Use of synteny to identify candidate genes underlying QTL controlling stomatal traits in faba bean (Vicia faba L.)
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We have identified QTLs for stomatal characteristics on chromosome II of faba bean by applying SNPs derived from M. truncatula , and have identified candidate genes within these QTLs using synteny between the two species.
Faba bean (Vicia faba L.) is a valuable food and feed crop worldwide, but drought often limits its production, and its genome is large and poorly mapped. No information is available on the effects of genomic regions and genes on drought adaptation characters such as stomatal characteristics in this species, but the synteny between the sequenced model legume, Medicago truncatula, and faba bean can be used to identify candidate genes. A mapping population of 211 F5 recombinant inbred lines (Mélodie/2 × ILB 938/2) were phenotyped to identify quantitative trait loci (QTL) affecting stomatal morphology and function, along with seed weight, under well-watered conditions in a climate-controlled glasshouse in 2013 and 2014. Canopy temperature (CT) was evaluated in 2013 under water-deficit (CTd). In total, 188 polymorphic single nucleotide polymorphisms (SNPs), developed from M. truncatula genome data, were assigned to nine linkage groups that covered ~928 cM of the faba bean genome with an average inter-marker distance of 5.8 cM. 15 putative QTLs were detected, of which eight (affecting stomatal density, length and conductance and CT) co-located on chromosome II, in the vicinity of a possible candidate gene—a receptor-like protein kinase found in the syntenic interval of M. truncatula chromosome IV. A ribose-phosphate pyrophosphokinase from M. truncatula chromosome V, postulated as a possible candidate gene for the QTL for CTd, was found some distance away in the same chromosome. These results demonstrate that genomic information from M. truncatula can successfully be translated to the faba bean genome.
KeywordsQuantitative Trait Locus Linkage Group Faba Bean Composite Interval Mapping Quantitative Trait Locus Region
H. Khazaei expresses his gratitude to the Emil Aaltonen Foundation (Emil Aaltosen Säätiö) and Niemi-säätiö for their financial support. The project was further supported by the University of Helsinki, Niemi-Säätiö and by “Legume Futures (Legume-supported cropping systems for Europe)”, a collaborative research project funding from the European Union’s Seventh Programme for research, technological development and demonstration under Grant Agreement No. 245216. In addition, we thank Markku Tykkyläinen, Sanna Peltola, Sini Lindström and Stefano Zanotto for their assistance in the glasshouse work. We thank Prof. Wolfgang Link (Georg-August-University, Göttingen, Germany) for providing the seeds of the parental lines.
Conflict of interest
The authors declare that they have no conflict of interest.
- Asfaw A, Blair MW, Struik P (2012) Multienvironment quantitative trait loci analysis of photosynthate acquisition, accumulation, and remobilization traits in common bean under drought stress. Genes Genomes Genet 5:579–595Google Scholar
- Box GEP, Cox DR (1964) An analysis of transformations. J Roy Stat Soc B 26:211–252Google Scholar
- Cottage A, Webb A, Hobbs D, Khamassi K, Maalouf F, Ogbannaya F, Stoddard FL, Duc G, Link W, Thomas JE, O’Sullivan DM (2012b) SNP discovery and validation for genomic-assisted breeding of faba bean (Vicia faba L.). In: VI international conference on legume genetics and genomics (ICLGG). Hyderabad, IndiaGoogle Scholar
- Cruz-Izquierdo S, Avila CM, Satovic Z, Palomino C, Gutierrez N, Ellwood SR, Phan HTT, Cubero JI, Torres AM (2012) Comparative genomics to bridge Vicia faba with model and closely-related legume species: stability of QTLs for flowering and yield-related traits. Theor Appl Genet 125:1767–1782PubMedCrossRefGoogle Scholar
- Doheny-Adams T, Hunt L, Franks PJ, Beerling DJ, Gray JE (2012) Genetic manipulation of stomatal density influences stomatal size, plant growth and tolerance to restricted water supply across a growth carbon dioxide gradient. Philos Trans R Soc Lond B Biol Sci 367:547–555PubMedCrossRefPubMedCentralGoogle Scholar
- FAO (2012) FAOSTAT. In: Food and Agriculture Organization of the United Nations. Rome, Italy. http://faostat.fao.org/
- Henry DC (2009) Genomic, proteomic and metabolomic approaches to study drought responses in Aquilegia. Ph.D. thesis. Clemson University USA. p 123Google Scholar
- Hiremath PJ, Kumar A, Penmetsa RV, Farmer A, Schlueter JA, Chamarthi SK, Whaley AM, Carrasquilla-Garcia N, Gaur PM, Upadhyaya HD, Kishor PBK, Shah TM, Cook DR, Varshney RK (2012) Large-scale development of cost-effective SNP marker assays for diversity assessment and genetic mapping in chickpea and comparative mapping in legumes. Plant Biotech J 10:716–732CrossRefGoogle Scholar
- IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DG et al (eds) A special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, Cambridge and New York, p 582Google Scholar
- Khazaei H, O’Sullivan DM, Sillanpää MJ, Stoddard FL (2014) Genetic analysis reveals a novel locus in Vicia faba decoupling pigmentation in the flower from that in the extra-floral nectaries. Mol Breed (in press). doi: 10.1007/s11032-014-0100-9
- Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, Batistic O, D’Angelo C, Bornberg-Bauer E, Kudla J, Harter K (2007) The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J 50:347–363PubMedCrossRefGoogle Scholar
- Marshall A, Aalen RB, Audenaert D, Beeckman T, Broadley MR, Butenko MA, Caño-Delgado A, De Vries S, Dresselhaus T, Felix G, Graham NS, Foulkes J, Granier C, Greb T, Grossniklaus U, Hammond JP, Heidstra R, Hodgman C, Hothorn M, Inzé D, Østergaard L, Russinova E, Simon R, Skirycz A, Stahl Y, Zipfel C, De Smete I (2012) Tackling drought stress: receptor-like kinase presents new approaches. Plant Cell 24:2262–2278PubMedCrossRefPubMedCentralGoogle Scholar
- Osakabe Y, Mizuno S, Tanaka H, Maruyama K, Osakabe K, Todaka D, Fujita Y, Kobayashi M, Shinozaki K, Yamaguchi-Shinozaki K (2010) Overproduction of the membrane-bound receptor-like protein kinase 1, RPK1, enhances abiotic stress tolerance in Arabidopsis. J Biol Chem 285:9190–9201PubMedCrossRefPubMedCentralGoogle Scholar
- Ricciardi L (1989) Plant breeding for resistance to drought. II. Relationship between stomata and agronomic traits in Vicia faba L. genotypes. Agric Mediterr 119:424–434Google Scholar
- Satovic Z, Avila CM, Cruz-Izquierdo S, Díaz-Ruíz R, García-Ruíz GM, Palomino C, Gutiérrez N, Vitale S, Ocaña-Moral S, Gutiérrez MV, Cubero JI, Torres AM (2013) A reference consensus genetic map for molecular markers and economically important traits in faba bean (Vicia faba L.). BMC Genom 14:932CrossRefGoogle Scholar
- Saxena RK, Varma Penmetsa RV, Upadhyaya HD, Kumar A, Carrasquilla-Garcia N, Schlueter JA, Farmer A, Whaley AM, Sarma BK, May GD, Cook DR, Varshney RK (2012) Large-scale development of cost-effective single-nucleotide polymorphism marker assays for genetic mapping in pigeon pea and comparative mapping in legumes. DNA Res 19:449–461PubMedCrossRefPubMedCentralGoogle Scholar
- Tayeh N, Bahrman N, Devaux R, Bluteau A, Prosperi JM, Delbreil B, Lejeune-Hénaut I (2013) A high-density genetic map of the Medicago truncatula major freezing tolerance QTL on chromosome 6 reveals colinearity with a QTL related to freezing damage on Pisum sativum linkage group VI. Mol Breed 32:279–289CrossRefGoogle Scholar
- Torres AM, Avila CM, Stoddard FL, Cubero JI (2012) Faba bean. In: Torres AM, Cubero JI, Kole C, Pérez de la Vega M (eds) Genetics, genomics and breeding in crop plants: cool season food legumes. Science Pubs Inc, New Hampshire, pp 50–97Google Scholar
- Wang S, Basten CJ, Zeng Z-B (2012) Windows QTL Cartographer 2.5. In: Department of Statistics, North Carolina State University, Raleigh, NC. http://statgen.ncsu.edu/qtlcart/WQTLCart.htm