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Disease Genes (disease + gene)
Selected AbstractsCalculation of IBD probabilities with dense SNP or sequence dataGENETIC EPIDEMIOLOGY, Issue 6 2008Jonathan M. Keith Abstract The probabilities that two individuals share 0, 1, or 2 alleles identical by descent (IBD) at a given genotyped marker locus are quantities of fundamental importance for disease gene and quantitative trait mapping and in family-based tests of association. Until recently, genotyped markers were sufficiently sparse that founder haplotypes could be modelled as having been drawn from a population in linkage equilibrium for the purpose of estimating IBD probabilities. However, with the advent of high-throughput single nucleotide polymorphism genotyping assays, this is no longer a reasonable assumption. Indeed, the imminent arrival of individual sequencing will enable high-density single nucleotide polymorphism genotyping on a scale for which current algorithms are not equipped. In this paper, we present a simple new model in which founder haplotypes are modelled as a Markov chain. Another important innovation is that genotyping errors are explicitly incorporated into the model. We compare results obtained using the new model to those obtained using the popular genetic linkage analysis package Merlin, with and without using the cluster model of linkage disequilibrium that is incorporated into that program. We find that the new model results in accuracy approaching that of Merlin with haplotype blocks, but achieves this with orders of magnitude faster run times. Moreover, the new algorithm scales linearly with number of markers, irrespective of density, whereas Merlin scales supralinearly. We also confirm a previous finding that ignoring linkage disequilibrium in founder haplotypes can cause errors in the calculation of IBD probabilities. Genet. Epidemiol. 2008. © 2008 Wiley-Liss, Inc. [source] Examining the statistical properties of fine-scale mapping in large-scale association studiesGENETIC EPIDEMIOLOGY, Issue 3 2008Steven Wiltshire Abstract Interpretation of dense single nucleotide polymorphism (SNP) follow-up of genome-wide association or linkage scan signals can be facilitated by establishing expectation for the behaviour of primary mapping signals upon fine-mapping, under both null and alternative hypotheses. We examined the inferences that can be made regarding the posterior probability of a real genetic effect and considered different disease-mapping strategies and prior probabilities of association. We investigated the impact of the extent of linkage disequilibrium between the disease SNP and the primary analysis signal and the extent to which the disease gene can be physically localised under these scenarios. We found that large increases in significance (>2 orders of magnitude) appear in the exclusive domain of genuine genetic effects, especially in the follow-up of genome-wide association scans or consensus regions from multiple linkage scans. Fine-mapping significant association signals that reside directly under linkage peaks yield little improvement in an already high posterior probability of a real effect. Following fine-mapping, those signals that increase in significance also demonstrate improved localisation. We found local linkage disequiliptium patterns around the primary analysis signal(s) and tagging efficacy of typed markers to play an important role in determining a suitable interval for fine-mapping. Our findings help inform the interpretation and design of dense SNP-mapping follow-up studies, thus facilitating discrimination between a genuine genetic effect and chance fluctuation (false positive). Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source] Affected-sib-pair test for linkage based on constraints for identical-by-descent distributions corresponding to disease models with imprinting,GENETIC EPIDEMIOLOGY, Issue 4 2004Michael Knapp Abstract Holmans' possible triangle test for affected sib pairs has proven to be a powerful tool for linkage analysis. This test is a likelihood-ratio test for which maximization is restricted to the set of possible sharing probabilities. Here, we extend the possible triangle test to take into account genomic imprinting, which is also known as parent-of-origin effect. While the classical test without imprinting looks at whether affected sib pairs share 0, 1, or 2 alleles identical-by-descent, the likelihood-ratio test allowing for imprinting further distinguishes whether the sharing of exactly one allele is through the father or mother. Thus, if the disease gene is indeed subject to imprinting, the extended test presented here can take into account that affecteds will have inherited the mutant allele preferentially from one particular parent. We calculate the sharing probabilities at a marker locus linked to a disease susceptibility locus. Using our formulation, the constraints on these probabilities given by Dudoit and Speed ([1999] Statistics in Genetics; New York: Springer) can easily be verified. Next, we derive the asymptotic distribution of the restricted likelihood-ratio test statistic under the null hypothesis of no linkage, and give LOD-score criteria for various test sizes. We show, for various disease models, that the test allowing for imprinting has significantly higher power to detect linkage if imprinting is indeed present, at the cost of only a small reduction in power in case of no imprinting. Altogether, unlike many methods currently available, our novel model-free sib-pair test adequately models the epigenetic parent-of-origin effect, and will hopefully prove to be a useful tool for the genetic mapping of complex traits. © 2004 Wiley-Liss, Inc. [source] Association and aggregation analysis using kin-cohort designs with applications to genotype and family history data from the Washington Ashkenazi StudyGENETIC EPIDEMIOLOGY, Issue 2 2001Nilanjan Chatterjee Abstract When a rare inherited mutation in a disease gene, such as BRCA1, is found through extensive study of high-risk families, it is critical to estimate not only age-specific penetrance of the disease associated with the mutation, but also the residual effect of family history once the mutation is taken into account. The kin-cohort design, a cross-sectional survey of a suitable population that collects DNA and family history data, provides an efficient alternative to cohort or case-control designs for estimating age-specific penetrance in a population not selected because of high familial risk. In this report, we develop a method for analyzing kin-cohort data that simultaneously estimate the age-specific cumulative risk of the disease among the carriers and non-carriers of the mutations and the gene-adjusted residual familial aggregation or correlation of the disease. We employ a semiparametric modeling approach, where the marginal cumulative risks corresponding to the carriers and non-carriers are treated non-parametrically and the residual familial aggregation is described parametrically by a class of bivariate failure time models known as copula models. A simple and robust two-stage method is developed for estimation. We apply the method to data from the Washington Ashkenazi Study [Struewing et al., 1997, N Engl J Med 336:1401,1408] to study the residual effect of family history on the risk of breast cancer among non-carriers and carriers of specific BRCA1/BRCA2 germline mutations. We find that positive history of a single first-degree relative significantly increases risk of the non-carriers (RR = 2.0, 95% CI = 1.6,2.6) but has little or no effect on the carriers. Genet. Epidemiol. 21:123,138, 2001. © 2001 Wiley-Liss, Inc. [source] UMD-predictor, a new prediction tool for nucleotide substitution pathogenicity,application to four genes: FBN1, FBN2, TGFBR1, and TGFBR2,HUMAN MUTATION, Issue 6 2009Mélissa Yana Frédéric Abstract Approximately half of gene lesions responsible for human inherited diseases are due to an amino acid substitution, showing that this mutational mechanism plays a large role in diseases. Distinguishing neutral sequence variations from those responsible for the phenotype is of major interest in human genetics. Because in vitro validation of mutations is not always possible in diagnostic settings, indirect arguments must be accumulated to define whether a missense variation is causative. To further differentiate neutral variants from pathogenic nucleotide substitutions, we developed a new tool, UMD-Predictor®. This tool provides a combinatorial approach that associates the following data: localization within the protein, conservation, biochemical properties of the mutant and wild-type residues, and the potential impact of the variation on mRNA. To evaluate this new tool, we compared it to the SIFT, PolyPhen, and SNAP software, the BLOSUM62 and Yu's Biochemical Matrices. All tools were evaluated using variations from well-validated datasets extracted from four UMD,LSDB databases (UMD,FBN1, UMD,FBN2, UMD,TGFBR1, and UMD,TGFBR2) that contain all published mutations of the corresponding genes, that is, 1,945 mutations, among which 796 different substitutions corresponding to missense mutations. Our results show that the UMD-Predictor® algorithm is the most efficient tool to predict pathogenic mutations in this context with a positive predictive value of 99.4%, a sensitivity of 95.4%, and a specificity of 92.2%. It can thus enhance the interpretation of variations in these genes, and could easily be applied to any other disease gene through the freely available UMD® generic software (http://www.umd.be). Hum Mutat 30:1,8, 2009. © 2009 Wiley-Liss, Inc. [source] Age-dependent and tissue-specific CAG repeat instability occurs in mouse knock-in for a mutant Huntington's disease geneJOURNAL OF NEUROSCIENCE RESEARCH, Issue 4 2001Hiroshi Ishiguro Abstract Huntington's disease (HD) is a neurodegenerative disorder characterized by the expansion of CAG repeats in exon 1 of the HD gene. To clarify the instability of expanded CAG repeats in HD patients, an HD model mouse has been generated by gene replacement with human exon 1 of the HD gene with expansion to 77 CAG repeats. Chimeric proteins composed of human mutated exon 1 and mouse huntingtin are expressed ubiquitously in brain and peripheral tissues. One or two CAG repeat expansion was found in litters from paternal transmission, whereas contraction of CAG repeat in litters was observed through maternal transmission. Elderly mice show greater CAG repeat instability than younger mice, and a unique case was observed of an expanded 97 CAG repeat mouse. Somatic CAG repeat instability is particularly pronounced in the liver, kidney, stomach, and brain but not in the cerebellum of 100-week-old mice. The same results of expanded CAG repeat instability as observed in this HD model mouse were confirmed in the human brain of HD patients. Glial fibrillary acidic protein (GFAP)-positive cells have been found to be increased in the substantia nigra (SN), globus pallidus (GP), and striatum (St) in the brains of 40-week-old affected mice, although without neuronal cell death. The CAG repeat instability and increase in GFAP-positive cells in this mouse model appear to mirror the abnormalities in HD patients. The HD model mouse may therefore have advantages for investigations of molecular mechanisms underlying instability of CAG repeats. J. Neurosci. Res. 65:289,297, 2001. © 2001 Wiley-Liss, Inc. [source] Retinopathy of prematurity: Mutations in the Norrie disease gene and the risk of progression to advanced stagesPEDIATRICS INTERNATIONAL, Issue 2 2001Mohammad Z Haider AbstractBackground: Retinopathy of prematurity (ROP) is a retinal vascular disease that occurs in infants with short gestational age and low birth weight and may lead to retinal detachment and blindness. Missense mutations in the Norrie disease (ND) gene have been associated with the risk of progression to advanced stages in cases of ROP from the US and also in clinically similar ND and familial exudative vitreoretinopathy. Methods: We have screened two ND gene mutations, namely A105T and Val60Glu, by polymerase chain reaction,restriction fragment length polymorphism (PCR-RFLP) and allele-specific PCR methods, respectively, in 210 Kuwaiti premature newborns to replicate these findings in a different ethnic group. Results: In the Kuwaiti premature newborn cohort, 115 of 210 babies had no eye problems and served as controls, while 95 were cases of ROP. In 71 of 95 ROP cases, the disease regressed spontaneously on or before stage 3, while in 24 of 95 ROP cases the disease progressed to advanced stages 4 and 5. In case of missense mutation (A105T), the AA genotype was detected in 96% of controls compared with 87% of ROP cases (NS); similarly no significant difference was found between spontaneously regressed ROP cases and those who progressed to advanced stages. For the Val60Glu mutation, no significant association was detected between the genotype and progression of ROP to advanced stages. Conclusions: Unlike data from the US, our findings from a Kuwaiti cohort of ROP cases and controls suggest a lack of association between the two ND gene mutations (A105T and Val60Glu) and ROP and the risk of progression of the disease to advanced stages. [source] Molecular Bases of Congenital Hypopigmentary Disorders in Humans and Oculocutaneous Albinism 1 in JapanPIGMENT CELL & MELANOMA RESEARCH, Issue 2000YASUSHI TOMITA The molecular bases of various types of congenital hypopigmentary disorders have been clarified in the past 10 years. Homozygous gene mutations of enzymes functional in melanogenesis such as tyrosinase, P protein and DHICA oxidase, result in oculocutaneous albinism (OCA) 1, OCA 2, and OCA 3, respectively. The genes responsible for Hermansky-Pudlak syndrome (HPS) and Chediak-Higashi syndrome (CHS) have also recently been isolated and cloned. The transcription factor paired box 3 (PAX3) works at the promoter region of the microphthalmia-associated transcription factor (MITF) gene, and the MITF transcription factor orders the expression of c-kit, which encodes the receptor for stem-cell factor, which in turn stimulates melanoblast migration from the neural tube to the skin in the embryo. Heterozygous mutations of PAX3, MITF, or c-kit genes induce Waardenburg syndrome (WS) 1/3, WS 2 or Piebaldism, respectively. A defect of endothelin-3 or the endothelin-B receptor produces WS 4. In our examination of 26 OCA 1 patients in Japan, all were found to have homozygous or heterozygous tyrosinase gene mutations at codons 77 or 310. Therefore, mutations at codons 77 and 310 are the major ones in Japanese patients with OCA 1. An autosomal dominant pigmentary disease of dyschromatosis symmetrica hereditaria (DSH) is well known in Japan, and is characterized by a mixture of hypo- and hyper-pigmented macules of various sizes on the backs of the hands and feet. The disease gene and its chromosomal localization have not been identified yet. Our trial of linkage analysis and positional cloning to determine the disease gene is presented. [source] Genetics of atrioventricular conduction disease in humansTHE ANATOMICAL RECORD : ADVANCES IN INTEGRATIVE ANATOMY AND EVOLUTIONARY BIOLOGY, Issue 2 2004D. Woodrow Benson Abstract Atrioventricular (AV) conduction disease (block) describes impairment of the electrical continuity between the atria and ventricles. Classification of AV block has utilized biophysical characteristics, usually the extent (first, second, or third degree) and site of block (above or below His bundle recording site). The genetic significance of this classification is unknown. In young patients, AV block may result from injury or be the major cardiac manifestation of neuromuscular disease. However, in some cases, AV block has unknown or idiopathic cause. In such cases, familial clustering has been noted and published pedigrees show autosomal dominant inheritance; associated heart disease is common (e.g., congenital heart malformation, cardiomyopathy). The latter finding is not surprising given the common origin of working myocytes and specialized conduction system elements. Using genetic models incorporating reduced penetrance (disease absence in some individuals with disease gene), variable expressivity (individuals with disease gene have different phenotypes), and genetic heterogeneity (similar phenotypes, different genetic cause), molecular genetic causes of AV block are being identified. Mutations identified in genes with diverse functions (transcription, excitability, and energy homeostasis) for the first time provide the means to assess risk and offer insight into the molecular basis of this important clinical condition previously defined only by biophysical characteristics. © 2004 Wiley-Liss, Inc. [source] Single-cell expression profiling of dopaminergic neurons combined with association analysis identifies pyridoxal kinase as Parkinson's disease gene,ANNALS OF NEUROLOGY, Issue 6 2009Matthias Elstner MD Objective The etiology of Parkinson disease (PD) is complex and multifactorial, with hereditary and environmental factors contributing. Monogenic forms have provided molecular clues to disease mechanisms but genetic modifiers of idiopathic PD are still to be determined. Methods We carried out whole-genome expression profiling of isolated human substantia nigra (SN) neurons from patients with PD vs. controls followed by association analysis of tagging single-nucleotide polymorphisms (SNPs) in differentially regulated genes. Association was investigated in a German PD sample and confirmed in Italian and British cohorts. Results We identified four differentially expressed genes located in PD candidate pathways, ie, MTND2 (mitochondrial, p = 7.14 × 10,7), PDXK (vitamin B6/dopamine metabolism, p = 3.27 × 10,6), SRGAP3 (axon guidance, p = 5.65 × 10,6), and TRAPPC4 (vesicle transport, p = 5.81 × 10,6). We identified a DNA variant (rs2010795) in PDXK associated with an increased risk of PD in the German cohort (p = 0.00032). This association was confirmed in the British (p = 0.028) and Italian (p = 0.0025) cohorts individually and reached a combined value of p = 1.2 × 10,7 (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.18,1.44). Interpretation We provide an example of how microgenomic genome-wide expression studies in combination with association analysis can aid to identify genetic modifiers in neurodegenerative disorders. The detection of a genetic variant in PDXK, together with evidence accumulating from clinical studies, emphasize the impact of vitamin B6 status and metabolism on disease risk and therapy in PD. Ann Neurol 2009;66:792,798 [source] The complete genome sequence of a dog: a perspectiveBIOESSAYS, Issue 6 2006Soohyun Lee A complete, high-quality reference sequence of a dog genome was recently produced by a team of researchers led by the Broad Institute, achieving another major milestone in deciphering the genomic landscape of mammalian organisms. The genome sequence provides an indispensable resource for comparative analysis and novel insights into dog and human evolution and history. Together with the survey sequence of a poodle previously published in 2003, the two dog genome sequences allowed identification of more than 2.5 million single nucleotide polymorphisms within and between dog breeds, which can be used in evolutionary analysis, behavioral studies and disease gene mapping.1 © 2005 Wiley Periodicals, Inc. BioEssays 28: 569,573, 2006. © 2006 Wiley Periodicals, Inc. [source] Ultrastructural features resembling those of harlequin ichthyosis in patients with severe congenital ichthyosiform erythrodermaBRITISH JOURNAL OF DERMATOLOGY, Issue 3 2001E. Virolainen Congenital ichthyoses are a group of heterogeneous disorders of cornification. Autosomal recessive congenital ichthyosis (ARCI) can be clinically subdivided into congenital ichthyosiform erythroderma and lamellar ichthyosis. Ultrastructurally, ARCI is classified into four groups: ichthyosis congenita (IC) types I,IV. The genetic background of the ARCI disorders is heterogeneous, but only one disease gene, transglutaminase 1, has been detected so far. We describe six patients with severe congenital ichthyosis from six different Scandinavian families. They could not be classified ultrastructurally into the four IC groups because of atypical findings of electron microscopy. These included abnormal lamellar bodies, alterations in keratohyalin, remnant organelles and lipid inclusions in the upper epidermal cells, which resembled the ultrastructural findings of harlequin ichthyosis (HI), although the HI phenotype was not present at birth. Some clinical features, such as thick scales, erythroderma, alopecia and ectropion were common to all patients. Ichthyosis was usually accentuated in the scalp and four patients had clumped fingers and toes. None of the patients carried the transglutaminase 1 mutation. We conclude that ultrastructural findings resembling those detected in previous HI cases (type 1 and 2) can also be found in patients who do not have classic clinical features of that rare ichthyosis. This may be due to lack of specificity of ultrastructural markers for HI or to its clinical heterogeneity. [source] 2163: Identification of novel disease gene for primary congenital glaucoma (PCG) through homozygosity mapping and next-generation sequencing strategies in a large consanguineous pedigreeACTA OPHTHALMOLOGICA, Issue 2010H VERDIN Purpose Primary congenital glaucoma (PCG) is caused by developmental anomalies of the trabecular meshwork and the anterior chamber angle resulting in an increased ocular pressure (IOP) and optic nerve damage. In general PCG displays an autosomal recessive inheritance pattern and is genetically heterogeneous. To date, three PCG loci are known, namely GLC3A, GLC3B and GLC3C, and two causal genes have been identified, CYP1B1 located in the GLC3A locus and LTPB2 located at 1.3 MB proximal to the GLC3C locus. The purpose of the current study is to identify the causal disease gene in a large consanguineous family with PCG, originating from Jordany. CYP1B1 mutations and linkage to the LTBP2, GLCB3 and GLCC3 locus were previously excluded. Methods In a first step, DNA from members from the consanguineous family will be genotyped by 250K GeneChip Mapping Affymetrix arrays. Homozygosity mapping will be applied to identify potential disease loci, using a homemade Perl script. Next, microsatellite analysis will be performed in order to confirm findings and to narrow down candidate regions. Subsequently, candidate regions of interest will be captured (Agilent) and sequenced on the Illumina Genome Analyser IIx (GAIIx). Gene and variant prioritization will be done using in-house developed software, followed by segregation analysis and screening in control individuals. At last, a cohort of 30 molecularly unsolved PCG patients will be screened for mutations in the newly identified disease. Conclusion The identification of a new disease gene for PCG may lead to better insights into the molecular pathogenesis of glaucoma, and might uncover novel therapeutic strategies. [source] The search for autism disease genesDEVELOPMENTAL DISABILITIES RESEARCH REVIEW, Issue 4 2004Thomas H Wassink Abstract Autism is a heritable disorder characterized by phenotypic and genetic complexity. This review begins by surveying current linkage, gene association, and cytogenetic studies performed with the goal of identifying autism disease susceptibility variants. Though numerous linkages and associations have been identified, they tend to diminish upon closer examination or attempted replication. The review therefore explores challenges to current methodologies presented by the complexities of autism that might underlie some of the current difficulties, and finishes by describing emerging phenotypic, statistical, and molecular investigational approaches that offer hope of overcoming those challenges. © 2004 Wiley-Liss, Inc. MRDD Research Reviews 2004;10:272,283. [source] Identification of a novel mutation in keratin 1 in a family with epidermolytic hyperkeratosisEXPERIMENTAL DERMATOLOGY, Issue 1 2000M. J. Arin Abstract: Epidermolytic hyperkeratosis (EHK) is a hereditary skin disorder typified by blistering due to cytolysis. One in 100,000 individuals is affected by this autosomal-dominant disease. The onset of the disease phenotype is typically at birth. Histological and ultrastructural examination of the epidermis shows a thickened stratum corneum and tonofilament clumping around the nucleus of suprabasal keratinocytes. Linkage studies localized the disease genes on chromosomes 12q and 17q which contain the type II and type I keratin gene clusters. Recently, several point mutations in the genes encoding the suprabasal keratins, K1 and K10, have been reported in EHK patients. We have investigated a large kindred affected by EHK and identified a new point mutation in the 2B region of keratin 1 (I107T), resulting from a T to C transition in codon 478. [source] Actin mutations in hypertrophic and dilated cardiomyopathy cause inefficient protein folding and perturbed filament formationFEBS JOURNAL, Issue 8 2005Sřren Vang Hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) are the most common hereditary cardiac conditions. Both are frequent causes of sudden death and are often associated with an adverse disease course. Alpha-cardiac actin is one of the disease genes where different missense mutations have been found to cause either HCM or DCM. We have tested the hypothesis that the protein-folding pathway plays a role in disease development for two actin variants associated with DCM and six associated with HCM. Based on a cell-free coupled translation assay the actin variants could be graded by their tendency to associate with the chaperonin TCP-1 ring complex/chaperonin containing TCP-1 (TRiC/CCT) as well as their propensity to acquire their native conformation. Some variant proteins are completely stalled in a complex with TRiC and fail to fold into mature globular actin and some appear to fold as efficiently as the wild-type protein. A fraction of the translated polypeptide became ubiquitinated and detergent insoluble. Variant actin proteins overexpressed in mammalian cell lines fail to incorporate into actin filaments in a manner correlating with the degree of misfolding observed in the cell-free assay; ranging from incorporation comparable to wild-type actin to little or no incorporation. We propose that effects of mutations on folding and fiber assembly may play a role in the molecular disease mechanism. [source] A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotypedGENETIC EPIDEMIOLOGY, Issue 1 2009Tao Wang Abstract Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index. Genet. Epidemiol. 2008. © 2008 Wiley-Liss, Inc. [source] Simultaneous localization of two linked disease susceptibility genesGENETIC EPIDEMIOLOGY, Issue 1 2005Joanna M. Biernacka Abstract For diseases with complex genetic etiology, more than one susceptibility gene may exist in a single chromosomal region. Extending the work of Liang et al. ([2001] Hum. Hered. 51:64,78), we developed a method for simultaneous localization of two susceptibility genes in one region. We derived an expression for expected allele sharing of an affected sib pair (ASP) at each point across a chromosomal segment containing two susceptibility genes. Using generalized estimating equations (GEE), we developed an algorithm that uses marker identical-by-descent (IBD) sharing in affected sib pairs to simultaneously estimate the locations of the two genes and the mean IBD sharing in ASPs at these two disease loci. Confidence intervals for gene locations can be constructed based on large sample approximations. Application of the described methods to data from a genome scan for type 1 diabetes (Mein et al. [1998] Nat. Genet. 19:297,300) yielded estimates of two putative disease gene locations on chromosome 6, approximately 20 cM apart. Properties of the estimators, including bias, precision, and confidence interval coverage, were studied by simulation for a range of genetic models. The simulations demonstrated that the proposed method can improve disease gene localization and aid in resolving large peaks when two disease genes are present in one chromosomal region. Joint localization of two disease genes improves with increased excess allele sharing at the disease gene loci, increased distance between the disease genes, and increased number of affected sib pairs in the sample. Genet. Epidemiol. © 2004 Wiley-Liss, Inc. [source] Maximum-likelihood estimation of haplotype frequencies in nuclear familiesGENETIC EPIDEMIOLOGY, Issue 1 2004Tim Becker Abstract The importance of haplotype analysis in the context of association fine mapping of disease genes has grown steadily over the last years. Since experimental methods to determine haplotypes on a large scale are not available, phase has to be inferred statistically. For individual genotype data, several reconstruction techniques and many implementations of the expectation-maximization (EM) algorithm for haplotype frequency estimation exist. Recent research work has shown that incorporating available genotype information of related individuals largely increases the precision of haplotype frequency estimates. We, therefore, implemented a highly flexible program written in C, called FAMHAP, which calculates maximum likelihood estimates (MLEs) of haplotype frequencies from general nuclear families with an arbitrary number of children via the EM-algorithm for up to 20 SNPs. For more loci, we have implemented a locus-iterative mode of the EM-algorithm, which gives reliable approximations of the MLEs for up to 63 SNP loci, or less when multi-allelic markers are incorporated into the analysis. Missing genotypes can be handled as well. The program is able to distinguish cases (haplotypes transmitted to the first affected child of a family) from pseudo-controls (non-transmitted haplotypes with respect to the child). We tested the performance of FAMHAP and the accuracy of the obtained haplotype frequencies on a variety of simulated data sets. The implementation proved to work well when many markers were considered and no significant differences between the estimates obtained with the usual EM-algorithm and those obtained in its locus-iterative mode were observed. We conclude from the simulations that the accuracy of haplotype frequency estimation and reconstruction in nuclear families is very reliable in general and robust against missing genotypes. © 2004 Wiley-Liss, Inc. [source] Evaluations of maximization procedures for estimating linkage parameters under heterogeneityGENETIC EPIDEMIOLOGY, Issue 3 2004Swati Biswas Abstract Locus heterogeneity is a major problem plaguing the mapping of disease genes responsible for complex genetic traits via linkage analysis. A common feature of several available methods to account for heterogeneity is that they involve maximizing a multidimensional likelihood to obtain maximum likelihood estimates. The high dimensionality of the likelihood surface may be due to multiple heterogeneity (mixing) parameters, linkage parameters, and/or regression coefficients corresponding to multiple covariates. Here, we focus on this nontrivial computational aspect of incorporating heterogeneity by considering several likelihood maximization procedures, including the expectation maximization (EM) algorithm and the stochastic expectation maximization (SEM) algorithm. The wide applicability of these procedures is demonstrated first through a general formulation of accounting for heterogeneity, and then by applying them to two specific formulations. Furthermore, our simulation studies as well as an application to the Genetic Analysis Workshop 12 asthma datasets show that, among other observations, SEM performs better than EM. As an aside, we illustrate a limitation of the popular admixture approach for incorporating heterogeneity, proved elsewhere. We also show how to obtain standard errors (SEs) for EM and SEM estimates, using methods available in the literature. These SEs can then be combined with the corresponding estimates to provide confidence intervals of the parameters. © 2004 Wiley-Liss, Inc. [source] Toward the ideal "Mutation Update" and "Locus Specific Database" for disease genesHUMAN MUTATION, Issue 9 2009Richard G.H. Cotton No abstract is available for this article. [source] Classifying MLH1 and MSH2 variants using bioinformatic prediction, splicing assays, segregation, and tumor characteristicsHUMAN MUTATION, Issue 5 2009Sven Arnold Abstract Reliable methods for predicting functional consequences of variants in disease genes would be beneficial in the clinical setting. This study was undertaken to predict, and confirm in vitro, splicing aberrations associated with mismatch repair (MMR) variants identified in familial colon cancer patients. Six programs were used to predict the effect of 13 MLH1 and 6 MSH2 gene variants on pre-mRNA splicing. mRNA from cycloheximide-treated lymphoblastoid cell lines of variant carriers was screened for splicing aberrations. Tumors of variant carriers were tested for microsatellite instability and MMR protein expression. Variant segregation in families was assessed using Bayes factor causality analysis. Amino acid alterations were examined for evolutionary conservation and physicochemical properties. Splicing aberrations were detected for 10 variants, including a frameshift as a minor cDNA product, and altered ratio of known alternate splice products. Loss of splice sites was well predicted by splice-site prediction programs SpliceSiteFinder (90%) and NNSPLICE (90%), but consequence of splice site loss was less accurately predicted. No aberrations correlated with ESE predictions for the nine exonic variants studied. Seven of eight missense variants had normal splicing (88%), but only one was a substitution considered neutral from evolutionary/physicochemical analysis. Combined with information from tumor and segregation analysis, and literature review, 16 of 19 variants were considered clinically relevant. Bioinformatic tools for prediction of splicing aberrations need improvement before use without supporting studies to assess variant pathogenicity. Classification of mismatch repair gene variants is assisted by a comprehensive approach that includes in vitro, tumor pathology, clinical, and evolutionary conservation data. Hum Mutat 0, 1,14, 2009. © 2009 Wiley-Liss, Inc. [source] Genetic association analysis: a primer on how it works, its strengths and its weaknessesINTERNATIONAL JOURNAL OF ANDROLOGY, Issue 6 2008Laura Rodriguez-Murillo Summary Currently, the most used approach to mapping disease genes is the genome wide association study, using large samples of cases and controls and hundreds of thousands of markers spread throughout the genome. This review focuses in explaining how an association study works, its strengths and its weaknesses, and the methods available to analyse the data. Issues related to sample size, genetic effect sizes, epistasis, replication and population stratification are specifically addressed, issues that an investigator must take into account when planning an association study of any complex disease. Finally, we include some special features concerning association studies in the Y chromosome, and we contrast the analysis characteristics of linkage and association. [source] The pure parsimony haplotyping problem: overview and computational advancesINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 5 2009Daniele Catanzaro Abstract Haplotyping estimation from aligned single-nucleotide polymorphism fragments has attracted more and more attention in recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from mapping of complex disease genes to inferring population histories, passing through designing drugs, functional genomics, and pharmacogenetics. The literature proposes a number of estimation criteria to select a set of haplotypes among possible alternatives. Usually, such criteria can be expressed under the form of objective functions, and the sets of haplotypes that optimize them are referred to as optimal. One of the most important estimation criteria is the pure parsimony, which states that the optimal set of haplotypes for a given set of genotypes is that having minimal cardinality. Finding the minimal number of haplotypes necessary to explain a given set of genotypes involves solving an optimization problem, called the pure parsimony haplotyping (PPH) estimation problem, which is notoriously -hard. This article provides an overview of PPH, and discusses the different approaches to solution that occur in the literature. [source] Inferring Haplotype/Disease Association by Joint Use of Case-Parents Trios and Case-Parent PairsANNALS OF HUMAN GENETICS, Issue 3 2010Yue-Qing Hu Summary Recently interest has been increasing in genetic association studies using several closely linked loci. The HAP-TDT method, which uses case-parents trios is powerful for such a task. However, it is not uncommon in practice that one parent is missing for some reason, such as late onset. The case-parents trios are thus reduced to case-parent pairs. Discarding such data could lead to a severe loss of power. In this paper, we propose the HAP-1-TDT method based on case-parent pairs to detect haplotype/disease association. A permutation-based randomisation technique is devised to assess the significance of the test statistic. Furthermore, the combined statistic HAP-C-TDT is developed to use jointly case-parents trios and case-parent pairs. These test statistics can be applied to either phase-known or phase-unknown data. A number of simulation studies are conducted to investigate the validity of the proposed tests; these studies show that the statistics are robust to population structure. Using several disease genes from the literature, we illustrate that incorporating case-parent pairs into an association study leads to noticeable power gain. Moreover, our simulation results suggest that our method has better size and power than UNPHASED. Finally, in simulated scenarios where there are only a few SNPs and risk is determined by two haplotypes that are complementary or near-complementary, our method has better power than TRIMM. [source] Investigation into the Ability of SNP Chipsets and Microsatellites to Detect Association with a Disease LocusANNALS OF HUMAN GENETICS, Issue 4 2008D. Curtis Summary We wished to investigate the ability of different SNP chipsets to detect association with a disease and to investigate the linkage disequilibrium (LD) relationships between microsatellites and nearby SNPs in order to assess their potential usefulness to detect association. SNP genotypes were obtained from HapMap and microsatellite genotypes from CEPH. 5000 SNPs were simulated as disease genes which increased penetrance from 0.01 to 0.02 in a sample of 400 cases and 400 controls. The power of flanking SNPs to detect association was tested using sets of 1, 2, 3 or 4 markers analysed with haplotype analysis or logistic regression and using either all HapMap markers or those from the Affymetrix 500K, Illumina 300K or Illumina 550K chipsets. Additionally, LD relationships between 10 microsatellites and SNPs within 2Mb of each other were studied. The power for one of the markers to detect association at p = 0.001 was around 0.4. Power was slightly better for logistic regression than haplotype analysis and for two-marker as opposed to single marker analysis but analysing with larger numbers markers had little benefit. The Illumina 550K marker set was better able to detect association than the other two and was almost as powerful as using all HapMap markers. Microsatellites had detectable LD with only a small number of nearby SNPs and the pattern of LD was very variable. Available chipsets have quite good ability to detect association although obviously results will be critically dependent on the nature of the genetic effect on risk, sample size and the actual LD relationships of the susceptibility polymorphisms involved. Microsatellites seem ill-suited for systematic studies to detect association. [source] Optimal Two-Stage Design for Case-Control Association Analysis Incorporating Genotyping ErrorsANNALS OF HUMAN GENETICS, Issue 3 2008Y. Zuo Summary Two-stage design is a cost effective approach for identifying disease genes in genetic studies and it has received much attention recently. In general, there are two types of two-stage designs that differ on the methods and samples used to measure allele frequencies in the first stage: (1) Individual genotyping is used in the first stage; (2) DNA pooling is used in the first stage. In this paper, we focus on the latter. Zuo et al. (2006) investigated statistical power of such a design, among other things, but the cost of the study was not taken into account. The purpose of this paper is to study the optimal design under the given overall cost. We investigate how to allocate the resources to the two stages. Note that in addition to the measurement errors associated with DNA pooling, genotyping errors are also unavoidable with individual genotyping. Therefore, we discuss the optimal design combining genotyping errors associated with individual genotyping. The joint statistical distributions of test statistics in the first and second stages are derived. For a fixed cost, our results show that the optimal design requires no additional samples in the second stage but only that the samples in the first stage be re-used. When the second stage uses an entirely independent sample, however, the optimal design under a given cost depends on the population allele frequency and allele frequency difference between the case and control groups. For the current genotyping costs, we can roughly allocate 1/3 to 1/2 of the total sample size to the first stage for screening. [source] European Mathematical Genetics Meeting, Heidelberg, Germany, 12th,13th April 2007ANNALS OF HUMAN GENETICS, Issue 4 2007Article first published online: 28 MAY 200 Saurabh Ghosh 11 Indian Statistical Institute, Kolkata, India High correlations between two quantitative traits may be either due to common genetic factors or common environmental factors or a combination of both. In this study, we develop statistical methods to extract the contribution of a common QTL to the total correlation between the components of a bivariate phenotype. Using data on bivariate phenotypes and marker genotypes for sib-pairs, we propose a test for linkage between a common QTL and a marker locus based on the conditional cross-sib trait correlations (trait 1 of sib 1 , trait 2 of sib 2 and conversely) given the identity-by-descent sharing at the marker locus. The null hypothesis cannot be rejected unless there exists a common QTL. We use Monte-Carlo simulations to evaluate the performance of the proposed test under different trait parameters and quantitative trait distributions. An application of the method is illustrated using data on two alcohol-related phenotypes from the Collaborative Study On The Genetics Of Alcoholism project. Rémi Kazma 1 , Catherine Bonaďti-Pellié 1 , Emmanuelle Génin 12 INSERM UMR-S535 and Université Paris Sud, Villejuif, 94817, France Keywords: Gene-environment interaction, sibling recurrence risk, exposure correlation Gene-environment interactions may play important roles in complex disease susceptibility but their detection is often difficult. Here we show how gene-environment interactions can be detected by investigating the degree of familial aggregation according to the exposure of the probands. In case of gene-environment interaction, the distribution of genotypes of affected individuals, and consequently the risk in relatives, depends on their exposure. We developed a test comparing the risks in sibs according to the proband exposure. To evaluate the properties of this new test, we derived the formulas for calculating the expected risks in sibs according to the exposure of probands for various values of exposure frequency, relative risk due to exposure alone, frequencies of latent susceptibility genotypes, genetic relative risks and interaction coefficients. We find that the ratio of risks when the proband is exposed and not exposed is a good indicator of the interaction effect. We evaluate the power of the test for various sample sizes of affected individuals. We conclude that this test is valuable for diseases with moderate familial aggregation, only when the role of the exposure has been clearly evidenced. Since a correlation for exposure among sibs might lead to a difference in risks among sibs in the different proband exposure strata, we also add an exposure correlation coefficient in the model. Interestingly, we find that when this correlation is correctly accounted for, the power of the test is not decreased and might even be significantly increased. Andrea Callegaro 1 , Hans J.C. Van Houwelingen 1 , Jeanine Houwing-Duistermaat 13 Dept. of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands Keywords: Survival analysis, age at onset, score test, linkage analysis Non parametric linkage (NPL) analysis compares the identical by descent (IBD) sharing in sibling pairs to the expected IBD sharing under the hypothesis of no linkage. Often information is available on the marginal cumulative hazards (for example breast cancer incidence curves). Our aim is to extend the NPL methods by taking into account the age at onset of selected sibling pairs using these known marginal hazards. Li and Zhong (2002) proposed a (retrospective) likelihood ratio test based on an additive frailty model for genetic linkage analysis. From their model we derive a score statistic for selected samples which turns out to be a weighed NPL method. The weights depend on the marginal cumulative hazards and on the frailty parameter. A second approach is based on a simple gamma shared frailty model. Here, we simply test whether the score function of the frailty parameter depends on the excess IBD. We compare the performance of these methods using simulated data. Céline Bellenguez 1 , Carole Ober 2 , Catherine Bourgain 14 INSERM U535 and University Paris Sud, Villejuif, France 5 Department of Human Genetics, The University of Chicago, USA Keywords: Linkage analysis, linkage disequilibrium, high density SNP data Compared with microsatellite markers, high density SNP maps should be more informative for linkage analyses. However, because they are much closer, SNPs present important linkage disequilibrium (LD), which biases classical nonparametric multipoint analyses. This problem is even stronger in population isolates where LD extends over larger regions with a more stochastic pattern. We investigate the issue of linkage analysis with a 500K SNP map in a large and inbred 1840-member Hutterite pedigree, phenotyped for asthma. Using an efficient pedigree breaking strategy, we first identified linked regions with a 5cM microsatellite map, on which we focused to evaluate the SNP map. The only method that models LD in the NPL analysis is limited in both the pedigree size and the number of markers (Abecasis and Wigginton, 2005) and therefore could not be used. Instead, we studied methods that identify sets of SNPs with maximum linkage information content in our pedigree and no LD-driven bias. Both algorithms that directly remove pairs of SNPs in high LD and clustering methods were evaluated. Null simulations were performed to control that Zlr calculated with the SNP sets were not falsely inflated. Preliminary results suggest that although LD is strong in such populations, linkage information content slightly better than that of microsatellite maps can be extracted from dense SNP maps, provided that a careful marker selection is conducted. In particular, we show that the specific LD pattern requires considering LD between a wide range of marker pairs rather than only in predefined blocks. Peter Van Loo 1,2,3 , Stein Aerts 1,2 , Diether Lambrechts 4,5 , Bernard Thienpont 2 , Sunit Maity 4,5 , Bert Coessens 3 , Frederik De Smet 4,5 , Leon-Charles Tranchevent 3 , Bart De Moor 2 , Koen Devriendt 3 , Peter Marynen 1,2 , Bassem Hassan 1,2 , Peter Carmeliet 4,5 , Yves Moreau 36 Department of Molecular and Developmental Genetics, VIB, Belgium 7 Department of Human Genetics, University of Leuven, Belgium 8 Bioinformatics group, Department of Electrical Engineering, University of Leuven, Belgium 9 Department of Transgene Technology and Gene Therapy, VIB, Belgium 10 Center for Transgene Technology and Gene Therapy, University of Leuven, Belgium Keywords: Bioinformatics, gene prioritization, data fusion The identification of genes involved in health and disease remains a formidable challenge. Here, we describe a novel bioinformatics method to prioritize candidate genes underlying pathways or diseases, based on their similarity to genes known to be involved in these processes. It is freely accessible as an interactive software tool, ENDEAVOUR, at http://www.esat.kuleuven.be/endeavour. Unlike previous methods, ENDEAVOUR generates distinct prioritizations from multiple heterogeneous data sources, which are then integrated, or fused, into one global ranking using order statistics. ENDEAVOUR prioritizes candidate genes in a three-step process. First, information about a disease or pathway is gathered from a set of known "training" genes by consulting multiple data sources. Next, the candidate genes are ranked based on similarity with the training properties obtained in the first step, resulting in one prioritized list for each data source. Finally, ENDEAVOUR fuses each of these rankings into a single global ranking, providing an overall prioritization of the candidate genes. Validation of ENDEAVOUR revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified YPEL1 as a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. Finally, we are currently evaluating a pipeline combining array-CGH, ENDEAVOUR and in vivo validation in zebrafish to identify novel genes involved in congenital heart defects. Mark Broom 1 , Graeme Ruxton 2 , Rebecca Kilner 311 Mathematics Dept., University of Sussex, UK 12 Division of Environmental and Evolutionary Biology, University of Glasgow, UK 13 Department of Zoology, University of Cambridge, UK Keywords: Evolutionarily stable strategy, parasitism, asymmetric game Brood parasites chicks vary in the harm that they do to their companions in the nest. In this presentation we use game-theoretic methods to model this variation. Our model considers hosts which potentially abandon single nestlings and instead choose to re-allocate their reproductive effort to future breeding, irrespective of whether the abandoned chick is the host's young or a brood parasite's. The parasite chick must decide whether or not to kill host young by balancing the benefits from reduced competition in the nest against the risk of desertion by host parents. The model predicts that three different types of evolutionarily stable strategies can exist. (1) Hosts routinely rear depleted broods, the brood parasite always kills host young and the host never then abandons the nest. (2) When adult survival after deserting single offspring is very high, hosts always abandon broods of a single nestling and the parasite never kills host offspring, effectively holding them as hostages to prevent nest desertion. (3) Intermediate strategies, in which parasites sometimes kill their nest-mates and host parents sometimes desert nests that contain only a single chick, can also be evolutionarily stable. We provide quantitative descriptions of how the values given to ecological and behavioral parameters of the host-parasite system influence the likelihood of each strategy and compare our results with real host-brood parasite associations in nature. Martin Harrison 114 Mathematics Dept, University of Sussex, UK Keywords: Brood parasitism, games, host, parasite The interaction between hosts and parasites in bird populations has been studied extensively. Game theoretical methods have been used to model this interaction previously, but this has not been studied extensively taking into account the sequential nature of this game. We consider a model allowing the host and parasite to make a number of decisions, which depend on a number of natural factors. The host lays an egg, a parasite bird will arrive at the nest with a certain probability and then chooses to destroy a number of the host eggs and lay one of it's own. With some destruction occurring, either natural or through the actions of the parasite, the host chooses to continue, eject an egg (hoping to eject the parasite) or abandon the nest. Once the eggs have hatched the game then falls to the parasite chick versus the host. The chick chooses to destroy or eject a number of eggs. The final decision is made by the host, choosing whether to raise or abandon the chicks that are in the nest. We consider various natural parameters and probabilities which influence these decisions. We then use this model to look at real-world situations of the interactions of the Reed Warbler and two different parasites, the Common Cuckoo and the Brown-Headed Cowbird. These two parasites have different methods in the way that they parasitize the nests of their hosts. The hosts in turn have a different reaction to these parasites. Arne Jochens 1 , Amke Caliebe 2 , Uwe Roesler 1 , Michael Krawczak 215 Mathematical Seminar, University of Kiel, Germany 16 Institute of Medical Informatics and Statistics, University of Kiel, Germany Keywords: Stepwise mutation model, microsatellite, recursion equation, temporal behaviour We consider the stepwise mutation model which occurs, e.g., in microsatellite loci. Let X(t,i) denote the allelic state of individual i at time t. We compute expectation, variance and covariance of X(t,i), i=1,,,N, and provide a recursion equation for P(X(t,i)=z). Because the variance of X(t,i) goes to infinity as t grows, for the description of the temporal behaviour, we regard the scaled process X(t,i)-X(t,1). The results furnish a better understanding of the behaviour of the stepwise mutation model and may in future be used to derive tests for neutrality under this model. Paul O'Reilly 1 , Ewan Birney 2 , David Balding 117 Statistical Genetics, Department of Epidemiology and Public Health, Imperial, College London, UK 18 European Bioinformatics Institute, EMBL, Cambridge, UK Keywords: Positive selection, Recombination rate, LD, Genome-wide, Natural Selection In recent years efforts to develop population genetics methods that estimate rates of recombination and levels of natural selection in the human genome have intensified. However, since the two processes have an intimately related impact on genetic variation their inference is vulnerable to confounding. Genomic regions subject to recent selection are likely to have a relatively recent common ancestor and consequently less opportunity for historical recombinations that are detectable in contemporary populations. Here we show that selection can reduce the population-based recombination rate estimate substantially. In genome-wide studies for detecting selection we observe a tendency to highlight loci that are subject to low levels of recombination. We find that the outlier approach commonly adopted in such studies may have low power unless variable recombination is accounted for. We introduce a new genome-wide method for detecting selection that exploits the sensitivity to recent selection of methods for estimating recombination rates, while accounting for variable recombination using pedigree data. Through simulations we demonstrate the high power of the Ped/Pop approach to discriminate between neutral and adaptive evolution, particularly in the context of choosing outliers from a genome-wide distribution. Although methods have been developed showing good power to detect selection ,in action', the corresponding window of opportunity is small. In contrast, the power of the Ped/Pop method is maintained for many generations after the fixation of an advantageous variant Sarah Griffiths 1 , Frank Dudbridge 120 MRC Biostatistics Unit, Cambridge, UK Keywords: Genetic association, multimarker tag, haplotype, likelihood analysis In association studies it is generally too expensive to genotype all variants in all subjects. We can exploit linkage disequilibrium between SNPs to select a subset that captures the variation in a training data set obtained either through direct resequencing or a public resource such as the HapMap. These ,tag SNPs' are then genotyped in the whole sample. Multimarker tagging is a more aggressive adaptation of pairwise tagging that allows for combinations of two or more tag SNPs to predict an untyped SNP. Here we describe a new method for directly testing the association of an untyped SNP using a multimarker tag. Previously, other investigators have suggested testing a specific tag haplotype, or performing a weighted analysis using weights derived from the training data. However these approaches do not properly account for the imperfect correlation between the tag haplotype and the untyped SNP. Here we describe a straightforward approach to testing untyped SNPs using a missing-data likelihood analysis, including the tag markers as nuisance parameters. The training data is stacked on top of the main body of genotype data so there is information on how the tag markers predict the genotype of the untyped SNP. The uncertainty in this prediction is automatically taken into account in the likelihood analysis. This approach yields more power and also a more accurate prediction of the odds ratio of the untyped SNP. Anke Schulz 1 , Christine Fischer 2 , Jenny Chang-Claude 1 , Lars Beckmann 121 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany 22 Institute of Human Genetics, University of Heidelberg, Germany Keywords: Haplotype, haplotype sharing, entropy, Mantel statistics, marker selection We previously introduced a new method to map genes involved in complex diseases, using haplotype sharing-based Mantel statistics to correlate genetic and phenotypic similarity. Although the Mantel statistic is powerful in narrowing down candidate regions, the precise localization of a gene is hampered in genomic regions where linkage disequilibrium is so high that neighboring markers are found to be significant at similar magnitude and we are not able to discriminate between them. Here, we present a new approach to localize susceptibility genes by combining haplotype sharing-based Mantel statistics with an iterative entropy-based marker selection algorithm. For each marker at which the Mantel statistic is evaluated, the algorithm selects a subset of surrounding markers. The subset is chosen to maximize multilocus linkage disequilibrium, which is measured by the normalized entropy difference introduced by Nothnagel et al. (2002). We evaluated the algorithm with respect to type I error and power. Its ability to localize the disease variant was compared to the localization (i) without marker selection and (ii) considering haplotype block structure. Case-control samples were simulated from a set of 18 haplotypes, consisting of 15 SNPs in two haplotype blocks. The new algorithm gave correct type I error and yielded similar power to detect the disease locus compared to the alternative approaches. The neighboring markers were clearly less often significant than the causal locus, and also less often significant compared to the alternative approaches. Thus the new algorithm improved the precision of the localization of susceptibility genes. Mark M. Iles 123 Section of Epidemiology and Biostatistics, LIMM, University of Leeds, UK Keywords: tSNP, tagging, association, HapMap Tagging SNPs (tSNPs) are commonly used to capture genetic diversity cost-effectively. However, it is important that the efficacy of tSNPs is correctly estimated, otherwise coverage may be insufficient. If the pilot sample from which tSNPs are chosen is too small or the initial marker map too sparse, tSNP efficacy may be overestimated. An existing estimation method based on bootstrapping goes some way to correct for insufficient sample size and overfitting, but does not completely solve the problem. We describe a novel method, based on exclusion of haplotypes, that improves on the bootstrap approach. Using simulated data, the extent of the sample size problem is investigated and the performance of the bootstrap and the novel method are compared. We incorporate an existing method adjusting for marker density by ,SNP-dropping'. We find that insufficient sample size can cause large overestimates in tSNP efficacy, even with as many as 100 individuals, and the problem worsens as the region studied increases in size. Both the bootstrap and novel method correct much of this overestimate, with our novel method consistently outperforming the bootstrap method. We conclude that a combination of insufficient sample size and overfitting may lead to overestimation of tSNP efficacy and underpowering of studies based on tSNPs. Our novel approach corrects for much of this bias and is superior to the previous method. Sample sizes larger than previously suggested may still be required for accurate estimation of tSNP efficacy. This has obvious ramifications for the selection of tSNPs from HapMap data. Claudio Verzilli 1 , Juliet Chapman 1 , Aroon Hingorani 2 , Juan Pablo-Casas 1 , Tina Shah 2 , Liam Smeeth 1 , John Whittaker 124 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK 25 Division of Medicine, University College London, UK Keywords: Meta-analysis, Genetic association studies We present a Bayesian hierarchical model for the meta-analysis of candidate gene studies with a continuous outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping markers (typically SNPs) in the same genetic region. Meta analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequlibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian hierarchical linear regression that models the observed genotype group means and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach is applied to the meta analysis of 24 studies assessing the effect of 7 variants in the C-reactive protein (CRP) gene region on plasma CRP levels, an inflammatory biomarker shown in observational studies to be positively associated with cardiovascular disease. Cathryn M. Lewis 1 , Christopher G. Mathew 1 , Theresa M. Marteau 226 Dept. of Medical and Molecular Genetics, King's College London, UK 27 Department of Psychology, King's College London, UK Keywords: Risk, genetics, CARD15, smoking, model Recently progress has been made in identifying mutations that confer susceptibility to complex diseases, with the potential to use these mutations in determining disease risk. We developed methods to estimate disease risk based on genotype relative risks (for a gene G), exposure to an environmental factor (E), and family history (with recurrence risk ,R for a relative of type R). ,R must be partitioned into the risk due to G (which is modelled independently) and the residual risk. The risk model was then applied to Crohn's disease (CD), a severe gastrointestinal disease for which smoking increases disease risk approximately 2-fold, and mutations in CARD15 confer increased risks of 2.25 (for carriers of a single mutation) and 9.3 (for carriers of two mutations). CARD15 accounts for only a small proportion of the genetic component of CD, with a gene-specific ,S, CARD15 of 1.16, from a total sibling relative risk of ,S= 27. CD risks were estimated for high-risk individuals who are siblings of a CD case, and who also smoke. The CD risk to such individuals who carry two CARD15 mutations is approximately 0.34, and for those carrying a single CARD15 mutation the risk is 0.08, compared to a population prevalence of approximately 0.001. These results imply that complex disease genes may be valuable in estimating with greater precision than has hitherto been possible disease risks in specific, easily identified subgroups of the population with a view to prevention. Yurii Aulchenko 128 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Compression, information, bzip2, genome-wide SNP data, statistical genetics With advances in molecular technology, studies accessing millions of genetic polymorphisms in thousands of study subjects will soon become common. Such studies generate large amounts of data, whose effective storage and management is a challenge to the modern statistical genetics. Standard file compression utilities, such as Zip, Gzip and Bzip2, may be helpful to minimise the storage requirements. Less obvious is the fact that the data compression techniques may be also used in the analysis of genetic data. It is known that the efficiency of a particular compression algorithm depends on the probability structure of the data. In this work, we compared different standard and customised tools using the data from human HapMap project. Secondly, we investigate the potential uses of data compression techniques for the analysis of linkage, association and linkage disequilibrium Suzanne Leal 1 , Bingshan Li 129 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, USA Keywords: Consanguineous pedigrees, missing genotype data Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al (2005) that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. The false-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. Which family members will aid in the reduction of false-positive evidence of linkage is highly dependent on which other family members are genotyped. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband's sibling-grandparents. When parental genotypes are not available, false-positive evidence for linkage can be reduced by including in the analysis genotype data from either unaffected siblings of the proband or the proband's married-in-grandparents. Najaf Amin 1 , Yurii Aulchenko 130 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Genomic Control, pedigree structure, quantitative traits The Genomic Control (GC) method was originally developed to control for population stratification and cryptic relatedness in association studies. This method assumes that the effect of population substructure on the test statistics is essentially constant across the genome, and therefore unassociated markers can be used to estimate the effect of confounding onto the test statistic. The properties of GC method were extensively investigated for different stratification scenarios, and compared to alternative methods, such as the transmission-disequilibrium test. The potential of this method to correct not for occasional cryptic relations, but for regular pedigree structure, however, was not investigated before. In this work we investigate the potential of the GC method for pedigree-based association analysis of quantitative traits. The power and type one error of the method was compared to standard methods, such as the measured genotype (MG) approach and quantitative trait transmission-disequilibrium test. In human pedigrees, with trait heritability varying from 30 to 80%, the power of MG and GC approach was always higher than that of TDT. GC had correct type 1 error and its power was close to that of MG under moderate heritability (30%), but decreased with higher heritability. William Astle 1 , Chris Holmes 2 , David Balding 131 Department of Epidemiology and Public Health, Imperial College London, UK 32 Department of Statistics, University of Oxford, UK Keywords: Population structure, association studies, genetic epidemiology, statistical genetics In the analysis of population association studies, Genomic Control (Devlin & Roeder, 1999) (GC) adjusts the Armitage test statistic to correct the type I error for the effects of population substructure, but its power is often sub-optimal. Turbo Genomic Control (TGC) generalises GC to incorporate co-variation of relatedness and phenotype, retaining control over type I error while improving power. TGC is similar to the method of Yu et al. (2006), but we extend it to binary (case-control) in addition to quantitative phenotypes, we implement improved estimation of relatedness coefficients, and we derive an explicit statistic that generalizes the Armitage test statistic and is fast to compute. TGC also has similarities to EIGENSTRAT (Price et al., 2006) which is a new method based on principle components analysis. The problems of population structure(Clayton et al., 2005) and cryptic relatedness (Voight & Pritchard, 2005) are essentially the same: if patterns of shared ancestry differ between cases and controls, whether distant (coancestry) or recent (cryptic relatedness), false positives can arise and power can be diminished. With large numbers of widely-spaced genetic markers, coancestry can now be measured accurately for each pair of individuals via patterns of allele-sharing. Instead of modelling subpopulations, we work instead with a coancestry coefficient for each pair of individuals in the study. We explain the relationships between TGC, GC and EIGENSTRAT. We present simulation studies and real data analyses to illustrate the power advantage of TGC in a range of scenarios incorporating both substructure and cryptic relatedness. References Clayton, D. G.et al. (2005) Population structure, differential bias and genomic control in a large-scale case-control association study. Nature Genetics37(11) November 2005. Devlin, B. & Roeder, K. (1999) Genomic control for association studies. Biometics55(4) December 1999. Price, A. L.et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics38(8) (August 2006). Voight, B. J. & Pritchard, J. K. (2005) Confounding from cryptic relatedness in case-control association studies. Public Library of Science Genetics1(3) September 2005. Yu, J.et al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics38(2) February 2006. Hervé Perdry 1 , Marie-Claude Babron 1 , Françoise Clerget-Darpoux 133 INSERM U535 and Univ. Paris Sud, UMR-S 535, Villejuif, France Keywords: Modifier genes, case-parents trios, ordered transmission disequilibrium test A modifying locus is a polymorphic locus, distinct from the disease locus, which leads to differences in the disease phenotype, either by modifying the penetrance of the disease allele, or by modifying the expression of the disease. The effect of such a locus is a clinical heterogeneity that can be reflected by the values of an appropriate covariate, such as the age of onset, or the severity of the disease. We designed the Ordered Transmission Disequilibrium Test (OTDT) to test for a relation between the clinical heterogeneity, expressed by the covariate, and marker genotypes of a candidate gene. The method applies to trio families with one affected child and his parents. Each family member is genotyped at a bi-allelic marker M of a candidate gene. To each of the families is associated a covariate value; the families are ordered on the values of this covariate. As the TDT (Spielman et al. 1993), the OTDT is based on the observation of the transmission rate T of a given allele at M. The OTDT aims to find a critical value of the covariate which separates the sample of families in two subsamples in which the transmission rates are significantly different. We investigate the power of the method by simulations under various genetic models and covariate distributions. Acknowledgments H Perdry is funded by ARSEP. Pascal Croiseau 1 , Heather Cordell 2 , Emmanuelle Génin 134 INSERM U535 and University Paris Sud, UMR-S535, Villejuif, France 35 Institute of Human Genetics, Newcastle University, UK Keywords: Association, missing data, conditionnal logistic regression Missing data is an important problem in association studies. Several methods used to test for association need that individuals be genotyped at the full set of markers. Individuals with missing data need to be excluded from the analysis. This could involve an important decrease in sample size and a loss of information. If the disease susceptibility locus (DSL) is poorly typed, it is also possible that a marker in linkage disequilibrium gives a stronger association signal than the DSL. One may then falsely conclude that the marker is more likely to be the DSL. We recently developed a Multiple Imputation method to infer missing data on case-parent trios Starting from the observed data, a few number of complete data sets are generated by Markov-Chain Monte Carlo approach. These complete datasets are analysed using standard statistical package and the results are combined as described in Little & Rubin (2002). Here we report the results of simulations performed to examine, for different patterns of missing data, how often the true DSL gives the highest association score among different loci in LD. We found that multiple imputation usually correctly detect the DSL site even if the percentage of missing data is high. This is not the case for the naďve approach that consists in discarding trios with missing data. In conclusion, Multiple imputation presents the advantage of being easy to use and flexible and is therefore a promising tool in the search for DSL involved in complex diseases. Salma Kotti 1 , Heike Bickeböller 2 , Françoise Clerget-Darpoux 136 University Paris Sud, UMR-S535, Villejuif, France 37 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany Keywords: Genotype relative risk, internal controls, Family based analyses Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRRs. We will analytically derive the GRR estimators for the 1:1 and 1:3 matching and will present the results at the meeting. Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRR. We will analytically derive the GRR estimator for the 1:1 and 1:3 matching and will present the results at the meeting. Luigi Palla 1 , David Siegmund 239 Department of Mathematics,Free University Amsterdam, The Netherlands 40 Department of Statistics, Stanford University, California, USA Keywords: TDT, assortative mating, inbreeding, statistical power A substantial amount of Assortative Mating (AM) is often recorded on physical and psychological, dichotomous as well as quantitative traits that are supposed to have a multifactorial genetic component. In particular AM has the effect of increasing the genetic variance, even more than inbreeding because it acts across loci beside within loci, when the trait has a multifactorial origin. Under the assumption of a polygenic model for AM dating back to Wright (1921) and refined by Crow and Felsenstein (1968,1982), the effect of assortative mating on the power to detect genetic association in the Transmission Disequilibrium Test (TDT) is explored as parameters, such as the effective number of genes and the allelic frequency vary. The power is reflected by the non centrality parameter of the TDT and is expressed as a function of the number of trios, the relative risk of the heterozygous genotype and the allele frequency (Siegmund and Yakir, 2007). The noncentrality parameter of the relevant score statistic is updated considering the effect of AM which is expressed in terms of an ,effective' inbreeding coefficient. In particular, for dichotomous traits it is apparent that the higher the number of genes involved in the trait, the lower the loss in power due to AM. Finally an attempt is made to extend this relation to the Q-TDT (Rabinowitz, 1997), which involves considering the effect of AM also on the phenotypic variance of the trait of interest, under the assumption that AM affects only its additive genetic component. References Crow, & Felsenstein, (1968). The effect of assortative mating on the genetic composition of a population. Eugen.Quart.15, 87,97. Rabinowitz,, 1997. A Transmission Disequilibrium Test for Quantitative Trait Loci. Human Heredity47, 342,350. Siegmund, & Yakir, (2007) Statistics of gene mapping, Springer. Wright, (1921). System of mating.III. Assortative mating based on somatic resemblance. Genetics6, 144,161. Jérémie Nsengimana 1 , Ben D Brown 2 , Alistair S Hall 2 , Jenny H Barrett 141 Leeds Institute of Molecular Medicine, University of Leeds, UK 42 Leeds Institute for Genetics, Health and Therapeutics, University of Leeds, UK Keywords: Inflammatory genes, haplotype, coronary artery disease Genetic Risk of Acute Coronary Events (GRACE) is an initiative to collect cases of coronary artery disease (CAD) and their unaffected siblings in the UK and to use them to map genetic variants increasing disease risk. The aim of the present study was to test the association between CAD and 51 single nucleotide polymorphisms (SNPs) and their haplotypes from 35 inflammatory genes. Genotype data were available for 1154 persons affected before age 66 (including 48% before age 50) and their 1545 unaffected siblings (891 discordant families). Each SNP was tested for association to CAD, and haplotypes within genes or gene clusters were tested using FBAT (Rabinowitz & Laird, 2000). For the most significant results, genetic effect size was estimated using conditional logistic regression (CLR) within STATA adjusting for other risk factors. Haplotypes were assigned using HAPLORE (Zhang et al., 2005), which considers all parental mating types consistent with offspring genotypes and assigns them a probability of occurence. This probability was used in CLR to weight the haplotypes. In the single SNP analysis, several SNPs showed some evidence for association, including one SNP in the interleukin-1A gene. Analysing haplotypes in the interleukin-1 gene cluster, a common 3-SNP haplotype was found to increase the risk of CAD (P = 0.009). In an additive genetic model adjusting for covariates the odds ratio (OR) for this haplotype is 1.56 (95% CI: 1.16-2.10, p = 0.004) for early-onset CAD (before age 50). This study illustrates the utility of haplotype analysis in family-based association studies to investigate candidate genes. References Rabinowitz, D. & Laird, N. M. (2000) Hum Hered50, 211,223. Zhang, K., Sun, F. & Zhao, H. (2005) Bioinformatics21, 90,103. Andrea Foulkes 1 , Recai Yucel 1 , Xiaohong Li 143 Division of Biostatistics, University of Massachusetts, USA Keywords: Haploytpe, high-dimensional, mixed modeling The explosion of molecular level information coupled with large epidemiological studies presents an exciting opportunity to uncover the genetic underpinnings of complex diseases; however, several analytical challenges remain to be addressed. Characterizing the components to complex diseases inevitably requires consideration of synergies across multiple genetic loci and environmental and demographic factors. In addition, it is critical to capture information on allelic phase, that is whether alleles within a gene are in cis (on the same chromosome) or in trans (on different chromosomes.) In associations studies of unrelated individuals, this alignment of alleles within a chromosomal copy is generally not observed. We address the potential ambiguity in allelic phase in this high dimensional data setting using mixed effects models. Both a semi-parametric and fully likelihood-based approach to estimation are considered to account for missingness in cluster identifiers. In the first case, we apply a multiple imputation procedure coupled with a first stage expectation maximization algorithm for parameter estimation. A bootstrap approach is employed to assess sensitivity to variability induced by parameter estimation. Secondly, a fully likelihood-based approach using an expectation conditional maximization algorithm is described. Notably, these models allow for characterizing high-order gene-gene interactions while providing a flexible statistical framework to account for the confounding or mediating role of person specific covariates. The proposed method is applied to data arising from a cohort of human immunodeficiency virus type-1 (HIV-1) infected individuals at risk for therapy associated dyslipidemia. Simulation studies demonstrate reasonable power and control of family-wise type 1 error rates. Vivien Marquard 1 , Lars Beckmann 1 , Jenny Chang-Claude 144 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Genotyping errors, type I error, haplotype-based association methods It has been shown in several simulation studies that genotyping errors may have a great impact on the type I error of statistical methods used in genetic association analysis of complex diseases. Our aim was to investigate type I error rates in a case-control study, when differential and non-differential genotyping errors were introduced in realistic scenarios. We simulated case-control data sets, where individual genotypes were drawn from a haplotype distribution of 18 haplotypes with 15 markers in the APM1 gene. Genotyping errors were introduced following the unrestricted and symmetric with 0 edges error models described by Heid et al. (2006). In six scenarios, errors resulted from changes of one allele to another with predefined probabilities of 1%, 2.5% or 10%, respectively. A multiple number of errors per haplotype was possible and could vary between 0 and 15, the number of markers investigated. We examined three association methods: Mantel statistics using haplotype-sharing; a haplotype-specific score test; and Armitage trend test for single markers. The type I error rates were not influenced for any of all the three methods for a genotyping error rate of less than 1%. For higher error rates and differential errors, the type I error of the Mantel statistic was only slightly and of the Armitage trend test moderately increased. The type I error rates of the score test were highly increased. The type I error rates were correct for all three methods for non-differential errors. Further investigations will be carried out with different frequencies of differential error rates and focus on power. Arne Neumann 1 , Dörthe Malzahn 1 , Martina Müller 2 , Heike Bickeböller 145 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany 46 GSF-National Research Center for Environment and Health, Neuherberg & IBE-Institute of Epidemiology, Ludwig-Maximilians University München, Germany Keywords: Interaction, longitudinal, nonparametric Longitudinal data show the time dependent course of phenotypic traits. In this contribution, we consider longitudinal cohort studies and investigate the association between two candidate genes and a dependent quantitative longitudinal phenotype. The set-up defines a factorial design which allows us to test simultaneously for the overall gene effect of the loci as well as for possible gene-gene and gene time interaction. The latter would induce genetically based time-profile differences in the longitudinal phenotype. We adopt a non-parametric statistical test to genetic epidemiological cohort studies and investigate its performance by simulation studies. The statistical test was originally developed for longitudinal clinical studies (Brunner, Munzel, Puri, 1999 J Multivariate Anal 70:286-317). It is non-parametric in the sense that no assumptions are made about the underlying distribution of the quantitative phenotype. Longitudinal observations belonging to the same individual can be arbitrarily dependent on one another for the different time points whereas trait observations of different individuals are independent. The two loci are assumed to be statistically independent. Our simulations show that the nonparametric test is comparable with ANOVA in terms of power of detecting gene-gene and gene-time interaction in an ANOVA favourable setting. Rebecca Hein 1 , Lars Beckmann 1 , Jenny Chang-Claude 147 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Indirect association studies, interaction effects, linkage disequilibrium, marker allele frequency Association studies accounting for gene-environment interactions (GxE) may be useful for detecting genetic effects and identifying important environmental effect modifiers. Current technology facilitates very dense marker spacing in genetic association studies; however, the true disease variant(s) may not be genotyped. In this situation, an association between a gene and a phenotype may still be detectable, using genetic markers associated with the true disease variant(s) (indirect association). Zondervan and Cardon [2004] showed that the odds ratios (OR) of markers which are associated with the disease variant depend highly on the linkage disequilibrium (LD) between the variant and the markers, and whether the allele frequencies match and thereby influence the sample size needed to detect genetic association. We examined the influence of LD and allele frequencies on the sample size needed to detect GxE in indirect association studies, and provide tables for sample size estimation. For discordant allele frequencies and incomplete LD, sample sizes can be unfeasibly large. The influence of both factors is stronger for disease loci with small rather than moderate to high disease allele frequencies. A decline in D' of e.g. 5% has less impact on sample size than increasing the difference in allele frequencies by the same percentage. Assuming 80% power, large interaction effects can be detected using smaller sample sizes than those needed for the detection of main effects. The detection of interaction effects involving rare alleles may not be possible. Focussing only on marker density can be a limited strategy in indirect association studies for GxE. Cyril Dalmasso 1 , Emmanuelle Génin 2 , Catherine Bourgain 2 , Philippe Broët 148 JE 2492 , Univ. Paris-Sud, France 49 INSERM UMR-S 535 and University Paris Sud, Villejuif, France Keywords: Linkage analysis, Multiple testing, False Discovery Rate, Mixture model In the context of genome-wide linkage analyses, where a large number of statistical tests are simultaneously performed, the False Discovery Rate (FDR) that is defined as the expected proportion of false discoveries among all discoveries is nowadays widely used for taking into account the multiple testing problem. Other related criteria have been considered such as the local False Discovery Rate (lFDR) that is a variant of the FDR giving to each test its own measure of significance. The lFDR is defined as the posterior probability that a null hypothesis is true. Most of the proposed methods for estimating the lFDR or the FDR rely on distributional assumption under the null hypothesis. However, in observational studies, the empirical null distribution may be very different from the theoretical one. In this work, we propose a mixture model based approach that provides estimates of the lFDR and the FDR in the context of large-scale variance component linkage analyses. In particular, this approach allows estimating the empirical null distribution, this latter being a key quantity for any simultaneous inference procedure. The proposed method is applied on a real dataset. Arief Gusnanto 1 , Frank Dudbridge 150 MRC Biostatistics Unit, Cambridge UK Keywords: Significance, genome-wide, association, permutation, multiplicity Genome-wide association scans have introduced statistical challenges, mainly in the multiplicity of thousands of tests. The question of what constitutes a significant finding remains somewhat unresolved. Permutation testing is very time-consuming, whereas Bayesian arguments struggle to distinguish direct from indirect association. It seems attractive to summarise the multiplicity in a simple form that allows users to avoid time-consuming permutations. A standard significance level would facilitate reporting of results and reduce the need for permutation tests. This is potentially important because current scans do not have full coverage of the whole genome, and yet, the implicit multiplicity is genome-wide. We discuss some proposed summaries, with reference to the empirical null distribution of the multiple tests, approximated through a large number of random permutations. Using genome-wide data from the Wellcome Trust Case-Control Consortium, we use a sub-sampling approach with increasing density to estimate the nominal p-value to obtain family-wise significance of 5%. The results indicate that the significance level is converging to about 1e-7 as the marker spacing becomes infinitely dense. We considered the concept of an effective number of independent tests, and showed that when used in a Bonferroni correction, the number varies with the overall significance level, but is roughly constant in the region of interest. We compared several estimators of the effective number of tests, and showed that in the region of significance of interest, Patterson's eigenvalue based estimator gives approximately the right family-wise error rate. Michael Nothnagel 1 , Amke Caliebe 1 , Michael Krawczak 151 Institute of Medical Informatics and Statistics, University Clinic Schleswig-Holstein, University of Kiel, Germany Keywords: Association scans, Bayesian framework, posterior odds, genetic risk, multiplicative model Whole-genome association scans have been suggested to be a cost-efficient way to survey genetic variation and to map genetic disease factors. We used a Bayesian framework to investigate the posterior odds of a genuine association under multiplicative disease models. We demonstrate that the p value alone is not a sufficient means to evaluate the findings in association studies. We suggest that likelihood ratios should accompany p values in association reports. We argue, that, given the reported results of whole-genome scans, more associations should have been successfully replicated if the consistently made assumptions about considerable genetic risks were correct. We conclude that it is very likely that the vast majority of relative genetic risks are only of the order of 1.2 or lower. Clive Hoggart 1 , Maria De Iorio 1 , John Whittakker 2 , David Balding 152 Department of Epidemiology and Public Health, Imperial College London, UK 53 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: Genome-wide association analyses, shrinkage priors, Lasso Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants of small effect, which is a plausible scenario for many complex diseases. Moreover, many simulation studies assume a single causal variant and so more complex realities are ignored. Analysing large numbers of variants simultaneously is now becoming feasible, thanks to developments in Bayesian stochastic search methods. We pose the problem of SNP selection as variable selection in a regression model. In contrast to single SNP tests this approach simultaneously models the effect of all SNPs. SNPs are selected by a Bayesian interpretation of the lasso (Tibshirani, 1996); the maximum a posterior (MAP) estimate of the regression coefficients, which have been given independent, double exponential prior distributions. The double exponential distribution is an example of a shrinkage prior, MAP estimates with shrinkage priors can be zero, thus all SNPs with non zero regression coefficients are selected. In addition to the commonly-used double exponential (Laplace) prior, we also implement the normal exponential gamma prior distribution. We show that use of the Laplace prior improves SNP selection in comparison with single -SNP tests, and that the normal exponential gamma prior leads to a further improvement. Our method is fast and can handle very large numbers of SNPs: we demonstrate its performance using both simulated and real genome-wide data sets with 500 K SNPs, which can be analysed in 2 hours on a desktop workstation. Mickael Guedj 1,2 , Jerome Wojcik 2 , Gregory Nuel 154 Laboratoire Statistique et Génome, Université d'Evry, Evry France 55 Serono Pharmaceutical Research Institute, Plan-les-Ouates, Switzerland Keywords: Local Replication, Local Score, Association In gene-mapping, replication of initial findings has been put forwards as the approach of choice for filtering false-positives from true signals for underlying loci. In practice, such replications are however too poorly observed. Besides the statistical and technical-related factors (lack of power, multiple-testing, stratification, quality control,) inconsistent conclusions obtained from independent populations might result from real biological differences. In particular, the high degree of variation in the strength of LD among populations of different origins is a major challenge to the discovery of genes. Seeking for Local Replications (defined as the presence of a signal of association in a same genomic region among populations) instead of strict replications (same locus, same risk allele) may lead to more reliable results. Recently, a multi-markers approach based on the Local Score statistic has been proposed as a simple and efficient way to select candidate genomic regions at the first stage of genome-wide association studies. Here we propose an extension of this approach adapted to replicated association studies. Based on simulations, this method appears promising. In particular it outperforms classical simple-marker strategies to detect modest-effect genes. Additionally it constitutes, to our knowledge, a first framework dedicated to the detection of such Local Replications. Juliet Chapman 1 , Claudio Verzilli 1 , John Whittaker 156 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: FDR, Association studies, Bayesian model selection As genomewide association studies become commonplace there is debate as to how such studies might be analysed and what we might hope to gain from the data. It is clear that standard single locus approaches are limited in that they do not adjust for the effects of other loci and problematic since it is not obvious how to adjust for multiple comparisons. False discovery rates have been suggested, but it is unclear how well these will cope with highly correlated genetic data. We consider the validity of standard false discovery rates in large scale association studies. We also show that a Bayesian procedure has advantages in detecting causal loci amongst a large number of dependant SNPs and investigate properties of a Bayesian FDR. Peter Kraft 157 Harvard School of Public Health, Boston USA Keywords: Gene-environment interaction, genome-wide association scans Appropriately analyzed two-stage designs,where a subset of available subjects are genotyped on a genome-wide panel of markers at the first stage and then a much smaller subset of the most promising markers are genotyped on the remaining subjects,can have nearly as much power as a single-stage study where all subjects are genotyped on the genome-wide panel yet can be much less expensive. Typically, the "most promising" markers are selected based on evidence for a marginal association between genotypes and disease. Subsequently, the few markers found to be associated with disease at the end of the second stage are interrogated for evidence of gene-environment interaction, mainly to understand their impact on disease etiology and public health impact. However, this approach may miss variants which have a sizeable effect restricted to one exposure stratum and therefore only a modest marginal effect. We have proposed to use information on the joint effects of genes and a discrete list of environmental exposures at the initial screening stage to select promising markers for the second stage [Kraft et al Hum Hered 2007]. This approach optimizes power to detect variants that have a sizeable marginal effect and variants that have a small marginal effect but a sizeable effect in a stratum defined by an environmental exposure. As an example, I discuss a proposed genome-wide association scan for Type II diabetes susceptibility variants based in several large nested case-control studies. Beate Glaser 1 , Peter Holmans 158 Biostatistics and Bioinformatics Unit, Cardiff University, School of Medicine, Heath Park, Cardiff, UK Keywords: Combined case-control and trios analysis, Power, False-positive rate, Simulation, Association studies The statistical power of genetic association studies can be enhanced by combining the analysis of case-control with parent-offspring trio samples. Various combined analysis techniques have been recently developed; as yet, there have been no comparisons of their power. This work was performed with the aim of identifying the most powerful method among available combined techniques including test statistics developed by Kazeem and Farrall (2005), Nagelkerke and colleagues (2004) and Dudbridge (2006), as well as a simple combination of ,2-statistics from single samples. Simulation studies were performed to investigate their power under different additive, multiplicative, dominant and recessive disease models. False-positive rates were determined by studying the type I error rates under null models including models with unequal allele frequencies between the single case-control and trios samples. We identified three techniques with equivalent power and false-positive rates, which included modifications of the three main approaches: 1) the unmodified combined Odds ratio estimate by Kazeem & Farrall (2005), 2) a modified approach of the combined risk ratio estimate by Nagelkerke & colleagues (2004) and 3) a modified technique for a combined risk ratio estimate by Dudbridge (2006). Our work highlights the importance of studies investigating test performance criteria of novel methods, as they will help users to select the optimal approach within a range of available analysis techniques. David Almorza 1 , M.V. Kandus 2 , Juan Carlos Salerno 2 , Rafael Boggio 359 Facultad de Ciencias del Trabajo, University of Cádiz, Spain 60 Instituto de Genética IGEAF, Buenos Aires, Argentina 61 Universidad Nacional de La Plata, Buenos Aires, Argentina Keywords: Principal component analysis, maize, ear weight, inbred lines The objective of this work was to evaluate the relationship among different traits of the ear of maize inbred lines and to group genotypes according to its performance. Ten inbred lines developed at IGEAF (INTA Castelar) and five public inbred lines as checks were used. A field trial was carried out in Castelar, Buenos Aires (34° 36' S , 58° 39' W) using a complete randomize design with three replications. At harvest, individual weight (P.E.), diameter (D.E.), row number (N.H.) and length (L.E.) of the ear were assessed. A principal component analysis, PCA, (Infostat 2005) was used, and the variability of the data was depicted with a biplot. Principal components 1 and 2 (CP1 and CP2) explained 90% of the data variability. CP1 was correlated with P.E., L.E. and D.E., meanwhile CP2 was correlated with N.H. We found that individual weight (P.E.) was more correlated with diameter of the ear (D.E.) than with length (L.E). Five groups of inbred lines were distinguished: with high P.E. and mean N.H. (04-70, 04-73, 04-101 and MO17), with high P.E. but less N.H. (04-61 and B14), with mean P.E. and N.H. (B73, 04-123 and 04-96), with high N.H. but less P.E. (LP109, 04-8, 04-91 and 04-76) and with low P.E. and low N.H. (LP521 and 04-104). The use of PCA showed which variables had more incidence in ear weight and how is the correlation among them. Moreover, the different groups found with this analysis allow the evaluation of inbred lines by several traits simultaneously. Sven Knüppel 1 , Anja Bauerfeind 1 , Klaus Rohde 162 Department of Bioinformatics, MDC Berlin, Germany Keywords: Haplotypes, association studies, case-control, nuclear families The area of gene chip technology provides a plethora of phase-unknown SNP genotypes in order to find significant association to some genetic trait. To circumvent possibly low information content of a single SNP one groups successive SNPs and estimates haplotypes. Haplotype estimation, however, may reveal ambiguous haplotype pairs and bias the application of statistical methods. Zaykin et al. (Hum Hered, 53:79-91, 2002) proposed the construction of a design matrix to take this ambiguity into account. Here we present a set of functions written for the Statistical package R, which carries out haplotype estimation on the basis of the EM-algorithm for individuals (case-control) or nuclear families. The construction of a design matrix on basis of estimated haplotypes or haplotype pairs allows application of standard methods for association studies (linear, logistic regression), as well as statistical methods as haplotype sharing statistics and TDT-Test. Applications of these methods to genome-wide association screens will be demonstrated. Manuela Zucknick 1 , Chris Holmes 2 , Sylvia Richardson 163 Department of Epidemiology and Public Health, Imperial College London, UK 64 Department of Statistics, Oxford Center for Gene Function, University of Oxford, UK Keywords: Bayesian, variable selection, MCMC, large p, small n, structured dependence In large-scale genomic applications vast numbers of markers or genes are scanned to find a few candidates which are linked to a particular phenotype. Statistically, this is a variable selection problem in the "large p, small n" situation where many more variables than samples are available. An additional feature is the complex dependence structure which is often observed among the markers/genes due to linkage disequilibrium or their joint involvement in biological processes. Bayesian variable selection methods using indicator variables are well suited to the problem. Binary phenotypes like disease status are common and both Bayesian probit and logistic regression can be applied in this context. We argue that logistic regression models are both easier to tune and to interpret than probit models and implement the approach by Holmes & Held (2006). Because the model space is vast, MCMC methods are used as stochastic search algorithms with the aim to quickly find regions of high posterior probability. In a trade-off between fast-updating but slow-moving single-gene Metropolis-Hastings samplers and computationally expensive full Gibbs sampling, we propose to employ the dependence structure among the genes/markers to help decide which variables to update together. Also, parallel tempering methods are used to aid bold moves and help avoid getting trapped in local optima. Mixing and convergence of the resulting Markov chains are evaluated and compared to standard samplers in both a simulation study and in an application to a gene expression data set. Reference Holmes, C. C. & Held, L. (2006) Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis1, 145,168. Dawn Teare 165 MMGE, University of Sheffield, UK Keywords: CNP, family-based analysis, MCMC Evidence is accumulating that segmental copy number polymorphisms (CNPs) may represent a significant portion of human genetic variation. These highly polymorphic systems require handling as phenotypes rather than co-dominant markers, placing new demands on family-based analyses. We present an integrated approach to meet these challenges in the form of a graphical model, where the underlying discrete CNP phenotype is inferred from the (single or replicate) quantitative measure within the analysis, whilst assuming an allele based system segregating through the pedigree. [source] A Combinatorial Searching Method for Detecting a Set of Interacting Loci Associated with Complex TraitsANNALS OF HUMAN GENETICS, Issue 5 2006Qiuying Sha Summary Complex diseases are presumed to be the results of the interaction of several genes and environmental factors, with each gene only having a small effect on the disease. Mapping complex disease genes therefore becomes one of the greatest challenges facing geneticists. Most current approaches of association studies essentially evaluate one marker or one gene (haplotype approach) at a time. These approaches ignore the possibility that effects of multilocus functional genetic units may play a larger role than a single-locus effect in determining trait variability. In this article, we propose a Combinatorial Searching Method (CSM) to detect a set of interacting loci (may be unlinked) that predicts the complex trait. In the application of the CSM, a simple filter is used to filter all the possible locus-sets and retain the candidate locus-sets, then a new objective function based on the cross-validation and partitions of the multi-locus genotypes is proposed to evaluate the retained locus-sets. The locus-set with the largest value of the objective function is the final locus-set and a permutation procedure is performed to evaluate the overall p-value of the test for association between the final locus-set and the trait. The performance of the method is evaluated by simulation studies as well as by being applied to a real data set. The simulation studies show that the CSM has reasonable power to detect high-order interactions. When the CSM is applied to a real data set to detect the locus-set (among the 13 loci in the ACE gene) that predicts systolic blood pressure (SBP) or diastolic blood pressure (DBP), we found that a four-locus gene-gene interaction model best predicts SBP with an overall p-value = 0.033, and similarly a two-locus gene-gene interaction model best predicts DBP with an overall p-value = 0.045. [source] Spectrin mutations in spinocerebellar ataxia (SCA)BIOESSAYS, Issue 8 2006Peter Bauer Recently, ,III spectrins have been recognized as ataxia disease genes, with the identification by Ikeda and co-workers of pathogenic mutations in the SPTBN2 gene in three large (and mapped) SCA5 families of American and European origin.(1) With their discovery, the large "Lincoln" family has been traced back to the underlying genetic defect for the slowly progressive cerebellar ataxia. In addition, the involvement of this component of the cytoskeleton directs attention towards the possible role of organelle stability during neurodegeneration. The findings suggest that the mechanical properties of neurons and their dynamics may be as important as altered Ca2+ homeostasis, transcriptional dysregulation, and impaired protein degradation in neurodegeneration conditions. BioEssays 28: 785,787, 2006. © 2006 Wiley Periodicals, Inc. [source] | ||||||||||||||||