Types of markers in plant breeding-Molecular markers and Functional molecular markers

1. CHANDANA, B. R.
Jr. MSc (GPB)
UAHS, SHIMOGA

2. CONTENTS
INTRODUCTION1.
FUNCTIONAL MARKERS
FMs DEVELOPMENT
CASE STUDY
3.
4.
5.
MARKER TYPES2.
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3. INTRODUCTION
• Marker (biology), generally refers to a
measurable indicator of some biological
state or condition.
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Markers
Biochemical Morphological Molecular
Protein Banding
Pattern
Chromosomal
Isozyme

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Molecular
Hybridization
based
PCR based
RFLP
AFLP
RAPD
SNP
SSR
Microsatellites

6. Morphological markers
• Identification based on morphology (external
appearance).
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Advantages
Easily monitored
Disadvantages
• Affected by the environment.
• Limited in number
• Some (e.g. flower color) appear late in plant
development, making early scoring impossible.
• PLEIOTROPIC gene action
• Complete genome assays, required for quantitative trait
locus (QTL) analyses are not feasible.

8. Protein molecular markers
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Markers related to the variations in protein and amino
acid banding pattern.
Isozyme markers: Multiple forms of the same enzyme
coded by the different genes
Allozyme : one enzyme, one locus; two or more alleles
in a population.

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Advantages
• Simple,
• Inexpensive,
• Electrophoretically resolvable, and detectable
• Does not require DNA extraction or the availability of sequence
information, primers or probes,
• Quick and easy to use, codominant markers that have high
reproducibility

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Applications
• Used for detection of the gene introgression and recombination
• Comparative mapping, and
• Determination of the genetic diversity and phylogenetic
relationships.
Disadvantages
• Relatively low abundance and low level of polymorphism
• Affected by environmental conditions
• May change depending on the type of tissue used for the analysis.

11. Molecular Markers
• Do not represent target genes themselves but
act as ‘signs’ or ‘flags’.
• Genetic markers located in close proximity to
genes (i.e. tightly linked) may be referred to as
gene ‘tags’.
• Do not affect the phenotype of the trait of
interest because they are located only near or
‘linked’ to genes controlling the trait.
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Collard et al. 2005

12. • Genetic markers were originally used in
genetic mapping to determine the order of
genes along chromosomes.
• In 1913, Alfred H. Sturtevant generated the
first genetic map using six morphological traits
in the fruit fly (Drosophila melanogaster).
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13. • Karl Sax produced evidence for genetic linkage
between a qualitative and a QUANTITATIVE
TRAIT LOCUS in the common bean (Phaseolus
vulgaris)
• These pioneer studies, genetic markers have
evolved from morphological markers through
isozyme markers to DNA markers.
Morhological markers Isozyme
markers Molecular markers
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14. Molecular markers include
1)Non-PCR based markers
 RFLPs (Botstein et al. 1980)
2)PCR-based markers
 RAPD (Williams et al. 1990),
 AFLP (Vos et al. 1995),
 Microsatellite or (SSR) (Powell et al. 1996),
 SCAR(Paran and Michelmore 1993),
 CAPS (Konieczny and Ausubel 1993),
3) Sequence-based markers
 SNP markers (Gupta et al. 2005)
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15. Polymorphism
• Codominant markers indicate differences in size of
DNA segment based on whether individual is
homozygote and heterozygote.
• Whereas dominant markers are either present or
absent.
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16. • First plant DNA markers were based on
restriction fragment length polymorphisms
(RFLPs)
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Early hybridization-based, isotopically-labeled RFLP
techniques were
Inherently challenging
Time consuming,
Were eventually replaced by
 Less complex,
 More cost-effective
 PCR-based markers.

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 DNA markers have been the most widely-used
molecular markers in crop improvement, owing to
their abundance and polymorphisms.
 Most of these markers are selectively neutral
 They are usually located in non-coding and non-
regulatory regions of DNA
McKay and Latta 2002

19. Functional Markers
• DNA markers derived from functionally
characterized sequence motifs.
• ‘Functional Markers’ term - Andersen &
Lu¨ bberstedt
Functional markers (FMs) are developed from
polymorphic sites within genes that causally affect
target trait variation i.e. based on functional
characterization of the polymorphisms
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21. • The characterization of genes and gene families
suggests that conserved regions may be used to define
gene function.
• These conserved regions are typically functional
• Domains which correspond to conserved DNA
sequences within genes, conserved DNA regions are
often conserved across different plant species.
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Collard & Mackill (2009)

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Collard & Mackill (2009)
• Easier to develop functional markers in plants
where either complete or nearly complete genome
sequence information is available than in others in
which little or no genomic information is
available.
• Based on sequence homology, putative functions
can be assigned to 30–50% of expressed
sequences in any species.

23. FM polymorphisms may be
• SNPs,
• Insertions or deletions (INDELs), including partial or
complete loss of the gene
• Different numbers of repeat motifs within SSRs
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Gupta and Rustgi 2004

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26. Indirect functional markers(IFMs)
• Role for phenotypic trait variation is indirectly known
• Association studies provide only indirect (statistical) evidence
of sequence motif function.
• This approach relies on LINKAGE DISEQUILIBRIUM(LD)
mapping based on nonrandom occurrence of allele haplotypes
in the genome.
• The genetic background might affect results from association
studies, and
• Statistical approaches have been developed to control
unknown population structures.
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27. Direct functional markers(DFMs)
• The role for the phenotypic trait variation is
well proven.
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28. Types of FMMs
SSR based FMMs
• ‘Microsatellite markers’ or ‘Short Tandem Repeats’,
are 2–6 base pairs of repeating DNA sequences.
• Development is very easy and cost effective as they
are electronically developed from publicly available
ESTs or gene sequence information using different
softwares.
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Udaykumar K et al. 2015

29. Used in ;
• Genetic mapping,
• Functional diversity studies, and
• Can be transferred among distantly related
species.
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30. SNP or Insertion and Deletion (InDel)
based FMMs
• Functional SNPs and InDels can directly
contribute to the phenotypic variation
• Such polymorphisms are indispensable for the
development of functional markers.
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Udaykumar K et al. 2015

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34. (1) Functional markers do not require validation,
(2) They can be applied directly to other populations.
(3) Provide a better estimate of allelic diversity of
genes/QTLs
(4) A better estimate of genetic diversity of the
species.
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Merits(5) Generate knowledge about the nature and the
physical location of sequences involved in phenotypic
expression of the concerned traits (Anderson and
Lubberstedt2003).
(6) The number of markers required for foreground
selection will be reduced to the number of genes to be
selected,
(7) There will be no recombination between a marker and
the linked gene

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Merits
(5) Generate knowledge about the nature and the physical
location of sequences involved in phenotypic expression
of the concerned traits (Anderson and Lubberstedt2003).
(6) The number of markers required for foreground
selection will be reduced to the number of genes to be
selected,
(7) There will be no recombination between a marker and
the linked gene

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Limitations
• Only a small fraction of the genes of different crop
species have been functionally characterized
• Reliably characterize and distinguish among the
phenotypic effects of the different alleles of a given
gene/QTL and to develop suitable allele-specific
markers.
• Once functional markers have been developed, they
need to be evaluated in different genetic backgrounds in
order to obtain more precise estimates of the phenotypic
effects of different marker (¼ gene/QTL) alleles

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Case Study

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Objective
To use EST-SSR markers to estimate the genetic diversity in
progeny derived from FSRRS of a maize program to assist in
the selection step of the most divergent genotypes to compose
the recombination blocks.
MATERIAL AND METHODS
20 EST-SSR loci
80 genotypes (S1 progenies)
40 from each population
(CIMMYT and Piranão)
13th RS cycle,
selected in agronomic
evaluation step from
210 previously
evaluated genotypes.

41. Softwares UsedSamples from each S1 progeny
(10 seedlings)
DNA extraction (Kit method)
DNA Quantified (NanoDrop met) &
Electrophoreized(0.8% agarose gel)
DNA amplification (20 polymorphic
EST-SSR primers)
Capillary electrophoresis of amplified
products (250-bp DNA ladder )
Molecular analysis
Estimation of genetic diversity , observed
heterozygosity , polymorphism
information content (PIC), and the
inbreeding coefficient (F) -
PowerMarker software v3.25
Genetic dissimilarity matrix -GENES
software (Cruz, 2013)
Genetic variability within and between
groups - Analysis of molecular variance
(AMOVA) (Excoffier et al., 2005)
Analysis of allele frequency -
GenAlEx 6.3 software (Peakall and
Smouse, 2009)
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42. Results
20 EST-SSR loci, a total of 93 alleles, with allele numbers
per locus ranging from 2 to 8 and an average of 4.65
Highly informative –PIC > 0.5,
Moderately informative- 0.5 to 0.25
Not informative- < 0.25
• 20 microsatellite loci analyzed, 14 (70%) can be
considered highly informative, maximum PIC was 0.76
bip2 locus, lowest value (0.27) umc1108 locus, and
mean PIC was 0.55.
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45. • Among the alleles identified, 24 (25.8%) were
unique, being detected in 13 of the 20 loci .
• The genic SSR markers were effective in
clustering genotypes into their respective
populations.
• It is expected that such variability and genetic
distance are more directly associated with
heterosis of future hybrids, given the nature of
the sampled genomic region, as well as
providing higher genetic gain per cycle of RRS
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46. Conclusion
Functional markers has the potential to initiate a
new ‘Green Revolution,’ which is of vital
importance for the development of drastically-
improved crop germplasm. Increasingly the exact
linkage of markers and genes to traits will lead to
more efficient plant breeding in the future aid to
translate to unprecedented crop improvement.
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