Accuracy and responses of genomic selection on key traits in apple breeding

Abstract

The application of genomic selection in fruit tree crops is expected to enhance breeding efficiency by increasing prediction accuracy, increasing selection intensity and decreasing generation interval. The objectives of this study were to assess the accuracy of prediction and selection response in commercial apple breeding programmes for key traits. The training population comprised 977 individuals derived from 20 pedigreed full-sib families. Historic phenotypic data were available on 10 traits related to productivity and fruit external appearance and genotypic data for 7829 SNPs obtained with an Illumina 20K SNP array. From these data, a genome-wide prediction model was built and subsequently used to calculate genomic breeding values of five application full-sib families. The application families had genotypes at 364 SNPs from a dedicated 512 SNP array, and these genotypic data were extended to the high-density level by imputation. These five families were phenotyped for 1 year and their phenotypes were compared to the predicted breeding values. Accuracy of genomic prediction across the 10 traits reached a maximum value of 0.5 and had a median value of 0.19. The accuracies were strongly affected by the phenotypic distribution and heritability of traits. In the largest family, significant selection response was observed for traits with high heritability and symmetric phenotypic distribution. Traits that showed non-significant response often had reduced and skewed phenotypic variation or low heritability. Among the five application families the accuracies were uncorrelated to the degree of relatedness to the training population. The results underline the potential of genomic prediction to accelerate breeding progress in outbred fruit tree crops that still need to overcome long generation intervals and extensive phenotyping costs.

References

[1] Hackett WP . Juvenility, maturation, and rejuvenation in woody plants. In: Janick J, editor. Horticultural Reviews. Hoboken, NJ: John Wiley & Sons, Inc.; 1985. pp 109-155.

[2] Kumar S, Bink M, Volz R, Bus V, Chagné D . Towards genomic selection in apple (Malus × domestica Borkh.) breeding programmes: prospects, challenges and strategies. Tree Genet Genomes 2012; 8: 1-14.

[3] van Nocker S, Gardiner SE . Breeding better cultivars, faster: applications of new technologies for the rapid deployment of superior horticultural tree crops. Hort Res 2014; 1: 14022.

[4] Lande R, Thompson R . Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 1990; 124: 743-756.

[5] Muranty H, Jorge V, Bastien C, Lepoittevin C, Bouffier L, Sanchez L . Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of MAS in crops. Tree Genet Genomes 2014; 10: 1491-1510.

[6] Baumgartner IO, Patocchi A, Frey JE, Peil A, Kellerhals M . Breeding elite lines of apple carrying pyramided homozygous resistance genes against apple scab and resistance against powdery mildew and fire blight. Plant Mol Biol Report 2015; 33: 1573-1583.

[7] Meuwissen T, Hayes B, Goddard M . Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001; 157: 1819-1829.

[8] Riedelsheimer C, Technow F, Melchinger AE . Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines. BMC Genomics 2012; 13: 452.

[9] Bernardo R, Yu JM . Prospects for genomewide selection for quantitative traits in maize. Crop Sci 2007; 47: 1082-1090.

[10] Albrecht T, Wimmer V, Auinger H-J et al. Genome-based prediction of testcross values in maize. Theor Appl Genet 2011; 123: 339-350.

[11] Guo Z, Tucker DM, Lu J, Kishore V, Gay G . Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 2012; 124: 261-275.

[12] Piepho HP . Ridge regression and extensions for genomewide selection in maize. Crop Sci 2009; 49: 1165.

[13] Zhao Y, Gowda M, Liu W et al. Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 2012; 124: 769-776.

[14] Zhong S, Dekkers JCM, Fernando RL, Jannink J-L . Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 2009; 182: 355-364.

[15] Heffner EL, Jannink J-L, Iwata H, Souza E, Sorrells ME . Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 2011; 51: 2597-2606.

[16] Beaulieu J, Doerksen T, Clément S, MacKay J, Bousquet J . Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity 2014; 113: 343-352.

[17] Iwata H, Hayashi T, Tsumura Y . Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica. Tree Genet Genomes 2011; 7: 747-758.

[18] Resende MFR Jr, Muñoz P, Resende MDV et al. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 2012; 190: 1503-1510.

[19] Resende MDV, Resende MFR Jr, Sansaloni CP et al. Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 2012; 194: 116-128.

[20] Lipka AE, Lu F, Cherney JH, Buckler ES, Casler MD, Costich DE . Accelerating the switchgrass (Panicum virgatum L.) breeding cycle using genomic selection approaches. PLoS One 2014; 9: e112227.

[21] Cros D, Denis M, Sánchez L et al. Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.). Theor Appl Genet 2015; 128: 397-410.

[22] Iwata H, Hayashi T, Terakami S, Takada N, Sawamura Y, Yamamoto T . Potential assessment of genome-wide association study and genomic selection in Japanese pear Pyrus pyrifolia. Breed Sci 2013; 63: 125-140.

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

[24] Asoro FG, Newell MA, Beavis WD, Scott MP, Tinker NA, Jannink J-L . Genomic, marker-assisted, and pedigree-BLUP selection methods for b-glucan concentration in elite oat. Crop Sci 2013; 53: 1894-1906.

[25] Gianfranceschi L, Soglio V . The European project HiDRAS: innovative multidisciplinary approaches to breeding high quality disease resistant apples. In: XI Eucarpia Symposium on Fruit Breeding and Genetics 663; 2003. pp 327-330.

[26] Bink MCAM, Uimari P, Sillanpää MJ, Janss L, Jansen R . Multiple QTL mapping in related plant populations via a pedigree-analysis approach. Theor Appl Genet 2002; 104: 751-762.

[27] Kouassi A, Durel C-E, Costa F et al. Estimation of genetic parameters and prediction of breeding values for apple fruit-quality traits using pedigreed plant material in Europe. Tree Genet Genomes 2009; 5: 659-672.

[28] Voorrips RE, Bink MCAM, van de Weg WE . Pedimap: software for the visualization of genetic and phenotypic data in pedigrees. J Hered 2012; 103: 903-907.

[29] Bink MCAM, Jansen J, Madduri M et al. Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple. Theor Appl Genet 2014; 127: 1073-1090.

[30] Bianco L, Cestaro A, Sargent DJ et al. Development and validation of a 20K single nucleotide polymorphism (SNP) whole genome genotyping array for apple (Malus × domestica Borkh). PLoS One 2014; 9: e110377.

[31] Chagné D, Crowhurst RN, Troggio M et al. Genome-wide SNP detection, validation, and development of an 8K SNP array for apple. PLoS One 2012; 7: e31745.

[32] Hickey JM, Kinghorn BP, Tier B, van der Werf JH, Cleveland MA . A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Genet Sel Evol 2012; 44: 9.

[33] Muñoz F, Sanchez L . breedR: statistical methods for forest genetic resources analysts. R package version 0.7-9. [Internet]; 2014. Available at https://github.com/famuvie/breedR.

[34] R Core Team . R: a language and environment for statistical computing. [Internet]. Vienna: R Foundation for Statistical Computing; 2014. Available at http://www.R-project.org/.

[35] Coster A. Pedigree: pedigree functions. R package version 1.4.[Internet]; 2012. Available at http://CRAN.R-project.org/package=pedigree.

[36] Luan T, Woolliams JA, Ødegård J et al. The importance of identity-by-state information for the accuracy of genomic selection. Genet Sel Evol 2012; 44: 28.

[37] Strandén I, Christensen OF . Allele coding in genomic evaluation. Genet Sel Evol 2011; 43: 25.

[38] Clark SA, Hickey JM, Daetwyler HD, van der Werf JH . The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet Sel Evol 2012; 44: 4.

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

[40] Habier D, Tetens J, Seefried F-R, Lichtner P, Thaller G . The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol 2010; 42: 5.

[41] Pszczola M, Strabel T, Mulder HA, Calus MPL . Reliability of direct genomic values for animals with different relationships within and to the reference population. J Dairy Sci 2012; 95: 389-400.

[42] Wolc A, Arango J, Settar P et al. Persistence of accuracy of genomic estimated breeding values over generations in layer chickens. Genet Sel Evol 2011; 43: 23.

[43] Hickey JM, Dreisigacker S, Crossa J et al. Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci 2014; 54: 1476.

[44] Liebhard R, Kellerhals M, Pfammatter W, Jertmini M, Gessler C . Mapping quantitative physiological traits in apple (Malus × domestica Borkh.). Plant Mol Biol 2003; 52: 511-526.

[45] Costa F. MetaQTL analysis provides a compendium of genomic loci controlling fruit quality traits in apple. Tree Genet Genomes 2015; 11: 819.

[46] Segura V, Cilas C, Costes E . Dissecting apple tree architecture into genetic, ontogenetic and environmental effects: mixed linear modelling of repeated spatial and temporal measures. New Phytol 2008; 178: 302-314.

[47] Calenge F, Durel C-E . Both stable and unstable QTLs for resistance to powdery mildew are detected in apple after four years of field assessments. Mol Breed 2006; 17: 329-339.

[48] Calenge F, Faure A, Goerre M et al. Quantitative trait loci (QTL) analysis reveals both broad-spectrum and isolate-specific QTL for scab resistance in an apple progeny challenged with eight isolates of Venturia inaequalis. Phytopathology 2004; 94: 370-379.

[49] Durel C-E, Denancé C, Brisset M-N . Two distinct major QTL for resistance to fire blight co-localize on linkage group 12 in apple genotypes ‘Evereste’ and Malus floribunda clone 821. Genome 2009; 52: 139-147.

[50] Kenis K, Keulemans J, Davey MW . Identification and stability of QTLs for fruit quality traits in apple. Tree Genet Genomes 2008; 4: 647-661.

[51] Costa F, Peace CP, Stella S et al. QTL dynamics for fruit firmness and softening around an ethylene-dependent polygalacturonase gene in apple (Malus × domestica Borkh.). J Exp Bot 2010; 61: 3029-3039.

[52] Celton J-M, Martinez S, Jammes M-J et al. Deciphering the genetic determinism of bud phenology in apple progenies: a new insight into chilling and heat requirement effects on flowering dates and positional candidate genes. New Phytol 2011; 192: 378-392.

[53] Conner PJ, Brown SK, Weeden NF . Molecular-marker analysis of quantitative traits for growth and development in juvenile apple trees. Theor Appl Genet 1998; 96: 1027-1035.

[54] Kumar S, Molloy C, Munoz P, Daetwyler H, Chagne D, Volz R . Genome-enabled estimates of additive and non-additive genetic variances and prediction of apple phenotypes across environments. G3 Genes Genomes Genetics 2015; 5: 2711-2718.

[55] Goddard ME, Hayes BJ . Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 2009; 10: 381-391.

[56] Giraud H, Lehermeier C, Bauer E et al. Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize. Genetics 2014; 198: 1717-1734.

[57] Daetwyler HD, Villanueva B, Woolliams JA . Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 2008; 3: e3395.

[58] Goddard ME, Hayes BJ, Meuwissen TH . Using the genomic relationship matrix to predict the accuracy of genomic selection. J Anim Breed Genet 2011; 128: 409-421.

[59] Grattapaglia D, Resende MDV . Genomic selection in forest tree breeding. Tree Genet Genomes 2011; 7: 241-255.

[60] Brard S, Ricard A . Is the use of formulae a reliable way to predict the accuracy of genomic selection? J Anim Breed Genet 2014; 132: 207-217.

[61] Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, de Los Campos G, Eskridge K, Crossa J . Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding. G3 Genes Genomes Genetics 2015; 5: 291-300.

[62] Wang C-L, Ding X-D, Wang J-Y et al. Bayesian methods for estimating GEBVs of threshold traits. Heredity 2013; 110: 213-219.

[63] Kizilkaya K, Fernando RL, Garrick DJ . Reduction in accuracy of genomic prediction for ordered categorical data compared to continuous observations. Genet Sel Evol 2014; 46: 37.

[64] Durel CE, Laurens F, Fouillet A, Lespinasse Y . Utilization of pedigree information to estimate genetic parameters from large unbalanced data sets in apple. Theor Appl Genet 1998; 96: 1077-1085.

[65] 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.

[66] Habier D, Fernando RL, Dekkers JCM . Genomic selection using low-density marker panels. Genetics 2009; 182: 343-353.

[67] Cleveland MA, Hickey JM . Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation. J Anim Sci 2013; 91: 3583-3592.

[68] Noiton DAM, Alspach PA . Founding clones, inbreeding, coancestry, and status number of modern apple cultivars. J Am Soc Hortic Sci 1996; 121: 773-782.

[69] Patocchi A, Frei A, Frey JE, Kellerhals M . Towards improvement of marker assisted selection of apple scab resistant cultivars: Venturia inaequalis virulence surveys and standardization of molecular marker alleles associated with resistance genes. Mol Breed 2009; 24: 337-347.

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