause amino acid change. No significantly different allelic distribution of 13 SPR1 SNPs could be found between the patients with PV and controls after correction for multiple testing. If the frequencies of SPR1 SNPs were compared between the early onset psoriatics and control subjects, early onset patients were more likely to have G allele at position 988 (60% vs. 35.3%, P = 0.001). However, the significance disappeared upon stratification for the Cw6 status. Haplotype-based association analysis showed two susceptibility haplotypes (types 8 and 19) in early onset psoriasis patients. Nonetheless, the significance also disappeared after stratification of the Cw6 status. Our results suggest that HLA-Cw6 remains the major risk allele in Chinese psoriatics, and that the SPR1 gene might not play an important role in the causation of PV. [source]

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

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

Robust estimation and testing of haplotype effects in case-control studies,,

GENETIC EPIDEMIOLOGY, Issue 1 2008
Andrew S. Allen
Abstract Haplotype-based analyses are thought to play a major role in the study of common complex diseases. This has led to the development of a variety of statistical methods for detecting disease-haplotype associations from case-control study data. However, haplotype phase is often uncertain when only genotype data is available. Methods that account for haplotype ambiguity by modeling the distribution of haplotypes can, if this distribution is misspecified, lead to substantial bias in parameter estimates even when complete genotype data is available. Here we study estimators that can be derived from score functions of appropriate likelihoods. We use the efficient score approach to estimation in the presence of nuisance parameters to a derive novel estimators that are robust to the haplotype distribution. We establish key relationships between estimators and study their empirical performance via simulation. Genet. Epidemiol. 2007. Published 2007 Wiley-Liss, Inc. [source]

Estimating haplotype relative risks in complex disease from unphased SNPs data in families using a likelihood adjusted for ascertainment

GENETIC EPIDEMIOLOGY, Issue 8 2006
J. Carayol
Abstract The understanding of complex diseases and insights to improve their medical management may be achieved through the deduction of how specific haplotypes may play a joint effect to change relative risk information. In this paper we describe an ascertainment adjusted likelihood-based method to estimate haplotype relative risks using pooled family data coming from association and/or linkage studies that were used to identify specific haplotypes. Haplotype-based analysis tends to require a large amount of parameters to capture all the information that leads to efficiency problems. An adaptation of the Stochastic Expectation Maximization algorithm is used for haplotypes inference from genotypic data and to reduce the number of nuisance parameters for risk estimation. Using different simulations, we show that this method provides unbiased relative risk estimates even in case of departure from Hardy-Weinberg equilibrium. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source]

Haplotype and genotypes of the VDR gene and cutaneous melanoma risk in non-Hispanic whites in Texas: A case,control study

INTERNATIONAL JOURNAL OF CANCER, Issue 9 2008
Chunying Li
Abstract In a hospital-based case,control study of 805 non-Hispanic whites with cutaneous melanoma and 841 cancer-free age-, sex- and ethnicity-matched control subjects, 3 VDR polymorphisms (i.e., TaqI, BsmI and FokI) were genotyped using blood samples collected between 1994 and 2006. We tested the hypothesis that the haplotypes and combined genotypes of these polymorphisms were associated with melanoma risk by interacting with known risk factors. Haplotypes t-B-F (adjusted odds ratio [OR], 0.52; 95% confidence interval [CI], 0.34,0.80) and t-B-f (adjusted OR, 0.51; CI, 0.27,0.94) were associated with a reduced risk when compared to T-b-f. The combined genotypes Tt+tt/Bb+BB/Ff+ff (adjusted OR, 0.69; CI, 0.52, 0.90) and Tt+tt/Bb+BB/FF (adjusted OR, 0.58; CI, 0.43, 0.78) were also associated with reduced risk, whereas the combined genotype TT/Bb+BB/Ff+ff genotype (adjusted OR, 2.35; CI, 1.13, 4.98) was associated with increased risk when compared to TT/bb/Ff+ff genotypes. On multivariate analysis, only the TaqI polymorphism was an independent risk factor, while the FokI polymorphism interacted with skin color (p = 0.029), moles (p = 0.017) and first-degree relatives with any cancer (p = 0.013) in modifying risk. Together, these findings suggest that VDR polymorphisms may directly affect or modify the risk associated with known melanoma risk factors. Larger, population-based studies are needed to replicate our findings. © 2008 Wiley-Liss, Inc. [source]

MICA-STR, HLA-B haplotypic diversity and linkage disequilibrium in the Hunan Han population of southern China

INTERNATIONAL JOURNAL OF IMMUNOGENETICS, Issue 4 2006
W. Tian
Summary Major histocompatibility complex (MHC) class I chain-related gene A (MICA) is located 46 kb centromeric to HLA-B and encodes a stress-inducible protein. MICA allelic variation is thought to be associated with disease susceptibility and immune response to transplants. This study was aimed to investigate the haplotypic diversity and linkage disequilibrium between human leukocyte antigen (HLA)-B and (GCT)n short tandem repeat in exon 5 of MICA gene (MICA-STR) in a southern Chinese Han population. Fifty-eight randomly selected nuclear families with 183 members including 85 unrelated parental samples were collected in Hunan province, southern China. HLA-B generic typing was performed by polymerase chain reaction,sequence-specific priming (PCR,SSP), and samples showing novel HLA-B-MICA-STR linkage were further typed for HLA-B allelic variation by high-resolution PCR,SSP. MICA-STR allelic variation and MICA gene deletion (MICA*Del) were detected by fluorescent PCR,size sequencing and PCR,SSP. Haplotype was determined through family segregation analysis. Statistical analysis was applied to the data of the 85 unrelated parental samples. Nineteen HLA-B specificities and seven MICA-STR allelic variants were observed in 85 unrelated parental samples, the most predominant of which were HLA-B*46, -B60, -B*13, and -B*15, and MICA*A5, MICA*A5.1 and MICA*A4, respectively. Genotype distributions of HLA-B, MICA-STR loci were consistent with Hardy,Weinberg proportions. The HLA-B-MICA-STR haplotypic phases of all 85 unrelated parental samples were unambiguously assigned, which contained 30 kinds of HLA-B, MICA-STR haplotypic combinations, nine of them have not been reported in the literature. Significant positive linkage disequilibria between certain HLA-B and MICA-STR alleles, including HLA-B*13 and MICA*A4, HLA-B*38 and MICA*A9, HLA-B*58 and MICA*A9, HLA-B*46 and MICA*A5, HLA-B*51 and MICA*A6, HLA-B*52 and MICA*A6, and HLA-B60 and MICA*A5.1, were observed. HLA-B*48 was linked to MICA*A5, MICA*A5.1 and MICA*Del. HLA-B*5801-MICA*A10 linkage was found in a family. Our data indicated a high degree of haplotypic diversity and strong linkage disequilibrium between MICA-STR and HLA-B in a southern Chinese Han population, the data will inform future studies on anthropology, donor,recipient HLA matching in clinical transplantation and HLA-linked disease association. [source]

A Haplotype-Based Analysis of the LRP5 Gene in Relation to Osteoporosis Phenotypes in Spanish Postmenopausal Women,

JOURNAL OF BONE AND MINERAL RESEARCH, Issue 12 2008
Lídia Agueda
Abstract LRP5 encodes the low-density lipoprotein receptor-related protein 5, a transmembrane protein involved in Wnt signaling. LRP5 is an important regulator of osteoblast growth and differentiation, affecting bone mass in vertebrates. Whether common variations in LRP5 are associated with normal BMD variation or osteoporotic phenotypes is of great relevance. We used a haplotype-based approach to search for common disease-associated variants in LRP5 in a cohort of 964 Spanish postmenopausal women. Twenty-four SNPs were selected, covering the LRP5 region, including the missense changes p.V667M and p.A1330V. The SNPs were genotyped and evaluated for association with BMD at the lumbar spine (LS) or femoral neck (FN) and with osteoporotic fracture, at single SNP and haplotype levels, by regression methods. Association with LS BMD was found for SNP 1, rs312009, located in the 5,-flanking region (p = 0.011, recessive model). SNP 6, rs2508836, in intron 1, was also associated with BMD, both at LS (p = 0.025, additive model) and FN (p = 0.031, recessive model). Two polymorphisms were associated with fracture: SNP 11, rs729635, in intron 1, and SNP 15, rs643892, in intron 5 (p = 0.007 additive model and p = 0.019 recessive model, respectively). Haplotype analyses did not provide additional information, except for haplotype "GC" of the block located at the 3,end of the gene. This haplotype spans intron 22 and the 3, untranslated region and was associated with FN BMD (p = 0.029, one copy of the haplotype versus none). In silico analyses showed that SNP 1 (rs312009) lies in a putative RUNX2 binding site. Electro-mobility shift assays confirmed RUNX2 binding to this site. [source]

OPRM1 Asn40Asp Predicts Response to Naltrexone Treatment: A Haplotype-Based Approach

ALCOHOLISM, Issue 3 2009
Gabor Oroszi
Background:, Individualized pharmacotherapy requires identification of genetic variants predictive of treatment response. In OPRM1, Asn40Asp has been reported to be predictive of response to naltrexone treatment. Nevertheless, the in vitro function of the polymorphism remains elusive and over 300 OPRM1 sequence variants have been identified to date. Therefore we used a haplotype-based approach to capture information of other genetic variants that might predict treatment response to naltrexone in the COMBINE Study. Methods:, 5, nuclease genotyping assays (TaqMan®) were applied for 10 SNPs. Five-locus haplotypes in 2 OPRM1 haplotype blocks were assigned to Caucasian participants. The relationship of the haplotypes to medication reflected by "good clinical outcome" was analyzed in 306 Caucasians treated without Combined Behavioral Intervention and with either naltrexone or placebo. Results:, A significant haplotype by medication interaction (p = 0.03) was found in OPRM1 block 1. Naltrexone-treated alcoholics with haplotype AGCCC, the single haplotype carrying the Asp40 allele had the highest percent of good clinical outcome. When interaction of genotypes at each of the 5 loci comprising block 1 with medication was examined, only the Asn40/Asp40 and Asp40/Asp40 genotypes were found to significantly interact with naltrexone treatment. No haplotype by medication interaction was documented in OPRM1 block 2. Conclusions:, Our haplotype-based approach confirms that the single OPRM1 locus predictive of response to naltrexone treatment is Asn40Asp in exon 1. A substantial contribution of any other OPRM1 genetic variant to interindividual variations in response to naltrexone treatment (at least in terms of good clinical outcome) is not supported by our findings. [source]

A Haplotype of the DRD1 Gene Is Associated With Alcohol Dependence

ALCOHOLISM, Issue 4 2008
P. Batel
Background:, The D1 dopamine receptor has been involved in a number of brain functions, including motor control, inattentive symptoms and reward and reinforcement mechanisms. Indeed, DRD1 antagonists may reduce cocaine-seeking behavior and the acquisition of cocaine-cue associations. The D1.1/r4532 marker of the DRD1 gene has been associated with a large set of phenotypes including addictive behaviors, but none with alcohol dependence per se. Methods:, We analyzed a population of 134 patients with alcohol dependence, also assessing more homogeneous (severe) phenotypes, comparing this sample with a healthy control population, assessing two SNPs within the DRD1 gene in order to depict the role of DRD1 polymorphisms and haplotypes. Results:, The T allele of the rs686 polymorphism within DRD1 gene was significantly more frequent in patients with alcohol dependence (p = 0.0008), with a larger excess for patients with severe dependence (p = 6 × 10,6), and even more for patients with severe complications such as withdrawal seizures (p = 7 × 10,7). A specific haplotype rs686*T-rs4532*G within the DRD1 gene was significantly more precisely associated with alcohol dependence in our sample (p = 5 × 10,6). Conclusions:, Even though chance finding cannot be ruled out, convergent evidence is given that the DRD1 gene is a susceptibility gene in alcohol dependence, regarding the fact that relying on more homogeneous phenotypes (i.e., more severe patients) and more informative genetic markers (i.e., haplotypes) reinforce the initial association. [source]

A Novel Single Nucleotide Polymorphism of the Neuropeptide Y (NPY) Gene Associated With Alcohol Dependence

ALCOHOLISM, Issue 5 2005
Salim Mottagui-Tabar
Background: Neuropeptide Y (NPY) is a major endogenous regulator of anxiety-related behaviors and emotionality. Transgenic work with NPY and null-mutant mice have implicated NPY in the control of alcohol consumption, suggesting that genetic variation of the prepro-NPY gene may also contribute to the heritability of alcoholism. The aim of this study was to examine whether polymorphic variants of the NPY gene are associated with the diagnosis of alcohol dependence. Methods: We compared allele frequencies of 5 NPY polymorphisms (,883-ins/del, ,602, ,399, ,84, and +1128) in a Nordic population of alcohol-dependent individuals (n= 428 males; n= 149 females) and ethnically matched controls (n= 84 males; n= 93 females) for whom alcohol dependence or any diagnosis of substance disorder was excluded. Patients were further subtyped into type I (late-onset) and type II (early-onset) alcoholics. Results: The ,602 marker showed a significant association with alcohol dependence (p= 0.0035; OR, 2.3; 95% CI, 1.3-4.0); a trend level association was further observed for the ,399 marker (p= 0.058; OR, 1.3; 95% CI, 0.99-1.7) and the +1128 marker (p= 0.053; OR, 1.8; 95% CI, 0.99-3.1). The association for the ,602 marker remained and was strengthened when analyzed in type I subjects only, although this association was not seen in type II patients, and there also was a significant association in the female subjects but not in males. The ,602 single nucleotide polymorphism was in strong linkage dysequilibrium (r2= 0.7; p < 0.0001) with the +1128 single nucleotide polymorphism, which has previously been reported to be associated with a diagnosis of alcoholism. Haplotype-based association confirmed these results. Conclusions: We report a novel polymorphism at position ,602 in the 5, region of the NPY gene that is significantly associated with alcohol dependence. We also describe the haplotype frequencies and linkage dysequilibrium pattern of four variations in that region. [source]

Refined analysis of genetic variability parameters in hepatitis C virus and the ability to predict antiviral treatment response

JOURNAL OF VIRAL HEPATITIS, Issue 8 2008
J. M. Cuevas
Summary., Hepatitis C virus (HCV) infects approximately 3% of the world population. The chronicity of hepatitis C seems to depend on the level of genetic variability. We have recently (Torres-Puente et al., J Viral Hepat, 2008; 15: 188) reported genetic variability estimates from a large-scale sequence analysis of 67 patients infected with HCV subtypes 1a (23 patients) and 1b (44 patients) and related them to response, or lack of, to alpha-interferon plus ribavirin treatment.. Two HCV genome regions were analysed in samples prior to antiviral therapy, one compressing the three hypervariable regions of the E2 glycoprotein and another one including the interferon sensitive determining region and the V3 domain of the NS5A protein. Haplotype and nucleotide diversity measures showed a clear tendency to higher genetic variability levels in nonresponder than in responder patients. Here, we have refined the analysis of genetic variability (haplotype and nucleotide diversity, number of haplotypes and mutations) by considering their distribution in each of the biologically meaningful subregions mentioned above, as well as in their surrounding and intervening regions. Variability levels are very heterogeneous among the different subregions, being higher for nonresponder patients. Interestingly, significant differences were detected in the biologically relevant regions, but also in the surrounding regions, suggesting that the level of variability of the whole HCV genome, rather than exclusively that from the hypervariable regions, is the main indicator of the treatment response. Finally, the number of haplotypes and mutations seem to be better discriminators than haplotype and nucleotide diversity, especially in the NS5A region. [source]

Using Case-parent Triads to Estimate Relative Risks Associated with a Candidate Haplotype

ANNALS OF HUMAN GENETICS, Issue 3 2009
Min Shi
Summary Estimating haplotype relative risks in a family-based study is complicated by phase ambiguity and the many parameters needed to quantify relative risks for all possible diplotypes. This problem becomes manageable if a particular haplotype has been implicated previously as relevant to risk. We fit log-linear models to estimate the risks associated with a candidate haplotype relative to the aggregate of other haplotypes. Our approach uses existing haplotype-reconstruction algorithms but requires assumptions about the distribution of haplotypes among triads in the source population. We consider three levels of stringency for those assumptions: Hardy-Weinberg Equilibrium (HWE), random mating, and no assumptions at all. We assessed our method's performance through simulations encompassing a range of risk haplotype frequencies, missing data patterns, and relative risks for either offspring or maternal genetic effects. The unconstrained model provides robustness to bias from population structure but requires excessively large sample sizes unless there are few haplotypes. Assuming HWE accommodates many more haplotypes but sacrifices robustness. The model assuming random mating is intermediate, both in the number of haplotypes it can handle and in robustness. To illustrate, we reanalyze data from a study of orofacial clefts to investigate a 9-SNP candidate haplotype of the IRF6 gene. [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]

Single Nucleotide Polymorphisms and Haplotype of Four Genes Encoding Cardiac Ion Channels in Chinese and their Association with Arrhythmia

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2 2008
Yu Zhang Ph.D.
Background: Many studies revealed that variations in cardiac ion channels would cause cardiac arrhythmias or act as genetic risk factors. We hypothesized that specific single nucleotide polymorphisms in cardiac ion channels were associated with cardiac rhythm disturbance in the Chinese population. Method: We analyzed 160 nonfamilial cardiac arrhythmia patients and 176 healthy individuals from which 81 individuals were selected for association study, and a total of 19 previously reported SNPs in four cardiac ion channel genes (KCNQ1, KCNH2, SCN5A, KCNE1) were genotyped. Results: The frequency of KCNQ1 1638G>A, as well as the haplotype harboring KCNQ1 1638A, KCNQ1 1685 + 23G and 1732 + 43T (haplotype AGT) was significantly higher in healthy controls than in arrhythmia patients. This finding implicated that this haplotype (AGT) might be a protective factor against arrhythmias. Conclusions: Our study provided important information to elucidate the effect of SNPs of cardiac ion channel genes on channel function and susceptibility to cardiac arrhythmias in Chinese population. [source]

Haplotype-Based Regression Analysis and Inference of Case,Control Studies with Unphased Genotypes and Measurement Errors in Environmental Exposures

BIOMETRICS, Issue 3 2008
Iryna Lobach
Summary It is widely believed that risks of many complex diseases are determined by genetic susceptibilities, environmental exposures, and their interaction. Chatterjee and Carroll (2005, Biometrika92, 399,418) developed an efficient retrospective maximum-likelihood method for analysis of case,control studies that exploits an assumption of gene,environment independence and leaves the distribution of the environmental covariates to be completely nonparametric. Spinka, Carroll, and Chatterjee (2005, Genetic Epidemiology29, 108,127) extended this approach to studies where certain types of genetic information, such as haplotype phases, may be missing on some subjects. We further extend this approach to situations when some of the environmental exposures are measured with error. Using a polychotomous logistic regression model, we allow disease status to have K+ 1 levels. We propose use of a pseudolikelihood and a related EM algorithm for parameter estimation. We prove consistency and derive the resulting asymptotic covariance matrix of parameter estimates when the variance of the measurement error is known and when it is estimated using replications. Inferences with measurement error corrections are complicated by the fact that the Wald test often behaves poorly in the presence of large amounts of measurement error. The likelihood-ratio (LR) techniques are known to be a good alternative. However, the LR tests are not technically correct in this setting because the likelihood function is based on an incorrect model, i.e., a prospective model in a retrospective sampling scheme. We corrected standard asymptotic results to account for the fact that the LR test is based on a likelihood-type function. The performance of the proposed method is illustrated using simulation studies emphasizing the case when genetic information is in the form of haplotypes and missing data arises from haplotype-phase ambiguity. An application of our method is illustrated using a population-based case,control study of the association between calcium intake and the risk of colorectal adenoma. [source]

Conditional Likelihood Methods for Haplotype-Based Association Analysis Using Matched Case,Control Data

BIOMETRICS, Issue 4 2007
Jinbo Chen
Summary Genetic epidemiologists routinely assess disease susceptibility in relation to haplotypes, that is, combinations of alleles on a single chromosome. We study statistical methods for inferring haplotype-related disease risk using single nucleotide polymorphism (SNP) genotype data from matched case,control studies, where controls are individually matched to cases on some selected factors. Assuming a logistic regression model for haplotype-disease association, we propose two conditional likelihood approaches that address the issue that haplotypes cannot be inferred with certainty from SNP genotype data (phase ambiguity). One approach is based on the likelihood of disease status conditioned on the total number of cases, genotypes, and other covariates within each matching stratum, and the other is based on the joint likelihood of disease status and genotypes conditioned only on the total number of cases and other covariates. The joint-likelihood approach is generally more efficient, particularly for assessing haplotype-environment interactions. Simulation studies demonstrated that the first approach was more robust to model assumptions on the diplotype distribution conditioned on environmental risk variables and matching factors in the control population. We applied the two methods to analyze a matched case,control study of prostate cancer. [source]

Gastric parietal cell antibodies are associated with glutamic acid decarboxylase-65 antibodies and the HLA DQA10501-DQB10301 haplotype in Type 1 diabetes mellitus

DIABETIC MEDICINE, Issue 8 2000
C. E. M. De Block
SUMMARY Aims To assess the prevalence of thyrogastric autoimmunity in relation to age, sex, ,-cell antibody status and HLA DQ haplotypes in Type 1 diabetes mellitus. Methods One hundred and seventy-one patients with Type 1 diabetes mellitus were studied (male/female 86/85; mean age 19 ± 11 years; duration of diabetes 5 ± 4 years). Islet cell antibodies (ICA) and parietal cell antibodies (PCA) were measured using indirect immunofluorescence; glutamic acid decarboxylase-65 antibodies (GADA) by radiobinding assay and thyroid peroxidase antibodies (TPO) with an immunoradiometric assay (IRMA). Results The majority of subjects (81.3%) showed one or more autoantibodies. The prevalence rates were: GADA 64.9%, ICA 46.2%, PCA 19.9% and TPO 19.3%. Patients with ICA+ , 3 years after diagnosis had a higher prevalence of GADA (P = 0.03, odds ratio (OR) 2.66) and thyrogastric antibodies (P = 0.05, OR 2.23) than subjects ICA, after 3 years. PCA+ patients were older (P = 0.04), had a higher prevalence of GADA (P = 0.005, OR 3.89) and TPO (P = 0.05, OR 2.50) than PCA, subjects. Logistic regression analysis showed that PCA status was determined by the HLA DQA1*0501-DQB1*0301 haplotype (, = 2.94, P = 0.04) and GADA status (, = 2.44, P = 0.041). Conclusions Thyrogastric antibodies are highly prevalent in Type 1 diabetes mellitus, especially in patients with persisting ICA. Screening for gastric autoimmunity is particularly advised in patients who are positive for GADA and for the HLA DQA1*0501-DQB1*0301 haplotype. [source]

The p73 polymorphisms are not associated with susceptibility to esophageal squamous cell carcinoma in a high incidence region of China

DISEASES OF THE ESOPHAGUS, Issue 4 2007
H. Ge
SUMMARY., P73, a p53 homolog, has some p53-like activities and plays an important role in modulating cell cycle, apoptosis and DNA repair. The two linked polymorphisms in the non-coding region of exon2 of p73 gene, named G4C14-A4T14, may alter translation efficiency of the gene. The transcription of p73 gene is initiated by three promoters, termed P1-P3. There is a single nucleotide substitution (,386G/A) in the P3 promoter region with unknown function. To test the hypothesis that the genetic variations in the exon2 and P3 promoter play a role in the etiology of esophageal squamous cell carcinoma (ESCC), we conducted a population-based case-control study in 348 ESCC patients and 583 healthy controls from a high incidence region of Hebei province, China. The p73 polymorphisms were genotyped by polymerase chain reaction-restriction fragment length polymorphism analysis (PCR-RFLP). The results showed that the family history of upper gastrointestinal cancer (UGIC) significantly increased the risk of developing ESCC (the age, sex and smoking status adjusted OR = 2.02, 95% CI = 1.54,2.67). The overall distribution of the p73 genotype, allelotype and haplotype in cancer patients and controls were not significantly different (all P -values are above 0.05). Stratification analysis by smoking status and family history of UGIC also did not show the significant influence of the polymorphisms on the risk of ESCC development. The results suggested that the p73 exon2 G4C14-A4T14 and P3 promoter ,386G/A polymorphisms might not be used as potential markers to predicate the risk of ESCC development in northern China. [source]

Comparative phylogeography of salmonid fishes (Salmonidae) reveals late to post-Pleistocene exchange between three now-disjunct river basins in Siberia

DIVERSITY AND DISTRIBUTIONS, Issue 4 2003
E. Froufe
Abstract. We use a comparative phylogeographical framework to evaluate the hypothesis of hydrological exchange during the Pleistocene among the now disjunct Lena, Amur, and Enisei basins in Siberia, and to provide evidence on the causal mechanism of their present day faunal dissimilarities. Approximately 600 bases of the mitochondrial control region were sequenced in five distinct lineages among three genera of salmonid fishes, Hucho, Brachymystax and Thymallus. All three basins were fixed for divergent (2,5.4%) lineages of Thymallus whereas a single shared haplotype was present in all three basins for Hucho taimen (Pallas, 1773) and one shared haplotype between the Lena and Amur basins out of a total of five for blunt-snouted and one out of five for sharp-snouted Brachymystax lenok (Pallas, 1773). For both blunt- and sharp-snouted lenok the haplotypes found within each basin did not form clades, so no relationship between genotypes and geographical occurrence was found. Our data support relatively recent hydrological mixing of the major river drainage systems in eastern and far-eastern Siberia, congruent with the hypothesis of large-scale palaeo-hydrological exchange stemming from glacial advance, retreat and melting during Pleistocene climate fluctuations. Furthermore, these results in conjunction with a comparison of overall faunal composition suggest that environmental differences rather than historical contingency may be responsible for the faunal dissimilarities of the Amur, Lena, and Enisei river basins. [source]

Genetic structure of Japanese populations of an ambrosia beetle, Xylosandrus germanus (Curculionidae: Scolytinae)

ENTOMOLOGICAL SCIENCE, Issue 3 2008
Masaaki ITO
Abstract We examined the genetic structures of 13 Japanese populations of an ambrosia beetle, Xylosandrus germanus (Curculionidae: Scolytinae), to understand the effects of geographical barriers on the colonization dynamics of this species. The genetic structure was studied using portions of the mitochondrial cytochrome oxidase I (COI) gene. A phylogenetic analysis revealed three distinct lineages (clades A, B and C) within X. germanus. Clade A contained 21 haplotypes from all 13 populations; whereas clade B contained eight haplotypes from Hokkaido (Sapporo and Furano), Iwate and Nagano populations; and clade C contained only a single a haplotype from the Hokkaido (Furano) population. In the analysis of molecular variance (amova), the greatest amount of genetic variation was detected between populations in Hokkaido and those in Honshu and other southern islands. Between these two groups of populations, all the values of the coefficient of gene differentiation were significantly larger than zero, except for the Hokkaido (Sapporo) versus Nagano comparison. Our results confirm that for X. germanus, gene flow has been interrupted between Hokkaido and Honshu since the last glacial maximum. [source]

Vascular endothelial growth factor gene polymorphisms are associated with the risk of developing adenomyosis

ENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Issue 5 2009
Shan Kang
Abstract Vascular endothelial growth factor (VEGF), a major mediator of angiogenesis and vascular permeability, may play a key role in the development of adenomyosis. The aim of this study was to investigate whether these four VEGF polymorphisms (,2578C/A, ,1154G/A, ,460C/T, and +936C/T) were associated with the risk of adenomyosis development. Genotypes were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay in 174 adenomyosis patients and 199 frequency-matched control women. There were significant differences between patients and control group in allele frequencies and genotype distributions of the ,2578C/A polymorphisms (P = 0.010 and 0.044, respectively). Compared with the C/C genotype, the A/A + C/A genotype could significantly modify the risk of developing adenomyosis [odds ratio (OR) = 0.64, 95% confidence interval (CI) = 0.42,0.97]. For the ,1154G/A polymorphism, the allele frequencies and genotype distributions in patient group were significant different from those of the controls (P = 0.001 and 0.007, respectively). Compared with the G/G genotype, the A/A + G/A genotype could significantly decrease the risk of developing adenomyosis (OR = 0.51, 95% CI = 0.33,0.80). However, the genotype distributions and allele frequencies of the ,460C/T and +936C/T polymorphisms did not significantly differ between controls and patients (all P value > 0.05). The haplotype analysis suggested that the TGA (VEGF ,460/,1154/,2578) and CGA haplotypes exhibited a significant decrease in the risk of developing adenomyosis compared with the haplotype of TGC (OR = 0.64, 95% CI = 0.41,1.00; OR = 0.44, 95% CI = 0.21,0.93, respectively). The study indicated that the ,2578A or ,1154A allele of VEGF gene could significantly decrease the risk of adenomyosis and might be potentially protective factors for adenomyosis development. Environ. Mol. Mutagen., 2009. © 2009 Wiley-Liss, Inc. [source]

Characterization of bacterial pectinolytic strains involved in the water retting process

ENVIRONMENTAL MICROBIOLOGY, Issue 9 2003
Elena Tamburini
Summary Pectinolytic microorganisms involved in the water retting process were characterized. Cultivable mesophilic anaerobic and aerobic bacteria were isolated from unretted and water-retted material. A total of 104 anaerobic and 23 aerobic pectinolytic strains were identified. Polygalacturonase activity was measured in the supernatant of cell cultures; 24 anaerobic and nine aerobic isolates showed an enzymatic activity higher than the reference strains Clostridium felsineum and Bacillus subtilis respectively. We performed the first genotypic characterization of the retting microflora by a 16S amplified ribosomal DNA restriction analysis (ARDRA). Anaerobic isolates were divided into five different groups, and the aerobic isolates were clustered into three groups. 84.6% of the anaerobic and 82.6% of the aerobic isolates consisted of two main haplotypes. Partial 16S rRNA gene sequences were determined for 12 strains, representative of each haplotype. All anaerobic strains were assigned to the Clostridium genus, whereas the aerobic isolates were assigned to either the Bacillus or the Paenibacillus genus. Anaerobic isolates with high polygalacturonase (PG) activity belong to two clearly distinct phylogenetic clusters related to C. acetobutylicum,C. felsineum and C. saccharobutylicum species. Aerobic isolates with high PG activity belong to two clearly distinct phylogenetic clusters related to B. subtilisT and B. pumilusT. [source]

Clinical, neuropsychological, neurophysiologic, and genetic features of a new Italian pedigree with familial cortical myoclonic tremor with epilepsy

EPILEPSIA, Issue 5 2009
Antonio Suppa
Summary We studied the clinical, neuropsychological, neurophysiologic, and genetic features of an Italian family with familial cortical myoclonic tremor with epilepsy (FCMTE). Clinically affected members of the family had limb and voice tremor, seizures, and myoclonus involving the eyelids during blinking. Neuropsychological testing disclosed visuospatial impairment, possibly due to temporal lobe dysfunction. Neurophysiologic findings suggested increased primary motor cortex excitability with normal sensorimotor integration. Linkage analysis excluded the 8q24 locus, where patients shared a common haplotype spanning 14.5 Mb in the pericentromeric region of chromosome 2. [source]

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

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

,-Globin gene cluster haplotypes and HbF levels are not the only modulators of sickle cell disease in Lebanon

EUROPEAN JOURNAL OF HAEMATOLOGY, Issue 2 2003
A. Inati
Abstract: Sickle cell disease (SCD) is an inherited autosomal recessive disorder of the , -globin chain. Despite the fact that all subjects with SCD have the same single base pair mutation, the severity of the clinical and hematological manifestations is extremely variable. This study examined for the first time in Lebanon the correlation between the clinical manifestation of SCD and the , -globin gene haplotypes. The haplotypes of 50 patients diagnosed with SCD were determined using polymerase chain reaction amplification of fragments containing nine polymorphic restriction sites around and within the ,,G,,A,,,,,,,, -globin gene complex. Most reported haplotypes were found in our population with the Benin haplotype as the most prevalent one. When the patients were divided according to their HbF levels into three groups (Group A: HbF < 5%, Group B: HbF between 5 and 15%, and Group C: HbF > 15%), surprisingly, the highest levels of HbF were associated with the most severe clinical cases. Our findings suggest that fetal hemoglobin levels are important but not the only parameters that affect the severity of the disease. In addition, the high levels of HbF in patients with CAR haplotypes did not seem to ameliorate the severity of symptoms, suggesting that genetic factors other than haplotypes are the major determinants of increased HbF levels in Lebanon. [source]

Broad T cell immunity to the LcrV virulence protein is induced by targeted delivery to DEC-205/CD205-positive mouse dendritic cells

EUROPEAN JOURNAL OF IMMUNOLOGY, Issue 1 2008
Yoonkyung Do
Abstract There is a need for a more efficient vaccine against the bacterium Yersinia pestis, the agent of pneumonic plague. The F1-LcrV (F1-V) subunit vaccine in alhydrogel is known to induce humoral immunity. In this study, we utilized DC to investigate cellular immunity. We genetically engineered the LcrV virulence protein into the anti-DEC-205/CD205 mAb and thereby targeted the conjugated protein directly to mouse DEC-205+ DC in situ. We observed antigen-specific CD4+ T cell immunity measured by intracellular staining for IFN-, in three different mouse strains (C57BL/6, BALB/c, and C3H/HeJ), while we could not observe such T cell responses with F1-V vaccine in alhydrogel. Using a peptide library for LcrV protein, we identified two or more distinct CD4+ T cell mimetopes in each MHC haplotype, consistent with the induction of broad immunity. When compared to nontargeted standard protein vaccine, DC targeting greatly increased the efficiency for inducing IFN-,-producing T cells. The targeted LcrV protein induced antibody responses to a similar extent as the F1-V subunit vaccine, but Th1-dependent IgG2a and IgG2c isotypes were observed only after anti-DEC-205:LcrV mAb immunization. This study sets the stage for the analysis of functional roles of IFN-,-producing T cells in Y.,pestis infection. [source]

GENETIC STUDY: H2 haplotype at chromosome 17q21.31 protects against childhood sexual abuse-associated risk for alcohol consumption and dependence

ADDICTION BIOLOGY, Issue 1 2010
Elliot C. Nelson
ABSTRACT Animal research supports a central role for corticotropin-releasing factor (CRF) in actions of ethanol on brain function. An examination of alcohol consumption in adolescents reported a significant genotype × environment (G × E) interaction involving rs1876831, a corticotropin-releasing hormone receptor 1 (CRHR1) polymorphism, and negative events. CRHR1 and at least four other genes are located at 17q21.31 in an extremely large block of high linkage disequilibrium resulting from a local chromosomal inversion; the minor allele of rs1876831 is contained within the H2 haplotype. Here, we examine whether G × E interactions involving this haplotype and childhood sexual abuse (CSA) are associated with risk for alcohol consumption and dependence in Australian participants (n = 1128 respondents from 476 families) of the Nicotine Addiction Genetics project. Telephone interviews provided data on DSM-IV alcohol dependence diagnosis and CSA and enabled calculation of lifetime alcohol consumption factor score (ACFS) from four indices of alcohol consumption. Individuals reporting a history of CSA had significantly higher ACFS and increased risk for alcohol dependence. A significant G × E interaction was found for ACFS involving the H2 haplotype and CSA (P < 0.017). A similar G × E interaction was associated with protective effects against alcohol dependence risk (odds ratio 0.42; 95% confidence interval 0.20,0.89). For each outcome, no significant CSA-associated risk was observed in H2 haplotype carriers. These findings support conducting further investigation of the H2 haplotype to determine the gene(s) responsible. Our results also suggest that severe early trauma may prove to be an important clinical covariate in the treatment of alcohol dependence. [source]

Marginal zone B cell enrichment and strong follicular B cell reduction correlate with a delayed IgG response in a light chain diversity restricted mouse model

EUROPEAN JOURNAL OF IMMUNOLOGY, Issue 10 2004
Yacine
Abstract Recently developed B6.,,,SEG mice (by crossing ,, and C57BL/6 mice congenic for the wild Mus spretus SEG strain , locus lacking genes coding for ,1 and ,3) have a very reduced light chain diversity. B6.,,,SEG mice produce only ,2 and ,x light chains. Regardless of their Igh haplotype, B6.,,,SEG mice show a restricted B cell distribution by light chain subtype with ,x dominance in all peripheral compartments except peritoneal cavity where ,2 is dominant. This distribution suggests that selection mechanisms act differently in different B cell compartments on ,2 and ,x bearing B cells. Sequence analysis before or following immunization did not reveal unusual mechanisms of diversification. B6.,,,SEG mice still respond to various challenging antigens using new Ab patterns. In particular, regardless of Igha or Ighb haplotypes, the anti-2,4-dinitrophenyl response is characterized by a restricted diversity for both heavy and light chains and a delayed IgG response when compared to B6 and B6.,, mice. We suggest that the delayed IgG response is due to the expansion of marginal zone B cells whereas follicular B cells are strongly reduced. [source]

IL-10 promoter haplotype influence on interferon treatment response in multiple sclerosis

EUROPEAN JOURNAL OF NEUROLOGY, Issue 3 2005
S. Wergeland
The level of interleukin-10 (IL-10) expression is related to polymorphisms -1082 (G/A), -819 (T/C) and -592 (A/C) in the promoter region of the IL-10 gene, which constitute three haplotypes, GCC, ATA, and ACC. The ATA (a non-GCC) haplotype, which is associated with low IL-10 expression, has been shown to improve interferon (IFN) treatment response in hepatitis C. We analysed the distribution of IL-10 promoter haplotype combinations to determine whether they could influence initial IFN treatment response in 63 patients with relapsing-remitting multiple sclerosis (MS). The patients were grouped into non-GCC or GCC haplotypes, and the clinical and magnetic resonance imaging (MRI) disease activity was compared in the two groups. During the first 6 months of treatment, MS patients with non-GCC haplotypes experienced fewer new MRI T1-contrast enhancing lesions [0.77 ± 0.36 (SEM)] than patients with the GCC haplotype (2.45 ± 0.57) (P = 0.05, Mann-Whitney U test). No differences were detected on clinical disease activity. The results suggest an influence of IL-10 promoter polymorphisms on IFN treatment response in MS. [source]

Haplotypes

Selected Abstracts

Expression of individual immunoglobulin genes occurs in an unusual system consisting of multiple independent loci