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Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava

Theoretical and Applied Genetics volume 135, pages 145–171 (2022)Cite this article

Abstract

Key message

GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture.

Abstract

Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of − 0.001–0.71 for the four yield-related traits and 0.33–0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.

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Data availability

Phenotypic data used in the analyses and the genotypic data are available in Cassavabase (http://cassavabase.org/search/traits). The imputed SNP genotypic data obtained from the 276 genotypes used in this study are available in Cassavabase.

References

  • Aiemnaka P, Wongkaew A, Chanthaworn J, Nagashima SK, Boonma S, Authapun J et al (2012) Molecular characterization of a spontaneous waxy starch mutation in cassava. Crop Sci 52:2121–2130. https://doi.org/10.2135/cropsci2012.01.0058

    CAS  Article  Google Scholar 

  • de Albuquerque HYG, Carmo CDD, Brito AC, Oliveira EJD (2018) Genetic diversity of manihot esculenta crantz germplasm based on single-nucleotide polymorphism markers. Annals Appl Biol 173(3):271–284. https://doi.org/10.1111/aab.12460

    Article  Google Scholar 

  • Aldana AS, Quintero AF (2013) Physicochemical characterization of two cassava (Manihot esculenta Crantz) starches and flours. Rev Sci Agroaliment 1:19–25

    Google Scholar 

  • Andrade LRB, Sousa MBE, Oliveira EJ, Resende MDV, Azevedo CF (2019) Cassava yield traits predicted by genomic selection methods. PLoS One 14(11):e0224920. https://doi.org/10.1371/journal.pone.0224920

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Asoro FG, Newell MA, Beavis WD, Scott MP, Jannink J-L (2011) Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Gen 4:132–144. https://doi.org/10.3835/plantgenome2011.02.0007

    Article  Google Scholar 

  • Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Soft 67(1):1–48. https://doi.org/10.18637/jss.v067.i01

    Article  Google Scholar 

  • Begum H, Spindel JE, Lalusin A, Borromeo T, Gregorio G, Hernandez J et al (2015) Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa). PLoS ONE 10(3):e0119873. https://doi.org/10.1371/journal.pone.0119873

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) Tassel: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19):2633–2635

    CAS  PubMed  Google Scholar 

  • BredesonJ V, Lyons JB, Prochnik SE, Wu GA, Ha CM, Edsinger-Gonzales E et al (2016) Sequencing wild and cultivated cassava and related species reveals extensive interspecific hybridization and genetic diversity. Nat Biotechnol 34:562–570. https://doi.org/10.1038/nbt.3535

    CAS  Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Ceballos H, Iglesias CA, Perez JC, Dixon AG (2004) Cassava breeding: opportunities and challenges. Plant Mol Biol 56:503–516. https://doi.org/10.1007/s11103-004-5010-5

    CAS  Article  PubMed  Google Scholar 

  • Cach NT, Perez JC, Lenis JI, Calle F, Morante N et al (2005) Epistasis in the expression of relevant traits in cassava (Manihot esculenta Crantz) for subhumid conditions. J Hered 96:586–592

    CAS  PubMed  Google Scholar 

  • Calle F, Perez JC, Gaitán W, Morante N, Ceballos H, Llano G et al (2005) Diallel inheritance of relevant traits in cassava (Manihot esculenta Crantz) adapted to acid-soil savannas. Euphytica 144:177–186

    Google Scholar 

  • Ceballos H, Pérez JC, Barandica OJ, Lenis JI, Morante N, Calle F, Pino L, Hershey CH (2016) Cassava breeding I: the value of breeding value. Front Plant Sci 7:1227

    PubMed  PubMed Central  Google Scholar 

  • Ceballos H, Kawuki RS, Gracen VE, Yencho GC, Hershey CH (2015) Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava. Theor Appl Genet 128:1647–1667

    PubMed  PubMed Central  Google Scholar 

  • Ceballos H, Rojanaridpiched C, Phumichai C et al (2020) Excellence in cassava breeding: perspectives for the future. Crop Breed Gene Genom 2:e200008

    Google Scholar 

  • Chaengsee P, Kongsil P, Siriwong N, Kittipadakul P, Piyachomkwan K, Petchpoung K (2020) Potential yield and cyanogenic glucoside content of cassava root and pasting properties of starch and flour from cassava Hanatee var and breeding lines grown under rain-fed condition. Agri Natural Resour 54(3):237–244

    Google Scholar 

  • Combs E, Bernardo R (2013) Accuracy of genomewide selection for different traits with constant population size, heritability, and number of markers. Plant Genome 6:1–7. https://doi.org/10.3835/plantgenome2012.11.0030

    CAS  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, Jannink J-L, Lopez Cruz MA, Babu R (2013) Genomic prediction in maize breeding populations with genotyping-by-sequencing. G3 Genes|Genomes|Genetics 3(11):1903–1926. https://doi.org/10.1534/g3.113.008227

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Crossa J et al (2014) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112:48–60

    CAS  PubMed  Google Scholar 

  • Crossa J et al (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22:961–975

    CAS  PubMed  Google Scholar 

  • Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185:1021–1031

    CAS  PubMed  PubMed Central  Google Scholar 

  • de losCampos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375–385. https://doi.org/10.1534/genetics.109.101501

    CAS  Article  Google Scholar 

  • de losCampos G, Gianola D, Rosa GJM, Weigel KA, Cross J (2010) Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet Res 92:295–308. https://doi.org/10.1017/S0016672310000285

    CAS  Article  Google Scholar 

  • de losCampos, G., Pérez, P. 2013. BGLR: Bayesian generalized linear regression. R package version 1.0.4. https://cran.r-project.org/web/packages/BGLR

  • Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601

    CAS  PubMed  Google Scholar 

  • Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15

    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):e19379

    CAS  PubMed  PubMed Central  Google Scholar 

  • Ehret A, Hochstuhl D, Gianola D, Thaller G (2015) Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle. Genet Sel Evol 47:22

    PubMed  PubMed Central  Google Scholar 

  • Elango D, Xue W, Chopra S (2020) Genome wide association mapping of epi-cuticular wax genes in Sorghum bicolor. Physio Mol Biol Plants 26(8):1727–1737. https://doi.org/10.1007/s12298-020-00848-5

  • Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. The Plant Genome 4:250–255

    Google Scholar 

  • Endelman JB, Jannink JL (2012) Shrinkage estimation of the realized relationship matrix. G3 Genes|Genomes|Genetics 2:1405–1413

  • Evans ID, Lips A (1992) Viscoelasticity of gelatinized starch dispersions. J Texture Stud 23:69–86. https://doi.org/10.1111/j.1745-4603.1992.tb00512.x

  • Ezenwaka L, Del Carpio DP, Jannink J-L, Rabbi I, Danquah E, Asante I, Danquah A, Blay E, Egesi C (2018) Genome-wide association study of resistance to cassava green mite pest and related traits in cassava. Crop Sci 58:1907–1918

    CAS  Google Scholar 

  • FAO. 2018 Food Outlook - biannual report on global food markets – November 2018. Rome. 104 pp.

  • Flint-Garcia SA, Thornsberry JM, Buckler ESIV (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374. https://doi.org/10.1146/annurev.arplant.54.031902.134907

    CAS  Article  PubMed  Google Scholar 

  • Gelandi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17

    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):e90346

    PubMed  PubMed Central  Google Scholar 

  • González-Camacho JM, Ornella L, Pérez-Rodríguez P, Gianola D, Dreisigacker S, Crossa J (2018) Applications of machine learning methods to genomic selection in breeding wheat for rust resistance. Plant Genome. https://doi.org/10.3835/plantgenome2017.11.0104

    Article  PubMed  Google Scholar 

  • Gouy M, Rousselle Y, Bastianelli D et al (2013) Experimental assessment of the accuracy of genomic selection in sugarcane. Theor Appl Genet 126:2575–2586. https://doi.org/10.1007/s00122-013-2156-z

    CAS  Article  PubMed  Google Scholar 

  • Gracen VE, Kogsil P, Napasintuwong O, Duangjit J, Phumichai C. The story of Kasetsart 50. The most important cassava variety in the world. Bangkok-Kamphaeng Saen (Thailand): Center for Agricultural Biotechnology, Kasetsart University; 2018.

  • Guo G, Zhao F, Wang Y, Zhang Y, Du L et al (2014) Comparison of single-trait and multiple-trait genomic prediction models. BMC Genet 15:30. https://doi.org/10.1186/1471-2156-15-30

  • Hayes BJ, Pryce J, Chamberlain AJ, Bowman PJ, Goddard ME (2010) Genetic Architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in holstein cattle as contrasting model traits. PLoS Genet 6(9):e1001139. https://doi.org/10.1371/journal.pgen.1001139

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Habier D, Fernando R, Kizilkaya K, Garrick D (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12:186

    PubMed  PubMed Central  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12

    CAS  Google Scholar 

  • Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690

    Google Scholar 

  • Heslot N, Yang H, Sorrells M, Jannink J (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160. https://doi.org/10.2135/cropsci2011.09.0297

    Article  Google Scholar 

  • Hickey JM, Chiurugwi T, Mackay I, Powell W (2017) Implementing genomic selection in CBPWP genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nat Genet 49:1297–1303

    CAS  PubMed  Google Scholar 

  • Howard R, Carriquiry AL, Beavis WD (2014) Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures. G3 Genes|Genomes|Genetics 4:1027–1046

    PubMed  PubMed Central  Google Scholar 

  • Ikeogu UN, Akdemir D, Wolfe MD, Okeke UG, Amaefula C, Jannink J-L, Egesi CN (2019) Genetic correlation, genome-wide association and genomic prediction of portable NIRS predicted carotenoids in cassava roots. Front Plant Sci 10:1570–1570

    PubMed  PubMed Central  Google Scholar 

  • Isidro J, Jannink JL, Akdemir D, Poland J, Heslot N, Sorrells ME (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128(1):145–158. https://doi.org/10.1007/s00122-014-2418-4

    Article  PubMed  Google Scholar 

  • Jaramillo G, Morante N, Pérez JC, Calle F, Ceballos H et al (2005) Diallel analysis in cassava adapted to the midaltitude valleys environment. Crop Sci 45:1058–1063

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  • Juliana P, Singh RP, Singh PK, Poland JA, Bergstrom GC, Huerta-Espino J, Bhavani S, Crossa J, Sorrells ME (2018) Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes. Theor Appl Genet 131(7):1405–1422. https://doi.org/10.1007/s00122-018-3086-6

    Article  PubMed  PubMed Central  Google Scholar 

  • Kaler AS, Purcell LC (2019) Estimation of a significance threshold for genome-wide association studies. BMC Genomics 20:618. https://doi.org/10.1186/s12864-019-5992-7

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Karlström A, Calle F, Salazar S, Morante N, Dufour D, Ceballos H (2016) Biological implications in cassava for the production of amylose-free starch: impact on root yield and related traits. Front Plant Sci 7:604. https://doi.org/10.3389/fpls.2016.00604

  • Kawano K, Fukuda WMG, Cenpukdee U (1987) Genetic and environmental effects on dry matter content of cassava root. Crop Sci 27(1):69–74

    Google Scholar 

  • Kawano K (2003) Thirty years of cassava breeding for productivity – biological and social factors for success. Crop Sci 43:1325–1335. https://doi.org/10.2135/cropsci2003.1325

    Article  Google Scholar 

  • Kayondo SI, Pino Del Carpio D, Lozano R, Ozimati A, Wolfe M et al (2018) Genome-wide association mapping and genomic prediction for CBSD resistance in Manihot esculenta. Sci Rep 8:1549. https://doi.org/10.1038/s41598-018-19696-1

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Kulembeka HP, Ferguson M, Herselman L, Kanju E, Mkamilo G et al (2012) Diallel analysis of field resistance to brown streak disease in cassava (Manihot esculenta Crantz) landraces from Tanzania. Euphytica 187:277–288

    Google Scholar 

  • Kuon J-E, Qi W, Schläpfer P, Hirsch-Hoffmann M, Rogalla P, von Bieberstein A, Patrignani LP et al (2019) Haplotype-resolved genomes of geminivirus-resistant and geminivirus-susceptible african cassava cultivars. BMC Biol 17(1):75

    PubMed  PubMed Central  Google Scholar 

  • Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760

    CAS  PubMed  PubMed Central  Google Scholar 

  • Liaw A. 2013 Breiman and Cutler’s random forests for classification and regression. Available 403 at: http://cran.r-project.org/web/packages/randomForest/index.html.

  • Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ et al (2012) GAPIT, genome association and prediction integrated tool. Bioinformatics 28:2397–2399. https://doi.org/10.1093/bioinformatics/bts444

    CAS  Article  PubMed  Google Scholar 

  • Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet 12(2):e1005767. https://doi.org/10.1371/journal.pgen.1005767

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Lorenz AJ, Smith KP, Jannink J-L (2012) Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci 52:1609–1621

    Google Scholar 

  • Lorenz AJ (2013) Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment. G3 Genes|Genomes|Genetics 3:481–91

    PubMed  PubMed Central  Google Scholar 

  • Maenhout S, De Baets B, Haesaert G, Van Bockstaele E (2007) Support vector machine regression for the prediction of maize hybrid performance. Theor Appl Genet 115:1003–1013

    CAS  PubMed  Google Scholar 

  • McKey D, Elias M, Pujol B, Duputié A (2010) The evolutionary ecology of clonally propagated domesticated plants. New Phytol 186(2):318–332

    PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • Momen M, Mehrgardi AA, Sheikhi A, Kranis A, Tusell L et al (2018) Predictive ability of genome-assisted statistical models under various forms of gene action. Sci Rep 8:12309. https://doi.org/10.1038/s41598-018-30089-2

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Morota G, Gianola D (2014) Kernel-based whole-genome prediction of complex traits: a review. Front Gene 5:363

    Google Scholar 

  • Newport Scientific operation manual of the series 4 rapid visco analyzer. Australia. 1995. p 93

  • Nwokocha LM, Aviara NA, Senan C, Williams PA (2009) A comparative study of some properties of cassava and cocoyam starches. Carbohydr Polym 76:362–367. https://doi.org/10.1016/j.carbpol.2008.10.034

    CAS  Article  Google Scholar 

  • Ogbonna AC, Braatz de Andrade LR, Mueller LA et al (2021) Comprehensive genotyping of a Brazilian cassava (Manihot esculenta Crantz) germplasm bank: insights into diversification and domestication. Theor Appl Genet. https://doi.org/10.1007/s00122-021-03775-5

    Article  PubMed  PubMed Central  Google Scholar 

  • Okeke UG, Akdemir D, Rabbi I et al (2017) Accuracies of univariate and multivariate genomic prediction models in African cassava. Genet Sel Evol 49:88. https://doi.org/10.1186/s12711-017-0361-y

    Article  PubMed  PubMed Central  Google Scholar 

  • Ozimati AR, Kawuki W, Esuma IS, Kayondo M, Wolfe et al (2018) Training population optimization for prediction of cassava brown streak disease resistance in west african clones. G3 Genes|Genomes|Genetics 8:3903–3913

  • Ozimati A, Kawuki R, Esuma W, Kayondo SI, Pariyo A, Wolfe M, Jannink JL (2019) Genetic variation and trait correlations in an East African cassava breeding population for genomic selection. Crop Sci 59(2):460–473

    PubMed  PubMed Central  Google Scholar 

  • Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103:681–686

    CAS  Google Scholar 

  • Pérez JC, Ceballos H, Calle F, Morante N, Gaitán W et al (2005) Within-family genetic variation and epistasis in cassava (Manihot esculenta Crantz) adapted to the acid-soils environment. Euphytica 145:77–85

    Google Scholar 

  • Pérez P, de Loscampos G (2014) Genome-wide regression and prediction with the bglr statistical package. Genetics 198:483–495. https://doi.org/10.1534/genetics.114.164442

    Article  PubMed  PubMed Central  Google Scholar 

  • Pérez-Rodríguez P, Gianola D, González-Camacho JM, Crossa J, Manès Y, Dreisigacker S (2012) Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 Genes|Genomes|Genetics 2(12):1595–1605. https://doi.org/10.1534/g3.112.003665

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Pérez L, Soto E, Farré G, Juanos J, Villorbina G, Bassie L, Medina V, Serrato AJ, Sahrawy M, Rojas JA, Romagosa I, Muñoz P, Zhu C, Christou P (2019) CRISPR/Cas9 mutations in the rice Waxy/GBSSI gene induce allele specific and zygosity dependent feedback effects on endosperm starch biosynthesis. Plant cell Reports 38:417–433. https://doi.org/10.1007/s00299-019-02388-z

  • R Core Team (2017). R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. URL https://www.R-project.org/.

  • Rabbi IY, Udoh LI, Wolfe M, Parkes EY, Gedil MA, Dixon A, Ramu P, Jannink J-L, Kulakow P (2017) Genome wide association mapping of correlated traits in cassava: dry matter and total carotenoid content. Plant Genome 10(3):944. https://doi.org/10.3835/plantgenome2016.09.0094

    CAS  Article  Google Scholar 

  • Rabbi IY, Kayondo SI, Bauchet G et al (2020) Genome-wide association analysis reveals new insights into the genetic architecture of defensive, agro-morphological and quality-related traits in cassava. Plant Mol Biol. https://doi.org/10.1007/s11103-020-01038-3

    Article  PubMed  Google Scholar 

  • Raemakers K, Schreuder M, Suurs L et al (2005) Improved cassava starch by antisense inhibition of granule-bound starch synthase I. Mol Breed 16:163–172. https://doi.org/10.1007/s11032-005-7874-8

  • Ramu P, Esuma W, Kawuki R, Rabbi IY, Egesi C, Bredeson JV, Bart RS, Verma J, Buckler ES, Fei Lu (2017) Cassava haplotype map highlights fixation of deleterious mutations during clonal propagation. Nat Genet. https://doi.org/10.1038/ng.3845

    Article  PubMed  PubMed Central  Google Scholar 

  • Riedelsheimer C et al (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44:217–220

    CAS  PubMed  Google Scholar 

  • Rojanaridpiched, C., V. Vichukit, S. Thongsri, O. Boonseng, A. Limsila and D. Suparhan (2010) Recent progress in cassava breeding and varietal adoption in Thailand. In: Howeler, R. (ed.) A new future for cassava in Asia: its use as food and fuel to benefit the poor. Proceedings of the 8th regional workshop, Vientiane, Lao PDR, pp. 202–210

  • Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink JL, Sorrells M (2012) Evaluation of genomic prediction methods for Fusarium head blight resistance in wheat. Plant Genome 5:51–61. https://doi.org/10.3835/plantgenome2012.02.0001

    CAS  Article  Google Scholar 

  • Salas-Tovar JA, Flores-Gallegos AC, Contreras-Esquivel JC et al (2017) Analytical methods for pectin methylesterase activity determination: a review. Food Anal Methods 10:3634–3646. https://doi.org/10.1007/s12161-017-0934-y

    Article  Google Scholar 

  • Sánchez T, Dufour D, Moreno IX, Ceballos H (2010) Comparison of pasting and gel stabilities of waxy and normal starches from potato, maize, and rice with those of a novel waxy cassava starch under thermal, chemical, and mechanical stress. J Agric Food Chem 58:5093–5099

    PubMed  Google Scholar 

  • Sauvage C, Segura V, Bauchet G, Stevens R, Do PT, Nikoloski Z, Fernie AR, Causse M (2014) Genome-wide association in tomato reveals 44 candidate loci for fruit metabolic traits. Plant Physiol 165(3):1120–1132. https://doi.org/10.1104/pp.114.241521

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Schirmer M, Höchstötter A, Jekle M, Arendt E, Becker T (2013) Physicochemical and morphological characterization of different starches with variable amylose/amylopectin ratio. Food Hydrocoll 32:52–63

    CAS  Google Scholar 

  • Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44(7):825–30

    CAS  PubMed  PubMed Central  Google Scholar 

  • Somo M, Kulembeka H, Mtunda K et al (2020) Genomic prediction and quantitative trait locus discovery in a cassava training population constructed from multiple breeding stages. Crop Sci 60:896–913. https://doi.org/10.1002/csc2.20003

    CAS  Article  Google Scholar 

  • Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E et al (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite. Tropical Rice Breed Lines Plos Genet 11(2):e1004982. https://doi.org/10.1371/journal.pgen.1004982

    CAS  Article  PubMed  Google Scholar 

  • Stapleton G. (2012). Global starch market outlook and competing starch raw materials for by product segment and region. Pricing outlook and cassava growth potential. Cassava Starch World 2010. Centre for Management Technology (CMT), Phnom Penh

  • Su G, Christensen OF, Janss L, Lund MS (2014) Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. J Dairy Sci 97(10):6547–6559. https://doi.org/10.3168/jds.2014-8210

    CAS  Article  PubMed  Google Scholar 

  • Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947–1958

    CAS  PubMed  Google Scholar 

  • Swarts K, Li H, Romero Navarro JA, An D, Romay MC, Hearne S, Acharya C, Glaubitz JC, Mitchell S, Elshire RJ, Buckler ES, Bradbury PJ (2014) Novel methods to optimize genotypic imputation for low-coverage, next-generation sequence data in crop plants. Plant Genome. https://doi.org/10.3835/plantgenome2014.05.0023

    Article  Google Scholar 

  • Thanyasiriwat T, Sraphet S, Whankaew S, Boonseng O, Bao J, Lightfoot DA, Tangphatsornruang S, Triwitayakorn K (2014) Quantitative trait loci and candidate genes associated with starch pasting viscosity characteristics in cassava (Manihot esculenta Crantz). Plant Biol (stuttg) 16(1):197–207. https://doi.org/10.1111/plb.12022 (Epub 2013 Apr 24 PMID: 23614826)

    CAS  Article  Google Scholar 

  • Toae R, Sriroth K, Rojanaridpiched C, Vichukit V, Chotineeranat S, Wansuksri R, Chatakanonda P, Piyachomkwan K (2019) Outstanding characteristics of Thai non-gm bred waxy cassava starches compared with normal cassava starch, waxy cereal starches and stabilized cassava starches. Plants 8(11):447. https://doi.org/10.3390/plants8110447

    CAS  Article  PubMed Central  Google Scholar 

  • Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B Methodol 58:267–288

    Google Scholar 

  • Tumuhimbise R, Melis R, Shanahan P (2014) Diallel analysis of early storage root yield and disease resistance traits in cassava (Manihot esculenta Crantz). F Crop Res 167:86–93

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  • Wang X, Yang Z, Xu C (2015) A comparison of genomic selection methods for breeding value prediction. Sci Bull 60:925–935

    Google Scholar 

  • Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z (2017) Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity 118:302–310

    CAS  PubMed  Google Scholar 

  • Wickham H (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. ISBN 978–3–319–24277–4, https://ggplot2.tidyverse.org.

  • Wolfe MD, Rabbi IY, Egesi C, Hamblin M, Kawuki R, Kulakow P et al (2016) Genome-wide association and prediction reveals the genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement. Plant Genome 9(2):1–13. https://doi.org/10.3835/plantgenome2015.11.0118

    CAS  Article  Google Scholar 

  • Wolfe MD, Kulakow P, Rabbi IY, Jannink J-L (2016b) Marker-based estimates reveal significant nonadditive effects in clonally propagated cassava (Manihot esculenta): implications for the prediction of total genetic value and the selection of varieties. G3 Genes|Genomes|Genetics 6:3497. https://doi.org/10.1534/g3.116.033332

    Article  PubMed  PubMed Central  Google Scholar 

  • Wolfe MD, Del Carpio DP, Alabi O, Ezenwaka LC, Ikeogu UM, Kayondo IS et al (2017) Prospects for genomic selection in cassava breeding. Plant Genome 10(3):1–19

    Google Scholar 

  • Wolfe MD, Bauchet GJ, Chan AW, Lozano R, Ramu P et al (2019) Historical introgressions from a wild relative of modern cassava improved important traits and may be under balancing selection. Genetics 213:1237–1253

    PubMed  PubMed Central  Google Scholar 

  • Xu S (2017) Predicted residual error sum of squares of mixed models: an application for genomic prediction. G3 Genes|Genomes|Genetics 7:895–909

    PubMed Central  Google Scholar 

  • Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc Natl Acad Sci USA 111:12456–12461

    CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Wang X, Ding X, Zheng X, Yang Z, Xu C, Hu Z (2018) Genomic selection of agronomic traits in hybrid rice using an NCII population. Rice 10, 11(1):32. https://doi.org/10.1186/s12284-018-0223-4. PMID: 29748895; PMCID:PMC5945574

  • Yabe S, Hara T, Ueno M, Enoki H, Kimura T, Nishimura S, Yasui Y, Ohsawa R, Iwata H (2018) Potential of genomic selection in mass selection breeding of an allogamous crop: an empirical study to increase yield of common buckwheat. Front Plant Sci. https://doi.org/10.3389/fpls.2018.00276

    Article  PubMed  PubMed Central  Google Scholar 

  • Yasui T, Ashida K (2011) Waxy endosperm accompanies increased fat and saccharide contents in bread wheat (Triticum aestivum L.) grain. Cereal Sci 53:104–111. https://doi.org/10.1016/j.jcs.2010.10.004

  • Yang N, Lu Y, Yang X, Huang J, Zhou Y, Ali F et al (2014) Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet 10(9):e1004573. https://doi.org/10.1371/journal.pgen.1004573

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Yin L, Zhang H, Tang Z, Xu J, Yin D, Zhang Z, Yuan X, Zhu M, Zhao S, Li X (2021) rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study, genomics. Proteom Bioinform. https://doi.org/10.1016/j.gpb.2020.10.007

    Article  Google Scholar 

  • Yonis BO, Pino del Carpio D, Wolfe M et al (2020) Improving root characterisation for genomic prediction in cassava. Sci Rep 10:8003. https://doi.org/10.1038/s41598-020-64963-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Yun MS, Kawagoe Y (2009) Amyloplast division progresses simultaneously at multiple sites in the endosperm of rice. Plant Cell Physiol 50(9):1617–1626. https://doi.org/10.1093/pcp/pcp104

  • Zacarias AM, Labuschagne MT (2010) Diallel analysis of cassava brown streak disease, yield and yield related characteristics in Mozambique. Euphytica 176:309–320

    Google Scholar 

  • Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM (2010) Buckler ES. Nat Genet 42(4):355–360

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang N et al (2015) Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population. Plant Physiol 168:575–583

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang S, Chen X, Lu C, Ye J, Zou M, Lu K, Feng S, Pei J, Liu C, Zhou X, Ma P, Li Z, Liu C, Liao Q, Xia Z, Wang W (2018) Genome-wide association studies of 11 agronomic traits in cassava (Manihot esculenta Crantz). Front Plant Sci 9:503. https://doi.org/10.3389/fpls.2018.00503

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank James Tanaka for his assistance in the laboratory of Dr. Mark Sorrells.

Funding

This work was supported by Kasetsart University Research and Development Institute (KURDI), Grant Number รหัส ศ-ข(กษ)2.57, and was partially supported by the Center of Excellence on Agricultural Biotechnology, Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation (AG-BIO/MHESI) Grant Number (60-005-001)J. The authors would also like to thank TTDI for providing supporting plant materials.

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Authors and Affiliations

  1. Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand

    Chalermpol Phumichai, Sirikan Hunsawattanakul, Phasakorn Fungfoo, Pasajee Kongsil, Piya Kittipadakul & Wannasiri Wannarat

  2. Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand

    Chalermpol Phumichai, Sirikan Hunsawattanakul & Pumipat Tongyoo

  3. Center of Excellence On Agricultural Biotechnology: (AG-BIO/MHESI), Bangkok, 10900, Thailand

    Chalermpol Phumichai & Sirikan Hunsawattanakul

  4. Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand

    Pornsak Aiemnaka, Piyaporn Nathaisong, Chareinsuk Rojanaridpiched & Vichan Vichukit

  5. Department of Horticulture, Faculty of Agriculture Kamphaeng Saen, Kasetsart University, Nakhon Pathom, 73140, Thailand

    Julapark Chunwongse

  6. Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand

    Chookiat Kijkhunasatian, Sunee Chotineeranat & Kuakoon Piyachomkwan

  7. United States Department of Agriculture - Agriculture Research Service, Ithaca, NY, 14850, USA

    Jean-Luc Jannink

  8. Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, 14850, USA

    Marnin D. Wolfe & Mark E. Sorrells

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  1. Chalermpol Phumichai
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Contributions

CP and JC designed the experiment. PA, PN, SH, PJK, PK, WW, and CP conducted the field experiments. CR and VV provided germplasm. CK, SC, and KP conducted the starch pasting properties experiments. CP, PF, and PT performed data analysis. CP, SC, and PJK wrote the first draft of the manuscript. CP, MS, JJ, and MW revised the manuscript. All authors read and approved the manuscript.

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Correspondence to Chalermpol Phumichai.

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Communicated by Damaris Odeny.

Supplementary Information

Below is the link to the electronic supplementary material.

List of 276 cassava genotypes including 247 wild-type and 29 waxy cassava starch types (PDF 189 KB)

122_2021_3956_MOESM2_ESM.pdf

Manhattan and quantile–quantile plots (QQ) plots comparing different yield-related traits data using compressed mixed linear (CMLM) model (PDF 194 KB)

122_2021_3956_MOESM3_ESM.pdf

Manhattan and quantile–quantile plots (QQ) plots comparing different yield-related traits data using multi‐locus mixed model (MLMM) (PDF 85 KB)

122_2021_3956_MOESM4_ESM.pdf

Manhattan and quantile–quantile plots (QQ) plots comparing different waxy and wild-type starch cassava using compressed mixed linear (CMLM) model, multi‐locus mixed model (MLMM) model, and fixed and random model circulating probability unification (FarmCPU) model (PDF 61 KB)

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Phumichai, C., Aiemnaka, P., Nathaisong, P. et al. Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava. Theor Appl Genet 135, 145–171 (2022). https://doi.org/10.1007/s00122-021-03956-2

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