A Comparison of the Adoption of Genomic Selection Across Different Breeding Institutions

Overview

Journal Front Plant Sci

Date 2021 Dec 6

PMID 34868114

Citations 3

Authors

Mahmood Gholami

Valentin Wimmer

Carolina Sansaloni

Cesar Petroli

Sarah J Hearne

Giovanny Covarrubias-Pazaran

Stefan Rensing

Johannes Heise

Paulino Perez-Rodriguez

Susanne Dreisigacker

Jose Crossa

Johannes W R Martini

Affiliations

Soon will be listed here.

Citing Articles

Within-family genomic selection in strawberry: Optimization of marker density, trial design, and training set composition.

Sleper J, Tapia R, Lee S, Whitaker V Plant Genome. 2025; 18(1):e20550.

PMID: 39789751 PMC: 11718141. DOI: 10.1002/tpg2.20550.

Modeling within and between Sub-Genomes Epistasis of Synthetic Hexaploid Wheat for Genome-Enabled Prediction of Diseases.

Cuevas J, Gonzalez-Dieguez D, Dreisigacker S, Martini J, Crespo-Herrera L, Lozano-Ramirez N Genes (Basel). 2024; 15(3).

PMID: 38540321 PMC: 10970072. DOI: 10.3390/genes15030262.

A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data.

Liu H, Yu S Plant Cell Rep. 2023; 42(11):1825-1832.

PMID: 37750948 DOI: 10.1007/s00299-023-03069-8.

Results from rapid-cycle recurrent genomic selection in spring bread wheat.

Dreisigacker S, Perez-Rodriguez P, Crespo-Herrera L, Bentley A, Crossa J G3 (Bethesda). 2023; 13(4).

PMID: 36702618 PMC: 10085763. DOI: 10.1093/g3journal/jkad025.

References

Albrecht T, Wimmer V, Auinger H, Erbe M, Knaak C, Ouzunova M . Genome-based prediction of testcross values in maize. Theor Appl Genet. 2011; 123(2):339-50. DOI: 10.1007/s00122-011-1587-7. View

Hickey J, Chiurugwi T, Mackay I, Powell W . Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nat Genet. 2017; 49(9):1297-1303. DOI: 10.1038/ng.3920. View

Covarrubias-Pazaran G, Martini J, Quinn M, Atlin G . Strengthening Public Breeding Pipelines by Emphasizing Quantitative Genetics Principles and Open Source Data Management. Front Plant Sci. 2021; 12:681624. PMC: 8313805. DOI: 10.3389/fpls.2021.681624. View

Beyene Y, Gowda M, Olsen M, Robbins K, Perez-Rodriguez P, Alvarado G . Empirical Comparison of Tropical Maize Hybrids Selected Through Genomic and Phenotypic Selections. Front Plant Sci. 2019; 10:1502. PMC: 6883373. DOI: 10.3389/fpls.2019.01502. View

Maxmen A . How African scientists are improving cassava to help feed the world. Nature. 2019; 565(7738):144-146. DOI: 10.1038/d41586-019-00014-2. View

Aguilar I, Misztal I, Johnson D, Legarra A, Tsuruta S, Lawlor T . Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci. 2010; 93(2):743-52. DOI: 10.3168/jds.2009-2730. View

Lund M, Roos A, Vries A, Druet T, Ducrocq V, Fritz S . A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol. 2011; 43:43. PMC: 3292506. DOI: 10.1186/1297-9686-43-43. View

Nyine M, Uwimana B, Swennen R, Batte M, Brown A, Christelova P . Trait variation and genetic diversity in a banana genomic selection training population. PLoS One. 2017; 12(6):e0178734. PMC: 5460855. DOI: 10.1371/journal.pone.0178734. View

Atanda S, Olsen M, Burgueno J, Crossa J, Dzidzienyo D, Beyene Y . Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program. Theor Appl Genet. 2020; 134(1):279-294. PMC: 7813723. DOI: 10.1007/s00122-020-03696-9. View

10.

Wiggans G, Cole J, Hubbard S, Sonstegard T . Genomic Selection in Dairy Cattle: The USDA Experience. Annu Rev Anim Biosci. 2016; 5:309-327. DOI: 10.1146/annurev-animal-021815-111422. View

11.

Sansaloni C, Franco J, Santos B, Percival-Alwyn L, Singh S, Petroli C . Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints. Nat Commun. 2020; 11(1):4572. PMC: 7486412. DOI: 10.1038/s41467-020-18404-w. View

12.

Crossa J, de Los Campos G, Perez P, Gianola D, Burgueno J, Araus J . Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics. 2010; 186(2):713-24. PMC: 2954475. DOI: 10.1534/genetics.110.118521. View

13.

Wolfe M, Del Carpio D, Alabi O, Ezenwaka L, Ikeogu U, Kayondo I . Prospects for Genomic Selection in Cassava Breeding. Plant Genome. 2018; 10(3). PMC: 7822052. DOI: 10.3835/plantgenome2017.03.0015. View

14.

Zhang X, Perez-Rodriguez P, Burgueno J, Olsen M, Buckler E, Atlin G . Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population. G3 (Bethesda). 2017; 7(7):2315-2326. PMC: 5499138. DOI: 10.1534/g3.117.043141. View

15.

Wang D, Fan J, Siao C, Berno A, Young P, Sapolsky R . Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science. 1998; 280(5366):1077-82. DOI: 10.1126/science.280.5366.1077. View

16.

Crossa J, Martini J, Gianola D, Perez-Rodriguez P, Jarquin D, Juliana P . Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials. Front Genet. 2020; 10:1168. PMC: 6913188. DOI: 10.3389/fgene.2019.01168. View

17.

Esuma W, Ozimati A, Kulakow P, Gore M, Wolfe M, Nuwamanya E . Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava. G3 (Bethesda). 2021; 11(9). PMC: 8496257. DOI: 10.1093/g3journal/jkab160. View

18.

Crossa J, Jarquin D, Franco J, Perez-Rodriguez P, Burgueno J, Saint-Pierre C . Genomic Prediction of Gene Bank Wheat Landraces. G3 (Bethesda). 2016; 6(7):1819-34. PMC: 4938637. DOI: 10.1534/g3.116.029637. View

19.

Martini J, Molnar T, Crossa J, Hearne S, Pixley K . Opportunities and Challenges of Predictive Approaches for Harnessing the Potential of Genetic Resources. Front Plant Sci. 2021; 12:674036. PMC: 8281018. DOI: 10.3389/fpls.2021.674036. View

20.

Crossa J, Perez-Rodriguez P, Cuevas J, Montesinos-Lopez O, Jarquin D, de Los Campos G . Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Trends Plant Sci. 2017; 22(11):961-975. DOI: 10.1016/j.tplants.2017.08.011. View