Validation of existing molecular markers

“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
2
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.,
3
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.
4
References
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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 genomespecific 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.
5
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.,
6
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).
7
References
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
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). Markerassisted 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 nonlethal, uncovers a cryptic paralogous mutation, and produces novel
tocopherol (vitamin E) profiles in sunflower. Theor Appl Genet 113:783-793.
8
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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.
9
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)
References
 Adugna W, Labuschagne MT and Viljoen CD (2006). The use of
morphological and AFLP markers in diversity analysis of linseed. Biodivers
Conserv 15:3193-3205.
 Fu YB, Guerin S, Peterson GW, Diederichsen A, Rowland GG and Richards
KW (2003). RAPD analysis of genetic variability of regenerated seeds in
the Canadian flax cultivar CDC Normandy. Seed Sci Technol 31:207-211.
 McBreen K, Lockhart PJ, McLenachan PA, Scheele S and Robertson AW
(2003). The use of molecular techniques to resolve relationships among
traditional weaving cultivars of Phormium. N Zeal J Bot 41(2):301-310.
 Oh TJ, Gorman M and Cullis CA (2000). RFLP and RAPD mapping in flax
(Linum usitatissimum). Theor Appl Genet 101:590-593.
 Roose Amsaleg C, Cariou Pham E, Vautrin D, Tavernier R and Solignac M
(2006). Polymorphic microsatellite loci in Linum usitatissimum. Mol Ecol
Notes 6:796-799.
 Spielmeyer W, Green AG, Bittisnich D, Mendham N and Lagudah ES
(1998). Identification of quantitative trait loci contributing to Fusarium wilt
resistance on an AFLP linkage map of flax (Linum usitatissimum). Theor
Appl Genet 97:633-641.
 Soto-Cerda BJ, Saavedra HU, Navarro C and Ortega PM (2011)
Characterization of novel genic SSR markers in Linum usitatissimum (L.)
and their transferability across eleven Linum species. DOI: 10.2225/vol14issue2-fulltext-6.
10
Hemp (Cannabis sativa L)
Cannabis sativa L. has a long association with humans, as a source of fiber,
oil and food, and for its medicinal and intoxicating properties. Microsatellite markers
were developed for Cannabis sativa L. to be used for DNA typing (genotype
identification) and to measure the genetic relationships between the different plants.
Eleven microsatellite markers have been validated for DNA typing and for assessing
genetic relatedness in Cannabis (Alghanim and Almirall, 2003). The effectiveness of
two different types of markers associated to the locus determining the chemotype in
Cannabis has been evaluated as possible tools in marker-assisted selection in hemp,
but also for possible applications in the forensic and pharmaceutical fields (Pacifico et
al., 2006).
References
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Alghanim HJ and Almirall JR (2003) Development of microsatellite markers in
Cannabis sativa for DNA typing and genetic relatedness analyses. Anal Bioanl
Chem 376:1225-1233.
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Pacifico D, Miselli F, Micheler M, Carboni A, Ranalli P and Mandolino G (2006)
Genetics and Marker-assisted Selection of the Chemotype in Cannabis sativa L.
Mol Breeding 17: 257–268.
11
Kenaf (Hibiscus cannabinus L)
Kenaf (Hibiscus cannabinus L.) is one of the most economically important
crops for fibre production. The genetic diversity and phylogenetic relationship has
been analyzed by sequence-related amplified polymorphism (SRAP) marker system
on 84 varieties of kenaf germplasm collected from 26 countries and regions around
the world (Qi et al., 2011).
However, genetic information for kenaf, especially, at the molecular level is
limited. Most of the genetic improvements of the kenaf broodstocks have resulted
from the use of traditional selective breeding techniques, such as selection,
crossbreeding and hybridization, which have worked best on traits with additive
genetic variation, but not well enough on traits with low heritability. Genomic
research and especially QTL mapping will eventually lead to marker-assisted
selection (MAS) for efficient and precise selection. To implement MAS, researchers
should first find molecular markers that were linked closely with the given
performance or production trait and then determine the location of this trait on the
linkage map. Only recently, the kenaf genetic linkage map was constructed,
providing new insight into the genetic structure of the species and serving as a
reference to increase the resolution of future maps and will be very useful in
consolidating linkage groups (Chen et al., 2011).
References
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Chen M-X, Wei C-L, Qi J-M, Chen X-B, Su J-G, Li A-Q, Tao A-F and Wu W-R
(2011) Genetic linkage map construction for kenaf using SRAP, ISSR and
RAPD markers. DOI: 10.1111/j.1439-0523.2011.01879.x
Qi J, Xua J, Liab A, Wanga X, Zhanga G, Suad J and Liue A (2011)
Analysis of Genetic Diversity and Phylogenetic Relationship of Kenaf
Germplasm by SRAP. J Natur Fiber 8:99-110.
12
CARBOHYDRATE CROPS
Maize (Zea mays L)
Recent developments in plant molecular genetics have provided plant
breeders with powerful tools to identify and select Mendelian components underlying
both simple and complex agronomic traits. Inexpensive PCR-based markers for
provitamin A has been developed and validated at CIMMYT from the corresponding
gene sequence (unpublished results, CIMMYT) using seed DNA-based genotyping
method, which will enable developing-country breeders to more effectively produce
maize cultivars with higher provitamin A levels.
In maize, a trait that has been extensively investigated as an indirect
measure of drought tolerance is the capacity of ABA accumulation. The presence of a
major QTL for root features (root-ABA1) was mapped on bin 2.04 in Os420 ×
IABO78. This major QTL affecting abscisic acid (ABA) concentration in the leaf, root
traits and relative water content was further evaluated in maize using NILs (Landi et
al., 2005). Interestingly, the QTL allele for larger root mass and higher ABA
concentration negatively affected grain yield (Landi et al., 2007). Grain yield
(Graham et al., 1997) and flowering time (Szalma et al., 2007) traits have also been
mapped using this method. Laurie et al. (2004) were able to detect 50 QTL
accounting for genetic variance in maize oil content with a resolution of the order of
a few centimorgans across generations.
Quantitative trait loci conditioning resistance to plant pathogens (rQTL) have
been discovered and reviewed by several authors (Balint-Kurti and Johal 2008;
Redinbaugh and Pratt 2009). To date only a few QTL conferring resistance to Maize
streak mastrevirus, Cercospora zeae-maydis, Exserohilum turcicum (Pass.) and
Peronosclerospora sorghiin maize have been validated (Abalo et al. 2009; Asea et al.
2009; Nair et al., 2005). For Cercospora resistance in maize, QTLs have been
validated across genetic backgrounds (Pozar et al., 2009) and environments (Juliatti
et al., 2009). Furthermore, a major QTL controlling maize streak virus resistance
explains 50–70% of total phenotypic variation (Pernet et al., 1999). Several
microsatellite markers associated with this QTL were validated across populations
and have been successfully used for the selection of resistant lines (William et al.,
2007).
13
Maize (Zea mays L.) stalk lodging is breakage of the stalk at or below the ear,
which may result in loss of the ear at harvest. Stalk lodging is often intensified by the
stalk tunneling action of the second-generation of the European corn borer (2-ECB)
[Ostrinia nubilalis (Hübner)]. Rind penetrometer resistance (RPR) has been used to
measure stalk strength and improve stalk lodging resistance, and quantitative trait
loci (QTL) have been identified for both RPR and 2-ECB damage. Validation studies
of QTL for resistance to ECB tunneling in the stalk, plant height, and the number of
days to anthesis, have often found relatively few QTL in common between the
generations or samples (Austin and Lee, 1996; Melchinger et al., 1998). However, it
has been demonstrated that rQTL in several maize populations were effective in
improving both stalk strength (rind penetrometer resistance) and European corn
borer (Ostrinia nubilalis) resistance related to stalk lodging (Flint-Garcia et al., 2003).
Analyses for evaluating the significance of QTL x genetic background
interactions in several diverse mapping populations, have been performed in maize
for grain moisture, silking date and grain yield (Blanc et al., 2006). QTL metaanalysis is another approach to identify consensus QTL across studies, to validate
QTL effects across environments/genetic backgrounds, and also to refine QTL
positions on the consensus map (Goffinet and Gerber, 2000). The concept of metaanalysis has been applied to the analysis of QTL/genes for flowering time (Chardon
et al., 2004) and drought tolerance in maize (Hao et al., 2010). A meta-analysis of
quantitative trait loci (QTL) associated with plant digestibility and cell wall
composition in maize has been carried out and fifteen metaQTL with confidence
interval (CI) smaller than 10 cM were identified (Truntzler et al., 2010).
14
References
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


Abalo G, Edema R, Derera J and Tongoona P (2009) A comparative analysis
of conventional and marker-assisted selection methods in breeding maize
streak virus resistance in maize. Crop Sci 49:509–520.
Asea G, Vivek B, Bigirwa G, Lipps PE and Pratt RC (2009) Validation of
consensus quantitative trait loci for resistance to multiple foliar pathogens of
maize. Phytopathology 99:540–547.
Austin DF and Lee M (1996) Comparative mapping in F2:3 and F6:7
generations of quantitative trait loci for grain yield and yield component in
maize. Theor Appl Genet 92:817-826.
Balint-Kurti PJ and Johal GS (2008) Maize disease resistance. In: Bennetzen
JL, Hake SC (eds) Maize genetics handbook: its biology. Springer, New York,
pp 251–270.
Blanc G, Charcosset A, Mangin B, Gallais A and Moreau L (2006) Connected
populations for detecting quantitative trait loci and testing for epistasis: an
application in maize. Theor Appl Genet 113:206-24.
Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, Murigneux A
and Charcosset A (2004) Genetic architecture of flowering time in maize as
inferred from quantitative trait loci meta-analysis and synteny conservation
with the rice genome. Genet 168:2169-2185.
Flint-Garcia S.A, Darrah L.L, McMullen M.D, Hibbard B.E (2003) Phenotypic
versus marker-assisted selection for stalk strength and second-generation
European corn borer resistance in maize. Theor. Appl. Genet. 107:1331–1336.
Goffinet B and Gerber S (2000) Quantitative trait loci: a meta-analysis. Genet
155:463-473.
Graham GI, Wolff DW and Stuber CW (1997) Characterization of a yield
quantitative trait locus on chromosome five of maize by fine mapping. Crop
Sci 37:1601–1610.
Hao Z, Li X, Liu X, Xie C, Li M, Zhang D and Zhang S (2010) Meta-analysis of
constitutive and adaptive QTL for drought tolerance in maize. Euphytica
174:165–177.
Juliatti FC, Pedrosa MG, Silva HD and Corrêa da Silva JV (2009) Genetic
mapping for resistance to gray leaf spot in maize. Euphytica 169:227-238.
Landi P, Sanguineti MC, Liu C, Li Y, Wang TY, S. Giuliani S, Bellotti M, Salvi S
and Tuberosa R (2007) Root-ABA1 QTL affects root lodging, grain yield, and
other agronomic traits in maize grown under well-watered and water-stressed
conditions. J Exp Bot 58:319–326.
Landi P, Sanguineti MC, Salvi S, Giuliani S, Bellotti M, Maccaferri M, Conti S
and Tuberosa R (2005) Validation and characterization of a major QTL
affecting leaf ABA concentration in maize. Mol Breed 15: 291-303.
Laurie CC, Chasalow SD, LeDeaux JR, McCarroll R, Bush D, Hauge B, Lai C,
Clark D, Rocheford TR and Dudley JW (2004) The genetic architecture of
response to long-term artificial selection for oil concentration in the maize
kernel. Genetics 168:2141-2155.
Melchinger AE, Utz HF and Schön CC (1998) Quantitative trait locus (QTL)
mapping using different testers and independent population samples in maize
15
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reveals low power of QTL detection and large bias in estimates of QTL effects.
Genetics 149:383-403.
Nair SK, Prasanna BM, Garg A, Rathore RS, Setty TA and Singh NN (2005)
Identification and validation of QTLs conferring resistance to sorghum downy
mildew (Peronosclerospora sorghi) and Rajasthan downy mildew (P.
heteropogoni) in maize. Theor Appl Genet 110:1384-1392.
Pernet A, Hoisington D, Dintinger J et al. (1999) Genetic mapping of maize
streak virus resistance from the Mascarene source. II. Resistance in line
CIRAD390 and stability across germplasm. Theor Appl Genet 99:540–553
Pozar G, Butruille D, Silva HD, McCuddin ZP and Penna JC (2009) Mapping
and validation of quantitative trait loci for resistance to Cercospora zeaemaydis infection in tropical maize (Zea mays L.). Theor Appl Genet 18:553-64.
Redinbaugh M, Pratt RC (2009) Virus Resistance. In: Bennetzen JL, Hake SC
(eds) Handbook of maize: its biology, 2nd edn. Springer, New York, pp 251–
270.
Szalma SJ, Hostert BM, LeDeaux JR, Stuber CW and Holland J (2007). QTL
mapping with near-isogenic lines in maize. Theor Appl Genet 114:1211–1228.
Truntzler M, Barrière Y, Sawkins MC, Lespinasse D, Betran J, Charcosset A
and Moreau L (2010) Meta-analysis of QTL involved in silage quality of maize
and comparison with the position of candidate genes. Theor Appl Genet
121:1465-82.
William HM, Morris M, Warburton M et al. (2007) Technical, economic and
policy considerations on marker-assisted selection in crops: lessons from the
experience at an international agricultural research centre. In: Guimarães E,
Ruane J, Scherf B, Sonnino A, Dargie J (eds) Marker-assisted selection.
Current status and future perspectives in crops, livestock, forestry and fish.
Food and Agriculture Organization of the United Nations, Rome
16
Potato (Solanum spp L)
Simple sequence repeat (SSR) or microsatellite markers are a valuable tool
for genetic research. By using potato publicly available EST sequences, Tang et al.
(2008) created a PolySSR tool which can serve as a pipeline for the identification of
polymorphic SSRs. The markers identified by this tool have been all validated. This
greatly improves the efficiency of marker development, especially in species where
there are low levels of polymorphism.
It is known that most agronomic plant traits result from complex molecular
networks involving multiple genes and from environmental factors. Tuber
susceptibility to bruising is a complex trait of the cultivated potato (Solanum
tuberosum) that is crucial for crop quality. Recently, diagnostic markers for tuber
bruising and enzymatic discoloration have been validated (Urbany et al., 2011). The
markers diagnostic for increased or decreased bruising susceptibility is expected to
facilitate the combination of superior alleles in breeding programs.
Potato germplasm requiries the use of sources of resistance to pests and
diseases in order to breed varieties cheaper to grow. Potatoes contract many
different viruses. One of the worst is Potato virus Y (PVY) which can reduce yield up
to 80 percent. Also, if the virus is relatively symptomless, it can prevent certification
of seed, which is appeared to be healthy, devastating the seed grower with an
unexpected loss of livelihood. Genes that encode resistance to PVY have been
identified. Although the actual copy number of the genes is not known, DNA markers
located close to these genes have been identified and validated(Ryon et al., 2009).
The use of these markers reduces time, expense, and unrealiability of determining
which potato breeding materials are resistant to PVY, thus accelerating the breeding
process of potato varieties. Furthermore, CAPs and SCARs have allowed the breeding
of genotypes resistant to PVY (Kasai et al. 2000). The successful employment of four
PCR-based diagnostic assays to combine the Ry adg gene for extreme resistance to
PVY with Gro1 for nematode resistance and with Rx1 for extreme resistance to
potato virus X (PVX, genus Potexvirus), or with Sen1 for wart resistance
(Synchytrium endobioticum) has been reported (Gebhardt et al., 2006).
The potato cyst nematode (Globodera pallida) is one of the most significant
soilborne pests of potatoes worldwide. The availability of DNA-based markers, which
are easy to score, cost-effective and diagnostic for resistance to G. pallida Pa2/3
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would greatly speed up the process of new variety development. A set of markers
have been validated for GpaIV adg across a wide range of germplasm (Moloney et al.,
2010). Phytophthora infestans (Mont.) de Bary, the causal agent of late blight, is a
very devastating pathogen in potato cultivars. Field resistance has been
characterized in a potato segregating family of 230 full-sub progenies derived from a
cross between two hybrid S. phureja x S. stenotomum clones. QTLs have been
identified and validated for the new genetic loci in this diploid potato family
contributing to general resistance against late blight (Constanzo et al., 2005).
Since the numbers of new potato breeding cultivars is increasing yearly, the
reliable maintenance of large culture collections is becoming more problematic.
Additionally, the differentiation of cultivars based on morphological characteristics is
a highly skilled and time-consuming task and for these reasons a rapid and robust
method for variety differentiation has become highly desirable. The validation of a
set of six SSRs markers that can be used to differentiate over 400 potato cultivars
has been reported (Reid and Kerr, 2007).
18
References









Constanzo S, Simko I, Christ BJ and Haynes KG (2005). QTL analysis of late
blight resistance in a biploid potato family of Solanum phureja x S.
stenotomum. Theor Appl Genet 111:609-617.
Marker-assisted combination of major genes for pathogen resistance in
potato.
Gebhardt C, Bellin D, Henselewski H, Lehmann W, Schwarzfischer J and
Valkonen JP (2006) Marker-assisted combination of major genes for pathogen
resistance in potato. Theor Appl Genet 112:1458–1464.
Kasai K, Morikawa Y, Sorri VA et al. (2000) Development of SCAR markers to
the PVY resistance gene Ryadg based on a common feature of plant disease
resistance genes. Genome 43:1–8.
Moloney C, Griffin D, Jones PW, Bryan GJ, McLean K, Bradshaw JE and
Milbourne D (2010) Development of diagnostic markers for use in breeding
potatoes resistant to Globodera pallida pathotype Pa2/3 using germplasm
derived from Solanum tuberosum ssp.andigena CPC 2802. Theor Appl Genet
120:679-89.
Reid A and Kerr Em (2007). A rapid sequence repeat (SSR)-based
identification method for potato cultivars. Plant Genetic Resources:
Characterization and Utilization 5: 7-13.
Ryon O, Hane D, Brown C, Yilma S, James S, Mosley A, crosslink J and Vales
M (2009). Validation and implementation of marker-assisted selection (MAS)
for PVY resistance (Ryadg gene) in a tetraploid potato breeding program.
American Journal of potato research.
Tang J, Baldwin SJ, Jacobs JME, van der Linden CG, Voorrips RE, Leunissen
JAM, van Eck H and Vosman B (2008). Large-scale identification of
polymorphic microsatellites using an in silico approach. BMC bioinformatics
9:374.
Urbany C, Stich B, Schmidt L, Simon L, Berding H, Junghans H, Niehoff KH,
Braun A, Tacke E, Hofferbert HR, Lubeck J, Strahwald J and Gebhardt C
(2011). Association genetics in Solanum tuberosum provides new insights into
potato tuber bruising and enzymatic tissue discoloration. BMC genomics 12:7.
19
Sorghum (Sorghum bicolor L)
Identification of genomic regions/ quantitative trait loci (QTL) associated with
important agronomic traits is essential. The identification of QTLs controlling
significant traits in sorghum would improve our understanding of inheritance of these
traits, enable us to analyze association between these traits, clarify the relationships
of QTLs to candidate genes and finally provide the basis for MAS of these traits.
Therefore, it is important to validate these putative QTLs across various genetic
backgrounds and environments before using them in marker-assisted selection
programs.
There are some good examples of QTLs validation in sorghum. Three SSR
markers (Xtxp43, Xtxp51, and Xtxp211), each representing a QTL, have been
validated across populations and environments, demonstrating the utility of MAS for
a quantitative trait, early-season cold tolerance (Knoll and Ejeta, 2008). For
Atherigona resistance, 25 QTLs have been validated across different environments
and genetic backgrounds (Aruna et al., 2011). A significant growing constraint to
sorghum production in sub-Saharan Africa is the hemi-parasitic weed Striga
hermonthica (Del.) Benth. Reliable QTL for striga field resistance in sorghum have
been identified (Haussmann et al., 2004). Since their effects were validated across
environments, years and independent recombinant inbred sorghum populations
samples, these QTL are excellent candidates for marker-assisted selection. Sorghum
ergot, caused predominantly by Claviceps africana Frederickson, Mantle, de Milliano,
is a significant threat to the sorghum industry worldwide. QTLs underlying ergot
resistance have been identified and validated across sorghum populations (Parh et
al., 2008). Anthracnose, one of the destructive foliar diseases of sorghum, is incited
by the fungus Colletotrichum graminicola. RAPD and SCAR markers linked to
anthracnose resistance gene in sorghum have been developed and validated.
Therefore, these identified RAPD and SCAR markers can be used in the resistancebreeding program of sorghum anthracnose by marker-assisted selection (Singh et al.,
2005).
The stay-green trait has been reported as an important component of
terminal drought tolerance in sorghum because it assures normal grain filling under
20
water-limited conditions (Xu et al. 2000). The validation of the the stay-green QTLs
from line E36-1 across genetic backgrounds and years has been reported
(Haussmann et al., 2002). Breeding for drought tolerance, particularly through
avoidance mechanisms, is likely to involve root characteristics. The identification and
validation of nodal root angle QTL across a range of diverse sorghum germplasm
presents new opportunities for improving drought adaptation via molecular breeding
to manipulate a trait for which selection has previously been very difficult (Mace et
al., 2011b). An analysis of QTL location of 771 QTL across 44 studies and 161 traits
has been conducted in sorghum, based on the adjusted confidence intervals of QTL
location, projected onto the sorghum consensus map. 400 individual QTL were
validated across all trait categories, with the exception of the leaf trait category
(Mace and Jordan, 2011a).
21
References
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Aruna C, Bhagwat VR, Madhusudhana R, Sharma V, Hussain T, Ghorade
RB, Khandalkar HG, Audilakshmi S, Seetharama N (2011) Identification
and validation of genomic regions that affect shoot fly resistance in
sorghum [Sorghum bicolor (L.) Moench]. Theor Appl Genet 122:1617-30.
Haussmann BIG, Hess DE, Omanya GO, Folkertsma RT, Reddy BVS,
Kayentao M, Welz HG and Geiger HH (2004) Genomic regions influencing
resistance to the parasitic weed Striga hermonthica in two recombinant
inbred populations of sorghum. Theor Appl Genet 109:1005-1016.
Haussmann BI, Mahalakshmi V, Reddy BV, Seetharama N, Hash CT and
Geiger HH (2002) QTL mapping of stay-green in two sorghum
recombinant inbred populations. Theor Appl Genet 106:133-142.
Knoll J and Ejeta G (2008) Marker-assisted selection for early-season cold
tolerance in sorghum: QTL validation across populations and
environments. Theor Appl Genet 16:541-53.
Mace ES and Jordan RD (2011a) Integrating sorghum whole genome
sequence information with a compendium of sorghum QTL studies reveals
uneven distribution of QTL and of gene-rich regions with significant
implications for crop improvement. Theor Appl Genet 123:169-191.
Mace ES, Singh V, Van Oosterom EJ, Hammer GL, Hunt CH and Jordan
DR (2011b) QTL for nodal root angle in sorghum (Sorghum bicolor L.
Moench) co-locate with QTL for traits associated with drought adaptation.
DOI: 10.1007/s00122-011-1690-9.
Parh DK, Jordan DR, Aitken EAB, Mace ES, Jun-ai P, McIntyre CL and
Godwin ID (2008) QTL analysis of ergot resistance in sorghum. Theor
Appl Genet 117:369-382.
Singh M, Chaudhary K, Singal HR, Magill CW and Boora KS (2006)
Identification and characterization of RAPD and SCAR markers linked to
anthracnose resistance gene in sorghum [Sorghum bicolor (L.) Moench].
Euphytica 149: 179–187.
22
SPECIALTY CROPS
Coneflower (Echinacea angustifolia DC)
Pepermint (Mentha piperita L)
Pot marigold (Calendula officinalis L)
To date there are no reports available of molecular marker-based approaches
to Echinacea angustifolia DC, Mentha piperita L and Calendula officinalis L
improvement, and not even the most skeletal of genetic maps are available for any
of the above species.
23