Abstract
Genotyping costs can be reduced without decreasing the genomic selection accuracy through methodologies of markers subsets assortment. Thus, we compared two strategies to obtain markers subsets. The former uses the primary and the latter the re-estimated markers effects. Moreover, we analyzed each subset via prediction accuracy, bias, and relative efficiency by main genotypic effect model (MGE) fitted, using genomic best linear unbiased predictor linear kernel (GB), and Gaussian nonlinear kernel (GK). All scenarios (subset of markers × kernels models) were applied to a public dataset of rice diversity panel (RICE) and two hybrids maize datasets (HEL and USP). The highest prediction accuracies were obtained by MGE-GB and MGE-GK for grain yield and plant height when we decrease the number of markers. Overall, marker subsets via re-estimated effects method showed a higher relative efficiency of genomic selection. Based on a high-density panel, we can conclude that it is possible to select the most informative markers in order to improve accuracy and build a low-cost SNP chip to implement genomic selection in breeding programs. In addition, we recommend REE (re-estimated effect) strategies to find markers subsets in training population, increasing accuracy of genomic selection.
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References
Bassi FM, Bentley AR, Charmet G et al (2015) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci 242:23–36. https://doi.org/10.1016/j.plantsci.2015.08.021
Bhat JA, Ali S, Salgotra RK et al (2016) Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front Genet 7:1–11. https://doi.org/10.3389/fgene.2016.00221
Bian Y, Holland JB (2017) Enhancing genomic prediction with genome-wide association studies in multiparental maize populations. Heredity (Edinb) 118:585–593. https://doi.org/10.1038/hdy.2017.4
Browning BL, Browning SR (2008) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84:210–223. https://doi.org/10.1016/j.ajhg.2009.01.005
Crossa J, de los Campos G, Perez-Rodriguez P et al (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724. https://doi.org/10.1534/genetics.110.118521
Crossa J, Pérez P, Hickey J et al (2013) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity (Edinb) 112:48–60. https://doi.org/10.1038/hdy.2013.16
Crossa J, Pérez-Rodríguez P, Cuevas J et al (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22:961–975
Cuevas J, Crossa J, Montesinos-Lopez O et al (2016a) Bayesian genomic prediction with genotype × environment interaction kernel models. G3 (Bethesda). https://doi.org/10.1534/g3.116.035584
Cuevas J, Crossa J, Soberanis V et al (2016b) Genomic prediction of genotype × environment interaction kernel regression models. Plant Genome. https://doi.org/10.3835/plantgenome2016.03.0024
Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185:1021–1031. https://doi.org/10.1534/Genetics.110.116855
de los Campos G, Perez-Rodriguez P (2016) BGLR: Bayesian generalized linear regression. R package version 1.0.5. http://CRAN.R-project.org/package=BGLR. Accessed 10 Aug 2016
e Souza MB, Cuevas J, de Couto EGO et al (2017) Genomic-enabled prediction in maize using kernel models with genotype × environment interaction. G3 Genes Genomes Genet. https://doi.org/10.1534/g3.117.042341
Endelman JB (2011) Ridge regression and other kernels for genomic selection with R Package rrBLUP. Plant Genome J 4:250–255. https://doi.org/10.3835/plantgenome2011.08.0024
Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761–1776. https://doi.org/10.1534/genetics.105.049510
Gianola D, Weigel KA, Krämer N et al (2014) Enhancing genome-enabled prediction by bagging genomic BLUP. PLoS ONE. https://doi.org/10.1371/journal.pone.0091693
Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml user guide release 3.0. VSN International, Hemel Hempstead
Gorjanc G, Bijma P, Hickey JM (2015a) Reliability of pedigree-based and genomic evaluations in selected populations. Genet Sel Evol 47:65. https://doi.org/10.1186/s12711-015-0145-1
Gorjanc G, Cleveland MA, Houston RD, Hickey JM (2015b) Potential of genotyping-by-sequencing for genomic selection in livestock populations. Genet Sel Evol 47:12. https://doi.org/10.1186/s12711-015-0102-z
Guo Z, Tucker DM, Basten CJ et al (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127:749–762. https://doi.org/10.1007/s00122-013-2255-x
Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397. https://doi.org/10.1534/genetics.107.081190
Habier D, Fernando RL, Dekkers JCM (2009) Genomic selection using low-density marker panels. Genetics 182:343–353. https://doi.org/10.1534/genetics.108.100289
He S, Schulthess AW, Mirdita V et al (2016) Genomic selection in a commercial winter wheat population. Theor Appl Genet 129:641–651. https://doi.org/10.1007/s00122-015-2655-1
Heslot N, Yang H-P, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146. https://doi.org/10.2135/cropsci2011.09.0297
Hoffstetter A, Cabrera A, Huang M, Sneller C (2016a) Optimizing training population data and validation of genomic selection for economic traits in soft winter wheat. G3 (Bethesda) 6:2919–2928. https://doi.org/10.1534/g3.116.032532
Hoffstetter A, Cabrera A, Sneller C (2016b) Identifying quantitative trait loci for economic traits in an elite soft red winter wheat population. Crop Sci 56:547–558. https://doi.org/10.2135/cropsci2015.06.0332
Li B, Zhang N, Wang YG et al (2018) Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Front Genet 9:1–20. https://doi.org/10.3389/fgene.2018.00237
Ma P, Lund MS, Nielsen US et al (2015) Single-step genomic model improved reliability and reduced the bias of genomic predictions in Danish Jersey. J Dairy Sci 98:9026–9034. https://doi.org/10.3168/jds.2015-9703
Ma Y, Reif JC, Jiang Y et al (2016) Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.). Mol Breed 36:1–10. https://doi.org/10.1007/s11032-016-0504-9
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
Moser G, Khatkar MS, Hayes BJ, Raadsma HW (2010) Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genet Sel Evol 42:37. https://doi.org/10.1186/1297-9686-42-37
Neves HHR, Carvalheiro R, Queiroz SA (2012) A comparison of statistical methods for genomic selection in a mice population. BMC Genet 13:1. https://doi.org/10.1186/1471-2156-13-100
Ogawa S, Matsuda H, Taniguchi Y et al (2014) Effects of single nucleotide polymorphism marker density on degree of genetic variance explained and genomic evaluation for carcass traits in Japanese Black beef cattle. BMC Genet 15:15. https://doi.org/10.1186/1471-2156-15-15
Perez-Rodriguez P, Gianola D, Gonzalez-Camacho JM et al (2013) Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 Genes Genomes Genet 2:1595–1605. https://doi.org/10.1534/g3.112.003665
Pérez-Rodríguez P, Gianola D, González-Camacho JM et al (2012) Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 (Bethesda) 2:1595–1605. https://doi.org/10.1534/g3.112.003665
Porto-Neto LR, Barendse W, Henshall JM et al (2015) Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection. Genet Sel Evol 47:84. https://doi.org/10.1186/s12711-015-0162-0
Resende MFR, Munoz P, Resende MDV et al (2012) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190:1503–1510. https://doi.org/10.1534/genetics.111.137026
Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE (2008) Genomic selection using different marker types and densities. J Anim Sci 86:2447–2454. https://doi.org/10.2527/jas.2007-0010
Spindel J, Begum H, Akdemir D et al (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet. https://doi.org/10.5061/dryad.7369p.funding
Spindel JE, Begum H, Akdemir D et al (2016) Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity (Edinb) 116:395–408. https://doi.org/10.1038/hdy.2015.113
Su G, Christensen OF, Janss L, Lund MS (2014) Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. J Dairy Sci 97:6547–6559. https://doi.org/10.3168/jds.2014-8210
Szyda J, Zukowski K, Kamiński S, Zarnecki A (2013) Testing different single nucleotide polymorphism selection strategies for prediction of genomic breeding values in dairy cattle based on low density panels. Czech J Anim Sci 58:136–145
Tayeh N, Klein A, Le Paslier M-C et al (2015) Genomic prediction in pea: effect of marker density and training population size and composition on prediction accuracy. Front Plant Sci 6:1–11. https://doi.org/10.3389/fpls.2015.00941
Thomson MJ (2014) High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol 2:195–212. https://doi.org/10.9787/PBB.2014.2.3.195
Unterseer S, Bauer E, Haberer G et al (2014) A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genom 15:823. https://doi.org/10.1186/1471-2164-15-823
VanRaden PM (2007) Genomic measures of relationship and inbreeding. Interbull Annu Meet Proc 37:33–36. https://doi.org/10.1007/s13398-014-0173-7.2
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423. https://doi.org/10.3168/jds.2007-0980
VanRaden PM, Tooker ME, O’Connell JR et al (2017) Selecting sequence variants to improve genomic predictions for dairy cattle. Genet Sel Evol 49:32. https://doi.org/10.1186/s12711-017-0307-4
Vazquez AI, Rosa GJM, Weigel KA et al (2010) Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins. J Dairy Sci 93:5942–5949. https://doi.org/10.3168/jds.2010-3335
Wang Q, Yu Y, Yuan J et al (2017) Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei. BMC Genet 18:1–9. https://doi.org/10.1186/s12863-017-0507-5
Weigel KA, de los Campos G, González-Recio O et al (2009) Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. J Dairy Sci 92:5248–5257. https://doi.org/10.3168/jds.2009-2092
Wimmer AV, Auinger H, Albrecht T et al (2015) synbreed: framework for the analysis of genomic prediction data using R, pp 1–43
Wu XL, Xu J, Feng G et al (2016) Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications. PLoS ONE 11(9):e0161719
Yu H, Xie W, Li J et al (2014) A whole-genome SNP array (RICE6 K) for genomic breeding in rice. Plant Biotechnol J 12:28–37. https://doi.org/10.1111/pbi.12113
Zhang Z, Liu J, Ding X et al (2010) Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS ONE 5:1–8. https://doi.org/10.1371/journal.pone.0012648
Zhang Z, Erbe M, He J et al (2015) Accuracy of whole genome prediction using a genetic architecture enhanced variance–covariance matrix. G3 Genes Genomes Genet 5:615–627. https://doi.org/10.1534/g3.114.016261
Acknowledgements
We thank Helix Sementes® (São Paulo, Brazil) for the dataset, and Allogamous Plant Breeding Laboratory for the technical and scientific support. Funding was provided by National Council for Scientific and Technological Development (CNPq).
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e Sousa, M.B., Galli, G., Lyra, D.H. et al. Increasing accuracy and reducing costs of genomic prediction by marker selection. Euphytica 215, 18 (2019). https://doi.org/10.1007/s10681-019-2339-z
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DOI: https://doi.org/10.1007/s10681-019-2339-z