Strategies to Identify Quantitative Trait Loci in Plants

WEL COME
MAHESH R HAMPANNAVAR
PGS16AGR6978
Dept. Genetics and Plant Breeding

2
• QTL analysis requirement
Mapping population
Linkage map
Identify the polymorphic
markers in parents
18/11/2018 Mahesh R Hampannavar (mahi5295@gmail.com)

5
Strategies to
Identify the
Quantitative
Trait Loci
(QTLs)- Trait
Introgression
Genetics of
QTLs
Markers
selection
Mapping
population
Linkage map
construction
Various
approaches
to identify
the QTLs
QTL identify
through QTL
ICImapping
software

6
QTL identified Marker
Assisted
selection
®
Fine linkage mapping Meta analysis Marker conversion
Multi
environment/populati-
-on trail (Economic)
how can we make the QTLs examined in genetic mapping populations universal?
Fine QTL Mapping-
A step towards Marker Assisted Selection

7
Trait
introgression
Positional
Mapping
Marker assisted
selection
• Map based cloning
• Isolate the region
governing the trait.
• Evaluation
• Screening
QTL and Marker
association
require in

8
Increase the
no. of
recombinants
Multi parent
population
Increase the
no. of
markers
Fine linkage mapping
Fine linkage mapping

Number of markers and marker spacing
• No absolute value for the number of DNA marker.
• A relatively sparse ‘framework’ (‘skeletal’ or ‘scaffold’) map consisting of
evenly spaced markers is adequate.
• preliminary genetic mapping studies generally contain between 100 and
200 markers (Mohan et al., 1997).

• Genome size of the species
• Darvasi et al. (1993) reported that the power of detecting a QTL was
virtually the same for a marker spacing of 10 cM as for an infinite
number of markers, and only slightly decreased for marker spacing of
20 or even 50 cM.

• The kilobase pair to centimorgan ratio
varied from 30 to 550 kb per cM
(Schmidt et al., 1995).
• In rice 1 cM on average equals to 258.5
kb (The Rice Genome Sequencing
Project, 2005).
• Therefore, genetically close markers
may actually be far apart in terms of
base pairs (or vice versa) due to
differences in the frequency of
recombination along the length of a
chromosome.
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Genetic distances in
centimorgan (cM)
physical distances in
kilobase pairs (kb).

• The ultimate physical map is the whole genome DNA sequence.
• Its better to develop the high dense map by using the frame work
linkage map fine qtl mapping.
12
Saturated Map help to search the all region of genome
More land
marker leads
easy travel

• The pearl millet map obtained in this study spans 303 cM.
13
X1
•In an intervarietal F2 population, derived from a single
F1 plant.
•Most clones were derived from a pearl millet PstI
genomic library and restriction enzymeEcoRI, EcoRV,
HindIII and DraI.
•The probe-enzyme combination which gave the clearest
polymorphism between the respective parental
genotypes was used in the mapping experiments.

• 181 loci were placed on a linkage map.
• The total length of this map, which comprised seven linkage groups,
was 303 cM
• average map distance between loci was about 2 cM, although a few
intervals in excess of 10 cM were present at the ends of a few linkage
groups.

• Continuation of previous study after 10 years(2004)
• Added some more SSR markers derived from the BAC clone and
genomic library.
• Genetic maps produced in four different crosses have been integrated
to develop a consensus map of 353 RFLP and 65 SSR markers.

• 242 loci and spans 473 cM.
• The difference in length is
mainly due to the addition of
12 distally located markers.
• So much gap in distal region
need to fill     

• A mapping population of 140 F7 RILs from the cross
H77/833-2 (female parent) 9 PRLT2/89-33 (male parent)
was used to generate a linkage map.
• Linkage map was constructed by integrating DArT and
SSR marker data

• A set of 95 diverse
genotypes
representing the entire
diversity of wild and
cultivated pearl millet
accessions held in the
ICRISAT Genebank, was
used to develop the
discovery array.
• This array was used to
genotype a set of 24
diverse pearl millet
inbreds and 574
polymorphic DArT
markers were
identified.
• 258 DArTs were used
for genotyping

• This map contains
321 loci (258 DArTs
and 63 SSRs)
• The genetic map
spans 1,148 cM
corresponding to an
average of 3.6 cM per
marker.
• The linkage map
constructed in this
study is more highly
saturated, includes
more markers and
has smaller marker
intervals.

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DArT GBS

Genotyping By Sequencing

• GBS is a robust, simple,
and affordable
procedure for SNP
discovery and mapping.
• Overall, this approach
reduces genome
complexity with
restriction enzymes
(REs) in high-diversity,
large genomes species
for efficient high-
throughput, highly
multiplexed sequencing.

• the occurrences of
crossing over as close to
the target QTL
• It helps to identify the
markers close to QTL
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Increase the
no. of
recombinants

• Fallowing methods facilitate the occurrences of crossing over as close
to the target QTL
a. Homozygous Lines Derived from Near-Isogenic Lines
b. Intercross Recombinant Inbred Lines
c. Recurrent Selection Backcross QTL Mapping
d. Genetically Heterogeneous Stocks
e. Multiparent Advanced Generation Intercross Population

• These strategies aim to keep the relevant genomic regions in
heterozygote states for prolonged period of time so crossing over
may occur very close to the QTL.
• Which help to know the marker very close to QTLs.

• In IRILs the recombination rate observed between any two loci will be
(n+2) times the normal recombination rate (r) Rn=r(n+2)
• Therefore a distance of 1 cM would be detected as 10 cM after eight
generations of intermating
(de Vienne and Causse, 2003)

• Recurrent Selection Backcross QTL Mapping reduces the confidence
interval to 1cM or less.
• The resolution of mapping increases with increase in recurrent
selection of backcrossing.
• (Luo et al., 2002)

• Use of MAGIC population for mapping the major QTL -The confidence
interval reduced to 300-kb interval in A. thaliana as against 2-20mb
for mapping in biparental mapping population.
Kover et al., 2009

Modification of genotyping
• For position based cloning – markers distribution should be <0.1cM
Or
10 markers per cM
• Assume that genome size of crop is 4000cM
• No. of markers requires for saturated mapping is 4,00,000
• Genotyping of 500 mapping population total marker assay requires
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500*400000= 20,00,00,000

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M1 M2
M1 M2 M1
M2
Use the large new
markers in these
subpopulation only,
which reduces the
no. of assay
Mapping population
Churchill et al., 1993

Selective mapping
• Selective genotyping (also known
as ‘distribution extreme analysis’
or ‘trait-based marker analysis’)
involves selecting individuals from
a population that represent the
phenotypic extremes or tails of
the trait being analysed.
• Linkage map construction and
QTL analysis is performed using
only the individuals with extreme
phenotypes.

Bulk segregant analysis
• BSA is a method used to detect
markers located in specific
chromosomal regions
• Markers are screened across the
two bulks. Polymorphic markers
may represent markers that are
linked to a gene or QTL of
interest
• The entire population is then
genotyped with these
polymorphic markers

33
•SG overestimate the R2
and additive affect
•No effect on QTL
identify via BSA and SG

• BSA is a powerful, effort-saving, and cost-effective approach.
• There are a few samples to be genotyped, which in turn reduces the
number of data points
• BSA can be applied in multiple rice populations simultaneously to identify
consistent-effect drought grain yield QTLs worthy for MAS.

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In this study, the genetic
analysis of an F2 population
consisting of 224 plants
derived from a crossing of
Híbrido de Timor UFV 427-
15 (resistant) with Catuaí
Amarelo IAC 30
(susceptible) showed that a
dominant gene confers the
resistance of coffee to race
II of H. vastatrix.

36
501

Detecting stable QTLs
• Expression of genes
depends on the particular
environment (Mather
1941).
• Quantitative traits are
instable to the
environment.
• QTLs examined under
single environments are
considered instable and
are ignored
• Recognizing and studying
environment-specific QTL
(the so-called instable
QTL) is an important issue
of plant QTL analysis.

38
•Two upland cotton strains 0–153 and
sGK9708.
•A total of 851 polymorphic primers were
finally selected and used to genotype 196
recombinant inbred lines
• The RIL population was evaluated for fiber
quality traits in six locations in China for five
years.
•Identified 165 QTLs for fiber quality traits, of
which 47 QTLs were determined to be stable
across multiple environments.
Fiber strength
•A total of 35 QTLs for FS were identified on 13
chromosomes
•Eight QTLs were detected in three or more
environments and declared as stable QTLs.
•The QTL on c7, qFS-C7-1, was identified in 10
environments,
•explaining 12.2–26.7 % of the observed PV.

39
•QTL of flowering time traits, we detected
the main-effect QTL of flowering (LOD=36,
explained 52% of phenotypic variance) in
the N10 linkage group in the spring cultivate
environments for Brassica napus.
•which then disappeared in the winter
environments.
•A large number of important instable QTLs
were found in the QTL analysis of B. napus. For
example, 36 flowering time QTL are detected in
multiple-location-multiple-year environments,
in which 23 QTL are “instable”.

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Genome-wide QTL analysis for SY and seven SYRTs in
eight environments was conducted in a doubled
haploid population containing 348 lines.
Totally, 18 and 208 QTLs for SY and SYRTs were
observed, respectively.
Three major QTLs for SY were observed,
including cqSY-C6-2 and cqSY-C6-3 that were
expressed stably in winter cultivation area for 3 years
and cqSY-A2-2 only expressed in spring rapeseed
area.18/11/2018 Mahesh R Hampannavar (mahi5295@gmail.com)

Statistically significant level (SL-QTL) vs Micro Real QTL
• MR-QTLs refer to QTLs under the threshold (P ≤ 0.05) but above
certain standard (P ≤ 0.5) in multiple-environmental trials
• MR-QTL as those repeatedly detected at a LOD value above the
threshold of suggestive linkage while below the threshold of P = 0.05
level,
• In other words, one SL-QTL in an environment might be an MR-QTL
at another environment, and an MR-QTL might also be a SL-QTL if
the environment is good enough to induce the significantly
differential expression of the two alleles.
• micro-real QTL to protect the minor QTLs

Consensus map
• Making comparisons between maps of
different population to detect the stablr
QTL
• Mapping population * markers = Linkage
map
• Anchor markers
• Bins are used to integrate maps
• Consensus maps are produced by
combining or merging different maps,
constructed from different genotypes,
together.
42
Anchor markers
groups Anchor markers
groups
BINS

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• Consensus map of barley
• SSR RFLP and AFLP
• 5 linkage map of double haploids
• Joinmap 2.0
• Consensus Map order good agreement with all crosses
• It helps to develop the saturated map

QTL Meta analysis
• Attempt to combine the results from many different studies
• Helps to find the actual position, number, affect of QTL.
Assumption of QTL meta analysis-
• Different QTL mapping are independent
• QTLs controlling the traits are finite
• QTLs co segregates in different mapping population
• QTL detected in single study are independent each other

46
Results depicted graphically
The QTL subject to clustering analysis: Number or location of meta qtl or real QTL determined
Estimation of confidence interval
Projection of QTLs from different study on the consensus map
Construction of consensus linkage map
Collect the finding from different QTL studies on the particular traits

• How many “real” QTL do the QTL detected in the different studies
represent— one, two, three,
or
• As many as the number detected throughout the studies?
• Once this question is answered, the positions of the real QTL can be
estimated.

•The different models:
Let k = 1, . . . , n represent
different models for the real
position xi of the n QTL.
•In model k = 1, we consider
that all the n QTL located at a
single position.
•In model k, we consider that
there are k different positions
for the n QTL, and model n
corresponds to the case
where the n QTL are located
at n different positions
48
The model with lowest AIC value was selected
Akaike information criterion

• The length of the confidence interval at 95 % for the position of the
QTL.
49
The QTLs within the region of 20 cM on the consensus
map were considered as part of same cluster as earlier
CI (95) = 530/(N)
CI (95) = 163/(N)
CI = confidence interval significance @ 95%
N= size of mapping population
= Phenotypic variance
F2 and other population
RIL population
Calculation of
confidence
interval

50
220  PH4CV
220 Y1518
P9-10  PH4CV
• Emergence rate and
germination Index,
seedling root length,
shoot length, and total
length
• 1,382, 1,500, and 1,419
SNP markers
F 2:3 mapping
population
A total of 43 QTL were identified to be
associated with LTGA with 19, 13, and 11
from 220  PH4CV, 220  Y1518
and P9-10  PH4CV, respectively

52
Meta-QTL (mQTL) detected from the consensus linkage map.

53
QTLs for different races or different
studies were classified into different
clusters if their confidence regions had
no region in common and were > 20 cM
away from each other
A total of
62 marker–
quantitative
trait locus
(QTL)18/11/2018 Mahesh R Hampannavar (mahi5295@gmail.com)

54
(i) Suggestive QTL: LOD > 3.0
(p value = 0.001),
(ii) significant QTL: LOD > 4.0
(p value = 0.0001)
(iii) confirmed (replicated) QTL.
If the QTLs from the same cluster
came from at least three
independent studies, they were
regarded as being confirmed in this
study.

• a total of 432 QTL from 11
individual maps.
• Although the sets of QTL
found for the same fiber
related trait in different
experiments showed
nonrandom correspondence.
• This suggests that lint
fiber development may
involve a complex gene
network

57
A meta-analysis was performed using
Biomercator V4.2 to integrate QTLs
from 11 environmental datasets on the
RIL populations
165 identified QTLs, 90 were identified
as common QTLs, 75 QTLs were
determined to be novel QTLs.
Meta-clusters on chr4, chr7, chr14 and
chr25 were identified as stable QTL
clusters and were considered more
valuable in MAS for the improvement of
fiber quality of upland cotton.

58
Dissecting the QTLs

• Using a strategy of forward genetics, many QTL have been dissected
into single genes for finemapping and map-based cloning in recent
years.
• It requires the sequence of particular position in both contrasting
parents or lines

• This work was performed using a set of recombinant chromosome nearly
isogenic lines (RCNILs) derived from a BC2S3 population produced using the
inbred maize line W22 and teosinte (Zea mays ssp. parviglumis) as the
parents
• Using GBS technology identify the SNPs
• KRN was a highly polygenic traits with a minimum of 25 QTL detected
(Shannon LM. Ph.D thesis)
• KRN 1.4 shown larger effect (50.48% of the variance).

KRN1.4 contains seven genes from the maize
Filtered Gene Set
Genes 1 to 4 code for proteins whose
function could not be inferred from
orthology to genes described in other
species.
61
The 2.0-LOD interval of the QTL for KRN spanned
203 kb, from position 292,686,855 bp to
292,890,107 bp of the physical map18/11/2018 Mahesh R Hampannavar (mahi5295@gmail.com)

• Ids1 would seem a strong candidate for KRN1.4 given its role in
regulating inflorescence branching.
62
• An alfin-like transcription factor
• It is a PHD zinc finger protein that has the potential to bind to
cis-acting elements in the promoter regions of target genes
Gene 5
• small-subunit ribosomal gene
Gene 6
(AC225147.4_FG002)
• Ids1 is a gene that encodes an APETALA2-like
transcription factor that plays a role in the
ABC model of flower development.
• Regulate inflorescence branching,
• floral meristem determinacy
• spikelet meristem determinacy
Gene 7,
Indeterminate
spikelet1 (ids1)

• The qMi-C11 and qMi-C14 loci map to chromosome 11 and 14, respectively acted
in epistatically
• (SSR marker BNL3545-CGR5668 sequence this region , thought to contain qMi-
C14)
• cotton D-genome sequence as reference genome for comparison.
63
M-120
RNR
• Upland cotton
line
• Resistant
Pima
S-6
• G.barbadense
• RKN
susceptible
cultivar
F2 populations,
SSR markers

• Identify 20 candidate resistance
genes with conserved LRR
domains clustering in three
groups.
• only four to have non-
synonymous mutations in the
coding region between
resistance and susceptible
• SNP alleles in coding regions of
four LRR genes in the RKN
resistant line GA120R1B3 –
Gorai.005G026500,
Gorai.005G028600,
Gorai.005G029200 and
Gorai.005G 029400 – resulted
in changes in amino acids
relative to Acala Maxxa

Marker conversion
• SNP detection and genotyping platforms often rely on expensive
equipment or consumables, which results in considerable high cost.
• SNP assay is another limiting factor for SNP application in plant
genetics and breeding.
• RFLP and RAPD have low reproducibility result in wrong selection in
MAS
• Economic and reproducible are the major concern in Markers

66
RAPD to SCAR

SNP TO CAPS
• CAPS means cleaved amplified polymorphic
sequence.
• This is used when the restriction sequence is
naturally present because of the SNP
between both parents.
67
P1 ATTCGCATCTACGTACTGGAATTCAGCTTCGTCATGGTCA
TAAGCGTTGATGCATGACCTTAAGTCGAAGCAGTACCAGT
P2 ATTCGCATCTACGTACTGGAATTTAGCTTCGTCATGGTCA
TAAGCGTTGATGCATGACCTTAAATCGAAGCAGTACCAGT
EcoR1 G AATT C
C TTAA G

• When SNPs do not change
a restriction site, dCAPS can
overcome the limitation.
• dCAPS is available when a
restriction site can be
produced by one or two
base changes using a
mismatch primer.
68
SNP TO dCAPS

• A screening panel consisting of nine
soybean varieties was used for PCR
amplification.
• The sequences of SNP mutation site
were analyzed using the restrict
program embedded in EMBOSS, and
the restriction enzyme recognition
sites for DraI and PstI were
identified.
• The primers were designed by
Primers 3
69
• In soybean, 5551 SNPs have been identified by resequencing six
genotypes
• pyrosequencing ESTs with whole genome sequences in different soybean
varieties and identified 3899 SNPs.
• In total, 1747 SNP target genes were putatively annotated
• A total of 36 SNPs displayed as potential CAPS candidate18/11/2018 Mahesh R Hampannavar (mahi5295@gmail.com)

• validated 16 SNPs by resequencing, and converted 7 SNP markers into
CAPS.
• the marker CAPS282 on the 3′-UTR of gene Glyma07g03490 was
identified as associated with 100-seed weight of soybean.

71
QTL identified Marker
Assisted
selection
®
Fine linkage mapping Meta analysis Marker conversion
Multi
environment
field trail (Economic)
how can we make the QTLs examined in genetic mapping populations universal?
ConclusionsThank you

• A rough estimate of the QTL position.
• Measure of statistical significance: P-value
• phenotypic variance R2%
• Source of the favorable allele (Parent A or Parent B)
• Estimates of additive and dominance effects
73
Additive effects = (Mean of A marker class – Mean of B marker class) / 2.
Dominance effects = Mean of heterozygous (H) class – [(Mean
of A class + Mean of B class) / 2].
Understanding interval mapping results

• Confidence intervals for QTLs
• position of a QTL is the map
position at which the highest
LOD
• But…..
• Bootstrapping- statistical way to
find the confidence interval map

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