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A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora

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Abstract

Genomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-by-sequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodness-of-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10–17% higher) and lower prediction errors than traditional GBLUP. The results may be used as basis for additional studies into the genus Coffea and can be expanded for similar perennial crops.

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References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  Google Scholar 

  • Bates D, Vazquez AI (2014) pedigreemm: Pedigree-based mixed-effects models. https://CRAN.R-project.org/package=pedigreemm https://CRAN.R-project.org/package=pedigreemm, r package version 0.3-3

  • Burgueño J, Crossa J, Cotes JM, Vicente FS, Das B (2011) Prediction assessment of linear mixed models for multienvironment trials. Crop Sci 51(3):944–954

    Article  Google Scholar 

  • Burgueño J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52(2):707

    Article  Google Scholar 

  • de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182(1):375–385

    Article  Google Scholar 

  • de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193(2):327–45

    Article  PubMed  Google Scholar 

  • Cilas C, Montagnon C, Bar-Hen A (2011) Yield stability in clones of coffea canephora in the short and medium term: longitudinal data analyses and measures of stability over time. Tree Genet Genomes 7(2):421–429

    Article  Google Scholar 

  • Crossa J, Burgueño J, Cornelius PL, McLaren G, Trethowan R, Krishnamachari A (2006) Modeling genotype × environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop Sci 46(4):1722–1733

    Article  Google Scholar 

  • Crossa J, Beyene Y, Kassa S, Pérez P, Hickey JM, Chen C, de los Campos G, Burgueño J, Windhausen VS, Buckler E et al (2013) Genomic prediction in maize breeding populations with genotyping-by-sequencing. G3: Genes–Genomes–Genetics 3(11):1903–1926

    Article  PubMed  PubMed Central  Google Scholar 

  • Cubry P, De Bellis F, Avia K, Bouchet S, Pot D, Dufour M, Legnate H, Leroy T (2013) An initial assessment of linkage disequilibrium (ld) in coffee trees: Ld patterns in groups of coffea canephora pierre using microsatellite analysis. BMC Genomics 14 (1):10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST et al (2011) The variant call format and vcftools. Bioinformatics 27(15):2156–2158

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R, Pietrella M, Zheng C, Alberti A, Anthony F, Aprea G et al (2014) The coffee genome provides insight into the convergent evolution of caffeine biosynthesis. Science 345(6201):1181–1184

    Article  CAS  PubMed  Google Scholar 

  • Elshire RJ, Glaubitz JC, Sun Q, Poland Ja, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PloS One 6(5):e19,379

    Article  CAS  Google Scholar 

  • Falconer DS, Mackay TFC (1996) Quantitative genetics. Pearson Education Limited, England

    Google Scholar 

  • Ferrão LFV, Caixeta ET, Souza FdF, Zambolim EM, Cruz CD, Zambolim L, Sakiyama NS (2013) Comparative study of different molecular markers for classifying and establishing genetic relationships in coffea canephora. Plant Syst Evol 299(1):225–238

    Article  Google Scholar 

  • Ferrão LFV, Caixeta ET, Pena G, Zambolim EM, Cruz CD, Zambolim L, Ferrão MAG, Sakiyama NS (2015) New EST–SSR markers of Coffea arabica: transferability and application to studies of molecular characterization and genetic mapping. Mol Breed 35(1):1–5

    Article  Google Scholar 

  • Ferrão LFV, Ferrão RG, Ferrão MAG, Fonseca A, Stephens M, Garcia AAF (2016) Genomic prediction in Coffea canephora using Bayesian polygenic modeling. In: 5th international conference on quantitative genetics. WI, Madison, p 203

  • Ferrão RG, Ferrão MAG, Fonseca A, Pacova B (2007) Melhoramento genético de Coffea canephora. In: Ferrão R, Fonseca A, Bragança S, Ferrão M, Muner LD (eds) Cafe conilon, incaper edn Vitória-ES, pp 123–173

  • Gelman A, Carlin JB, Stern HS, Rubin DB (2014) Bayesian data analysis, vol 2. Chapman & hall/CRC Boca Raton, FL, USA

    Google Scholar 

  • Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) Tassel-gbs: a high capacity genotyping by sequencing analysis pipeline. PLoS One 9(2):e90,346

    Article  Google Scholar 

  • GNU P (2007) Free Software Foundation. Bash (3.2.48) [Unix shell program].Retrieved from http://ftp.gnu.org/gnu/bash/bash-3.2.48.tar.gz

  • Grattapaglia D, Resende MDV (2010) Genomic selection in forest tree breeding. Tree Genet Genomes 7(2):241–255

    Article  Google Scholar 

  • Heslot N, Akdemir D, Sorrells ME, Jannink JL (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127(2):463–480

    Article  PubMed  Google Scholar 

  • Hu Y, Yan C, Hsu CH, Chen QR, Niu K, Komatsoulis GA, Meerzaman D (2014) Omiccircos: a simple-to-use r package for the circular visualization of multidimensional omics data. Cancer Informat 13:13

    Article  CAS  Google Scholar 

  • IOC (2016) International Coffee Organization - Trade Statistics Tables. http://www.ico.org/

  • Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9(2):166–177

    Article  CAS  PubMed  Google Scholar 

  • Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M et al (2014a) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 127(3):595–607

    Article  PubMed  Google Scholar 

  • Jarquín D, Kocak K, Posadas L, Hyma K, Jedlicka J, Graef G, Lorenz A (2014b) Genotyping by sequencing for genomic prediction in a soybean breeding population. BMC Genomics 15(1):740

    Article  PubMed  PubMed Central  Google Scholar 

  • Kelly AM, Cullis BR, Gilmour AR, Eccleston JA, Thompson R (2009) Estimation in a multiplicative mixed model involving a genetic relationship matrix. Genet Sel Evol 41(1):1

    Article  Google Scholar 

  • Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL, Singh RP, Autrique E, de los Campos G (2015) Increased prediction accuracy in wheat breeding trials using a marker x environment interaction genomic selection model. G3: Genes–Genomes–Genetics 5(4):569–82

    Article  PubMed  PubMed Central  Google Scholar 

  • Malosetti M, Ribaut JM, van Eeuwijk FA (2014) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Drought phenotyping in crops: From theory to practice 4(44):53

    Google Scholar 

  • Malosetti M, Bustos-Korts D, Boer MP, van Eeuwijk FA (2016) Predicting responses in multiple environments: issues in relation to genotype × environment interactions. Crop Sci 56(5):2210–2222

    Article  Google Scholar 

  • Margarido GRA, Pastina MM, Souza AP, Garcia AAF (2015) Multi-trait multi-environment quantitative trait loci mapping for a sugarcane commercial cross provides insights on the inheritance of important traits. Mol Breed 35(8):175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mérot-L’Anthoëne V, Mangin B, Lefebvre-Pautigny F, Jasson S, Rigoreau M, Husson J, Lambot C, Crouzillat D (2014) Comparison of three qtl detection models on biochemical, sensory, and yield characters in coffea canephora. Tree Genet Genomes 10(6):1541–1553

    Article  Google Scholar 

  • Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829

    CAS  PubMed  PubMed Central  Google Scholar 

  • Meyer K (2009) Factor-analytic models for genotype × environment type problems and structured covariance matrices. Genet Sel Evol 41(1):21

    Article  PubMed  PubMed Central  Google Scholar 

  • Mrode RA (2014) Linear models for the prediction of animal breeding values. Cabi

  • Oakey H, Cullis B, Thompson R, Comadran J, Halpin C, Waugh R (2016) Genomic selection in multi-environment crop trials. G3: Genes–Genomes–Genetics 6(5):1313–1326

    Article  PubMed  PubMed Central  Google Scholar 

  • Pastina MM, Malosetti M, Gazaffi R, Mollinari M, Margarido GRA, Oliveira KM, Pinto LR, Souza AP, van Eeuwijk FA, Garcia AAF (2012) A mixed model QTL analysis for sugarcane multiple-harvest-location trial data. Theor Appl Genet 124:835–849

    Article  CAS  PubMed  Google Scholar 

  • Payne RW, Murray DA, Harding SA (2011) An introduction to the genstat command language (14th edn)

  • Piepho H, Möhring J, Melchinger A, Büchse A (2008) Blup for phenotypic selection in plant breeding and variety testing. Euphytica 161(1-2):209–228

    Article  Google Scholar 

  • Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink JL (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome J 5(3):103

    Article  CAS  Google Scholar 

  • R Core Team (2013) R: A Language and Environment for Statistical Computing

  • Schulz-Streeck T, Ogutu JO, Piepho HP (2013) Comparisons of single-stage and two-stage approaches to genomic selection. Theor Appl Genet 126(1):69–82

    Article  PubMed  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464

    Article  Google Scholar 

  • Smith AB, Cullis BR, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57(4):1138–1147

    Article  CAS  PubMed  Google Scholar 

  • Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143(06):449

    Article  Google Scholar 

  • Smith AB, Stringer JK, Wei X, Cullis BR (2007) Varietal selection for perennial crops where data relate to multiple harvests from a series of field trials. Euphytica 157(1-2):253–266

    Article  Google Scholar 

  • Smith KF, Casler M (2004) Spatial analysis of forage grass trials across locations, years, and harvests. Crop Sci 44(1):56–62

    Article  Google Scholar 

  • Spindel JE, Begum H, Akdemir D, Collard B, Redona E, Jannink Jl, Mccouch S (2016) Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity 116:395–408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tran HTM, Lee LS, Furtado A, Smyth H, Henry RJ (2016) Advances in genomics for the improvement of quality in coffee. J Sci Food Agric 96(10):3300–3312

    Article  CAS  PubMed  Google Scholar 

  • VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423

    Article  CAS  PubMed  Google Scholar 

  • Wang W, Gelman A (2014) Difficulty of selecting among multilevel models using predictive accuracy. Statistics at its Interface 7(1):1–88

    Google Scholar 

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Acknowledgments

This work is partially supported by FAPESP/CAPES (São Paulo Research Foundation), grants 2014/20389-2 for L.F.V.F and A.A.F.G. Phenotypic evaluations and GBS data is supported by Fapes (Espírito Santo Research Foundation), grants 55207464/11 and 65192036/14. Additional support is provided by the Instituto Capixaba de Pesquisa, Assitência Tecnica e Extensão Rural (Incaper) and Embrapa Cafe. A.A.F.G, R.G.F, M.A.G.F and A.F have a fellowship from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). The author thank Livia Souza and Anete P. de Souza (CBMEG, Unicamp/Brazil) by the assistance in the DNA extraction step; and Paulo Volpi (Incaper/Brazil) by the support on the phenotypic evaluation.

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Authors and Affiliations

  1. Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz (ESALQ), Universidade de São Paulo (USP), Av. Padua Dias, 11, CP 83, CEP 13400-970, Piracicaba, SP, Brazil

    Luís Felipe Ventorim Ferrão & Antonio Augusto Franco Garcia

  2. Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural (Incaper), Rua Afonso Sarlo, 160, Bento Ferreira, CEP 29052-010, Vitória, ES, Brazil

    Romário Gava Ferrão

  3. Embrapa Café/Incaper, Rua Afonso Sarlo, 160, Bento Ferreira, CEP 29052-010, Vitória, ES, Brazil

    Maria Amélia Gava Ferrão & Aymbiré Francisco

Authors
  1. Luís Felipe Ventorim Ferrão
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  2. Romário Gava Ferrão
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  3. Maria Amélia Gava Ferrão
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  4. Aymbiré Francisco
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  5. Antonio Augusto Franco Garcia
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Contributions

L.F.V.F, A.A.F.G, R.G.F, M.A.G.F and A.F conceived the study and designed the experiments. R.G.F, M.A.G.F and A.F installed the experimental design and collected the phenotypic data. L.F.V.F performed the DNA extraction. L.F.V.F and A.A.F.G performed the genomic prediction analysis and proposed the theoretical idea of the model. L.F.V.F wrote the paper.

Corresponding author

Correspondence to Antonio Augusto Franco Garcia.

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The authors declare that they have no conflict of interest.

Data Archiving Statement

The genotypes in the study belong to the germplasm collection and breeding program of the Incaper institution (ES,Brazil). Phenotypic and genotypic data were submitted as Supplementary Material.

Additional information

Communicated by: J. Beaulieu

This article is part of the Topical Collection on Breeding

Key Message: First insights into the Genotyping-by-Sequencing (GBS) in Coffea canephora and a genomic prediction model considering the theory about multiplicative mixed model to accommodate the interaction effects across sites and harvests

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Ventorim Ferrão, L., Gava Ferrão, R., Ferrão, M. et al. A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora . Tree Genetics & Genomes 13, 95 (2017). https://doi.org/10.1007/s11295-017-1171-7

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