Allele mining in crops: Prospects and potentials

Biotechnology Advances 28 (2010) 451–461

Contents lists available at ScienceDirect

Biotechnology Advances j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / b i o t e c h a d v

Research review paper

Allele mining in crops: Prospects and potentials G. Ram Kumar 1, K. Sakthivel 1, R.M. Sundaram, C.N. Neeraja, S.M. Balachandran, N. Shobha Rani, B.C. Viraktamath, M.S. Madhav ⁎ Biotechnology Laboratory, Crop Improvement Section, Directorate of Rice Research, Rajendranagar, Hyderabad 500030, India

a r t i c l e

i n f o

Article history: Received 2 June 2009 Received in revised form 21 September 2009 Accepted 25 September 2009 Available online 25 February 2010 Keywords: Allele mining Promoter mining Allelic variation Crop gene pool EcoTilling

a b s t r a c t Enormous sequence information is available in public databases as a result of sequencing of diverse crop genomes. It is important to use this genomic information for the identification and isolation of novel and superior alleles of agronomically important genes from crop gene pools to suitably deploy for the development of improved cultivars. Allele mining is a promising approach to dissect naturally occurring allelic variation at candidate genes controlling key agronomic traits which has potential applications in crop improvement programs. It helps in tracing the evolution of alleles, identification of new haplotypes and development of allele-specific markers for use in marker-assisted selection. Realizing the immense potential of allele mining, concerted allele mining efforts are underway in many international crop research institutes. This review examines the concepts, approaches and applications of allele mining along with the challenges associated while emphasizing the need for more refined ‘mining’ strategies for accelerating the process of allele discovery and its utilization in molecular breeding. © 2010 Elsevier Inc. All rights reserved.

Contents 1. 2. 3.

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Introduction . . . . . . . . . . . . . . . . . . . . . . Evolution of new alleles . . . . . . . . . . . . . . . . Allele mining . . . . . . . . . . . . . . . . . . . . . 3.1. ‘True’ allele mining . . . . . . . . . . . . . . . 3.2. Approaches . . . . . . . . . . . . . . . . . . . 3.2.1. EcoTilling . . . . . . . . . . . . . . . 3.2.2. Sequencing-based allele mining . . . . . 3.3. Tools required for allele mining . . . . . . . . . 3.4. Considerations for allele mining . . . . . . . . . Applications . . . . . . . . . . . . . . . . . . . . . . 4.1. Characterization of allelic diversity in gene banks . 4.2. Identification of new haplotypes . . . . . . . . . 4.3. Development of allele-specific markers for MAS . . 4.4. Allelic synteny and evolutionary relationship . . . Challenges . . . . . . . . . . . . . . . . . . . . . . . 5.1. Selection of genotypes . . . . . . . . . . . . . . 5.1.1. Development of core/mini core collections 5.1.2. Accurate phenotyping methods . . . . . 5.1.3. Flexible computational tools . . . . . . 5.2. Handling genomic resources . . . . . . . . . . . 5.3. Demarcation of promoter region . . . . . . . . . 5.4. Characterization of regulatory region . . . . . . . 5.5. Higher sequencing costs . . . . . . . . . . . . .

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⁎ Corresponding author. Tel.: + 91 40 24015036x226; fax: + 91 040 24015308. E-mail addresses: [email protected], [email protected] (M.S. Madhav). 1 These authors have contributed equally. 0734-9750/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.biotechadv.2010.02.007

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6. Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Progress in plant breeding in terms of development of superior and high yielding varieties of agricultural crops was made possible by accumulation of beneficial alleles from vast plant genetic resources existing worldwide. Still, a significant portion of these beneficial/ superior alleles were not utilized as these were left behind during evolution and domestication. This untapped genetic variation existing in wild relatives and land races of crop plants could be exploited gainfully for development of agronomically superior cultivars. Introgressions of novel alleles from wild relatives of crop plants into cultivated varieties (deVicente and Tanksley, 1993; Xiao et al., 1996; 1998; McCouch et al., 2007) have clearly demonstrated that certain alleles and their combinations potentially make dramatic changes in trait expression when moved to a suitable genetic background by overcoming the genetic bottlenecks which restricted their introgression to cultivars. Hence, the vast germplasm resources need to be relooked for novel alleles to further enhance the genetic potential of crop varieties for various agronomic traits. Enormous progress has been made in the last 15 years in depositing an exponential amount of sequence information into GenBank (Chan, 2005; Mardis, 2008). With rapid accumulation of sequence and expression data in various genomic databases, accelerated discovery and annotation of new genes can be expected which would enable the development of allele-specific markers (Spooner et al., 2005). Based on gene and genome sequences, polymerase chain reaction (PCR) strategies are devised to isolate useful alleles of genes from a wide range of species (Latha et al., 2004). This capability enables direct access to key alleles conferring resistance to biotic and abiotic stresses, greater nutrient use efficiency, enhanced yield and improved quality. Using novel genomic tools, similar alleles responsible for a given trait and their variants in other genotypes can be identified. This is often referred to as ‘dissection of naturally occurring variation at candidate genes/loci’ or simply ‘allele mining’. Identification of allelic variants from germplasm collections not only provides new germplasm for delivering novel alleles to targeted trait improvement but also categorizes the germplasm entries for their conservation. Realizing the importance of allele mining in genomics-driven plant breeding era, we discuss the concept of allele mining and its strategies along with the prospects and challenges. 2. Evolution of new alleles Mutation is considered as an evolutionary driving force which underlies existing allelic diversity in any crop species. For creation of new alleles or causing variations in the existing allele and allelic combinations, mutations in the genic regions of the genome either as single nucleotide polymorphism (SNP) or as insertion and deletion (InDel) are important. The mutations in coding regions and/or regulatory regions may have tremendous effect on the phenotype by altering the encoded protein structure and/or function while those that occur in noncoding regions of a gene could often be silent without any effect on the phenotype. Even though most of the mutations are deleterious, in general 0.1% of the mutations are supervital leading to alterations in gene function which may be highly necessary for the survival of the plant (Singh, 2005). Several such instances are generally observed in diverse crop species. For example, the prostrate growth habit of wild rice is controlled by a single gene PROG1. The wild type allele is replaced by mutant allele (prog1) in most of the Oryza sativa cultivars, which disrupts the PROG1 function

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thereby inactivating it's expression, leading to erect growth, greater grain number and higher grain yield in cultivated rice (Tan et al., 2008). Similarly, recent work of Huang et al. (2009) showed that presence of dominant mutant allele of Dep 1 (dense and erect panicle) in majority of Chinese high yielding varieties codes for the panicle architecture leading to higher grain yield. Since rice is the first crop species sequenced, natural variants for genes controlling several agronomically important traits were identified. Many superior alleles like Sh4 for grain shattering (Li and Sang, 2006), Rc7 for grain pericarp color (Sweeney et al., 2007), Wx for granule-bound starch synthase (GBSS) (Wang et al., 1995) and GS3 grain size/shape (Fan et al., 2006) were favored and selected by nature or artificial selection during evolution and domestication from land races of rice. Alternatively, several studies suggest that many disease resistant alleles like bacterial leaf blight resistance gene Xa21 from Oryza longistaminata (Khush et al., 1991), blast resistance genes like Pi9 from Oryza minuta (Sitch et al., 1989; Amante-Bordeos et al., 1992), Pi40 from Oryza australiensis (Jeung et al., 2007) and trait-enhancing alleles like grain filling GIF1 (Wang et al., 2008a) were dominant and better expressed in wild species than cultivated rice and such superior alleles were left behind during evolution and domestication of present-day cultivated crop species (Tanksley and McCouch, 1997; Kovach and McCouch, 2008). Transferring these beneficial wild species -derived alleles to elite genetic backgrounds resulted in increased trait performance of elite varieties in rice (Xiao et al., 1996; 1998; McCouch et al., 2007) and tomato (deVicente and Tanksley, 1993). Despite the fact that the propagation of these altered/novel alleles (either through natural or artificial selection) depends upon their role in the plant's adaptation, it can be suggested that such altered/novel alleles of key genes are preserved in many related wild species, land races and cultivated varieties of a crop species. Therefore, exploring the natural variation in important genes is highly indispensable in modern crop improvement programs. 3. Allele mining 3.1. ‘True’ allele mining Initial studies of allele mining have focused only on the identification of SNPs/InDels at coding sequences or exons of the gene, since these variations were expected to affect the encoded protein structure and/or function. Ample examples are available to demonstrate the effect of such sequence variations in genic regions in altering the phenotypes. However, recent reports indicate that the nucleotide changes in non-coding regions (5′ UTR) including promoter, introns and 3′ UTR) also have significant effect on transcript synthesis and accumulation which in turn alter the trait expression. Role of intronic mutations in gene regulation was evident in the expression of some genes like tubulin (components of microtubules) (Fiume et al., 2004) and rubi3 (polyubiquitin gene) (Samadder et al., 2008) in rice as well VRN-1 (which affect vernalization response) in barley and wheat (Fu et al., 2005). A mutation in 5′ splice site of the first intron of the waxy (Wx) gene had resulted in tenfold increase in the gene activity in rice (Isshiki et al., 1998). Intronic mutations including insertions of tranposons or differential expansion of microsatellite repeat sequences were correlated with alteration of gene expression (Sureshkumar et al., 2009). In recent years, mining for sequence variation especially in regulatory region of genes is gaining more importance in context of gene expression. Since, it is well known that promoter elements play a key role in gene regulation and any changes in their sequences will dramatically

G.R. Kumar et al. / Biotechnology Advances 28 (2010) 451–461

influence gene expression resulting in variable trait expression. Such differential gene expression is usually due to sequence polymorphism in cis-acting/trans-acting regulatory elements binding sites present in upstream region. For instance, in maize the gene expression changes are clearly illustrated with the mutations located in distant 5′-regions of domestication gene Tb1 (Clark et al., 2006) and flowering time control geneVgt1 (Salvi et al., 2007). Chu et al. (2006) demonstrated the role of mutations in the promoter region of the candidate gene for xa13 (a recessive bacterial blight resistance gene) in conferring disease resistance in rice. Similarly, the accumulated mutations in the promoter regions of a gene associated with incomplete grain filling (GIF1) were also reported to be the underlying cause for restricted expression pattern in cultivars as compared to a broad expression pattern of its wild type allele in the wild species of Oryza (Wang et al., 2008a). Thus, ‘true’ allele mining should also include analysis of non-coding and regulatory regions of the candidate genes in addition to analyzing sequence variations in the coding regions of the agronomically important genes so as to cover most of the functional variations of relevance in the genes (Rangan et al., 1999; Latha et al., 2004) (Fig. 1). Realizing the importance of ‘mining’ for variation in regulatory regions, promoters of genes have been chosen as the potential target for allele mining which is termed as ‘promoter mining’. Promoter mining coupled with in silico analysis aimed to identify nucleotide variation in transcription factor binding motif (TFBM), number/frequency, and location of regulatory elements binding sites in promoter regions and their corresponding transcription factors which are associated with specific expression pattern in response to constitutive, developmental, tissue-specific, hormonal and environmental regulation (Veerla and Hoglund, 2006).Such analysis of promoter sequences also provide the knowledge on conservation of regulatory motifs specific to particular differentiated cell/tissue types. Ultimately, promoter mining would offer insights into the molecular mechanism of gene expression and isolation of novel and efficient promoters for use in genetic engineering applications. As the global gene expression experiments to identify sets of genes with similar expression patterns are rapidly increasing, it is highly essential to incorporate approaches like in vitro and in silico promoter mining in regular allele mining programs to identify and categorize cisregulatory elements and to understand their role in gene regulation. 3.2. Approaches Two major approaches are available for the identification of sequence polymorphisms for a given gene in the naturally occurring populations. They are (i) modified TILLING (Targeting Induced Local Lesions in Genomes) procedure called EcoTilling and (ii) sequencingbased allele mining. A brief description of these two approaches is given in this section and steps involved in these approaches are illustrated in Fig. 2. 3.2.1. EcoTilling TILLING 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). A variation of this technique, EcoTilling, represents a means to determine the extent of natural variation in selected genes in crops. The method is essentially same as TILLING except that the mutations are not induced artificially and are detected from naturally occurring alleles in the primary and secondary

Fig. 1. “True” allele mining includes coding, non-coding and regulatory regions of a gene.

453

Fig. 2. Steps involved in allele mining.

crop gene pools (Comai et al., 2004; Comai and Henikoff, 2006). Like TILLING, EcoTilling also relies on the enzymatic cleavage of heteroduplexed DNA (formed due to single nucleotide mismatch in sequence between reference and test genotype) with a single strand specific nuclease (i.e., Cel-1, mung bean nuclease, S1 nuclease, etc.) under specific conditions followed by detection through Li-Cor genotypers (Li-Cor, USA). At point mutations, there will be a cleavage by the nuclease to produce two cleaved products whose sizes will be equal to the size of full length product. The presence, type and location of point mutation or SNP will be confirmed by sequencing the amplicon from the test genotype that carry the mutation. Although TILLING and EcoTilling were proposed as costeffective approaches for haplotyping and SNP discovery, these techniques require more sophistication and involve several steps starting from making DNA pools of reference and test genotypes, specific conditions for efficient cleavage by nuclease, detection in polyacrylamide gels using LiCor genotyper and confirming through sequencing. Recently, a rapid and cost-effective method for detecting novel allelic variants of known candidate on agarose gels and its utility in candidate gene mapping has been described (Raghavan et al. 2007). We can anticipate that the cost of EcoTilling can be significantly reduced by the adoption of such innovative strategies for allele mining. 3.2.2. Sequencing-based allele mining This technique involves amplification of alleles in diverse genotypes through PCR followed by identification of nucleotide variation by DNA sequencing. Sequencing-based allele mining would help to analyze individuals for haplotype structure and diversity to infer genetic association studies in plants. Unlike EcoTilling, sequencing-based allele mining does not require much sophisticated equipment or involve tedious steps, but involves huge costs of sequencing. A comparison between these two procedures is given in Table 1. Despite the claim that EcoTilling can be

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done at a fraction of the cost of SNP/haplotyping methods, the elaborate equipment and expertise required and the need for confirmatory sequencing in EcoTilling procedure, draws reconsideration for its wide applicability. However, each technology has its strengths and weaknesses that need to be carefully considered in the light of the intended application. 3.2.2.1. Next generation sequencing technologies in allele mining. Though the DNA sequence reveals profound insights into genetic organization, the cost, cumbersomeness and time were major limitations of the earlier versions of sequencers which depend upon either chain termination sequencing or cycle sequencing. With the advent of faster sequencing platforms, sequencing process has become more simple and cheaper. In the last few years, ‘massively parallel’ methods have also emerged and lead to the development of ‘next generation’ sequencing platforms with increased throughput and accuracy. These methods were mainly used for resequencing, alignment of the sequence data and their comparison with reference genome. The first of this kind was commercialized by 454 Life Sciences (Margulies et al., 2005) and this technique relied on pyrosequencing while eliminating the need for cloning. With this 454 sequencing platform, it was possible to produce 100 Mb of sequence with 99.5% accuracy and increased read length averaging over 250 bases. Another massively parallel sequencer, Illumina/Solexa genome analyzer has been developed (Bennett, 2004) and this is capable of sequencing one billion bases (1 Gb) of 30–40 base sequence reads in a single run in a short timer period of 3–4 days. In addition to the above, Applied Biosystems Inc (USA) has also developed a massively parallel sequencer using supported oligonucleotide ligation and detection system (SOLiD) (Hutchison, 2007). This system, despite its shorter read length of about 25–35, can generate 2–3 Gb of sequences per run. Recently, Helicos Heliscope (www.helicosbio.com) has commercialized a new massively parallel system based on single molecule sequencing (SMS) technology. SMS technology is much faster and cheaper than second generation sequencing technologies of Applied Biosystems and Illumina and has been described as third generation or next to next generation sequencing technology (Gupta, 2008). Another such third generation sequencing technology is expected to be commercialized by Pacific Biosciences SMRT (www.pacificbiosciences.com) in early 2010. As advances in DNA sequencing technologies progress towards long envisioned “US$ 1000 genome” (or sequencing a human genome for US$ 1000), and as the sequencing costs are reduced by a factor of

Table 1 Comparison of techniques available for identifying variation in naturally occurring alleles. S. Parameters No. 1

2 3

4

5

6 7

Technical expertise

EcoTilling

Requires high technical expertise starting from DNA pooling, to detection of cleavage of heteroduplexes Complexity More Efficiency Less efficient due to high chances of false positives, nonspecific cleavage and chances of non-detection in DNA pools Utility Proposed as effective in detection of SNPs rather than InDels Cost per Comparatively high as it needs data point the intervention of confirmatory sequencing step Time Requires more time especially for sample preparation Throughput Associated complexity reduces the throughput and only less samples can be processed

Sequencing-based allele mining Require less expertise with direct sequencing of PCR products Less Highly efficient since it involves one step direct identification of sequence variations. Effective in detection of any type of nucleotide polymorphism. Comparatively less cost is involved Comparatively less time is required Throughput and sample size increases with massively parallel sequencing platforms

two or three each year (Pettersson et al., 2009), sequencing at a lower cost would soon become a reality. Given the availability of low cost, more read length and high throughput sequencing platforms, sequencing-based allele mining in future, would result in faster generation of allelic data at a cheaper cost. In addition, sequencingbased allele mining of specific genes in identified accessions and their association with phenotypic variations will give a tremendous impetus to precision breeding programs in crop plants. 3.3. Tools required for allele mining An important step in allele mining, irrespective of the methods used, essentially involves the identification of polymorphism by comparative analysis of sequences of various genotypes. Several software tools are available for handling the complex nucleotide data, prediction of putative functional or structural components of complex macromolecules, prediction of transcription factor binding sites, identification of sequence polymorphisms and to predict the amino acid changes which are responsible for changes in encoded protein structure and/or function. A brief account of the bioinformatic tools used for allele mining analysis is given in Table 2. These tools simplify the access and analysis of DNA sequence; help in identification of sequence polymorphism, and probe whether the variation correspond to the variation in TFBMs. These tools are also useful to identify the over-represented motifs in the upstream regions while delimiting the core promoter region and such identification narrows down the focus to identify new variation in regulatory elements. A typical output of many such computational methods will be a list of motifs, which are most likely to correspond to the actual binding sites, along with a large number of random variations. It should be noted that, the computational based predictions depend on the pre-designed algorithms which are largely developed based on the pre-characterized sequences available in the databases. Hence, such predictions should always be validated through systematic wet-lab experimental approaches. 3.4. Considerations for allele mining Successful and efficient allele mining activity would depend mainly on the existing genetic base and the availability of genome and gene sequences information of a particular crop species. Several other important factors include availability of efficient and reliable phenotyping techniques; genomic resources; availability of high throughput techniques for quick and easy generation of allelic data points; cost-effective sequencing platforms; efficient bioinformatic tools for identification of nucleotide variation and molecular marker construction technologies for marker-assisted selection (MAS). Considering these, a set of criteria for allele mining is detailed in Table 3. Based on these criteria, allele mining can be initiated for any crop with sequence information and even for a related member of its family. 4. Applications Allele mining can be effectively used for discovery of superior alleles, through ‘mining’ the gene of interest from diverse genetic resources. It can also provide insight into molecular basis of novel trait variations and identify the nucleotide sequence changes associated with superior alleles. In addition, the rate of evolution of alleles; allelic similarity/dissimilarity at a candidate gene and allelic synteny with other members of the family can also be studied. Allele mining may also pave way for molecular discrimination among related species, development of allele-specific molecular markers, facilitating introgression of novel alleles through MAS or deployment through genetic engineering (GE). While general applications of allele mining are described here (see also Fig. 3), the current status on the applications of allele mining is detailed in Table 4.

G.R. Kumar et al. / Biotechnology Advances 28 (2010) 451–461 Table 2 List of bioinformatic tools/web resources/databases useful for allele/promoter mining.

Table 3 Considerations for allele mining activities.

S. Name No.

Utility

Available at

Reference

S Activities/steps No.

1

Database of Transcription Factor (TF) binding motifs

http://www.dna. affrc.go.jp/PLACE/ index.html

Higo et al. (1999)

1

2

3

4 5

6

7

8

9 10

11

12

13

14

15

16

17

18

PLACE (plant cis acting regulatory DNA elements) plantCARE

2 Plant cis acting regulatory elements database

http:// bioinformatics. psb.ugent.be/ webtools/ plantcare/html TRANSFAC Database of TF and TF http://www. binding motifs gene-regulation. com/pub/ programs.html JASPAR Transcription Factor http://jaspar. Binding Site Database genereg.net/ W-AlignACE Motif discovery tool http://www1. spms.ntu.edu.sg/ ∼chenxin/WAlignACE/ Motif discovery tool http://meme. MEME nbcr.net/ (Multiple EM meme4_1/cgifor Motif bin/meme.cgi Elicitation) Plantprom DB Plant promoter http://mendel.cs. database rhul.ac.uk/ mendel.php? topic=plantprom MATinspector To predict TFBS and of http://www. promoter analysis genomatix.de/ products/ MatInspector/ EPD Eukaryotic Promoter http://www.epd. Database isb-sib.ch/ http://wwwmgs. TRRD Regulatory regions bionet.nsc.ru/ database (description of Regulatory Elements mgs/gnw/trrd/ and TFBS) http://rulai.cshl. SCPD Saccharomyces edu/SCPD/ cerevisiae promoter database DCPD Drosophila core http://wwwpromoter database biology.ucsd.edu/ labs/Kadonaga/ DCPD.html http://rulai.cshl. Collection of TRED (transcriptional mammalian regulatory edu/cgi-bin/ elements regulatory TRED/tred.cgi? element process=home database) http://www.ifti. ooTFD Object oriented org/ootfd/ -transcription factors database AGRIS TF and RE databases http:// arabidopsis.med. ohio-state.edu – ModuleFinder Gene co-expression and CoReg and link with promoter elements – FastPCR Nucleotide sequence analysis and primer design Primer 3 Primer design http://frodo.wi. mit.edu/primer3/

19

BioEdit

Nucleotide sequence analysis

20

Clustal W

Sequence alignment

www.mbio.ncsu. edu/BioEdit/ BioEdit.html www.ebi.ac.uk/ Tools/clustralw/

Lescot et al. (2002)

Matys et al. (2003)

Bryne et al. (2008) Chen et al. (2008a)

3 4 4

Bailey et al. (2006)

Shahmuradov et al. (2003)

Cartharius et al. (2005) 3 Schmid et al. (2004) Kolchanov et al. (2000)

Zhu and Zhang (1999) 4 Kutach and Kadonaga (2000) Jiang et al. (2007)

455

5.

Criteria for selection/design

Germplasm identification

Reference collection representing maximum genetic diversity in a minimum possible number. Crop species Availability of genetic map, segregating populations, ESTs or gene/genome sequences Relatedness to model species for which target gene sequence is available Availability of comparative map/sequence colinearity Availability of mini core collections or 200– 300 reference accessions covering all varietal groups selected from mini core Target trait Traits of interest Phenotypic characterization Precise phenotyping using efficient protocols in suitable environmental condition Candidate gene targets A priority gene list considering the traits targeted Cross-species: genes with known homologues in related crops Within species: genes for important agronomic traits. For complex traits, genes that co-localize with reported major QTL For crops with no full genome sequences: EST sequences and information from cDNA libraries can be used The genes under consideration should be candidates with strong evidence, or be part of a well-understood metabolic pathway or be related by clear orthology across species, to the extent possible Primer design Comparative genomics based prediction of the conserved regions that may be suitable for amplification of the homologs in related species Covering the coding and upstream regulatory regions Primer pairs with a minimum overlap of 100– 200 bp to maximize their utility for SNP detection and sequencing Sequencing analysis for Sequencing of single and clear amplicons detection of SNPs and InDels Amplicons of genotypes with high trait intensity in desired direction Database of candidate gene Well classified and easily accessible database diversity for each nucleotide polymorphism associated with trait expression Tools for designing and modeling superior alleles for enhanced trait expression

Ghosh (1998)

Davuluri et al. (2003) Holt et al. (2006) Kalendar (2009) Rozen and Skaletsky (2000) –

–

4.1. Characterization of allelic diversity in gene banks Identification and access to allelic variation that affects the plant phenotype is of utmost importance for the utilization of genetic resources

in crop improvement. Exploitation of gene banks for efficient utilization depends on the knowledge of genetic diversity, in general, and allelic diversity at candidate gene(s) of interest, in particular. Hence, characterization of genetic diversity or allelic/genic diversity among the accessions of the collection is highly important to establish the genetic relationships among them and to assess their genetic worthiness in terms of its utility for improving a target trait. Allele mining seems to be a promising, although largely untested method to unlock the diversity in the collections of genetic resources in the world gene banks (Kaur et al., 2008a). The availability of sequence of the candidate genes that underlie agronomic traits will facilitate a pragmatic gene-based use of genetic resources, which, at least in theory, can be extended to any given trait (Graner, 2006). Realizing the potential of allele mining in plant genetic resources (PGR) management applications, many of the international crop research institutes which are maintaining crop germplasm collections have initiated studies to characterize the allelic diversity of crop plants. Due importance on this aspect has also been given by the Generation Challenge Program (GCP) through its several project initiatives for unlocking the genetic diversity in crops for the benefit of the resource-poor (www. generationcp.org). Although many hurdles are still need to be overcome,

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et al., 2008); a TT deletion in intron 2; (AT)n insertion in intron 4 (Chen et al., 2008b) and several SNPs across the coding region (Kovach et al., 2009). Of them, Shi et al. (2008) have successfully developed allelespecific molecular markers based on the identified functional nucleotide polymorphism in badh2 for use in MAS. Successful cases of allele mining to find novel alleles and allelic combinations are also increasingly accumulating in many crops in addition to rice. Large scale haplotype diversity at vernalization locus (VRN-H1/H4) in European barley germplasm has been reported and allele-specific molecular markers have been developed (Cockram et al., 2007).These studies clearly support the existence of large amount of unexploited allelic diversity for many traits in the crop gene pool and provide tremendous opportunity to identify and exploit the allelic variation for trait enhancement. Fig. 3. Applications of allele mining.

4.4. Allelic synteny and evolutionary relationship the application of allele mining strategies is expected to result in a quantum leap in the use of PGRs. 4.2. Identification of new haplotypes Allele mining can be potentially employed in the identification of nucleotide variation at a genomic region (candidate gene) associated with phenotypic variation for a trait. Through this, one can evaluate the frequency, type and the extent of occurrence of new haplotypes and the resulting phenotypic changes. Many case studies are available for analyzing the haplotype diversity and identification of new haplotypes at candidate genes (see Table 4). Knowledge on the most common haplotype changes and their frequency in the populations would form the basis for association mapping studies. 4.3. Development of allele-specific markers for MAS Identification of sequence variation will pave the way to develop allele-specific marker assay for precise introgression of the identified ‘superior and/or novel’ alleles to suitable genetic background. In recent years, several case studies are being reported which demonstrate the existence of sequence variation at key genes while some studies have demonstrated the utility of these variations through development of allele-specific molecular markers for MAS. For instance, in rice, comparison of nucleotide sequences of Waxy gene (codes for a granule-bound starch synthase) in 18 different accessions revealed the presence of five different alleles, which are characterized with a unique replacement, frame shift or splice donor site mutations. All the alleles were clearly associated with the observed phenotypic alterations (Mikami et al., 2008). With the identification of the candidate gene for gelatinization temperature (GT), another quality parameter, Bao et al. (2006) studied the nucleotide diversity in starch synthase IIa (SSIIa) locus involved in GT. Based on the 24 SNPs and one InDel variation among 30 rice varieties, allele-specific markers were developed for their use marker-assisted backcross breeding programs targeted to improve grain quality in rice. Allele mining at Pi-ta locus (responsible for resistance to rice blast disease) revealed 91 nucleotide polymorphisms and 18 InDel variations among wild rice accessions of Oryza rufipogon and Oryza barthii (Huang et al., 2008). Wang et al. (2008b) identified 16 different sequences with numerous insertion and deletions in the exon and intron regions of the Pita gene in cultivated rice and its wild relatives. Very recently, 86 SNPs and 28 InDels have been identified in GS3 gene responsible for grain size through comparative sequencing of 54 accessions of O. sativa (Takanokai et al., 2009). Identification of allelic variation was much evident in case of rice fragrant gene, badh2 (Sakthivel et al., 2009a, b). Since its discovery by Bradbury et al. (2005), at least seven allelic variants have been independently identified including a 7-bp insertion in exon 8 (Amarawathi et al., 2008); a 7-bp deletion in exon 2 (Shi et al., 2008); absence of MITE (miniature interspersed transposable element) in the promoter (Bourgis et al., 2008); two new SNPs in the central section of intron 8 (Sun

Using the sequence information obtained from allele mining studies, syntenic relationships can be assessed among the identified loci/genes across the species/genera. In rye, superior homologue alleles for aluminum tolerance were isolated using syntenic allele sequence information from wheat, and the same technique has been employed to isolate agronomically superior alleles in Phaseolus vulgaris and other grasses (Fontecha et al., 2007). Genes/QTL controlling quantitative resistance to the blast pathogen, Magnaporthe grisea showed high similarity in their sequence and as well as genetic/ physical position in both rice and barley, suggesting a common evolutionary origin for this resistance gene (Chen et al., 2002). Wang et al. (2008c) analyzed the allelic frequency and compared the variation of two late blight resistant genes Rpi-blb1 and Rpi-blb2 with ancestral genes in Solanum. They suggested that Rpi-blb1 might have evolved from the intragenic recombination of two ancestral genes RGA3-blb and RGA1-blb and RGA1-blb has originated well before the tuber and non-tuber divergence. Identification and characterization of a resistance gene, Rpi-blb1 in a non-crossable species S. bulbocastanum has lead to the identification of putatively functional Rpi-blb1 homologues in a crossable species Solanum stoloniferum (Song et al., 2003). In tomato, Rose et al. (2005) amplified and sequenced the alleles of Pto gene (conferring resistance to bacterial pathogen) in 49 genotypes and tested a subset of these alleles for its function, identified several nucleotide changes responsible for pathogen recognition and hypersensitive resistance response and attributed these changes to the natural variation in resistance to Pseudomonas syringae pv. tomato (Pst) strains. In wheat, map-based cloning (Yahiaoui et al. 2004) followed by haplotype and sequence analysis of powdery mildew resistance (Pm3) locus among the accessions carrying various Pm3 alleles have led to the identification of nucleotide polymorphism among various alleles (Srichumpa et al., 2005; Yahiaoui et al., 2006) and development of functional molecular markers for each allele (Tommasini et al., 2006). Besides, these reports, in order to utilize the naturally occurring diversity at Pm3 locus, allele mining approach was employed and two new functional Pm3 alleles, which differ mainly in the LRR domain have been identified, and based on this study, a set of resistant lines harbouring new potentially functional alleles for this gene have been identified (Kaur et al. 2008a). These new functional alleles will be suitable candidates for transfer to susceptible but economically important wheat varieties to achieve efficient control of powdery mildew. Besides its practical utility of such strategies in wheat breeding, the analysis of allelic diversity and availability of different allelic sequences will also contribute to a better understanding of the processes involved in resistance gene evolution and function (Kaur et al., 2008b). Graner (2006) had demonstrated the allele-based use of PGRs in barley while studying the sequence variation at rym4 locus which confers resistance to barley yellow mosaic virus and identified several novel, functional alleles which

G.R. Kumar et al. / Biotechnology Advances 28 (2010) 451–461 Table 4 (continued)

Table 4 Status of allele mining in crop plants. S. Crop No.

Allele/locus

Trait/name of the protein

Author

1

GluDy

y subunit of glutenin

HMW-GS Mal d 3 s Amy32b

Endosperm storage proteins Allergenicity Self-incompatibility α amylase

Giles and Brown (2006) Liu et al. (2008)

transcription factor -GAMYB

GAMYB-involved in gibberellin signaling Vernalization requirement β-amylase I — starch break down enzyme β-amylase I — starch break down enzyme Grain protein content

2 3 4 5 6

Aegilops sp. Agropyron sp. Apple Apricot Barley

Gao et al. (2005) Halasz et al. (2005) Polakova et al. (2005) Haseneyer et al. (2008)

7

Barley and wheat Barley

8

Barley

VRN-H1 and VRN-H2 Bmy1

9

Barley

Bmy1

10

Barley

Gpc-B1

11 12

Barley rps2 Grapevine VvmybA1

13

Paspalum sp. Pea

Apomixis locus

Lectin locus

16 17

Phaseolus sp. Potato Rice

18 19

Rice Rice

Badh2 Badh2

20 21 22

Rice Rice Rice

Badh2 Badh2 Badh2

23 24 25

Rice Rice Rice

OsC1 Pi ta Pi ta

26

Rice

Pikh

27

Rice

28

Rice

NBS-LRR class R-genes Adh2

29

Rice

wx locus

30

Rice

Rc locus

31

Rice

SSIIa

32

Rye

Alt3

Physicochemical properties Aluminum tolerance

33

Rye

Alt4

Aluminum tolerance

34

Rye

EST

–

35

Soybean

GmDGAT

36

Soybean

SKTI

37 38

Tomato Wheat

Pto Pm3

39

Wheat

Pm3

40

Wheat

Wx-A1

41

Wheat

Ap1 and PhyC

Final acylation of the Kennedy pathway Soybean Kunitz trypsin inhibitor Disease resistance Powdery mildew resistance Powdery mildew resistance Waxy gene and amylose biosynthesis Vernalization response

14 15

TI1

Rpi-blb1 Badh2

457

Cockram et al. (2007) Chiapparino et al. (2006) Malysheva-Otto and Roder (2006) Distelfeld et al. (2008) Ribosomal protein S2 Kubo et al. (2005) Transcriptional regulator This et al. (2007) of anthocyanin biosynthesis Gametophytic apomixes Calderini et al. (2006) Trypsin inhibitors Sonnante et al. characterization (2005) Storage and defense Lioi et al. (2007) proteins Late blight resistance Wang et al. (2008c) Fragrance Amarawathi et al. (2008) Fragrance Shi et al. (2008) Fragrance Bourgis et al. (2008) Fragrance Sun et al. (2008) Fragrance Chen et al. (2008b) Fragrance Sakthivel et al. (2006, 2009a) Apicullus coloration Saitoh et al. (2004) Blast resistance Huang et al. (2008) Blast resistance Wang et al. (2008b) Blast resistance Ram kumar et al. (2008) Disease resistance of Yang et al. (2008) the plant Alcohol dehydrogenase Yoshida and Miyashita (2005) Mikami et al. Granule-bound starch synthase (2008) Pericarp color Brooks et al. (2008) Bao et al. (2006) Miftahudin et al. (2005) Fontecha et al. (2007) Varshney et al. (2007) Wang et al. (2006)

Wang et al. (2008d) Rose et al. (2005) Yahiaoui et al. (2006) Kaur et al. (2008a) Saito and Nakamura (2005) Beales et al. (2005)

(continued on next page)

S. Crop No.

Allele/locus

Trait/name of the protein

Author

42

Wheat

SSII

43

Wheat

Wx-B1

Endosperm starch synthesis Waxy protein

44

Wheat

Shimbata et al. (2005) Monari et al. (2005) Zhang and Dubcovsky (2008) Xia et al. (2008)

45 46 47

PSY-1 and PSY-2 Grain yellow pigment content (GYPC) Wheat Viviparous-1 Pre-harvest sprouting tolerance Wheat TaALMT1 Aluminum resistant Zizania sp. Adh1 and Adh2 Alcohol dehydrogenase I

Raman et al. (2008) Xu et al. (2008)

were further studied at phenotypic level. Several such examples of allele mining studies have been summarized in Table 4. 5. Challenges Considering the huge number of accessions that are held collectively in various gene banks, genetic resources collections are deemed to harbour a wealth of undisclosed allelic variants. Now the challenge is to efficiently identify and exploit the useful variation for crop improvement. Here, we describe the challenges in allele mining and suggest the ways to overcome them in order to increase the efficiency. 5.1. Selection of genotypes The foremost challenge in unlocking the existing variation is the selection of germplasm to be ‘mined’. Given a reliable protocol for characterizing a gene, screening the entire collection would certainly be helpful to find rare alleles, but this is an enormous and inefficient way of screening. Hence, we need to find a way forward from genotyping the whole composite collections to efficiently screening the accessions to discover new alleles and allele combinations while minimizing the number of accessions to be screened. Several approaches through which the prioritization of genotypes for allele mining can be done are described hereunder. 5.1.1. Development of core/mini core collections Handling the entire germplasm is a Herculean task, be it for conventional plant breeding or for allele mining and hence requires sampling strategies to narrow down to a manageable size while maintaining the variability. Development of core and mini collections out of the entire collection has been proposed to simplify the conservation of germplasm resources and effective utilization of the existing variation in gene banks. A core collection is a subset of accessions from the entire collection that capture most of the available genetic diversity of the species (Brown, 1989). For each crop, the first step consists of collating information on various existing collections (the ‘composite set’) and to apply a simple rationale for extracting a representative sample (the ‘core sample’). For defining core collections, several parameters pertaining to geographical, agronomical and botanical descriptors have been used in many crops such as finger millet (Upadhyaya et al., 2006a), chickpea (Upadhyaya et al., 2001; 2006b; 2007), groundnut (Upadhyaya et al., 2003), and cowpea (Mahalakshmi, 2007). The development of core collections is coordinated by the IARC (International Agricultural Research Centers) in-charge of the crop and has been completed for 18 crops (Glaszmann, 2006). The size of the core samples depends heavily on the global amount of resources available in the collections, and may range from several hundred accessions to a maximum of 3000 for the most important crops. Generally, 10% of the crop accessions constitute the core collection representing the variability of the entire collection. However, when the size of whole collection is too large and a core collection (10% of

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entire collection) becomes unmanageable, a strategy has been suggested (Upadhyaya and Ortiz, 2001) in terms of selecting a mini core (core of core) collection (10% of the core or 1% of the entire collection). Mini core collections are developed by using the qualitative parameters to develop trait-specific subsets and by characterizing the genetic diversity of each core sample using molecular markers to reveal the structure of its diversity. Molecular marker-based precise establishment of mini core collections were reported for many crops (Upadhyaya et al., 2006c; Upadhyaya et al., 2008a). Molecular characterization of mini core and trait-specific subsets was suggested to further reveal the genetic usefulness of the germplasm accessions in allele mining (Upadhyaya et al., 2008a). The data generated from morphological and molecular characterization studies could be used to define the genetic structure of the global composite collection and to finally select a ‘reference sample’ of approximately 300 accessions (depending on the size and genetic diversity of the core collection of that particular crop) representing the maximum diversity for the purpose of identification and isolation of allelic variants of candidate genes associated with beneficial traits (Upadhyaya et al., 2006a). Molecular marker and qualitative trait-based reference sets of 300 accessions each in chickpea, groundnut, pigeonpea and finger millet, 384 accessions in sorghum and 200 accessions in foxtail millet have been established (Upadhyaya et al., 2008a). We suggest that the mini core collections or the reference sets (subset of mini core) could constitute the starting materials for dissecting the naturally occurring allelic variation at candidate genes through allele mining efforts. 5.1.2. Accurate phenotyping methods Phenotyping would be the key for success in short listing the genotypes for allele mining as phenotypes are considered as the best clues for genotypes. Increase in precision in phenotyping techniques maximizes the chances of ‘mining’ the prospective genotypes. Also, with the development of trait-specific subsets obtained from phenotyping experiments the number of genotypes for ‘mining’ is significantly reduced. Hence, there is a need to refine phenotyping protocols to increase the efficiency of allele mining. 5.1.3. Flexible computational tools Ever increasing number of plant genetic resources in gene banks created the problem of repetitiveness and genetic redundancy within the existing crop gene pools, which poses a challenge to effective management and potential utilization of these resources. Therefore, efficient computational tools are necessary to ensure the access to useful alleles. A major challenge that exists is to develop flexible computational tools for the selection of desirable alleles which are seen as an avenue for the rapid expansion and extension of GenBank DNA data. Park (2007) has developed a computational tool called ‘Power Core’ to support the development of core and allele mining sets by enhancing the richness and reducing the redundancy of useful alleles. Many such tools are essential to accelerate the future development of core subsets with all the observed characteristics or classes in larger crop collections during the deployment of useful alleles from gene banks. 5.2. Handling genomic resources To keep pace with rapid accumulation of nucleotide and gene expression data, computational tools need to be developed for analyzing the functional nucleotide diversity and to predict specific nucleotide changes responsible for altered function. Exploiting the developments in allele mining, association genetics and comparative genomics by combining expertise from several disciplines, including molecular genetics, statistics and bioinformatics is the suggested way forward.

5.3. Demarcation of promoter region Demarcation of putative promoter regions poses one of the challenges in promoter mining analysis unlike defining the coding region for allele mining. Promoters and regulatory elements are spread across the upstream region and their locations are variable from one gene to another. Available literature clearly suggests the lack of consensus in fixing the size of the region for mining promoter and regulatory elements. This window size varies from as low as −100 bp to −3000 bp (Brazma et al. 1998; Molina and Grotewold, 2005; Park et al. 2002; Veerla and Hoglund, 2006). However, high densities of regulatory elements have been reported only between −100 and −600 bp in the upstream region. Still, there is a need to develop specific software tools which can accurately predict the core promoter region based on the representation/over-representation of consensus regulatory motifs. 5.4. Characterization of regulatory region In contrast to identifying variation in coding regions of the genome, characterizing the extent of cis-acting regulatory variation presents a much greater challenge, since it is not possible to discern this even in the fully sequenced genomes. Screening for regulatory variants based on differences in transcript levels between individuals is confounded by potential trans-acting factors or environmental differences (Dai et al., 2007). As the promoter regions can function in bi-directional pathway, prediction of the orientation will be difficult even by the software's. Moreover, complexity of analyzing these promoters is increased due to the fact that the transcription factors usually control more than one gene (Hehl and Wingender, 2001). We suggest using a suit of different computational methods to get the snapshot of the regulatory elements which can be further examined through suitable experiments. 5.5. Higher sequencing costs One of the important challenges is to minimize the time and efforts required while reducing the cost per data point. These challenges may partly be overcome by resorting to cheaper and faster sequencing platforms for high throughput detection of allelic variations. 6. Perspective Due to its tremendous potentials and applications, allele mining can be visualized as a vital link between effective utilization of genetic and genomic resources in genomics-driven modern plant breeding. In order to keep pace with ever increasing sequence data in GenBank and ever expanding crop gene banks, it is highly imperative to evolve novel and efficient ‘mining’ strategies. Efforts to develop tools and strategies should be equally focused on handling both genetic and genome resources. Firstly, there is a need to develop more refined sampling strategies or tools which can aid in such selection to enrich useful alleles in a minimum number of genotypes with desired levels of trait expression. More efforts are needed to develop computational tools for efficient identification of core sets from large germplasm collections which give a correct representation of all phenotypic classes (both qualitative and quantitative). Genetic diversity information at molecular level can be best utilized to shortlist the genotypes while retaining the diversity among the mini core sets. However, further studies are required on number and type of markers to be used while down sizing the mini core sets. Through careful and accurate phenotyping, trait-specific subsets with all phenotypic classes can be developed which would further result in selection of ‘key’ genotypes for ‘mining’ for the novel alleles underlying particular trait. Secondly, to effectively extract and apply information from large genomic resources, several molecular biological and bioinformatic tools are currently available. With these tools, identification of

G.R. Kumar et al. / Biotechnology Advances 28 (2010) 451–461

functional nucleotide polymorphisms in superior alleles and comparison of sequence variation in a range of alleles for a given gene with the corresponding gradient of strength in trait expression across genotypes is now possible and this information can be used to elucidate the most preferable allele with desired levels of function. Subsequently, improved marker construction techniques coupled with highly powerful, yet simple, assay to discriminate even a single base pair changes would be most required to transfer the identified allele in to desired background through MAS. The outcome of international genome sequencing projects will make allele mining possible for many genes in different crop species and possibly from related species within the limits of genome synteny and sequence conservation. New technologies that allow us to harness the process of allele discovery and direct it more efficiently to integrate it into the varietal development process would greatly enhance the power of this approach. As we are entering an era of low cost and high throughput sequencing, routine allele mining through sequencing would soon become a universal tool to score variations at candidate genes between individuals and become an effective and economically viable method of choice for identification and development of novel genetic resources for deployment in plant breeding. Whole genome SNP detections at key candidate regions through array chips are becoming available and this is expected to further increase the speed for detection of mutations and allow rapid identification and characterization of allelic variants. Further, advanced allele mining strategies which involves designing of artificial allelic variants with more relevant function by domain shuffling or gene shuffling among related genes offers new opportunities and provides powerful tools for crop improvement. Now, the challenge before us is in the integration of information from whole genome sequencing, genome-scale SNP assays and targeted genemapping studies, with the phenotypic trait variation for more efficient utilization of genetic and genomic resources for crop improvement. There is also a need to bridge the gap between channelizing the information about genes and gene function to the generation of new productive varieties that are an essential component of a sustainable food supply for the future. Once after the identification of genes/loci and the germplasm sources carrying trait-enhancing alleles, cataloguing of novel and superior alleles and their behavior in appropriate genetic backgrounds and environments needs to be documented. As efficient tools for data mining and cost-effective and high throughput sequencing platforms become available, it is certainly expected that sequencing-based allele mining would emerge as a method of choice in revealing natural variations and in providing novel and effective alleles and would take center stage for all crop improvement activities. Acknowledgments Research in our laboratory on this topic is funded by the Department of Biotechnology, Government of India, New Delhi. We thank Dr. P. Rajendrakumar, NRCS, Hyderabad for his critical review of the manuscript. We also thank the anonymous reviewers whose suggestions have helped to improve the manuscript to the present form. References Amante-Bordeos A, Sitch LA, Nelson R, Damacio RD, Oliva NP, Aswidinnoor H, et al. Transfer of bacterial blight and blast resistance from the tetraploid wild rice Oryza minuta to cultivated rice. Oryza sativa Theor Appl Genet 1992;84:345–54. Amarawathi Y, Singh R, Singh AK, Singh VP, Mohapatra T, Sharma TR, et al. Mapping of quantitative trait loci for basmati quality traits in rice (Oryza sativa L.). Mol Breeding 2008;21:49–65. Bailey TL, Williams N, Misleh C, Li WW. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 2006;34:W369–73. Bao JS, Corke H, Sun M. Nucleotide diversity in starch synthase IIa and validation of single nucleotide polymorphisms in relation to starch gelatinization temperature and other physicochemical properties in rice (Oryza sativa L.). Theor Appl Genet 2006;113:1171–83.

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