Elsevier

Biotechnology Advances

Volume 28, Issue 4, July–August 2010, Pages 451-461
Biotechnology Advances

Research review paper
Allele mining in crops: Prospects and potentials

https://doi.org/10.1016/j.biotechadv.2010.02.007Get rights and content

Abstract

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.

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, Xiao et al., 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 re-looked 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.

Section snippets

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

‘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

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

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.

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.

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.

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