Marker-assisted prediction of non-additive genetic values

Genetica

Volumn 139, Issue 7, 2011, Pages 843-854

  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

Non additive gene action; Prediction; RBF regression

Indexed keywords

ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; DOMINANT GENE; EPISTASIS; FEMALE; GENETIC MARKER; GENETIC SELECTION; GENETICS; GENOTYPE; MALE; PHENOTYPE; PREDICTIVE VALUE; QUANTITATIVE TRAIT; REGRESSION ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL;

ANIMALS; BAYES THEOREM; COMPUTER SIMULATION; EPISTASIS, GENETIC; FEMALE; GENES, DOMINANT; GENETIC MARKERS; GENOTYPE; LINEAR MODELS; MALE; MODELS, GENETIC; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PREDICTIVE VALUE OF TESTS; QUANTITATIVE TRAIT, HERITABLE; REGRESSION ANALYSIS; SELECTION, GENETIC;

EID: 79960833949     PISSN: 00166707     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10709-011-9588-7     Document Type: Article

References (42)

1. Bogdan M, Ghosh JK, Doerge RW (2004) Modifying the Schwarz Bayesian information criterion to locate multiple interacting quantitative trait loci. Genet Mol Res 167: 989-999.
2. Broomhead D, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2: 321-355.
3. Bulmer MG (1971) Effect of selection on genetic variance. Am Nat 105(943): 201-211.
4. Carlborg O, Jacobsson L, Ahgren P, Siegel P, Andersson L (2006) Epistasis and the release of genetic variation during long-term selection. Nat Genet 38(4): 418-420.
5. Cockerham CC (1954) An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39(6): 859-882.
6. Crow JF (2010) On epistasis: why it is unimportant in polygenic directional selection. Philos Trans R Soc B Biol Sci 365(1544): 1241-1244.
7. Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3: e3395.
8. de los Campos G, Gianola D, Rosa GJM (2009) Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation. J Anim Sci 87(6): 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. de los Campos G, Gianola D, Rosa GJM, Weigel KA, Vazquez AI, Allison DB (2010b) Semi-parametric marker-enabled prediciton of genetic values using reproducing kernel Hilbert spaces regressions. In: 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany.
11. 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.
12. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Philos Trans R Soc Edinb 52: 399-433.
13. Gianola D, van Kaam J (2008) Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178(4): 2289-2303.
14. Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 4(177): 2389-2397.
15. Hallander J, Waldmann P (2007) The effect of non-additive genetic interactions on selection in multi-locus genetic models. Heredity 98: 349-359.
16. Hill WG (2010) Understanding and using quantitative genetic variation. Philos Trans R Soc B Biol Sci 365(1537): 73-85.
17. Hill WG, Goddard ME, Visscher PM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4(2): e1000008.
18. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K (2002) A comprehensive review of genetic association studies. Genet Med 4: 45-61.
19. Kroymann J, Mitchell-Olds T (2005) Epistasis and balanced polymorphism influencing complex trait variation. Nature 435(7038): 95-98.
20. Lee SH, van der Werf JHJ, Hayes BJ, Goddard ME, Visscher PM (2008) Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genet 4(10): e1000231.
21. Long N, Gianola D, Rosa GJM, Weigel KA, Avendano S (2007) Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet 124(6): 377-389.
22. 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.
23. Łukaszewicz M (2001) Non-additive genetic effects in animal selection. J Appl Genet 42(4): 467-478.
24. 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.
25. Meuwissen THE (2009) Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping. Genet Sel Evol 41: 35.
26. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4): 1819-1829.
27. Micchelli CA (1986) Interpolation of scattered data: distance matrices and conditionally positive definite functions. Constr Approx 2: 11-22.
28. Mitchell T (1997) Machine learning. McGraw Hill, Hightstown, NJ, USA.
29. Muir WM (2007) Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J Anim Breed Genet 124(6): 342-355.
30. Orr MJL (1995) Regularization in the selection of radial basis function centers. Neural Comput 7(3): 606-623.
31. Palucci V, Schaeffer LR, Miglior F, Osborne V (2007) Non-additive genetic effects for fertility traits in Canadian Holstein cattle. Genet Sel Evol 39(2): 181-193.
32. Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103: 681-686.
33. Poggio T, Girosi F (1989) A theory of networks for approximation and learning. A. I. Memo No. 1140, Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
34. Powell MJD (1987) Radial basis functions for multivariable interpolation: a review. In: Algorithms for approximation. Clarendon Press, New York, pp 143-167.
35. Schölkopf B, Burges C, Vapnik V (1995) Extracting support data for a given task. In: Proceedings, first international conference on knowledge discovery and data mining, Menlo Park, pp 252-257.
36. Sonesson AK, Meuwissen THE (2009) Testing strategies for genomic selection in aquaculture breeding programs. Genet Sel Evol 41: 37.
37. Tikka J (2009) Simultaneous input variable and basis function selection for RBF networks. Neurocomputing 72(10-12): 2649-2658.
38. Varona L, Misztal I (1999) Prediction of parental dominance combinations for planned matings, methodology, and simulation results. J Dairy Sci 82: 2186-2191.
39. Walsh B, Lynch M (2011) Evolution and selection of quantitative traits. online draft version: http://nitro. biosci. arizona. edu/zbook/NewVolume_2/newvol2. html#2A.
40. Yi N, Shriner D, Banerjee S, Mehta T, Pomp D, Yandell BS (2007) An efficient Bayesian model selection approach for interacting quantitative trait loci models with many effects. Genetics 176(3): 1865-1877.
41. Yi N, Xu S (2002) Mapping quantitative trait loci with epistatic effects. Genet Res 79: 185-198.
42. Yi N, Xu S (2008) Bayesian LASSO for quantitative trait loci mapping. Genet Mol Res 179(2): 1045-1055.