Genomic prediction in CIMMYT maize and wheat breeding programs

Heredity

Volumn 112, Issue 1, 2014, Pages 48-60

  Crossa, J ^a   Perez P ^b   Hickey, J ^a,c   Burgueno J ^a   Ornella, L ^d   Ceron Rojas J ^a   Zhang, X ^a   Dreisigacker, S ^a   Babu, R ^a   Li, Y ^a   Bonnett, D ^a   Mathews, K ^a  

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

^b Colegio de Postgraduados ^*  (Mexico)

^c UNIVERSITY OF NEW ENGLAND   (Australia)

^d French Argentine International Center for Information and Systems Sciences ^*  (Argentina)

Author keywords

Bayesian LASSO; Environment interaction; Genomic selection; Genotype; International Maize and Wheat Improvement Center; Reproducing kernel Hilbert space regression

Indexed keywords

BIOMARKER; GENOMICS; GENOTYPE; HERITABILITY; MAIZE; PLANT BREEDING; POPULATION STRUCTURE; RESEARCH; WHEAT;

ANIMALIA; TRITICUM AESTIVUM; ZEA MAYS;

ARTICLE; BIOLOGICAL MODEL; GENETIC SELECTION; GENETICS; GENOTYPE; GENOTYPE ENVIRONMENT INTERACTION; MAIZE; PHENOTYPE; PLANT GENOME; POPULATION GENETICS; QUANTITATIVE TRAIT; WHEAT;

GENE-ENVIRONMENT INTERACTION; GENETICS, POPULATION; GENOME, PLANT; GENOTYPE; MODELS, GENETIC; PHENOTYPE; QUANTITATIVE TRAIT, HERITABLE; SELECTION, GENETIC; TRITICUM; ZEA MAYS;

EID: 84890309817     PISSN: 0018067X     EISSN: 13652540     Source Type: Journal    
DOI: 10.1038/hdy.2013.16     Document Type: Article

References (35)

1. Bernardo R (2008). Molecular markers and selection of complex traits in plants: learning from the last 20 years. Crop Sci 48: 1649-1664.
2. Bernardo R, Yu Y (2007). Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 47: 1082-1090.
3. Burgueno J, Crossa J, Cornelius PL, Trethowan R, McLaren G, Krishnamachari A (2007). Modeling additive × environment and additive × additive × environment using genetic covariances of relatives of wheat genotypes. Crop Sci 43: 311-320.
4. Burgueno J, Crossa J, Cotes JM, San Vicente F, Das B (2011). Prediction assessment of linear mixed models for multienvironment trials. Crop Sci 51: 944-954.
5. Burgueno J, de los Campos GDL, 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.
6. Crossa J, de los Campos G, Perez P, Gianola D, Burgueno J, Araus JL et al. (2010). Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186: 713-724.
7. Crossa J, Perez P, de los Campos G, Mahuku G, Dreisigacker S, Magorokosho C (2011). Genomic selection and prediction in plant breeding. J Crop Improv 25: 239-261.
8. Daetwyler HD, Villanueva B, Woolliams J (2008). Accuracy of predicting the genetic risk of disease using a genome-wide approach. PloS ONE 3: e3395.
9. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E et al. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182: 375-385.
10. de los Campos G, Gianola D, Rosa GJM, Weigel K, Crossa J (2010). Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet Res 92: 295-308.
11. Gianola D, Fernando R, Stella A (2006). Genomic-assisted prediction of genetic values with semiparametric procedures. Genetics 173: 1761-1776.
12. Gianola D, Okut H, Weigel KA, Rosa GJM (2011). Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genet 12: 87.
13. Goddard ME, Hayes BJ (2009). Genomic selection. J Anim Breed Genet 124: 323-330.
14. Gonzalez-Camacho JM, de los Campos G, Perez P, Gianola D, Cairns J, Mahuku G et al. (2012). Genome-enabled prediction of genetic values using radial basis function neural networks. Theor Appl Genet 125: 759-771.
15. Habier D, Fernando RL, Dekkers JCM (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics 177: 2389-2397.
16. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009). Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92: 433-443.
17. Hastie T, Tibshirani R, Friedman J (2009). The Elements of Statistical Learning, 2nd edn. Springer-Verlag: NY, US.A.
18. Heffner EL, Jannink J-L, Sorrells M (2011). Genomic selection accuracy using multifamily prediction models in a wheat breeding program. The Plant Genome 4: 65-75.
19. Hickey JM, Crossa J, Babu R, de los Campos G (2012). Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs. Crop Sci 52: 654-663.
20. Lorenzana RE, Bernardo R (2009). Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120: 151-161.
21. McKinney BA, Pajewski NM (2012). Six degrees of epistasis: statistical network models for GWAS. Front Genet 2: 1-6.
22. Meuwissen THE, Hayes BJ, Goddard ME (2001). Prediction of total genetic values using genome-wide dense marker maps. Genetics 157: 1819-1829.
23. Ornella L, Singh S, Perez P, Burgueno J, Singh R, Tapia E et al. (2012). Genomic prediction of genetic values for resistance to wheat rusts. The Plant Genome 5: 136-148.
24. Park T, Casella G (2008). The Bayesian LASSO. J Am Stat Assoc 103: 681-686.
25. Perez P, de los Campos G, Crossa J, Gianola D (2010). Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian Linear Regression Package in R. The Plant Genome 3: 106-116.
26. Perez P, Gianola D, Gonzalez-Camacho JM, Crossa J, Manes Y, Dreisigacker S (2012). A comparison between linear and non-parametric regression models for genomeenabled prediction in wheat. Genes Genomes Genet 2: 1595-1605.
27. Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R et al. (2012). Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44: 217-220.
28. VanRaden PM (2007). Genomic measures of relationship and inbreeding. Interbull Annu Meeting Proc, Interbull Bulletin 37: 33-36.
29. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF et al. (2008). Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92: 16-24.
30. VanRaden PM (2008). Efficient methods to compute genomic predictions. J Dairy Sci 91: 4414-4423.
31. Wang D, El-Basyoni IS, Baenziger PS, Crossa J, Eskridge K, Dweikat I (2012). Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations. Heredity 109: 313-319.
32. Wang D, Eskridge K, Crossa J (2011). Identifying QTLs and epistasis in structured plant breeding populations using adaptive mixed LASSO. J Agric Biol Environ Stat 16: 170-184.
33. Windhausen VS, Atlin GN, Crossa J, Hickey JM, Grudloyma P, Terekegne A et al. (2012). Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. Genes Genomes Genet 2: 1427-1436.
34. Zhang YM, Xu S (2005). A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity 95: 96-104.
35. Zhao Y, Gowda M, Liu W, Wurschum T, Maurer HP, Longin FH et al. (2012). Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124: 769-776.