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Molecular Genetics and Genomics

, Volume 294, Issue 1, pp 57–68 | Cite as

Development of sequence-based markers for seed protein content in pigeonpea

  • Jimmy Obala
  • Rachit K. Saxena
  • Vikas K. Singh
  • C. V. Sameer Kumar
  • K. B. Saxena
  • Pangirayi Tongoona
  • Julia Sibiya
  • Rajeev K. VarshneyEmail author
Original Article

Abstract

Pigeonpea is an important source of dietary protein to over a billion people globally, but genetic enhancement of seed protein content (SPC) in the crop has received limited attention for a long time. Use of genomics-assisted breeding would facilitate accelerating genetic gain for SPC. However, neither genetic markers nor genes associated with this important trait have been identified in this crop. Therefore, the present study exploited whole genome re-sequencing (WGRS) data of four pigeonpea genotypes (~ 12X coverage) to identify sequence-based markers and associated candidate genes for SPC. By combining a common variant filtering strategy on available WGRS data with knowledge of gene functions in relation to SPC, 108 sequence variants from 57 genes were identified. These genes were assigned to 19 GO molecular function categories with 56% belonging to only two categories. Furthermore, Sanger sequencing confirmed presence of 75.4% of the variants in 37 genes. Out of 30 sequence variants converted into CAPS/dCAPS markers, 17 showed high level of polymorphism between low and high SPC genotypes. Assay of 16 of the polymorphic CAPS/dCAPS markers on an F2 population of the cross ICP 5529 (high SPC) × ICP 11605 (low SPC), resulted in four of the CAPS/dCAPS markers significantly (P < 0.05) co-segregated with SPC. In summary, four markers derived from mutations in four genes will be useful for enhancing/regulating SPC in pigeonpea crop improvement programs.

Keywords

Seed protein content Cajanus cajan Whole-genome resequencing Next generation sequencing Sequence variants Common variant analysis 

Notes

Acknowledgements

Authors are thankful to the United States Agency for International Development (USAID) and ICRISAT for funding various projects related to pigeonpea genomics at ICRISAT. We also thank Mr. Aamir Khan (ICRISAT) for his help in processing the WGRS data and Ms. Anu Chitikineni (ICRISAT) for coordinating Sanger sequencing and primer ordering. This work has been undertaken as part of the CGIAR Research Program on Grain Legumes and Dryland Cereals. ICRISAT is a member of CGIAR Consortium.

Compliance with ethical standards

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

Jimmy Obala declares that he has no conflict of interest. Rachit K. Saxena declares that he has no conflict of interest. Vikas K. Singh declares that he has no conflict of interest, C.V. Sameer Kumar declares that he has no conflict of interest. K.B. Saxena declares that he has no conflict of interest, Pangirayi Tongoona declares that he has no conflict of interest. Julia Sibiya declares that she has no conflict of interest. Rajeev K. Varshney declares that he has no conflict of interest.

Data availability statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

Supplementary material

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References

  1. Akibode CS, Maredia M (2011) Global and regional trends in production, trade and consumption of food legume crops. Report submitted to the Standing Panel on Impact Assessment (SPIA) of the CGIAR Science Council. FAO, RomeGoogle Scholar
  2. Araújo WL, Trofimova L, Mkrtchyan G, Steinhauser D, Krall L, Graf A, Fernie AR, Bunik VI (2013) On the role of the mitochondrial 2-oxoglutarate dehydrogenase complex in amino acid metabolism. Amino Acids 44:683–700CrossRefGoogle Scholar
  3. Azam S, Thakur V, Ruperao P, Shah T, Balaji J, Amindala B, Farmer AD, Studholme DJ, May GD, Edwards D, Jones JD, Varshney RK (2012) Coverage-based consensus calling (CbCC) of short sequence reads and comparison of CbCC results to identify SNPs in chickpea (Cicer arietinum; Fabaceae), a crop species without a reference genome. Am J Bot 99:186–192CrossRefGoogle Scholar
  4. Bansal V, Harismendy O, Tewhey R, Murray SS, Schork NJ, Topol EJ, Frazer KA (2010) Accurate detection and genotyping of SNPs utilizing population sequencing data. Genome Res 20:537–545CrossRefGoogle Scholar
  5. Blanco A, Mangini G, Giancaspro A, Giove S, Colasuonno P, Simeone R, Signorile A, De Vita P, Mastrangelo L, Cattivelli AM, Gadaleta A (2012) Relationships between grain protein content and grain yield components through quantitative trait locus analyses in a recombinant inbred line population derived from two elite durum wheat cultivars. Mol Breed 30:79–92CrossRefGoogle Scholar
  6. Bolon YT, Joseph B, Cannon SB, Graham MA, Diers BW, Farmer AD, May GD, Muehlbauer GJ, Specht JE, Tu ZJ, Weeks N, Xu WW, Shoemaker RC, Vance PC (2010) Complementary genetic and genomic approaches help characterize the linkage group I seed protein QTL in soybean. BMC Plant Biol 10:41CrossRefGoogle Scholar
  7. Burstin J, Marget P, Huart M, Moessner A, Mangin B, Duchene C, Desprez B, Munier-Jolain N, Duc G (2007) Developmental genes have pleiotropic effects on plant morphology and source capacity, eventually impacting on seed protein content and productivity in pea. Plant Physiol 144:768–781CrossRefGoogle Scholar
  8. Cabezas JA, González-Martínez SC, Collada C, Guevara MA, Boury C, de María N, Eveno E, Aranda I, Garnier-Géré PH, Brach J, Alía R, Plomion C, Cervera MT (2015) Nucleotide polymorphisms in a pine ortholog of the Arabidopsis degrading enzyme cellulase KORRIGAN are associated with early growth performance in Pinus pinaster. Tree Physiol 35:1000–1006CrossRefGoogle Scholar
  9. Church DM, Schneider VA, Graves T, Auger K, Cunningham F, Bouk N, Chen H-C, Agarwala R, McLaren WM, Ritchie GRS, Albracht D, Kremitzki M, Rock S, Kotkiewicz H, Kremitzki C, Wollam A, Trani L, Fulton L, Fulton R, Matthews L, Whitehead S, Chow W, Torrance J, Dunn M, Harden G, Threadgold G, Wood J, Collins J, Heath P, Griffiths G, Pelan S, Grafham D, Eichler EE, Weinstock G, Mardis ER, Wilson RK, Howe K, Flicek P, Hubbard T (2011) Modernizing reference genome assemblies. PLoS Biol 9:e1001091CrossRefGoogle Scholar
  10. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Ruden DM, Lu X (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w118. Fly 6:1–13CrossRefGoogle Scholar
  11. Cohen H, Israeli H, Matityahu I, Amir R (2014) Seed-specific expression of a feedback-insensitive form of cystathionine-g-synthase in Arabidopsis stimulates metabolic and transcriptomic responses associated with desiccation stress. Plant Physiol 166:1575–1592CrossRefGoogle Scholar
  12. Coonrod EM, Durtschi JD, Margraf RL, Voelkerding KV (2013) Developing genome and exome sequencing for candidate gene identification in inherited disorders: an integrated technical and bioinformatics approach. Arch Pathol Lab Med 137:415–433CrossRefGoogle Scholar
  13. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis KS, Gabriel B, Altshuler D, Daly MJ (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498CrossRefGoogle Scholar
  14. Deschamps S, la Rota M, Ratashak JP, Biddle P, Thureen D, Farmer A, Luck S, Beatty M, Nagasawa N, Michael L, Llaca V, Sakai H, May G, Lightner J, Campbell MA (2010) Rapid genome-wide single nucleotide polymorphism discovery in soybean and rice via deep resequencing of reduced representation libraries with the Illumina Genome Analyzer. Plant Genome 3:53–68CrossRefGoogle Scholar
  15. FAO (2016) FAOSTAT. Food and Agriculture Organisation of the United Nations. http://faostat3.fao.org. Acccessed 28 Aug 2018
  16. Feng X, Yu X, Tong J (2014) Novel single nucleotide polymorphisms of the insulin-like growth factor-1 gene and their associations with growth traits in common carp (Cyprinus carpio L.). Int J Mol Sci 15:22471–22482CrossRefGoogle Scholar
  17. Gaj T, Sirk SJ, Shui S-L, Liu J (2016) Genome-editing technologies; principles and applications. Cold Spring Harb Perspect Biol 8:a023754CrossRefGoogle Scholar
  18. Gilissen C, Hoischen A, Brunner HG, Veltman JA (2012) Disease gene identification strategies for exome sequencing. Eur J Hum Genet 20:490–497CrossRefGoogle Scholar
  19. Grattapaglia D (2008) Genomics of Eucalyptus, a global tree for energy, paper and wood. In: Moore P, Ming R (eds) Genomics of tropical crop plants. Springer, New York, pp 257–295Google Scholar
  20. Greilhuber J, Obermayer R (1998) Genome size variation in Cajanus cajan (Fabaceae): a reconsideration. Plant Syst Evol 212:135–141CrossRefGoogle Scholar
  21. Guo Y, Yang X, Chander S, Yan J, Zhang J, Song T, Li J (2013) Identification of unconditional and conditional QTL for oil, protein and starch content in maize. Crop J 1:34–42CrossRefGoogle Scholar
  22. Hwang E-Y, Song Q, Jia G, Specht JE, Hyten DL, Costa J, Cregan PB (2014) A genome-wide association study of seed protein and oil content in soybean. BMC Genom 15:1CrossRefGoogle Scholar
  23. Hyten DL, Cannon SB, Song Q, Weeks N (2010a) High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. BMC Genom 11:38CrossRefGoogle Scholar
  24. Hyten DL, Song Q, Fickus EW, Quigley CV (2010b) High throughput SNP discovery and assay development in common bean. BMC Genom 11:475CrossRefGoogle Scholar
  25. Janninks J (2001) Using interconnected populations to find quantitative trait loci. http://www.reeis.usda.gov/web/crisprojectpages/0189427-using-interconnected-populations-to-find-quantitative-trait-loci.html. Accessed 24 Feb 2016
  26. Joshi NA, Fass JN (2011) Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. https://github.com/najoshi/sickle. Accessed 20 Jan 2016
  27. Korbie DJ, Mattick JS (2008) Touchdown PCR for increased specificity and sensitivity in PCR amplification. Nat Protoc 3:1452–1456CrossRefGoogle Scholar
  28. Krajewski P, Bocianowski J, Gawłowska M, Kaczmarek Z, Pniewski T, Święcicki W, Wolko B (2012) QTL for yield components and protein content: a multi-environment study of two pea (Pisum sativum L.) populations. Euphytica 183:323–336CrossRefGoogle Scholar
  29. Krishnan HB, Natarajan SS, Oehrle NW, Garrett WM, Darwish O (2017) Proteomic analysis of pigeonpea (Cajanus cajan) seeds reveals the accumulation of numerous stress-related proteins. J Agric Food Chem 65:4572–4581CrossRefGoogle Scholar
  30. Kumar V, Khan AW, Saxena RK, Garg V, Varshney RK (2016) First-generation HapMap in Cajanus spp. reveals untapped variations in parental lines of mapping populations. Plant Biotech J 14:1673–1681CrossRefGoogle Scholar
  31. Lam H-M, Wong P, Chan H-K, Yam K-M, Chen L, Chow C-M, Coruzzi G-M (2003) Overexpression of the ASN1 gene enhances nitrogen status in seeds of Arabidopsis. Plant Physiol 132:926–935CrossRefGoogle Scholar
  32. Langmead B, Salzberg S (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefGoogle Scholar
  33. Lee S (2012) Converting SNPs to CAPS and dCAPS marker using dCAPS Finder. http://articles.extension.org/pages/32594/converting-snps-to-caps-and-dcaps-marker-using-dcaps-finder. Accessed 17 July 2016
  34. Lestari P, Koh HJ (2013) Development of new CAPS/dCAPS and SNAP markers for rice eating quality. HAYATI J Biosci 20:15–23CrossRefGoogle Scholar
  35. Lestari P, Van K, Lee J, Kang YJ, Lee S-H (2013) Gene divergence of homeologous regions associated with a major seed protein content QTL in soybean. Front Plant Sci 4:176CrossRefGoogle Scholar
  36. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefGoogle Scholar
  37. Li J, Dai X, Liu T, Zhao PX (2012) LegumeIP: an integrative database for comparative genomics and transcriptomics of model legumes. Nucleic Acids Res 40:D1221–D1229CrossRefGoogle Scholar
  38. Lim J-H, Yang H-J, Jung K-H, Yoo S-C, Paek N-C (2014) Quantitative trait locus mapping and candidate gene analysis for plant architecture traits using whole genome re-sequencing in rice. Mol Cells 37:149–160CrossRefGoogle Scholar
  39. Lovén J, Orlando DA, Sigova AA, Lin CY, Rahl PB, Burge CB, Levens DL, Lee TI, Young RA (2012) Revisiting global gene expression analysis. Cell 151:476–482CrossRefGoogle Scholar
  40. Mace ES, Buhariwalla HK, Crouch JH (2003) A high throughput DNA extraction protocol for molecular breeding programs. Plant Mol Biol Rep 21:459–459CrossRefGoogle Scholar
  41. Machado M, Magalhães WC, Sene A, Araújo B, Faria-Campos AC, Chanock SJ, Scott L, Oliveira G, Tarazona-Santos E, Rodrigues MR (2011) Phred-Phrap package to analyses tools: a pipeline to facilitate population genetics re-sequencing studies. Investig Genet 2:3CrossRefGoogle Scholar
  42. Mahender A, Anandan A, Pradhan SK, Pandit E (2016) Rice grain nutritional traits and their enhancement using relevant genes and QTLs through advanced approaches. SpringerPlus 5:2086CrossRefGoogle Scholar
  43. Mligo JK, Craufurd PQ (2005) Adaptation and yield of pigeonpea in different environments in Tanzania. Field Crop Res 94:43–53CrossRefGoogle Scholar
  44. Mula MG, Saxena KB (2010) Lifting the level of awareness on pigeonpea—a global perspective. International Crops Research Institute for the Semi-Arid Tropics, PatancheruGoogle Scholar
  45. Neff MM, Turk E, Kalishman M (2002) Web-based primer design for single nucleotide polymorphism analysis. Trend Genet 18:613–661CrossRefGoogle Scholar
  46. Nigro D, Gu YQ, Huo N, Marcotuli I, Blanco A, Gadaleta A, Anderson OD (2013) Structural analysis of the wheat genes encoding NADH-dependent glutamine-2-oxoglutarate amidotransferases and correlation with grain protein content. PLoS One 8:e73751CrossRefGoogle Scholar
  47. Obala J (2017) Study of inheritance and identification of molecular markers for seed protein content in pigeonpea (Cajanus cajan). PhD Thesis, University of KwaZulu-Natal, South AfricaGoogle Scholar
  48. Odeny DA (2007) The potential of pigeonpea (Cajanus cajan (L.) Millsp.) in Africa. Nat Resour Forum 31:297–305CrossRefGoogle Scholar
  49. Ohta M, Wakasa Y, Takahashi H, Hayashi S, Kudo K, Takaiwa F (2013) Analysis of rice ER-resident J-proteins reveals diversity and functional differentiation of the ER-resident Hsp70 system in plants. J Exp Bot 64:5429–5441CrossRefGoogle Scholar
  50. Olson ND, Lund SP, Colman RE, Foster JT, Sahl JW, Schupp JM, Keim P, Morrow JB, Salit ML, Zook JM (2015) Best practices for evaluating single nucleotide variant calling methods for microbial genomics. Front Genet 6:235CrossRefGoogle Scholar
  51. Pandurangan S, Pajak A, Molnar SJ, Cober ER, Dhaubhadel S, Hernández-Sebastiá C, Kaiser WM, Nelson RL, Huber SC, Marsolais F (2012) Relationship between asparagine metabolism and protein concentration in soybean seed. J Exp Bot 63:3173–3184CrossRefGoogle Scholar
  52. Pereira CS, da Costa DS, Pereira S, Nogueira FD, Albuquerque PM, Teixeira J, Faro C, Pissarra J (2008) Cardosins in postembryonic development of cardoon: towards an elucidation of the biological function of plant aspartic proteinases. Protoplasma 232:203–213CrossRefGoogle Scholar
  53. Pflieger S, Lefebvre V, Causse M (2001) The candidate gene approach in plant genetics: a review. Mol Breed 7:275–291CrossRefGoogle Scholar
  54. Rahaie M, Xue GP, Schenk PM (2013) The role of transcription factors in wheat under different abiotic stress. In: Vahdati K, Leslie C (eds) Abiotic stress-plant responses and applications in agriculture. InTechOpen, London.  https://doi.org/10.5772/54795 Google Scholar
  55. Rao PP, Birthal PS, Bhagavatula S, Bantilan MCS (2010) Chickpea and pigeonpea economies in Asia: facts, trends and outlook. International Crops Research Institute for the Semi-Arid Tropics, PatancheruGoogle Scholar
  56. Rios J, Stein E, Shendure J, Hobbs HH, Cohen JC (2010) Identification by whole genome resequencing of gene defect responsible for severe hypercholesterolemia. Hum Mol Genet 19:4313–4318CrossRefGoogle Scholar
  57. Roach JC, Glusman G, Smit AFA, Hu VCD, Hubley R, Shannon RT, Rowen L, Pant KP, Goodman N, Bamshad M, Shendure J, Drmanac R, Jorde LB, Hood L, Galas DJ (2010) Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328:636–639CrossRefGoogle Scholar
  58. Saxena KB, Sawargaonkar SL (2015) Genetic enhancement of seed proteins in pigeonpea—methodologies, accomplishments and opportunities. Int J Sci Res 4:254–258Google Scholar
  59. Saxena RK, Prathima C, Saxena KB, Hoisington DA, Singh NK, Varshney RK (2010) Novel SSR markers for polymorphism detection in pigeonpea (Cajanus spp.). Plant Breed 129:142–148CrossRefGoogle Scholar
  60. Saxena RK, Obala J, Sinjushin A, Sameer-Kumar CV, Saxena KB, Varshney RK (2017) Characterization and mapping of Dt1 locus which co-segregates with CcTFL1 for growth habit in pigeonpea. Theor Appl Genet 130:1773–1784CrossRefGoogle Scholar
  61. Schoenbeck MA, Temple SJ, Trepp GB, Blumenthal JM, Samac DA, Gantt SJ, Hernandez G, Vance CP (2000) Decreased NADH-glutamate synthase activity in nodules and flowers of alfalfa (Medicago sativa L.) transformed with an antisense glutamate synthase transgene. J Exp Bot 51:29–39Google Scholar
  62. Silva J, Scheffler B, Sanabria Y, de Guzman C, Galam D, Farmer A, Woodward J, May G, Oard J (2012) Identification of candidate genes in rice for resistance to sheath blight disease by whole genome sequencing. Theor Appl Genet 124:63–74CrossRefGoogle Scholar
  63. Singh VK, Khan AW, Saxena RK, Kumar V, Kale SM, Sinha P, Chitikineni A, Pazhamala LT, Garg V, Sharma M, Sameer-Kumar CV, Parupalli S, Vechalapu S, Patil S, Muniswamy S, Ghanta A, Yamini KN, Dharmaraj PS, Varshney RK (2016a) Next-generation sequencing for identification of candidate genes for Fusarium wilt and sterility mosaic disease in pigeonpea (Cajanus cajan). Plant Biotechnol J 14:1183–1194CrossRefGoogle Scholar
  64. Singh VK, Khan AW, Jaganathan D, Thudi M, Roorkiwal M, Takagi H, Garg V, Kumar V, Chitikineni A, Gaur PM, Sutton T, Terauchi R, Varshney RK (2016b) QTL-seq for rapid identification of candidate genes for 100-seed weight and root/total plant dry weight ratio under rainfed conditions in chickpea. Plant Biotechnol J 14:2110–2119CrossRefGoogle Scholar
  65. Singh VK, Khan AW, Saxena RK, Sinha P, Kale SM, Parupalli S, Kumar V, Chitikineni A, Vechalapu S, Sameer-Kumar CV, Sharma M, Ghanta A, Yamini KN, Muniswamy S, Varshney RK (2017) Indel-seq: a fast-forward genetics approach for identification of trait-associated putative candidate genomic regions and its application in pigeonpea (Cajanus cajan). Plant Biotechnol J 15:906–914CrossRefGoogle Scholar
  66. Sobreira NLM, Cirulli ET, Avramopoulos D, Wohler E, Oswald GL, Stevens EL, Ge D, Shianna KV, Smith JP, Maia JM, Gumbs CE, Pevsner J, Thomas G, Valle D, Hoover-Fong JE, Goldstein DB (2010) Whole-genome sequencing of a single proband together with linkage analysis identifies a Mendelian disease gene. PLoS Genet 6:e1000991CrossRefGoogle Scholar
  67. The UniProt Consortium (2008) The universal protein resource (UniProt). Nucleic Acids Res 36:D190–D195CrossRefGoogle Scholar
  68. Upadhyaya HD, Reddy KN, Sastry DVSSR, Gowda CLL (2007a) Identification of photoperiod insensitive sources in the world collection of pigeonpea at ICRISAT. J SAT Agric Res 3:46–49Google Scholar
  69. Upadhyaya HD, Reddy KN, Gowda CLL, Silim SN (2007b) Patterns of diversity in pigeonpea (Cajanus cajan (L.) Millsp.) germplasm collected from different elevations in Kenya. Genet Resour Crop Evol 54:1787–1795CrossRefGoogle Scholar
  70. Upadhyaya HD, Bajaj D, Narnoliya L, Das S, Kumar V, Gowda CLL, Sharma S, Tyagi AK, Parida SK (2016) Genome-wide scans for delineation of candidate genes regulating seed protein content in chickpea. Front Plant Sci 7:302CrossRefGoogle Scholar
  71. Varshney RK, Chen W, Li Y, Bharti AK, Saxena RK, Schlueter JA, Donoghue MTA, Azam S, Fan G, Whaley AM, Farmer AD, Sheridan J, Iwata A, Tuteja R, Penmetsa RV, Wu W, Upadhyaya HD, Yang S, Shah T, Saxena KB, Michael T, McCombie WR, Yang B, Zhang G, Yang H, Wang J, Spillane C, Cook DR, May GD, Xu X, Jackson SA (2012) Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers. Nat Biotechnol 30:83–89CrossRefGoogle Scholar
  72. Varshney RK, Saxena RK, Upadhyaya HD, Khan AW, Yu Y, Kim C, Rathore A, Kim D, Kim J, An S, Kumar V, Anuradha G, Yamini KN, Zhang W, Muniswamy S, Kim J, Penmetsa RV, von Wettberg E, Datta SK (2017) Whole-genome resequencing of 292 pigeonpea accessions identifies genomic regions associated with domestication and agronomic traits. Nat Genet 49:1082–1088CrossRefGoogle Scholar
  73. Xu J, Yuan Y, Xu Y, Zhang G, Guo X, Wu F, Wang Q, Rong T, Pan G, Cao M, Tang Q, Gao S, Liu Y, Wang J, Lan H, Lu Y (2014) Identification of candidate genes for drought tolerance by whole-genome re-sequencing in maize. BMC Plant Biol 14:83CrossRefGoogle Scholar
  74. Yamada T, Mori Y, Yasue K, Maruyama N, Kitamura K, Abe J (2014) Knockdown of the 7S globulin subunits shifts distribution of nitrogen sources to the residual protein fraction in transgenic soybean seeds. Plant Cell Rep 33:1963–1976CrossRefGoogle Scholar
  75. Yang GH, Dong YB, Li YL, Wang QI, Shi QL, Zhou Q (2014) QTL verification of grain protein content and its correlation with oil content by using connected RIL populations of high-oil maize. Genet Mol Res 13:881–894CrossRefGoogle Scholar
  76. You FM, Huo N, Gu YQ, Luo M-C, Ma Y, Hane D, Lazo GR, Dvorak J, Anderson OD (2008) BatchPrimer3: a high throughput web application for PCR and sequencing primer design. BMC Bioinform 9:253CrossRefGoogle Scholar
  77. You FM, Huo N, Deal KR, Gu YQ, Luo M-C, McGuire PE, Dvorak J, Anderson OD (2011) Annotation-based genome-wide SNP discovery in the large and complex Aegilops tauschii genome using next-generation sequencing without a reference genome sequence. BMC Genom 12:59CrossRefGoogle Scholar
  78. Zeng Y-D, Sun J-L, Bu S-H, Deng K-S, Tao T, Zhang Y-M, Zhang T-Z, Du X-M, Zhou B-L (2016) EcoTILLING revealed SNPs in GhSus genes that are associated with fiber- and seed-related traits in upland cotton. Sci Rep 6:29250CrossRefGoogle Scholar
  79. Zhang YH, Liu MF, He JB, Wang YF, Xing GN, Li Y, Yang SP, Zhao TJ, Gai JH (2015) Marker-assisted breeding for transgressive seed protein content in soybean [Glycine max (L.) Merr.]. Theor Appl Genet 128:1061–1072CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jimmy Obala
    • 1
    • 2
  • Rachit K. Saxena
    • 1
  • Vikas K. Singh
    • 1
  • C. V. Sameer Kumar
    • 1
  • K. B. Saxena
    • 1
  • Pangirayi Tongoona
    • 2
  • Julia Sibiya
    • 2
  • Rajeev K. Varshney
    • 1
    Email author
  1. 1.International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)HyderabadIndia
  2. 2.University of KwaZulu-Natal, African Center for Crop ImprovementPietermaritzburgSouth Africa

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