This document discusses allele mining as a technique for improving crops. It defines allele mining as identifying allelic variation within genetic resources collections to find superior alleles. There are two main approaches - TILLING based allele mining which uses mutagenized populations and enzymatic cleavage to find mutations, and sequencing-based allele mining which uses PCR and sequencing to identify natural variation. Both have benefits and limitations. Applications of allele mining include finding alleles for resistance, abiotic stress tolerance, and improved yield and quality. Overall, allele mining is a promising approach for utilizing genetic resources to discover variants that can aid crop breeding.

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2. Allele Mining in Crop Improvement
Supervisor: Prof. J. P. Shahi
Presented by-
Miss. Gayatri Kumawat
ID No. = 17412GPB006
Genetics and Plant breeding
M. Sc. 2nd year

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Contents
Dept. of Genetics & Plant Breeding
What is allele mining ?
Allele mining set
Approaches
Comparison
Challenges
Case study
Introduction
Applications

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 Development of superior and high yielding varieties
 Accumulation of beneficial alleles from vast PGR
 Beneficial or superior alleles left behind during evolution
e.g. wild relatives and land races
 Introgressions of novel alleles into cultivated varieties make
dramatic changes in trait expression (Mc Couch et al., 2007)
 Germplasm resources need to be relooked for novel alleles
Introduction

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Plant Genetic Resources (PGR)
• A wealth of germplasm collections is available worldwide
• Around 7.4 million accessions held in over 1,700 gene
banks (http://www.fao.org)
• These harbour a wealth of undisclosed allelic variants

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National Bureau of Plant Genetic Resources (NBPGR)
(http://www.nbpgr.ernet.in)

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The challenge is how to unlock this variation ????
Solution
Allele mining

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What is allele and allele mining ?
Dept. of Genetics & Plant Breeding
Allele : Alternative form of a gene
Allele mining:
 Identifying allelic variation of relevant traits within genetic
resources collections
 Dissection of naturally occurring variation at candidate
genes/loci
 Searching for new superior alleles

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Evolution of new alleles
Mutation:
- Evolutionary driving force
- SNP or InDel in coding or regulatory regions may have tremendous
effect on the phenotype e.g. prostrate growth habit of wild rice gene
PROG1 (Tan et al., 2008)
Dept. of Genetics & Plant Breeding

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True allele mining
Dept. of Genetics & Plant Breeding
Analysis of non-coding and regulatory regions of the candidate
genes in addition to coding regions of genes.
e.g. 5'UTR, 3'UTR, intron and promoter
Intronic mutations –
e.g. waxy (Wx) gene in rice (Isshiki et al., 1998)
VRN-1 in barley and wheat (Fu et al., 2005)
Mutations in the promoter -
e.g. xa13 in rice (Chu et al., 2006)
GIF1 in rice cultivars (Wang et al., 2008)

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Requirements for allele mining ?
Direct access to genes conferring:
• Resistance to biotic stresses
• Tolerance against abiotic stresses
• Greater nutrient use efficiency
• Enhancing the yield of crops
• Improved the quality
 Enormous progress has been made in depositing an exponential
amount of sequence information into GeneBank
 Based on gene and genome sequences, PCR strategies are
devised to isolate useful alleles of genes from a wide range of
species (Latha et al., 2004).

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(Gowda CLL et al., 2013)
Construction of core and mini-core collections
Dept. of Genetics & Plant Breeding

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Allele mining set
(Umesha and Amruta, 2014)
Red- Cultivated
Blue- Mini core
Light green- wild
Pigeonpea

16. Approaches for allele mining
1) TILLING based allele mining
2) PCR based or Sequencing based allele mining
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17. TILLING
 TILLING expanded as Targeted Induced Local Lesions IN Genomes
 TILLING is a non transgenic reverse genetics approach that allows
directed identification of point mutations in a specific gene
 Developed by McCallum et al., 2000
 This technique was designed to detect mutations in Arabidopsis
thaliana treated with the chemical mutagen, EMS.
 TILLING has since been used as a method in other organisms such
as zebrafish, corn, wheat, rice, soybean, tomato and lettuce.

18. Forward and Reverse Genetics
Phenotype
Search
Reverse Genetics
Forward Genetics
Phenotype
Mutations
18

19. What are SNPs ?
• SNPs (snips) are
single base variants
of the wild
nucleotide sequence
• May be at DNA level
or at mRNA level
• SNPs at mRNA are
known as cSNPs
SNP
19

20. TILLING Procedure
(a) EMS Mutagenesis to obtain mutagenized population
(b) DNA extraction from each mutated plants (M2) and Pooling
individual DNA
(c) Mutation discovery
 PCR amplification with fluorescent labeled primers
 Heteroduplex formation
 Enzymatic mismatch cleavage (Cutting the annealed products with a
single-strand specific nuclease CEL I enzyme)
 Gel electrophoresis and mutation discovery
• Purifying the products and run on gels or capillaries to detect the
mismatched products
• Fragments are denatured and separated typically on a LI-COR DNA
analyser (Colbert et al.,2001).

21. TILLING
Ashkani et al.2015

22. Eco-TILLING
• Modified TILLING
• Developed by Comai et al., 2004
• Good in heterozygous population
• To determine natural polymorphisms
• TILLING used as a simple nucleotide polymorphism
(SNP) discovery tool to examine DNA variation in
natural populations is called as EcoTILLING.
• EcoTILLING was found to be robust at characterizing
natural genetic variation.

23. Self Eco TILLING:
Developed by Wang et al., 2008
Modified Eco- TILLING method for the discovery of mutation in
a multi-gene family
Procedure:
 Instead of mutant population use any germplasm accession,
land races, etc.,
 DNA extraction and pooling
 PCR amplification
 Denaturation and Annealing - heteroduplexes
 Enzymatic Mismatch Cleavage
 Identification of the individual
Mutation Discovery (Electrophoresis and fragment separation by
capillary electrophoresis, identification of variation).

24. Eco Tilling
Ashkani et al., 2015

25. TILLING vs Eco-TILLING
• Mutant population is
created
• Sequence information
needed
• Natural population or
association mapping
population is used
• Sequence information is
not needed

26. Sequencing-Based Allele Mining
• Developed by Huang in 2009
• Based on PCR and sequencing
Procedure
Collection of accession
DNA extraction
Primer design
PCR amplification
Sequencing and identification of allelic variation.

27. Sequencing-Based Allele Mining
Ashkani et al., 2015

28. Sl. No Name Utility Available at Reference
1 AGRIS TF database Arabidopsis.me
d.ohiostate.edu
Arabidopsis.me
d.ohiostate.edu
2 FastPCR Nucleotide
sequence
analysis
(Kalender, 2009)
3 BioEdit Nucleotide
sequence
analysis
www.mbio.ncs
u.edu
4 Clustal W Sequence
alignment
www.ebi.ac.uk
-
Bioinformatics tools for allele mining

29. Different steps involved in two approaches of allele mining
Kumari et al., 2018

30. Limitations of TILLING
• A successful TILLING project depends on the
development of a densely mutagenized population
and the preparation of DNA of suitable quality for PCR.
• Because, protocols for chemical mutagenesis in
different plant and animal species can vary
dramatically, no single protocol can be considered
general.
• Highly heterozygous species where the large number
of bands from natural polymorphisms could potentially
inhibit the detection of rare induced mutations.

31. Applications of Allele Mining

32. Parameters Eco-TILLING
Sequencing-based
allele mining
DNA pooling Yes No
Heteroduplex formation Yes No
Primer labeling by
fluorescent dyes Yes No
Primer cost High Low
Enzymatic mismatch cleavage Yes No
Electrophoresis
Fragments are denatured and
separated typically on a LI-
COR DNA analyser
Simple Agarose Gel
Comparison of allele mining techniques

33. Cost of Sequencing Medium-to-high High
Samples size for sequencing Less More
Time required
More( especially
for sample preparation) Less
Technical expertise
High ( especially for DNA
Pooling and detection of
cleavage of heteroduplexes) Low
Lab facilities High Sophisticated Low Sophisticated
Nucleotide detection
Effective in detection of
SNPs rather than InDels
Effective in detection of
any type of nucleotide
Throughput Medium-to-high High
Complexity More Less
Parameters Eco-TILLING
Sequencing-based allele
mining

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Challenges
• Development of core/mini core collections
• Accurate phenotyping methods
• Flexible computational tools
- Repetitiveness and genetic redundancy
- Challenge to effective management
• Handling genomic resources
• Demarcation of promoter region

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(Kumar et al., 2010)
Status of Allele Mining in Crop Improvement

36. Case Study 1..

37. Case study 2..

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Conclusion
 Allele mining can be visualized as a vital link between
effective utilization of genetic and genomic resources
in genomics.
 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 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 centre stage for all crop
improvement activities.
Dept. of Genetics & Plant Breeding

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