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Parental Genotypes (parental + genotype)
Selected AbstractsCombining ability and heterosis under pest epidemics in a broad-based global wheat-breeding populationPLANT BREEDING, Issue 3 2008R. Ortiz Abstract Wheat breeders rarely apply population improvement schemes or select parental sources according to combining ability and heterotic patterns. They rely on pedigree selection methods for breeding new cultivars. This experiment was undertaken to assess the advantages of using diallel crosses to define combining ability and understand heterosis in a broad-based wheat-breeding population across different environments affected by yellow rust. Sixty-four genotypes derived from a full diallel mating scheme were assessed for grain yield in two contrasting growing seasons at two locations for two consecutive years. Parental genotypes showed significant combining ability for grain yield that was affected by yellow rust and genotype-by-environment (GE) interactions, both of which affected heterosis for grain yield. Significant GE interactions suggested that decentralized selection for specific environments could maximize the use of this wheat germplasm. Cultivar effects and specific heterosis were the most important factors influencing grain yield. Some crosses capitalized on additive genetic variation for grain yield. This research shows the power of available quantitative breeding tools to help breeders choose parental sources in a population improvement programme. [source] Effect of parental genotypes and paternal heterosis on litter traits in crossbred goatsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2 2008Y. Tsukahara Summary The effect of parental genotype and paternal heterosis on litter size (LS), total litter birth weight (TLW) and average litter birth weight (ALW) was analysed utilizing data from a crossbreeding programme involving the exotic German Fawn goats and local Katjang goats in Malaysia. In this study, these traits were regarded as traits of the litter to consider the effect of service sire genotype. The results revealed that LS was significantly influenced by the genotype of sire. The genotypes of sire and dam had significant effects on TLW and ALW. Estimates of crossbreeding parameter showed significant and negative influence of paternal heterosis on TLW and ALW while there was no significant effect of paternal heterosis on LS. The results of this study stress the need to reconsider the use of local males in the tropics. [source] FAP-1-mediated activation of NF-,B induces resistance of head and neck cancer to fas-induced apoptosisJOURNAL OF CELLULAR BIOCHEMISTRY, Issue 1 2007Eva Wieckowski Abstract Molecular mechanisms responsible for tumor resistance to apoptosis often involve the Fas/FasL pathway. While squamous cell carcinomas of the head and neck (SCCHN) express both Fas and FasL, their resistance to self-induced apoptosis or apoptosis mediated by Fas agonistic antibody (CH-11Ab) was independent of the level of Fas surface expression or the presence of soluble Fas in supernatants of primary or metastatic SCCHN cell lines. By in vitro immunoselection, using PCI-15A cell line treated with successive cycles of CH-11 Ab, Fas-resistant sublines with the parental genotype were selected. Such sublines failed to cleave caspase-8 upon Fas engagement and were resistant to CH-11 Ab, although they remained sensitive to VP-16 or staurosporin. In the presence of cycloheximide, the selected SCCHN sublines become susceptible to CH-11 Ab, and showed cleavage of caspase-8, suggesting that apoptosis resistance was mediated by an inhibitory protein(s) acting upstream of caspase-8. Overexpression of Fas-associated phosphatase 1 (FAP-1), but not cellular FLICE-inhibitory protein (cFLIP) in SCCHN sublines was documented by Western blots and RT-PCR analyses. The FAP-1+ selected sublines also downregulated cell surface Fas. A high phosphorylation level of I,B,, NF,B activation and upregulation of Bcl-2 expression were observed in the FAP-1+ sublines. Treatment with the phosphatase inhibitor, orthovanadate, or silencing of FAP-1 with siRNA abolished their resistance to apoptosis, suggesting that FAP-1 phosphatase activity could be responsible for NF-,B activation and resistance of SCCHN cells to Fas-mediated apoptosis. J. Cell. Biochem. 100: 16,28, 2007. © 2006 Wiley-Liss, Inc. [source] Determination of sex and scrapie resistance genotype in preimplantation ovine embryosMOLECULAR REPRODUCTION & DEVELOPMENT, Issue 2 2009Florence Guignot Abstract The aim of this study was to test the accuracy of genotype diagnosis after pre-amplification of DNA extracted from biopsies obtained by microblade cutting of ovine embryos and to evaluate the viability of biopsied embryos after vitrification/warming and transfer to recipients. Sex and PrP genotypes were determined. Sex diagnosis was done by PCR amplification of ZFX/ZFY and SRY sequences after PEP-PCR while PrP genotype determination was performed after specific pre-amplification of specific target including codons 136, 154 and 171. Embryos were collected at Day 7 after oestrus. Blastocysts and expanded blastocysts were biopsied immediately after collection whereas compacted morulae were biopsied after 24 hr of in vitro culture. Eighty-nine biopsied embryos were frozen by vitrification. Fresh and vitrified whole embryos were kept as control. DNA of biopsies was extracted and pre-amplified. Sex diagnosis was efficient for 96.6% of biopsies and PrP genotyping was determined in 95.8% of codons. After embryo transfer, no significant difference was observed in lambing rate between biopsied, vitrified control and fresh embryos (54.5%, 60% and 66.6%, respectively). Embryo survival rate was not different between biopsied and whole vitrified embryos (P,=,0.38). At birth, 96.7% of diagnosed sex and 95.4% of predetermined codons were correct. Lamb PrP profiles were in agreement with parental genotype. PEP-PCR coupled with sex diagnosis and nested PCR coupled with PrP genotype predetermination are very accurate techniques to genotype ovine embryo before transfer. These original results allow planning of selection of resistant genotype to scrapie and sex of offspring before transfer of cryopreserved embryo. Mol. Reprod. Dev. 76: 183,190, 2009. © 2008 Wiley-Liss, Inc. [source] High embryo sac fertility and diversity of abnormal embryo sacs detected in autotetraploid indica/japonica hybrids in rice by whole-mount eosin B-staining confocal laser scanning microscopyPLANT BREEDING, Issue 2 2009C. Y. Hu Abstract An eosin B staining procedure for use with confocal microscopy (WE-CLSM) was used to examine the mature embryo sacs in 24 typical autotetraploid (2n = 4x = 48) indica/japonica hybrids and to compare with their original diploid (2n = 2x = 24) indica/japonica hybrids in rice. Four of the 24 hybrids showed >80% embryo sac fertility, and 13 hybrids over 70%. The average embryo sac fertility of the 24 autotetraploid indica/japonica hybrids was 68.08%, which was 17.33% higher than that in diploid hybrids. When compared with the diploid hybrids, 79.17% of the autotetraploid hybrids had higher embryo sac fertility. A diversity of abnormal embryo sacs occurred in autotetraploid indica/japonica hybrids. Embryo sac abnormalities were similar for both autotetraploid and diploid hybrid, but their frequencies were different. Some novel abnormal types were found in autotetraploid hybrids, such as enlarged egg apparatus. The embryo sac fertilities and frequencies of various abnormalities in autotetraploid hybrids varied with parental genotype and environment. The average seed set of the autotetraploid hybrids were higher than their diploid hybrids in different growing seasons. [source] The redistribution of protein sulfur in transgenic rice expressing a gene for a foreign, sulfur-rich proteinTHE PLANT JOURNAL, Issue 1 2003N. D. Hagan Summary Sulfur amino acid composition is an important determinant of seed protein quality. A chimeric gene encoding sunflower seed albumin (SSA), one of the most sulfur-rich seed storage proteins identified so far, was introduced into rice (Oryza sativa) in order to modify cysteine and methionine content of the seed. Analysis of a transgenic line expressing SSA at approximately 7% of total seed protein revealed that the mature grain showed little change in the total sulfur amino acid content compared to the parental genotype. This result indicated that the transgenic rice grain was unable to respond to the added demand for cysteine and methionine imposed by the production of SSA. Analysis of the protein composition of the transgenic grain showed changes in the relative levels of the major seed storage proteins, as well as some non-storage proteins, compared to non-transgenic controls. Changes observed at the protein level were concomitant with differences in mRNA accumulation but not always with the level of transcription. The limited sulfur reserves appeared to be re-allocated from endogenous proteins to the new sulfur sink in the transgenic grain. We hypothesize that this response is mediated by a signal transduction pathway that normally modulates seed storage protein composition in response to environmental fluctuations in sulfur availability, via both transcriptional and post-transcriptional control of gene expression. [source] Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic densityGENETIC EPIDEMIOLOGY, Issue 4 2010J. Stone Abstract Haplotype-based association studies have been proposed as a powerful comprehensive approach to identify causal genetic variation underlying complex diseases. Data comparisons within families offer the additional advantage of dealing naturally with complex sources of noise, confounding and population stratification. Two problems encountered when investigating associations between haplotypes and a continuous trait using data from sibships are (i) the need to define within-sibship comparisons for sibships of size greater than two and (ii) the difficulty of resolving the joint distribution of haplotype pairs within sibships in the absence of parental genotypes. We therefore propose first a method of orthogonal transformation of both outcomes and exposures that allow the decomposition of between- and within-sibship regression effects when sibship size is greater than two. We conducted a simulation study, which confirmed analysis using all members of a sibship is statistically more powerful than methods based on cross-sectional analysis or using subsets of sib-pairs. Second, we propose a simple permutation approach to avoid errors of inference due to the within-sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the VDR gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4-SNP VDR haplotype. In conclusion, our proposed method retains the benefits of the between- and within-pair analysis for pairs of siblings and can be implemented in standard software. Genet. Epidemiol. 34: 309,318, 2010. © 2009 Wiley-Liss, Inc. [source] Testing association in the presence of linkage , a powerful score for binary traitsGENETIC EPIDEMIOLOGY, Issue 6 2007Gudrun Jonasdottir Abstract We present a score for testing association in the presence of linkage for binary traits. The score is robust to varying degrees of linkage, and it is valid under any ascertainment scheme based on trait values as well as under population stratification. The score test is derived from a mixed effects model where population level association is modeled using a fixed effect and where correlation among related individuals is allowed for by using log-gamma random effects. The score, as presented in this paper, does not assume full information about the inheritance pattern in families or parental genotypes. We compare the score to the semi-parametric family-based association test (FBAT), which has won ground because of its flexible and simple form. We show that a random effects formulation of co-inheritance can improve the power substantially. We apply the method to data from the Collaborative Study on the Genetics of Alcoholism. We compare our findings to previously published results. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source] Informative-Transmission Disequilibrium Test (i-TDT): combined linkage and association mapping that includes unaffected offspring as well as affected offspringGENETIC EPIDEMIOLOGY, Issue 2 2007Chao-Yu Guo Abstract To date, there is no test valid for the composite null hypothesis of no linkage or no association that utilizes transmission information from heterozygous parents to their unaffected offspring as well as the affected offspring from ascertained nuclear families. Since the unaffected siblings also provide information about linkage and association, we introduce a new strategy called the informative-transmission disequilibrium test (i-TDT), which uses transmission information from heterozygous parents to all of the affected and unaffected offspring in ascertained nuclear families and provides a valid chi-square test for both linkage and association. The i-TDT can be used in various study designs and can accommodate all types of independent nuclear families with at least one affected offspring. We show that the transmission/disequilibrium test (TDT) (Spielman et al. [1993] Am. J. Hum. Genet. 52:506,516) is a special case of the i-TDT, if the study sample contains only case-parent trios. If the sample contains only affected and unaffected offspring without parental genotypes, the i-TDT is equivalent to the sibship disequilibrium test (SDT) (Horvath and Laird [1998] Am. J. Hum. Genet. 63:1886,1897. In addition, the test statistic of i-TDT is simple, explicit and can be implemented easily without intensive computing. Through computer simulations, we demonstrate that power of the i-TDT can be higher in many circumstances compared to a method that uses affected offspring only. Applying the i-TDT to the Framingham Heart Study data, we found that the apolipoprotein E (APOE) gene is significantly linked and associated with cross-sectional measures and longitudinal changes in total cholesterol. Genet. Epidemiol. © 2006 Wiley-Liss, Inc. [source] Power calculations for likelihood ratio tests for offspring genotype risks, maternal effects, and parent-of-origin (POO) effects in the presence of missing parental genotypes when unaffected siblings are availableGENETIC EPIDEMIOLOGY, Issue 1 2007E. Rampersaud Abstract Genotype-based likelihood-ratio tests (LRT) of association that examine maternal and parent-of-origin effects have been previously developed in the framework of log-linear and conditional logistic regression models. In the situation where parental genotypes are missing, the expectation-maximization (EM) algorithm has been incorporated in the log-linear approach to allow incomplete triads to contribute to the LRT. We present an extension to this model which we call the Combined_LRT that incorporates additional information from the genotypes of unaffected siblings to improve assignment of incompletely typed families to mating type categories, thereby improving inference of missing parental data. Using simulations involving a realistic array of family structures, we demonstrate the validity of the Combined_LRT under the null hypothesis of no association and provide power comparisons under varying levels of missing data and using sibling genotype data. We demonstrate the improved power of the Combined_LRT compared with the family-based association test (FBAT), another widely used association test. Lastly, we apply the Combined_LRT to a candidate gene analysis in Autism families, some of which have missing parental genotypes. We conclude that the proposed log-linear model will be an important tool for future candidate gene studies, for many complex diseases where unaffected siblings can often be ascertained and where epigenetic factors such as imprinting may play a role in disease etiology. Genet. Epidemiol. © 2006 Wiley-Liss, Inc. [source] Using sex-averaged genetic maps in multipoint linkage analysis when identity-by-descent status is incompletely knownGENETIC EPIDEMIOLOGY, Issue 5 2006Tasha E. Fingerlin Abstract The ratio of male and female genetic map distances varies dramatically across the human genome. Despite these sex differences in genetic map distances, most multipoint linkage analyses use sex-averaged genetic maps. We investigated the impact of using a sex-averaged genetic map instead of sex-specific maps for multipoint linkage analysis of affected sibling pairs when identity-by-descent states are incompletely known due to missing parental genotypes and incomplete marker heterozygosity. If either all or no parental genotypes were available, for intermarker distances of 10, 5, and 1,cM, we found no important differences in the expected maximum lod score (EMLOD) or location estimates of the disease locus between analyses that used the sex-averaged map and those that used the true sex-specific maps for female:male genetic map distance ratios 1:10 and 10:1. However, when genotypes for only one parent were available and the recombination rate was higher in females, the EMLOD using the sex-averaged map was inflated compared to the sex-specific map analysis if only mothers were genotyped and deflated if only fathers were genotyped. The inflation of the lod score when only mothers were genotyped led to markedly increased false-positive rates in some cases. The opposite was true when the recombination rate was higher in males; the EMLOD was inflated if only fathers were genotyped, and deflated if only mothers were genotyped. While the effects of missing parental genotypes were mitigated for less extreme cases of missingness, our results suggest that when possible, sex-specific maps should be used in linkage analyses. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source] Method for using complete and incomplete trios to identify genes related to a quantitative trait,GENETIC EPIDEMIOLOGY, Issue 1 2004Emily O. Kistner Abstract A number of tests for linkage and association with qualitative traits have been developed, with the most well-known being the transmission/disequilibrium test (TDT). For quantitative traits, varying extensions of the TDT have been suggested. The quantitative trait approach we propose is based on extending the log-linear model for case-parent trio data (Weinberg et al. [1998] Am. J. Hum. Genet. 62:969,978). Like the log-linear approach for qualitative traits, our proposed polytomous logistic approach for quantitative traits allows for population admixture by conditioning on parental genotypes. Compared to other methods, simulations demonstrate good power and robustness of the proposed test under various scenarios of the genotype effect, distribution of the quantitative trait, and population stratification. In addition, missing parental genotype data can be accommodated through an expectation-maximization (EM) algorithm approach. The EM approach allows recovery of most of the lost power due to incomplete trios. Published 2004 Wiley-Liss, Inc. [source] Properties of case/pseudocontrol analysis for genetic association studies: Effects of recombination, ascertainment, and multiple affected offspringGENETIC EPIDEMIOLOGY, Issue 3 2004Heather J. Cordell Abstract The case/pseudocontrol approach is a general framework for family-based association analysis, incorporating several previously proposed methods such as the transmission/disequilibrium test and log-linear modelling of parent-of-origin effects. In this report, I examine the properties of methods based on a case/pseudocontrol approach when applied to a linked marker rather than (or in addition to) the true disease locus or loci, and when applied to sibships that have been ascertained on, or that may simply contain, multiple affected sibs. Through simulations and analytical calculations, I show that the expected values of the observed relative risk parameters (estimating quantities such as effects due to a child's own genotype, maternal genotype, and parent-of-origin) depend crucially on the ascertainment scheme used, as well as on whether there is non-negligible recombination between the true disease locus and the locus under study. In the presence of either recombination or ascertainment on multiple affected offspring, methods based on conditioning on parental genotypes are shown to give unbiased genotype relative risk estimates at the true disease locus (or loci) but biased estimates of population genotype relative risks at a linked marker, suggesting that the resulting estimates may be misleading when used to predict the power of future studies. Methods that allow for exchangeability of parental genotypes are shown (in the presence of either recombination or ascertainment on multiple affected offspring) to produce false-positive evidence of maternal genotype effects when there are true parent-of-origin or mother-child interaction effects, even when analyzing the true locus. These results suggest that care should be taken in both the interpretation and application of parameter estimates obtained from family-based genetic association studies. © 2004 Wiley-Liss, Inc. [source] Tests for genetic association using family dataGENETIC EPIDEMIOLOGY, Issue 2 2002Mei-Chiung Shih Abstract We use likelihood-based score statistics to test for association between a disease and a diallelic polymorphism, based on data from arbitrary types of nuclear families. The Nonfounder statistic extends the transmission disequilibrium test (TDT) to accommodate affected and unaffected offspring, missing parental genotypes, phenotypes more general than qualitative traits, such as censored survival data and quantitative traits, and residual correlation of phenotypes within families. The Founder statistic compares observed or inferred parental genotypes to those expected in the general population. Here the genotypes of affected parents and those with many affected offspring are weighted more heavily than unaffected parents and those with few affected offspring. We illustrate the tests by applying them to data on a polymorphism of the SRD5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of these family-based statistics to that of the score statistic based on Cox's partial likelihood for censored survival data, and find that the family-based statistics have considerably more power when there are many untyped parents. The software program FGAP for computing test statistics is available at http://www.stanford.edu/dept/HRP/epidemiology/FGAP. Genet. Epidemiol. 22:128,145, 2002. © 2002 Wiley-Liss, Inc. [source] Effect of parental genotypes and paternal heterosis on litter traits in crossbred goatsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2 2008Y. Tsukahara Summary The effect of parental genotype and paternal heterosis on litter size (LS), total litter birth weight (TLW) and average litter birth weight (ALW) was analysed utilizing data from a crossbreeding programme involving the exotic German Fawn goats and local Katjang goats in Malaysia. In this study, these traits were regarded as traits of the litter to consider the effect of service sire genotype. The results revealed that LS was significantly influenced by the genotype of sire. The genotypes of sire and dam had significant effects on TLW and ALW. Estimates of crossbreeding parameter showed significant and negative influence of paternal heterosis on TLW and ALW while there was no significant effect of paternal heterosis on LS. The results of this study stress the need to reconsider the use of local males in the tropics. [source] The genetics of adaptation to novel environments: selection on germination timing in Arabidopsis thalianaMOLECULAR ECOLOGY, Issue 7 2010BROOK T. MOYERS When studying selection during adaptation to novel environments, researchers have often paid little attention to an organism's earliest developmental stages. Despite this lack of attention, early life history traits may be under strong selection during colonization, as the expression of adaptive phenotypes at later points is contingent upon early survival. Moreover, the timing of early developmental transitions can constrain the timing of later transitions, with potentially large effects on fitness. In this issue, Huang et al. (2010) underscore the importance of early life history traits in the adaptation of Arabidopsis thaliana to old-field sites in North America. Using a new population of mapped recombinant inbred lines, the authors examined germination timing and total lifetime fitness of A. thaliana while varying site latitude, dispersal season, and maternal photoperiod. Huang et al. (2010) discovered several Quantitative Trait Loci (QTL) with large effects on fitness that colocalized with QTL for field germination timing and seed dormancy,demonstrating that fitness is genetically associated with these early life history traits, and that these loci are likely under strong selection during adaptation to novel environments. In the epistatic interactions of some loci, recombinant genotypes outperformed parental genotypes, supporting the potentially adaptive role of recombination. This study provides elegant evidence that traits expressed early in an organism's development can play an important role during adaptive evolution. [source] Cytonuclear disequilibrium in a hybrid zone involving deep-sea hydrothermal vent mussels of the genus BathymodiolusMOLECULAR ECOLOGY, Issue 11 2003Y. Won Abstract A hybrid zone involving the deep-sea mussels, Bathymodiolus azoricus and B. puteoserpentis, was recently discovered at Broken Spur hydrothermal vent field (29°10, N, 43°10, W) along an intermediate segment of the Mid-Atlantic Ridge axis. Examination of nuclear (allozymes) and cytoplasmic (mitochondrial DNA) gene markers in a new sample from Broken Spur revealed significant cytonuclear disequilibrium caused by an excess of the parental types (coupling phase) and a deficiency of recombinants (repulsion phase). An assignment test of individual multilocus genotypes also revealed an excess of parental genotypes in the admixed population. These results support the hypothesis that the Broken Spur mussel population comprises a nonequilibrium mixture of parental immigrants and hybrid individuals. [source] Natural selection for salt tolerance quantitative trait loci (QTLs) in wild sunflower hybrids: Implications for the origin of Helianthus paradoxus, a diploid hybrid speciesMOLECULAR ECOLOGY, Issue 5 2003C. Lexer Abstract For a new diploid or homoploid hybrid species to become established, it must diverge ecologically from parental genotypes. Otherwise the hybrid neospecies will be overcome by gene flow or competition. We initiated a series of experiments designed to understand how the homoploid hybrid species, Helianthus paradoxus, was able to colonize salt marsh habitats, when both of its parental species (H. annuus×H. petiolaris) are salt sensitive. Here, we report on the results of a quantitative trait locus (QTL) analysis of mineral ion uptake traits and survivorship in 172 BC2 hybrids between H. annuus and H. petiolaris that were planted in H. paradoxus salt marsh habitat in New Mexico. A total of 14 QTLs were detected for mineral ion uptake traits and three for survivorship. Several mineral ion QTLs mapped to the same position as the survivorship QTLs, confirming previous studies, which indicated that salt tolerance in Helianthus is achieved through increased Ca uptake, coupled with greater exclusion of Na and related mineral ions. Of greater general significance was the observation that QTLs with effects in opposing directions were found for survivorship and for all mineral ion uptake traits with more than one detected QTL. This genetic architecture provides an ideal substrate for rapid ecological divergence in hybrid neospecies and offers a simple explanation for the colonization of salt marsh habitats by H. paradoxus. Finally, selection coefficients of +0.126, ,0.084 and ,0.094 for the three survivorship QTLs, respectively, are sufficiently large to account for establishment of new, homoploid hybrid species. [source] ALSPAC,The Avon Longitudinal Study of Parents and ChildrenPAEDIATRIC & PERINATAL EPIDEMIOLOGY, Issue 1 2001Golding ALSPAC (The Avon Longitudinal Study of Parents and Children, formerly the Avon Longitudinal Study of Pregnancy and Childhood) was specifically designed to determine ways in which the individual's genotype combines with environmental pressures to influence health and development. To date, there are comprehensive data on approximately 10 000 children and their parents, from early pregnancy until the children are aged between 8 and 9. The study aims to continue to collect detailed data on the children as they go through puberty noting, in particular, changes in anthropometry, attitudes and behaviour, fitness and other cardiovascular risk factors, bone mineralisation, allergic symptoms and mental health. The study started early during pregnancy and collected very detailed data from the mother and her partner before the child was born. This not only provided accurate data on concurrent features, especially medication, symptoms, diet and lifestyle, attitudes and behaviour, social and environmental features, but was unbiased by parental knowledge of any problems that the child might develop. From the time of the child's birth many different aspects of the child's environment have been monitored and a wide range of phenotypic data collected. By virtue of being based in one geographic area, linkage to medical and educational records is relatively simple, and hands-on assessments of children and parents using local facilities has the advantage of high quality control. The comprehensiveness of the ALSPAC approach with a total population sample unselected by disease status, and the availability of parental genotypes, provides an adequate sample for statistical analysis and for avoiding spurious results. The study has an open policy in regard to collaboration within strict confidentiality rules. [source] Effect of parental genotypes and colchicine treatment on the androgenic response of wheat F1 hybridsPLANT BREEDING, Issue 4 2003I. Zamani Abstract The effect of the parental genotypes and colchicine treatment on the androgenic response of wheat (Triticum aestivum L.) F1 hybrids was studied. For this, anthers from three F1 hybrids and their parents were cultured on W14 initiation medium and W14 supplemented with 0.03% colchicine. The number of responding anthers, microspore-derived structures/100 anthers, green plants/embryos cultured, green plants/100 anthers and albino plants/100 anthers were recorded. It was observed that embryo formation and plant regeneration ability were genetically controlled and genotype dependent. In both treatments the variety Kavkaz had a significantly higher percentage of responding anthers, microspore-derived structures and green plants/100 anthers than the other genotypes. On the other hand, the variety Myconos also demonstrated high microspore-derived structure production and green plant regeneration when treated with colchicine. The good response observed in these two varieties indicates the importance of colchicine treatment only for certain genotypes. Green plant production capacity of the hybrids was intermediate to that of the parental varieties. As one parent with a high or even an intermediate response to anther culture could lead to the production of sufficient (for breeding purposes) green plants from the F1 hybrids, it was concluded that screening the inbred lines for the response to anther culture with and without colchicine treatment could contribute to utilization of breeding material with a low response to anther culture via the proper hybrid combinations. [source] Genetic variation in Arabidopsis thaliana for night-time leaf conductancePLANT CELL & ENVIRONMENT, Issue 8 2008MAIRGARETH A. CHRISTMAN ABSTRACT Night-time leaf conductance (gnight) and transpiration may have several adaptive benefits related to plant water, nutrient and carbon relations. Little is known, however, about genetic variation in gnight and whether this variation correlates with other gas exchange traits related to water use and/or native habitat climate. We investigated gnight in 12 natural accessions and three near isogenic lines (NILs) of Arabidopsis thaliana. Genetic variation in gnight was found for the natural accessions, and gnight was negatively correlated with native habitat atmospheric vapour pressure deficit (VPDair), suggesting lower gnight may be favoured by natural selection in drier habitats. However, there were also significant genetic correlations of gnight with daytime gas exchange traits expected to affect plant fitness [i.e. daytime leaf conductance, photosynthesis and intrinsic water-use efficiency (WUEi)], indicating that selection on daytime gas exchange traits may result in indirect selection on gnight. The comparison of three NILs to their parental genotypes identified one quantitative trait locus (QTL) contributing to variation in gnight. Further characterization of genetic variation in gnight within and among populations and species, and of associations with other traits and native habitats will be needed to understand gnight as a putatively adaptive trait. [source] Genotypic Association Analysis Using Discordant-Relative-PairsANNALS OF HUMAN GENETICS, Issue 1 2009T. Yan Summary In practice, family-based design has been widely used in disease-gene association analysis. The major advantage of such design is that it is not subject to spurious association due to population structure such as population stratification (PS) and admixture. A disadvantage is that parental genotypes are hard to obtain if the disease is late onset for which a discordant-relative-pair design is useful. Designs of such kind include full-sib-pair, half-sib-pair, first-cousin-pair, and so on. The closer the relatedness of the pair, the less possible that they are subject to population stratification. On the other hand, the association test using close relative-pairs may be less powerful due to over-matching. Trade-off between these two factors (population structure and over-matching) is the major concern of this study. Some tests, namely McNemar's test, matched Cochran-Armitage trend tests (CATTs), matched maximum efficient robust test (MERT), and Bhapkar's test, are used for testing disease-gene association based on relative-pair designs. These tests are shown to be valid in the presence of PS but not admixture. Numerical studies show that the McNemar's test, additive CATT, MERT, and Bhapkar's test are robust in power, but none of them is uniformly more powerful than the others. In most simulations, the power of any of the tests increases as the pair is more distant. The proposed methods are applied to two real examples. [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] Allelic association between the NRAMP1 gene and susceptibility to tuberculosis in Guinea,ConakryANNALS OF HUMAN GENETICS, Issue 6 2000A. C. L. CERVINO Forty four families from Guinea,Conakry were analysed to test for association between NRAMP1 (Natural Resistance Associated Macrophage Protein 1) polymorphisms and tuberculosis. Each family included at least one affected sib and one parent. Healthy sibs were also analysed and on average the families included four members. A total of 160 individuals were included in the final dataset. The analysis of association was performed using an extended TDT test, TRANSMIT, to allow for missing information in the parental genotypes. Three polymorphisms in the NRAMP1 gene were typed: a microsatellite (CA) repeat, a 4 bp deletion in the 3, untranslated region and a single nucleotide change in intron 4. The single base change in intron 4 was significantly associated (p= 0.036) with tuberculosis. Our results therefore confirm, using a family-based approach on a newly studied population, the previously reported association between this polymorphism and tuberculosis in a population-based study of West Africans. [source] Genetic characterization and gonad development of artificially produced interspecific hybrids of the abalones, Haliotis discus discus Reeve, Haliotis gigantea Gmelin and Haliotis madaka HabeAQUACULTURE RESEARCH, Issue 5 2008Faruq Ahmed Abstract Hybridization among abalone species has been suggested as a possible means to increase their growth rates for aquaculture. As a first step to test the usefulness of the hybrids of Japanese abalone species (Haliotis discus discus, Haliotis gigantea and Haliotis madaka) for aquaculture, we characterized the genetic background and gonad development of hybrids that were produced by artificial insemination. The hybrid status of the resulting offspring was confirmed by assaying 14 allozymes and by RFLP analysis of the 16s rRNA and cytochrome oxidase I (COI) regions of mtDNA using 13 restriction enzymes. Histological examination of the gonads of the hybrids was conducted in comparison with those of the parental species. Cross-breeding among the three species was conducted successfully in all combinations although with lower fertilization rates (means of 1.3,60.8%) than the parental species (34.3,90%). Crosses between H. discus discus and H. madaka had higher fertilization rates (22.4,60.8%) than those involving H. gigantea (1.3,19.9%). The hybrids were ascertained by the presence of both parental genotypes at the LDH-A, ME-A, MDH-A and GPI loci. The maternal origin of the hybrid mtDNA was confirmed by digestion with DdeI, TaqI, HpaII of the COI region. No polymorphism was observed in the 16S rRNA region. The hybrids had gonadal development and maturity stages similar to the parental species up to fully mature oocytes and sperm. They spawned upon stimulation and produced viable offspring with high fertilization rates and successful development to the juvenile stage in back- and homologous hybrid crosses. [source] | |||||||||||||||||||||||||