Skip to main content
SpringerLink
Log in

Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program

  • Original Article
  • Published:
Theoretical and Applied Genetics Aims and scope Submit manuscript

Abstract

Key message

We review and propose easily implemented and affordable indicators to assess the genetic diversity and the potential of a breeding population and propose solutions for its long-term management.

Abstract

Successful plant breeding programs rely on balanced efforts between short-term goals to develop competitive cultivars and long-term goals to improve and maintain diversity in the genetic pool. Indicators of the sustainability of response to selection in breeding pools are of key importance in this context. We reviewed and proposed sets of indicators based on temporal phenotypic and genotypic data and applied them on an early maize grain program implying two breeding pools (Dent and Flint) selected in a reciprocal manner. Both breeding populations showed a significant positive genetic gain summing up to 1.43 qx/ha/year but contrasted evolutions of genetic variance. Advances in high-throughput genotyping permitted the identification of regions of low diversity, mainly localized in pericentromeric regions. Observed changes in genetic diversity were multiple, reflecting a complex breeding system. We estimated the impact of linkage disequilibrium (LD) and of allelic diversity on the additive genetic variance at a genome-wide and chromosome-wide scale. Consistently with theoretical expectation under directional selection, we found a negative contribution of LD to genetic variance, which was unevenly distributed between chromosomes. This suggests different chromosome selection histories and underlines the interest to recombine specific chromosome regions. All three sets of indicators valorize in house data and are easy to implement in the era of genomic selection in every breeding program.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The datasets analyzed in this study are not publicly available.

References

  • Akdemir D, Sánchez JI (2016) Efficient breeding by genomic mating. Front Genet 7:210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Akdemir D, Beavis W, Fritsche-Neto R, Singh AK, Isidro-Sánchez J (2018) Multi-objective optimized genomic breeding strategies for sustainable food improvement. Heredity 1:17

    Google Scholar 

  • Avery PJ, Hill WG (1977) Variability in genetic parameters among small populations. Genet Res 29:193–213

    Article  CAS  PubMed  Google Scholar 

  • Bernardo R (2014) Essentials of plant breeding. Stemma Press, Woodbury

    Google Scholar 

  • Bernardo R (2017) Prospective targeted recombination and genetic gains for quantitative traits in maize. Plant Genome 10:2

    Article  CAS  Google Scholar 

  • Betrán FJ, Hallauer AR (1996) Characterization of interpopulation genetic variability in three hybrid maize populations. J Hered 87:319–328

    Article  Google Scholar 

  • Brisbane JR, Gibson JP (1995) Balancing selection response and rate of inbreeding by including genetic relationships in selection decisions. Theor Appl Genet 91:421–431

    Article  CAS  PubMed  Google Scholar 

  • Browning SR, Browning BL (2007) 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bulmer M (1971) The stability of equilibria under selection. Heredity 27:157–162

    Article  CAS  PubMed  Google Scholar 

  • Bulmer M (1980) The mathematical theory of quantitative genetics. Oxford University Press, New York

    Google Scholar 

  • Butler D, Cullis B, Gilmour A, Gogel B (2009) {ASReml}-R reference manual

  • Comstock RE, Robinson HF, Harvey PH (1949) A breeding procedure designed to make maximum use of both general and specific combining ability. Agron J 41:360–367

    Article  Google Scholar 

  • de los Campos G, Sorensen D, Gianola D (2015) Genomic heritability: what is it? PLoS Genet 11:e1005048

    Article  CAS  PubMed Central  Google Scholar 

  • Duvick DN (1984) Chapter 2, genetic contributions to yield gains of US Hybrid Maize, 1930 to 1980. In: Genetic contributions to yield gains of five major crop plants, ASA, CSSA, 677 South Segoe Road

  • Duvick DN, Smith JSC, Cooper M (2004) Long-term selection in a commercial hybrid maize breeding program. Plant Breed. Rev. J Janick Ed Vol 24 Part 2 Long Term Sel. Crops Anim. Bact., Wiley, New York, pp 109–151

  • Eberhart SA (1964) Least squares method for comparing progress among recurrent selection methods 1. Crop Sci 4:230–231

    Article  Google Scholar 

  • Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longmans Green, Harlow, Essex, UK

    Google Scholar 

  • Falke KC, Flachenecker C, Melchinger AE, Piepho H-P, Maurer HP et al (2007a) Temporal changes in allele frequencies in two European F(2) flint maize populations under modified recurrent full-sib selection. TAG Theor Appl Genet Theor Angew Genet 114:765–776

    Article  CAS  Google Scholar 

  • Falke KC, Maurer HP, Melchinger AE, Piepho H-P, Flachenecker C et al (2007b) Linkage disequilibrium in two European F(2) flint maize populations under modified recurrent full-sib selection. TAG Theor Appl Genet Theor Angew Genet 115:289–297

    Article  CAS  Google Scholar 

  • Felsenstein J (1965) The effect of linkage on directional selection. Genetics 52(2):349–363

    CAS  PubMed  PubMed Central  Google Scholar 

  • Feng L, Sebastian S, Smith S, Cooper M (2006) Temporal trends in SSR allele frequencies associated with long-term selection for yield in maize. Maydica 51:293–300

    Google Scholar 

  • Fischer S, Möhring J, Schön CC, Piepho H-P, Klein D et al (2008) Trends in genetic variance components during 30 years of hybrid maize breeding at the University of Hohenheim. Plant Breed 127:446–451

    Article  Google Scholar 

  • Fischer S, Möhring J, Maurer HP, Piepho H-P, Thiemt E-M et al (2009) Impact of genetic divergence on the ratio of variance due to specific vs. general combining ability in winter triticale. Crop Sci 49:2119–2122

    Article  Google Scholar 

  • Flachenecker C, Frisch M, Falke KC, Melchinger AE (2006) Trends in population parameters and best linear unbiased prediction of progeny performance in a European F2 maize population under modified recurrent full-sib selection. Theor Appl Genet 112:483–491

    Article  CAS  PubMed  Google Scholar 

  • Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES et al (2011) A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS ONE 6:e28334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gerke JP, Edwards JW, Guill KE, Ross-Ibarra J, McMullen MD (2015) The genomic impacts of drift and selection for hybrid performance in maize. Genetics 201:1201–1211

    Article  PubMed  PubMed Central  Google Scholar 

  • Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R (2009) Additive genetic variability and the bayesian alphabet. Genetics 183:347–363

    Article  PubMed  PubMed Central  Google Scholar 

  • Gonen S, Battagin M, Johnston SE, Gorjanc G, Hickey JM (2017) The potential of shifting recombination hotspots to increase genetic gain in livestock breeding. Genet Sel Evol 49:55

    Article  PubMed  PubMed Central  Google Scholar 

  • Gore MA, Chia J-M, Elshire RJ, Sun Q, Ersoz ES et al (2009) A first-generation haplotype map of maize. Science 326:1115–1117

    Article  CAS  PubMed  Google Scholar 

  • Gorjanc G, Gaynor RC, Hickey JM (2018) Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection. Theor Appl Genet 131:1953–1966

    Article  PubMed  PubMed Central  Google Scholar 

  • Gusev A, Lee SH, Trynka G, Finucane H, Vilhjálmsson BJ et al (2014) Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am J Hum Genet 95:535–552

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hallauer AR, Darrah LL (1985) Compendium of recurrent selection methods and their application. Crit Rev Plant Sci 3:1–33

    Article  Google Scholar 

  • Hill WG (1981) Estimation of effective population size from data on linkage disequilibrium 1. Genet Res 38:209–216

    Article  Google Scholar 

  • Hill WG, Robertson A (1966) The effect of linkage on limits to artificial selection. Genet Res 8:269–294

    Article  CAS  PubMed  Google Scholar 

  • Hospital F, Chevalet C (1996) Interactions of selection, linkage and drift in the dynamics of polygenic characters. Genet Res 67:77–87

    Article  CAS  PubMed  Google Scholar 

  • Inghelandt DV, Melchinger AE, Lebreton C, Stich B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 120:1289–1299

    Article  PubMed  PubMed Central  Google Scholar 

  • Jensen J, Su G, Madsen P (2012) Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle. BMC Genet 13:44

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jiao Y, Peluso P, Shi J, Liang T, Stitzer MC et al (2017) Improved maize reference genome with single-molecule technologies. Nature 546:524–527

    Article  CAS  PubMed  Google Scholar 

  • Labate JA, Lamkey KR, Lee M, Woodman WL (1999) Temporal changes in allele frequencies in two reciprocally selected maize populations. Theor Appl Genet 99:1166–1178

    Article  Google Scholar 

  • Lehermeier C, de los Campos G, Wimmer V, Schön C-C (2017) Genomic variance estimates: with or without disequilibrium covariances? J Anim Breed Genet 134:232–241

    Article  CAS  PubMed  Google Scholar 

  • Lush JL (1937) Animal breeding plans. Iowa State College Press, Iowa

    Google Scholar 

  • Lynch M, Walsh B (1999) Evolution and selection of quantitative traits, Sunderland

  • MacLeod IM, Meuwissen THE, Hayes BJ, Goddard ME (2009) A novel predictor of multilocus haplotype homozygosity: comparison with existing predictors. Genet Res 91:413–426

    Article  CAS  Google Scholar 

  • Meuwissen TH (1997) Maximizing the response of selection with a predefined rate of inbreeding. J Anim Sci 75:934–940

    Article  CAS  PubMed  Google Scholar 

  • Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    CAS  PubMed  PubMed Central  Google Scholar 

  • Nei M (1975) Molecular population genetics and evolution. Front Biol 40:I-288

    PubMed  Google Scholar 

  • Nei M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583–590

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pérez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198:483–495

    Article  PubMed  PubMed Central  Google Scholar 

  • Peripolli E, Munari DP, Silva MVGB, Lima ALF, Irgang R et al (2017) Runs of homozygosity: current knowledge and applications in livestock. Anim Genet 48:255–271

    Article  CAS  PubMed  Google Scholar 

  • R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Reif JC, Gumpert F-M, Fischer S, Melchinger AE (2007) Impact of interpopulation divergence on additive and dominance variance in hybrid populations. Genetics 176:1931–1934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D et al (2014) Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. TAG Theor Appl Genet Theor Angew Genet 127:2313–2331

    Article  CAS  Google Scholar 

  • Russell WA (1991) Genetic improvement of maize yields. Adv Agron 46:245–298

    Article  Google Scholar 

  • Rutkoski J (2018) Estimation of realized rates of genetic gain and indicators for breeding program assessment. bioRxiv 409342

  • Smith JM, Haigh J (1974) The hitch-hiking effect of a favourable gene. Genet Res 23:23–35

    Article  CAS  PubMed  Google Scholar 

  • Sorensen D, Fernando R, Gianola D (2001) Inferring the trajectory of genetic variance in the course of artificial selection. Genet Res 77:83–94

    Article  CAS  PubMed  Google Scholar 

  • Speed D, Balding DJ (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res 24:1550–1557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM et al (2007) Recent human effective population size estimated from linkage disequilibrium. Genome Res 17:520–526

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Truntzler M, Ranc N, Sawkins MC, Nicolas S, Manicacci D et al (2012) Diversity and linkage disequilibrium features in a composite public/private dent maize panel: consequences for association genetics as evaluated from a case study using flowering time. Theor Appl Genet 125:731–747

    Article  CAS  PubMed  Google Scholar 

  • VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423

    Article  CAS  PubMed  Google Scholar 

  • Waples RS, England PR (2011) Estimating contemporary effective population size on the basis of linkage disequilibrium in the face of migration. Genetics 189:633–644

    Article  PubMed  PubMed Central  Google Scholar 

  • Weir BS, Hill WG (1980) Effect of mating structure on variation in linkage disequilibrium. Genetics 95:477–488

    CAS  PubMed  PubMed Central  Google Scholar 

  • Woolliams JA, Berg P, Dagnachew BS, Meuwissen THE (2015) Genetic contributions and their optimization. J Anim Breed Genet 132:89–99

    Article  CAS  PubMed  Google Scholar 

  • Wright S (1931) Evolution in mendelian populations. Genetics 16:97–159

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the experimental staff at RAGT2n for managing field experiments and data extractions. This research was funded by RAGT2n and the ANRT CIFRE Grant No. 2016/1281 for AA.

Author information

Authors and Affiliations

  1. GQE ‑ Le Moulon, INRA, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France

    Antoine Allier, Laurence Moreau & Alain Charcosset

  2. RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France

    Antoine Allier, Simon Teyssèdre, Christina Lehermeier, Bruno Claustres, Stéphane Maltese & Stéphane Melkior

Authors
  1. Antoine Allier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simon Teyssèdre
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christina Lehermeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bruno Claustres
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stéphane Maltese
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stéphane Melkior
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Laurence Moreau
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alain Charcosset
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alain Charcosset.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest. The experiments reported in this study comply with the current laws in Europe.

Additional information

Communicated by Benjamin Stich.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 358 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Allier, A., Teyssèdre, S., Lehermeier, C. et al. Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program. Theor Appl Genet 132, 1321–1334 (2019). https://doi.org/10.1007/s00122-019-03280-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00122-019-03280-w

Search

Navigation