Genomic Prediction of Gene Bank Wheat Landraces

Abstract

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 44

1. 1.

   Akdemir D. Sanchez J. I. Jannink J.-L., 2015 Optimization of genomic selection training populations with a genetic algorithm. Genet. Sel. Evol. 47: 38.

   Not in our library10.1186/s12711-015-0116-6

2. 2.

   Ayala M. Guzman C. Alvarez J. B. Peña R. J., 2013 Characterization of genetic diversity of puroindoline genes in Mexican wheat landraces. Euphytica 190: 53–63.

   Not in our library

3. 3.

   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.

   Not in our library

4. 4.

   Brown A. H. D., 1989 Core collections: a practical approach to genetic resource management. Genome 31: 818–824.

   Not in our library

5. 5.

   Caballero L. Peña R. J. Martin L. M. Alvarez J. B., 2010 Characterization of Mexican Creole wheat landraces in relation to morphological characteristics and HMW glutenin subunit composition. Genet. Resour. Crop Evol. 57: 657–665.

   Not in our library

6. 6.

   Carling J. Uszynska K. Jaccoud D. Machado A. Hopper A. (2015 ) DArT and DArTseq genome profiling for breeding, pre-breeding and population genetics. P0052. Plant and Animal Genome Conference XXIII.

   Not in our library

7. 7.

   Crossa J. de los Campos G. Pérez-Rodríguez P. Gianola D. Burgueño J. 2010 Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186: 713–724.

   Not in our library10.1534/genetics.110.118521

8. 8.
   Genomic Prediction in Maize Breeding Populations with Genotyping-by-Sequencing · 2013

   No notice
9. 9.

   Daetwyler H. D. Kemper K. E. van der Werf J. H. J. Hayes B. J., 2015 Components of the accuracy of genomic prediction in a multi-breed sheep population. J. Anim. Sci. 90: 3375–3384.

   Not in our library10.2527/jas2011-4557

10. 10.

    de los Campos, G., and P. Pérez-Rodríguez, 2014 Bayesian generalized linear regression. R package version 1.0.4. Available at: https://r-forge.r-project.org/R/?group_id=1525.

    Not in our library

11. 11.

    de los Campos G. Naya H. Gianola D. Crossa J. Legarra A. 2009 Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182: 375–385.

    Not in our library10.1534/genetics.109.101501

12. 12.

    de los Campos G. Gianola D. Rosa G. J. M. Weigel K. Crossa J., 2010 Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet. Res. 92: 295–308.

    Not in our library10.1017/s0016672310000285

13. 13.

    de los Campos G. Veturi Y. Vazquez A. I. C. Lehermeier, and P. Pérez-Rodríguez, 2015 Incorporating genetic heterogeneity in whole-genome regressions using interactions. J. Agric. Biol. Environ. Stat. 20: 467–490.

    Not in our library10.1007/s13253-015-0222-5

14. 14.

    Franco J. Crossa J. Taba S. Shands H. A., 2005 Sampling strategy for conserving genetic diversity when forming core subsets. Crop Sci. 45: 1035–1044.

    Not in our library

15. 15.

    Franco J. Crossa J. Warburton M. L. Taba S., 2006 Sampling strategies for conserving maize diversity when forming core subsets using genetic markers. Crop Sci. 46: 854–864.

    Not in our library

16. 16.

    Franco J. Crossa J. Desphande S., 2010 Hierarchical multiple-factor analysis for classifying genotypes based on phenotypic and genetic data. Crop Sci. 50: 105–117.

    Not in our library

17. 17.

    Frankel O. H. Brown A. H. D., 1984 Plant genetic resources today: a critical appraisal, pp. 249–257 in Crop genetic resources: Conservation and evaluation, edited by Holden J. H. W. Williams J. T. George Allen and Unwin, London.

    Not in our library

18. 18.

    Gilmour A. R. Cullis B. R. Verbyla A. P., 1997 Accounting for natural and extraneous variation in the analysis of field experiments. J. Agric. Biol. Environ. Stat. 2(3): 269–293.

    Not in our library

19. 19.

    Gilmour, A. R., B. J. Gogel, B. R. Cullis, and R. Thompson, 2009 ASReml User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. Available at: www.vsni.co.uk/downloads/asreml/release3/UserGuide.pdf.

    Not in our library

20. 20.
    Genome-enabled prediction of genetic values using radial basis function neural networks · 2012

    No notice
21. 21.

    Gorjanc G. Jenk J. Hearne S. J. Hickey J. M., 2016 Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations. BMC Genomics 17: 30.

    Not in our library10.1186/s12864-015-2345-z

22. 22.

    Gou Z. Tucker D. M. Basten C. J., H. Gandhi, E. Ersoz 2014 The impact of population structure on genomic prediction in stratified populations. Theor. Appl. Genet. 127(3): 749–762.

    Not in our library10.1007/s00122-013-2255-x

23. 23.

    Guzman C. Medina-Larque A. S. Velu G. González-Santoyo H. Singh R. P. 2014 Use of wheat genetic resources to develop biofortified wheat with enhanced grain zinc and iron concentrations and desirable processing quality. J. Cereal Sci. 60: 617–622.

    Not in our library

24. 24.

    Guzman C. Ortega R. Yamamori M. Peña R. J. Alvarez J. B., 2015 Molecular characterization of two novel null waxy alleles in Mexican bread wheat landraces. J. Cereal Sci. 62: 8–14.

    Not in our library

25. 25.

    Heslot N. Yang H. P. Sorrells M. E. Jannink J. L., 2012 Genomic selection in plant breeding: a comparison of models. Crop Sci. 52: 146–160.

    Not in our library

26. 26.
    Dynamics of long-term genomic selection · 2010

    No notice
27. 27.

    Janss L. de los Campos G. Sheehan N. Sorensen D., 2012 Inferences from genomic models in stratified populations. Genetics 192(2): 693–704.

    Not in our library10.1534/genetics.112.141143

28. 28.
    A reaction norm model for genomic selection using high-dimensional genomic and environmental data · 2013

    No notice
29. 29.
    Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker × Environment Interaction Genomic Selection Model · 2015

    No notice
30. 30.

    Meuwissen T. H. E. Hayes B. J. Goddard M. E., 2001 Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4): 1819–1829.

    Not in our library10.1093/genetics/157.4.1819

31. 31.

    Pérez-Rodríguez P., and G. de los Campos, 2014 Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2): 483–495.

    Not in our library10.1534/genetics.114.164442

32. 32.
    Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat · 2012

    No notice
33. 33.

    R Core Team, 2016 R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: http://www.R-project.org.

    Not in our library

34. 34.

    Reif J. C. Melchinger A. E. Frisch M., 2005 Genetic and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop Sci. 45: 1–7.

    Not in our library

35. 35.

    Rutkoski J. Singh R. P. Huerta-Espino J. Bhavani B. Poland J. 2015 Genetic gain from phenotypic and genomic selection for quantitative resistance to stem rust of wheat. Plant Genome (in press).

    Not in our library10.3835/plantgenome2014.10.0074

36. 36.
    Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population · 2015

    Not checked
37. 37.

    Sansaloni, C., C. Petroli, D. Jaccoud, J. Carling, F. Detering et al. 2011 Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high-throughput, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proceedings 5(Suppl 7): P54.

    Not in our library

38. 38.

    Schulz-Streeck T. Ogutu J. O. Gordillo A. Karaman Z. Knaak C. 2013 Genomic selection allowing for marker-by-environment interaction. Plant Breed. 132(2): 532–538.

    Not in our library

39. 39.

    VanRaden P. M., 2007 Genomic measures of relationship and inbreeding. Interbull Bull 37: 33–36.

    Not in our library

40. 40.

    VanRaden P. M., 2008 Efficient methods to compute genomic predictions. J. Dairy Sci. 91: 4414–4423.

    Not in our library10.3168/jds.2007-0980

41. 41.

    Vikram P. Franco J. Burgueno-Ferrera J. Li H. Sehgal D. 2016 Unlocking the genetic diversity of Creole wheats. Sci. Rep. 6: 23092.

    Not in our library10.1038/srep23092

42. 42.

    Ward J. H. Jr, 1963 Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58: 236–244.

    Not in our library

43. 43.
    Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments · 2012

    No notice
44. 44.

    Zhang X. Pérez-Rodríguez P. Semagn K. Beyene Y. Babu R. 2014 Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS. Heredity 114: 291–299.

    Not in our library10.1038/hdy.2014.99

Cited by papers we hold 10

• Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize · 2022
  Not checked
• Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction · 2021
  Not checked
• A review of deep learning applications for genomic selection · 2021
  No notice
• Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints · 2020
  No notice
• Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits · 2018
  No notice
• Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar · 2018
  No notice
• Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture · 2018
  No notice
• Optimising Genomic Selection in Wheat: Effect of Marker Density, Population Size and Population Structure on Prediction Accuracy · 2018
  No notice
• Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico · 2017
  Not checked
• Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding · 2016
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.