Plant Breeding 133, 1–11 (2014) © 2013 Blackwell Verlag GmbH doi:10.1111/pbr.12117 Review Genomic resources for breeding crops with enhanced abiotic stress tolerance K A I L A S H C B A N S A L 1,4, S A N G R A M K L E N K A 2,3 and T A P A N K M O N D A L 1 National Bureau of Plant Genetic Resources, Pusa, New Delhi, 110012, India; 2Department of Biology, University of Massachusetts, Amherst, MA, 01003, USA; Present 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 figure and 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 modern 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 generation of genomic resources and their utilization 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 climate change demands the development of stress-tolerant crop cultivars Development of such tolerant cultivars through conventional 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 widened 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 constitute 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 responsible 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 locationspecific, 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 significantly by generating genomic resources Recent advancements in ‘omics’ techniques have enabled generation of genomic resources much more efficiently (Mondal and Sutoh 2013) Ultimately, these genomic resources are utilized for developing better cultivars resilient to climate change either through markerassisted 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 genomics resources However, high degree of redundancy in the different in situ as well as ex situ collections creates major bottlenecks 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 common 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 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 wileyonlinelibrary.com K C BANSAL, S K LENKA AND T K MONDAL Fig 1: Generation and utilization of plant genomic resources There are two major approaches to utilize the genomic resources (one ‘molecular breeding’ denoted by left box and the other ‘transgenic approach’ indicated by right box) Central circle denotes the identification of diverse PGRs by genotyping 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 considered to be the basic genomic resources, denoted by blue oval circles These genomic resources are utilized for various crop improvement applications 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) Similarly, 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 eliminate 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 alternative (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 agroclimatic zones of India with an objective to develop the ‘wheat CRS’ Preliminary evaluation of the wheat germplasm has indicated 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 NGSbased 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 collections (Ossowski et al 2010, Pool et al 2010) Generation of mutants with novel genetic variation Although natural variation is the main criterion for selecting parents of mapping population, yet in several instances, natural variants either 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 importance has always remained a challenge for the scientific community In recent past, significant achievements have been made in the development of molecular biology-based techniques for generating 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 Plant genomic resources for abiotic stress tolerance chemical 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 genotypic screening for allelic variations before commencing the phenotyping (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 highthroughput 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 biological diversity available in domesticated rice (Negr~ao et al 2011) It detected natural allelic variants in 3′-untranslated region attributed to the regulation of gene expression under salt stress Naredo Ma et al (2009) demonstrated the utility of EcoTILLING for the detection of SNPs in the upland and low-land rice cultivars and registered their contributions for drought stress tolerance Several polymorphisms of candidate genes were detected, which were associated with tolerance to drought stress Similarly, 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 identifying the trait-specific phenotypes With the advancement of phenomics 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 period 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 phenotypes 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 shadow on the effective use of phenomics for phenotyping the germplasm We therefore strongly advocate precise field phenotyping using non-destructive methods to obtain accurate association 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 commercially, 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 genome 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 information 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 genomewide variation Once the genome of a plant is sequenced, it can serve as a reference 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 resequencing of several genotypes or targeted resequencing 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 relationships 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 largest 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 polymorphisms 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 identi- fied by comparing the spectrum of mutations that have accumulated since A thaliana diverged from its closest relative around 10 million years ago Subsequently, several whole-genome resequencing 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 recombination events’ from many lineages (Abdurakhmonov and Abdukarimov 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 drawbacks (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 supplement 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 germplasm collections, which offer new perspectives towards the discovery 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, effective genotyping techniques for plants, high-density maps, phenotyping 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 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 associated 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 genotypes 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) K C BANSAL, S K LENKA AND T K MONDAL 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, Diatchenko et al 1996), MPSS (massive parallel sequence signature, Brenner et al 2000) and cDNA-AFLP (cDNA amplified fragment 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 Affymetrixbased 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 Due to higher sensitivity and wider applicability, transcriptome sequencing or RNA-seq is gaining popularity Microarray-based transcriptome profiling experiments are suitable to the model organism only (for example, Affymetrix offers microarrays chips for approximately 30 organisms only) However, 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 differentially expressed genes Differential gene expression analyses in response to osmotic stress and ABA treatment revealed a strong interplay among various metabolic pathways including abscisic acid and 13-lipoxygenase, salicylic acid, jasmonic acid, and plant defence pathways (Dugas et al 2011) Recently, drought-responsive genes of Gossypium herbaceum were identified 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 (Rowland et al 2012) Small RNA: non-coding genomic resources Apart from the genic (exon) and non-genic (intron)-based markers, several non-coding genomic resources such as small RNA, cis-element and intergenic region are gaining importance as valuable 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 conventional 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 diversity in plants, information on allelic variations of abiotic Plant genomic resources for abiotic stress tolerance 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 genomic resources Identification of drought-responsive transcripts of roots Identification of salinity stress-induced transcripts 454 Molina et al (2008) To detect transcriptional changes under osmotic stress Identification of genes associated with nitrogen-use efficiency Identification of drought-responsive genes Illumina 17 493 unigenes Of these, 880 were up-regulated by drought stress 51 301 (shoot) and 54 491 (root) transcripts tags Of these, 213 (shoot) and 436 (root) were differentially expressed under salinity stress 28 335 unigenes Of these, 50 differentially expressed genes identified under osmotic stress 3231 genes related to nitrogen-use efficiency were identified 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 13 115 unigenes Of which, 363 and 106 specific transcripts, respectively, were up- or down-regulated under salinity stress 17 154 drought-responsive genes and 8319 SSRs were identified Hiremath et al (2011) 611 up- and 728 down-regulated genes in PEG-treated root tips were identified 15 493 unigenes were identified Of which, 4880 were cold responsive Yang et al (2011) 5787 genes were differentially expressed under waterlogged condition 16 283 unigenes identified Of these, 275 were up-regulated under drought stress 15 000 contigs and 124 000 singletons identified Of which, 17 were up-regulated under cold stress Also 15 886 EST-SSR were mined 18 833 unigenes were identified, of these, 40 were highly up-regulated under atrazine stress condition 29 056 unigenes were identified, of which, 827 were drought responsive 75 404 unigenes were identified Of these, 213 were up-regulated under drought stress 74 336 unigenes Of these, several genes of picroside biosynthesis pathways were up-regulated under low temperature Qi et al (2012) Chickpea (Cicer arietinum L.) Rice (Oryza sativa L.) 740 Sorghum (Sorghum bicolor) 739 Soybean (Glycine max) 975 Chickpea (C arietinum L.) 740 Chickpea (C arietinum L.) 9740 Identification of salt-responsive genes 454 Wild oat (Avena barbata) 8729 Differentially expressed transcriptomes under drought stress Polyethylene glycol-induced transcriptomes in the root tips Identification of cold-responsive genes Identification of waterlogging stress-inducible genes Identification of drought-responsive genes Identification of genes responsive to cold acclimatization 454 Common bean (Phaseolus vulgaris) Sugar beet (Beta vulgaris sp vulgaris) Cucumber (Cucumis sativus L.) Cotton (Gossypium herbaceum) Blueberry (Vaccinium corymbosum) Rice (O sativa L.) Ammopiptanthus mongolicus 489 587 1223 880 1667 650 489 Not known Sugarcane (Saccharum spp.) 3961 Picrorhiza kurroa 1720 Illumina Illumina 454/Illumina 454 Illumina Illumina 454 454 Identification of atrazine-responsive genes Illumina Identification of drought-inducible transcripts Identification of drought-responsive genes Identification of picroside-containing genes under low-temperature treatment 454 Illumina Illumina stress-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 domestication or by selection as well as cross-breeding that paved the way to today’s elite cultivars Therefore, owing to a lack of efficient strategies to screen, isolate and transfer important alleles, several gene banks nationally or internationally have remained underexplored The most effective strategy for determining Reference Mizuno et al (2010) Dugas et al (2011) Hao et al (2011a) Molina et al (2011) Swarbreck et al (2011) Mutasa-G€ottgens et al (2012) Ranjan et al (2012) Rowland et al (2012) Zhang et al (2012) Zhou et al (2012) Kido et al (2012) Gahlan et al (2012) 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 domestication, evolution, speciation and ecological adaptation Due to high cost of sequencing, early studies were limited to resequencing of K C BANSAL, S K LENKA AND T K MONDAL Table 2: Summary of miRNAs that are associated with abiotic stresses Stress Plant miRNA ABA treatment Cadmium homeostasis Cold Phaseolus vulgaris Oryza sativa Arabidopsis thaliana miR159, miR393, miR1514 19 different miRNAs miR165/166, miR169, miR172, miR393, miR396, miR397, miR408 144 conserved miRNAs belong to 33 miRNA families and 29 new miRNAs miR397, miR398, miR408, miR857 Populus tomentosa Copper assimilation A thaliana Drought P vulgaris A thaliana Iron deficiency Low nitrate Populus trichocarpa P tomentosa Medicago truncatula M truncatula O sativa O sativa P tomentosa Zea mays A thaliana A thaliana Z mays Mechanical stress Phosphate assimilation P trichocarpa A thaliana Salinity A thaliana Submergence responsive Sulphate homeostasis UV-radiation P trichocarpa Saccharum officinarum Z mays A thaliana Populus tremula A thaliana Nicotiana tabacum Flooding Hypoxia Wound responsive References miR2118 miR157, miR167, miR168, miR171, miR408, miR319 and miR397 miR393, miR396 miR1446a-e, miR1444a, miR1447, miR1450 17 conserved miRNA families and nine novel miRNAs miR398 and miR408 different miRNAs miR393 30 different miRNAs Seven conserved miRNA families and five novel miRNAs miR167, miR166, miR171 and miR396 19 different miRNAs miR169b, miR169c, miR172c and miR394a miR164, miR169, miR172, miR397, miR398, miR399, miR408, miR528, and miR827, miR160, miR167, miR168, miR169, miR319, miR395, miR399, miR408, and miR528 miR156, miR162, miR164, miR475, miR480, and miR481 miR399 miR396, miR168, miR167, miR165, miR319, miR159, miR394, miR156, miR393, miR171, miR158 and miR169 miR530a, miR1445, miR1446a-e, miR1447 and miR1711 miR319 39 different miRNAs miR395 miR169, miR395, miR472, miR168, miR398 and miR408 miR156, miR160, miR165/166, miR167 and miR398 Various 21- or 24-nt small RNAs (including ta-siRNAs) a 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 Arenas-Huertero et al (2009) Ding et al (2011) Zhou et al (2008) Chen et al (2012a) Sunkar et al (2006), Burkhead et al (2009) Arenas-Huertero et al (2009) Liu et al (2008) Lu et al (2008) Ren et al (2012), Chen et al (2012b) Trindade et al (2010) Wang et al (2011) Zhao et al (2007) Zhou et al (2010) Ren et al (2012) Zhang et al (2008) Moldovan et al (2010) Kong and Yang (2010) Xu et al (2011) Lu et al (2005) Fujii et al (2005), Chiou et al (2006) Liu et al (2008) Lu et al (2008) Thiebaut et al (2012) Zhang et al (2008) Kawashima et al (2009) Jia et al (2009) Zhou et al (2007) Tang et al (2012) tolerance was detected at the 558th base pair (an A/G transition) of SiDREB2 gene in a CRS consisting of 45 foxtail millet accessions 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 nonfermenting1-related protein kinase (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 attributed to non-synonymous substitutions, hence were suitable candidates 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 Plant genomic resources for abiotic stress tolerance wheat under irrigated and rainfed conditions among the 189 genotypes 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 inheritance as compared to other markers, such as simple sequence repeats (SSRs), SNP provides enhanced possibilities for studying PGRs management in several ways, such as cultivar identification, 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 compared among the large number of PGRs comprising diverse genetic background For example, ESTs as well as transcriptomic sequences were used to detect large-scale SNPs in grapevine (Lijavetzky et al 2007) and black cottonwood (Populus trichocarpa) (Geraldes et al 2011) In wheat, multialignments 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 validated on several genotypes of durum wheat that were differentially 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 programmes by marker-assisted selection Conclusions In conclusion, while omics approaches are suitable for generating large-scale genomics resources, yet phenotyping 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 Developing a suitable abiotic stress-tolerant cultivar needs either tightly linked markers or an allelic form of gene that contributes significantly towards the target traits Current and fast emerging technologies, 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 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Identification of abiotic stress- related genomic resources by high-throughput sequencing Plant Approximate genome size (Mbp) Objectives of sequencing Platform used Abiotic stress- related genomic resources. .. 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