Genomic selection of juvenile height across a single-generational gap in Douglas-fir

. 2019 Jun ; 122 (6) : 848-863. [epub] 20190110

Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid30631145
Odkazy

PubMed 30631145
PubMed Central PMC6781123
DOI 10.1038/s41437-018-0172-0
PII: 10.1038/s41437-018-0172-0
Knihovny.cz E-zdroje

Here, we perform cross-generational GS analysis on coastal Douglas-fir (Pseudotsuga menziesii), reflecting trans-generational selective breeding application. A total of 1321 trees, representing 37 full-sib F1 families from 3 environments in British Columbia, Canada, were used as the training population for (1) EBVs (estimated breeding values) of juvenile height (HTJ) in the F1 generation predicting genomic EBVs of HTJ of 136 individuals in the F2 generation, (2) deregressed EBVs of F1 HTJ predicting deregressed genomic EBVs of F2 HTJ, (3) F1 mature height (HT35) predicting HTJ EBVs in F2, and (4) deregressed F1 HT35 predicting genomic deregressed HTJ EBVs in F2. Ridge regression best linear unbiased predictor (RR-BLUP), generalized ridge regression (GRR), and Bayes-B GS methods were used and compared to pedigree-based (ABLUP) predictions. GS accuracies for scenarios 1 (0.92, 0.91, and 0.91) and 3 (0.57, 0.56, and 0.58) were similar to their ABLUP counterparts (0.92 and 0.60, respectively) (using RR-BLUP, GRR, and Bayes-B). Results using deregressed values fell dramatically for both scenarios 2 and 4 which approached zero in many cases. Cross-generational GS validation of juvenile height in Douglas-fir produced predictive accuracies almost as high as that of ABLUP. Without capturing LD, GS cannot surpass the prediction of ABLUP. Here we tracked pedigree relatedness between training and validation sets. More markers or improved distribution of markers are required to capture LD in Douglas-fir. This is essential for accurate forward selection among siblings as markers that track pedigree are of little use for forward selection of individuals within controlled pollinated families.

Zobrazit více v PubMed

Atefi A, Shadparvar AA, Ghavi Hossein-Zadeh N. Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods. Acta Sci Anim Sci. 2016;38:447. doi: 10.4025/actascianimsci.v38i4.32023. DOI

Avendanõ S, Woolliams JAE, Villanueva B. Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding. Genetics Research. 2004;83:55–64. doi: 10.1017/S0016672303006566. PubMed DOI

Bartholomé J, Van Heerwaarden J, Isik F, Boury C, Vidal M, Plomion C, Bouffier L. Performance of genomic prediction within and across generations in maritime pine. BMC Genom. 2016;17:604. doi: 10.1186/s12864-016-2879-8. PubMed DOI PMC

Beaulieu J, Doerksen T, Clément S, MacKay J, Bousquet J. Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity. 2014;113:343–352. doi: 10.1038/hdy.2014.36. PubMed DOI PMC

Beaulieu J, Doerksen TK, MacKay J, Rainville A, Bousquet J. Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genom. 2014;15:1048. doi: 10.1186/1471-2164-15-1048. PubMed DOI PMC

Cappa EP, Stoehr MU, Xie CY, Yanchuk AD. Identification and joint modeling of competition effects and environmental heterogeneity in three Douglas-Fir (Pseudotsuga menziesii var. menziesii) trials. Tree Genet Genomes. 2016;12:102. doi: 10.1007/s11295-016-1061-4. DOI

Dean CA, Stonecypher RW. Early Selection of Douglas-Fir across South Central Coastal Oregon, USA. Silvae Genet. 2006;55:135–141. doi: 10.1515/sg-2006-0019. DOI

Dutkowski GW, Silva JCe, Gilmour AR, Lopez GA. Spatial analysis methods for forest genetic trials. Can J Res. 2002;32:2201–2214. doi: 10.1139/x02-111. DOI

El-Kassaby YA. Associations between Allozyme Genotypes and Quantitative Traits in Douglas-Fir [PSEUDOTSUGA MENZIESII (Mirb.) Franco] Genetics. 1982;101:103–115. PubMed PMC

El-Kassaby YA, Cappa EP, Liewlaksaneeyanawin C, Klápšte J, Lstiburek M. Breeding without breeding: is a complete pedigree necessary for efficient breeding? PLoS ONE. 2011;6:e25737. doi: 10.1371/journal.pone.0025737. PubMed DOI PMC

El-Kassaby YA, Lstibůrek M. Breeding without breeding. Genet Res. 2009;91:111. doi: 10.1017/S001667230900007X. PubMed DOI

Fuentes-Utrilla P, Goswami C, Cottrell JE, Pong-Wong R, Law A, A’Hara SW, Lee SJ, Woolliams JA. QTL analysis and genomic selection using RADseq derived markers in Sitka spruce: the potential utility of within family data. Tree Genet Genomes. 2017;13:33. doi: 10.1007/s11295-017-1118-z. DOI

Gamal El-Dien O, Ratcliffe B, Klápště J, Chen C, Porth I, El-Kassaby YA. Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genom. 2015;16:370. doi: 10.1186/s12864-015-1597-y. PubMed DOI PMC

Garrick DJ, Taylor JF, Fernando RL. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet Sel Evol. 2009;41:55. doi: 10.1186/1297-9686-41-55. PubMed DOI PMC

Gezan SA, Osorio LF, Verma S, Whitaker VM. An experimental validation of genomic selection in octoploid strawberry. Hortic Res. 2017;4:16070. doi: 10.1038/hortres.2016.70. PubMed DOI PMC

Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml Use Guide Release. 2009;3:0.

Grattapaglia D. Breeding forest trees by genomic selection: current progress and the way forward. In: Tuberosa R, Graner A, Frison E, editors. Genomics of plant genetic resources. Dordrecht: Springer Netherlands; 2014. pp. 651–682.

Grattapaglia D. Status and perspectives of genomic selection in forest tree breeding. In: Varshney RK, Roorkiwal M, Sorrells ME, editors. Genomic selection for crop improvement. Cham: Springer International Publishing; 2017. pp. 199–249.

Grattapaglia D, Resende MDV. Genomic selection in forest tree breeding. Tree Genet Genomes. 2011;7:241–255. doi: 10.1007/s11295-010-0328-4. DOI

Habier D, Fernando RL, Dekkers JCM. The impact of genetic relationship information on genome-assisted breeding values. Genetics. 2007;177:2389–2397. doi: 10.1534/genetics.107.081190. PubMed DOI PMC

Hallander, J (2009). Novel methods for improved tree breeding (Doctoral thesis, Swedish University of Agricultural Sciences, Umea). Retrieved from: Epsilon Open Archive. ISBN 978-91-86195-60-1

Hallander J, Waldmann P. Optimum contribution selection in large general tree breeding populations with an application to Scots pine. Theor Appl Genet. 2009;118(6):1133–1142. doi: 10.1007/s00122-009-0968-7. PubMed DOI

Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci. 2009;92:433–443. doi: 10.3168/jds.2008-1646. PubMed DOI

Heffner EL, Sorrells ME, Jannink JL. Genomic selection for crop improvement. Crop Sci. 2009;49:1. doi: 10.2135/cropsci2008.08.0512. DOI

Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME. Plant breeding with genomic selection: gain per unit time and cost. Crop Sci. 2010;50:1681. doi: 10.2135/cropsci2009.11.0662. DOI

Henderson CR. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics. 1976;32:69. doi: 10.2307/2529339. DOI

Howe GT, Yu J, Knaus B, Cronn R, Kolpak S, Dolan P, Lorenz WW, Dean JF. A SNP resource for Douglas-fir: de novo transcriptome assembly and SNP detection and validation. BMC Genom. 2013;14:137. doi: 10.1186/1471-2164-14-137. PubMed DOI PMC

Isik F, Bartholomé J, Farjat A, Chancerel E, Raffin A, Sanchez L, Plomion C, Bouffier L. Genomic selection in maritime pine. Plant Sci. 2016;242:108–119. doi: 10.1016/j.plantsci.2015.08.006. PubMed DOI

Isik F, Holland J, Maltecca C. Genetic data analysis for plant and animal breeding. Cham: Springer; 2017.

Ivanova NV, Fazekas AJ, Hebert PDN. Semi-automated, membrane-based protocol for DNA isolation from plants. Plant Mol Biol Rep. 2008;26:186–198. doi: 10.1007/s11105-008-0029-4. DOI

Jaramillo-Correa JP, Prunier J, Vázquez-Lobo A, Keller SR, Moreno-Letelier A (2015). Molecular signatures of adaptation and selection in forest trees. In: Advances in botanical research, vol. 74 (ed. Plomion, C., and Adam-Blondon, A.), Elsevier, pp 265–306

Lerceteau E, Szmidt AE, Andersson B. Detection of quantitative trait loci in Pinus sylvestris L. across years. Euphytica. 2001;121:117–122. doi: 10.1023/A:1012076825293. DOI

Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink J-L (2011). Genomic selection in plant breeding. Advances in agronomy, vol. 110 (ed. Sparks, D.L.) Elsevier, pp 77–123

Lorenz AJ, Smith KP, Jannink JL. Potential anD Optimization of Genomic Selection for Fusarium Head Blight Resistance in Six-row Barley. Crop Sci. 2012;52:1609. doi: 10.2135/cropsci2011.09.0503. DOI

Lindgren D, Gea LD, Jefferson PA. Status number for measuring genetic diversity. For Genet. 1997;4:762–764.

Märtens K, Hallin J, Warringer J, Liti G, Parts L. Predicting quantitative traits from genome and phenome with near perfect accuracy. Nat Commun. 2016;7:11512. doi: 10.1038/ncomms11512. PubMed DOI PMC

Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–1829. PubMed PMC

Müller BSF, Neves LG, de Almeida Filho JE, Resende MFR, Muñoz PR, dos Santos PET, Filho EP, Kirst M, Grattapaglia D. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genom. 2017;18:524. doi: 10.1186/s12864-017-3920-2. PubMed DOI PMC

Müller D, Schopp P, Melchinger AE. Persistency of prediction accuracy and genetic gain in synthetic populations under recurrent genomic selection. G3 (Bethesda) 2017;7:801–811. doi: 10.1534/g3.116.036582. PubMed DOI PMC

Munoz PR, Resende MFR, Huber DA, Quesada T, Resende MDV, Neale DB, Wegrzyn JL, Kirst M, Peter GF. Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. Crop Sci. 2014;54(3):1115–1123. doi: 10.2135/cropsci2012.12.0673. DOI

Neale DB, Kremer A. Forest tree genomics: growing resources and applications. Nat Rev Genet. 2011;12:111–122. doi: 10.1038/nrg2931. PubMed DOI

Neale DB, Savolainen O. Association genetics of complex traits in conifers. Trends Plant Sci. 2004;9:325–330. doi: 10.1016/j.tplants.2004.05.006. PubMed DOI

Neale DB, McGuire PE, Wheeler NC, Stevens KA, Crepeau MW, Cardeno C, Zimin AV, Puiu D, Pertea GM, Sezen UU, et al. The Douglas-Fir genome sequence reveals specialization of the photosynthetic apparatus in pinaceae. G3 (Bethesda) 2017;7:3157–3167. doi: 10.1534/g3.117.300078. PubMed DOI PMC

Neves LG, Davis JM, Barbazuk WB, Kirst M. Whole-exome targeted sequencing of the uncharacterized pine genome. Plant J. 2013;75:146–156. doi: 10.1111/tpj.12193. PubMed DOI

Perez P, de los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics. 2014;198:483–495. doi: 10.1534/genetics.114.164442. PubMed DOI PMC

Ratcliffe B, El-Dien OG, Klápště J, Porth I, Chen C, Jaquish B, El-Kassaby YA. A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods. Heredity. 2015;115:547–555. doi: 10.1038/hdy.2015.57. PubMed DOI PMC

Resende MDV, Resende MFR, Sansaloni CP, Petroli CD, Missiaggia AA, Aguiar AM, Abad JM, Takahashi EK, Rosado AM, Faria DA, et al. Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 2012;194:116–128. doi: 10.1111/j.1469-8137.2011.04038.x. PubMed DOI

Resende MFR, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol. 2012;193:617–624. doi: 10.1111/j.1469-8137.2011.03895.x. PubMed DOI

Resende MFR, Munoz P, Resende MDV, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.) Genetics. 2012;190:1503–1510. doi: 10.1534/genetics.111.137026. PubMed DOI PMC

Resende RT, Resende MDV, Silva FF, Azevedo CF, Takahashi EK, Silva-Junior OB, Grattapaglia D. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity. 2017;119:245–255. doi: 10.1038/hdy.2017.37. PubMed DOI PMC

Sallam A. H., Endelman J. B., Jannink J.-L., Smith K. P. Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population. The Plant Genome. 2015;8(1):0. doi: 10.3835/plantgenome2014.05.0020. PubMed DOI

Shen X, Alam M, Ronnegard L (2014). Package “bigRR”: Generalized Ridge Regression (with special advantage for p >> n cases)

Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE. Genomic selection using different marker types and densities. J Anim Sci. 2008;86:2447–2454. doi: 10.2527/jas.2007-0010. PubMed DOI

Tan B, Grattapaglia D, Martins GS, Ferreira KZ, Sundberg B, Ingvarsson PK. Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol. 2017;17:110. doi: 10.1186/s12870-017-1059-6. PubMed DOI PMC

Thistlethwaite FR, Ratcliffe B, Klápště J, Porth I, Chen C, Stoehr MU, El-Kassaby YA. Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform. BMC Genom. 2017;18:930. doi: 10.1186/s12864-017-4258-5. PubMed DOI PMC

Thomson MJ. High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol. 2014;2:195–212. doi: 10.9787/PBB.2014.2.3.195. DOI

Ukrainetz NK, Kang KY, Aitken SN, Stoehr M, Mansfield SD. Heritability and phenotypic and genetic correlations of coastal Douglas-fir (Pseudotsuga menziesii) wood quality traits. Can J Res. 2008;38:1536–1546. doi: 10.1139/X07-234. DOI

Van Eenennaam AL, Weigel KA, Young AE, Cleveland MA, Dekkers JCM. Applied Animal genomics: results from the field. Annu Rev Anim Biosci. 2014;2:105–139. doi: 10.1146/annurev-animal-022513-114119. PubMed DOI

Varshney Rajeev K., Roorkiwal Manish, Sorrells Mark E. Genomic Selection for Crop Improvement. Cham: Springer International Publishing; 2017. Genomic Selection for Crop Improvement: An Introduction; pp. 1–6.

Woolliams, J. A., and Thompson, R. (1994). A theory of genetic contributions. In Proceedings of the 5th World Congress on Genetics Applied to Livestock Production, vol. 19 (ed. Smith, C., Gavora, J. S., Benkel, B., Chesnais, J., Fairfull, W., Gibson, J. P., Kennedy, B. W. and Burnside, E. B.), pp. 127–134. Guelph

Whittaker JC, Thompson R, Denham MC. Marker-assisted selection using ridge regression. Genet Res. 2000;75:249–252. doi: 10.1017/S0016672399004462. PubMed DOI

Wray N, Thompson R. Prediction of rates of inbreeding in selected populations. Genetical Research. 1990;55:41–54. doi: 10.1017/S0016672300025180. PubMed DOI

Wright S. Coefficients of inbreeding and relationship. Am Nat. 1922;56:330–338. doi: 10.1086/279872. DOI

Wu X, Lund MS, Sun D, Zhang Q, Su G. Impact of relationships between test and training animals and among training animals on reliability of genomic prediction. J Anim Breed Genet. 2015;132:366–375. doi: 10.1111/jbg.12165. PubMed DOI

Yanchuk AD. General and specific combining ability from disconnected partial diallels of coastal Douglas-fir. Silvae Genet. 1996;45:37–45.

Yeh FC, Heaman C. Heritabilities and genetic and phenotypic correlations for height and diameter in coastal Douglas-fir. Can J Res. 1982;12:181–185. doi: 10.1139/x82-027. DOI

Zhong S, Dekkers JCM, Fernando RL, Jannink JL. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics. 2009;182:355–364. doi: 10.1534/genetics.108.098277. PubMed DOI PMC

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...