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Search Results: 1 - 10 of 5758 matches for " genomic selection. "
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Use of Observed Genomic Information to Infer Linkage Disequilibrium between Markers and QTLs  [PDF]
El Hamidi Hay, Romdhane Rekaya
Agricultural Sciences (AS) , 2018, DOI: 10.4236/as.2018.911102
Abstract: Conducting genomic selection in admixed populations is challenging and its accuracy in this case largely depends on the persistence of linkage disequilibrium between single nucleotide polymorphisms (SNP) and quantitative trait loci (QTL). Inferring linkage disequilibrium (LD) between SNP markers and QTLs could be important in understanding the change of SNP marker effects across different breeds. Predicting the change in linkage disequilibrium between markers and QTLs across two divergent breeds was explored using information from the genotype data. Two different models (M1, M2) that differ in the definition of the explanatory variables were used to infer the level of LD between SNP markers and QTLs using all markers in the panel or windows of fixed number of markers. Three simulation scenarios were conducted using different number of SNPs and QTLs. In the first scenario, the resulting coefficient of determination (R2) was 0.65 and 0.52 using M1 and M2, respectively. In the second scenario, average R2 equaled 0.12 using all markers in the panel and 0.25 using 100 marker windows. Across the three simulation scenarios, it was clear that a significant portion of the variation in the change in LD between SNP markers and QTLs could be explained by information already available in the observed SNP marker data.
Mutations in normal breast tissue and breast tumours
Ian PM Tomlinson
Breast Cancer Research , 2001, DOI: 10.1186/bcr311
Abstract: A few years ago, a fascinating article by Deng et al [1] showed loss of heterozygosity (LOH) on chromosome 3p in apparently normal breast tissue adjacent to a carcinoma. Evidence was presented to show that the carcinoma also showed 3p loss. The importance accorded to these findings was reflected by their publication in Science. Readers inferred that 3p loss may sometimes be an early event in breast tumorigenesis that might have produced a selective advantage and clonal expansion, but led to no change in morphology. This model of tumorigenesis was therefore subtly different from that proposed for the pathogenesis of colorectal tumours, in which even the earliest lesions were morphologically distinct from normal tissue [2].Lakhani et al [3] studied short-term cultures of luminal epithelial and myoepithelial breast cells derived from macroscopically normal fresh breast tissue adjacent to carcinoma. This method was used to exclude the possibility that the 'normal' tissue studied by Deng et al [1] might actually have been heavily contaminated by cancer cells that had undergone Pagetoid spread. Microdissected normal duct-lobular units were also analysed, thus providing a direct comparison with the study of Deng et al [1]. Five of 13 cases showed some LOH in normal tissue, although this was variable in its origin (microdissected or cultured, adjacent to cancer or distant from cancer). Loss was seen in normal tissue, most often at markers near BRCA2 on chromosome 13q and rarely at markers on chromosome 3p near those studied by Deng et al [1]. Loss in normal tissue was not always observed in any adjacent carcinoma.No large sample series has ever confirmed the existence of detectable mutations in apparently normal breast tissue (perhaps no one has tried) and each of the two studies above had potential technical problems, which were almost unavoidable. The ability of polymerase chain reaction to produce consistent, artefactual allelic loss, especially in archival samples, is b
Marcadores SNP: conceitos básicos, aplica??es no manejo e no melhoramento animal e perspectivas para o futuro
Caetano, Alexandre Rodrigues;
Revista Brasileira de Zootecnia , 2009, DOI: 10.1590/S1516-35982009001300008
Abstract: the first studies to identify, characterize and use molecular markers to characterize genetic resources and generate tools for animal breeding and management date from the end of the 80s. in the last 20 years the technologies to generate molecular data went through several innovation cycles. the last wave of technological innovations represents a true revolution, bringing methods to identify and genotype snp (single nucleotide polymorphism) markers in large scale. high density dna chips were generated to genotype from tens of thousands to hundreds of thousands of snps in a single assay. furthermore, other medium density technologies allow for the genotyping of tens to hundreds of makers, in high numbers of samples, with very high speed and automation. these new technologies allowed for the generation of new applications, such as the methods to genetically evaluate and select animals based on their genomic value (genomic estimated breeding value - gebv). the statistical methods for genomic evaluation and selection are in full development, but the technology already became reality with the release of the first bull summary for the holstein breed with gebvs for milk production and quality traits in january 2009. in addition, these technologies brought new options for development of diagnostic tests for paternity testing, individual identification, traceability, etc. also, these new technologies to genotype snp markers facilitated the development of outsourcing companies to generate molecular data, allowing any group to conduct advanced experiments, always using the most advanced technologies, without the need of investments into equipment.
Using LASSO to estimate marker effects for Genomic Selection
Mario Graziano Usai,Mike E. Goddard,Ben J. Hayes
Italian Journal of Animal Science , 2010, DOI: 10.4081/ijas.2009.s2.168
Abstract: Here we suggest a least absolute shrinkage and selection operator (LASSO) approach to estimate the marker effects for genomic selection using the least angle regression (LARS) algorithm, modified to include a cross–validation step to define the best subset of markers to involve in the model. The LASSO-LARS was tested on simulated data which consisted of 5,865 individuals and 6,000 SNPs. The last generations of this dataset were the selection candidates. Using only animals from generations prior to the candidates, three approaches to splitting the population into training and validation sets for cross-validation were evaluated. Furthermore, different sizes of the validation sample were tested. Moreover, BLUP and Bayesian methods were carried out for comparison. The most reliable cross-validation method was the random splitting of overall population with a validation sample size of 50% of the reference population. The accuracy of the GEBVs (correlation with true breeding values) in the candidate population obtained by LASSO-LARS was 0.89 with 156 explanatory SNPs. This value was higher then those obtained by using BLUP and Bayesian methods, which were 0.75 and 0.84 respectively. It was concluded that LASSO-LARS approach is a good alternative way to estimate markers effects for genomic selection.
Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs
Ezequiel Luis Nicolazzi,Riccardo Negrini,Corrado Dimauro
Italian Journal of Animal Science , 2010, DOI: 10.4081/ijas.2009.s2.117
Abstract: Two methods of SNPs pre-selection based on single marker regression for the estimation of genomic breeding values (G-EBVs) were compared using simulated data provided by the XII QTL-MAS workshop: i) Bonferroni correction of the significance threshold and ii) Permutation test to obtain the reference distribution of the null hypothesis and identify significant markers at P<0.01 and P<0.001 significance thresholds. From the set of markers significant at P<0.001, random subsets of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%). The permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively). Interestingly, halving or dividing by four the number of SNPs significant at P<0.001 resulted in an only slightly decrease of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying large effects, might have favoured the Bonferroni method.
Advances in genomic selection in domestic animals
Zhe Zhang,Qin Zhang,XiangDong Ding
Chinese Science Bulletin , 2011, DOI: 10.1007/s11434-011-4632-7
Abstract: Genomic selection (GS) is a marker-assisted selection method, in which high density markers covering the whole genome are used simultaneously for individual genetic evaluation via genomic estimated breeding values (GEBVs). GS can increase the accuracy of selection, shorten the generation interval by selecting individuals at the early stage of life, and accelerate genetic progress. With the availability of high density whole genome SNP (single nucleotide polymorphism) chips for livestock, GS is reshaping the conventional animal breeding systems. In many countries, GS is becoming the major genetic evaluation method for bull selection in dairy cattle and GS may soon completely replace the traditional genetic evaluation system. In recent years, GS has become an important research topic in animal, plant and aquiculture breeding and many exciting results have been reported. In this paper, the methods for obtaining GEBVs, factors affecting the accuracy of GEBVs, and the current status of implementation of GS in livestock are reviewed. Some unresolved issues related to GS in livestock are also discussed.
Applied Genetics and Genomics in Alfalfa Breeding
Xuehui Li,E. Charles Brummer
Agronomy , 2012, DOI: 10.3390/agronomy2010040
Abstract: Alfalfa ( Medicago sativa L.), a perennial and outcrossing species, is a widely planted forage legume for hay, pasture and silage throughout the world. Currently, alfalfa breeding relies on recurrent phenotypic selection, but alternatives incorporating molecular marker assisted breeding could enhance genetic gain per unit time and per unit cost, and accelerate alfalfa improvement. Many major quantitative trait loci (QTL) related to agronomic traits have been identified by family-based QTL mapping, but in relatively large genomic regions. Candidate genes elucidated from model species have helped to identify some potential causal loci in alfalfa mapping and breeding population for specific traits. Recently, high throughput sequencing technologies, coupled with advanced bioinformatics tools, have been used to identify large numbers of single nucleotide polymorphisms (SNP) in alfalfa, which are being developed into markers. These markers will facilitate fine mapping of quantitative traits and genome wide association mapping of agronomic traits and further advanced breeding strategies for alfalfa, such as marker-assisted selection and genomic selection. Based on ideas from the literature, we suggest several ways to improve selection in alfalfa including (1) diversity selection and paternity testing, (2) introgression of QTL and (3) genomic selection.
Methods to address poultry robustness and welfare issues through breeding and associated ethical considerations
William M. Muir,Heng-Wei Cheng
Frontiers in Genetics , 2014, DOI: 10.3389/fgene.2014.00407
Abstract: As consumers and society in general become more aware of ethical and moral dilemmas associated with intensive rearing systems, pressure is put on the animal and poultry industries to adopt alternative forms of housing. This presents challenges especially regarding managing competitive social interactions between animals. However, selective breeding programs are rapidly advancing, enhanced by both genomics and new quantitative genetic theory that offer potential solutions by improving adaptation of the bird to existing and proposed production environments. The outcomes of adaptation could lead to improvement of animal welfare by increasing fitness of the animal for the given environments, which might lead to increased contentment and decreased distress of birds in those systems. Genomic selection, based on dense genetic markers, will allow for more rapid improvement of traits that are expensive or difficult to measure, or have a low heritability, such as pecking, cannibalism, robustness, mortality, leg score, bone strength, disease resistance, and thus has the potential to address many poultry welfare concerns. Recently selection programs to include social effects, known as associative or indirect genetic effects (IGEs), have received much attention. Group, kin, multi-level, and multi-trait selection including IGEs have all been shown to be highly effective in reducing mortality while increasing productivity of poultry layers and reduce or eliminate the need for beak trimming. Multi-level selection was shown to increases robustness as indicated by the greater ability of birds to cope with stressors. Kin selection has been shown to be easy to implement and improve both productivity and animal well-being. Management practices and rearing conditions employed for domestic animal production will continue to change based on ethical and scientific results. However, the animal breeding tools necessary to provide an animal that is best adapted to these changing conditions are readily available and should be used, which will ultimately lead to the best possible outcomes for all impacted.
Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice
Dagmar NRG Kapell, Daniel Sorensen, Guosheng Su, Luc LG Janss, Cheryl J Ashworth, Rainer Roehe
BMC Genetics , 2012, DOI: 10.1186/1471-2156-13-42
Abstract: Genomic selection showed a high predictive ability (PA) in comparison to traditional polygenic selection, especially for traits of moderate heritability and when cross-validation was between families. This occurred although the proportion of genomic variance of traits using genomic models was 22 to 33% smaller than using polygenic models. Using a 2.5% mixture genomic model, the proportion of genomic variance was 79% smaller relative to the polygenic model. Although the proportion of variance explained by the markers was reduced further when a smaller number of SNPs was assumed to have a substantial effect on the trait, PA of genomic selection for most traits was little affected. These low mixture percentages resulted in improved estimates of single SNP effects. Genomic models implemented for traits with fewer QTLs showed even lower PA than the polygenic models.Genomic selection generally performed better than traditional polygenic selection, especially in the context of between family cross-validation. Reducing the number of markers considered to affect the trait did not significantly change PA for most traits, particularly in the case of within family cross-validation, but increased the number of markers found to be associated with QTLs. The underlying number of QTLs affecting the trait has an effect on PA, with a smaller number of QTLs resulting in lower PA using the genomic model compared to the polygenic model.
Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection
Pimentel, Eduardo da Cruz Gouveia;Sargolzaei, Mehdi;Simianer, Henner;Schenkel, Flávio Schramm;Liu, Zengting;Fries, Luiz Alberto;Queiroz, Sandra Aidar de;
Genetics and Molecular Biology , 2010, DOI: 10.1590/S1415-47572010005000014
Abstract: the aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. one iterative and two direct methods were used: gauss-seidel with residual update, cholesky decomposition and gentleman-givens rotations. for resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. methods were also applied to real data comprising 3081 individuals genotyped for 45181 snps. results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. use of a direct solver may allow for computing (co)variances of snp effects. when applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. from results with real data, gentleman-givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). it would indeed be the preferred method whenever computer resources allow its use.
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