Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
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TLDR
An overview of available methods for implementing parametric WGR models is provided, selected topics that emerge in applications are discussed, and a general discussion of lessons learned from simulation and empirical data analysis in the last decade are presented.Abstract:
Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.read more
Citations
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Efficient Bayesian mixed-model analysis increases association power in large cohorts
Po-Ru Loh,George Tucker,Brendan Bulik-Sullivan,Bjarni J. Vilhjálmsson,Bjarni J. Vilhjálmsson,Hilary K. Finucane,Rany M. Salem,Daniel I. Chasman,Paul M. Ridker,Benjamin M. Neale,Benjamin M. Neale,Bonnie Berger,Nick Patterson,Alkes L. Price +13 more
TL;DR: BOLT-LMM is presented, which requires only a small number of O(MN) time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes.
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Genome-Wide Regression and Prediction with the BGLR Statistical Package
TL;DR: The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures, which allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner.
Journal ArticleDOI
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
José Crossa,Paulino Pérez-Rodríguez,Jaime Cuevas,Osval A. Montesinos-López,Diego Jarquin,Gustavo de los Campos,Juan Burgueño,Juan Manuel González-Camacho,Sergio Pérez-Elizalde,Yoseph Beyene,Susanne Dreisigacker,Ravi P. Singh,Xuecai Zhang,Manje Gowda,Manish Roorkiwal,Jessica Rutkoski,Rajeev K. Varshney +16 more
TL;DR: Based on GP results, it is speculated how GS in germplasm enhancement programs could accelerate the flow of genes from gene bank accessions to elite lines and recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
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Polygenic modeling with bayesian sparse linear mixed models.
TL;DR: This work applies Bayesian sparse linear mixed model (BSLMM) and compares it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction, and demonstrates that BSLMM considerably outperforms either of the other two methods.
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Pitfalls of predicting complex traits from SNPs
Naomi R. Wray,Jian Yang,Ben J. Hayes,Ben J. Hayes,Alkes L. Price,Michael E. Goddard,Peter M. Visscher +6 more
TL;DR: Some of the limitations and pitfalls of prediction analysis are discussed and how naive implementations can lead to severe bias and misinterpretation of results are shown.
References
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Journal ArticleDOI
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree
Gustavo de los Campos,Hugo Naya,Daniel Gianola,José Crossa,Andres Legarra,Eduardo Manfredi,Kent A. Weigel,José Miguel Cotes +7 more
TL;DR: This article adapts the Bayesian least absolute shrinkage and selection operator (LASSO) to arrive at a regression model where markers, pedigrees, and covariates other than markers are considered jointly, and results indicate that inclusion of markers in the regression further improved the predictive ability of models.
Journal ArticleDOI
Plant Breeding with Genomic Selection: Gain per Unit Time and Cost
TL;DR: An analytical framework is developed to compare gains from MAS and GS for complex traits and provide a plant breeding context for interpreting results from studies on GEBV accuracy, and indicates that GS can outperform MAS on a per-year basis even at lowGEBV accuracies.
Journal ArticleDOI
Marker assisted selection using best linear unbiased prediction
Rohan L. Fernando,M. Grossman +1 more
TL;DR: This approach allows simultaneous evaluation of fixed effects, effects of MQTL alleles, and effects of alleles at the remaining QTLs, using known relationships and phenotypic and marker information.
Journal ArticleDOI
QTL analysis in plants; where are we now?
M. J. Kearsey,A G L Farquhar +1 more
Abstract: We have briefly reviewed the methods currently available for QTL analysis in segregating populations and summarized some of the conclusions arising from such analyses in plant populations We show that the analytical methods locate QTL with poor precision (10-30 cM), unless the heritability of an individual QTL is high Also the estimates of the QTL effects, particularly the dominance effects tend to be inflated because only large estimates are significant Estimates of numbers of QTL per trait are generally low ( 1, but seldom significantly greater These latter cases need further analysis Many QTL map close to candidate genes, and there is growing evidence from synteny studies of corresponding chromosome regions carrying similar QTL in different species However, unreliability of QTL location may suggest false candidates
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Accuracy of genomic breeding values in multi-breed dairy cattle populations
TL;DR: Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived, however in multi-breed populations, Bayesian approaches give higher accuracies for some traits.