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
Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments
Vanessa S. Windhausen,Gary Atlin,John M. Hickey,José Crossa,Jean-Luc Jannink,Mark E. Sorrells,Babu Raman,Jill E. Cairns,Amsal Tarekegne,Kassa Semagn,Yoseph Beyene,Pichet Grudloyma,Frank Technow,Christian Riedelsheimer,Albrecht E. Melchinger +14 more
TL;DR: In this paper, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations.
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Computationally efficient whole-genome regression for quantitative and binary traits.
Joelle Mbatchou,Leland Barnard,Joshua D. Backman,Anthony Marcketta,Jack A. Kosmicki,Andrey Ziyatdinov,Christian Benner,Colm O'Dushlaine,Mathew Barber,Boris Boutkov,Lukas Habegger,Manuel A. R. Ferreira,Aris Baras,Jeffrey S. Reid,Gonçalo R. Abecasis,Evan Maxwell,Jonathan Marchini +16 more
TL;DR: RegenerIE as mentioned in this paper is a whole-genome regression method based on ridge regression that enables highly parallelized analysis of quantitative and binary traits in biobank-scale data with reduced computational requirements.
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Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement.
Jennifer Spindel,Hasina Begum,Deniz Akdemir,Bertrand C. Y. Collard,Edilberto D. Redoña,Jean-Luc Jannink,Jean-Luc Jannink,Susan R. McCouch +7 more
TL;DR: An extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains is proposed.
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A Mixed-Model Quantitative Trait Loci (QTL) Analysis for Multiple-Environment Trial Data Using Environmental Covariables for QTL-by-Environment Interactions, with an Example in Maize
Martin P. Boer,Deanne Wright,L. Feng,Dean Podlich,Lang Luo,Mark E. Cooper,Fred A. van Eeuwijk +6 more
TL;DR: The modeling approach proposed explains genotype-by-environment interaction by differential quantitative trait locus (QTL) expression in relation to environmental variables with a better understanding of the genetic architecture of such traits as observed across environments.
Book ChapterDOI
Maize production in a changing climate: Impacts, adaptation, and mitigation strategies
Jill E. Cairns,Kai Sonder,Pervez Haider Zaidi,Nele Verhulst,Nele Verhulst,George Mahuku,Raman Babu,Sudha K. Nair,Biswajit Das,Bram Govaerts,M.T. Vinayan,Zerka Rashid,J.J. Noor,P. Devi,F. M. San Vicente,Boddupalli M. Prasanna +15 more
TL;DR: In this article, a review focusing on achievements in stress tolerance breeding and physiology and presents future tools for quick and efficient germplasm development is presented to increase maize system resilience to climate-related stresses and mitigate the effects of future climate change.
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