Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
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TLDR
It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.Abstract:
Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.read more
Citations
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Journal ArticleDOI
Genome-Wide Association for Growth Traits in Canchim Beef Cattle
Marcos Eli Buzanskas,D. A. Grossi,Ricardo Vieira Ventura,Flavio S Schenkel,Mehdi Sargolzaei,Sarah Laguna Conceição Meirelles,Fabiana Barichello Mokry,R. H. Higa,Maurício A. Mudadu,Marcos Vinícius Gualberto Barbosa da Silva,S. C. M. Niciura,Roberto Augusto de Almeida Torres Júnior,Maurício Mello de Alencar,Luciana Correia de Almeida Regitano,Danísio Prado Munari +14 more
TL;DR: Genomic regions and genes that play roles in birth weight, weaning weight, and long-yearling weight adjusted for 420 days of age (LYW) in Canchim cattle are identified and new candidate regions for body weight traits were detected.
Journal ArticleDOI
Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens.
Anna Wolc,Jesus Arango,Petek Settar,Janet E. Fulton,Neil P. O'Sullivan,Rudolf Preisinger,David Habier,Rex D. Fernando,Dorian J. Garrick,William G. Hill,Jack C. M. Dekkers +10 more
TL;DR: Genomic prediction model Bayes-B was used to identify genomic regions associated with the mean and standard deviation of egg weight at three ages in a commercial brown egg layer line and a novel approach using the posterior distribution of window variances from the Monte Carlo Markov Chain samples were used to describe genetic architecture.
Journal ArticleDOI
Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle
TL;DR: Using BEAGLE for pre-phasing and IMPUTE2 for imputation is a fast and accurate strategy to increase the size of the reference data and in turn the accuracy of imputation when only few animals are available.
Book ChapterDOI
Implementing a QTL detection study (GWAS) using genomic prediction methodology.
TL;DR: How to make inferences from commonly used Bayesian methods for genomic prediction about genome-wide association study results is described and how to interpret the results is commented on.
Journal ArticleDOI
Prospects for Genomic Selection in Cassava Breeding
Marnin D. Wolfe,Dunia Pino Del Carpio,Olumide Alabi,Lydia Ezenwaka,Ugochukwu N. Ikeogu,Ismail Siraj Kayondo,Roberto Lozano,Uche Godfrey Okeke,Alfred Ozimati,Esuma Williams,Chiedozie Egesi,Robert Kawuki,Peter Kulakow,Ismail Y. Rabbi,Jean-Luc Jannink +14 more
TL;DR: Prospects for GS in cassava are good and improving, with accuracy generally similar across breeding populations, and data sharing across programs improves predictions in some circumstances.
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