Genomic selection strategies for clonally propagated crops.


Journal

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
ISSN: 1432-2242
Titre abrégé: Theor Appl Genet
Pays: Germany
ID NLM: 0145600

Informations de publication

Date de publication:
23 Mar 2023
Historique:
received: 25 08 2022
accepted: 14 01 2023
entrez: 23 3 2023
pubmed: 24 3 2023
medline: 28 3 2023
Statut: epublish

Résumé

For genomic selection in clonally propagated crops with diploid (-like) meiotic behavior to be effective, crossing parents should be selected based on genomic predicted cross-performance unless dominance is negligible. For genomic selection (GS) in clonal breeding programs to be effective, parents should be selected based on genomic predicted cross-performance unless dominance is negligible. Genomic prediction of cross-performance enables efficient exploitation of the additive and dominance value simultaneously. Here, we compared different GS strategies for clonally propagated crops with diploid (-like) meiotic behavior, using strawberry as an example. We used stochastic simulation to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included (1) a breeding program that introduced GS in the first clonal stage, and (2) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were (1) parent selection based on genomic estimated breeding values (GEBVs) and (2) parent selection based on genomic predicted cross-performance (GPCP). Selection of parents based on GPCP produced faster genetic gain than selection of parents based on GEBVs because it reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using GPCP always produced the most genetic gain unless dominance was negligible. We conclude that (1) in clonal breeding programs with GS, parents should be selected based on GPCP, and (2) a two-part breeding program with parent selection based on GPCP to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

Identifiants

pubmed: 36952013
doi: 10.1007/s00122-023-04300-6
pii: 10.1007/s00122-023-04300-6
pmc: PMC10036424
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

74

Subventions

Organisme : Biotechnology and Biological Sciences Research Council
ID : BBS/E/D/30002275
Pays : United Kingdom
Organisme : Innovate UK
ID : 132748

Informations de copyright

© 2023. The Author(s).

Références

Bassil NV, Davis TM, Zhang H, Ficklin S, Mittmann M et al (2015) Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa. BMC Genom. https://doi.org/10.1186/s12864-015-1310-1
doi: 10.1186/s12864-015-1310-1
Bingham ET (1998) Role of chromosome blocks in heterosis and estimates of dominance and overdominance. In: Larnkey KR, Staub JE (eds) CSSA special publications. Crop Science Society of America, Madison, pp 71–87
Bingham ET, Groose RW, Woodfield DR, Kidwell KK (1994) Complementary gene interactions in alfalfa are greater in autotetraploids than diploids. Crop Sci 34(4):823–829. https://doi.org/10.2135/cropsci1994.0011183X003400040001x
doi: 10.2135/cropsci1994.0011183X003400040001x
Bisognin DA (2011) Breeding vegetatively propagated horticultural crops. Crop Breed Appl Biotechnol 11(spe):35–43. https://doi.org/10.1590/S1984-70332011000500006
doi: 10.1590/S1984-70332011000500006
Bradshaw J (2016) Plant breeding: past, present and future. Springer, Cham
doi: 10.1007/978-3-319-23285-0
Chen GK, Marjoram P, Wall JD (2009) Fast and flexible simulation of DNA sequence data. Genome Res 19(1):136–142. https://doi.org/10.1101/gr.083634.108
doi: 10.1101/gr.083634.108 pubmed: 19029539 pmcid: 2612967
Comstock RE, Kelleher T, Morrow EB (1958) Genetic variation in an asexual species. Gard Strawb Genet 43(4):634–646. https://doi.org/10.1093/genetics/43.4.634
doi: 10.1093/genetics/43.4.634
Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D et al (2017) 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
doi: 10.1016/j.tplants.2017.08.011 pubmed: 28965742
de Freitas JPX, da Silva Santos V, de Oliveira EJ (2016) Inbreeding depression in cassava for productive traits. Euphytica 209(1):137–145. https://doi.org/10.1007/s10681-016-1649-7
doi: 10.1007/s10681-016-1649-7
Edger PP, Poorten TJ, VanBuren R et al (2019) Origin and evolution of the octoploid strawberry genome. Nat Genet 51:541–547. https://doi.org/10.1038/s41588-019-0356-4
doi: 10.1038/s41588-019-0356-4 pubmed: 30804557 pmcid: 6882729
Falconer DS (1985) A note on fisher’s ‘average effect’ and ‘average excess.’ Genet Res 46(3):337–347. https://doi.org/10.1017/S0016672300022825
doi: 10.1017/S0016672300022825 pubmed: 4092925
Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Pearson, Harlow
Gaynor RC, Gorjanc G, Bentley AR, Ober ES, Howell P et al (2017) A two-part strategy for using genomic selection to develop inbred lines. Crop Sci 57(5):2372–2386. https://doi.org/10.2135/cropsci2016.09.0742
doi: 10.2135/cropsci2016.09.0742
Gaynor RC, Gorjanc G, Hickey JM (2021) AlphaSimR: an R package for breeding program simulations. G3 Genes Genom Genet 11(2):jkaa017
Gaynor RC, Gorjanc G, Wilson D, Hickey JM (2019) AlphaSimR: breeding program simulations.
Gemenet DC, Khan A (2017) Opportunities and challenges to implementing genomic selection in clonally propagated crops. In: Varshney RK, Roorkiwal M, Sorrells ME (eds) Genomic selection for crop improvement. Springer, Cham, pp 185–198
doi: 10.1007/978-3-319-63170-7_8
Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136(2):245–257. https://doi.org/10.1007/s10709-008-9308-0
doi: 10.1007/s10709-008-9308-0 pubmed: 18704696
Goddard ME, Hayes BJ (2007) Genomic selection: genomic selection. J Anim Breed Genet 124(6):323–330. https://doi.org/10.1111/j.1439-0388.2007.00702.x
doi: 10.1111/j.1439-0388.2007.00702.x pubmed: 18076469
Gorjanc G, Gaynor RC, Hickey JM (2017) Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection. bioRxiv. https://doi.org/10.1101/227215
doi: 10.1101/227215
Grüneberg W, Mwanga R, Andrade M, Espinoza J (2009) Selection methods Part: 5 breeding clonally propagated crops. In: Ceccarelli S, Guimarães EP, Weltzien E (eds) Plant breeding and farmer participation. Food and Agriculture Organization of the United Nations, Rome
Hill WG, Goddard ME, Visscher PM (2008) data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4(2):e1000008. https://doi.org/10.1371/journal.pgen.1000008
doi: 10.1371/journal.pgen.1000008 pubmed: 18454194 pmcid: 2265475
Kawuki R, Nuwamanya E, Labuschagne M, Herselman L, Ferguson M (2011) Segregation of selected agronomic traits in six S1 cassava families. J Plant Breed Crop Sci 3(8):154–160
Meuwissen T, Hayes B, Goddard M (2016) Genomic selection: a paradigm shift in animal breeding. Anim Front 6(1):6–14. https://doi.org/10.2527/af.2016-0002
doi: 10.2527/af.2016-0002
Niemirowicz-Szczytt K (1989) Preliminary studies on inbreeding in strawberry Fragaria x ananassa Duch. Acta Hortic 265:97–104. https://doi.org/10.17660/ActaHortic.1989.265.10
doi: 10.17660/ActaHortic.1989.265.10
Pujol B, Mckey D (2006) Size asymmetry in intraspecific competition and the density-dependence of inbreeding depression in a natural plant population: a case study in cassava (Manihot esculenta Crantz, Euphorbiaceae). J Evol Biol 19(1):85–96. https://doi.org/10.1111/j.1420-9101.2005.00990.x
doi: 10.1111/j.1420-9101.2005.00990.x pubmed: 16405580
Rho IR, Woo JG, Jeong HJ, Jeon HY, Lee C-H (2012) Characteristics of F1 Hybrids and Inbred lines in Octoploid Strawberry (Fragaria × ananassa Duchesne): characteristics of F1 hybrid and Inbred lines in Octoploid Strawberry. Plant Breed 131(4):550–554. https://doi.org/10.1111/j.1439-0523.2012.01958.x
doi: 10.1111/j.1439-0523.2012.01958.x
Rojas MC, Pérez JC, Ceballos H, Baena D, Morante N et al (2009) Analysis of inbreeding depression in eight S
doi: 10.2135/cropsci2008.07.0419
Sargent DJ, Fernandéz-Fernandéz F, Ruiz-Roja JJ, Sutherland BG, Passey A et al (2009) A genetic linkage map of the cultivated strawberry Fragaria × ananassa and its comparison to the diploid Fragaria reference map. Mol Breed 24(3):293–303. https://doi.org/10.1007/s11032-009-9292-9
doi: 10.1007/s11032-009-9292-9
Sargent DJ, Yang Y, Šurbanovski N, Bianco L, Buti M et al (2016) HaploSNP affinities and linkage map positions illuminate subgenome composition in the octoploid, cultivated strawberry (Fragaria × ananassa). Plant Sci 242:140–150. https://doi.org/10.1016/j.plantsci.2015.07.004
doi: 10.1016/j.plantsci.2015.07.004 pubmed: 26566832
Shaw DV (1990) Response to selection and associated changes in genetic variance for soluble solids and titratable acids contents in strawberries. J Am Soc Hortic Sci 115(5):839–843. https://doi.org/10.21273/JASHS.115.5.839
doi: 10.21273/JASHS.115.5.839
Shaw DV (1995) Comparison of ancestral and current-generation inbreeding in an experimental strawberry breeding population. Theor Appl Genet 90(2):237–241. https://doi.org/10.1007/BF00222207
doi: 10.1007/BF00222207 pubmed: 24173896
Shaw DV (1997) Trait mean depression for second-generation inbred strawberry populations with and without parent selection: theor. Appl Genet 95(1–2):261–264. https://doi.org/10.1007/s001220050557
doi: 10.1007/s001220050557
Shaw DV, Bringhurst RS, Voth V (1987) Genetic variation for quality traits in an advanced-cycle breeding population of strawberries. J Am Soc Hortic Sci 112(4):699–702. https://doi.org/10.21273/JASHS.112.4.699
doi: 10.21273/JASHS.112.4.699
Su G, Christensen OF, Ostersen T, Henryon M, Lund MS (2012) Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS ONE 7(9):e45293. https://doi.org/10.1371/journal.pone.0045293
doi: 10.1371/journal.pone.0045293 pubmed: 23028912 pmcid: 3441703
van Dijk T, Pagliarani G, Pikunova A, Noordijk Y, Yilmaz-Temel H et al (2014) Genomic rearrangements and signatures of breeding in the allo-octoploid strawberry as revealed through an allele dose based SSR linkage map. BMC Plant Biol 14(1):55. https://doi.org/10.1186/1471-2229-14-55
doi: 10.1186/1471-2229-14-55 pubmed: 24581289 pmcid: 3944823
Varona L, Legarra A, Toro MA, Vitezica ZG (2018) Non-additive effects in genomic selection. Front Genet. https://doi.org/10.3389/fgene.2018.00078
doi: 10.3389/fgene.2018.00078 pubmed: 29559995 pmcid: 5845743
Whitaker VM, Osorio LF, Hasing T, Gezan S (2012) Estimation of genetic parameters for 12 Fruit and vegetative traits in the University of Florida strawberry breeding population. J Am Soc Hortic Sci 137(5):316–324. https://doi.org/10.21273/JASHS.137.5.316
doi: 10.21273/JASHS.137.5.316
Wolfe MD, Chan AW, Kulakow P, Rabbi I, Jannink J-L (2021) Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices. Genetics 219(3):iyab122. https://doi.org/10.1093/genetics/iyab122
doi: 10.1093/genetics/iyab122 pubmed: 34740244 pmcid: 8570794
Woolliams JA, Berg P, Dagnachew BS, Meuwissen THE (2015) Genetic contributions and their optimization. J Anim Breed Genet 132(2):89–99. https://doi.org/10.1111/jbg.12148
doi: 10.1111/jbg.12148 pubmed: 25823835
Xiang T, Christensen OF, Vitezica ZG, Legarra A (2016) Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs. Genet Sel Evol. https://doi.org/10.1186/s12711-016-0271-4
doi: 10.1186/s12711-016-0271-4 pubmed: 27887565 pmcid: 5123321
Zingaretti LM, Monfort A, Pérez-Enciso M (2021) Automatic fruit morphology phenome and genetic analysis: an application in the octoploid strawberry. Plant Phenom 2021:1–14. https://doi.org/10.34133/2021/9812910
doi: 10.34133/2021/9812910

Auteurs

Christian R Werner (CR)

The Roslin Institute and Royal (Dick) School of Veterinary Studies, Easter Bush Research Centre, University of Edinburgh, Midlothian, EH25 9RG, UK. c.werner@cgiar.org.

R Chris Gaynor (RC)

The Roslin Institute and Royal (Dick) School of Veterinary Studies, Easter Bush Research Centre, University of Edinburgh, Midlothian, EH25 9RG, UK.

Daniel J Sargent (DJ)

NIAB EMR, New Road, East Malling, Kent, ME19 6BJ, UK.
East Malling Enterprise Centre, Driscoll's Genetics Ltd, New Road, East Malling, Kent, ME19 6BJ, UK.

Alessandra Lillo (A)

East Malling Enterprise Centre, Driscoll's Genetics Ltd, New Road, East Malling, Kent, ME19 6BJ, UK.

Gregor Gorjanc (G)

The Roslin Institute and Royal (Dick) School of Veterinary Studies, Easter Bush Research Centre, University of Edinburgh, Midlothian, EH25 9RG, UK.

John M Hickey (JM)

The Roslin Institute and Royal (Dick) School of Veterinary Studies, Easter Bush Research Centre, University of Edinburgh, Midlothian, EH25 9RG, UK.

Articles similaires

A scenario for an evolutionary selection of ageing.

Tristan Roget, Claire Macmurray, Pierre Jolivet et al.
1.00
Aging Selection, Genetic Biological Evolution Animals Fertility
Coal Metagenome Phylogeny Bacteria Genome, Bacterial
Biological Evolution History, 20th Century Selection, Genetic History, 19th Century Biology
Genome, Bacterial Virulence Phylogeny Genomics Plant Diseases

Classifications MeSH