XIXth INTERNATIONAL CONFERENCE OF GENETIC DAYS, 5th to 7th September, 2000, Nitra, Slovenska Republika

Main sub-headings

 Definition
 Principle
 Experimental design
 Experimental design to locate the QTL

region through selective DNA pooling in

dairy cattle.

 Microsatellite genotyping
 Statistical methods for accurate estimation

of gene frequency from pooled samples.

 Problems in determination of gene

frequency.

 Problems in interpretating 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.

Definitio

•
“Selective DNA pooling” is an advanced

methodology

for

linkage

mapping

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).

•

Figure A and B: showing allelic patterns
of a linked marker.

Here figure A is displaying a shift of
marker allelein affected individuals pool.

Figure C and D: showing allelic
patterns of a unlinked marker.

Here both figures are not displaying
any shift or deviation of the alleles.

Affected pool

Unaffected pool

Affected pool

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 ABI-Prism,

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.

frst 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:

n.i

= D

n.i

/ D

Where,

n = is the number of repeats in the native genomic
tract of the allele A

I = is the order of the shadow band
RI

n.i

= is the relative intensity of the ith shadow

band derived from the genomic tract of A

= is the densitometric intensity of the main

band derived from the genomic tract of A

n.i

=is the densitometric intensity of the ith

shadow band derived from the genomic tract of A

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 X

values from

individual genotyping were compared. The
results demonstrated a high correlation
between AIPs and X

values.

3rd method: Collins et al.. 2000

Measuring Total Allelic

content (TAC) from the pools

• This is a modifed 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.

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

amplifcation.

•
• 1. Sutter banding
• 2. Differential amplifcation.

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 fles.
There are 2 problems encountered
during visual inspection of the
peak image fles.

True negative peaks

False positive peaks

Figure1: Showing shifting of
microsatelliteallele in affected group.

This figure represents the True result
with correct peak profile image.

Figure3: Showing no shifting of
microsatellitealleles but there is one
linked marker allele in this locus.

This figure represents a good example of
True Negative peak profile image.

Shifted allele

control

unaffected

False .Shifted allele

Figure2: Showing shifting of
microsatelliteallele in affected group.

This figure represents a good example
of False positive peak profile image.

Example of correct result

Example of false positive result

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.

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 10

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).

Document Outline