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Marker Sets (marker + set)

Distribution by Scientific Domains

Life Sciences	70%
Medical Sciences	20%

Selected Abstracts

A Data-driven Segmentation for the Shoulder Complex

COMPUTER GRAPHICS FORUM, Issue 2 2010
Q Youn Hong
Abstract The human shoulder complex is perhaps the most complicated joint in the human body being comprised of a set of three bones, muscles, tendons, and ligaments. Despite this anatomical complexity, computer graphics models for motion capture most often represent this joint as a simple ball and socket. In this paper, we present a method to determine a shoulder skeletal model that, when combined with standard skinning algorithms, generates a more visually pleasing animation that is a closer approximation to the actual skin deformations of the human body. We use a data-driven approach and collect ground truth skin deformation data with an optical motion capture system with a large number of markers (200 markers on the shoulder complex alone). We cluster these markers during movement sequences and discover that adding one extra joint around the shoulder improves the resulting animation qualitatively and quantitatively yielding a marker set of approximately 70 markers for the complete skeleton. We demonstrate the effectiveness of our skeletal model by comparing it with ground truth data as well as with recorded video. We show its practicality by integrating it with the conventional rendering/animation pipeline. [source]

Genotyping errors, pedigree errors, and missing data

GENETIC EPIDEMIOLOGY, Issue S1 2005
Anthony L. Hinrichs
Abstract Our group studied the effects of genotyping errors, pedigree errors, and missing data on a wide range of techniques, with a focus on the role of single-nucleotide polymorphisms (SNPs). Half of our group used simulated data, and half of our group used data from the Collaborative Study on the Genetics of Alcoholism (COGA). The simulated data had no missing genotypes and no genotyping errors, so our group, as a whole, removed data and introduced artificial errors to study the robustness of various techniques. Our teams showed that genotyping errors are less detectable and may have a greater impact on SNPs than on microsatellites, but recently developed methods that account for genotyping errors help reduce false positives, and the assumptions of these methods appear to be supported by observations from repeated genotyping. The ability to detect linkage disequilibrium (LD) was also substantially reduced by missing data; this in turn could affect tagging SNPs chosen to generate haplotypes. In the COGA sample, genotyping measurements were repeated in three ways. First, full-genome screens were performed on three sets of markers: 328 microsatellites, 11,560 SNPs from the Affymetrix GeneChip Mapping 10,K Array marker set, and 4,720 SNPs from the Illumina Linkage III panel. Second, the entire Affymetrix marker set was typed on the same 184 individuals by two different laboratories. Finally, the Affymetrix and Illumina marker panels had 94 SNPs in common. Our teams showed that both SNPs and microsatellites can be readily used to identify pedigree errors, and that SNPs have fewer genotyping errors and a low inconsistency rate. However, a fairly high rate of no-calls, especially for the Affymetrix platform, suggests that the inconsistency rate may be higher than observed. Genet. Epidemiol. 29(Suppl. 1):S120,S124, 2005. © 2005 Wiley-Liss, Inc. [source]

Validation of whole genome linkage-linkage disequilibrium and association results, and identification of markers to predict genetic merit for twinning

ANIMAL GENETICS, Issue 4 2010
C. D. Bierman
Summary A previous genome-wide search with a moderate density 10K marker set identified many marker associations with twinning rate, either through single-marker analysis or combined linkage-linkage disequilibrium (LLD; haplotype) analysis. The objective of the current study was to validate putative marker associations using an independent set of phenotypic data. Holstein bulls (n = 921) from 100 paternal half-sib families were genotyped. Twinning rate predicted transmitting abilities were calculated using calving records from 1994 to 1998 (Data I) and 1999 to 2006 (Data II), and the underlying liability scores from threshold model analysis were used as the trait in marker association analyses. The previous analysis used 201 bulls with daughter records in Data I. In the current analysis, this was increased to 434, providing a revised estimate of effect and significance. Bulls with daughter records in Data II totaled 851, and analysis of this data provided the validation of results from analysis of Data I. Single nucleotide polymorphisms (SNPs) were selected to validate previously significant single-marker associations and LLD results. Bulls were genotyped for a total of 306 markers. Nine of 13 LLD regions located on chromosomes 1, 2, 3, 6, 9, 22, 23(2) and 26 were validated, showing significant results for both Data I and II. Association analysis revealed 55 of 174 markers validated, equating to a single-marker validation rate of 31%. Stepwise backward elimination and cross-validation analyses identified 18 SNPs for use in a final reduced marker panel explaining 34% of the genetic variation, and to allow prediction of genetic merit for twinning rate. [source]

Optimal Robust Two-Stage Designs for Genome-Wide Association Studies

ANNALS OF HUMAN GENETICS, Issue 6 2009
Thuy Trang Nguyen
Summary Optimal robust two-stage designs for genome-wide association studies are proposed using the maximum of the recessive, additive and dominant linear trend test statistics. These designs combine cost-saving two-stage genotyping with robustness against misspecification of the genetic model and are much more efficient than designs based on a single model specific test statistic in detecting multiple loci with different modes of inheritance. For given power of 90%, typical cost savings of 34% can be realised by increasing the total sample size by about 13% but genotyping only about half of the sample for the full marker set in the first stage and carrying forward about 0.06% of the markers to the second stage analysis. We also present robust two-stage designs providing optimal allocation of a limited budget for pre-existing samples. If a sample is available which would yield a power of 90% when fully genotyped, genotyping only half of the sample due to a limited budget will typically cause a loss of power of more than 55%. Using an optimal two-stage approach in the same sample under the same budget restrictions will limit the loss of power to less than 10%. In general, the optimal proportion of markers to be followed up in the second stage strongly depends on the cost ratio for chips and individual genotyping, while the design parameters of the optimal designs (total sample size, first stage proportion, first and second stage significance limit) do not much depend on the genetic model assumptions. [source]

Prevalence of various radiographic manifestations of osteochondrosis and their correlations between and within joints in Dutch Warmblood horses

EQUINE VETERINARY JOURNAL, Issue 1 2009
E. M. Van Grevenhof
Summary Reasons for performing study: Osteochondrosis (OC) is the most important orthopaedic developmental disorder in horses and may manifest in several different forms. No detailed study on the prevalence and/or interrelation of these forms is available, even though these data are a prerequisite for conclusive genetic studies. Objectives: To assess the prevalence of the various manifestations of OC as detected radiographically and to evaluate possible relationships between their occurrence within the same joint and between different joints. Methods: The FP (femoropatellar), TC (tarsocrural) and MCP/MTP (metacarpophalangeal/metatarsophalangeal) joints of 811 yearlings selected randomly, descending from 32 representative stallions, were radiographed and scored for the presence and grade of osteochondrotic lesions. Results were compared at the sire, animal, joint and predilection site levels. Results: In the FP joint, the percentage of animals showing normal joint contours in all sites was 60.7%. For the TC joint and the combined MCP/MTP joints, these figures were 68.6 and 64.6%, respectively. For all joints combined, the percentage dropped to 30.5%. Sedation improved detection of OC lesions in the FP joint. There was a high correlation between the right and left joints. The correlation between flattened bone contours and fragments was considerably less. Conclusions: Scoring on a detailed scale is necessary to achieve good insight into the prevalence of OC. Observations on the right and left joints can be combined in further analyses, whereas flattened bone contours and fragments should be evaluated as statistically different disorders. Potential relevance: This study provides insight into the prevalences of various manifestations of OC and their relationships, within and between joints. These results form the basis for detailed quantitative and/or molecular genetic studies that should lead to the establishment of breeding indices and/or genetic marker sets for OC. [source]

Imputation aware meta-analysis of genome-wide association studies

GENETIC EPIDEMIOLOGY, Issue 6 2010
Noah Zaitlen
Abstract Genome-wide association studies have recently identified many new loci associated with human complex diseases. These newly discovered variants typically have weak effects requiring studies with large numbers of individuals to achieve the statistical power necessary to identify them. Likely, there exist even more associated variants, which remain to be found if even larger association studies can be assembled. Meta-analysis provides a straightforward means of increasing study sample sizes without collecting new samples by combining existing data sets. One obstacle to combining studies is that they are often performed on platforms with different marker sets. Current studies overcome this issue by imputing genotypes missing from each of the studies and then performing standard meta-analysis techniques. We show that this approach may result in a loss of power since errors in imputation are not accounted for. We present a new method for performing meta-analysis over imputed single nucleotide polymorphisms, show that it is optimal with respect to power, and discuss practical implementation issues. Through simulation experiments, we show that our imputation aware meta-analysis approach outperforms or matches standard meta-analysis approaches. Genet. Epidemiol. 34: 537,542, 2010. © 2010 Wiley-Liss, Inc. [source]

Linkage mapping methods applied to the COGA data set: Presentation Group 4 of Genetic Analysis Workshop 14

GENETIC EPIDEMIOLOGY, Issue S1 2005
E. Warwick Daw
Abstract Presentation Group 4 participants analyzed the Collaborative Study on the Genetics of Alcoholism data provided for Genetic Analysis Workshop 14. This group examined various aspects of linkage analysis and related issues. Seven papers included linkage analyses, while the eighth calculated identity-by-descent (IBD) probabilities. Six papers analyzed linkage to an alcoholism phenotype: ALDX1 (four papers), ALDX2 (one paper), or a combination both (one paper). Methods used included Bayesian variable selection coupled with Haseman-Elston regression, recursive partitioning to identify phenotype and covariate groupings that interact with evidence for linkage, nonparametric linkage regression modeling, affected sib-pair linkage analysis with discordant sib-pair controls, simulation-based homozygosity mapping in a single pedigree, and application of a propensity score to collapse covariates in a general conditional logistic model. Alcoholism linkage was found with ,2 of these approaches on chromosomes 2, 4, 6, 7, 9, 14, and 21. The remaining linkage paper compared the utility of several single-nucleotide polymorphism (SNP) and microsatellite marker maps for Monte Carlo Markov chain combined oligogenic segregation and linkage analysis, and analyzed one of the electrophysiological endophenotypes, ttth1, on chromosome 7. Linkage was found with all marker sets. The last paper compared the multipoint IBD information content of several SNP sets and the microsatellite set, and found that while all SNP sets examined contained more information than the microsatellite set, most of the information contained in the SNP sets was captured by a subset of the SNP markers with ,1-cM marker spacing. From these papers, we highlight three points: a 1-cM SNP map seems to capture most of the linkage information, so denser maps do not appear necessary; careful and appropriate use of covariates can aid linkage analysis; and sources of increased gene-sharing between relatives should be accounted for in analyses. Genet. Epidemiol. 29(Suppl. 1):S29,S34, 2005. © 2005 Wiley-Liss, Inc. [source]

Genome-wide pleiotropy of osteoporosis-related phenotypes: The framingham study

JOURNAL OF BONE AND MINERAL RESEARCH, Issue 7 2010
David Karasik
Abstract Genome-wide association studies offer an unbiased approach to identify new candidate genes for osteoporosis. We examined the Affymetrix 500K,+,50K SNP GeneChip marker sets for associations with multiple osteoporosis-related traits at various skeletal sites, including bone mineral density (BMD, hip and spine), heel ultrasound, and hip geometric indices in the Framingham Osteoporosis Study. We evaluated 433,510 single-nucleotide polymorphisms (SNPs) in 2073 women (mean age 65 years), members of two-generational families. Variance components analysis was performed to estimate phenotypic, genetic, and environmental correlations (,P, ,G, and ,E) among bone traits. Linear mixed-effects models were used to test associations between SNPs and multivariable-adjusted trait values. We evaluated the proportion of SNPs associated with pairs of the traits at a nominal significance threshold ,,=,0.01. We found substantial correlation between the proportion of associated SNPs and the ,P and ,G (r,=,0.91 and 0.84, respectively) but much lower with ,E (r,=,0.38). Thus, for example, hip and spine BMD had 6.8% associated SNPs in common, corresponding to ,P,=,0.55 and ,G,=,0.66 between them. Fewer SNPs were associated with both BMD and any of the hip geometric traits (eg, femoral neck and shaft width, section moduli, neck shaft angle, and neck length); ,G between BMD and geometric traits ranged from ,0.24 to +0.40. In conclusion, we examined relationships between osteoporosis-related traits based on genome-wide associations. Most of the similarity between the quantitative bone phenotypes may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in defining the best phenotypes to be used in genetic studies of osteoporosis. © 2010 American Society for Bone and Mineral Research [source]

Isolation and characterization of microsatellite markers from the marine isopods Serolis paradoxa and Septemserolis septemcarinata (Crustacea: Peracarida)

MOLECULAR ECOLOGY RESOURCES, Issue 4 2008
FLORIAN LEESE
Abstract This study reports the successful isolation of highly informative microsatellite marker sets for two marine serolid isopod species. For Serolis paradoxa (Fabricius, 1775), 13, and for Septemserolis septemcarinata (Miers, 1875), eight polymorphic microsatellite markers were isolated using the reporter genome enrichment protocol. The number of alleles per locus (NA) and the observed heterozygosity (HO) encompass a wide range of variation within S. paradoxa (NA 3,31, HO 6,89%) and S. septemcarinata (NA 2,18, HO 9,94%). The suitability of the newly isolated markers for population genetic studies is evaluated. [source]

Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: Presentation Groups 1, 2, and 3

GENETIC EPIDEMIOLOGY, Issue S1 2005
Marsha A. Wilcox
Abstract The papers in presentation groups 1,3 of Genetic Analysis Workshop 14 (GAW14) compared microsatellite (MS) markers and single-nucleotide polymorphism (SNP) markers for a variety of factors, using multiple methods in both data sets provided to GAW participants. Group 1 focused on data provided from the Collaborative Study on the Genetics of Alcoholism (COGA). Group 2 focused on data simulated for the workshop. Group 3 contained analyses of both data sets. Issues examined included: information content, signal strength, localization of the signal, use of haplotype blocks, population structure, power, type I error, control of type I error, the effect of linkage disequilibrium, and computational challenges. There were several broad resulting observations. 1) Information content was higher for dense SNP marker panels than for MS panels, and dense SNP markers sets appeared to provide slightly higher linkage scores and slightly higher power to detect linkage than MS markers. 2) Dense SNP panels also gave higher type I errors, suggesting that increased test thresholds may be needed to maintain the correct error rate. 3) Dense SNP panels provided better trait localization, but only in the COGA data, in which the MS markers were relatively loosely spaced. 4) The strength of linkage signals did not vary with the density of SNP panels, once the marker density was ,1 SNP/cM. 5) Analyses with SNPs were computationally challenging, and identified areas where improvements in analysis tools will be necessary to make analysis practical for widespread use. Genet. Epidemiol. 29:(Suppl. 1): S7,S28, 2005. © 2005 Wiley-Liss, Inc. [source]