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Recombination Fractions (recombination + fraction)
Selected AbstractsA Bayesian approach to the transmission/disequilibrium test for binary traitsGENETIC EPIDEMIOLOGY, Issue 1 2002Varghese George Abstract The transmission/disequilibrium test (TDT) for binary traits is a powerful method for detecting linkage between a marker locus and a trait locus in the presence of allelic association. The TDT uses information on the parent-to-offspring transmission status of the associated allele at the marker locus to assess linkage or association in the presence of the other, using one affected offspring from each set of parents. For testing for linkage in the presence of association, more than one offspring per family can be used. However, without incorporating the correlation structure among offspring, it is not possible to correctly assess the association in the presence of linkage. In this presentation, we propose a Bayesian TDT method as a complementary alternative to the classical approach. In the hypothesis testing setup, given two competing hypotheses, the Bayes factor can be used to weigh the evidence in favor of one of them, thus allowing us to decide between the two hypotheses using established criteria. We compare the proposed Bayesian TDT with a competing frequentist-testing method with respect to power and type I error validity. If we know the mode of inheritance of the disease, then the joint and marginal posterior distributions for the recombination fraction (,) and disequilibrium coefficient (,) can be obtained via standard MCMC methods, which lead naturally to Bayesian credible intervals for both parameters. Genet. Epidemiol. 22:41,51, 2002. © 2002 Wiley-Liss, Inc. [source] Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairsGENETIC EPIDEMIOLOGY, Issue 4 2001Darlene R. Goldstein Abstract Identification of genes involved in complex traits by traditional (lod score) linkage analysis is difficult due to many complicating factors. An unfortunate drawback of non-parametric procedures in general, though, is their low power to detect genetic effects. Recently, Dudoit and Speed [2000] proposed using a (likelihood-based) score test for detecting linkage with IBD data on sib pairs. This method uses the likelihood for ,, the recombination fraction between a trait locus and a marker locus, conditional on the phenotypes of the two sibs to test the null hypothesis of no linkage (, = ½). Although a genetic model must be specified, the approach offers several advantages. This paper presents results of simulation studies characterizing the power and robustness properties of this score test for linkage, and compares the power of the test to the Haseman-Elston and modified Haseman-Elston tests. The score test is seen to have impressively high power across a broad range of true and assumed models, particularly under multiple ascertainment. Assuming an additive model with a moderate allele frequency, in the range of p = 0.2 to 0.5, along with heritability H = 0.3 and a moderate residual correlation , = 0.2 resulted in a very good overall performance across a wide range of trait-generating models. Generally, our results indicate that this score test for linkage offers a high degree of protection against wrong assumptions due to its strong robustness when used with the recommended additive model. Genet. Epidemiol. 20:415,431, 2001. © 2001 Wiley-Liss, Inc. [source] Bivariate combined linkage and association mapping of quantitative trait lociGENETIC EPIDEMIOLOGY, Issue 5 2008Jeesun Jung Abstract In this paper, bivariate/multivariate variance component models are proposed for high-resolution combined linkage and association mapping of quantitative trait loci (QTL), based on combinations of pedigree and population data. Suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In the region, multiple markers such as single nucleotide polymorphisms are typed. Two regression models, "genotype effect model" and "additive effect model", are proposed to model the association between the markers and the trait locus. The linkage information, i.e., recombination fractions between the QTL and the markers, is modeled in the variance and covariance matrix. By analytical formulae, we show that the "genotype effect model" can be used to model the additive and dominant effects simultaneously; the "additive effect model" only takes care of additive effect. Based on the two models, F -test statistics are proposed to test association between the QTL and markers. By analytical power analysis, we show that bivariate models can be more powerful than univariate models. For moderate-sized samples, the proposed models lead to correct type I error rates; and so the models are reasonably robust. As a practical example, the method is applied to analyze the genetic inheritance of rheumatoid arthritis for the data of The North American Rheumatoid Arthritis Consortium, Problem 2, Genetic Analysis Workshop 15, which confirms the advantage of the proposed bivariate models. Genet. Epidemiol. 2008. © 2008 Wiley-Liss, Inc. [source] Parent-of-origin, imprinting, mitochondrial, and X-linked effects in traits related to alcohol dependence: Presentation Group 18 of Genetic Analysis Workshop 14GENETIC EPIDEMIOLOGY, Issue S1 2005Konstantin Strauch Abstract The participants of Presentation Group 18 of Genetic Analysis Workshop 14 analyzed the Collaborative Study on the Genetics of Alcoholism data set to investigate sex-specific effects for phenotypes related to alcohol dependence. In particular, the participants looked at imprinting (which is also known as parent-of-origin effect), differences between recombination fractions for the two sexes, and mitochondrial and X-chromosomal effects. Five of the seven groups employed newly developed or existing methods that take imprinting into account when testing for linkage, or test for imprinting itself. Single-marker and multipoint analyses were performed for microsatellite as well as single-nucleotide polymorphism markers, and several groups used a sex-specific genetic map in addition to a sex-averaged map. Evidence for paternal imprinting (i.e., maternal expression) was consistently obtained by at least two groups at genetic regions on chromosomes 10, 12, and 21 that possibly harbor genes responsible for alcoholism. Evidence for maternal imprinting (which is equivalent to paternal expression) was consistently found at a locus on chromosome 11. Two groups applied extensions of variance components analysis that model a mitochondrial or X-chromosomal effect to latent class variables and electrophysiological traits employed in the diagnosis of alcoholism. The analysis, without using genetic markers, revealed mitochondrial or X-chromosomal effects for several of these traits. Accounting for sex-specific environmental variances appeared to be crucial for the identification of an X-chromosomal factor. In linkage analysis using marker data, modeling a mitochondrial variance component increased the linkage signals obtained for autosomal loci. Genet. Epidemiol. 29(Suppl. 1):S125,S132, 2005. © 2005 Wiley-Liss, Inc. [source] Spatial autocorrelation and linkage of Mendelian RAPD markers in a population of Picea abies KarstMOLECULAR ECOLOGY, Issue 3 2002Gabriele Bucci Abstract The spatial clustering of single- and di-locus genotypes in a natural, continuous population of Norway spruce was investigated using 69 Mendelian Random Amplified Polymorphic DNA (RAPD) markers that covered about 15% of the species' genome, and whose linkage relationships were known. Spatial autocorrelation techniques and randomization tests, applied to both single- and di-locus genotypes, revealed a weak, though significant, spatial structure at the scale 0,200 m (5% of single-locus and 7% of di-locus genotypes). To assess the relative importance of isolation by distance and linkage between markers on their spatial genetic structuring, we grouped joins between sampled trees into ,equivalence categories' expected to show similar, specific patterns of spatial distribution under isolation by distance. Results from both single- and di-locus analyses were consistent with the existence of patches of like homozygotes (about 8% and 11% of loci at the single- and di-locus level, respectively) surrounded by a mix of like heterozygotes. Similar structuring has been predicted by simulation models under isolation by distance and selective neutrality. Overall, linkage between markers accounted for an increase of spatial clumping of di-locus genotypes involving tightly linked loci with recombination fractions up to 0.1, a consequence of limited, stochastic spread of single-locus genotypes in space. Our results support the hypothesis that isolation by distance and linkage have a small, though significant, effect even within continuous forest tree populations. In general, the spatial distribution of multilocus genotypes within populations should be interpreted with caution when linkage relationships among the markers used are unknown. [source] The Essence of Linkage-based Imprinting Detection: Comparing Power, Type 1 Error, and the Effects of Confounders in Two Different Analysis ApproachesANNALS OF HUMAN GENETICS, Issue 3 2010David A. Greenberg Summary Imprinting is critical to understanding disease expression. It can be detected using linkage information, but the effects of potential confounders (heterogeneity, sex-specific penetrance, and sex-biased ascertainment) have not been explored. We examine power and confounders in two imprinting detection approaches, and we explore imprinting-linkage interaction. One method (PP) models imprinting by maximising lod scores w.r.t. parent-specific penetrances. The second (DRF) approximates imprinting by maximising lods over differential male-female recombination fractions. We compared power, type 1 error, and confounder effects in these two methods, using computer-simulated data. We varied heterogeneity, penetrance, family and dataset size, and confounders that might mimic imprinting. Without heterogeneity, PP had more imprinting-detecting power than DRF. PP's power increased when parental affectedness status was ignored, but decreased with heterogeneity. With heterogeneity, type 1 error increased dramatically for both methods. However, DRF's power also increased under heterogeneity, more than was attributable to inflated type 1 error. Sex-specific penetrance could increase false positives for PP but not for DRF. False positives did not increase on ascertainment through an affected "mother". For PP, non-penetrant individuals increased information, arguing against using affecteds-only methods. The high type 1 error levels under some circumstances means these methods must be used cautiously. [source] | ||||||||||