53 resultados para Linkage (Genetics)

em CentAUR: Central Archive University of Reading - UK


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Background Plant domestication occurred independently in four different regions of the Americas. In general, different species were domesticated in each area, though a few species were domesticated independently in more than one area. The changes resulting from human selection conform to the familiar domestication syndrome, though different traits making up this syndrome, for example loss of dispersal, are achieved by different routes in crops belonging to different families. Genetic and Molecular Analyses of Domestication Understanding of the genetic control of elements of the domestication syndrome is improving as a result of the development of saturated linkage maps for major crops, identification and mapping of quantitative trait loci, cloning and sequencing of genes or parts of genes, and discoveries of widespread orthologies in genes and linkage groups within and between families. As the modes of action of the genes involved in domestication and the metabolic pathways leading to particular phenotypes become better understood, it should be possible to determine whether similar phenotypes have similar underlying genetic controls, or whether human selection in genetically related but independently domesticated taxa has fixed different mutants with similar phenotypic effects. Conclusions Such studies will permit more critical analysis of possible examples of multiple domestications and of the origin(s) and spread of distinctive variants within crops. They also offer the possibility of improving existing crops, not only major food staples but also minor crops that are potential export crops for developing countries or alternative crops for marginal areas.

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Diploid Fragaria provide a potential model for genomic studies in the Rosaceae. To develop a genetic linkage map of diploid Fragaria, we scored 78 markers (68 microsatellites, one sequence-characterised amplified region, six gene-specific markers and three morphological traits) in an interspecific F2 population of 94 plants generated from a cross of F.vesca f. semperflorens × F. nubicola. Co-segregation analysis arranged 76 markers into seven discrete linkage groups covering 448 cM, with linkage group sizes ranging from 100.3 cM to 22.9 cM. Marker coverage was generally good; however some clustering of markers was observed on six of the seven linkage groups. Segregation distortion was observed at a high proportion of loci (54%), which could reflect the interspecific nature of the progeny and, in some cases, the self-incompatibility of F. nubicola. Such distortion may also account for some of the marker clustering observed in the map. One of the morphological markers, pale-green leaf (pg) has not previously been mapped in Fragaria and was located to the mid-point of linkage group VI. The transferable nature of the markers used in this study means that the map will be ideal for use as a framework for additional marker incorporation aimed at enhancing and resolving map coverage of the diploid Fragaria genome. The map also provides a sound basis for linkage map transfer to the cultivated octoploid strawberry.

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An apple rootstock progeny raised from the cross between the very dwarfing ‘M.27’ and the more vigorous ‘M.116’ (‘M.M.106’ × ‘M.27’) was used for the construction of a linkage map comprising a total of 324 loci: 252 previously mapped SSRs, 71 newly characterised or previously unmapped SSR loci (including 36 amplified by 33 out of the 35 novel markers reported here), and the self-incompatibility locus. The map spanned the 17 linkage groups (LG) expected for apple covering a genetic distance of 1,229.5 cM, an estimated 91% of the Malus genome. Linkage groups were well populated and, although marker density ranged from 2.3 to 6.2 cM/SSR, just 15 gaps of more than 15 cM were observed. Moreover, only 17.5% of markers displayed segregation distortion and, unsurprisingly in a semi-compatible backcross, distortion was particularly pronounced surrounding the self-incompatibility locus (S) at the bottom of LG17. DNA sequences of 273 SSR markers and the S locus, representing a total of 314 loci in this investigation, were used to anchor to the ‘Golden Delicious’ genome sequence. More than 260 of these loci were located on the expected pseudo-chromosome on the ‘Golden Delicious’ genome or on its homeologous pseudo-chromosome. In total, 282.4 Mbp of sequence from 142 genome sequence scaffolds of the Malus genome were anchored to the ‘M.27’ × ‘M.116’ map, providing an interface between the marker data and the underlying genome sequence. This will be exploited for the identification of genes responsible for traits of agronomic importance such as dwarfing and water use efficiency.

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Multilocus digenic linkage disequilibria (LD) and their population structure were investigated in eleven landrace populations of barley (Hordeum vulgare ssp. vulgare L.) in Sardinia, using 134 dominant simple-sequence amplified polymorphism markers. The analysis of molecular variance for these markers indicated that the populations were partially differentiated (F ST = 0.18), and clustered into three geographic areas. Consistent with this population pattern, STRUCTURE analysis allocated individuals from a bulk of all populations into four genetic groups, and these groups also showed geographic patterns. In agreement with other molecular studies in barley, the general level of LD was low (13 % of locus pairs, with P < 0.01) in the bulk of 337 lines, and decayed steeply with map distance between markers. The partitioning of multilocus associations into various components indicated that genetic drift and founder effects played a major role in determining the overall genetic makeup of the diversity in these landrace populations, but that epistatic homogenising or diversifying selection was also present. Notably, the variance of the disequilibrium component was relatively high, which implies caution in the pooling of barley lines for association studies. Finally, we compared the analyses of multilocus structure in barley landrace populations with parallel analyses in both composite crosses of barley on the one hand and in natural populations of wild barley on the other. Neither of these serves as suitable mimics of landraces in barley, which require their own study. Overall, the results suggest that these populations can be exploited for LD mapping if population structure is controlled.

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The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

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Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its message passing interface (MPI)-based C implementation are reported.

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Motivation: We compare phylogenetic approaches for inferring functional gene links. The approaches detect independent instances of the correlated gain and loss of pairs of genes from species' genomes. We investigate the effect on results of basing evidence of correlations on two phylogenetic approaches, Dollo parsminony and maximum likelihood (ML). We further examine the effect of constraining the ML model by fixing the rate of gene gain at a low value, rather than estimating it from the data. Results: We detect correlated evolution among a test set of pairs of yeast (Saccharomyces cerevisiae) genes, with a case study of 21 eukaryotic genomes and test data derived from known yeast protein complexes. If the rate at which genes are gained is constrained to be low, ML achieves by far the best results at detecting known functional links. The model then has fewer parameters but it is more realistic by preventing genes from being gained more than once. Availability: BayesTraits by M. Pagel and A. Meade, and a script to configure and repeatedly launch it by D. Barker and M. Pagel, are available at http://www.evolution.reading.ac.uk .

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In 2006 we celebrated the centenary of a remarkable year that saw the birth of genetics as a scientific discipline. This birth had its origins in horticulture and was supervised by a remarkable Cambridge academic, accompanied by a loyal group of female colleagues who worked together in underfunded conditions with little institutional support. Despite this deprivation, they established the foundations of an ongoing revolution, with huge academic and commercial consequences that we can recognize today in the shape of genomics and its application to biomedicine.

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We previously described the use of an established reverse genetics system for the generation of recombinant human influenza A viruses from cloned cDNAs. Here, we have assembled a set of plasmids to allow recovery of the avian H5N1 influenza virus A/Turkey/England/50-92/91 entirely from cDNA. This system enables us to introduce mutations or truncations into the cDNAs to create mutant viruses altered specifically in a chosen gene. These mutant viruses can then be used in future pathogenesis studies in chickens and in studies to understand the host range restrictions of avian influenza viruses in humans.

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A total of 45 microsatellites (SSRs) were developed for mapping in Fragaria. They included 31 newly isolated codominant genomic SSRs from F. nubicola and a further 14 SSRs, derived from an expressed sequence tagged library (EST-SSRs) of the cultivated strawberry, F. × ananassa. These, and an additional 64 previously characterised but unmapped SSRs and EST-SSRs, were scored in the diploid Fragaria interspecific F2 mapping population (FV×FN) derived from a cross between F. vesca 815 and F. nubicola 601. The cosegregation data of these 109 SSRs, and of 73 previously mapped molecular markers, were used to elaborate an enhanced linkage map. The map is composed of 182 molecular markers (175 microsatellites, six gene specific markers and one sequence-characterised amplified region) and spans 424 cM over seven linkage groups. The average marker spacing is 2.3 cM/marker and the map now contains just eight gaps longer than 10 cM. The transferability of the new SSR markers to the cultivated strawberry was demonstrated using eight cultivars. Because of the transferable nature of these markers, the map produced will provide a useful reference framework for the development of linkage maps of the cultivated strawberry and for the development of other key resources for Fragaria such as a physical map. In addition, the map now provides a framework upon which to place transferable markers, such as genes of known function, for comparative mapping purposes within Rosaceae.

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Bayesian statistics allow scientists to easily incorporate prior knowledge into their data analysis. Nonetheless, the sheer amount of computational power that is required for Bayesian statistical analyses has previously limited their use in genetics. These computational constraints have now largely been overcome and the underlying advantages of Bayesian approaches are putting them at the forefront of genetic data analysis in an increasing number of areas.