Wheat quality improvement at CIMMYT and the use of genomic selection on it

Applied and Translational Genomics

Volumn 11, Issue , 2016, Pages 3-8

  Guzman, Carlos ^a   Peña, Roberto Javier ^a   Singh, Ravi ^a   Autrique, Enrique ^a   Dreisigacker, Susanne ^a   Crossa, Jose ^a   Rutkoski, Jessica ^a   Poland, Jesse ^b   Battenfield, Sarah ^c  

^a INTERNATIONAL MAIZE AND WHEAT IMPROVEMENT CENTER CIMMYT   (Mexico)

^b KANSAS STATE UNIVERSITY   (United States)

^c SYNGENTA CROP PROTECTION   (United States)

Author keywords

Genomic selection; Wheat breeding; Wheat quality

Indexed keywords

ABIOTIC STRESS; ACCURACY; AGRICULTURAL PARAMETERS; CROPPING SYSTEM; DEVELOPING COUNTRY; DISEASE RESISTANCE; DROUGHT TOLERANCE; GENETIC SELECTION; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; GRAIN YIELD; HEAT TOLERANCE; NONHUMAN; PHENOTYPE; PLANT GENOME; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE TRAIT; REVIEW; SEQUENCE ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; TOTAL QUALITY MANAGEMENT; WHEAT QUALITY;

EID: 85006372734     PISSN: 22120661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atg.2016.10.004     Document Type: Review

References (39)

1. American Association of Cereal Chemists, Approved Methods of the AACC. 2010 (St. Paul, MN, USA).
2. Battenfield, S.D., Guzman, C., Gaynor, R.C., Singh, R.P., Peña, R.J., Dreisigacker, S., et al. Applying genomic selection for prediction of processing and end-use quality traits in CIMMYT spring bread wheat breeding program. The Plant Genome. In press., 2016, 10.3835/plantgenome2016.01.0005.
3. Bernardo, R., Yu, J., Prospects for genome-wide selection for quantitative traits in maize. Crop Sci. 47 (2007), 1082–1090.
4. Blakeney, A.B., Cracknell, R.L., Crosbie, G.B., Jefferies, S.P., Miskelly, D.M., O'Brien, L., Panozzo, J.F., Suter, D.A.I., Solah, S., Watts, T., Westcott, T., Williams, R.M., Understanding Australian Wheat Quality. 2009, Grains Research & Development Corporation.
5. Burgueño, J., de los Campos, G., Weigel, K., Crossa, J., Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci. 52 (2012), 707–719.
6. Crossa, J., de los Campos, G., Pérez, P., Gianola, D., Burgueño, J., et al. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186 (2010), 713–724.
7. Crossa, J., Pérez, P., de los Campos, G., Mahuku, G., Dreisigacker, S., et al. Genomic selection and prediction in plant breeding. Journal of Crop Improvement 25 (2011), 239–261.
8. Crossa, J., Jarquin, D., Franco, J., Perez-Rodriguez, P., Burgueño, J., Saint-Pierre, C., et al. Genomic prediction of gene bank wheat landraces. G3 6 (2016), 1819–1834.
9. de los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., et al. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182 (2009), 375–385.
10. de los Campos, G., Gianola, D., GJM, R., Weigel, K., Crossa, J., Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet. Res. 92 (2010), 295–308.
11. de los Campos, G., JM, H., Pong-Wong, R., HD, D., PL, C., Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193 (2013), 327–345.
12. Edwards, M.A., Osborne, B.G., Henry, R.J., Puroindoline genotype, starch granule size distribution and milling quality of wheat. J. Cereal Sci. 52 (2010), 314–320.
13. Gianola, D., Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 194 (2013), 573–596.
14. Goddard, M.E., Genomic selection: prediction of accuracy and maximization of long-term response. Genetica 136 (2009), 245–257.
15. Guzman, C., Posadas-Romano, G., Hernandez-Espinosa, N., Morales-Dorantes, A., Peña, R.J., A new standard water absorption criteria based on solvent retention capacity (SRC) to determine dough mixing properties, viscoelasticity, and bread-making quality. J. Cereal Sci. 66 (2015), 59–65.
16. Guzman, C., Alvarez, J.B., Wheat waxy proteins: polymorphism, molecular characterization and effects on starch properties. Theor. Appl. Genet. 129 (2016), 1–16.
17. Guzman, C., Mondal, S., Govindan, V., Autrique, J.E., Posadas-Romano, G., Cervantes, F., et al. Use of rapid tests to predict quality traits of CIMMYT bread wheat genotypes grown under different environments. LWT Food Sci. Technol. 69 (2016), 327–333.
18. Guzman, C., Xiao, Y., Crossa, J., Gonzalez-Santoyo, H., Huerta, J., Singh, R., Dreisigacker, S., Sources of the highly expressed wheat bread making (wbm) gene in CIMMYT spring wheat germplasm and its effect on processing and bread-making quality. Euphytica 209 (2016), 689–692.
19. Hayes, B., Visscher, P.M., Goddard, M.E., Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. 91 (2009), 47–60.
20. Heffner, E.L., Sorrells, M.E., Jannink, J.L., Genomic selection for crop improvement. Crop Sci. 49 (2009), 1–12.
21. Heffner, E.L., Jannink, J.L., Sorrells, M.E., Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4 (2011), 65–75.
22. Heslot, N., Yang, H.P., Sorrells, M.E., Jannink, J.L., Genomic selection in plant breeding: a comparison of models. Crop Sci. 52 (2012), 146–160.
23. Heslot, N., Akdemir, D., Sorrells, M.E., Jannink, J.L., Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor. Appl. Genet. 127 (2014), 463–480.
24. Jarquin, D., Crossa, J., Lacaze, X., Cheyron, P.D., Daucourt, J., et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor. Appl. Genet. 127 (2014), 595–607.
25. Lantican, M.A., Braun, H.J., Payne, T.S., Singh, R.P., Sonder, K., Baum, M., van Ginkel, M., Erenstein, O., Impacts of International Wheat Improvement Research, 1994-2014. 2016, CIMMYT, Mexico, D.F.
26. Lillemo, M., Chen, F., Xia, X., William, M., Peña, R.J., Trethowan, R., et al. Puroindoline grain hardness alleles in CIMMYT bread wheat germplasm. J. Cereal Sci. 44 (2006), 86–92.
27. Lorenz, A.J., Chao, S., Asoro, F.G., Heffner, E.L., Hayashi, T., et al. Genomic selection in plant breeding: knowledge and prospects. Adv. Agron. 110 (2011), 77–123.
28. Matsuo, R.R., Dexter, J.E., Relationship between some durum wheat physical characteristics and semolina milling properties. Can. J. Plant Sci. 60 (1980), 49–53.
29. Meuwissen, T.H., Hayes, B., Goddard, M., Prediction of total genetic value using genome-wide dense marker maps. Genetics 157 (2001), 1819–1829.
30. Mondal, S., Rutkoski, J.E., Velu, G., Singh, P.K., Crespo-Herrera, L.A., Guzman, C., et al. Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect Pest resistance and nutrition through conventional and modern breeding approaches. Front. Plant Sci., 7, 2016, 991.
31. Morris, C.F., Puroindolines: the molecular genetic basis of wheat grain hardness. Plant Mol. Biol. 48 (2002), 633–647.
32. Peña, R.J., Amaya, A., Rajaram, S., Mujeeb-Kazi, A., Variation in quality characteristics associated with some spring 1B/1R translocation wheats. J. Cereal Sci. 12 (1990), 105–112.
33. Payne, P.I., Nightingale, M.A., Krattiger, A.F., Holt, L.M., The relationship between HMW glutenin subunit composition and the breadmaking quality of British-grown wheat varieties. J. Sci. Food Agric. 40 (1987), 51–65.
34. Pérez-Rodriguez, P., Gianola, D., Gonzalez-Camacho, J.M., Crossa, J., Manes, Y., et al. A comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 (Bethesda) 2 (2012), 1595–1605.
35. Poland, J.A., Rife, T.W., Genotyping-by-sequencing for plant breeding and genetics. Plant Genome, 5, 2012, 92.
36. Poland, J., Endelman, J., Dawson, J., Rutkoski, J., Wu, S., Manes, Y., et al. Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome, 5, 2012, 103.
37. VanRaden, P.M., Efficient methods to compute genomic predictions. J. Dairy Sci. 91 (2008), 4414–4423.
38. Velu, G., Guzman, C., Mondal, S., Autrique, J.E., Huerta, J., Rp, S., Effect of drought and elevated temperature on grain zinc and iron concentrations in CIMMYT spring wheat. J. Cereal Sci. 69 (2016), 182–186.
39. Verbyla, K.L., Bowman, P.J., Hayes, B.J., Goddard, M.E., Sensitivity of genomic selection to using different prior distributions. BMC Proc., 4(Supp. 1), 2010, S5.