Accelerating the domestication of trees using genomic selection: Accuracy of prediction models across ages and environments

New Phytologist

Volumn 193, Issue 3, 2012, Pages 617-624

  Resende, M F R ^a   Munoz P ^a   Acosta, J J ^a   Peter, G F ^a   Davis, J M ^a   Grattapaglia, D ^b,c   Resende, M D V ^d,e   Kirst, M ^a  

^a UNIVERSITY OF FLORIDA   (United States)

^b EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA   (Brazil)

^c UNIVERSIDADE CATÓLICA DE BRASÍLIA   (Brazil)

^d EMBRAPA CERRADOS   (Brazil)

^e FEDERAL UNIVERSITY OF VIÇOSA   (Brazil)

Author keywords

Genomic selection; Genotype by environment interaction; Model estimation; Pinus taeda; Plant breeding

Indexed keywords

ACCURACY ASSESSMENT; AGE; CONIFEROUS TREE; DOMESTICATION; FEASIBILITY STUDY; GENETIC MARKER; GENOMICS; NUMERICAL MODEL; PHENOTYPE; POLYMORPHISM; PREDICTION; REGRESSION ANALYSIS; REPRODUCTIVE CYCLE; SELECTION;

AGRICULTURE; ARTICLE; BIOLOGICAL MODEL; ENVIRONMENT; GENETIC SELECTION; GENETICS; GENOMICS; GROWTH, DEVELOPMENT AND AGING; HISTOLOGY; INHERITANCE; METHODOLOGY; PHENOTYPE; PINE; QUANTITATIVE TRAIT; REPRODUCIBILITY; STATISTICAL MODEL; TIME; TREE;

AGRICULTURE; ENVIRONMENT; GENOMICS; INHERITANCE PATTERNS; LINEAR MODELS; MODELS, GENETIC; PHENOTYPE; PINUS; QUANTITATIVE TRAIT, HERITABLE; REPRODUCIBILITY OF RESULTS; SELECTION, GENETIC; TIME FACTORS; TREES;

PINUS TAEDA;

EID: 84855802587     PISSN: 0028646X     EISSN: 14698137     Source Type: Journal    
DOI: 10.1111/j.1469-8137.2011.03895.x     Document Type: Article

References (44)

1. Baltunis BS, Huber DA, White TL, Goldfarb B, Stelzer HE. 2005. Genetic effects of rooting loblolly pine stem cuttings from a partial diallel mating design. Canadian Journal of Forest Research 35: 1098-1108.
2. Baltunis BS, Huber DA, White TL, Goldfarb B, Stelzer HE. 2007. Genetic analysis of early field growth of loblolly pine clones and seedlings from the same full-sib families. Canadian Journal of Forest Research 37: 195-205.
3. Bernardo R, Yu J. 2007. Prospects for genomewide selection for quantitative traits in maize. Crop Science 47: 1082-1090.
4. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC et al. 2009. The genetic architecture of maize flowering time. Science 325: 714-718.
5. Daetwyler HD, Hickey JM, Henshall JM, Dominik S, Gredler B, van der Werf JHJ, Hayes BJ. 2010. Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population. Animal Production Science 50: 1004-1010.
6. Dekkers JCM. 2004. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of Animal Science 82(E-Suppl): E313-E328.
7. Eckert AJ, van Heerwaarden J, Wegrzyn JL, Nelson CD, Ross-Ibarra J, Gonzalez-Martinez SC, Neale DB. 2010. Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae). Genetics 185: 969-982.
8. Emebiri LC, Devey ME, Matheson AC, Slee MU. 1998. Age-related changes in the expression of QTLs for growth in radiata pine seedlings. Theoretical and Applied Genetics 97: 1053-1061.
9. FAO. 2002. World agriculture: towards 2015/2030. Summary Report. Rome, Italy: Food and Agriculture Organization of the United Nations.
10. Garrick DJ, Taylor JF, Fernando RL. 2009. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genetics Selection Evolution 41: 55.
11. Gianola D, Campos G, Hill WG, Manfredi E, Fernando R. 2009. Additive genetic variability and the Bayesian alphabet. Genetics 183: 347-363.
12. Gilmour AR, Cullis BR, Harding SA, Thompson R. 2006. ASRemlUpdate: what's new in Release 2.00. Hemel Hempstead, UK: VSN International Ltd.
13. Gonzalez-Martinez SC, Huber D, Ersoz E, Davis JM, Neale DB. 2008. Association genetics in Pinus taeda L. II. Carbon isotope discrimination. Heredity 101: 19-26.
14. Gonzalez-Martinez SC, Wheeler NC, Ersoz E, Nelson CD, Neale DB. 2007. Association genetics in Pinus taeda L. I. Wood property traits. Genetics 175: 399-409.
15. Grattapaglia D, Kirst M. 2008. Eucalyptus applied genomics: from gene sequences to breeding tools. New Phytologist 179: 911-929.
16. Grattapaglia D, Resende M. 2010. Genomic selection in forest tree breeding. Tree Genetics and Genomes 7: 241-255.
17. Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME. 2009. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41: 51.
18. Hayes B, Goddard M. 2010. Genome-wide association and genomic selection in animal breeding. Genome 53: 876-883.
19. Heffner EL, Sorrells ME, Jannink JL. 2009. Genomic selection for crop improvement. Crop Science 49: 1-12.
20. Humblot P, Le Bourhis D, Fritz S, Colleau JJ, Gonzalez C, Guyader Joly C, Malafosse A, Heyman Y, Amigues Y, Tissier M et al. 2010. Reproductive technologies and genomic selection in cattle. Veterinary Medicine International 2010: 192787.
21. IPCC. 2007. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, eds. Climate change 2007: the physical science basis. Contribution of Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK & New York, NY, USA: Cambridge University Press.
22. Iwata H, Hayashi T, Tsumura Y. 2011. Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica. Tree Genetics and Genomes 7: 1-12.
23. Kaya Z, Sewell MM, Neale DB. 1999. Identification of quantitative trait loci influencing annual height- and diameter-increment growth in loblolly pine (Pinus taeda L.). Theoretical and Applied Genetics 98: 586-592.
24. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, Durrant C, Mott R. 2009. A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genetics 5: e1000551.
25. Lande R, Thompson R. 1990. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124: 743-756.
26. Legarra A, Robert-Granie C, Manfredi E, Elsen JM. 2008. Performance of genomic selection in mice. Genetics 180: 611-618.
27. Lerceteau E, Szmidt AE, Andersson B. 2001. Detection of quantitative trait loci in Pinus sylvestris L. across years. Euphytica 121: 117-122.
28. Long AD, Langley CH. 1999. The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Research 9: 720-731.
29. Meuwissen TH, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819-1829.
30. Muir WM. 2007. Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics 124: 342-355.
31. Paterson AH, Saranga Y, Menz M, Jiang CX, Wright RJ. 2003. QTL analysis of genotype×environment interactions affecting cotton fiber quality. Theoretical and Applied Genetics 106: 384-396.
32. Paterson AH, Tanksley SD, Sorrells ME. 1991. DNA markers in plant improvement. Advances in Agronomy 46: 39-90.
33. Resende MDV, Lopes PS, Silva RL, Pires IL. 2008. Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesquisa Florestal Brasileira 56: 63-77.
34. Resende MDV, Resende MFRJ, Aguiar AM, Abad JIM, Missiaggia AA, Sansaloni CP, Petroli CD, Grattapaglia D. 2010. Computação da seleção genômica ampla (GWS). Documentos EMBRAPA 210 - ISSN 1679-2599. Colombo, Brazil: EMBRAPA Florestas.
35. Strauss SH, Lande R, Namkoong G. 1992. Limitations of molecular-marker-aided selection in forest tree breeding. Canadian Journal of Forest Research 22: 1050-1061.
36. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES. 2011. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nature Genetics 43: 159-162.
37. Usai MG, Goddard ME, Hayes BJ. 2009. Lasso with cross-validation for genomic selection. Genetics Research 91: 427-436.
38. Verbyla KL, Calus MPL, Mulder HA, de Haas Y, Veerkamp RF. 2010. Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information. Journal of Dairy Science 93: 2757-2764.
39. Visscher PM. 2008. Sizing up human height variation. Nature Genetics 40: 489-490.
40. Wear DN, Carter DR, Prestemon J. 2007. The U.S. South's timber sector in 2005: a prospective analysis of recent change. Asheville, NC, USA: USDA Forest Service, Southern Research Station. General Technical Report No. SRS-99.
41. White TL, Adams WT, Neale DB. 2007. Forest genetics. Wallingford, UK and Cambridge, MA, USA: CABI Publishing.
42. White TL, Carson M. 2004. Breeding program of conifers. In: Walter C, Carson M, eds. Plantation forest biotechnology for the 21st century. Kerala, India: Research Signpost, 61-85.
43. Williams ER, Matheson AC, Harwood CE. 2002. Experimental design and analysis for tree improvement, 2nd edn. Collingwood, Vic., Australia: CSIRO Publishing.
44. Yamada Y. 1962. Genotype by environment interaction and genetic correlation of the same trait under different environments. Japan Journal of Genetics 37: 498-509.