An experimental validation of genomic selection in octoploid strawberry

Abstract

The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013–2014 and 2014–2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values.

References

[1] Meuwissen TH, Hayes BJ, Goddard ME . Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001; 157: 1819-1829.

[2] Henderson CR . Applications of Linear Models in Animal Breeding. University of Guelph: Guelph, Ontario, Canada, 1984.

[3] Heffner EL, Sorrels ME, Jannick JL . Genomic selection for crop improvement. Crop Sci 2009; 49: 1-12.

[4] Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME . Invited review: Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 2009; 92: 433-443.

[5] Resende MFR Jr, Muñoz P, Resende MDV, Garrick DJ, Fernando RL, Davis JM et al. Accuracy of genomic selection methods in a standard dataset of Loblolly pine (Pinus taeda L.). Genetics 2012; 190: 1503-1510.

[6] Combs E, Bernardo R . Accuracy of genomewide selection for different traits with constant population size, heritability and numbers of markers. Plant Genome 2013; 6: 1-7.

[7] de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL . Whole genome regression and prediction methods applied to plant and animal breeding. Genetics 2013; 193: 327-345.

[8] Muranty H, Troggio M, Ben-Sadok I, Rifaï MA, Auwerkerken A, Banchi E et al. Accuracy and responses of genomic selection on key traits in apple breeding. Hort Res 2015; 2: 15060.

[9] Daetwyler HD, Villanueva B, Bijma P, Woolliams JA . Inbreeding in genome-wide selection. J Anim Breed Genet 2007; 124: 369-376.

[10] Pszczola M, Veerkamp RF, de Haas Y, Wall E, Strabel T, MPL Calus . Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population. Animal 2013; 7: 1759-1768.

[11] Gianola D . Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 2013; 90: 525-540.

[12] Habier D, Fernando RL, Kizilkaya K, Garrick D . Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 2011; 12: 186.

[13] Fodor A, Segura V, Denis M, Neuenschwander S, Fournier-Level A, Chatelet P et al. Genome-wide predictions methods in highly diverse and heterozygous species: proof-of-concept through simulation in grapevine. PLoS ONE 2014; 9: e110436.

[14] Kumar S, Chagné D, Bink MC, Volz RK, Whitworth C, Carlisle C . Genomic selection for fruit quality traits in apple (Malus × domestica Borkh.) . PloS ONE 2012; 7: e36674.

[15] Biscarini F, Stevanato P, Broccanello C, Stella A, Saccomani M . Genome enabled predictions for binomial traits in sugar beet populations. BMC Genet 2014; 15: 87.

[16] Duangjit J, Causse M, Sauvage C . Efficiency of genomic selection for tomato fruit quality. Mol Breeding 2016; 36: 29.

[17] Bassil NV, Davis TM, Zhang H, Ficklin S, Mittmann M, Webster T et al. Development and preliminary evaluation of a 90K Axiom SNP Array for the allo-octoploid cultivated strawberry Fragaria × ananassa . BMC Genomics 2015; 16: 155.

[18] Verma S, Bassil N, van de Weg E, Harrison R, Monfort A, Hidalgo JM et al. Development and evaluation of the Axiom^s IStraw35 384HT array for the allo-octoploid cultivated strawberry Fragaria × ananassa . Acta Hort 2016 (in press).

[19] Haymes KM, Henken B, Davis TM, van de Weg WE . Identification of RAPD markers linked to a Phytophthora fragariae resistance gene (Rpf1) in the cultivated strawberry . Theor Appl Genet 1997; 94: 1097-1101.

[20] Roach JA, Verma S, Peres NA, Jamieson AR, van de Weg WE, Bink MC et al. FaRXf1: a locus conferring resistance to angular leaf spot caused by Xanthomonas fragariae in octoploid strawberry . Theor Appl Genet 2016; 129: 1191-1201.

[21] Mangandi J, Verma S, Peres N, Bink MCAM, Van de Weg EW, Bassil N, Whitaker VM . Pc1: A large effect QTL conferring resistance to Phytophthora cactorum in strawberry . Plant and Animal Genome XXIV Conference, Plant and Animal Genome. 2016. Available from: https://pag.confex.com/pag/xxiv/webprogram/Paper21810.html.

[22] Lerceteau-Köhler E, Moing A, Guérin G, Renaud C, Petit A, Rothan C et al. Genetic dissection of fruit quality traits in the octoploid cultivated strawberry highlights the role of homoeo-QTL in their control. Theor Appl Genet 2012; 124: 1059-1077.

[23] Whitaker VM, Osorio LF, Hasing T, Gezan S . Estimation of genetic parameters for twelve fruit and vegetative traits in the University of Florida strawberry breeding population. J Amer Soc Hort Sci 2012; 137: 316-324.

[24] Yamada Y. Genotype by environment interaction and genetic correlation of the same trait under different environments. Jap J Genet 1962; 37: 498-509.

[25] Gilmour AR, Gogel B, Cullis BR, Thompson R . ASReml user guide release 3.0. VSN International Ltd.Hemel Hempstead, HP1 1ES, UK 2009. Available at www.vsni.co.uk.

[26] Hoerl AE, Kennard RW . Ridge regression: biased estimation for non-orthogonal problems. Technometrics 1970; 12: 55-67.

[27] Park T, Casella G . The Bayesian LASSO. J Am Stat Assoc 2008; 103: 681-686.

[28] Gianola D, Fernando RL, Stella A . Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 2006; 173: 1761-1776.

[29] VanRaden PM . Efficient methods to compute genomic predictions. J Dairy Sci 2008; 91: 4414-4423.

[30] de los Campos G, Perez P . BGLR: Bayesian Generalized Linear Regression. 2015; R package version 1.0.4. Available at https://CRAN.R-project.org/.

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

[32] Perez P, de los Campos G . Genome-wide regression and prediction with the BGLR statistical package. Genetics 2014; 198: 483-495.

[33] Tusell L, Perez-Rodriguez P, Forni S, Wu X-L, Gianola D . Genome-enabled methods for predicting litter size in pigs: a comparison. Animal 2013; 7: 1739-1749.

[34] de los Campos G, Gianola D, Rosa GJM, Weigel KA, Crossa J . Semi-parametric Genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet Res Camb 2010; 92: 395-308.

[35] Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010; 42: 565-569.

[36] Schaefer LR . Modification of negative eigenvalues to create positive definite matrices and approximation of standard errors of correlation estimates. Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph: Guelph, Ontario, Canada, 2010.

[37] Nazarian A, Gezan SA . GenoMatrix: a software package for pedigree-based and genomic prediction analyses on complex traits. J of Hered 2016; 107: 372-379.

[38] Butler DG, Cullis BR, Gilmour AR, Gogel B . ASReml-R reference manual release 20. The State of Queensland, Department of Primary Industries and Fisheries: Brisbane, Qld. 2007.

[39] Van Ooijen JW . Multipoint maximum likelihood mapping in a full-sib family of an outbreeding species. Genet Res 2011; 93: 343-349.

[40] van Dijk T, Pagliarani G, Pikunova A, Noordijk Y, Yilmaz-Temel H, Meulenbroek B et al. Genomic rearrangements and signatures of breeding in the allo-octoploid strawberry as revealed through an allele dose based SSR linkage map. BMC Plant Biol 2014; 14: 55.

[41] Shin JH, Blay S, McNeney B, Graham J . LDheatmap: An R function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. J Stat Soft 2006; 16: 3.

[42] Mangin B, Siberchicot A, Nicolas S, Doligez A, This P, Cierco-Ayrolles C . Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity 2012; 108: 285-291.

[43] Gouy M, Rousselle Y, Bastianelli D, Lecomte P, Bonnal L, Roques D et al. Experimental assessment of the accuracy of genomic selection in sugarcane. Theor Appl Genet 2013; 126: 2575-2586.

[44] Michell S, Ametz C, Gungor H . Genomic selection across multiple breeding cycles in applied bread wheat breeding. Theor Appl Genet 2016; 129: 1179-1189.

[45] Resende MFR Jr, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D et al. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 2012; 193: 617-624.

[46] Kumar S, Molloy C, Muñoz P, Daetwyler H, Chagné D, Volz R . Genome-enabled estimates of additive and nonadditive genetic variances and prediction of apple phenotypes across environments. G3 2015; 5: 2711-2718.

[47] Isik F, Bartholome J, Farjat A, Chancerel E, Raffin A, Sanchez L et al. Genomic selection in maritime pine. Plant Sci 2016; 242: 108-119.

[48] Würschum T, Reif JC, Kraft T, Janssen G, Zhao Y . Genomic selection in sugar beet breeding populations. BMC Genet 2013; 14: 85.

[49] Sun C, VanRaden PM, Cole JB, O’Connell JR . Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects. PLoS ONE 2014; 9: e103934.

[50] Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C et al. Predicting rice hybrid breeding performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity 2016, 1-9.

[51] Gonzalez-Recio O, Gianola D, Long N, Weigel KA, Rosa GJM, Avendano S . Nonparametric methods for incorporating genomic information into genomic evaluations: an application to mortality in broilers. Genetics 2008; 178: 2305-2313.

[52] Onogi A, Ideta O, Inoshita Y, Ebana K, Yoshioka T, Yamasaki M et al. Exploring the areas of applicability of whole-genome prediction methods for Asian rice. Theor Appl Genet 2015; 128: 41-53.

[53] Clark SA, Hickey JM, van der Wef JHJ . Different models of genetic variation and their effect on genomic evaluation. Genet Sel Evol 2011; 43: 18.

[54] Zhong S, Dekkers J, Fernando RL, Jannick JL . Factors affecting accuracy from genomic selection in populations derived from multiple inbreed lines: a barley case study. Genetics 2009; 182: 355-364.

[55] Habier D, Fernando RL, Dekker JCM . The impact of genetic relationship information on genome-assisted breeding values. Genetics 2007; 177: 2389-2397.

[56] Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R . Additive genetic variability and the Bayesian alphabet. Genetics 2009; 183: 347-363.

[57] Habier D, Fernando RL, Garrick DJ . Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 2013; 194: 597-607.