The presentation brief about candidate gene identification and candidate gene approach methods to identify QTLs in crop plants.

1. Candidate Gene Approach In
Crop Improvement
Bonipas Antony J
PG19AGR8128
M. Sc.
Dept. of Genetics and Plant Breeding
MASTER SEMINAR-II
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2. Introduction
Candidate gene identification strategies
Methodology for Candidate gene
Approach
Application
Conclusions
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3. • One of the main
objectives of
molecular genetics is
to identify and isolate
the gene of interest.
Introduction
This Photo by Unknown Author is licensed under CC BY-SA
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4. • Three main approaches lead to identify the
genes of interest.
A. Positional map based cloning
B. Insertional mutagenesis
C. Candidate gene (CG) approach
Positional cloning and insertional mutagenesis
methods are limited by genome size and/or by the
lack of transposons in the species being studied.
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5. • One of the main focus of plant breeding
program is to identify QTL of important
traits.
• Biparental based QTL mapping is most
commonly used in plant breeding to identify
QTL’s and marker associated with it.
• CG approach is a promising approach to
identify genetic disorder causing genes and
QTL’s in animals and humans.
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6. Candidate gene
Candidate genes are sequenced genes of
known biological action involved in the
development or physiology of a trait.
They may structural genes or genes in a
regulatory or biochemical pathway affecting trait of
interest.
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7. Putative gene:
Sequenced genes biological function or
protein product of which is not known,
Candidate gene:
Sequenced genes of known biological action
involved in the development or physiology of a trait.
Putative candidate genes:
Genes share sequence similarity of
characterized genes, based on which protein
products can be detected but exact protein
product/function yet to discover.
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8. Candidate gene
Identification
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9. Multi-Disciplinary Approach
Multi-
disciplinary
approach
• Mutagenesis
• Biochemical analysis
• Expression profiling
• Comparative genome
mapping
• Bioinformatics
• Linkage mapping
Candidate
genes
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10. Comparative
genomics
strategy
Combined
strategy
Digital candidate
gene
identification
strategy
Function-
dependent
strategy
Position-
dependent
strategy
CG
Identification
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11. Function-dependent strategy
• This approach is mainly focused on identification
differentially expressed genes.
• If we understand the physiological pathway of
trait of interest, we study expression profiling of
genes in volved in those pathway to identify CGs.
• Candidate genes can be mined from gene
expression profile, or by transcriptomics and
proteomics analysis.
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12. • Function dependent strategy is based on the
hypothesis that states, those genes show variable
expression of gene also responsible for variation
of trait.
• But it is not true in all the cases, therefore more
differentially expressed genes are selected for
association study.
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13. This Photo by Unknown Author is licensed under CC BY
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14. • Genetic maps of markers for genes of known
functions can assist in choosing positional CGs.
• Identification of candidate gene is mainly based on
the physical linkage information in a QTL-
identified chromosomal segment.
• This approach aims at the vicinity of known QTLs,
and candidate genes are sought out from tens to
hundreds of gene members harbored in the targeted
chromosomal region.
Position-dependent strategy
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15. Define the
candidate region
Identify all the
genes in the
region
Screen the genes
for polymorphism
Select the
candidate gene
and validate it
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16. Comparative genomics strategy
• Comparative genomics strategy makes the utility of
cross-species information to identify and characterize
the effect of putative candidates.
• In this strategy, candidate genes may be functionally
conserved or structurally homologous genes identified
from other related species.
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17. • Which also named as Digital candidate gene approach
(DigiCGA) or in silico candidate gene approach
• This is a web resource-based candidate gene identification
approach.
• DigiCGA can be defined as an approach that objectively
“extract, filter, (re)assemble, or (re)analyze all
possible resources available derived from the public web
databases mainly in accordance with the principles of
biological ontology and complex statistical methods to
make computational identification of the potential
candidate genes of specific interest”
Digital candidate gene identification strategy
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18. • Gene Ontology (GO) is an annotation system which tries
to describe attributes of gene and gene products
• The Gene Ontology considers three distinct aspects of
how gene functions can be described:
1) Cellular component: the place in all cells where a gene
product is active.
2) Molecular function: the elemental activities of a gene
product at the molecular level, such as binding or catalysis.
3) Biological process: biological role or objective to which
the gene or gene product contributes and this process is
accomplished via one or more ordered assemblies of
molecular functions.
Gene Ontology (GO)
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19. QuickGO
agriGO
AmiGO
GOrilla
Software for gene ontology
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20. • A total of 795 annotated drought stress responsive gene IDs
of Arabidopsis thaliana identified from STIFDB V.2.0
• The extracted FASTA sequences of 795 drought responsive
unigenes from NCBI were subjected to BLAST for organism
solanum.
• Out of 795, a total of 109 stress responsive unigenes which
were showing more than 80 per cent identities or sequence
similarities were selected from solanum.
Faizan et al., 2021
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21. Faizan et al., 2021
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22. Faizan et al., 2021
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23. Faizan et al., 2021
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24. • 19 transcription factors (TFs) which are involved in stress
responsive gene regulation. Among those, MYB/MYC,
bZIP, AP2/EREBP, WRKY, DREB (AP2/ERF), HSF and
NAC are well-known TF families that are known to play
prominent role in the regulation of different genes
associated with drought stress.
• The present in silico analysis has revealed that, the genes
like CDPK, galactinol synthase 2, lipoxygenase 3,
cinnamyl alcohol dehydrogenase 5 and Cellulose synthase
6 are known to be associated with tolerance to stresses
like drought.
Faizan et al., 2021
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25. Combined strategy
• At least two strategies together to mine
candidate genes, has begun to show its onset
in some applications.
• A single methods above mentioned may not
effective always, combination of those
methods will make the process easy to identify
candidate genes.
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26. Candidate Gene approach for identifying
trait of interest
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27. “The candidate gene hypothesis states that a
significant proportion of the QTL affecting trait
variation are in fact candidate genes associated
with the traits.”
Candidate Gene hypothesis
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28. 1. Choosing the putative candidate genes.
2. Uncovering polymorphism in the candidate genes.
3. Developing a convenient procedure for large-scale
genotyping.
4. Identifying a population for association
studies/linkage study.
5. Carrying out association studies/linkage studies.
6. Verifying association that are uncovered.
Candidate Gene approach for identifying
trait of interest
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29. Pflieger et al., 2001
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30. 1. Choice of Candidate Gene
1. Choice of functional CGs
CGs are proposed based on molecular and
physiological studies
2. Choice of positional CGs
Based on linkage data of the locus being
characterized
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31. Characterization of disease resistance loci
• RGA involved in pathogen recognition, signal
transduction and defense. Many RGA are
identified and characterized.
• At the molecular level, gene features of RGA
are highly conserved.
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32. • These genes were classified into three groups
based on the presence offunctional domains:
1. Nucleotide binding site (NBS) putatively
involved in nucleotide binding,
2. Leucine rich repeat (LRR) domain involved in
protein-protein interactions
3. Reporter like Kinase domain involved in
intracellular signal transduction.
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33. • Based on conserved motifs these RGAs will be
identified in different crop species.
• RGAs mapped to the vicinity of either qualitative or
quantitative resistance loci .
• These RGAs were thus CGs for new R-genes.
• RGAs can be detected using bioinformatics tools
based on their conserved structural features.
• High-density genome-wide RGA genetic maps are
useful for designing diagnostic markers and
identifying quantitative trait loci (QTL) or markers
associated with plant disease resistance.
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34. • Once CGs have been chosen, association experiments
should be performed to select the most likely CGs
1. Co segregation analysis
Genetic map positions of functional CGs and
target loci involved in the studied trait can be compared.
2. Statistical association
Statistical analyses between phenotypic variation
and molecular polymorphisms within the CG can be
conducted.
It is important to notice that these two strategies are
fundamentally identical and can be conducted together or
successively.
2. Screening of CG
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35. Co-segregation Analysis
• For loci controlling qualitative traits (MTLs), absolute co-
segregation without any recombinant individuals in a large
population, is required to select the most accurate CGs.
• QTL positions are quite imprecise because the associated
confidence interval covers several mega bases.
• Fine-mapping experiments are necessary to develop near
isogenic-lines segregating for shorter chromosome segments
and that have homogenous genetic backgrounds.
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36. Capsicum annuum cv TF68 X Capsicum chinense cv Habanero
(red) (Orange)
103 F2 individuals were used as a population for genetic linkage
analysis.
Cloning of candidate genes involved in carotenoid biosynthesis
Farnesyl pyrophosphate synthase (FPS)
Geranylgeranyl pyrophosphate synthase (GGPS)
Phytoene synthase (PSY)
Phytoene desaturase (PDS)
Lycopene β-cyclase (LCYB) and
Capsanthin-capsorubin synthase (CCS) Huh et al., 2001
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37. • F1 plants bore the same red-coloured fruits as TF68.
Out of 103 F2 plants, 78R: 25O,(3:1)
• Amplified candidate genes were converted into RFLP
markers.
• The candidate genes, GPS, PDS, LCYB, CCS and PSY,
were assigned on linkage group 7, 2, 10, 4 and 7.
• Gene PSY revealed polymorphism and complete co
segragtion with locus determining mature fruit colour fall is
the same region of earlier identified c2 locus.
Huh et al., 2001
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38. Hybridization of the PSY probe to a Southern blot containing
DNA from parents and F2 plants digested with EcoRI.
Quantification of carotenoids by HPLC Huh et al., 2001
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39. Huh et al., 2001
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40. • Statistical association between polymorphism of a
CG and a phenotype.
The molecular variation of the CG can be analyzed by,
1. Linkage analysis between polymorphic CG and
trait of interest in mapping population.
2. Linkage disequilibrium analysis/ Association study
in germplasm.
Statistical Analysis
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41. Candidate gene association
mapping
Genome wide association
studies
• It investigates associations
between within
prespecified gene of
interest and phenotype
• GWAS investigate
associations between
genetic variation of
entire genome and
phenotype
• Genetic variation is limited
to preselected genes.
• Genetic variation is
analysed in the entire
genome
• Cost involved relatively
lower
• Cost involved relatively
higher
• Prior-Hypothesis driven
approach
• Hypothesis free
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42. Candidate gene association mapping of Sclerotinia stalk
rot resistance in sunflower (Helianthus annuus L.)
uncovers the importance of COI1 homologs
• The candidate gene association mapping (CG-AM)
population consists of 260 cultivated sunflower lines.
• The CG-AM population was evaluated for
Sclerotinia stalk rot resistance for two years at three
locations.
Talukder et al., 2014
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43. Eight Arabidopsis genes,
• COI1 (Coronatine Insensitive 1),
• NPR1 (Nonexpresser of PR genes 1),
• EIN2 (Ethylene Insensitive 2),
• ABI1 (ABA Insensitive 1),
• ABI2 (ABA Insensitive 2),
• DET3 (De-Etiolated 3),
• PAD3 (Phytoalexin Deficient 3), and
• LACS2 (Long-Chain Acyl-CoA Synthetase 2), were
selected based on a defense response study in
Arabidopsis against Sclerotinia sclerotiorum
Candidate gene selection
Talukder et al., 2014
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44. Talukder et al., 2014
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45. Primers were used to amplify a portion of the
candidate gene genomic regions within a subset of 104
sunflower genotypes from our CG-AM population.
Talukder et al., 2014
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46. AM analysis was performed in two stages.
1. First, they analyzed the marker-trait association with
identified SNP/InDels in the subset of 104 lines. From the
preliminary analysis 10 independent or tag SNPs. Among
them, three were from HaABI1-2, two each were from
HaCOI1-1 and HaCOI1-2, and one each were from
HaEIN2-1, HaEIN2-2 and HaDET3-1 candidate genes.
2. Second, primers were designed for the 10 tag SNPs to
genotype the remaining 156 germplasm lines of the AM
population and to analyze the complete set of 260 lines.
The most significant polymorphism was found at SNP
position HaCOI1-1_251.
Talukder et al., 2014
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47. Talukder et al., 2001
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48. However, a statistical correlation or a map co segregation
between a polymorphism of the CG and the trait variation
does not definitively demonstrate a causal relationship.
1. Indeed, the putative polymorphic CG may be in linkage
disequilibrium with the actual polymorphism responsible
for variation in the trait.
2. This candidate gene effect may be due to chance.
3. A candidate gene effect can be due to interaction of alleles
at the candidate gene with specific genetic background of
the population studied.
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49. Physiological Analysis
Genetic Transformation
Sexual complementation
3. Validation
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50. Physiological analyses consist of
• Measuring CG expression at the mRNA level (by
quantitative RT-PCR or northern blotting)
• The protein level (by western blotting)
• By determining the enzyme activity of the CG.
Physiological Analysis
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51. • Identify candidate gene for potato quality trait viz,
tuber flesh color and tuber cooking quality.
• Diploid backcross population (C × E) consisting of 94
individuals was used in the experiment.
• Clone C is hybrid between S.phureja and S.tuberosum
dihaploid USW42.
• Clone E is the result of a cross between Clone C and
the S.vernei- S.tuberosum backcross clone VH34211.
From QTL to candidate gene: Pooling
strategy in potato
Kloosterman et al., 2010
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52. C clone E clone
Kloosterman et al., 2010
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53. Tuber flesh color:
• A total of four bulks were constructed, each consisting of
10 non-repeated genotypes; two bulks for yellow fleshed
tubers (Y1, Y2) and two bulks for white fleshed tubers
(W1, W2).
• 101 features/genes showed lower expression in white
bulk and higher expression in yellow bulk.
• A gene with high homology to beta-carotene
hydroxylase (bch) exhibited strong differential expression
and was, on average, more than 140-fold higher
expressed in the yellow fleshed tuber bulks.
Kloosterman et al., 2010
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54. Kloosterman et al., 2010
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55. • For validation of CG qRT-PCR conducted using
gene specific primer of bch
Kloosterman et al., 2010
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56. Texture after cooking:
78 differentially expressed genes were identified using
microarray technique in bulked sample.
Contig Micro.187.C2 showed high sequence homology
to a class of tyrosine and lysine rich cell wall proteins
(TLRP).
This is located on chromosome 9 in the same region of
previously identified potato texture QTL.
Kloosterman et al., 2010
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57. Kloosterman et al., 2010
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58. qRT-PCR analysis was done, amplification of the
targeted gene region was only detected in a small
number of genotypes.
Sequence analysis showed 21bp deletion in the coding
region unique for the parental E-allele within the
region of the oligo design.
This TLPR_Δ7 variant is negatively correlated with
mealiness.
Kloosterman et al., 2010
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59. Kloosterman et al., 2010
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60. Genetic transformation is the ultimate way to validate
a CG.
• The most common experiment is to complement a
deficient phenotype for the trait of interest with a
sense construction of the CG.
• Transformation of non-deficient plants with an
antisense construction is another approach of
validation.
Genetic Transformation
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61. 1. Identification economically important QTL in crop plants
especially identification of disease resistance QTL.
2. Validated candidate genes could be used in marker
assisted selection (MAS).
3. The importance of regulatory genes in the variation of a
trait can now be evaluated using the CG approach.
4. It help to identify disease resistant QTL function which
help to understand the biological mechanisms
determining a partial and polygenically inherited
resistance.
Applications
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62. CONCLUSION
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63. References
Faizan, M., Babu, H., Fakrudin, B., Lakshmana, D. and Rakshith, M., 2021. In Silico Identification
and Annotation of Drought Responsive Candidate Genes in Solanaceous Plants. IJCRT, 2320-
2882.
Huh, J.H., Kang, B.C., Nahm, S.H., Kim, S., Ha, K.S., Lee, M.H. and Kim, B.D., 2001. A candidate
gene approach identified phytoene synthase as the locus for mature fruit color in red pepper
(Capsicum spp.). Theor. Appl. Genet., 102(4): 524-530.
Kloosterman, B., Oortwijn, M., America, T., de Vos, R., Visser, R.G. and Bachem, C.W., 2010.
From QTL to candidate gene: genetical genomics of simple and complex traits in potato using
a pooling strategy. BMC genomics, 11: 1-16.
Pflieger, S., Lefebvre, V. and Causse, M., 2001. The candidate gene approach in plant genetics: a
review. Mol. Breed., 7(4): 275-291.
Talukder, Z.I., Hulke, B.S., Qi, L., Scheffler, B.E., Pegadaraju, V., McPhee, K. and Gulya, T.J.,
2014. Candidate gene association mapping of Sclerotinia stalk rot resistance in sunflower
(Helianthus annuus L.) uncovers the importance of COI1 homologs. Theor. Appl.
Genet., 127(1): 193-209.
Zhu, M. and Zhao, S., 2007. Candidate gene identification approach: progress and challenges. Int.
J. Biol. Sci, 3(7): 420.
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