The apple REFPOP-a reference population for genomics-assisted breeding in apple

Abstract

Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates. Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs, and measurements of phenotypes in different environments can facilitate the application of the approach under various climatic conditions. Here we present an apple reference population: the apple REFPOP, a large collection formed of 534 genotypes planted in six European countries, as a unique tool to accelerate apple breeding. The population consisted of 269 accessions and 265 progeny from 27 parental combinations, representing the diversity in cultivated apple and current European breeding material, respectively. A high-density genome-wide dataset of 303,239 SNPs was produced as a combined output of two SNP arrays of different densities using marker imputation with an imputation accuracy of 0.95. Based on the genotypic data, linkage disequilibrium was low and population structure was weak. Two well-studied phenological traits of horticultural importance were measured. We found marker–trait associations in several previously identified genomic regions and maximum predictive abilities of 0.57 and 0.75 for floral emergence and harvest date, respectively. With decreasing SNP density, the detection of significant marker–trait associations varied depending on trait architecture. Regardless of the trait, 10,000 SNPs sufficed to maximize genomic prediction ability. We confirm the suitability of the apple REFPOP design for genomics-assisted breeding, especially for breeding programs using related germplasm, and emphasize the advantages of a coordinated and multinational effort for customizing apple breeding methods in the genomics era.

References

[1] Food and Agriculture Organization of the United Nations. FAOSTAT (FAO, 2017). Access 19 Mar 2019. http://www.fao.org/faostat/en/#home.

[2] Urrestarazu, J. et al. Analysis of the genetic diversity and structure across a wide range of germplasm reveals prominent gene flow in apple at the European level. BMC Plant Biol. 16, 130 (2016).

[3] Urrestarazu, J. et al. Genome-wide association mapping of flowering and ripening periods in apple. Front. Plant Sci. 8, 1923 (2017).

[4] Kumar, S., Volz, R. K., Chagné, D. & Gardiner, S. in Genomics of Plant Genetic Resources: Vol. 2. Crop Productivity, Food Security and Nutritional Quality (eds Tuberosa, R., Graner, A. & Frison, E.) 387-416 (Springer Netherlands, Dordrecht, 2014).

[5] Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819 (2001).

[6] Georges, M., Charlier, C. & Hayes, B. Harnessing genomic information for livestock improvement. Nat. Rev. Genet. 20, 135-156 (2019).

[7] Laurens, F. et al. An integrated approach for increasing breeding efficiency in apple and peach in Europe. Hortic. Res. 5, 11 (2018).

[8] Kumar, S. et al. Genomic selection for fruit quality traits in apple (Malus×domestica Borkh.) . PLoS ONE 7, e36674 (2012).

[9] Muranty, H. et al. Accuracy and responses of genomic selection on key traits in apple breeding. Hortic. Res. 2, 15060 (2015).

[10] Migicovsky, Z. et al. Genome to phenome mapping in apple using historical data. Plant Genome 9, https://doi.org/10.3835/plantgenome2015.11.0113 (2016).

[11] McClure, K. A. et al. A genome-wide association study of apple quality and scab resistance. Plant Genome 11, 170075 (2018).

[12] McClure, K. A. et al. Genome-wide association studies in apple reveal loci of large effect controlling apple polyphenols. Hortic. Res. 6, 107 (2019).

[13] Roth, M. et al. Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple. Hortic. Res. 7, 148 (2020).

[14] Kumar, S., Hilario, E., Deng, C. H. & Molloy, C. Turbocharging introgression breeding of perennial fruit crops: a case study on apple. Hortic. Res. 7, 47 (2020).

[15] Covarrubias-Pazaran, G. et al. Multivariate GBLUP improves accuracy of genomic selection for yield and fruit weight in biparental populations of Vaccinium macrocarpon Ait. Front. Plant Sci. 9, 1310 (2018).

[16] Peace, C. P. et al. Apple whole genome sequences: recent advances and new prospects. Hortic. Res. 6, 59 (2019).

[17] Bianco, L. et al. Development and validation of a 20K single nucleotide polymorphism (SNP) whole genome genotyping array for apple (Malus × domestica Borkh) . PLoS ONE 9, e110377 (2014).

[18] Bianco, L. et al. Development and validation of the Axiom®Apple480K SNP genotyping array. Plant J. 86, 62-74 (2016).

[19] Muranty, H. et al. Using whole-genome SNP data to reconstruct a large multi-generation pedigree in apple germplasm. BMC Plant Biol. 20, 2 (2020).

[20] Rodríguez-Álvarez, M. X., Boer, M. P., van Eeuwijk, F. A. & Eilers, P. H. Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spat. Stat. 23, 52-71 (2018).

[21] Lassois, L. et al. Genetic diversity, population structure, parentage analysis, and construction of core collections in the French apple germplasm based on SSR markers. Plant Mol. Biol. Report. 34, 827-844 (2016).

[22] Kumar, S., Volz, R. K., Alspach, P. A. & Bus, V. G. M. Development of a recurrent apple breeding programme in New Zealand: a synthesis of results, and a proposed revised breeding strategy. Euphytica 173, 207-222 (2010).

[23] McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279-1283 (2016).

[24] Pégard, M. et al. Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population. BMC Genom. 20, 302 (2019).

[25] Vanderzande, S., Micheletti, D., Troggio, M., Davey, M. W. & Keulemans, J. Genetic diversity, population structure, and linkage disequilibrium of elite and local apple accessions from Belgium using the IRSC array. Tree Genet. Genom. 13, 125 (2017).

[26] Daccord, N. et al. High-quality de novo assembly of the apple genome and methylome dynamics of early fruit development. Nat. Genet. 49, 1099-1106 (2017).

[27] Cornille, A. et al. New insight into the history of domesticated apple: secondary contribution of the European wild apple to the genome of cultivated varieties. PLoS Genet. 8, e1002703 (2012).

[28] Segura, V. et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat. Genet. 44, 825-830 (2012).

[29] Celton, J.-M. 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. New Phytol. 192, 378-392 (2011).

[30] Allard, A. et al. Detecting QTLs and putative candidate genes involved in budbreak and flowering time in an apple multiparental population. J. Exp. Bot. 67, 2875-2888 (2016).

[31] Chagné, D. et al. Genetic and environmental control of fruit maturation, dry matter and firmness in apple (Malus × domestica Borkh.) . Hortic. Res. 1, 14046 (2014).

[32] Larsen, B. et al. Genome-wide association studies in apple reveal loci for aroma volatiles, sugar composition, and harvest date. Plant Genome 12, 180104 (2019).

[33] Gezan, S. A., Osorio, L. F., Verma, S. & Whitaker, V. M. An experimental validation of genomic selection in octoploid strawberry. Hortic. Res. 4, 16070, https://doi.org/10.1038/hortres.2016.70 (2017).

[34] Hardner, C. M. et al. Prediction of genetic value for sweet cherry fruit maturity among environments using a 6K SNP array. Hortic. Res. 6, 6 (2019).

[35] Schulthess, A. W. et al. Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor. Appl. Genet. 129, 273-287 (2016).

[36] Lado, B. et al. Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality . Theor. Appl. Genet. 131, 2719-2731 (2018).

[37] Fernández-Fernández, F. SID 5 (Research Project Final Report) (Fingerprinting the National Apple & Pear Collections, Defra, 2010).

[38] Bink, M. C. A. M. et al. Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple. Theor. Appl. Genet. 127, 1073-1090 (2014).

[39] Jansen, J. & van Hintum, T. Genetic distance sampling: a novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuce. Theor. Appl. Genet. 114, 421-428 (2007).

[40] European Environment Agency . Biogeographical Regions in Europe https://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2 (2017).

[41] Vanderzande, S. et al. High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow. PLoS ONE 14, e0210928 (2019).

[42] Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084-1097 (2007).

[43] Clayton, D. snpStats: SnpMatrix and XSnpMatrix Classes and Methods (2017).

[44] Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289-290 (2004).

[45] Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1-18 (2008).

[46] Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655-1664 (2009).

[47] International Union for the Protection of New Varieties of Plants (UPOV). Guidelines for the conduct of tests for distinctness, uniformity and stability: TG/14/9 Apple (fruit varieties) (International Union for the Protection of New Varieties of Plants (UPOV), Geneva, 2005).

[48] Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1-48 (2015).

[49] Højsgaard, S. & Halekoh, U. doBy: Groupwise statistics, LSmeans, Linear Contrasts, Utilities (2018).

[50] Tang, Y. et al. GAPIT version 2: an enhanced integrated tool for genomic association and prediction. Plant Genome 9, https://doi.org/10.3835/plantgenome2015.11.0120 (2016).

[51] Endelman, J. B. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4, 250-255 (2011).

[52] Bermingham, M. L. et al. Application of high-dimensional feature selection: evaluation for genomic prediction in man. Sci. Rep. 5, 10312 (2015).

[53] Elsen, J.-M. An analytical framework to derive the expected precision of genomic selection. Genet. Sel. Evol. 49, 95 (2017).

[54] R Core Team . R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2014).

[55] Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).