The genomic resources thus generated will be useful for various plant breeding applications such as marker-assisted breeding for gene introgression, mapping QTLs or identifying new or ra
Trang 1Genomic resources for breeding crops with enhanced abiotic stress tolerance
KA I L A S H C BA N S A L1,4, SA N G R A M K LE N K A2,3and TA P A N K MO N D A L1
1
National Bureau of Plant Genetic Resources, Pusa, New Delhi, 110012, India;2Department of Biology, University of Massachusetts, Amherst, MA, 01003, USA;3Present address: Reliance Industries Limited, Reliance Technology Group, Reliance Corporate Park, Navi Mumbai, 400701, India;4Corresponding author, E-mail: kailashbansal@hotmail.com; director@nbpgr.ernet.in
With 1 figure and 2 tables
Received April 13, 2013/Accepted August 28, 2013
Communicated by R Tuberosa
Abstract
To meet the challenges of climate change, exploring natural diversity in
the existing plant genetic resource pool as well as creation of new
mutants through chemical mutagenesis and molecular biology is needed
for developing climate-resilient elite genotypes Ever-increasing area
under existing abiotic stresses as well as emerging abiotic stress factors
and their combinations have further added to the problems of the current
crop improvement programmes However, with the advancement in
mod-ern techniques such as next-generation sequencing technologies, it is
now possible to generate on a whole-genome scale, genomic resources
for crop species at a much faster pace with considerably less efforts and
money The genomic resources thus generated will be useful for various
plant breeding applications such as marker-assisted breeding for gene
introgression, mapping QTLs or identifying new or rare alleles associated
with a particular trait In this article, we discuss various aspects of
gener-ation of genomic resources and their utilizgener-ation for developing abiotic
stress-tolerant crops to ensure sustainable agricultural production and
food security in the backdrop of rapid climate change
Key words: genomic resources — abiotic stress — breeding —
next generation sequencing — association mapping —
phenomics
Feeding the ever-increasing world population in the era of
cli-mate change demands the development of stress-tolerant crop
cultivars Development of such tolerant cultivars through
con-ventional breeding methods depends heavily on the availability
of natural genetic variations in a given crop species However,
genetic variability that exists is quite low and needs to be
wid-ened for further improving the tolerance capacity of crops to
meet the challenges of the climate change Further, efforts are
needed to protect the loss of genetic diversity in several plant
species Efforts have been made since long to collect, conserve
and evaluate plant genetic resources (PGRs), to support the plant
breeders with diverse genetic materials, to widen the genetic
base and to create new crop varieties to combat the climate
change Although there are 240 000 species of plants estimated
to grow on earth, yet only 25–30 of them are used for human
consumption, and of these, rice, wheat and maize together
con-stitute about 75% of global grain production (Cordain 1999).
Therefore, conservation, multiplication and sustainable utilization
of the existing PGRs, which comprise cultivars, landraces and
wild relatives, are essential to combat not only the food shortage
but also for mitigating the crop loss due to climate change.
Various abiotic stresses such as high and low temperature,
excess and deficient water stress, salinity, heavy metals toxicity,
high radiations, high and low nutrient content in the soil, etc are consequences of the rapidly changing climate and are responsi-ble for loss in crop production and productivity Despite the advancements in modern technologies, research strategies for developing climate-resilient cultivars are scanty An integrated strategy based on molecular breeding and genetic engineering approaches utilizing the PGRs is gaining momentum (Varshney
et al 2011) Thus, there is an urgent need to accelerate research efforts to harness the genetic potential of PGRs in general and specifically the wild or alien gene pool by prebreeding and by modern genomics approaches to develop superior stress-tolerant cultivars.
Strategies for generation of genomic resources
As agriculture is becoming more intensified and location-specific, crop improvement objectives are also becoming more and more trait-oriented To meet these objectives, it is not only necessary to conserve available genetic variability, but also important to utilize it Utilization of PGRs can be enhanced sig-nificantly by generating genomic resources Recent advance-ments in ‘omics’ techniques have enabled generation of genomic resources much more efficiently (Mondal and Sutoh 2013) Ulti-mately, these genomic resources are utilized for developing bet-ter cultivars resilient to climate change either through marker-assisted breeding or by genetic transformation for transgenic crop development (Fig 1).
Omics for conservation and utilization of PGRs
Augmentation, characterization and conservation of diversified plant genetic resources are the prerequisite for generation of ge-nomics resources However, high degree of redundancy in the different in situ as well as ex situ collections creates major bot-tlenecks for the management of PGRs Although DNA-based molecular markers are used to identify duplicate samples in the gene bank, yet they suffer from the difficulty to use as a com-mon set of markers for a given set of germplasm of a species Additionally, often the problem of reproducibility of DNA markers data among the laboratories has been encountered Importantly, high-throughput sequencing or next-generation sequencing (NGS) data do not suffer from such shortcomings and therefore are the most suitable to address the issue of redundancy However, sequencing of ex situ collections only to
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Trang 2eliminate redundancy would be too expensive Practically, it is
impossible to sequence each genotype in a large crop collection.
Therefore, there is a need to develop the ‘core reference set’
(CRS, a set of germplasm that is true representative, with 10%
genetic resources of the entire crop genetic diversity) as an
alter-native (Glaszmann et al 2010) This CRS serves as a valuable
resource for the scientific communities for reference purposes,
comparative studies, future reanalysis and integrative genomic
analysis (Hawkins et al 2010) Almost all major crop-specific
CRSs have been developed either by international centres or by
national crop-based institutions In India, CRSs for several crops
are available Recently, we have initiated a massive evaluation
experiment with 22 000 wheat accessions at three different
agro-climatic zones of India with an objective to develop the ‘wheat
CRS ’ Preliminary evaluation of the wheat germplasm has
indi-cated the presence of promising landraces with higher terminal
heat tolerance (unpublished data).
With the advancements of the NGS technologies, sequencing
of CRS is becoming relatively easy with low coverage to
develop genome-wide markers for facilitating the rejection of
duplicates (Bansal et al 2010, Davey et al 2011) Several
NGS-based technologies such as reduced representation libraries
(RRLs) (Gompert et al 2010, You et al 2011), complexity
reduction of polymorphic sequences (CRoPS) (van Orsouw et al.
2007, Mammadov et al 2010), restriction-site-associated DNA
sequencing (RAD-seq) (Baxter et al 2011) and low-coverage
sequencing for genotyping (Huang et al 2009, Andolfatto et al.
2011, Elshire et al 2011) have been developed recently for genetic analysis of plants including the non-model species, wild
as well as alien species, species with high levels of repetitive DNA or breeding lines with low levels of polymorphism These methods can be applied to compare SNP or haplotype diversity within and between closely related plant species or within wild natural populations to avoid redundancy in germplasm collec-tions (Ossowski et al 2010, Pool et al 2010).
Generation of mutants with novel genetic variation
Although natural variation is the main criterion for selecting par-ents of mapping population, yet in several instances, natural vari-ants either do not exist or are difficult to identify by the breeders for breeding purposes On the contrary, it is easy to search the mutants among the controlled or structured population rather than identifying the natural variants among the vast genetic resources Therefore, creating new mutants of agronomic impor-tance has always remained a challenge for the scientific commu-nity In recent past, significant achievements have been made in the development of molecular biology-based techniques for gen-erating mutants such as activation tagging (Borevitz et al 2000), gene/promoter trapping (Pothier et al 2007) and RNA silencing (Lindbo 2012), which are responsible for creating loss or gain of function in higher plants for reverse genetic applications On the other hand, physical or chemical mutagenesis although remains the main choice for decades, several techniques consisting of
Fig 1: Generation and utilization of plant genomic resources There are two major approaches to utilize the genomic resources (one‘molecular breed-ing’ denoted by left box and the other ‘transgenic approach’ indicated by right box) Central circle denotes the identification of diverse PGRs by geno-typing and development of‘core reference set’ (CRS) by phenotyping Genomic DNA (gDNA) clones such as bacterial artificial chromosomes (BAC), mitochondrial or chloroplast DNA (mtDNA, cpDNA), specific regions of the genome (targeted DNA), coding mRNAs or non-coding RNAs are con-sidered to be the basic genomic resources, denoted by blue oval circles These genomic resources are utilized for various crop improvement applica-tions Genomics techniques that can be used are shown by the solid arrows For instance, gDNA samples can be used for genome-wide analysis (GWA) by NGS-based approaches, such as reduced representation libraries (RRL), sequenced restriction-site-associated DNA (S-RAD), genotype by sequencing (GBS), whole-genome resequencing (WGreS), etc These approaches can be used for nucleotide variation profiling (dotted arrows) Simi-larly, targeted DNA can be generated by PCR for amplicon sequencing or through sequence capture The main applications of molecular breeding and transgenic approaches are described in the top boxes Other approaches shown here are: WGS, whole-genome sequencing; miRNA, microRNA; RNAi, RNA induced; and VIGS, Virus-induced gene silencing
Trang 3chemical mutagenesis coupled with molecular breeding have
been evolved (Henikoff and Comai, 2003).
Mutant population has long been a valuable resource in plant
breeding and genomics research (Henikoff and Comai 2003, Till
et al 2003) However, the methods employed (irradiation or
chemical) to induce a mutated population affect its usefulness
and application for genomics research (Comai and Henikoff
2006) TILLING (Targeting Induced Local Lesions IN Genomes)
is a technique that can identify polymorphisms (more specifically
point mutations) resulting from induced mutations in a target
gene by heteroduplex analysis (Till et al 2003) It allows
geno-typic screening for allelic variations before commencing the
phe-notyping (Henikoff et al 2004) It is rapidly becoming a
mainstream technology for the characterization of mutants
(Comai and Henikoff 2006) and discovery of SNPs (Cordeiro
et al 2006) To further expedite the genetic screening of
mutants, a very sensitive high-throughput screening method
based on capillary electrophoresis has been developed (Cross
et al 2007), which used Endonucleolytic Mutation Analysis by
Internal Labeling (EMAIL) to improve the effectiveness of this
new reverse genetics approach for crop improvement.
Similarly, EcoTILLING, which allows us to assign haplotypes,
facilitates reducing the number of accessions to be sequenced
and is fast becoming a cost-effective, time-saving and
high-throughput method of preference This method was recently used
to detect 15 and 23 representative SNPs of OsCPK17 and SalT
gene, respectively, across 375 accessions representing the
biolog-ical diversity available in domesticated rice (Negr~ao et al 2011).
It detected natural allelic variants in 3′-untranslated region
attrib-uted to the regulation of gene expression under salt stress
Na-redo Ma et al (2009) demonstrated the utility of EcoTILLING
for the detection of SNPs in the upland and low-land rice
culti-vars and registered their contributions for drought stress
toler-ance Several polymorphisms of candidate genes were detected,
which were associated with tolerance to drought stress
Simi-larly, EcoTILLING was conducted to identify drought-related
candidate genes in a panel of 96 barley genotypes Overall, 185
SNPs and 46 Indels were discovered and found to be associated
with drought tolerance Based on overlapping haplotype
sequences, markers were developed for four candidate genes:
HvARH1, HvSRG6, HvDRF1 and HVA1, which distinguished
between the tolerant and susceptible cultivars (Cseri et al 2011).
Phenomics: prerequisite for large-scale phenotypic
screening
Once the CRS is developed, the next important step is
identify-ing the trait-specific phenotypes With the advancement of
phe-nomics or high-throughput phenotyping technologies, it is
becoming possible to identify abiotic stress-tolerant genotypes
(Tuberosa 2012) One of the latest developments is automated
greenhouse system for high-throughput plant phenotyping Such
systems allow the non-destructive screening of plants over a
per-iod of time by means of image acquisition techniques During
such screening, different images of each plant are recorded and
analysed with the help of advanced image analysis algorithms to
identify plants with special phenotypes (Hartmann et al 2011).
It is noteworthy to mention that the plants with tolerant
pheno-types are the best source for the generation of genomic resources
and therefore the target for various molecular analyses including
the high-throughput sequencing to identify the alleles of interest.
However, as against the phenomics, field phenotyping under
natural stress conditions should be encouraged to obtain
mean-ingful data across crops and crop seasons Further, a lack of clear correlation between the values obtained in the pot culture
vs field experiment with crop yield data will always cast a sha-dow on the effective use of phenomics for phenotyping the germplasm We therefore strongly advocate precise field pheno-typing using non-destructive methods to obtain accurate associa-tion between genotyping and phenotyping.
Whole-genome de novo sequencing
Although ‘Sanger sequencing’ remained predominant for several decades for decoding the genomes, the ability to sequence the whole genome of an organism with new technology at lower cost with less time has become one of the landmark discoveries in the area of ‘omics’ Until recently, even sequencing a small genome would have required a multi-institutional effort with huge funding With the development of NGS technologies, genome sequencing has become much efficient, faster and cost-effective by several folds Ever since the first 454 NGS platform was launched com-mercially, several other platforms such as Illumina, ABI SOLiD, Helicos, Pac-bio, Ion Torrent and Oxford Nanopore are available for high-throughput sequencing presently Whole genomes of more than 30 plants have already been sequenced de novo (http://genomevolution.org), thus generating enormous genomic resources for further utilization for crop improvement Recently, Beijing Genome Institute of China has undertaken ‘The Million Plant and Animal Genomes Project’ that aims to sequence the gen-ome of thousands of economically and scientifically important plant/animal species This largest genome sequencing project will
be carried out in collaboration with scientists worldwide, which will ultimately aim to generate huge genomic resources and infor-mation This will ultimately help to accelerate the development of tools to ensure food security, improve ecological conservation, and develop new energy sources (www.genomics.cn) Using NGS technologies, pigeon pea and chickpea genomes have been sequenced in India recently (Singh et al 2012, Varshney et al.
2012, 2013, Jain et al 2013, Mir et al 2013).
Genome resequencing for the discovery of genome-wide variation
Once the genome of a plant is sequenced, it can serve as a refer-ence genome for studying genetic resources of the same species
or related species to detect genetic variations for large number of accessions within limited time Thus, whole-genome resequenc-ing of several genotypes or targeted resequencresequenc-ing of CRS becomes practically feasible to generate useful genomic resources and information This has also eliminated important bottlenecks of ascertainment bias (i.e the presence of rare alleles) obtained through biparental mapping population in the estimation of linkage disequilibrium (LD) and genetic relation-ships between accessions (Moragues et al 2010, Cosart et al.
2011, Schuenemann et al 2011) One of the best whole-genome resequencing efforts is 1001 Genomes Project, perhaps the larg-est resequencing project that was launched at the beginning of
2008 to discover genome-wide sequence variations of 1001 accessions of Arabidopsis thaliana Several Arabidopsis lines have been sequenced since then (Lister and Ecker 2009, Cao
et al 2011) It described the majority of small-scale polymor-phisms as well as many larger insertions and deletions in the
A thaliana pan-genome, their effects on gene function and the patterns of local and global linkage among these variants The action of processes other than spontaneous mutations is
Trang 4identi-fied by comparing the spectrum of mutations that have
accumu-lated since A thaliana diverged from its closest relative around
10 million years ago Subsequently, several whole-genome
rese-quencing projects have been initiated in various crop species, for
example rice and maize (Lai et al 2010, He et al 2011, Huang
et al 2013).
Genome-wide association studies (GWAS)
Association mapping (AM) is a promising alternative to classical
linkage mapping to elucidate the genetic basis of complex traits
such as abiotic stress tolerance, which uses the ‘historical
recom-bination events ’ from many lineages (Abdurakhmonov and
Ab-dukarimov 2008, Zhao et al 2011) Although linkage mapping
based on bi-parental progeny has been considered useful so far
for identifying major genes and mapping QTLs (Frary et al.
2000, Komatsuda et al 2007), yet it suffers from several
draw-backs (Moragues et al 2010, Cosart et al 2011, Schuenemann
et al 2011) To overcome the shortfall of the bi-parental-based
linkage mapping, association genetics has served well to
supple-ment these efforts in several crops (Gupta et al 2005, Hall et al.
2010, Maccaferri et al 2011) Further advancement to this
nested association mapping, which combines the advantages of
bi-parental linkage analysis along with association mapping in
single unified mapping population, is also being used for the
genome-wide dissection of complex traits, as reported in maize
(Yu et al 2008) GWAS are very much useful in diverse
germ-plasm collections, which offer new perspectives towards the
dis-covery of new genes and alleles specially for complex traits,
such as tolerance to abiotic stress in plants (Mackay et al 2009,
Hall et al 2010) However, GWAS require a genome-wide scan
of genetic diversity (preferably based on a reference genome
sequence and re-sequenced parts thereof), patterns of population
structure and the decay of LD Therefore to achieve this,
effec-tive genotyping techniques for plants, high-density maps,
pheno-typing resources, and if possible, a high-quality reference
genome sequence are required (Rafalski 2010) Finally, the
results of GWAS need to be validated through linkage analysis.
Drought at flowering stage is very critical as it causes loss
of kernel set and hence reduces the productivity of maize LD
mapping approach was used to identify loci involved in the
accumulation of carbohydrates and ABA metabolites during
low water stress To do so, 350 tropical and subtropical maize
inbred lines, well-watered or water stressed during flowering,
were genotyped with a panel of SNPs, which were identified
in the coding region of the genes that are associated with the
trait of interest It was found that among the 1229 SNPs in
540 candidate genes, one SNP in the maize homologue of the
Arabidopsis MADS-box gene, PISTILLATA, was significantly
associated with phaseic acid in ears of well-watered plants.
Similarly, one SNP of pyruvate dehydrogenase kinase, a key
regulator of carbon flux into respiration, was found to be
asso-ciated with silk sugar content in maize Additionally, a third
SNP of aldehyde oxidase gene was significantly associated with
ABA contents in silks of the low water-stressed plants (Setter
et al 2011) Therefore, these three SNPs will be most valuable
genomic resources for identifying the low water stress-tolerant
cultivars of maize Several agronomic QTLs related to abiotic
stress have been mapped, cloned and transferred to elite
geno-types In many cases, they are well documented through web
portals One such example is QlicRice: a web interface for
abiotic stress-responsive QTLs and loci interaction channels in
rice (Smita et al 2011).
Transcriptomics: coding genomic resources
Being polygenic in nature, gene expression under abiotic stress
is complex which poses a greater challenge to identify the alleles that expressed differentially due to the change of environment Although, differentially expressed genes can be identified in a number of ways such as DDRT (differential display of reverse transcriptase, Liang and Pardee 1992), SAGE (serial analysis of gene expression, Velculescu et al 1995), microarray (Lipshutz
et al 1999), SSH (suppression subtractive hybridization, Diat-chenko et al 1996), MPSS (massive parallel sequence signature, Brenner et al 2000) and cDNA-AFLP (cDNA amplified frag-ment length polymorphism, Lievens et al 2001), yet each one of them has relative advantages and disadvantages Until recently, microarray technology was one of the most powerful tools to identify the differentially expressed genes Using Affymetrix-based platform, drought-responsive transcriptomes in Indica rice genotypes with contrasting drought sensitivity were compared (Lenka et al 2011) This study identified genotype-dependent drought tolerance genomic resources in tolerant vs susceptible genotypes of rice However, in recent times, NGS technologies are contributing significantly for the discovery of large number
of genomic resources associated with abiotic stress tolerance, as listed in Table 1 Due to higher sensitivity and wider applicabil-ity, transcriptome sequencing or RNA-seq is gaining popularity Microarray-based transcriptome profiling experiments are suit-able to the model organism only (for example, Affymetrix offers microarrays chips for approximately 30 organisms only) How-ever, RNA-seq gives unprecedented details about transcriptional features that arrays cannot, such as novel transcribed regions, allele-specific expression, RNA editing and a comprehensive capability to capture alternative splicing Therefore, RNA-seq becomes popular choice for a number of purposes to identify dif-ferentially expressed genes Differential gene expression analyses
in response to osmotic stress and ABA treatment revealed a strong interplay among various metabolic pathways including ab-scisic acid and 13-lipoxygenase, salicylic acid, jasmonic acid, and plant defence pathways (Dugas et al 2011) Recently, drought-responsive genes of Gossypium herbaceum were identi-fied using RNA-seq (Ranjan et al 2012) Differentially expressed genes using 454 platform under cold acclimation, chilling unit accumulation, have also been identified, which were further used to develop SSR markers that are currently being used for construction of genetic linkage maps in blueberry (Row-land et al 2012).
Small RNA: non-coding genomic resources
Apart from the genic (exon) and non-genic (intron)-based mark-ers, several non-coding genomic resources such as small RNA, cis-element and intergenic region are gaining importance as valu-able genomic resources Small RNAs are recently emerging group of non-coding RNAs (Katiyar et al 2012), which play important role in gene expression under diverse stress conditions (Das and Mondal 2010) Several novel small RNAs specific to various abiotic stresses have been discovered either by conven-tional cloning or by small RNA-seq analysis (Table 2).
Allele mining for discovery of gene isoforms
Although phenotypic variations associated with different physiological processes are the consequences of allelic diver-sity in plants, information on allelic variations of abiotic
Trang 5stress-responsive genes is scanty (Tao et al 2011) PGRs that
conserved either in situ or ex situ are rich repertoire of alleles
that have been left behind by the selective processes of
domesti-cation or by selection as well as cross-breeding that paved the
way to today’s elite cultivars Therefore, owing to a lack of
effi-cient strategies to screen, isolate and transfer important alleles,
several gene banks nationally or internationally have remained
underexplored The most effective strategy for determining
allelic richness at a given locus is currently to determine its DNA sequence in a representative or core collection of a species
of interest.
Targeted sequencing of candidate genes from a large number
of accessions using ‘Sanger sequencing’ has been applied to study phylogenetic relationships of crop plants, their domestica-tion, evoludomestica-tion, speciation and ecological adaptation Due to high cost of sequencing, early studies were limited to resequencing of
Table 1: Identification of abiotic stress-related genomic resources by high-throughput sequencing
Plant
Approximate genome size (Mbp)
Objectives
of sequencing
Platform used
Abiotic stress-related
Chickpea
(Cicer
arietinum L.)
740 Identification of
drought-responsive transcripts of roots
454 17 493 unigenes Of these,
880 were up-regulated by drought stress
Molina et al (2008)
Rice
(Oryza sativa L.)
489 Identification of salinity
stress-induced transcripts
Illumina 51 301 (shoot) and 54 491 (root)
transcripts tags Of these, 213 (shoot) and 436 (root) were differentially expressed under salinity stress
Mizuno et al (2010)
Sorghum
(Sorghum bicolor)
739 To detect transcriptional
changes under osmotic stress
Illumina 28 335 unigenes Of these, 50
differentially expressed genes identified under osmotic stress
Dugas et al (2011)
Soybean
(Glycine max)
975 Identification of
genes associated with nitrogen-use efficiency
Illumina 3231 genes related to nitrogen-use
efficiency were identified
Hao et al (2011a)
Chickpea
(C arietinum L.)
740 Identification of
drought-responsive genes
454/Illumina 44 639 tentative unigenes identified
Of which, 728 SSRs,
495 SNPs, 387 conserved orthologous sequence markers and 2088 intron-spanning region markers were identified under drought stress
Hiremath et al (2011)
Chickpea
(C arietinum L.)
9740 Identification of
salt-responsive genes
454 13 115 unigenes Of which, 363 and
106 specific transcripts, respectively, were up- or down-regulated under salinity stress
Molina et al (2011)
Wild oat
(Avena barbata)
8729 Differentially expressed
transcriptomes under drought stress
454 17 154 drought-responsive genes
and 8319 SSRs were identified
Swarbreck et al (2011)
Common bean
(Phaseolus
vulgaris)
587 Polyethylene glycol-induced
transcriptomes in the root tips
454 611 up- and 728 down-regulated
genes in PEG-treated root tips were identified
Yang et al (2011)
Sugar beet
(Beta vulgaris
sp vulgaris)
1223 Identification of
cold-responsive genes
Illumina 15 493 unigenes were identified
Of which, 4880 were cold responsive
Mutasa-G€ottgens
et al (2012) Cucumber
(Cucumis
sativus L.)
880 Identification of
waterlogging stress-inducible genes
Illumina 5787 genes were differentially
expressed under waterlogged condition
Qi et al (2012)
Cotton
(Gossypium
herbaceum)
1667 Identification of
drought-responsive genes
454 16 283 unigenes identified
Of these, 275 were up-regulated under drought stress
Ranjan et al (2012)
Blueberry
(Vaccinium
corymbosum)
650 Identification of
genes responsive
to cold acclimatization
454 15 000 contigs and 124 000
singletons identified Of which,
17 were up-regulated under cold
stress Also 15 886 EST-SSR were mined
Rowland et al (2012)
Rice (O sativa L.) 489 Identification of
atrazine-responsive genes
Illumina 18 833 unigenes were identified,
of these, 40 were highly up-regulated under atrazine stress condition
Zhang et al (2012)
Ammopiptanthus
mongolicus
Not known Identification of
drought-inducible transcripts
454 29 056 unigenes were identified,
of which, 1 827 were drought responsive
Zhou et al (2012)
Sugarcane
(Saccharum spp.)
3961 Identification of
drought-responsive genes
Illumina 75 404 unigenes were identified
Of these, 213 were up-regulated under drought stress
Kido et al (2012)
Picrorhiza kurroa 1720 Identification of
picroside-containing genes under low-temperature treatment
Illumina 74 336 unigenes Of these, several
genes of picroside biosynthesis pathways were up-regulated under low temperature
Gahlan et al (2012)
Trang 6a single locus or few loci in only few individuals of a species
(Kellog et al 1996 and Lin et al 2001) Reduced costs of
‘Sanger sequencing’ technique with using capillary instruments
along with 96-well formats facilitated multilocus studies of allele
mining in larger collections (Vaughan et al 2003, Wright et al.
2005, Hyten et al 2006, Labate et al 2009) Nevertheless, NGS
technologies will be cost-effective for allele mining in near
future (Kumar et al 2010).
Several genes were mined to detect their allelic isoforms that
were associated with a particular abiotic stress tolerance trait; for
instance, dehydrin gene of sessile oak for drought tolerance
(Vornam et al 2011), PPDH2 allele for vernalization-responsive
genotypes of spring barley (Casao et al 2011), plant invertases
gene (ivr2 encoding plant acid-soluble invertase) of maize for
drought tolerance (Li et al 2011b), ABA stress and ripening
(ASR) allele for drought tolerance among the 204 accessions of
Oryza sativa L and 14 accessions of wild relatives such as
Oryza rufipogon and Oryza nivara (Philippe et al 2010),
sub1A-1 gene of O sativa for submergence tolerance (Fukao
et al 2009) and dehydrin gene of scots pine (Pinus sylvestris)
for cold stress (Wachowiak et al 2009) The allelic variations
were also reported in rye for alt3 gene (Miftahudin et al 2005)
and alt4 gene (Fontecha et al.2007) and in wheat for TaALMT1
gene (Raman et al 2008) that are associated with aluminium
stress tolerance A synonymous SNP associated with dehydration
tolerance was detected at the 558th base pair (an A/G transition)
of SiDREB2 gene in a CRS consisting of 45 foxtail millet acces-sions Based on the identified SNP, primers were designed to develop an allele-specific marker associated with dehydration tolerance (Lata et al 2011, Lata and Prasad 2013) Sucrose non-fermenting1-related protein kinase 2 (SnRK2) gene, which plays
a key role in abiotic stress signalling transduction pathways in plants, had also been mined to discover new alleles for abiotic stress tolerance (Zhang et al 2010), phosphorus utilization and accumulation efficiency of stem water-soluble carbohydrates (Zhang et al 2011a,b,c) in wheat accessions Rye (Secale cereale L.) is the most frost-tolerant cereal species As an outcrossing in nature, rye exhibits high levels of intraspecific diversity, which makes it well-suited for allele mining in genes involved in the frost-responsive network Therefore, alleles were mined, and haplotypes related to frost were discovered in ScCbf14, ScVrn1, ScDhn1, ScCbf2, ScCbf6, ScCbf9b, ScCbf11, ScCbf12, ScCbf15, ScIce2 and ScDhn3 genes Several SNPs were discovered in the promoters or non-coding regions, which attrib-uted to non-synonymous substitutions, hence were suitable candi-dates for association mapping (Li et al 2011a) Similarly, allelic diversity as well as haplotypes had been detected in European barley for frost tolerance in four CBF genes namely HvCbf3, HvCbf6, HvCbf9 and HvCbf14 (Fricano et al 2009) Allelic diversity of the genes responsible for dough mixing property of
Table 2: Summary of miRNAs that are associated with abiotic stresses
Cold Arabidopsis thaliana miR165/166, miR169, miR172, miR393, miR396,
miR397, miR408
Zhou et al (2008) Populus tomentosa 144 conserved miRNAs belong to 33
miRNA families and 29 new miRNAs
Chen et al (2012a)
Burkhead et al (2009)
A thaliana miR157, miR167, miR168, miR171,
miR408, miR319 and miR397 miR393, miR396
Liu et al (2008) Populus trichocarpa miR1446a-e, miR1444a, miR1447, miR1450 Lu et al (2008)
P tomentosa 17 conserved miRNA families and nine novel miRNAs Ren et al (2012), Chen et al (2012b)
Flooding P tomentosa Seven conserved miRNA families and five novel miRNAs Ren et al (2012)
miR398, miR399, miR408, miR528, and miR827, miR160, miR167, miR168, miR169, miR319, miR395,
miR399, miR408, and miR528
Xu et al (2011)
Mechanical stress P trichocarpa miR156, miR162, miR164, miR475, miR480, and miR481 Lu et al (2005)
Chiou et al (2006)
miR159, miR394, miR156, miR393, miR171, miR158 and miR169
Liu et al (2008)
P trichocarpa miR530a, miR1445, miR1446a-e, miR1447 and miR1711 Lu et al (2008)
UV-radiation Populus tremula miR169, miR395, miR472, miR168, miR398 and miR408 Jia et al (2009)
A thaliana miR156, miR160, miR165/166, miR167 and miR398 Zhou et al (2007) Wound responsive Nicotiana tabacum Various 21- or 24-nt small RNAs (including ta-siRNAs) Tang et al (2012)
Trang 7wheat under irrigated and rainfed conditions among the 189
geno-types of a RIL population was also studied, and allelic variations
were identified (Zheng et al 2010).
Single nucleotide polymorphism (SNP) or haplotype
Detection of allelic differences or variations in the PGRs is an
important application of genomic resources, which can be
achieved by highly robust DNA-based marker such as SNP or
haplotype (i.e group of SNPs that are linked to a particular
trait) Due to higher availability and stability during
inheri-tance as compared to other markers, such as simple sequence
repeats (SSRs), SNP provides enhanced possibilities for
study-ing PGRs management in several ways, such as cultivar
iden-tification, construction of genetic maps, assessment of genetic
diversity, detection of genotype vs phenotype associations and
marker-assisted breeding (Ganal et al 2009) Large-scale SNPs
have been discovered in several crops using various sources
of sequences Traditionally, the sequence variations are
com-pared among the large number of PGRs comprising diverse
genetic background For example, ESTs as well as
transcrip-tomic sequences were used to detect large-scale SNPs in
grapevine (Lijavetzky et al 2007) and black cottonwood
(Pop-ulus trichocarpa) (Geraldes et al 2011) In wheat,
multialign-ments of conserved domains in DREB1, WRKY1 transcription
factors (TFs) and HKT-1 had been utilized to design specific
primers to identify functional SNPs These primers were
vali-dated on several genotypes of durum wheat that were
differen-tially tolerant to salt and drought stress (Mondini et al 2012).
Similarly, genic SNPs linked to cold tolerance in barley
(Tondelli et al 2007), frost tolerance in rye (Li et al 2011c),
drought tolerance in maize (Hao et al 2011b, and Lu et al.
2010) as well as in Arabidopsis (Hao et al 2004, 2008) had
been discovered These trait-specific SNPs could be converted
into functional markers for respective crop improvement
pro-grammes by marker-assisted selection.
Conclusions
In conclusion, while omics approaches are suitable for
generat-ing large-scale genomics resources, yet phenotypgenerat-ing followed by
marker-assisted breeding is required to utilize those genomics
resources for developing stress-tolerant cultivars, a need of the
present-day agriculture due to rapid changes in climate
Develop-ing a suitable abiotic stress-tolerant cultivar needs either tightly
linked markers or an allelic form of gene that contributes
signifi-cantly towards the target traits Current and fast emerging
tech-nologies, such as NGS, high-throughput phenomics, RNAi,
chromosome engineering, marker-assisted breeding, GWS, and
bioinformatics will tremendously accelerate the development of
improved designer abiotic stress-tolerant crops by efficiently
har-nessing the genetic potential of PGRs.
Acknowledgements
The authors are grateful to Dr K V Bhat, National Bureau of Plant
Genetic Resources, New Delhi-12, India, for valuable suggestions
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