CoChat
Sign Up
PreprintCited by 36Genetic and phenotypic traits in livestockGenetics and Plant BreedingGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record.

Back to the future: Implications of genetic complexity for hybrid breeding strategies

Open access
Technow, Frank, Podlich, Dean, Cooper, Mark·2020·G3 Genes Genomes Genetics
DOI 10.1101/2020.10.21.349332PMC PMC8495936
Open this paper in CoChat — free accountOpen PDF

Abstract

Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate inbred use reduce effective population size and constrain the accessible genetic space. Under these conditions, long term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study we therefore aim to propose an alternative rational for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space and reducing effective population size, through program decentralization and disproportionate inbred use, is required to expose additive genetic variation and thus facilitate heritable genetic gains. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2020.10.21.349332

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 109

  1. Altenberg, L. (1994). Evolving better representations through selective genome growth. In Proceedings of the First Ieee Conference on Evolutionary Computation IEEE World Congress on Computational Intelligence ICEC-94, pp. 182–187. IEEE. · DOI
  2. 10.1016/j.tplants.2013.09.008 · DOI · Paper page
  3. 1969, Choice of Population Size and Use of Variation Between Replicate Populations in Plant Breeding Selection Programs 1, Crop Sci, 9, 555, 10.2135/cropsci1969.0011183X000900050011x · DOI
  4. 10.1016/j.tpb.2017.06.001 · DOI
  5. 2015, Fitting linear mixed-effects models using lme4, J Stat Soft, 67, 1
  6. 2001, breeding potential of intra- and interheterotic group crosses in maize, Crop Sci, 41, 68, 10.2135/cropsci2001.41168x · DOI
  7. 2016, Bandwagons I, too, have known, Theor Appl Genet, 129, 2323âĂŞ2332
  8. 10.2135/cropsci2013.12.0837 · DOI
  9. 10.1007/s00122-017-2856-x · DOI
  10. 10.1051/gse:19970101 · DOI
  11. 10.3835/plantgenome2018.08.0062 · DOI
  12. 10.1038/nrg1407 · DOI
  13. 10.1038/ng1761 · DOI
  14. 10.2307/2410645 · DOI
  15. Comstock, R. E. (1977). Quantitative genetics and the design of breeding programs. In Proceedings of the International Conference on Quantitative Genetics, Ames, IA, pp. 16–21. Iowa State University Press.
  16. 10.1007/BF01240919 · DOI · Paper page
  17. 2014, Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction, Crop Pasture Sci, 65, 311, 10.1071/CP14007 · DOI · Paper page
  18. 2002, The E(NK) model: Extending the NK model to incorporate gene-by-environment interactions and epistasis for diploid genomes, Complexity, 7, 31, 10.1002/cplx.10044 · DOI
  19. 10.1071/AR05154 · DOI · Paper page
  20. 10.1111/j.1439-0388.2012.01003.x · DOI
  21. 2013, Sustainable plant breeding, Plant Breed, 132, 1, 10.1111/pbr.12026 · DOI
  22. 10.2307/2411143 · DOI
  23. 10.1186/s41469-018-0039-0 · DOI
  24. 10.1016/j.pbi.2018.01.003 · DOI
  25. Dudley, J. W. and R. J. Lambert (2010). 100 Generations of Selection for Oil and Protein in Corn. In Plant Breeding Reviews, pp. 79–110. John Wiley & Sons, Ltd. · DOI
  26. 10.1186/s12862-015-0382-5 · DOI
  27. 10.1186/1471-2148-10-2 · DOI
  28. Duvick, D. (1999). Heterosis: feeding people and protecting natural resources. In J. Coors and S. Pandey (Eds.), The genetics and exploitation of heterosis in crops, pp. 19–29. Madison, WI: CSSA. · DOI
  29. Duvick, D. , J. Smith , and M. Cooper (2004). Long-term selection in a commercial hybrid maize breeding program. In J. Janick (Ed.), Plant Breeding Reviews, pp. 109–152. Hoboken, NJ: John Wiley & Sons, Inc. · DOI · Paper page
  30. 10.1016/j.biotechadv.2015.07.001 · DOI
  31. 1936, Heterosis, Genetics, 21, 375, 10.1093/genetics/21.4.375 · DOI
  32. Falconer, D. S. and T. F. C. Mackay (1996). Introduction to Quantitative Genetics (4 ed.). London: Pearson.
  33. 10.1007/s00122-009-1203-2 · DOI
  34. 10.3389/fgene.2018.00159 · DOI
  35. 10.2135/cropsci2008.12.0689 · DOI
  36. 2008, Trends in genetic variance components during 30 years of hybrid maize breeding at the University of Hohenheim, Plant Breed, 127, 446, 10.1111/j.1439-0523.2007.01475.x · DOI
  37. 10.1186/s12711-017-0340-3 · DOI
  38. 10.1038/s41587-020-0444-0 · DOI
  39. 10.2135/cropsci2016.09.0742 · DOI
  40. 10.1534/genetics.115.182410 · DOI
  41. 10.1038/nrg1426 · DOI · Paper page
  42. 10.2307/2409030 · DOI
  43. 10.2307/2410274 · DOI
  44. 2012, Complexity in the telecommunications industry: When integrating infrastructure and services backfires, Telecomm Policy, 36, 40, 10.1016/j.telpol.2011.11.019 · DOI
  45. Hallauer, A. R. , M. J. Carena , and J. B. M. Filho (2010). Quantitative Genetics in Maize Breeding (3 ed.). Handbook of Plant Breeding. New York: Springer-Verlag. · DOI
  46. 10.1016/j.tplants.2006.10.006 · DOI · Paper page
  47. 10.2135/cropsci2008.03.0152 · DOI · Paper page
  48. 10.1073/pnas.0908755106 · DOI
  49. 2019, Breeding crops to feed 10 billion, Nat Biotechnol, 37, 744, 10.1038/s41587-019-0152-9 · DOI
  50. 10.1371/journal.pgen.1000008 · DOI · Paper page
  51. 10.1371/journal.pgen.1006421 · DOI
  52. 10.3389/fpls.2018.00985 · DOI · Paper page
  53. 10.1038/ng.3974 · DOI
  54. 1975, Selection for high adult body weight in Drosophila populations with different structures, Genetics, 81, 163, 10.1093/genetics/81.1.163 · DOI
  55. Kauffman, S. A. (1993). The Origins of Order: Self-Organization and Selection in Evolution. New York: Oxford University Press. · DOI · Paper page
  56. 10.1007/s001220051321 · DOI
  57. 10.1007/s00122-016-2822-z · DOI
  58. 10.2189/asqu.52.4.667 · DOI · Paper page
  59. 10.1016/j.tig.2006.12.006 · DOI
  60. 10.1007/s00122-013-2172-z · DOI
  61. 10.1038/nrg3627 · DOI
  62. Melchinger, A. E. (1999). Genetic diversity and heterosis. In J. Coors and S. Pandey (Eds.), The genetics and exploitation of heterosis in crops, pp. 99–118. Madison, WI: ASA, CSSA, and SSSA. · DOI
  63. 10.1007/BF00274716 · DOI
  64. Melchinger, A. E. and R. K. Gumber (1998). Overview of heterosis and heterotic groups in agronomic crops. In Concepts and Breeding of Heterosis in Crop Plant, pp. 29–44. CSSA. · DOI
  65. 2018, Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP), Eur J Agron, 100, 151, 10.1016/j.eja.2018.01.007 · DOI · Paper page
  66. 10.1093/jxb/erq329 · DOI · Paper page
  67. 10.2135/cropsci1992.0011183X003200060001x · DOI
  68. 10.1093/nar/gkv1344 · DOI · Paper page
  69. 10.2135/cropsci2005.10-0371 · DOI · Paper page
  70. 10.1093/bioinformatics/bti689 · DOI
  71. 10.1554/0014-3820(2003)057[0706:TAGVAB]2.0.CO;2 · DOI
  72. 2019, Long-term selection in hybrid sorghum breeding programs, Crop Sci, 59, 150, 10.2135/cropsci2018.05.0345 · DOI
  73. 10.1038/nrg2452 · DOI
  74. 10.1111/j.1439-037X.1998.tb00526.x · DOI
  75. 10.1016/j.jspi.2003.03.002 · DOI
  76. 10.1007/3-540-48873-1_23 · DOI
  77. 10.2135/cropsci2004.1560 · DOI · Paper page
  78. 2012, Genotyping-by-sequencing for plant breeding and genetics, Plant Genome, 5, 92
  79. Press, W. H. , S. A. Teukolsky , W. T. Vetterling , and B. P. Flannery (1992). Numerical Recipes in C: The Art of Scientific Computing. Cambridge: Cambridge University Press.
  80. 2002, Numerical and theoretical studies of noise effects in the Kauffman model, J Stat Phys, 109, 967, 10.1023/A:1020416308456 · DOI
  81. 10.2135/cropsci1997.0011183X003700020001x · DOI
  82. 1980, Artificial selection with differing population structures, Genet Res, 36, 117, 10.1017/S0016672300019753 · DOI
  83. 10.1007/s00122-002-1140-9 · DOI
  84. 10.1534/genetics.107.074146 · DOI
  85. 10.2135/cropsci2004.0517 · DOI
  86. 10.1371/journal.pone.0021784 · DOI · Paper page
  87. 2020, Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs, Theor Appl Genet, 133, 1995, 10.1007/s00122-020-03573-5 · DOI
  88. Shull, G. H. (1908). The composition of a field of maize. J Hered os-4, 296–301. · DOI
  89. 2010, Impact of improved vegetable cultivars in overcoming food insecurity, Euphytica, 176, 125, 10.1007/s10681-010-0237-5 · DOI
  90. 10.2134/agronj1942.00021962003400100008x · DOI
  91. 10.1534/g3.118.200994 · DOI
  92. 10.1371/journal.pone.0190271 · DOI
  93. 10.1371/journal.pone.0130855 · DOI · Paper page
  94. 10.1534/genetics.114.165860 · DOI · Paper page
  95. 10.1007/s00122-014-2396-6 · DOI
  96. 2002, Yield potential, yield stability and stress tolerance in maize, Field Crop Res, 75, 161, 10.1016/S0378-4290(02)00024-2 · DOI
  97. 10.1038/s41540-019-0101-8 · DOI
  98. 10.1534/genetics.107.082768 · DOI
  99. 10.1007/s00122-018-3270-8 · DOI · Paper page
  100. 10.1046/j.1420-9101.2002.00413.x · DOI
  101. 10.2307/2411328 · DOI
  102. 10.1146/annurev-genet-120116-024846 · DOI
  103. 2020, Diversity and heterotic patterns in North American proprietary dent maize germplasm, Crop Sci, 60, 100, 10.1002/csc2.20050 · DOI
  104. 10.1105/tpc.16.00158 · DOI
  105. 2016, Implementation of genomic selection in the poultry industry, Anim Fron, 6, 23, 10.2527/af.2016-0004 · DOI
  106. 10.1007/BF02459575 · DOI
  107. Wright, S. (1932). The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In Proceedings of the Sixth International Congress of Genetics, Ithaca, NY, pp. 356–366. Brooklyn Botanic Garden.
  108. Wright, S. (1977). Evolution and the genetics of populations. Chicago: University of Chicago Press.
  109. 10.1038/NPLANTS.2016.150 · DOI

Cited by

Sorted by citations

Metadata

Authors
Technow, F., Podlich, D., & Cooper, M.
Affiliations
  • Corteva (United States) (US)
  • The University of Queensland (AU)
Corresponding author
Technow, Frank
ORCID
Venue
G3 Genes Genomes Genetics
Publisher
openRxiv
Published
2020-10-22
Volume
11
Issue
7
ISSN
2160-1836 (ISSN-L)
DOI
10.1101/2020.10.21.349332
PMC
PMC8495936
OpenAlex
W3157080093
Cited by
36
References
0
Citations by year
2026: 10 · 2025: 7 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 7
FWCI
2.69
Type
journal article
Language
en
Open access
Licence
Locations
  • G3 Genes Genomes Genetics (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • Corteva Agriscience
  • Australian Research Council (CE200100015)
  • Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture (CE 200100015)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
maize, inbred lines, genetic complexity, NK model, genetic architecture, effective population size, population bottleneck, additive genetic variance, selection response, infinitesimal model
MeSH terms
Plant Breeding, Zea mays, Crops, Agricultural

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review