Genome-enabled prediction of genetic values using radial basis function neural networks

Theoretical and Applied Genetics

Volumn 125, Issue 4, 2012, Pages 759-771

  Gonzalez Camacho J M ^a   de los Campos, G ^b   Perez P ^a   Gianola, D ^c   Cairns, J E ^d   Mahuku, G ^d   Babu, R ^d   Crossa, J ^d  

^a Colegio de Postgraduados ^*  (Mexico)

^b UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

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

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; FORECASTING; FUNCTIONS; GENES; HEAT CONDUCTION; IMAGE SEGMENTATION; REGRESSION ANALYSIS;

NON-LINEAR REGRESSION; NON-PARAMETRIC REGRESSION; PREDICTION ACCURACY; PREDICTIVE ACCURACY; QUANTITATIVE TRAITS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; REPRODUCING KERNEL HILBERT SPACES;

RADIAL BASIS FUNCTION NETWORKS;

ANIMALIA; ZEA MAYS;

EID: 84866328184     PISSN: 00405752     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00122-012-1868-9     Document Type: Article

References (38)

1. Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, Minneapolis.
2. Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2: 321-355.
3. Chen S, Cowan CF, Grant PM (1991) Orthogonal least squares learning algorithms for radial basis function networks. IEEE Trans Neural Netw 2(2): 302-309.
4. Crossa J, de los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Dreisigacker S, Yan J, Arief V, Banziger M, Braun H-J (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186: 713-724.
5. Crossa J, Pérez P, de los Campos G, Mahuku G, Dreisigacker S, Magorokosho C (2011) Genomic selection and prediction in plant breeding. J Crop Improv 25(3): 239-261.
6. de los Campos G, Perez P (2010) BLR: Bayesian Linear Regression R package version 1. 2, http://www. r-project. org/.
7. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009a) Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182: 375-385.
8. de los Campos G, Gianola D, Rosa GJM (2009b) Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation. J Anim Sci 87: 1883-1887.
9. de los Campos G, Gianola D, Rosa GJM, Weigel KA, Crossa J (2010) Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet Res 92: 295-308.
10. Dudley JW (2008) Epistatic interactions in crosses of Illinois high oil × Illinois low oil and of Illinois high protein × Illinois low protein. Crop Sci 48: 59-68.
11. Dudley JW, Johnson GR (2010) Epistatic models improve between years prediction and prediction of testcross performance in corn. Crop Sci 50: 763-769.
12. Gianola D, van Kaam JBCHM (2008) Reproducing kernel Hilbert space regression methods for genomic-assisted prediction of quantitative traits. Genetics 178: 2289-2303.
13. Gianola D, Fernando R, Stella A (2006) Genomic-assisted prediction of genetic values with semiparametric procedures. Genetics 173: 1761-1776.
14. Gianola D, Okut H, Weigel KA, Rosa GJM (2011) Predicting complex quantitative traits with neural networks: a case study with Jersey cows and wheat. BMC Genet 12: 87. doi: 101186/1471-2156-12-87.
15. Gonzalez-Recio O, Gianola D, Long N, Wiegel K, Rosa GJM, Avendaño S (2008) Non parametric methods for incorporating genomic information into genetic evaluation: an application to mortality in broilers. Genetics 178: 2305-2313.
16. Hallauer AR, Miranda JB (1981) Quantitative genetics in maize breeding. Iowa State University Press, Ames.
17. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer, New York.
18. 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.
19. Haykin S (1994) Neural networks: a comprehensive foundation. MacMillan, New York, p 696.
20. Hill WG, Goddard ME, Visscher PM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4(2): e1000008. doi: 10. 1371/journal. pgen. 1000008.
21. Holland JB (2001) Epistasis and plant breeding. Plant Breed Rev 21: 27-92.
22. Holland JB (2006) Theoretical and biological foundations of plant breeding. In: Lamkey KR, Lee M (eds) Plant breeding: the Arnel R Hallauer International Symposium. Blackwell Publishing, Ames.
23. Jayawardena AW, Fernando DAK (1998) Use of radial basis function type artificial neural networks for runoff simulation. Comput-Aided Civ Inf Eng 13: 91-99.
24. Kecman V (2001) Learning and soft computing: support vector machines, neural networks and fuzzy logic models. MIT Press Cambridge, London, p 541.
25. Long N, Gianola D, Rosa GJM, Weigel KA, Kranis A, Gonzalez-Recio O (2010) Radial basis function regression methods for predicting quantitative traits using SNP markers. Genet Res 92: 209-225.
26. Long N, Gianola D, Rosa GJM, Weigel KA (2011) Marker-assisted prediction of non-additive genetic values. Genetica. doi: 10. 1007/s10709-011-9588-7.
27. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic values using genome-wide dense marker maps. Genetics 157: 1819-1829.
28. Park T, Casella G (2008) The Bayesian LASSO. J Am Stat Assoc 103: 681-686.
29. Park J, Sandberg IW (1991) Universal approximation using radial basis function networks. Neural Comput 3(2): 246-259.
30. Pérez P, de los Campos G, Crossa J, Gianola D (2010) Genomic-enabled prediction based on molecular markers and pedigree using the BLR package in R. Plant Genome 3(2): 106-116.
31. Poggio T, Girosi F (1990) Networks for approximation and learning. Proc IEEE 78: 1481-1497.
32. Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric regression. Cambridge University Press, Cambridge.
33. Takasaki S, Kawamura Y (2007) Using adial basis function networks and significance testing to select effective siRNA sequences. Comput Stat Data Anal 51: 6476-6487.
34. VanRaden PM (2008) Efficient methods to compute genomic prediction. J Dairy Sci 91: 4414-4423.
35. Wahba G (1978) Improper priors, spline smoothing and the problem of guarding against model errors in regression. J Roy Stat Soc B 40: 364-372.
36. Wahba G (1990) Spline models for observational data. Society for Industrial and applied Mathematics, Philadelphia.
37. Zhang YM, Xu S (2005) A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity 95: 96-104.
38. Zheng S, Li Z, Wang H (2011) A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams. Expert Syst Appl 38: 11837-11842.