Application of support vector regression to genome-assisted prediction of quantitative traits

Theoretical and Applied Genetics

Volumn 123, Issue 7, 2011, Pages 1065-1074

  Long, Nanye ^a   Gianola, Daniel ^a,b   Rosa, Guilherme J M ^a,b   Weigel, Kent A ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DAIRY CATTLES; DISEASE RISKS; GAUSSIAN RADIAL BASIS FUNCTIONS; GENOME-WIDE ASSOCIATION; GRAIN YIELD; KERNEL FUNCTION; LEAST SQUARE; LINEAR COMBINATIONS; LINEAR KERNEL; LINEAR REGRESSION MODELS; MEAN SQUARED ERROR; MILK YIELD; MOLECULAR MARKER; NON-LINEAR RELATIONSHIPS; NONLINEAR DEPENDENCIES; OPTIMAL CHOICE; PREDICTIVE CORRELATION; PREDICTIVE PERFORMANCE; QUANTITATIVE TRAITS; RBF KERNELS; SUPPORT VECTOR REGRESSIONS;

GENES; GRAIN (AGRICULTURAL PRODUCT); LINEAR REGRESSION; RADIAL BASIS FUNCTION NETWORKS;

FORECASTING;

ALGORITHM; ALLELE; ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGY; CATTLE; GENETIC ASSOCIATION; GENETICS; GENOMICS; GENOTYPE; HUMAN; METHODOLOGY; NORMAL DISTRIBUTION; PHENOTYPE; PREDICTIVE VALUE; REGRESSION ANALYSIS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; WHEAT;

ALGORITHMS; ALLELES; ANIMALS; BAYES THEOREM; CATTLE; COMPUTATIONAL BIOLOGY; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; GENOTYPE; HUMANS; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PHENOTYPE; PREDICTIVE VALUE OF TESTS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; TRITICUM;

BOS; TRITICUM AESTIVUM;

EID: 80855131458     PISSN: 00405752     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00122-011-1648-y     Document Type: Article

References (37)

1. Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Ratsch G (2008) Support vector machines and kernels for computational biology. PLoS Comput Biol 4(10): e1000173.
2. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York.
3. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2: 121-167.
4. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
5. Cherkassky V, Ma Y (2004a) Comparison of loss functions for linear regression. In: Proceedings of the International Joint Conference on Neural Network.
6. Cherkassky V, Ma Y (2004b) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1): 113-126.
7. Cherkassky VS, Mulier F (2007) Learning from data: concepts, theory, and methods, 2nd edn. Wiley, Hoboken.
8. Coen T, Saeys W, Ramon H, Baerdemaeker JD (2006) Optimizing the tuning parameters of least squares support vector machines regression for NIR spectra. J Chemometr 20: 184-192.
9. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New York.
10. Crossa J, Burgueño J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, Singh RP, Trethowan R, Warburton M, Franco J, Reynolds M, Crouch JH, Ortiz R (2007) Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177(3): 1889-1913.
11. Crossa J, de los Campos G, Perez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh R, 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.
12. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel KA, Cotes J (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182(1): 375-385.
13. de los Campos G, Gianola D, Allison DB (2010) Predicting genetic predisposition in humans: the promise of whole-genome markers. Nat Rev Genet 11: 880-886.
14. Gianola D, van Kaam J (2008) Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178(4): 2289-2303.
15. Gianola D, Fernando R, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173(3): 1761-1776.
16. González-Recio O, Gianola D, Rosa G, Weigel K, Kranis A (2009) Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens. Genet Sel Evol 41(1): 3.
17. Goodman M, Stuber C (1983) Races of maize: VI. Isozyme variation among races of maize in Bolivia. Maydica 28: 169-187.
18. Long N, Gianola D, Rosa GJM, Weigel KA, Kranis A, González-Recio O (2010) Radial basis function regression methods for predicting quantitative traits using SNP markers. Genet Res 92(3): 209-225.
19. Maccaferri M, Sanguineti MC, Corneti S, Ortega JLA, Salem MB, Bort J, DeAmbrogio E, del Moral LFG, Demontis A, El-Ahmed A, Maalouf F, Machlab H, Martos V, Moragues M, Motawaj J, Nachit M, Nserallah N, Ouabbou H, Royo C, Slama A, Tuberosa R (2008) Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability. Genetics 178(1): 489-511.
20. Maenhout S, Baets BD, Haesaert G, Bockstaele EV (2007) Support vector machine regression for the prediction of maize hybrid performance. Theor Appl Genet 115: 1003-1013.
21. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4): 1819-1829.
22. Moser G, Tier B, Crump RE, Khatkar MS, Raadsma HW (2009) A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet Sel Evol 41(1): 56.
23. Nocedal J, Wright SJ (1999) Numerical optimization. Springer, New York.
24. Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103: 681-686.
25. Pelckmans K, Suykens JAK, Gestel TV, Brabanter JD, Lukas L, Hamers B, Moor BD, Vandewalle J (2007) LS-SVMlab: a MATLAB/C toolbox for least squares support vector machines. Software available at http://www.esat.kuleuven.be/sista/lssvmlab/.
26. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, New York.
27. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3): 199-222.
28. Suykens J, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Leaset squares support vector machines. World Scientific, Singapore.
29. Vapnik V (1995) The nature of statistical learning theory, 2nd edn. Springer, New York.
30. Vázquez AI, Rosa GJM, Weigel KA, de los Campos G, Gianola D, Allison DB (2010) Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins. J Dairy Sci 93: 5942-5949.
31. Visscher PM (2008) Sizing up human height variation. Nat Genet 40(5): 489-490.
32. Watkins C (2000) Dynamic alignment kernels. In: Smola AJ, Bartlett PL, Schölkopf B, Schuurmans D (eds) Advances in large margin classifiers. MIT Press, Cambridge.
33. Wei Z, Wang K, Qu H-Q, Zhang H, Bradfield J, Kim C, Frackleton E, Hou C, Glessner JT, Chiavacci R, Stanley C, Monos D, Grant SFA, Polychronakos C, Hakonarson H (2009) From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes. PLoS Genet 5(10): e1000678.
34. Weigel KA, de los Campos G, González-Recio O, Naya H, Wu XL, Long N, Rosa GJM, Gianola D (2009) Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. J Dairy Sci 92(10): 5248-5257.
35. Wright S (1978) Variability within and among natural populations. In: Evolution and the genetics of populations.
36. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42(7): 565-569.
37. Yi N, Xu S (2008) Bayesian LASSO for quantitative trait loci mapping. Genetics 179(2): 1045-1055.