Transcript XIXth INTERNATIONAL CONFERENCE OF GENETIC DAYS, 5th …

 Novel approaches for linkage mapping
in dairy cattle. ”Selective DNA pooling”
Chandra S. Pareek
Dept. of Animal Genetics,
Faculty of Animal Bio-Engineering,
University of Warmia and Mazury,
Olsztyn, Poland.
Main sub-headings
 Definition
 Principle
 Experimental design
 Experimental design to locate the QTL region through selective DNA pooling.
 Microsatellite genotyping
 Statistical methods for accurate estimation of gene frequency from pooled samples.
 Problems in determination of gene frequency.
 Problems in interpreting pooling results by visual inspection.
 Application of Selective DNA pooling in farm animals.
 Advantages of Selective DNA pooling.
 Success of selective DNA pooling in dairy cattle.
Definition
•
“Selective DNA pooling” is an advanced methodology for
linkage mapping of quantitative, binary and complex
traits in farm animals.
•
In human, this methodology is termed as DNA pooling
where it serves as mapping of complex disease traits.
•
It is defined as densitometric genotyping of physically
pooled samples from phenotypically extreme
individuals.
•
The DNA pooling is performed by taking equal aliquots of
DNA from the pooled individuals.
Principle
•
 ¨
The principle is based on densitometric estimates of marker allele
frequency from the pooled phenotypically extreme individuals.
•
 ¨ In this regards, the marker of choice is: STR or microsatellite markers.
•
 ¨ The microsatellite allele linked to any QTL gene can be identified by
any shift or deviation of allele from the pools.
•
 ¨
The QTL linked allele, and then further tested for its feasibility by
statistical analysis.
•
 ¨
The power of statistics is relied on the accurate estimates of gene
frequency from the pooled samples.
•
 ¨ Several methods have been described for accurate estimation of gene
frequencies (Daniels et al. 1998, Barcellos et al. 1998, Lipkin et al. 1998,
and Collins et al. 2000).
•
Affected pool
Figure A and B: showing allelic patterns
of a linked marker.
Unaffected pool
Here figure A is displaying a shift of
marker allele in affected individuals pool
Affected pool
Figure C and D: showing allelic
patterns of a unlinked marker.
Unaffected pool
Here both figures are not displaying
any shift or deviation of the alleles.
Experimental design
 A well-defined experimental design is an essential prerequisite to perform
the selective DNA pooling.
•
 The experimental design should include the following conditions.
• 1. Identification of resource families having extreme phenotypic values for
the given analysed trait.
• 2. Systemic selection of highly polymorphic STR markers from the
analysed genome.
•
 Experimental design to locate the QTL region through selective DNA
pooling
•
Daughter design: In case of cattle, by utilizing multiple half-sib
families with multiple STR markers.
Granddaughter design: In case of cattle, poultry and swine, by
utilizing F2 full-sib daughters including sire and grand sire.
Microsatellite genotyping
•
The most commonly used touch down
protocol of Don et al. 1991, can be used for
typing of microsatellte markers, followed by
visualisation of electrophoresis results in any
DNA sequencing machine (Perkin Elmer ABIPrism, Pharmacia ALF, and LICOR genetic
analyser).
 Statistical methods for accurate estimation of
gene frequency from pooled samples
The following three methods have been described:
By measuring the relative intensity of shadow bands
(RI):
Method proposed by Lipkin et al. 1998.
By measuring the Allelic Image Pattern (AIP) from
the pools:
Method proposed by Daniels et al. 1998.
By measuring the Total Allelic content (TAC) from
the pools:
Method proposed by Collins et al. 2000.
first Method: Lipkin et al. 1998
Measuring relative intensity of shadow bands (RI)
By giving the densitometric values of main and shadow bands,
the relative intensity of a given shadow band for a given allele
can be calculated as:
RIn.i = Dn.i / Dn
Where,
n = is the number of repeats in the native genomic tract of the
allele An
I = is the order of the shadow band
RIn.i = is the relative intensity of the ith shadow band derived
from the genomic tract of An
Dn = is the densitometric intensity of the main band derived
from the genomic tract of An
Dn.i =is the densitometric intensity of the ith shadow band
derived from the genomic tract of An
2nd method: Daniels et al.. 1998
Measuring Allelic Image Patterns (AIP)
from the pools
• The principle of this method is based on the analysis of
microsatellite allele image patterns (AIP) generated
from the DNA pools.
• The AIP statistic is calculated from the differences in
the area between two allele image pattern expressed as a
fraction of total shared and non-shared area.
AIP = Dif / (Dif + Com)
The AIPs from the pools and X2 values from
individual genotyping were compared. The results
demonstrated a high correlation between AIPs and X2
values.
Figure showing overlaid AIPs of two different pools amplified with the
microsatellite marker. Area ”Dif” and ”Com” are the non-shared and
common areas between the two AIPs.
3rd method: Collins et al.. 2000
Measuring Total Allelic content (TAC) from
the pools
• This is a modified method of Daniels et al.. 1998.
• The principle of this method is based on simple
measurement of total allele differences by comparing
the two pools.
• The pool comparison is done by comparing the
relative peak height differences between
electrophoregrams for each allele of a microsatellite.
Figure A: Showing peak image profile from individual genotyping
illustrating sutter profile and amplitude variation.
Figure B: showing peak image profiles from pooled genotyping.
Problems in determination of gene
frequency
Feasibility and reliability of selective DNA
pooling is depend upon the accurate
estimates of the gene frequency from
pooled samples, which is mostly
confounded with Sutter banding and
Differential amplification.
• 1. Sutter banding
• 2. Differential amplification.
Problems in interpretating pooling
results by visual inspection
Visual inspection of numerous STR
genotyping of pooled samples can be
performed by visual eye balling of the peak
image files.
There are 2 problems encountered during
visual inspection of the peak image files.
True negative peaks
False positive peaks
control
Shifted allele
Figure 1: Showing shifting of
microsatellite allele in affected group.
This figure represents the True result
with correct peak profile image.
unaffected
Example of correct result
Figure 2: Showing shifting of
microsatellite allele in affected group.
False .Shifted allele
This figure represents a good example
of False positive peak profile image.
Example of false positive result
Figure 3: Showing no shifting of
microsatellite alleles but there is one
linked marker allele in this locus.
This figure represents a good example of
True Negative peak profile image.
Example of true negative result
Application of selective DNA pooling in farm
animals
In rapid genome scanning for the identification of
unknown gene or linked gene fragment.
In rapid estimation of STR gene frequency. More recently
in estimation of SNP frequencies as well.
In identification of complex gene fragment within the
genome through linkage analysis of STR marker linked
to that gene fragment.
In QTL mapping of the identified gene or gene fragment.
To detect genes with small effect, for e.g., complex disease
traits in human.
Figure representing detection of linked allele by comparing affected and
unaffected DNA pools.
In this figure: Marker D5S393 is showing the linked allele to the disease
trait whereas, marker D5S410 showing no allele linked to the disease trait.
Advantages of selective DNA
pooling
¨To detect any linkage between marker and QTL:
Multiple families with large numbers of daughters are
required to get reasonable statistical power.
This requirement leads to genotyping of hundreds of
thousands individuals with high cost of experiment.
By means of selective DNA pooling, the cost of numerous
genotyping can be substantially reduced.
Thus selective DNA pooling is an ideal and potential
approach for analysing multiple large families with
multiple microsatellite markers.
¨
Selective DNA pooling reduces not only the genotype
cost by many folds, but also minimizes the valuable
experimental time.
For example:individual v/s Pooled genotyping
In case of individual G: 2000 markers x 2000 individuals
= 4 x 106 individuals
In case of Pooled G: The genotyping becomes 2000 x 2 =
4000.
 Success of selective DNA pooling in dairy cattle
¨ Mapping of QTL genes for milk protein percentage in
Israeli HF cattle (Lipkin et al. 1998).
¨ Detection of loci that affect quantitative traits like milk
production in New Zealand HF and Jersey cattle
population (Spelman et al. 1998).