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Particular Loci (particular + locus)
Selected AbstractsINTEGRATING EVOLUTIONARY AND FUNCTIONAL APPROACHES TO INFER ADAPTATION AT SPECIFIC LOCIEVOLUTION, Issue 9 2010Jay F. Storz Inferences about adaptation at specific loci are often exclusively based on the static analysis of DNA sequence variation. Ideally, population-genetic evidence for positive selection serves as a stepping-off point for experimental studies to elucidate the functional significance of the putatively adaptive variation. We argue that inferences about adaptation at specific loci are best achieved by integrating the indirect, retrospective insights provided by population-genetic analyses with the more direct, mechanistic insights provided by functional experiments. Integrative studies of adaptive genetic variation may sometimes be motivated by experimental insights into molecular function, which then provide the impetus to perform population genetic tests to evaluate whether the functional variation is of adaptive significance. In other cases, studies may be initiated by genome scans of DNA variation to identify candidate loci for recent adaptation. Results of such analyses can then motivate experimental efforts to test whether the identified candidate loci do in fact contribute to functional variation in some fitness-related phenotype. Functional studies can provide corroborative evidence for positive selection at particular loci, and can potentially reveal specific molecular mechanisms of adaptation. [source] Genetic evaluation of dairy cattle using a simple heritable genetic groundJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 11 2010Josef Pribyl Abstract The evaluation of an animal is based on production records, adjusted for environmental effects, which gives a reliable estimation of its breeding value. Highly reliable daughter yield deviations are used as inputs for genetic marker evaluation. Genetic variability is explained by particular loci and background polygenes, both of which are described by the genomic breeding value selection index. Automated genotyping enables the determination of many single-nucleotide polymorphisms (SNPs) and can increase the reliability of evaluation of young animals (from 0.30 if only the pedigree value is used to 0.60 when the genomic breeding value is applied). However, the introduction of SNPs requires a mixed model with a large number of regressors, in turn requiring new algorithms for the best linear unbiased prediction and BayesB. Here, we discuss a method that uses a genomic relationship matrix to estimate the genomic breeding value of animals directly, without regressors. A one-step procedure evaluates both genotyped and ungenotyped animals at the same time, and produces one common ranking of all animals in a whole population. An augmented pedigree,genomic relationship matrix and the removal of prerequisites produce more accurate evaluations of all connected animals. Copyright © 2010 Society of Chemical Industry [source] Why does elevated CO2 affect time of flowering?NEW PHYTOLOGIST, Issue 2 2009An exploratory study using the photoperiodic flowering mutants of Arabidopsis thaliana Summary ,,Evidence is accumulating that the effect of CO2 on time of flowering involves interactions with photoperiod, but the basis for this interaction is unclear. Here, which components of the photoperiod flowering pathway account for this interaction in Arabidopsis thaliana were examined. ,,Ten mutants deficient in particular loci in the photoperiod pathway, as well as the wild type, were grown under short and long days at either ambient or elevated CO2. Leaf number at flowering and the number of days required for induction of flowering were determined. ,,Elevated CO2 interacted with both the photoreceptors and the subsequent transduction reactions in the photoperiod pathway. The direction and magnitude of the effects varied with photoperiod. Elevated CO2 also affected flowering by increasing rate of leaf production. ,,The net effect of elevated CO2 on time of flowering varies because CO2 has a complex array of effects on different elements of the developmental pathway leading to flower induction that may either hasten or delay flowering depending upon the influence of other environmental factors such as photoperiod. [source] Modeling maternal-offspring gene-gene interactions: the extended-MFG testGENETIC EPIDEMIOLOGY, Issue 5 2010Erica J. Childs Abstract Maternal-fetal genotype (MFG) incompatibility is an interaction between the genes of a mother and offspring at a particular locus that adversely affects the developing fetus, thereby increasing susceptibility to disease. Statistical methods for examining MFG incompatibility as a disease risk factor have been developed for nuclear families. Because families collected as part of a study can be large and complex, containing multiple generations and marriage loops, we create the Extended-MFG (EMFG) Test, a model-based likelihood approach, to allow for arbitrary family structures. We modify the MFG test by replacing the nuclear-family based "mating type" approach with Ott's representation of a pedigree likelihood and calculating MFG incompatibility along with the Mendelian transmission probability. In order to allow for extension to arbitrary family structures, we make a slightly more stringent assumption of random mating with respect to the locus of interest. Simulations show that the EMFG test has appropriate type-I error rate, power, and precise parameter estimation when random mating holds. Our simulations and real data example illustrate that the chief advantages of the EMFG test over the earlier nuclear family version of the MFG test are improved accuracy of parameter estimation and power gains in the presence of missing genotypes. Genet. Epidemiol. 34: 512,521, 2010.© 2010 Wiley-Liss, Inc. [source] Contrasting effects of heterozygosity on survival and hookworm resistance in California sea lion pupsMOLECULAR ECOLOGY, Issue 7 2006KARINA ACEVEDO-WHITEHOUSE Abstract Low genetic heterozygosity is associated with loss of fitness in many natural populations. However, it remains unclear whether the mechanism is related to general (i.e. inbreeding) or local effects, in particular from a subset of loci lying close to genes under balancing selection. Here we analyse involving heterozygosity,fitness correlations on neonatal survival of California sea lions and on susceptibility to hookworm (Uncinaria spp.) infection, the single most important cause of pup mortality. We show that regardless of differences in hookworm burden, homozygosity is a key predictor of hookworm-related lesions, with no single locus contributing disproportionately. Conversely, the subsequent occurrence of anaemia due to blood loss in infected pups is overwhelmingly associated with homozygosity at one particular locus, all other loci showing no pattern. Our results suggest contrasting genetic mechanisms underlying two pathologies related to the same pathogen. First, relatively inbred pups are less able to expel hookworms and prevent their attachment to the intestinal mucosa, possibly due to a weakened immune response. In contrast, infected pups that are homozygous for a gene near to microsatellite Hg4.2 are strongly predisposed to anaemia. As yet, this gene is unknown, but could plausibly be involved in the blood-coagulation cascade. Taken together, these results suggest that pathogenic burden alone may not be the main factor regulating pathogen-related mortality in natural populations. Our study could have important implications for the conservation of small, isolated or threatened populations, particularly when they are at a risk of facing pathogenic challenges. [source] Statistical power when testing for genetic differentiationMOLECULAR ECOLOGY, Issue 10 2001N. Ryman Abstract A variety of statistical procedures are commonly employed when testing for genetic differentiation. In a typical situation two or more samples of individuals have been genotyped at several gene loci by molecular or biochemical means, and in a first step a statistical test for allele frequency homogeneity is performed at each locus separately, using, e.g. the contingency chi-square test, Fisher's exact test, or some modification thereof. In a second step the results from the separate tests are combined for evaluation of the joint null hypothesis that there is no allele frequency difference at any locus, corresponding to the important case where the samples would be regarded as drawn from the same statistical and, hence, biological population. Presently, there are two conceptually different strategies in use for testing the joint null hypothesis of no difference at any locus. One approach is based on the summation of chi-square statistics over loci. Another method is employed by investigators applying the Bonferroni technique (adjusting the P -value required for rejection to account for the elevated alpha errors when performing multiple tests simultaneously) to test if the heterogeneity observed at any particular locus can be regarded significant when considered separately. Under this approach the joint null hypothesis is rejected if one or more of the component single locus tests is considered significant under the Bonferroni criterion. We used computer simulations to evaluate the statistical power and realized alpha errors of these strategies when evaluating the joint hypothesis after scoring multiple loci. We find that the ,extended' Bonferroni approach generally is associated with low statistical power and should not be applied in the current setting. Further, and contrary to what might be expected, we find that ,exact' tests typically behave poorly when combined in existing procedures for joint hypothesis testing. Thus, while exact tests are generally to be preferred over approximate ones when testing each particular locus, approximate tests such as the traditional chi-square seem preferable when addressing the joint hypothesis. [source] | ||||||||||