DOI QR코드

DOI QR Code

Quantitative trait loci controlling the amino acid content in rice (Oryza sativa L.)

  • Yoo, Soo-Cheul (Department of Plant Life & Environmental Science, HanKyong National University)
  • Received : 2017.11.03
  • Accepted : 2017.11.19
  • Published : 2017.12.31

Abstract

The amino acid composition of rice is a major concern of rice breeders because amino acids are among the most important nutrient components in rice. In this study, a genetic map was constructed with a population of 134 recombinant inbred lines (RILs) from a cross between Dasanbyeo (Tongil-type indica) and TR22183 (temperate japonica), as a means to detect the main and epistatic effect quantitative trait loci (QTLs) for the amino acid content (AAC). Using a linkage map which covered a total of 1458 cM based on 239 molecular marker loci, a total of six main-effect QTLs (M-QTLs) was identified for the content of six amino acids that were mapped onto chromosome 3. For all the M-QTLs, the TR22183 allele increased the trait values. The QTL cluster (flanked by id3015453 and id3016090) on chromosome 3 was associated with the content of five amino acids. The phenotypic variation, explained by the individual QTLs located in this cluster, ranged from 10.2 to 12.4%. In addition, 26 epistatic QTLs (Ep-QTLs) were detected and the 25 loci involved in this interaction were distributed on all nine chromosomes. Both the M-QTLs and Ep-QTLs detected in this study will be useful in breeding programs which target the development of rice with improved amino acid composition.

Keywords

References

  1. D'Mello JPF. 2003. Amino acids as multifunctional molecules. In: D'Mello JPF, ed. Amino Acids in Animal Nutrition, 2nd edn. CABI Publishing, Cambridge, MA. pp 1-14
  2. Duan M, Sun SSM. 2005. Profiling the expression of genes controlling rice grain quality. Plant Mol. Biol. 59:165-178 https://doi.org/10.1007/s11103-004-7507-3
  3. Fan J, Oliphant A, Shen R, Kermani B, Garcia F, Gunderson K, Hansen M, Steemers F, Butler SL, Deloukas P, Galver L, Hunt S, McBride C, Bibikova M, Rubano T, Chen J, Wickham E, Doucet D, Chang W, Campbell D, Zhang B, Kruglyak S, Bentley D, Haas J, Rigault P, Zhou L, Stuelpnagel J, Chee MS. 2003. Highly parallel SNP genotyping. Cold Spring Harb. Symp. Quant. Biol. 68:69-78
  4. Henderson JW, Ricker RD, Bidlingmeyer BA, Woodward C. 2000. Rapid, accurate, sensitive and reproducible HPLC analysis of amino acids. Agilent Technol. Tech. Note 2000 1100:1-10
  5. Jiang W, Lee J, Jin YM, Qiao Y, Piao R, Jang SM, Woo MO, Kwon SW, Liu X, Pan HY, Du X, Koh HJ. 2011. Identification of QTLs for seed germination capability after various storage periods using two RIL populations in rice. Mol. Cells 31: 385-392
  6. Jiang XL, Deng ZY, Ru ZG, Wu P, Tian JC. 2013. Quantitative trait loci controlling amino acid contents in wheat (Triticum aestivum L.). Aust. J. Crop Sci. 7:820-829
  7. Liu HY, Quampah A, Chen JH, Li JR, Huang ZR, He QL, Shi CH, Zhu SJ. 2012. QTL analysis for gossypol and protein contents in upland cottonseeds with two different genetic systems across environments. Euphytica 188:453-463 https://doi.org/10.1007/s10681-012-0733-x
  8. Liu HY, Quampah A, Chen JH, Li JR, Huang ZR, He QL, Zhu SJ, Shi CH. 2013. QTL mapping based on different genetic systems for essential amino acid contents in cottonseeds in different environments. PLoS One 8: e57531 https://doi.org/10.1371/journal.pone.0057531
  9. Lu K, Li L, Zheng X, Zhang Z, Mou T, Hu Z. 2009. Genetic dissection of amino acid content in rice grain. J Sci Food Agric. 89:2377-2382 https://doi.org/10.1002/jsfa.3731
  10. Maclean JL, Dawe DC, Hardy B, Hettel GP. 2002. Rice almanac. 3rd edn. CABI Publishing: Wallingford, Oxon. 6-7
  11. Mather KA, Caicedo AL, Polato NR, Olsen KM, McCouch S, Purugganan MD. 2007. The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177:2223-2232 https://doi.org/10.1534/genetics.107.079616
  12. Meng L, Li H, Zhang L, Wang J. 2015. QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in bi-parental populations. Crop J. 3:265-279
  13. Munck L, Pram NJ, Moller B, Jacobsen S, Sondergaard I, Engelsen SB, Norgaard L, Bro R. 2001. Exploring the phenotypic expression of a regulatory proteome-altering gene by spectroscopy and chemometrics. Anal. Chim. Acta. 446:171-186
  14. Navea IP, Dwiyanti MS, Park J, Kim B, Lee S, Huang X, Koh HJ, Chin, JH. 2017. Identification of quantitative trait loci for panicle length and yield related traits under different water and P application conditions in tropical region in rice (Oryza sativa L.). Euphytica 213(2):37 https://doi.org/10.1007/s10681-016-1822-z
  15. Panthee DR, Pantalone VR, Sams CE, Saxton AM, West DR, Orf JH, Killam AS. 2006. Quantitative trait loci controlling sulfur containing amino acids, methionine and cysteine, in soybean seeds. Theor. Appl. Genet. 112:546-553 https://doi.org/10.1007/s00122-005-0161-6
  16. Quampah A, Liu HY, Xu HM, Li JR, Wu JG, Zhu SJ, Shi, CH. 2012. Mapping of quantitative trait loci for oil content in cotton seed kernel. J. Genet. 91:289-295 https://doi.org/10.1007/s12041-012-0184-0
  17. Thomson MJ. 2014. High-throughput SNP genotyping to access crop improvement. Plant Breed. Biotechnol. 2:195-212 https://doi.org/10.9787/PBB.2014.2.3.195
  18. Thomson MJ, Zhao K, Wright M, McNally K, Rey J, Tung CW, Reynolds A, Scheffler B, Eizenga G, McClung A, Kim H, Ismail AM, de Ocampo M, Mojica C, Reveche MY, Dilla-Ermita CJ, Mauleon R, Leung H, Bustamante C, McCouch SR. 2012. High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Mol. Breed. 29:875-886 https://doi.org/10.1007/s11032-011-9663-x
  19. Wang LQ, Zhong M, Li XH, Yuan DJ, Xu YB, Liu HF, He YQ, Luo LJ, Zhang QF. 2008. The QTL controlling amino acid content in grains of rice (Oryza sativa) are co-localized with the regions involved in the amino acid metabolism pathway. Mol. Breed. 21:127-137
  20. Wen J, Xu JF, Long Y, Wu JG, Xu HM, Meng JL, Shi CH. 2016. QTL mapping based on the embryo and maternal genetic systems for non-essential amino acids in rapeseed (Brassica napus L.) meal. J. Sci. Food Agric. 96:465-473 https://doi.org/10.1002/jsfa.7112
  21. WHO. 1973. Energy and protein requirements. WHO Tech. Rep. Ser. 522. World Health Organization, Geneva
  22. Wright MH, Tung CW, Zhao K, Reynolds A, McCouch SR, Bustamante CD. 2010. ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations. Bioinformatics 26:2952-2960 https://doi.org/10.1093/bioinformatics/btq533
  23. Zhao W, Park EJ, Chung JW, Park YJ, Chung IM, Ahn JK, Kim GH. 2009. Association analysis of the amino acid contents in rice. J. Integr. Plant Biol. 51:1126-1137 https://doi.org/10.1111/j.1744-7909.2009.00883.x
  24. Zheng X, Wu JG, Lou XH, Xu HM, Shi CH. 2008. QTL analysis of maternal and endosperm genomes for Histidine and Arginine in rice (Oryza sativa L.) across environments. Acta. Agron. Sin. 34:369-375 https://doi.org/10.1016/S1875-2780(08)60016-4
  25. Zhou Y, Cai HM, Xiao JH, Li XH, Zhang QF, Lian XM. 2009. Overexpression of aspartate aminotransferase genes in rice resulted in altered nitrogen metabolism and increased amino acid content in seeds. Theor. Appl. Genet. 118:13811390 https://doi.org/10.1007/s00122-009-0988-3