Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing

BMC Genomics

Yousry A El-Kassaby¹  Ilga Porth¹  Charles Chen²  Jaroslav Klápště³  Blaise Ratcliffe¹  Omnia Gamal El-Dien¹ 

[1] Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver V6T 1Z4, British Columbia, Canada;Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater 74078-3035, OK, USA;Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamycka 129, Prague 6, 165 21, Czech Republic

关键词: Multi-trait GS;    Imputation methods;    Genotype x environment interaction;    Open-pollinated families;    Genotyping-by-sequencing;    Genomic selection;    Interior spruce;   

Others  :  1204004
DOI  :  10.1186/s12864-015-1597-y

 received in 2015-01-21, accepted in 2015-04-28,  发布年份 2015

【 摘 要 】

Background

Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost.

Results

Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3.

Conclusions

The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima.

【 授权许可】

   
2015 Gamal El-Dien et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Grattapaglia D. Breeding Forest Trees by Genomic Selection:Current Progress and theWay Forward. In: Genomics Plant Genet Resour. Tuberosa R, Graner A, Frison E, editors. Springer Netherlands, Dordrecht; 2014: p.651-682.
• [2]El-Kassaby YA, Isik F, Whetten RW. Modern Advances in Tree Breeding. In: Challenges Oppor World’s For 21st Century. Fenning T, editor. Springer Science+Business Media, Dordrecht; 2014: p.441-459.
• [3]Lande R, Thompson R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics. 1990; 124:743-756.
• [4]Paterson AH, Tanksley SD, Sorrells ME. DNA markers in plant improvement. Adv Agron. 1991; 46:39-90.
• [5]Neale DB, Williams CG. Restriction-Fragment-Length-Polymorphism mapping in conifers and applications to forest genetics and tree improvement. Can J Res. 1991; 21:545-554.
• [6]Williams CG, Neale DB. Conifer wood quality and marker-aided selection—a case-study. Can J Res. 1992; 22:1009-1017.
• [7]Strauss SH, Lande R, Namkoong G. Limitations of molecular-marker-aided selection in forest tree breeding. Can J Res. 1992; 22:1050-1061.
• [8]Fisher RA. The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinb. 1918; 52:399-433.
• [9]Stuber CW, Polacco M, Senior ML. Synergy of empirical breeding, marker-assisted selection, and genomics to increase crop yield potential. Crop Sci. 1999; 39:1571-1583.
• [10]Dekkers JCM. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J Anim Sci. 2004; 82:313-328.
• [11]Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001; 157:1819-1829.
• [12]Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet. 2009; 10:381-391.
• [13]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.
• [14]Resende MFR, Muñoz P, Resende MD, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin T a, 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.
• [15]Zapata-Valenzuela J, Isik F, Maltecca C, Wegrzyn J, Neale D, McKeand S, Whetten R. SNP markers trace familial linkages in a cloned population of Pinus taeda-prospects for genomic selection. Tree Genet Genomes. 2012; 8:1307-1318.
• [16]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 (Edinb). 2014;113:343-352.
• [17]VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008; 91:4414-4423.
• [18]Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci. 2009; 92:4648-4655.
• [19]El-Kassaby YA, Lstibůrek M. Breeding without breeding. Genet Res (Camb). 2009; 91:111-120.
• [20]El-Kassaby YA, Cappa EP, Liewlaksaneeyanawin C, Klápště J, Lstibůrek M. Breeding without breeding: is a complete pedigree necessary for efficient breeding? PLoS One. 2011; 6: Article ID e25737
• [21]Isik F. Genomic selection in forest tree breeding: the concept and an outlook to the future. New For. 2014; 45:379-401.
• [22]Elshire RJ, Glaubitz JC, Sun Q, Poland J a, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011; 6: Article ID e19379
• [23]Chen C, Mitchell SE, Elshire RJ, Buckler ES, El-Kassaby YA. Mining conifers’ mega-genome using rapid and efficient multiplexed high-throughput genotyping-by-sequencing (GBS) SNP discovery platform. Tree Genet Genomes. 2013; 9:1537-1544.
• [24]Sutton BCS, Flanagan DJ, Gawley R, Newton CH, Lester DT, El-Kassaby YA. Inheritance of chloroplast and mitochondrial DNA in Picea and composition of hybrids from introgression zones. Theor Appl Genet. 1991; 82:242-248.
• [25]Birol I, Raymond A, Jackman SD, Pleasance S, Coope R, Taylor GA, et al. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics. 2013;29:1492-1497.
• [26]Porth I, White R, Jaquish B, Alfaro R, Ritland C, Ritland K. Genetical Genomics Identifies the Genetic Architecture for Growth and Weevil Resistance in Spruce. PLoS One. 2012;7:e44397.
• [27]Lu F, Lipka AE, Glaubitz J, Elshire R, Cherney JH, Casler MD, Buckler ES, Costich DE. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet. 2013; 9: Article ID e1003215
• [28]Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17:520-525.
• [29]Wang W, Wei Z, Lam T-W, Wang J. Next generation sequencing has lower sequence coverage and poorer CNP-detection capability in the regulatory regions. Sci Rep. 2011; 1:55.
• [30]Pan J, Wang B, Pei Z-Y, Zhao W, Gao J, Mao J-F, et al. Optimization of genotyping-by-sequencing strategy for population genomic analysis in conifers. Mol Ecol Resour. 2014.
• [31]Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B(Methodological). 1977; 39:1-38.
• [32]Solberg TR, Sonesson AK, Woolliams JA. Genomic selection using different marker types and densities. J Anim Sci. 2008; 86:2447-2454.
• [33]Namkoong G. Inbreeding effects on estimation of genetic additive variance. For Sci. 1966; 12:8-13.
• [34]Squillace AE. Average genetic correlations among offspring from open-pollinated forest trees. Silvae Genet. 1974; 23:149-156.
• [35]Askew GR, El-Kassaby YA. Estimation of relationship coefficients among progeny derived from wind-pollinated orchard seeds. Theor Appl Genet. 1994; 88:267-272.
• [36]Grattapaglia D, Resende MDV. Genomic selection in forest tree breeding. Tree Genet Genomes. 2011; 7:241-255.
• [37]Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink JL. Genomic selection in plant breeding. Knowledge and prospects. Adv Agron. 2011; 110:77-123.
• [38]Shen X, Alam M, Fikse F, Rönnegård L. A novel generalized ridge regression method for quantitative genetics. Genetics. 2013; 193:1255-1268.
• [39]Hofheinz N, Frisch M. Heteroscedastic ridge regression approaches for genome-wide prediction with a focus on computational efficiency and accurate effect estimation. G3 Genes Genomes Genet. 2014; 4:539-546.
• [40]Lorenzana RE, Bernardo R. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet. 2009; 120:151-161.
• [41]Luan T, Woolliams J a, Lien S, Kent M, Svendsen M. The accuracy of Genomic Selection in Norwegian red cattle assessed by cross-validation. Genetics. 2009; 183:1119-1126.
• [42]VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS. Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci. 2009; 92:16-24.
• [43]Resende MDV, Resende MFR, Sansaloni CP, Petroli CD, Missiaggia A a, Aguiar AM, Abad JM, Takahashi EK, Rosado AM, Faria D a, Pappas GJ, Kilian A, Grattapaglia D. 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.
• [44]Burdon RD. Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genet. 1977; 26:168-175.
• [45]Annicchiarico P. Genotype X Environment Interaction Challenges and Opportunities for Plant Breeding and Cultivar Recommendations. FAO Plant Production and Protection Paper No. 174. Rome,Italy; 2002:155.
• [46]Hazel LN. The genetic basis for constructing selection indices. Genetics. 1943; 28:476-490.
• [47]Bouffier L, Raffin A, Rozenberg P, Meredieu C, Kremer A. What are the consequences of growth selection on wood density in the French maritime pine breeding programme? Tree Genet Genomes. 2008; 5:11-25.
• [48]Hayes BJ, Visscher PM, Goddard ME. Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res (Camb). 2009; 91:47-60.
• [49]Van Grevenhof EM, Van Arendonk J a M, Bijma P. Response to genomic selection: the Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting. Genet Sel Evol. 2012; 44:26. BioMed Central Full Text
• [50]Bastiaansen JWM, Coster A, Calus MPL, van Arendonk J a M, Bovenhuis H. Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures. Genet Sel Evol. 2012; 44:3. BioMed Central Full Text
• [51]Burdon RD, Shelbourne CJA. Breeding populations for recurrent selection conflicts and possible solutions. New Zeal J For Sci. 1971; 1:174-193.
• [52]Jayawickrama KJS, Carson MJ. A breeding strategy for the New Zealand radiata pine breeding cooperative. Silvae Genet. 2000; 49:82-90.
• [53]Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin Y-C, Scofield DG, Vezzi F, Delhomme N, Giacomello S, Alexeyenko A, Vicedomini R, Sahlin K, Sherwood E, Elfstrand M, Gramzow L, Holmberg K, Hällman J, Keech O, Klasson L, Koriabine M, Kucukoglu M, Käller M, Luthman J, Lysholm F, Niittylä T, Olson A, Rilakovic N, Ritland C, Rosselló J a, Sena J et al.. The Norway spruce genome sequence and conifer genome evolution. Nature. 2013; 497:579-584.
• [54]Chaisurisri K, El-Kassaby YA. Genetic diversity in a seed production population vs. natural populations of Sitka Spruce. Biodivers Conserv. 1994; 3:512-523.
• [55]Stoehr MU, El-Kassaby YA. Levels of genetic diversity at different stages of the domestication cycle of interior spruce in British Columbia. Theor Appl Genet. 1997; 94:83-90.
• [56]Doyle JJ, Doyle JL. Isolation of plant DNA from fresh tissue. Focus (Madison). 1990; 12:13-15.
• [57]Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007; 23:2633-2635.
• [58]R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2014.
• [59]Perry PO. bcv: Cross-Validation for the SVD (Bi-Cross-Validation). 2009.
• [60]Hartigan JA, Wong MA. Algorithm AS 136: A K-means clustering algorithm. J R Stat Soc Ser C (Applied Stat). 1979; 28:100-108.
• [61]Rutkoski JE, Poland J, Jannink J-L, Sorrells ME. Imputation of unordered markers and the impact on genomic selection accuracy. G3 Genes Genomes Genet. 2013; 3:427-439.
• [62]El-Kassaby YA, Mansfield S, Isik F, Stoehr M. In situ wood quality assessment in Douglas-fir. Tree Genet Genomes. 2011; 7:553-561.
• [63]Auty D, Achim A. The relationship between standing tree acoustic assessment and timber quality in Scots pine and the practical implications for assessing timber quality from naturally regenerated stands. Forestry. 2008; 81:475-487.
• [64]Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml User. Guide release 3.0. VSN International Ltd., Hemel Hempstead, UK; 2009.
• [65]Endelman JB. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome. 2011; 4:250-255.
• [66]Henderson CR. Estimation of variance and covariance components. Biometrics. 1953; 9:226-252.
• [67]Gianola D, Okut H, Weigel K a, Rosa GJ. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genet. 2011; 12:87. BioMed Central Full Text
• [68]González-Camacho JM, de Los CG, Pérez P, Gianola D, Cairns JE, Mahuku G, Babu R, Crossa J. Genome-enabled prediction of genetic values using radial basis function neural networks. Theor Appl Genet. 2012; 125:759-771.
• [69]Crossa J, Beyene Y, Kassa S, Pérez P, Hickey JM, Chen C, de los Campos G, Burgueño J, Windhausen VS, Buckler E, Jannink J-L, Lopez Cruz M a, Babu R. Genomic prediction in maize breeding populations with genotyping-by-sequencing. G3 Genes Genomes Genet Genomes Genet. 2013; 3:1903-1926.
• [70]Lindgren D, Mullin TJ. Balancing gain and relatedness in selection. Silvae Genet. 1997; 46:124-129.
• [71]Lindgren D, Gea L, Jefferson P. Loss of genetic diversity monitored by status number. Silvae Genet. 1996; 45:52-59.
• [72]Caballero A, Toro M. Interrelations between effective population size and other pedigree tools for the management of conserved populations. Genet Res. 2000; 75:331-343.