Euphytica

, Volume 187, Issue 2, pp 263–276 | Cite as

Genome-wide selection in cassava

  • Eder Jorge de Oliveira
  • Marcos Deon Vilela de Resende
  • Vanderlei da Silva Santos
  • Cláudia Fortes Ferreira
  • Gilmara Alvarenga Fachardo Oliveira
  • Maiane Suzarte da Silva
  • Luciana Alves de Oliveira
  • Carlos Ivan Aguilar-Vildoso
Article

Abstract

The main objective of this study was to estimate the selection accuracy and to predict the genetic gain in cassava breeding using genomic selection methodologies. We evaluated 358 cassava genotypes for the following traits: shoot weight (SW), fresh root yield (FRY), starch fraction amylose content (AC), dry matter content (DMC), and starch yield (S-Y). Genotyping was performed using 390 single nucleotide polymorphisms (SNPs), which were used as covariates in the random regression-best linear unbiased prediction model for genomic selection. The heritability values detected by markers for the SW, FRY, AC, DMC, and S-Y traits were 0.25, 0.25, 0.03, 0.20, and 0.26, respectively. Because the low heritability detected for AC, this trait was eliminated from further analysis. Using only the most informative SNPs (118, 92, 56, and 97 SNPs for SW, FRY, DMC, and S-Y, respectively) we observed higher selection accuracy which were 0.83, 0.76, 0.67, and 0.77, respectively to SW, FRY, DMC, and S-Y. With these levels of accuracy and considering a selection cycle reduced by half the time, the theoretical gains with genomic selection compared to phenotypic selection for DMC, FRY, and SW would be 39.42 %, 56.90 %, and 73.96 %, respectively. These results indicate that in the cassava, genomic selection can substantially speed up selection cycles, thereby increasing gains per unit time. Although there are high expectations for incorporating this strategy into breeding programs, we still need to validate the model for other traits and evaluate whether the selection accuracy can be improved using more SNPs.

Keywords

Manihot esculenta Crantz Breeding SNP RR-BLUP 

References

  1. Asante IK, Dixon AGO (2002) Analysis of phenotypic stability in ten cassava genotypes in three West African countries. West Afr J Appl Ecol 3:43–48Google Scholar
  2. Bernardo R, Yu J (2007) Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  3. Cavalcanti JJ, Resende MDV (2012) Predição simultânea de efeitos de marcadores e seleção genômica ampla em cajueiro. Rev Bras Frutic (in press)Google Scholar
  4. Ceballos H, Iglesias CA, Perez JC, Dixon AGO (2004) Cassava breeding: opportunities and challenges. Plant Mol Biol 56:503–516PubMedCrossRefGoogle Scholar
  5. CIAT (2008) Annual report from SBA-2 Project. Improved cassava for the developing world. CIAT, CaliGoogle Scholar
  6. Cortez DF, Reilly K, Okogbenin E, Beeching JR, Iglesias C, Tohme J (2002) Mapping wound-response genes involved in post-harvest physiological deterioration (PPD) of cassava (Manihot esculenta Crantz). Euphytica 128:47–53CrossRefGoogle Scholar
  7. Crossa J, Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun H-J (2010) Prediction of genetic values of quantitative traits in Plant Breed using pedigree and molecular markers. Genetics 186:713–724PubMedCrossRefGoogle Scholar
  8. Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185:1021–1031PubMedCrossRefGoogle Scholar
  9. de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic predictions across multiple populations. Genetics 183:1545–1553PubMedCrossRefGoogle Scholar
  10. Dekkers JCM (2004) Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J Anim Sci 82:313–328Google Scholar
  11. Dekkers JCM (2007) Marker-assisted selection for commercial crossbred performance. J Anim Sci 85:2104–2114PubMedCrossRefGoogle Scholar
  12. FAO (2011) Disponível em, p. http://www.faostatfaoorg/defaultaspx. Accessed 11 Aug 2011
  13. Fernando RL, Habier D, Stricker C, Dekkers JCM, Tottir LR (2007) Genomic selection. Acta Agric Scand A 57:192–195Google Scholar
  14. Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183:347–363PubMedCrossRefGoogle Scholar
  15. Goddard ME, Hayes BJ (2007) Genomic selection. J Anim Breed Genet 124:323–330PubMedCrossRefGoogle Scholar
  16. Gomes JC, Silva J (2006) Correção da acidez e adubação. In: Souza LS, Farias ARN, Mattos PLP de, Fukuda WMG (eds) Aspectos socioeconômicos e agronômicos da mandioca. Embrapa Mandioca e Fruticultura Tropical, Cruz das Almas, pp 215–247Google Scholar
  17. Guo Z, Tucker DM, Lu J, Kishore V, Gay D (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124:261–275PubMedCrossRefGoogle Scholar
  18. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009a) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443PubMedCrossRefGoogle Scholar
  19. Hayes B, Bowman P, Chamberlain A, Verbyla K, Goddard M (2009b) Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet Sel Evol 41:51. doi:10.1186/1297-9686-41-51 PubMedCrossRefGoogle Scholar
  20. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  21. Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant Breed with genomic selection: potential gain per unit time and cost. Crop Sci 50:1681–1690CrossRefGoogle Scholar
  22. IBGE (2012) Produção agrícola municipal Disponível em. http://www.sidraibgegobr/bda/. Accessed 26 Jan 2012
  23. Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in Plant Breed: from theory to practice. Brief Funct Genomics 9:166–177PubMedCrossRefGoogle Scholar
  24. Jenkins S, Gibson N (2002) High-throughput SNP genotyping. Comp Funct Genomics 3:57–66PubMedCrossRefGoogle Scholar
  25. Jorge V (2000) Cartographie de la résistance du manioc à la bacteriose vasculaire du manioc causée par Xanthomonas axonopodis pv. manihotis. PhD Thesis, Université Paris XI, FranceGoogle Scholar
  26. Jorge V, Fregene M, Duque MC, Bonierbale MW, Tohme J, Verdier V (2000) Genetic mapping of resistance to bacterial blight disease in cassava (Manihot esculenta Crantz). Theor Appl Genet 101:865–872CrossRefGoogle Scholar
  27. Jorge V, Fregene M, Velez CM, Duque MC, Tohme J, Verdier V (2001) QTL analysis of field resistance to Xanthomonas axonopodis pv. manihotis in cassava. Theor Appl Genet 102:564–571CrossRefGoogle Scholar
  28. Kamau J, Melis R, Laing M, Derera J, Shanahan P, Ngugi E (2010) Combining the yield ability and secondary traits of selected cassava genotypes in the semi-arid areas of Eastern Kenya. J Plant Breed Crop Sci 2:181–191Google Scholar
  29. Kawano K, Narintaraporn K, Narintaraporn P, Sarakarn S, Limsila A, Limsila J, Suparhan D, Sarawat V, Watananonta W (1998) Yield improvement in a multistage breeding program for cassava. Crop Sci 38:325–332CrossRefGoogle Scholar
  30. Kawuki RS, Ferguson M, Labuschagne M, Herselman L, Kim D-J (2009) Identification characterisation and application of single nucleotide polymorphisms for diversity assessment in cassava (Manihot esculenta Crantz). Mol Breed 23:669–684CrossRefGoogle Scholar
  31. Kizito EB, Ronnberg-Wastljung AC, Egwang T, Gullberg U, Fregene M, Westerbergh A (2007) Quantitative trait loci controlling cyanogenic glucoside and dry matter content in cassava (Manihot esculenta Crantz) roots. Hereditas 144:129–136CrossRefGoogle Scholar
  32. Kunkeaw S, Yoocha T, Sraphet S, Boonchanawiwat A, Boonseng O, Lightfoot DA, Triwitayakorn K, Tangphatsornruang S (2011) Construction of a genetic linkage map using simple sequence repeat markers from expressed sequence tags for cassava (Manihot esculenta Crantz). Mol Breed 27:67–75CrossRefGoogle Scholar
  33. Long N, Gianola D, Rosa GJM, Weigel KA, Avendano S (2007) Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet 124:377–389PubMedCrossRefGoogle Scholar
  34. Lopez C, Piegu B, Cooke R, Delseny M, Tohme J, Verdier V (2005) Using cDNA and genomic sequences as tools to develop SNP strategies in cassava (Manihot esculenta Crantz). Theor Appl Genet 110:425–431PubMedCrossRefGoogle Scholar
  35. Lopez CE, Quesada-Ocampo LM, Bohorquez A, Duque MC, Vargas J, Tohme J, Verdier V (2007) Mapping EST-derived SSRs and ESTs involved in resistance to bacterial blight in Manihot esculenta. Genome 50:1078–1088PubMedCrossRefGoogle Scholar
  36. Macciotta NPP, Gaspa G, Steri R, Pieramati C, Carnier P, Dimauro C (2009) Pre-selection of most significant SNPS for the estimation of genomic breeding values. BMC Proc 3(Suppl 1):S14PubMedCrossRefGoogle Scholar
  37. Mayor PJ, Bernardo R (2009) Genome-wide selection and marker-assisted recurrent selection in double haploid versus F2 population. Crop Sci 49:1719–1725CrossRefGoogle Scholar
  38. Meuwissen THE, Goddard ME (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631PubMedCrossRefGoogle Scholar
  39. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  40. Muir WM (2007) Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J Anim Breed Genet 124:342–355PubMedCrossRefGoogle Scholar
  41. Nassar NMA, Ortiz R (2007) Cassava improvement: challenges and impacts. J Agric Sci 145:163–171CrossRefGoogle Scholar
  42. Ntawuruhunga P, Dixon AGO (2010) Quantitative variation and interrelationship between factors influencing cassava yield. J Appl Biosci 26:1594–1602Google Scholar
  43. Ojulong H, Labuschangne MT, Fregene M, Herselman L (2008) A cassava clonal evaluation trial based on a new cassava breeding scheme. Euphytica 160:119–129CrossRefGoogle Scholar
  44. Okogbenin E, Fregene M (2002) Genetic and QTL mapping of early root bulking in an F1 mapping population of non-inbred parents in cassava (Manihot esculenta Crantz). Theor Appl Genet 106:58–66PubMedGoogle Scholar
  45. Okogbenin E, Fregene M (2003) Genetic mapping of QTLs affecting productivity and plant architecture in a full-sib cross from non-inbred parents in cassava (Manihot esculenta Crantz). Theor Appl Genet 107:1452–1462PubMedCrossRefGoogle Scholar
  46. Olsen KM (2004) SNPs SSRs and inferences on cassava’s origin. Plant Mol Biol 56:517–526PubMedCrossRefGoogle Scholar
  47. Resende MDV (2007) Seleção genômica ampla (GWS) e modelos lineares mistos. In: Resende MDV. Matemática e estatística na análise de experimentos e no melhoramento genético, 1st edn. Embrapa Florestas, Colombo, pp 517–534Google Scholar
  48. Resende MDV (2008) Genômica quantitativa e seleção no melhoramento de plantas perenes e animais. Embrapa Florestas, ColomboGoogle Scholar
  49. Resende MDV, Lopes S, Silva RL, Pires IE (2008) Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesq Flor Bras 56:63–78Google Scholar
  50. Resende MDV, Resende MFR Jr, Aguiar AM, Abad JIM, Missiaggia AA, Sansaloni C, Petroli C, Grattapaglia D (2010) Computação da seleção genômica ampla (GWS). Embrapa Florestas, ColomboGoogle Scholar
  51. Resende MFR Jr, Muñoz M, Resende MDV, Garrick DJ, Fernando RL, Davis JM, Jokela EJ, Martin TA, Peter GF, Kirst M (2012a) Accuracy of genomic selection methods in a standard dataset of loblolly pine (Pinus taeda L.). Genetics 190:1503–1510PubMedCrossRefGoogle Scholar
  52. Resende MFR Jr, Munõz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M (2012b) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624PubMedCrossRefGoogle Scholar
  53. Resende MDV, Resende MFR Jr, Sansaloni CP, Petroli CD, Missiaggia AA, Aguiar AM, Abad JM, Takahashi EK, Rosado AM, Faria DA, Pappas GJ Jr, Kilian A, Grattapaglia D (2012c) Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 194:116–128Google Scholar
  54. Rutkoski JE, Heffner EL, Sorrells ME (2011) Genomic selection for durable stem rust resistance in wheat. Euphytica 179:161–173CrossRefGoogle Scholar
  55. Sánchez T, Mafla G, Morante N, Ceballos H, Dufour D, Calle F, Moreno X, Pérez JC, Debouck D (2009) Screening of starch quality traits in cassava (Manihot esculenta Crantz). Starch 61:12–19CrossRefGoogle Scholar
  56. Schaeffer LR (2006) Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 123:218–223PubMedCrossRefGoogle Scholar
  57. Schulz-Streeck T, Ogutu JO, Piepho H-P (2011) Pre-selection of markers for genomic selection. BMC Proc 5(Suppl 3):S12PubMedCrossRefGoogle Scholar
  58. Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE (2008) Genomic selection using different marker types and densities. J Anim Sci 86:2447–2454PubMedCrossRefGoogle Scholar
  59. Tian F, Bradbury J, Brown J, 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. Nat Genet 43:159–162PubMedCrossRefGoogle Scholar
  60. Villanueva B, Fernández J, García-Cortés LA, Varona L, Daetwyler HD, Toro MA (2011) Accuracy of genome-wide evaluation for disease resistance in aquaculture breeding programs. J Anim Sci 89:3433–3442PubMedCrossRefGoogle Scholar
  61. Weigel KA, de los Campos G, González-Recio O, Naya H, Wu XL, Long N, Rosa GJ, Gianola D (2009) Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. J Dairy Sci 92:5248–5257PubMedCrossRefGoogle Scholar
  62. Welsch R, Arango J, Bar C, Salazar B, Al-Babili S, Beltrán J, Chavarriaga P, Ceballos H, Tohme J, Beyer P (2010) Provitamin A accumulation in cassava (Manihot esculenta) roots driven by a single nucleotide polymorphism in a phytoene synthase gene. Plant Cell 22:3348–3356PubMedCrossRefGoogle Scholar
  63. Weng J, Xie C, Hao Z, Wang J, Liu C, Li M, Zhang D, Bai L, Zhang S, Li X (2011) Genome-wide association study identifies candidate genes that affect plant height in Chinese elite maize (Zea mays L.) inbred lines. PLoS ONE 6(12):e29229. doi:10.1371/journal.pone.0029229 PubMedCrossRefGoogle Scholar
  64. Whankaew S, Poopear S, Kanjanawattanawong S, Tangphatsornruang S, Boonseng O, Lightfoot DA, Triwitayakorn K (2011) A genome scan for quantitative trait loci affecting cyanogenic potential of cassava root in an outbred population. BMC Genomics 12:266PubMedCrossRefGoogle Scholar
  65. Wong CK, Bernardo R (2008) Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824PubMedCrossRefGoogle Scholar
  66. Wydra K, Zinsou V, Jorge V, Verdier V (2004) Identification of pathotypes of Xanthomonas axonopodis pv. manihotis in Africa and detection of quantitative trait loci and markers for resistance to bacterial blight. Phytopathology 94:1084–1093PubMedCrossRefGoogle Scholar
  67. Zhang Z, Liu JF, Ding XD, Bijma P, de Koning DJ, Zhang Q (2010) Best linear unbiased prediction of genomic breeding values using trait-specific marker-derived relationship matrix. PLoS ONE 5:9. doi:10.1371/journal.pone.0012648 Google Scholar
  68. Zhang Z, Ding XD, Liu JF, de Koning D-J, Zhang Q (2011) Genomic selection for QTL-MAS data using a trait-specific relationship matrix. BMC Proc 5(Suppl 3):S15PubMedCrossRefGoogle Scholar
  69. Zhong S, Dekkers JCM, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Eder Jorge de Oliveira
    • 1
  • Marcos Deon Vilela de Resende
    • 2
  • Vanderlei da Silva Santos
    • 1
  • Cláudia Fortes Ferreira
    • 1
  • Gilmara Alvarenga Fachardo Oliveira
    • 3
  • Maiane Suzarte da Silva
    • 3
  • Luciana Alves de Oliveira
    • 1
  • Carlos Ivan Aguilar-Vildoso
    • 4
  1. 1.Embrapa Cassava and FruitsCruz das AlmasBrazil
  2. 2.Embrapa Forestry ResearchColomboBrazil
  3. 3.Federal University of Recôncavo da BahiaCruz das AlmasBrazil
  4. 4.CNPq/Embrapa Cassava and FruitsCruz das AlmasBrazil

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