QGene

Report
Mapping a trait
Types of trait:
1. Monogenic
2. Polygenic-Quantitative-continuous
QT are the cumulative effect of many genes and are
environment dependent
Mapping a Quantitative trait:
Select a trait of interest
Chose contrasting parents
Develop mapping population
Phenotypic and genotypic characterization
Computation of phenotypic and genotypic data to identify Marker-Trait linkage
Mapping population:
• A population used for gene mapping is commonly called a mapping population.
• Mapping populations are usually obtained from controlled crosses.
• Decisions on selection of parents and mating design for development of mapping
population and the type of markers used depend upon the objectives of experiments,
availability of markers and the molecular map.
Characterization of Mapping Populations
Precise molecular and phenotypic characterization of mapping population is vital
for success of any mapping project.
The molecular genotype of any individual is independent of environment, it is not
influenced by G x E interaction.
Trait phenotype could be influenced by the environment, particularly in case of
quantitative characters.
It is important to precisely estimate the trait value by evaluating the genotypes in
multilocation testing over years using immortal mapping populations to have a
valid marker-trait association.
Statistical methods for QTL mapping:
• ANOVA/Regression: SMA/SMR= Single Marker Analysis/Single Marker Regression
• Likelihood ratio/LOD score method: SIM/CIM= Simple Interval Mapping/Composite
Interval Mapping
• Bayesian method: Its implemented reversible jump Markov chain Monte Carlo
(MCMC) algorithm
Softwares for QTL mapping:
1.
2.
3.
4.
5.
6.
7.
8.
QGENE
ICIM-ADD
WinQTL-Cartographer
MapManager QTX
QTL Network
R/qtl
G-Mendel
MapDisto
QTL mapping through QGENE software
QGENE for QTL mapping:
QGene is a program designed by Clare Nelson.
It is a very user friendly program for QTL analysis, NIL selection, and has many other
functions.
It is especially geared for Advanced Backcross populations, for which most current
software is either not applicable or user friendly for plant breeders.
Nelson JC (1997) QGENE: software for marker-based genomic analysis and breeding.
Molecular Breeding 3: 239-245.
System requirements
For the current version of QGene (4.3.10), you will need:
• Java JDK version 1.5 or higher. Version 1.6 is recommended
• 512 MB of memory or better
NOTE: While QGene may run under Java JRE instead of JDK, several
analyses will run much slower than expected, in particular permutation
analysis. This is because JRE does not fully support multi-threading. So be
sure to install the JDK, not just the JRE!
NOTE: QGene can run with memory as little as 64 MB. That may be
sufficient for simple QTL or trait analysis, but isn't recommended. However,
eQTL analysis needs at least 512 MB of memory.
Download and installation:
• It is free, so everybody can download by clicking the link:
www.qgene.org/qgene/download.php
• a download version was supplied, please install first JAVA (JDK or JRE)
and then Qgene and also create a shortcut to the desktop for quick tour
of Qgene all the time.
Data format:
• The standard format for importing marker, map, and trait data into QGene is as
a .qdf file
• Prepare a Excel file as per instruction and converted in to TEXT file by clicking file
format as TEXT TAB DELIMITED option.
•Then change the file extention name .txt to .qdf
[Header]
Study Name BoilamQTLs
Mating String bbsss
Genotype Symbols 123450
Parent1 BR
Parent2 BM
[locus]
RM3252
1
RM10115
1
RM490
1
RM154
2
RM12368
2
RM12476
2
[Trait]
RNa_K
M
TS
N
DS
N
0
7.6
25.6
0
0.8
6.76
1
1
1
1
2
1
2
1
1
3
1
1
1
1
0
1
2
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
2
1
3
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
3
1
1
1
0.238587 0.808835 3.529169 3.529169 2.823335 1.911633 2.426304 2.782614 1.676355
1063
497
319
411
281
411
671
573
340
38
29
19
26
22
33
48
33
31
0.45497
762
23
Explanation of file composition:
[Header] section
The Study name will be used to identify the data set, so use an informative (though not too
long) name.
The Mating string describes the genetic model to be used.
r (standard recombinant-inbred progeny), b (backcrossing), d (doubled-haploid) and s
(selfing).
To specify generation like a BC1F1 design, for example, write only b. An F2 will be s, an F3 ss, and
a series of three backcrosses followed by a selfing bbbs. Did you backcross twice, self once,
randomly intercross twice, and backcross again? No trouble: that's bbsiib.
The Genotype symbols are ABHCDX or 123450 system (A/1= Parent1, B/2= Parent2, H/3=
Heterozygous, C/4= A- , D/5= a- and X/0= missing)
The Parent1 and Parent2 entries provide QGene with labels for QTL effect plots, where
analysts wish to determine the parental origin of a superior QTL allele -- and may be used in
other plots such as for marker segregation, showing parental means on histograms, etc. If you
don't provide names for the parents, QGene will default to A and B.
[Locus] section
Here the map and marker genotype (technically, marker phenotype) data are given.
First word in the row is the marker name, followed by the chromosome and cM
position of the marker and finally the marker data.
[Trait] section
The first word on each line is the trait name.
The second word must be either N, O, or M -- indicating the trait to
be nominal, ordinal, or metric.
As with the marker data, QGene will verify that the numbers of trait values are
consistent among traits and also with those of marker data.
Run Qgene:
Make a new folder for storing input file, result files and images in a destination drive
Open Qgene by double clicking qgene desktop shortcut or by clicking qgene.exe
Click file menu and then click LOAD DATA
Select .qdf file (where you save the input file)
Data manager will appear (here you can view trait histogram, Linkage map and
marker data)
Save Linkage map and marker data by clicking view as image file
Trait Analysis:
Click Analysis on menu bar
Click again Trait Analysis a new window will appear for trait analysis (here you can see
the distribution of each trait by clicking each trait through histogram and by seeing
the P value of Kolmogorov-Smirnov normality test and other tests)
In trait analysis you can do Correlation and Regression among traits.
You can Export Analysis results and images by clicking file
QTL Mapping:
Click Analysis on the main menu bar and then click on QTL-mapping ( a new window
‘Main Analysis’ will appear)
In this window there are two panel (select 1 or all 12 chr on the left panel and select
at least 1 trait then go right panel and click + sign of Single Marker Regression)
There are Horizontal and Vertical Zoom button at upper and right panel (by using
these you can adjust whole picture in the window)
Click the LOD box and see there are any Significant Peak of QTL (LOD >3)
In general threshold LOD is 3 but you can calculate through permutation test
For permutation test: click ‘Resampling’ on the main analysis window, then click on
‘Permutation ‘- a new window will appear ‘Permutation Analysis’
Set ‘Iteration no. 1000 and click start button below the trait panel
Two line will appear- lower line is the value for significance at 5% and upper is 1%
(you can see the value at upper portion of window)
QTL mapping (contd):
For saving image and files, click file menu and export images and files
Following the same procedures you can analyzed all trait.
Close the permutation window
In main analysis window click the box of LOD, R2, Add effect and Dominance effect
and then go File menu for saving images and files in text format (text files can be
changed to Excel file by changing the extension as .xls)
Follow same procedure for analyzing through SIM and CIM (incase of CIM additional
option is to select co-factor as default from option of Main Analysis window )

similar documents