Genomic Selection in Multi-environment Crop Trials

G3: Genes, Genomes, Genetics - Tập 6 Số 5 - Trang 1313-1326 - 2016
Helena Oakey1, B. R. Cullis2, R. Thompson3, Jordi Comadran4, Claire Halpin1, Robbie Waugh4,1
1Division of Plant Sciences, University of Dundee at The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK
2National Institute for Applied Statistics Research Australia, University of Wollongong, NSW 2522, Australia
3Rothamsted Research, Harpenden, Hertfordshire AL5 3JQ, UK
4Department of Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK

Tóm tắt

AbstractGenomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.

Từ khóa


Tài liệu tham khảo

Akaike, 1974, New look at statistical-model identification., Transactions on Automatic Control., AC19, 716, 10.1109/TAC.1974.1100705

Burgueno, 2012, Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers., Crop Sci., 52, 707, 10.2135/cropsci2011.06.0299

Butler, 2009, ASReml R-reference manual

Comadran, 2012, Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley., Nat. Genet., 44, 1388, 10.1038/ng.2447

Crossa, 2010, Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers., Genetics, 186, 713, 10.1534/genetics.110.118521

Crossa, 2011, Genomic selection and prediction in plant breeding., J. Crop Improv., 25, 239, 10.1080/15427528.2011.558767

Cullis, 1998, Spatial analysis of multi-environment early generation trials., Biometrics, 54, 1, 10.2307/2533991

Cullis, 2006, On the design of early generation variety trials with correlated data., J. Agric. Biol. Environ. Stat., 11, 381, 10.1198/108571106X154443

Da, 2014, Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers., PLoS One, 9, e87666, 10.1371/journal.pone.0087666

de Boer, 1993, Genetic evaluation methods for populations with dominance and inbreeding., Theor. Appl. Genet., 86, 245, 10.1007/BF00222086

de los Campos, 2013, Whole-genome regression and prediction methods applied to plant and animal breeding., Genetics, 193, 327, 10.1534/genetics.112.143313

Desta, 2014, Genomic selection: genome-wide prediction in plant improvement., Trends Plant Sci., 19, 592, 10.1016/j.tplants.2014.05.006

Gilmour, 1997, Accounting for natural and extraneous variation in the analysis of field experiments., J. Agric. Biol. Environ. Stat., 2, 269, 10.2307/1400446

Guo, 2013, Accuracy of across-environment genome wide prediction in maize nested association mapping populations., G3 (Bethesda), 3, 263, 10.1534/g3.112.005066

Habier, 2007, The impact of genetic relationship information on genome assisted breeding values., Genetics, 177, 2389, 10.1534/genetics.107.081190

Habier, 2013, Genomic BLUP decoded: a look into the black box of genomic prediction., Genetics, 194, 597, 10.1534/genetics.113.152207

Heffner, 2010, Plant breeding with genomic selection: gain per unit time and cost., Crop Sci., 50, 10.2135/cropsci2009.11.0662

Heffner, 2011, Genomic selection accuracy for grain quality traits in biparental wheat populations., Crop Sci., 51, 2597, 10.2135/cropsci2011.05.0253

Heffner, 2011, Genomic selection accuracy using multifamily prediction models in a wheat breeding program., Plant Genome, 4, 65, 10.3835/plantgenome.2010.12.0029

Heslot, 2012, Genomic selection in plant breeding: a comparison of models., Crop Sci., 52, 146, 10.2135/cropsci2011.06.0297

Jannink, 2010, Genomic selection in plant breeding: from theory to practice., Brief. Funct. Genomics, 9, 166, 10.1093/bfgp/elq001

Jonas, 2013, Does genomic selection have a future in plant breeding?, Trends Biotechnol., 31, 497, 10.1016/j.tibtech.2013.06.003

Kelly, 2007, The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials., Crop Sci., 47, 1063, 10.2135/cropsci2006.08.0540

Lado, 2013, Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data., G3 (Bethesda), 2, 2015

Ly, 2013, Relatedness and genotype × environment interaction affect prediction accuracies in genomic selection: a study in cassava., Crop Sci., 53, 1312, 10.2135/cropsci2012.11.0653

Massman, 2013, Genomewide selection verses marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize., Crop Sci., 53, 58, 10.2135/cropsci2012.02.0112

Meuwissen, 2001, Prediction of total genetic value using genome-wide dense marker maps., Genetics, 157, 1819, 10.1093/genetics/157.4.1819

Munoz, 2014, Unravelling additive from nonadditive effects using genomic relationship matrices., Genetics, 198, 1759, 10.1534/genetics.114.171322

Oakey, 2013, Identification of crop cultivars with consistently high lignocellulosic sugar release requires the use of appropriate statistical design and modelling., Biotechnol. Biofuels, 6, 185, 10.1186/1754-6834-6-185

Patterson, 1977, Variability of yields of cereal varieties in U. K. trials., J. Agric. Sci., 89, 238, 10.1017/S002185960002743X

Piepho, 2012, Efficient computation of ridge-regression best linear unbiased prediction in genomic selection in plant breeding., Crop Sci., 52, 1093, 10.2135/cropsci2011.11.0592

R Core Team, 2015 R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org/.

Smith, 2001, Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend., Biometrics, 57, 1138, 10.1111/j.0006-341X.2001.01138.x

Smith, 2005, The analysis of crop cultivar breeding and evaluations trials: an overview of current mixed model approaches., J. Agric. Sci., 143, 1, 10.1017/S0021859605005587

Solberg, 2009, Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect., Genet. Sel. Evol., 41, 53, 10.1186/1297-9686-41-53

Stranden, 2009, Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit., J. Dairy Sci., 92, 2971, 10.3168/jds.2008-1929

Tibshirani, 1996, Regression shrinkage and selection via the LASSO., J. R. Stat. Soc., B, 58, 267, 10.1111/j.2517-6161.1996.tb02080.x

Wang, 2012, Comparison of five methods for genomic breeding value estimation for the common dataser of the 15th QTL-MAS workshop., BMC Proc., 6, S13, 10.1186/1753-6561-6-S2-S13

Wimmer, 2013, Genome-wide prediction of traits with different genetic architecture through efficient variable selection., Genetics, 195, 573, 10.1534/genetics.113.150078

Zhong, 2009, Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study., Genetics, 182, 355, 10.1534/genetics.108.098277

Zou, 2005, Regularization and variable selection via the elastic net., J. R. Stat. Soc. Ser. A Stat. Soc., 67, 301, 10.1111/j.1467-9868.2005.00503.x

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G3: Genes, Genomes, Genetics

Tập 6 Số 5

1313-1326

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