Hostname: page-component-5c6d5d7d68-ckgrl Total loading time: 0 Render date: 2024-08-22T02:04:12.506Z Has data issue: false hasContentIssue false

Quantitative trait loci underlying root yield and starch content in an F1 derived cassava population (Manihot esculenta Crantz)

Published online by Cambridge University Press:  19 September 2016

S. SRAPHET
Affiliation:
Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
A. BOONCHANAWIWAT
Affiliation:
Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
T. THANYASIRIWAT
Affiliation:
Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Department of Agriculture and Resource, Faculty of Natural Resources and Agro-Industry, Kasetsart University Chalermprakiat Sakon Nakhon Province Campus, Mueang Sakon Nakhon 47000, Thailand
R. THAIKERT
Affiliation:
Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
S. WHANKAEW
Affiliation:
Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
D. R. SMITH
Affiliation:
Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
O. BOONSENG
Affiliation:
Rayong Field Crops Research Center, Ministry of Agriculture and Cooperatives, Rayong 21150, Thailand
D. A. LIGHTFOOT
Affiliation:
Genomics Core-Facility, Southern Illinois University at Carbondale, Carbondale, Illinois 62901, USA
K. TRIWITAYAKORN*
Affiliation:
Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Center for Cassava Molecular Biotechnology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
*
*To whom all correspondence should be addressed. Email: kanokporn.tri@mahidol.ac.th

Summary

Cassava (Manihot esculenta Crantz) root yield measured as fresh weight (hereafter root yield) is declining in much of Asia and Africa. The current study aimed to identify quantitative trait loci (QTL) underlying both root and starch fresh weights in F1 cassava. Eight QTL were associated with root yield, underlying 12·9–40·0% of the phenotypic variation (PVE). Nine QTL were associated with starch content, underlying 11·3–27·3% of the PVE. Quantitative trait loci were identified from four different environments that encompassed two locations and 3 years. Consistent QTL for root yield, YLD5_R11 and YLD8_L09 on linkage group (LG) 16, were detected across years and locations. Quantitative trait loci for starch content, ST3_R09, ST6_R10 and ST7_R11 on LG 11, were found across 3 years. Co-localization of QTL for both traits with positive correlation was detected between YLD3_R10 and ST5_R10 on LG 9. Candidate genes within the QTL that were consistent across multiple environments were identified based on cassava genome sequences. Genes predicted to encode for glycosyl hydrolases, uridine 5’-diphospho-(UDP)-glucuronosyl transferases and UDP-glucosyl transferases were found among the 44 genes located within the region containing the QTL controlling starch content. Sixteen genes predicted to encode proteins that were possibly associated with root yield were identified. The QTL controlling root yield and starch content in the current study will be useful for molecular breeding of cassava through marker-assisted selection. The identification of candidate genes underlying both traits will be useful both as markers and for gene expression studies.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bainbridge, Z., Tomlins, K., Wellings, K. & Westby, A. (1996). Methods for Assessing Quality Characteristics of Non-Grain Starch Staples (Part 3 Laboratory Methods). Chatham, UK: Natural Resources Institute.Google Scholar
Balyejusa Kizito, E., Rönnberg-Wästljung, A. C., 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, 129136.CrossRefGoogle ScholarPubMed
Boonchanawiwat, A., Sraphet, S., Boonseng, O., Lightfoot, D. A. & Triwitayakorn, K. (2011). Quantitative trait loci underlying plant and first branch height in cassava (Manihot esculenta Crantz). Field Crops Research 121, 343349.CrossRefGoogle Scholar
Cantarel, B. L., Coutinho, P. M., Rancurel, C., Bernard, T., Lombard, V. & Henrissat, B. (2009). The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Research 37, D233D238.CrossRefGoogle ScholarPubMed
Chen, X., Fu, Y., Xia, Z., Jie, L., Wang, H., Lu, C. & Wang, W. (2012). Analysis of QTL for yield-related traits in cassava using an F 1 population from non-inbred parents. Euphytica 187, 227234.CrossRefGoogle Scholar
Collard, B. C. Y., Jahufer, M. Z. Z., Brouwer, J. B. & Pang, E. C. K. (2005). An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142, 169196.Google Scholar
FAO (2014). FAOSTAT. Rome: FAO. Available from: http://www.faostat.fao.org (verified 25 January 2014).Google Scholar
Kearsey, M. J. (1998). The principles of QTL analysis (a minimal mathematics approach). Journal of Experimental Botany 49, 16191623.CrossRefGoogle Scholar
Keeling, P. L. & Myers, A. M. (2010). Biochemistry and genetics of starch synthesis. Annual Review of Food Science and Technology 1, 271303.Google Scholar
Kunkeaw, S., Tangphatsornruang, S., Smith, D. R. & Triwitayakorn, K. (2010). Genetic linkage map of cassava (Manihot esculenta Crantz) based on AFLP and SSR markers. Plant Breeding 129, 112115.CrossRefGoogle Scholar
Lark, K. G., Chase, K., Adler, F., Mansur, L. M. & Orf, J. H. (1995). Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proceedings of the National Academy of Sciences of the United States of America 92, 46564660.Google Scholar
Lou, P., Zhao, J., Kim, J. S., Shen, S., Del Carpio, D. P., Song, X., Jin, M., Vreugdenhil, D., Wang, X., Koornneef, M. & Bonnema, G. (2007). Quantitative trait loci for flowering time and morphological traits in multiple populations of Brassica rapa . Journal of Experimental Botany 58, 40054016.CrossRefGoogle ScholarPubMed
Malosetti, M., Voltas, J., Romagosa, I., Ullrich, S. E. & Van Eeuwijk, F. A. (2004). Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica 137, 139145.CrossRefGoogle Scholar
Ntawuruhunga, P. & Dixon, A. G. O. (2010). Quantitative variation and interrelationship between factors influencing cassava yield. Journal of Applied Biosciences 26, 15941602.Google Scholar
Okogbenin, E. & Fregene, M. (2002). Genetic analysis and QTL mapping of early root bulking in an F 1 population of non-inbred parents in cassava (Manihot esculenta Crantz). Theoretical and Applied Genetics 106, 5866.CrossRefGoogle Scholar
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). Theoretical and Applied Genetics 107, 14521462.CrossRefGoogle Scholar
Okogbenin, E., Marin, J. & Fregene, M. (2006). An SSR-based molecular genetic map of cassava. Euphytica 147, 433440.CrossRefGoogle Scholar
Okogbenin, E., Marin, J. & Fregene, M. (2008). Quantitative trait loci analysis for early yield in a pseudo F2 population of cassava. African Journal of Biotechnology 7, 131138.Google Scholar
Onwueme, I. C. (2002). Cassava in Asia and the Pacific. In Cassava: Biology, Production and Utilization (Eds Hillocks, R. J., Thresh, J. M. & Bellotti, A.), pp. 5565. Wallingford, UK: CABI Publishing.Google Scholar
Paterson, A. H., Damon, S., Hewitt, J. D., Zamir, D., Rabinowitch, H. D., Lincoln, S. E., Lander, E. S. & Tanksley, S. D. (1991). Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments. Genetics 127, 181197.CrossRefGoogle ScholarPubMed
Redoña, E. D. & Mackill, D. J. (1998). Quantitative trait locus analysis for rice panicle and grain characteristics. Theoretical and Applied Genetics 96, 957963.CrossRefGoogle Scholar
Shi, J., Li, R., Qiu, D., Jiang, C., Long, Y., Morgan, C., Bancroft, I., Zhao, J. & Meng, J. (2009). Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus . Genetics 182, 851861.CrossRefGoogle ScholarPubMed
Song, X., Han, Y., Teng, W., Sun, G. & Li, W. (2010). Identification of QTL underlying somatic embryogenesis capacity of immature embryos in soybean (Glycine max (L.) Merr.). Plant Cell Reports 29, 125131.CrossRefGoogle ScholarPubMed
SPSS (2008). SPSS Statistics for Windows Version 17·0. Chicago, IL, USA: SPSS Inc.Google Scholar
Sraphet, S., Boonchanawiwat, A., Thanyasiriwat, T., Boonseng, O., Tabata, S., Sasamoto, S., Shirasawa, K., Isobe, S., Lightfoot, D. A., Tangphatsornruang, S. & Triwitayakorn, K. (2011). SSR and EST-SSR-based genetic linkage map of cassava (Manihot esculenta Crantz). Theoretical and Applied Genetics 122, 11611170.CrossRefGoogle ScholarPubMed
Sun, W., Zhang, Y., Le, W. & Zhang, H. (2009). Construction of a genetic linkage map and QTL analysis for some leaf traits in pear (Pyrus L.). Frontiers of Agriculture in China 3, 6774.CrossRefGoogle Scholar
Swamy, B. P. M. & Sarla, N. (2011). Meta-analysis of yield QTLs derived from inter-specific crosses of rice reveals consensus regions and candidate genes. Plant Molecular Biology Reporter 29, 663680.Google Scholar
Thanyasiriwat, T., Sraphet, S., Whankaew, S., Boonseng, O., Bao, J., Lightfoot, D. A., Tangphatsornruang, S. & Triwitayakorn, K. (2014). Quantitative trait loci and candidate genes associated with starch pasting viscosity characteristics in cassava (Manihot esculenta Crantz). Plant Biology 16, 197207.CrossRefGoogle ScholarPubMed
Timmerman-Vaughan, G. M., Mills, A., Whitfield, C., Frew, T., Butler, R., Murray, S., Lakeman, M., McCallum, J., Russell, A. & Wilson, D. (2005). Linkage mapping of QTL for seed yield, yield components, and developmental traits in pea. Crop Science 45, 13361344.Google Scholar
Van Ooijen, J. (1992). Accuracy of mapping quantitative trait loci in autogamous species. Theoretical and Applied Genetics 84, 803811.CrossRefGoogle ScholarPubMed
Van Ooijen, J. W., Boer, M. P., Jansen, R. C. & Maliepaard, C. (2002). MapQTL 4·0, Software for the Calculation of QTL Positions on Genetic Maps (User Guide). Wageningen, the Netherlands: Plant Research International.Google Scholar
Voorrips, R. E. (2002). MapChart: software for the graphical presentation of linkage maps and QTLs. Journal of Heredity 93, 7778.CrossRefGoogle ScholarPubMed
Wang, M. & Goldman, I. (1997). Transgressive segregation and reciprocal effect for free folic acid content in a red beet (Beta vulgaris L.) population. Euphytica 96, 317321.CrossRefGoogle Scholar
Whankaew, S., Poopear, S., Kanjanawattanawong, S., Tangphatsornruang, S., Boonseng, O., Lightfoot, D. A. & Triwitayakorn, K. (2011). A genome scan for quantitative trait loci affecting cyanogenic potential of cassava root in an outbred population. BMC Genomics 12, 266. DOI: 10.1186/1471-2164-12-266 CrossRefGoogle Scholar
Xiao, J., Grandillo, S., Ahn, S. N., McCouch, S. R., Tanksley, S. D., Li, J. & Yuan, L. (1996). Genes from wild rice improve yield. Nature 384, 223224.CrossRefGoogle Scholar
Zhang, Y., Li, Y. X., Wang, Y., Liu, Z. Z., Liu, C., Peng, B., Tan, W. W., Wang, D., Shi, Y. S., Sun, B. C., Song, Y. C., Wang, T. Y. & Li, Y. (2010). Stability of QTL across environments and QTL-by-environment interactions for plant and ear height in maize. Agricultural Sciences in China 9, 14001412.Google Scholar