904 resultados para Microsatellites (Genetics)


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Cytogenetic mapping of the arctic fox and the Chinese raccoon dog were performed using a set of canine probes derived from the Bacterial Artificial Chromosome (BAC) library. Altogether, 10 BAC clones containing sequences of selected genes (PAX3, HBB, ATP2A2, TECTA, PIT1, ABCA4, ESR2, TPH1, HTR2A, MAOA) and microsatellites were mapped by fluorescence in situ hybridization (FISH) experiments to chromosomes of the canids studied. At present, the cytogenetic map on the arctic fox and Chinese raccoon dog consists of 45 loci each. Chromosomal localization of the BAC clones was in agreement with data obtained by earlier independent comparative chromosome painting. However, two events of telomere-to-centromere inversions were tentatively identified while compared with assignments in the dog karyotype.

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Recurrent airway obstruction (RAO) is a multifactorial and polygenic disease. Affected horses are typically 7 years of age or older and show exercise intolerance, increased breathing effort, coughing, airway neutrophilia, mucus accumulation and hyperreactivity as well as cholinergic bronchospasm. The environmental factors responsible are predominantly allergens and irritants in haydust, but the immunological mechanisms underlying RAO are still unclear. Several studies have demonstrated a familiar predisposition for RAO and it is now proven that the disease has a genetic basis. In offspring, the risk of developing RAO is 3-fold increased when one parent is affected and increases to almost 5-fold when both parents have RAO. Segregation analysis in two high-prevalence families demonstrated a high heritability and a complex inheritance with several major genes. A whole genomescan showed chromosome-wide significant linkage of seven chromosomal regions with RAO. Of the microsatellites, which were located near atopy candidate genes, those in a region of chromosome 13 harboring the IL4R gene were strongly associated with the RAO phenotype in the offspring of one RAO-affected stallion. Furthermore, IgE-levels are influenced by hereditary factors in the horse, and we have evidence that RAO-affected offspring of the same stallion have increased levels of specific IgE against moldspore allergens. The identification of genetic markers and ultimately of the responsible genes will not only allow for an improved prophylaxis, i.e. early identification of susceptible individuals and avoidance of high-risk matings, but also improve our ability to find new therapeutic targets and to optimize existing treatments.

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Natural selection is one of the major factors in the evolution of all organisms. Detecting the signature of natural selection has been a central theme in evolutionary genetics. With the availability of microsatellite data, it is of interest to study how natural selection can be detected with microsatellites. ^ The overall aim of this research is to detect signatures of natural selection with data on genetic variation at microsatellite loci. The null hypothesis to be tested is the neutral mutation theory of molecular evolution, which states that different alleles at a locus have equivalent effects on fitness. Currently used tests of this hypothesis based on data on genetic polymorphism in natural populations presume that mutations at the loci follow the infinite allele/site models (IAM, ISM), in the sense that at each site at most only one mutation event is recorded, and each mutation leads to an allele not seen before in the population. Microsatellite loci, which are abundant in the genome, do not obey these mutation models, since the new alleles at such loci can be created either by contraction or expansion of tandem repeat sizes of core motifs. Since the current genome map is mainly composed of microsatellite loci and this class of loci is still most commonly studied in the context of human genome diversity, this research explores how the current test procedures for testing the neutral mutation hypothesis should be modified to take into account a generalized model of forward-backward stepwise mutations. In addition, recent literature also suggested that past demographic history of populations, presence of population substructure, and varying rates of mutations across loci all have confounding effects for detecting signatures of natural selection. ^ The effects of the stepwise mutation model and other confounding factors on detecting signature of natural selection are the main results of the research. ^

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The tropical abalone Haliotis asinina is a wild-caught and cultured species throughout the Indo-Pacific as well as being an emerging model species for the study of haliotids. H. asinina has the fastest recorded natural growth rate of any abalone and reaches sexual maturity within one year. As such, it is a suitable abalone species for selective breeding for commercially important traits such as rapid growth. Estimating the amount of variation in size that is attributable to heritable genetic differences can assist the development of such a selective breeding program. Here we estimated heritability for growth-related traits at 12 months of age by creating a single cohort of 84 families in a full-factorial mating design consisting of 14 sires and 6 dams. Of 500 progeny sampled, 465 were successfully assigned to their parents based on shared alleles at 5 polymorphic microsatellite loci. Using an animal model, heritability estimates were 0.48 +/- 0.15 for shell length, 0.38 +/- 0.13 for shell width and 0.36 +/- 0.13 for weight. Genetic correlations were > 0.98 between shell parameters and weight, indicating that breeding for weight gains could be successfully achieved by selecting for shell length. (c) 2006 Elsevier B.V. All rights reserved.

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The genetic variability of 28 sorghum genotypes of known senescence phenotype was investigated using 66 SSR markers well-distributed across the sorghum genome. The genotypes of a number of lines from breeding programmes for stay-green were also determined. This included lines selected phenotypically for stay-green and also RSG 03123, a marker-assisted backcross progeny of R16 (recurrent parent) and B35 (stay-green donor). A total of 419 alleles were detected with a mean of 6.2 per locus. The number of alleles ranged from one for Xtxp94 to 14 for Xtxp88. Chromosome SBI-10 had the highest mean number of alleles (8.33), while SBI-05 had the lowest (4.17). The PIC values obtained ranged from zero to 0.89 in Xtxp94 and Xtxp88, respectively, with a mean of 0.68. On a chromosome basis, mean PIC values were highest in SBI-10 (0.81) and lowest in SBI-05 (0.53). Most of the alleles from B35 in RSG 03123 were found on chromosomes SBI-01, SBI-02 and SBI-03, confirming the successful introgression of quantitative trait loci associated with stay-green from B35 into the senescent background R16. However, the alternative stay-green genetic sources were found to be distinct based on either all the SSRs employed or using only those associated with the stay-green trait in B35. Therefore, the physiological and biochemical basis of each stay-green source should be evaluated in order to enhance the understanding of the functioning of the trait in the various backgrounds. These genetic sources of stay-green could provide a valuable resource for improving this trait in sorghum breeding programmes.

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Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.

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Skipjack (SJT) (Katsuwonus pelamis) is a medium sized, pelagic, highly dispersive tuna species that occurs widely across tropical and subtropical waters. SJT constitute the largest tuna fishery in the Indian Ocean, and are currently managed as a single stock. Patterns of genetic variation in a mtDNA gene and 6 microsatellite loci were examined to test for stock structure in the northwestern Indian Ocean. 324 individuals were sampled from five major fishing grounds around Sri Lanka, and single sites in the Maldive Islands and the Laccadive Islands. Phylogenetic reconstruction of mtDNA revealed two coexisting divergent clades in the region. AMOVA (Analysis of Molecular Variance) of mtDNA data revealed significant genetic differentiation among sites (ΦST = 0.2029, P < 0.0001), also supported by SAMOVA results. AMOVA of microsatellite data also showed significant differentiation among most sampled sites (FST = 0.0256, P<0.001) consistent with the mtDNA pattern. STRUCTURE analysis of the microsatellite data revealed two differentiated stocks. While the both two marker types examined identified two genetic groups, microsatellite analysis indicates that the sampled SJT are likely to represent individuals sourced from discrete breeding grounds that are mixed in feeding grounds in Sri Lankan waters.

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Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.