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  • 1
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  Wissenschaft - eine Säule der Hebammenarbeit; 1. Internationale Fachtagung der Deutschen Gesellschaft für Hebammenwissenschaft; 20110923-20110923; Hildesheim; DOC11dghwi10 /20110915/
    Publication Date: 2011-09-15
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 2
    Keywords: CHROMOSOME-6 ; genetic analysis ; genetic association ; case-control ; German ; cancer epidemiology ; WEIGHT ; ALLELE ; case-control study ; case control study ; CHROMOSOMES ; HAPLOTYPE ; HAPLOTYPES ; LOCUS ; mantel statistics ; TESTS ; POWER ; analysis ; ALLELES ; methods ; LEVEL ; case control studies ; INTERVAL ; LOCI ; comparison ; case control ; RARE ; LINKAGE-DISEQUILIBRIUM ; SET ; UNIT ; cancer research ; genomic ; PHENOTYPE ; case-control studies ; LENGTH ; REGIONS ; LINKAGE DISEQUILIBRIUM ; MARKERS ; REGION ; statistics ; NUMBER ; study design ; DESIGN ; PAIRS ; LINKAGE ; chromosome ; ASSOCIATION ; MARKER ; DISEASE ; EPIDEMIOLOGY ; COMMON ; CANCER ; Germany
    Abstract: Measuring the association of haplotype similarities with phenotype similarities has been used to develop statistical tests of genetic association. Previously, we applied the general approach of Mantel statistics to correlate genetic and phenotype similarity, where genetic similarity was defined by the number of intervals flanked by markers identical by state for pairs of haplotypes. Here we investigated in the case-control study design the effect on power of the Mantel statistics for five different measures of genetic similarity based on haplotypes: 1) the number of shared intervals, 2) the physical length of the shared intervals, 3) the genetic length of the shared intervals in centimorgans, 4) the genetic length of the shared intervals in linkage disequilibrium units (LDU) and 5) Yu's measure that attaches more weight to the sharing of rare than common alleles. With prior knowledge of the answers of Genetic Analysis Workshop 15 Problem 3, we analyzed the simulated data sets in two genomic regions surrounding the disease loci on chromosomes 6 and 18. For the dense map on chromosome 6, all methods showed a very high power of comparable magnitude. For chromosome 18, we observed a power between 19% and 99% at the pointwise 5% significance level using 1000 cases and 1000 controls for all methods except Yu's measure. While it yielded a much lower power, Yu's measure had 80% power around the disease locus.
    Type of Publication: Book chapter
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  • 3
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    John Wiley & Sons, Ltd.
    Abstract: A convenient way to incorporate haplotypes into statistical analysis of complex diseases is the use of haplotype sharing measures. Statistical methods summarise evolutionary events such as mutation, recombination and coalescence into simple scores to improve the power of association tests. Existing methods provide flexible tools for various study designs such as pedigree data and case-control data, in candidate gene analysis and for genome-wide association analysis. Although haplotype sharing methods were powerful in detecting disease mutations in isolated populations, their applicability for complex diseases in general population deserves further investigation as their potential for possible extensions using a variety of genomic variants, such as copy number variation and uncommon sequence mutations.
    Type of Publication: Book
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  • 4
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  Brücken bauen - von der Evidenz zum Patientenwohl; 19. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin; 20180308-20180310; Graz, Österreich; DOC18ebmP7-2 /20180306/
    Publication Date: 2018-03-16
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 5
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  Klasse statt Masse - wider die wertlose Wissenschaft; 18. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin; 20170309-20170311; Hamburg; DOC17ebmW13 /20170223/
    Publication Date: 2017-02-23
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 6
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  GMDS 2014; 59. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS); 20140907-20140910; Göttingen; DOCAbstr. 247 /20140904/
    Publication Date: 2014-09-05
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 7
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  Komplexe Interventionen - Entwicklung durch Austausch; 13. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin; 20120315-20120317; Hamburg; DOC12ebm076 /20120305/
    Publication Date: 2012-03-06
    Keywords: ddc: 610
    Language: English
    Type: conferenceObject
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  • 8
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  Wissenschaft - eine Säule der Hebammenarbeit; 1. Internationale Fachtagung der Deutschen Gesellschaft für Hebammenwissenschaft; 20110923-20110923; Hildesheim; DOC11dghwi11 /20110915/
    Publication Date: 2011-09-15
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 9
    Keywords: human ; GENOME ; ACCURACY ; TIME ; MARKER ; recombination ; ASSOCIATION ; FREQUENCY ; LINKAGE ; polymorphism ; single nucleotide polymorphism ; LINKAGE ANALYSIS ; genetics ; SNP ; statistics ; MARKERS ; HUMAN GENOME ; REGION ; REGIONS ; LINKAGE DISEQUILIBRIUM ; LENGTH ; INDIVIDUALS ; SELECTION ; Monte Carlo ; MONTE-CARLO ; MAPS ; SINGLE ; GENOME-WIDE ANALYSIS ; SNPs ; SOFTWARE ; TASK ; CANDIDATE GENES ; association analysis ; HAPLOTYPE ; LOCUS ; single-nucleotide ; single-nucleotide polymorphism ; haplotype sharing ; mantel statistics ; TESTS ; ASSOCIATION TEST ; BLOCKS ; GENOTYPE DATA ; haplotype-sharing ; haplotype-tagging ; multiple testing ; NEED ; SHARING ANALYSIS
    Abstract: Moderately dense maps of single-nucleotide polymorphism (SNP) markers across the human genome for both the simulated data set and data from the Collaborative Study of the Genetics of Alcoholism were available at Genetic Analysis Workshop 14 for the first time. This allowed examination of various novel and existing methods for haplotype analyses. Three contributors applied Mantel statistics in different ways for both linkage and association analysis by using the shared length between two haplotypes at a marker locus as a measure of genetic similarity. The results indicate that haplotype-sharing based on Mantel statistics can be a powerful approach and needs further methodological evaluation. Four contributors investigated haplotype-tagging SNP (htSNP) selection procedures, two contributors examined the use of multilocus haplotypes compared to single loci in association tests, and two contributors compared the accuracy of various methods for reconstructing haplotypes and estimating haplotype frequencies for both pedigree data and data from unrelated individuals. For all three different tasks, software packages and procedures gave similar results in regions of high linkage disequilibrium (LD). However, they were not as consistent in regions of moderate to low LD. One coalescence-based approach for estimating haplotype frequencies, coupled with a Markov chain Monte Carlo technique, outperformed the other haplotype frequency estimation methods in regions of low LD. In conclusion, regardless of the task, results were similar in chromosomal regions of high LD. However, based on the differing results observed here, methodological improvements are required for chromosomal regions of low to moderate LD
    Type of Publication: Journal article published
    PubMed ID: 16342175
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  • 10
    Keywords: Germany ; DISEASE ; POPULATION ; GENE ; COMPLEX ; COMPLEXES ; MARKER ; ASSOCIATION ; LINKAGE ; statistics ; LENGTH ; PHENOTYPE ; case-control studies ; RECONSTRUCTION ; case-control study ; RE ; interaction ; case control studies ; HAPLOTYPE ; complex disease ; haplotype sharing ; mantel statistics ; TESTS ; haplotype-sharing ; SHARING ANALYSIS ; POWER ; SIZE
    Abstract: We applied a new approach based on Mantel statistics to analyze the Genetic Analysis Workshop 14 simulated data with prior knowledge of the answers. The method was developed in order to improve the power of a haplotype sharing analysis for gene mapping in complex disease. The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes from case-control studies. The genetic similarity is measured as the shared length between haplotype pairs around a genetic marker. The phenotypic similarity is measured as the mean corrected cross-product based on the respective phenotypes. Cases with phenotype P1 and unrelated controls were drawn from the population of Danacaa. Power to detect main effects was compared to the X-2-test for association based on 3-marker haplotypes and a global permutation test for haplotype association to test for main effects. Power to detect gene x gene interaction was compared to unconditional logistic regression. The results suggest that the Mantel statistics might be more powerful than alternative tests
    Type of Publication: Journal article published
    PubMed ID: 16451684
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