Journal ArticleDOI
Genomic Selection for Crop Improvement
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TLDR
Genomic selection would substantially accelerate the breeding cycle, enhancing gains per unit time and dramatically change the role of phenotyping, which would then serve to update prediction models and no longer to select lines.Abstract:
Despite important strides in marker technologies, the use of marker-assisted selection has stagnated for the improvement of quantitative traits. Biparental mating designs for the detection of loci affecting these traits (quantitative trait loci [QTL]) impede their application, and the statistical methods used are ill-suited to the traits' polygenic nature. Genomic selection (GS) has been proposed to address these deficiencies. Genomic selection predicts the breeding values of lines in a population by analyzing their phenotypes and high-density marker scores. A key to the success of GS is that it incorporates all marker information in the prediction model, thereby avoiding biased marker effect estimates and capturing more of the variation due to small-effect QTL. In simulations, the correlation between true breeding value and the genomic estimated breeding value has reached levels of 0.85 even for polygenic low heritability traits. This level of accuracy is sufficient to consider selecting for agronomic performance using marker information alone. Such selection would substantially accelerate the breeding cycle, enhancing gains per unit time. It would dramatically change the role of phenotyping, which would then serve to update prediction models and no longer to select lines. While research to date shows the exceptional promise of GS, work remains to be done to validate it empirically and to incorporate it into breeding schemes.read more
Citations
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Journal ArticleDOI
Breeding Technologies to Increase Crop Production in a Changing World
Mark Tester,Peter Langridge +1 more
TL;DR: New technologies must be developed to accelerate breeding through improving genotyping and phenotyping methods and by increasing the available genetic diversity in breeding germplasm.
Journal ArticleDOI
GAPIT: Genome Association and Prediction Integrated Tool
Alexander E. Lipka,Feng Tian,Qishan Wang,Jason A. Peiffer,Meng Li,Peter J. Bradbury,Michael A. Gore,Edward S. Buckler,Zhiwu Zhang +8 more
TL;DR: An R package called GAPIT is developed that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection and can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time.
Journal ArticleDOI
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
TL;DR: A new software package for R called rrBLUP, which is a fast maximum‐likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data.
Journal ArticleDOI
Plant tolerance to high temperature in a changing environment: scientific fundamentals and production of heat stress-tolerant crops
Craita E. Bita,Tom Gerats +1 more
TL;DR: There is a differential effect of climate change both in terms of geographic location and the crops that will likely show the most extreme reductions in yield as a result of expected extreme fluctuations in temperature and global warming in general.
Journal ArticleDOI
Extension of the bayesian alphabet for genomic selection
David Habier,Rohan L. Fernando,Kadir Kizilkaya,Kadir Kizilkaya,Dorian J. Garrick,Dorian J. Garrick +5 more
TL;DR: Estimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture, and it is believed that Bayes Cπ has merit for routine applications.
References
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Book
Genetics and Analysis of Quantitative Traits
Michael Lynch,Bruce Walsh +1 more
TL;DR: This book discusses the genetic Basis of Quantitative Variation, Properties of Distributions, Covariance, Regression, and Correlation, and Properties of Single Loci, and Sources of Genetic Variation for Multilocus Traits.
Journal ArticleDOI
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
TL;DR: 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.
Journal ArticleDOI
Mapping mendelian factors underlying quantitative traits using rflp linkage maps
Eric S. Lander,David Botstein +1 more
TL;DR: In this paper, a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs) are described, and explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.
Journal ArticleDOI
A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
Jianming Yu,Gaël Pressoir,William H. Briggs,Irie Vroh Bi,Masanori Yamasaki,John Doebley,Michael D. McMullen,Michael D. McMullen,Brandon S. Gaut,Dahlia M. Nielsen,James B. Holland,James B. Holland,Stephen Kresovich,Edward S. Buckler,Edward S. Buckler +14 more
TL;DR: A unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers is developed and provides a powerful complement to currently available methods for association mapping.
Journal ArticleDOI
Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes
Barbara E. Stranger,Matthew S. Forrest,Mark J Dunning,Catherine E. Ingle,Claude Beazley,Natalie P. Thorne,Richard Redon,Christine P. Bird,Anna De Grassi,Charles Lee,Charles Lee,Chris Tyler-Smith,Nigel P. Carter,Stephen W. Scherer,Stephen W. Scherer,Simon Tavaré,Simon Tavaré,Panagiotis Deloukas,Matthew E. Hurles,Emmanouil T. Dermitzakis +19 more
TL;DR: To determine the overall contribution of CNVs to complex phenotypes, association analyses of expression levels with SNPs and CNVs in individuals who are part of the International HapMap project show little overlap between the two types of variation.