Abed A, Pérez-Rodríguez P, Crossa J, Belzile F (2018) When less can be better: how can we make genomic selection more cost-effective and accurate in barley? Theor Appl Genet 131:1873–1890. https://doi.org/10.1007/s00122-018-3120-8
Alemu A, Åstrand J, Montesinos-Lopez OA, Sanchez JI et al (2024) Genomic selection in plant breeding: key factors shaping two decades of progress. Mol Plant 17(4):552–578
Article PubMed CAS Google Scholar
Allier A, Lehermeier C, Charcosset A, Moreau L, Teyssèdre S (2019) Improving short- and long-term genetic gain by accounting for within-family variance in optimal cross-selection. Front Genet 10:1006. https://doi.org/10.3389/fgene.2019.01006
Article PubMed PubMed Central Google Scholar
Amaresh AN, Krishnappa G, Durai AA, Manimekalai R et al (2025a) Next-generation molecular breeding tools to harness higher genetic gains in sugarcane. Planta 262(5):122. https://doi.org/10.1007/s00425-025-04842-7
Article PubMed CAS Google Scholar
Amaresh NA, Appunu C et al (2025b) Advanced genome editing technologies: potentials and prospects in improvement of sugar crops. Sugar Tech 27:14–28. https://doi.org/10.1007/s12355-024-01447-4
Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61. https://doi.org/10.1016/j.tplants.2013.09.008
Article PubMed CAS Google Scholar
Azodi CB, Tang J, Shiu SH (2020) Machine learning approaches for genomic prediction. Plant Genome 13(3):e20001
Badu-Apraku B, Talabi AO, Fakorede MAB, Fasanmade Y, Gedil M, Magorokosho C et al (2019) Yield gains and associated changes in an early yellow bi-parental maize population following genomic selection for Striga resistance and drought tolerance. BMC Plant Biol 19(1):129. https://doi.org/10.1186/s12870-019-1740-z
Article PubMed PubMed Central CAS Google Scholar
Banks R (2022) Evolution of genetics organisations’ strategies through the implementation of genomic selection: learnings and prospects. Agriculture 12:1524. https://doi.org/10.3390/agriculture12101524
Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J (2016) 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
Article PubMed CAS Google Scholar
Beche E, Gillman JD, Song Q, Nelson R, Beissinger T, Decker J, Shannon G, Scaboo AM (2021) Genomic prediction using training population design in interspecific soybean populations. Mol Breed 41(2):15
Article PubMed PubMed Central CAS Google Scholar
Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48(5):1649–1664. https://doi.org/10.2135/cropsci2008.03.0131
Bernardo R (2016) Bandwagons i, too, have known. Theor Appl Genet 129(12):2323–2332
Bhandari A, Bartholomé J, Cao-Hamadoun T-V, Kumari N, Frouin J, Kumar A et al (2019) Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice. PLoS One 14(1):e0208871. https://doi.org/10.1371/journal.pone.0208871
Article PubMed PubMed Central Google Scholar
Bhandari P, Kim J, Lee TG (2023) Genetic architecture of fresh-market tomato yield. BMC Plant Biol 23(1):18
Article PubMed PubMed Central CAS Google Scholar
Biscarini F, Nazzicari N, Bink M, Arús P, Aranzana MJ, Verde I et al (2017) Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies. BMC Genomics 18(1):1–15. https://doi.org/10.1186/s12864-017-4392-7
Brainard SH, Ellison SL, Simon PW, Dawson JC, Goldman IL (2022) Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values. Theor Appl Genet 135:605–622. https://doi.org/10.1007/s00122-021-03988-8
Article PubMed CAS Google Scholar
Brault C, Lazerges J, Doligez A et al (2022) Interest of phenomic prediction as an alternative to genomic prediction in grapevine. Plant Methods 18(1):108. https://doi.org/10.1186/s13007-022-00940-9
Article PubMed PubMed Central CAS Google Scholar
Browning BL, Browning SR (2016) Genotype imputation with millions of reference samples. Am J Hum Genet 98(1):116–126
Article PubMed PubMed Central CAS Google Scholar
Burgueño J, de Los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719
Cheng CH, Datson PM, Hilario E, Deng CH, Manako KI, McNeilage M, Hoeata K (2019) Genomic predictions in diploid Actinidia chinensis (kiwifruit). Eur J Hortic Sci 84(4):213–217
Cobb JN, Declerck G, Greenberg A, Clark R, McCouch S (2013) Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126:867–887. https://doi.org/10.1007/s00122-013-2066-0
Article PubMed PubMed Central Google Scholar
Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142(1–2):169–196. https://doi.org/10.1007/s10681-005-1681-5
Collard BCY, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci 363(1491):557–572. https://doi.org/10.1098/rstb.2007.2170
Article PubMed CAS Google Scholar
Crossa J, de los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186(2):713–724. https://doi.org/10.1534/genetics.110.118521
Article PubMed PubMed Central CAS Google Scholar
Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, Montesinos-Lopez A, Bentley AR (2021) The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data. Front Plant Sci 12:651480
Article PubMed PubMed Central Google Scholar
Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de Los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S (2017a) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975. https://doi.org/10.1016/j.tplants.2017.08.011
Article PubMed CAS Google Scholar
Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López OA, Jarquín D, de Los Campos G, Burgueno J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK (2017b) Genomic selection in plant breeding: methods, models, and perspectives. Crop Sci 57(1):1–20
D’Agostino N, Tripodi P (2017) NGS-based genotyping, high-throughput phenotyping and genome-wide association studies laid the foundations for next-generation breeding in horticultural crops. Diversity 9(3):38. https://doi.org/10.3390/d9030038
da Silva FA, Viana AP, Correa CCG, Santos EA, de Oliveira JAVS, Andrade JDG, Ribeiro RM, Glória LS (2021) Bayesian ridge regression shows the best fit for SSR markers in Psidiumguajava among Bayesian models. Sci Rep 11:13639. https://doi.org/10.1038/s41598-021-93120-z
Article PubMed PubMed Central CAS Google Scholar
Daetwyler HD, Bansal UK, Bariana HS, Hayden MJ, Hayes BJ (2014) Genomic prediction for rust resistance in diverse wheat landraces. Theor Appl Genet 127:1795–1803. https://doi.org/10.1007/s00122-014-2341-8
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