BIT150 – Fall 2008 –
Homework 8 KEY
Due on Thursday November 20th by email to TA: mfaricelli@ucdavis.edu as
Hwk8_Lastname BEFORE the Lab
Using Putty, login to ‘plantgenome’. In your home directory, into the Lab8 directory, there is a
qwork subdirectory, which contains a folder named rauh. Into this folder, there is another folder
named all. Make sure that you have the files rauhall.inp and rauhmap.inp into the all folder.
Using QTL Cartographer, map QTL in a map of molecular markers, using the provided files.
1.
1.1. Look at the rauhall.inp file.
-
What is the size of the population? What is the type of population?
101 individuals, RILs
-
What is the number of traits?
16 traits
1.2. Look at the rauhmap.inp file.
-
What mapping function was used in the provided genetic map of molecular markers?
Kosambi
-
How many linkage groups are?
5
2.
2.1. Using the Rmap command, open the files rauhmap.inp to translate the genetic map
information provided in this file into that required by QTL Cartographer. Give the name all to
the output file.
2.2. Using the Rcross command, open the files rauhall.inp.
Make sure that the following files have been created:
all.cro
all.log
all.map
3.
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3.1. Using the Qstats command, compute the statistics from your data. Look at the output file
(with the extension .qst).
- How many traits are listed? 16
- What are their names, means and variances? Make a table with these data.
Name
Mean
Variance
Root length 1
124.6264
824.1156
Aerial mass 1
0.1079
0.0015
Root mass 1
0.0935
0.0014
A/R ratio 1
1.7854
2.2823
Root length 2
188.7578
668.1094
Aerial mass 2
0.2253
0.0030
Root mass 2
0.1937
0.0037
A/R ratio 2
1.4873
0.7694
Root length 3
174.8176
577.4732
Aerial mass 3
0.2531
0.0064
Root mass 3
0.1638
0.0035
A/R ratio 3
1.8209
0.2356
Root length 4
175.2598
655.1415
Aerial mass 4
0.1338
0.0018
Root mass 4
0.1435
0.0020
A/R ratio 4
1.1816
0.1947
- Provide the histogram for each trait.
Trait 1: Root length 1
2
Trait 2: Aerial mass 1
3
Trait 3: Root mass 1
Trait 4: A/R ratio 1
4
Trait 5: Root length 2
Trait 6: Aerial mass 2
5
Trait 7: Root mass 2
6
Trait 8: A/R ratio 2
7
Trait 9: Root length 3
Trait 10: Aerial mass 3
8
Trait 11: Root mass 3
9
Trait 12: A/R ratio 3
Trait 13: Root length 4
10
Trait 14: Aerial mass 4
11
Trait 15: Root mass 4
Trait 16: A/R ratio 4
12
4.
4.1. Using the LRmapqtl command, perform a linear regression analysis for each individual
marker to test whether a marker is linked to a QTL. Look at the output file (extension .lr).
- Which chromosomes are most likely to have QTLs for the trait rootlength1?
Chromosomes 2, 3, and 5 are most likely to have QTLs for trait rootlength1.
- For each chromosome, list the markers significantly associated with QTLs for the trait
rootlenght1 according to their significance.
Chromosome 2
Chromosome 3
Chromosome 5
Marker
F
Marker
F
Marker
F
29
0.001
8
0.009
3
0.023
28
0.005
4
0.010
4
0.035
27
0.008
7
0.011
1
0.041
26
0.013
1
0.014
6
0.041
9
0.016
13
3
0.017
26
0.018
5
0.023
27
0.023
20
0.024
10
0.025
6
0.027
22
0.029
25
0.034
5.
5.1. Using the SRmapqtl command, perform a stepwise regression analysis. Look at the output
file (extension .sr).
- Which chromosomes and which markers in each chromosome are more closely linked
to QTLs for trait rootlenght1? Rank the markers.
Chromosomes 2: markers 18 and 29; 3: markers 8 and 15; 4: marker 35; 5: marker 1. The
rank is 29, 8, 15, 1, 35, and 18.
- Do these results agree with those obtained from the linear regression analysis?
Only chromosomes 2, 3, and 5 were shown to likely have QTLs for trait rootlength1 by
the linear regression analysis.
6.
6.1. Using the Zmapqtl command, perform an interval (model 3) and composite interval (model
6) mapping. Graphically display your results (using the commands Eqtl with a threshold of 12,
Preplot, and gnuplot).
- Present your graphs here.
- Which chromosomes have QTLs for the trait rootlength1? Indicate how many QTLs
each chromosome has and the model by which they were revealed.
No QTL in chromosome 1
14
1 QTL in chromosome 2, model 6
2 QTL in chromosome 3, model 6
15
No QTL in chromosome 4
1 QTL in chromosome 5, model 6
- What is the difference between model 3 and model 6? (Go to the manual)
Often, a trait is affected by more than one QTL. QTL other than the one being mapped
can be called "background" loci. These background QTL have two effects. Those which
are not linked to the QTL being mapped behave like additional environmental effects and
reduce the significance of any association. Those which are linked to the QTL being
mapped bias the estimated location of that QTL. So composite interval mapping extends
the ideas of interval mapping to include additional markers as cofactors — outside a
defined window of analysis — for the purpose of removing the variation that is
associated with other QTL in the genome.
Model 3, a simple interval mapping model, fits only the mean (Lander and Botstein
(1989) method). It doesn’t control for the genetic background.
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Model 6, a composite interval mapping model, has two additional parameters: window
size (ws) and number of markers for controlling background (np). This window extends
to 10 cM beyond the markers immediately flanking the test position. The number of
markers is set by the -n option.
You need to run SRmapqtl to rank the markers before using model 6.
- What extra information do you gain by testing multiple models?
Zmapqtl uses composite interval mapping to map quantitative trait loci to a map of
molecular markers.
The concept of composite interval mapping is to extend the ideas of interval mapping to
include additional markers as cofactors — outside a defined window of analysis — for
the purpose of removing the variation that is associated with other QTL in the genome.
However the number of background QTLs and their interactions are unknown. We have
to test different models (markers used as cofactors and window size) to search for
significant interactions.
By testing multiple models on the data, we can find QTLs that maybe hidden in other
models and also get better information of the interactions and locations of these QTLs.
- Which model provides better results?
Model 6 provides better results.
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