Unraveling additive from nonadditive effects using genomic relationship matrices

Genetics

Volumn 198, Issue 4, 2014, Pages 1759-1768

  Muñoz, Patricio R ^a   Resende, Marcio F R ^a   Gezan, Salvador A ^a   Resende, Marcos Deon Vilela ^b,c   de los Campos, Gustavo ^d   Kirst, Matias ^a   Huber, Dudley ^a   Peter, Gary F ^a  

^a UNIVERSITY OF FLORIDA   (United States)

^b EMBRAPA CERRADOS   (Brazil)

^c FEDERAL UNIVERSITY OF VIÇOSA   (Brazil)

^d UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MOLECULAR MARKER; GENETIC MARKER;

ADDITIVE BY ADDITIVE MODEL; ADDITIVE BY DOMINANCE MODEL; ARTICLE; BREEDING; DOMINANCE BY DOMINANCE MODEL; EPISTASIS; GENETIC ASSOCIATION; GENETIC MARKER; GENETIC MODEL; GENETIC REGULATION; GENETIC SELECTION; GENETIC VARIABILITY; HEREDITY; HERITABILITY; LOBLOLLY PINE; MOLECULAR PHYLOGENY; NONHUMAN; PEDIGREE; PLANT BREEDING; PLANT GENETICS; PLANT GENOME; PLANT HEIGHT; POPULATION GENETIC STRUCTURE; POPULATION GENETICS; QUANTITATIVE TRAIT; VARIANCE; ALGORITHM; BIOLOGICAL MODEL; CROSS BREEDING; GENETICS; GENOMICS; INHERITANCE; PROCEDURES;

ALGORITHMS; BREEDING; CROSSES, GENETIC; GENETIC MARKERS; GENOMICS; INHERITANCE PATTERNS; MODELS, GENETIC; PEDIGREE; PINUS TAEDA;

EID: 84915750520     PISSN: 00166731     EISSN: 19432631     Source Type: Journal    
DOI: 10.1534/genetics.114.171322     Document Type: Article

References (53)

1. Akaike, H., 1974 New look at statistical-model identification. Transactions on Automatic Control. AC19: 716-723.
2. Araujo, J. A., N. M. G. Borralho, and G. Dehon, 2012 The importance and type of non-additive genetic effects for growth in Eucalyptus globulus. Tree Genet. Genomes 8: 327-337.
3. Baltunis, B. S., D. A. Huber, T. L. White, B. Goldfarb, and H. E. Stelzer, 2007 Genetic gain from selection for rooting ability and early growth in vegetatively propagated clones of loblolly pine. Tree Genet. Genomes 3: 227-238.
4. Baltunis, B. S., T. A. Martin, D. A. Huber, and J. M. Davis, 2008 Inheritance of foliar stable carbon isotope discrimination and third-year height in Pinus taeda clones on contrasting sites in Florida and Georgia. Tree Genet. Genomes 4: 797-807.
5. Baltunis, B. S., H. X. Wu, H. S. Dungey, T. J. T. Mullin, and J. T. Brawner, 2009 Comparisons of genetic parameters and clonal value predictions from clonal trials and seedling base population trials of radiata pine. Tree Genet. Genomes 5: 269-278.
6. Cockerham, C. C., 1954 An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39: 859-882.
7. Costa E Silva, J., N. M. G. Borralho, and B. M. Potts, 2004 Additive and non-additive genetic parameters from clonally replicated and seedling progenies of Eucalyptus globulus. Theor. Appl. Genet. 108: 1113-1119.
8. Costa E Silva, J., N. M. G. Borralho, J. A. Araujo, R. E. Vaillancourt, and B. M. Potts, 2009 Genetic parameters for growth, wood density and pulp yield in Eucalyptus globulus. Tree Genet. Genomes 5: 291-305.
9. Crossa, J., G. D. L. Campos, P. Perez, D. Gianola, J. Burgueno et al., 2010 Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186: 713-724.
10. de los Campos, G., H. Naya, D. Gianola, J. Crossa, A. Legarra et al., 2009 Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182: 375-385.
11. de los Campos, G., J. M. Hickey, R. Pong-Wong, H. D. Daetwyler, and M. P. L. Calus, 2012 Whole genome regression and prediction methods applied to plant and animal breeding. Genetics 193(2): 327-345.
12. Eckert, A. J., J. van Heerwaarden, J. L. Wegrzyn, C. D. Nelson, J. Ross-Ibarra et al., 2010 Patterns of population structure and environmental associations to aridity across the range of Loblolly Pine (Pinus taeda L., Pinaceae). Genetics 185: 969-982.
13. Falconer, D. S., and T. F. C. Mackay, 1996 Introduction to Quantitative Genetics, Ed. 4. Addison Wesley Longman, Essex, England.
14. Foster, G. S., and D. V. Shaw, 1988 Using clonal replicates to explore genetic-variation in a perennial plant-species. Theor. Appl. Genet. 76: 788-794.
15. Gianola, D., and G. de los Campos, 2008 Inferring genetic values for quantitative traits non-parametrically. Genet. Res. 90: 525- 540.
16. Gilmour, A. R., R. Thompson, and B. R. Cullis, 1995 Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51: 1440-1450.
17. Gilmour, A. R., B. J. Gogel, B. R. Cullis, and R. Thompson, 2009 ASReml User Guide Release 3.0. VSN International, Hemel Hempstead, UK.
18. Habier, D., J. Tetens, F.-R. Seefried, P. Lichtner, and G. Thaller, 2010 The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet. Sel. Evol. 42: 5.
19. Hayes, B. J., P. M. Visscher, and M. E. Goddard, 2009 Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. 91: 47-60.
20. Henderson, C. R., 1985 Best linear unbiased prediction of nonadditive genetic merits in noninbred populations. J. Anim. Sci. 60: 111-117.
21. Heslot, N., H.-P. Yang, M. E. Sorrells, and J.-L. Jannink, 2012 Genomic selection in plant breeding: a comparison of models. Crop Sci. 52: 146-160.
22. Hill, W., 2010 Understanding and using quantitative genetic variation. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365: 73-85.
23. Hill, W. G., and B. S. Weir, 2011 Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genet. Res. 93: 47-64.
24. Hill, W., M. Goddard, and P. Visscher, 2008 Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4(2): e1000008.
25. Isik, F., B. L. Li, and J. Frampton, 2003 Estimates of additive, dominance and epistatic genetic variances from a clonally replicated test of loblolly pine. For. Sci. 49: 77-88.
26. Isik, F., B. Goldfarb, A. LeBude, B. L. Li, and S. McKeand, 2005 Predicted genetic gains and testing efficiency from two loblolly pine clonal trials. Can. J. Forest Research 35: 1754-1766.
27. Kempthorne, O., 1954 The correlation between relatives in a random mating population. Proc. R. Soc. Lond. B Biol. Sci. 143: 103-113.
28. Kohavi, R., 1995 The power of decision tables. Machine Learning: ECML-95. pp. 174-189.
29. Lee, S. H., M. E. Goddard, P. M. Visscher, and J. H. J. van der Werf, 2010 Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits. Genet. Sel. Evol. 42: 22.
30. Lu, P. X., D. A. Huber, and T. L. White, 1999 Potential biases of incomplete linear models in heritability estimation and breeding value prediction. Can. J. Forest Research 29: 724-736.
31. Lynch, M., and B. Walsh, 1998 Genetics and Analysis of Quantitative Traits. Sinauer Associates, Sunderland, MA.
32. Mrode, R. A., 2005 Linear Models for the Prediction of Animal Breeding Values. CABI Publishing Series, Cambridge, UK.
33. Mullin, T. J., E. K. Morgenstern, Y. S. Park, and D. P. Fowler, 1992 Genetic-parameters from a clonally replicated test of black spruce (Picea mariana). Can. J. Forest Research 22: 24-36.
34. Muñoz, P., M. F. Resende, D. A. Huber, T. Quezada, M. D. Resende et al., 2013 Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. Crop Sci. 54: 1115-1123.
35. Pante, M. J., B. Gjerde, I. McMillan, and I. Misztal, 2002 Estimation of additive and dominance genetic variances for body weight at harvest in rainbow trout Oncorhynchus mykiss Oncorhynchus mykiss. Aquaculture 204: 383-392.
36. Palucci, V., L. R. Schaeffer, F. Miglior, and V. Osborne, 2007 Nonadditive genetic effects for fertility traits in Canadian Holstein cattle. Genet. Sel. Evol. 39: 181-193.
37. Powell, J. E., P. M. Visscher, and M. E. Goddard, 2010 Reconciling the analysis of IBD and IBS in complex trait studies. Nat. Rev. Genet. 11: 800-805.
38. Resende, Jr., M. F. R., P. Muñoz, J. J. Acosta, G. F. Peter, J. M. Davis et al., 2012a Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol. 193: 617-624.
39. Resende, Jr., M. F., P. Muñoz, M. D. Resende, D. J. Garrick, R. L. Fernando et al., 2012b Accuracy of genomic selection methods in a standard data set of Loblolly Pine (Pinus taeda L.). Genetics 190: 1503-1510.
40. Rodriguez-Almeida, F. A., L. D. Van Vleck, R. L. Wilham, and S. L. Northcutt, 1995 Estimation of non-additive genetic variances in three synthetic lines of beef cattle using an animal model. J. Anim. Sci. 73: 1002-1011.
41. Simeone, R., I. Miztal, I. Aguilar, and A. Legarra, 2011 Evaluation of the utility of the diagonal elements of the genomic relationship matrix as a diagnostic tool to detect mislabelled genotyped animals in a broiler chicken population. J. Anim. Breed. Genet. 12895): 386-393.
42. Su, G., O. F. Christensen, T. Ostersen, M. Henryon, and M. S. Lund, 2012 Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS ONE 7(9): e45293.
43. Vanderwerf, J. H. J., and W. Deboer, 1989 Influence of nonadditive effects on estimation of genetic-parameters in dairy-cattle. J. Dairy Sci. 72: 2606-2614.
44. VanRaden, P. M., 2008 Efficient methods to compute genomic predictions. J. Dairy Sci. 91: 4414-4423.
45. Veerkamp, R. F., H. A. Mulder, R. Thompson, and M. P. L. Calus, 2011 Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped. J. Dairy Sci. 94: 4189-4197.
46. Visscher, P. M., 2009 Whole genome approaches to quantitative genetics. Genetica 136: 351-358.
47. Vitezica, Z.G., L. Varona, and A. Legarra. 2013 On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics 195: 1223-1230.
48. Wei, M., and J. H. J. van der Werf, 1993 Animal model estimation of additive and dominance variances in egg production traits of poultry. J. Anim. Sci. 71: 57-65.
49. Williams, E. R., A. C. Matheson, and C. E. Harwood, 2002 Experimental Design and Analysis for Tree Improvement, Ed. 2. Commonwealth Scientific and Industrial Research Organization, Melbourne.
50. Winkelman, A. M., and R. G. Peterson, 1994 Genetic parameters heritabilities, dominance ratios, and genetic correlations for body weight and length of chinook salmon after 9 and 22 months of saltwater rearing. Aquaculture 125: 30-36.
51. Wu, R. L., 1996 Detecting epistatic genetic variance with a clonally replicated design: models for low- vs. high-order non allelic interaction. Theor. Appl. Genet. 93: 102-109.
52. Yang, J., B. Benyamin, B. P. McEvoy, S. Gordon, A. K. Henders et al., 2010 Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42: 565-569.
53. Zuk, O., E. Hechter, S. R. Sunyaev, and E. S. Lander, 2012 The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl. Acad. Sci. USA 109: 1193-1198.