“Non-food Crops-to-Industry schemes in EU27”
WP2. Plant breeding
D2.4 Validation of existing molecular markers
(if any) (robustness and ease of use)
Lead beneficiary: Agricultural University of Athens
Authors: Dimitra Milioni
Theoni Margaritopoulou
November 2011
The project is a Coordinated Action supported by
Grant agreement no. 227299
Table of contents
OIL CROPS 3
Oilseed rape (Brassica napus) 3
Sunflower (Helianthus annus) 6
FIBER CROPS 10
Flax (Linum usitatissimum L) 10
Hemp (Cannabis sativa L) 11
Kenaf (Hibiscus cannabinus L) 12
CARBOHYDRATE CROPS 13
Maize (Zea mays L) 13
Potato (Solanum spp L) 17
Sorghum (Sorghum bicolor L) 20
SPECIALTY CROPS 23
Coneflower (Echinacea angustifolia DC) 23
Pepermint (Mentha piperita L) 23
Pot marigold (Calendula officinalis L) 23
WP2
DELIVERABLE 2.4
OIL CROPS
Oilseed rape (Brassica napus)
Canola/rapeseed (Brassica napus L.) is a major oilseed crop in Canada, Europe, Australia, China and the Indian subcontinent. Erucic acid, one of the main fatty acids in rapeseed oil, has several potential applications in the oleo-chemical industry. High throughput genome-specific and gene-specific molecular markers for erucic acid genes in Brassica napus have been developed and successfully implemented in canola/rapeseed breeding programs (Rahman et al., 2008).
Seed weight is an important component of grain yield in oilseed rape, but the genetic basis for this important quantitative trait is still not clear. Recently, a study using 10 natural environments and 2 related populations (DH lines and derived fixed homozygous F2 lines) was conducted to unravel the complex nature of seed yield and yield-related traits in rapeseed (Shi et al. 2009). A remarkable finding is that very few QTL were universally detected at all environments tested suggesting that they could be used for MAS. Additionally, two major QTLs, TSWA7a and TSWA7b, were stably identified and validated across years in a haploid (DH) population and an F2 population with different genetic backgrounds. The QTLs identified are well suitable to MAS due to no significant epistatic interactions that could interfere with each other in selection process (Fan et al., 2010).
Diseases are of major concern for all Brassica cultivars. A serious disease is the white rust, which is caused by Albugo candida. AFLP and CAPS (cleaved amplified polymorphic sequence) markers for the white rust resistance gene have been developed and validated in Brassica juncea, (Varshney et al., 2004). These data can serve as a very helpful database for the exploration of the white rust in rapeseed. Blackleg, caused by Leptosphaeria maculans (Desm.), is one of the most serious diseases of rapeseed, in Australia, Europe and Canada. In rapeseed, a number of qualitative and quantitative genes conferring blackleg resistance have been tagged using molecular markers in different mapping populations (Delourme et al., Dusabenyagasani and Fernando 2008; Yu et al. 2008). In order to use genetic markers for routine marker assisted selection in rapeseed breeding programs, blackleg resistance-molecular marker associations have been identified and validated in diverse genetic backgrounds (Raman et al., 2011).
Xu et al. (2010) constructed an integrated genetic linkage map for the genome of Brassica napus using simple sequence repeats (SSRs) markers derived from the sequenced BACs in Brassica rapa. A total of 890 SSR markers have been validated for the construction of the integrated map. Additionally, using validated EST-SSR markers Ramchiary et al. (2011) were able to create a high-density integrated map from 4 individual mapping populations of B.rapa. Transferability analysis of these markers to other cultivated brassica relatives showed 100% amplification for B. napus. These highly transferable genetic markers can facilitate to the molecular mapping of quantitative trait loci, the positioning of specific genes and additionally to marker assisted selection not only for B.rapa but for the relative species as well. Furthermore, a consortium of eleven industrial partners amongst with Agriculture and Agri-Food Canada (AAFC), DNA Landmarks (DLM) and Dow Agrosciences (DAS) have developed and validated a large number of single nucleotide polymorphisms (SNPs) through screening of 235 winter and spring oilseed rape lines. The use of these markers can provide a tool for the investigation of the genetic relationships between the DAS oilseed rape lines (Wiggins et al., 2010).
A very promising tool used either for efficient hybrid production or for assisting in recurrent selection, is Dominant Genic Male Sterility (DGMS). Song et al. (2006) have validated a series of eight amplified fragment length polymorphisms (AFLPs) which are tightly linked to the male sterility allele (Ms) and further developed a marker that is specific to the restore allele (Mf). These markers can facilitate breeding towards new elite homozygous sterile lines and allow further research on map-based cloning.
References
· Delourme R, Piel N, Horvais R, Pouilly N, Domin C, Vallée P, Falentin C, Manzanares-Dauleux MJ and Renard M (2008) Molecular and phenotypic characterization of near isogenic lines at QTL for quantitative resistance to Leptosphaeria maculans in oilseed rape (Brassica napus L.). Theor Appl Genet 117:1055-1067.
· Dusabenyagasani M and Fernando WGD (2008) Development of a SCAR marker to track canola resistance against blackleg caused by Leptosphaeria maculans pathogenicity group 3. Plant Disease 92:903-908.
· Fan C, Cai G, Qin J, Li Q, Yang M, Wu J, Fu T, Liu K and Zhou Y (2010) Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus. Theor Appl Genet 121:1289-1301.
· Rahman M, Sun Z, McVetty PB and Li G (2008) High throughput genome-specific and gene-specific molecular markers for erucic acid genes in Brassica napus (L.) for marker-assisted selection in plant breeding. Theor Appl Genet 117:895-904.
· Raman H, Raman R, Taylor B, Lindbeck K, CoombesN, Eckermann P, Batley J, Edwards D, Price A, Rehman A, Marcroft S, Luckett D, Hossain S and Salisbury P (2011) Blackleg resistance in rapeseed: phenotypic screen, molecular markers and genome wide linkage and association mapping in 17th Australian Research Assembly on Brassicas (ARAB), August 2011.
· Ramchiary N, Nguyen VD, Li X, Hong CP, Dhandapani V, Choi SR, Yu G, Piao ZY and Lim YP (2011). Genic microsatellite markers in Brassica rapa: development, characterization, mapping and their utility in other cultivated and wild Brassica relatives. DNA research 1-16.
· Shi J, Li R, Qiu D, Jiang C, Long Y, Morgan C, Bancroft I, Zhao J and Meng J (2009) Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus. Genetics 182:851–861
· Song LQ, Fu TD, Tu JX, Ma CZ and Yang GS (2006). Molecular validation of multiple allele inheritance for dominant genic male sterility gene in Brassica napus L. Theor Appl Genet 113:55-62.
· Varshney A, Mohapatra T and Sharma RP (2004). Development and validation of CAPS and AFLP markers for white rust resitance gene in Brassica juncea. Theor Appl Genet 109:153-159.
· Wiggins M, Tang S, Bai Y, Lu F, Powers C, Pita F, Ubayasena L, Ehlert Z, Kubik T, Gingera G, Stoll C, Ripley V, Greene T, Thompson S and Kumpatla S (2010). High-throughput single nucleotide polymorphism (SNP) discovery and marker validation in Brassica napus. Dow Agrosciences.
· Xu J, Qian X, Wang X, Li R, Cheng X, Yang Y, Fu J, Zhang S, King GJ, Wu J and Liu K (2010). Construction of an integrated genetic linkage map for the A genome of Brassica napus using SSR markers derived from sequenced BACs in B.rapa. BMC Genomics 11:594.
· Yu F, Lydiate DJ and Rimmer SR (2008) Identification and mapping of a third blackleg resistance locus in Brassica napus derived from B. rapa subsp. sylvestris. Genome 51:64-72.
Sunflower (Helianthus annus)
Markers’ validation assesses their linkage to and association with QTLs and their effectiveness in selection of the target phenotype in independent populations and different genetic backgrounds (Collard et al., 2005)
Stress responses are of great importance for all cultivated crops. In order to saturate a sunflower genetic map and facilitate marker-assisted selection (MAS) breeding for stress response, it is necessary to enhance map saturation with molecular markers localized in linkage groups associated to genomic regions involved in these traits. Validation of genic SSRs in four genotypes of sunflower (RHA266, PAC2, HA89 and RHA801) resulted in amplification of 74 sequences from a total of 127 analyzed. Out of them, 13% represented polymorphic loci, 45% monomorphic, 5% null alleles and the remaining 37% showed either no amplification product, nonspecific amplification or complex or difficult to resolve banding patterns (Talia et al., 2010). The percentage of polymorphism observed coincides with that reported by Heesacker et al. (2008), which conclude that less than 10% of the transcribed loci in sunflower can be genetically mapped using SSR, and in agreement with reports for other species (Eujayl et al. 2004; Fraser et al. 2004; Varshney et al. 2005).
Broomrape (Orobancche cumana) infects the roots of sunflower crop causing severe losses. Breeding for resistant sunflower cultivars is the most effective method to control the parasitic weed. A set of markers have been validated in a number of different genetic backgrounds for the Or5 gene conferring resistance to race E of broomrape (Luoras et al., 2004; Perez-Vich et al., 2004, Tang et al., 2003). Additionally, examples of markers validation across various genetic backgrounds have been reported for the PI2 gene determining resistance to different downy mildew races (Brahm et al., 2000) and to the R1 and Radv genes conferring resistance to rust (Lawson et al., 1998). Midstalk rot, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in sunflower (Helianthus annuus L.). QTLs controlling three resistant (stem lesion, leaf lesion and speed of fungal control) and two morphological (leaf length and leaf length with petiole) traits have been identified and validated for this devastating disease of sunflower (Micic et al., 2005) across generations. QTLs have also been validated across environments (Bert et al., 2002) and genetic backgrounds (Ronicke et al., 2005). For sunflower oil content, QTLs have been validated across generations, environments and mapping populations (Tang et al., 2006a; Leon et al., 2003).
Furthermore, markers have been developed in sunflower for simple traits selection, based on gene mutations underlying the trait of interest. Kolkman et al. (2004) identified a mutation in codon 205 in the acetohydroxyacid synthase gene AHAs-1 that confers resistance to imidazolinone (IMI) herbicides and developed a SNP genotyping assay diagnostic for it. A non-lethal knockout mutation in a MPBQ/MSBQ-MT locus on LG1 (MT-1), underlying beta-tocopherol accumulation in sunflower seeds, was identified. Robust STS markers diagnostic for wild type and mutant MT-1 alleles have been developed (Tang et al., 2006b).
References
· Bert PF, Jouan I, Tourvieille de Labrouhe D, Serre F, Nicolas P and Vear F (2002). Comparative genetic analysis of quantitative traits in sunflower ( Helianthus annuus L.) 1. Characterisation of QTL involved in resistance to Sclerotinia sclerotiorum and Diaporthe helianthi. Theor Appl Genet 105:985–993.
· Brahm L, Rocher T and Friedt W (2000). PCR-based markers facilitating marker assisted selection in sunflower for resistance to downy mildew. Crop Sci 40:676-682.
· Collard BCY, Jahufer MZZ, Brouwer JB and Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142:169-196.
· Eraser LG, Harvey CF, Crowhurst RN and Silve HN (2004). EST-derived microsatellites from Actinidia species and their potential for mapping. Theor Appl Genet 108:1010-1016.
· Heesacker A, Kishore VK, Gao W, Tang S, Kolkman JM, Gingle A, Matvienko M, Kozik A, Michelmore RM, Lai Z, Rieseberg LH and Knapp SJ (2008). SSRs and INDELs mined from the sunflower EST database: Abundance, polymorphisms and cross-taxa utility. Theor Appl Genet 117:1021-1029.
· Kolkman JM, Slabaugh MB, Bruniard JM, Berry S, Bushman BS, Olungu C, Maes N, Abratti G, Zambelli A, Miller JF, Leon A and Knapp SJ (2004). Acetohydroxyacid synthase mutations conferring resistance to imidazolinone or sulfonylurea herbicides in sunflower. Theor Appl Genet 109:1147-1155.
· Lawson WR, Goulter KC, Henry RJ, Kong GA and Kochman JK (1998). Marker-assisted selection for two rust resistance genes in sunflower. Mol Breed 4:227-234.
· Leon AJ, Andrade FH and.Lee M (2003) Genetic analyses of seed-oil concentration across generations and environments in sunflower. Crop Sci 43:135–140.
· Luoras M, Stanciu D, Ciuca M, Nastase D and Geronzi F. Preliminary studies to the use of marker assisted selection for resistance to Orobanche Cumana wallr in sunflower. Romanian agricultural research 21.
· Micic Z, Hahn V, Bauer E, Melchinger AE, Knapp SJ, Tang S and Schon CC. Identification and validation of QTL for Sclerotinia midstalk rot resistance in sunflower by selective genotyping. Theor Appl Genet 111:233-242.
· Talia P, Nishinakamasu V, Hopp HE, Heinz RA and paniego N (2010). Genetic mapping of EST-SSRs, SSR and InDels to improve saturation of genomic regions in a previously developed sunflower map.Electronic Journal of Biotechnology ISSN: 0717-3458. 113, Number 5, 783-799,
· Tang S, Leon A, Bridges WC and Knapp SJ (2006a). Quantitative trait loci for genetically correlated seed traits are tightly linked to branching and pericarp pigment loci in sunflower. Crop Sci. 46:721–734.
· Tang S, Hass CG and Knapp SJ (2006b) Ty3/gypsy-like retrotransposon knockout of a 2-methyl-6-phytyl-1,4-benzoquinone methyltransferase is non-lethal, uncovers a cryptic paralogous mutation, and produces novel tocopherol (vitamin E) profiles in sunflower. Theor Appl Genet 113:783-793.
· Tang S and Knapp S (2003) Microsatellites uncover extraordinary diversity in native American land races and wild populations of cultivated sunflower. Theor Appl Genet 106:990-1003.
· Pérez-Vich B, Akhtouch B, Knapp SJ, Leon AJ, Velasco V, Fernández-Martínez JM and Berry ST (2004b). Quantitative trait loci for broomrape (Orobanche cumana Wallr.) resistance. Theor Appl Genet 109: 92-102.
· Rönick, S, Hahn V, Vogler A and Friedt W (2005). Quantitative Trait Loci Analysis of Resistance to Sclerotinia sclerotiorum in Sunflower. Phytopathol., 95:834–839.
· Varshney RK, Graner A and Sorrells ME (2005). Genic microsatellite markers in plants: features and applications. Trends Biotech 23:48-55.
FIBER CROPS
Flax (Linum usitatissimum L)
Flax is the third largest natural fiber crop and one of the five major oil crops in the world. Different molecular marker techniques have been applied in the flax molecular marker development and in flax genetic resource evaluation. These include random amplified polymorphic DNA (RAPD), restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) (Roose-Amsaleg et al., 2006; Adugna et al., 2006; McBreen et al., 2003; Fu et al., 2003; Oh et al., 2000; Spielmeyer et al., 1998). However, the numbers of effective flax molecular markers are still limited. Fifty Expressed Sequence Tag-derived microsatellite markers (EST-SSRs) have been evaluated for polymorphism and transferability in 50 Linum usitatissimum cultivars/accessions and 11 Linum species (Soto-Cerda et al., 2011). The high rate of flax EST-SSRs markers’ transferability validates their potential application for fingerprinting, functional diversity, comparative mapping and Marker Assisted Selection (MAS)