A comparison of partial least squares (PLS) and sparse PLS regressions in genomic selection in French dairy cattle

Journal of Dairy Science

Volumn 95, Issue 4, 2012, Pages 2120-2131

  Colombani, C ^a   Croiseau, P ^b   Fritz, S ^c   Guillaume, F ^d   Legarra, A ^a   Ducrocq, V ^b   Robert Granie C ^a  

^a CEMAGREF   (France)

^b UMR1313 GÉNÉTIQUE ANIMALE ET BIOLOGIE INTÉGRATIVE   (France)

^c UNCEIA   (France)

^d Institut de l'Elevage   (France)

Author keywords

French dairy cattle; Genomic selection; Partial least squares regression; Sparse partial least squares

Indexed keywords

ANIMAL; ARTICLE; BREEDING; CATTLE; CHEMISTRY; COMPARATIVE STUDY; DAIRYING; FEMALE; FERTILIZATION; FRANCE; GENETIC SELECTION; GENETICS; GENOTYPE; LACTATION; MALE; MILK; PEDIGREE; PHENOTYPE; PREGNANCY; QUANTITATIVE TRAIT; REGRESSION ANALYSIS; REPRODUCIBILITY;

ANIMALS; BREEDING; CATTLE; DAIRYING; FEMALE; FERTILIZATION; FRANCE; GENOTYPE; LACTATION; LEAST-SQUARES ANALYSIS; MALE; MILK; PEDIGREE; PHENOTYPE; PREGNANCY; QUANTITATIVE TRAIT, HERITABLE; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SELECTION, GENETIC;

ANIMALIA; BOS;

EID: 84858980689     PISSN: 00220302     EISSN: 15253198     Source Type: Journal    
DOI: 10.3168/jds.2011-4647     Document Type: Article

References (32)

1. Abdi H. Partial least squares regression and projection on latent structure regression (PLS regression). Comput. Stat. 2010, 2:97-106.
2. Boichard D., Manfredi E. Genetic analysis of conception rate in French Holstein cattle. Acta Agric. Scand. A Anim. Sci. 1994, 44:138-145.
3. Chun H., Keles S. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J. R. Stat. Soc. Series B Stat. Methodol. 2010, 72:3-25.
4. Cole J.B., VanRaden P.M., O'Connell J.R., Van Tassell C.P., Sonstegard T.S., Schnabel R.D., Taylor J.F., Wiggans G.R. Distribution and location of genetic effects for dairy traits. J. Dairy Sci. 2009, 92:2931-2946.
5. Colombani C., Croiseau P., Hozé C., Fritz S., Guillaume F., Boichard D., Legarra A., Ducrocq V., Robert-Granié C. Could genomic selection be efficient to detect QTL?. 15th QTLMAS Workshop, Rennes, France. BMC Proceedings 2011.
6. Coster A., Bastiaansen J.W.M., Calus M.P.L., van Arendonk J.A.M., Bovenhuis H. Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance. Genet. Sel. Evol. 2010, 42:9.
7. Druet T., Georges M. A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics 2010, 184:789-798.
8. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression. Ann. Stat. 2004, 32:407-451.
9. Fikse W.F., Banos G. Weighting factors of sire daughter information in international genetic evaluations. J. Dairy Sci. 2001, 84:1759-1767.
10. Grisart B., Farnir F., Karim L., Cambisano N., Kim J.J., Kvasz A., Mni M., Simon P., Frere J.M., Coppieters W., Georges M. Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. Proc. Natl. Acad. Sci. USA 2004, 101:2398-2403.
11. Harris B.L., Johnson D.L., Spelman R.J. Genomic selection in New Zealand and the implications for national genetic evaluation. Proc. 36th ICAR Biennial Session: Identification, Breeding, Production, Health and Recording of Farm Animals 2009, 325-330.
12. Hayes B.J., Bowman P.J., Chamberlain A.J., Goddard M.E. Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 2009, 92:433-443.
13. Henderson C.R. Selection index and expected genetic advance. Statistical genetics and plant breeding. Nat. Acad. Sci. Nat. Res. Counc. Pub. 1963, 983:141-163.
14. Lê Cao K.A., Gonzalez I., Dejean S. integrOmics: An R package to unravel relationships between two omics datasets. Bioinformatics 2009, 25:2855-2856.
15. Lê Cao K.A., Rossouw D., Robert-Granie C., Besse P. A sparse PLS for variable selection when integrating omics data. Stat. Appl. Genet. Mol. Biol. 2008, 7:35.
16. Long N., Gianola D., Rosa G.J.M., Weigel K.A. Dimension reduction and variable selection for genomic selection: Application to predicting milk yield in Holsteins. J. Anim. Breed. Genet. 2011, 128:247-257.
17. Lorber A., Wangen L.E., Kowalski B.R. A theoretical foundation for the PLS algorithm. J. Chemometr. 1987, 1:19-31.
18. Meuwissen T.H.E., Hayes B.J., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001, 157:1819-1829.
19. Mevik B.H., Cederkvist H.R. Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR). J. Chemometr. 2004, 18:422-429.
20. Moser G., Khatkar M., Hayes B., Raadsma H. Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genet. Sel. Evol. 2010, 42:37.
21. Moser G., Tier B., Crump R., Khatkar M., Raadsma H. A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet. Sel. Evol. 2009, 41:56.
22. Mrode R.A., Swanson G.J. Calculating cow and daughter yield deviations and partitioning of genetic evaluations under a random regression model. Livest. Prod. Sci. 2004, 86:253-260.
23. Shen H.P., Huang J.H.Z. Sparse principal component analysis via regularized low rank matrix approximation. J. Multivariate Anal. 2008, 99:1015-1034.
24. Solberg T.R., Sonesson A.K., Woolliams J.A., Meuwissen T.H.E. Reducing dimensionality for prediction of genome-wide breeding values. Genet. Sel. Evol. 2009, 41:29.
25. Steiger J.H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 1980, 87:245-251.
26. VanRaden P.M. Efficient methods to compute genomic predictions. J. Dairy Sci. 2008, 91:4414-4423.
27. VanRaden P.M., Van Tassell C.P., Wiggans G.R., Sonstegard T.S., Schnabel R.D., Taylor J.F., Schenkel F.S. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 2009, 92:16-24.
28. VanRaden P.M., Wiggans G.R. Derivation, calculation, and use of national animal-model information. J. Dairy Sci. 1991, 74:2737-2746.
29. VanSickle J. Analysing correlations between stream and watershed attributes. J. Am. Water Resour. Assoc. 2003, 39:717-726.
30. Wold H. Estimation of principal components and related models by iterative least squares. Multivariate Analysis 1966, 391-420. Academic Press, New York, NY. P.R. Krishnaiah (Ed.).
31. Wold S., Eriksson L., Trygg J., Kettaneh N. The PLS method-Partial least squares projections to latent structures and its application in industrial RDP (research, development, and production) 2004, Technical report. Umea University, Sweden.
32. Wold S., Sjostrom M., Eriksson L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58:109-130.