linkage mapping and qtl analysis_lab
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Lab Manual
Linkage Mapping and QTL Analysis in
Experimental Populations
Linkage Mapping using MapMaker
Software download site: http://rna-informatics.uga.edu/malmberg/rlmlab/index.php?s=1&n=5&r=0
Latest version: MapMaker QTL 3.0b January 1993
Destination: C:\MMintelNT and unzip here (closest to root file “C” works better)
Executable file: MapMaker.exp (command prompt)
Citation: Lander E.S., P. Green, J. Abrahamson, A. Barlow, M.J. Daly, S.E. Lincoln and L.
Newburg (1987). MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1(2):174-181.
Sample Data File for Linkage Mapping
Step 1. Go to http://www.extension.org/pages/32510/mapmaker-tutorial#.ViLQfvlVikr
Step 2. Download “mapmakersampledataset.xls”, copy and paste “mapmakersampledataset.txt” to notepad (save as same)Save in folder where you would want to store the output files
Data source: Scott Wolfe (2012). MapMaker Tutorial. Web accessed: Oct 17, 2015 from:http://www.extension.org/pages/32510/mapmaker-tutorial#.ViLQfvlVikr
Sample Data File for Linkage Mapping
Mapping file information a:Total number of markers: 27Mapping population: F2 intercross (Parent A x Parent B)Total number of individuals: 104Marker symbol: - 1 = Parent A (homozygous for parent A alleles)- 2 = Heterozygous (both parent A and parent B alleles)- 3 = Parent B (homozygous for parent B alleles)- 4 = Not homozygous for parent A- 5 = Not homozygous for parent B
a Source: http://pbgworks.org/sites/pbgworks.org/files/MapMaker%20Tutorial%20Final.pdf
Sample Data File for Linkage MappingStep 3. Check input file format:
Source: http://pbgworks.org/sites/pbgworks.org/files/mapmakersampletextfile.txt
Type of PopulationPopulation
Size
Number of Markers
DefaultsGenotype Score
ScoresMarker Names
Start MapMaker
Step 4. Double click MapMaker.exe and run the program
Set Working Directory
Step 5. Change directory (cd command) to folder where your input file mapmakersampledataset.txt is located
Upload Input File
Step 6. Upload the input file using prepare commandHere, prepare mapmakersampledataset.txt
Saving work
Step 7. Save occasionally to avoid loss of work. Use photo command. Here, saved as “output1.out”.
Specify data
Step 8. Specify data to be used using sequence command. Here all marker data is selected
Grouping
Step 9. Build preliminary linkage groups using group command.Default thresholds are LOD = 3 and max. rf = 50
Two groups with 14 and 13
markers; no unlinked markers
Grouping
Step 10. Check at different LOD and max. rf values. Here, two groups remain unchanged at higher values.
One unlinked at LOD =7 and max. rf. = 30 (very stringent values).
Back to original grouping
Working on Group 1
Step 11. Specify the group (use seq) to start working on that group. Here, start with the first group identified as group 1.
Ordering Markers in Group 1
Step 12. Linear order of markers in a specified group can be obtained using order command
AutomaticOrdering steps:
1. Finds most informative subset and map them
2. Adds remaining markers individually
Ordering Markers in Group 1
Step 12. Linear order of markers in a specified group can be obtained using order command
AutomaticOrdering steps:
1. Finds most informative subset and map them
2. Adds remaining markers individually
3. Tries unmapped ones at lower threshold
Ordering Markers in Group 1
Step 12. Linear order of markers in a specified group can be obtained using order command
AutomaticOrdering steps:
1. Finds most informative subset and maps them
2. Adds remaining markers individually
3. Tries unmapped ones at lower threshold4. Reports markers that do not fit uniquely
Add Remaining Markers to Group 1
Step 13. First, seq order1 (best fitted group 1 markers). Then, add remaining markers with try command. Remember, different original subset could lead to different unassigned markers
Adding unassigned markers: 1. Try remaining markers. Start with
first one (marker 10 in this case)
2. Marker 10 best fits 3rd position
Update Group 1
Step 14. Make new sequence with additional marker at best fit position, add remaining markers, and build final sequence
Adding unassigned markers:
3. Make new sequence with marker 10 added to 3rd position
4. Try other unassigned markers sequentially
5. Make updated sequence
Finalize Linkage Group 1
Step 15. Finally, map command is used to build genetic linkage map of the first group.
Linkage Group 2
Step 16. Repeat Steps 11 to 15 to build remaining linkage groups (here, second linkage group)
Genetic Linkage Maps
MapChart a used for graphical presentation of genetic linkage map
a Source: https://www.wageningenur.nl/en/show/Mapchart.htm
QTL Analysis Using WinQTLCart
Software download site: http://statgen.ncsu.edu/qtlcart/WQTLCart.htm
Latest version: WinQTLCart v2.5_011 released at Aug 01, 2012
Destination: C:\NCSU and unzip here
Logo:
Citation: Wang S., C. J. Basten, and Z.-B. Zeng (2012). Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. ( http://statgen.ncsu.edu/qtlcart/WQTLCart.htm)
Sample Data Files for QTL Analysis
Step 1. Go to http://www.maizegdb.org/data_center/qtl-data
Step 2. Read Messmer1.txt (summary of files)Data summary: - Linkage map: 160 markers (79 RFLPs and 81 SSRs) - Population: 236 recombinant inbred lines (RILs) of maize- Phenotypic data: 6 traits evaluated in 7 field experiments
(42 separate phenotype data)
Citation: Messmer R., Y. Fracheboud, M. Banziger, M. Vargas, P. Stamp and J-M. Ribaut
(2009). Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits. Theor Appl Genet. 119:913-930.
Sample Data Files for QTL Analysis
Step 3. Download “Messmer1map.inp” (rename as map.inp) and “Messmer1cross.inp” (rename as cross.inp)Save in folder where you would want to store QTL mapping output files
Trait of interest for this lab. exercise: MFLW (time from sowing to male flowering , in days) in Mexico (M) under water stress (WS) and well-watered conditions (WW)1. MFLW-MWS1 (Under water stress in Mexico, first environment)2. MFLW-MWS2 (Under water stress in Mexico, second
environment)3. MFLW-MWW1 (Under well-watered condition in Mexico, first
environment)4. MFLW-MWW2 (Under well-watered condition in Mexico,
second environment)
Open Windows QTL CartographerStep 4. Double click WinQTLCart to open interface window.
Familiarize yourselves to the interface.1. Title Bar
2. Menu Bar3. Toolbar
6. Data Pane
5. Form Pane
7. Status Bar
4. Tree Pane
Set working directoryStep 5. Set working directory to folder where input files are located
. Output files will be stored in the working directory.
Import input file (or files)Step 6. Import source data files from working directory folder. We
have data in *.inp fomat. Click Next.
Upload input filesStep 7. Upload Map File (map.inp) and Cross Data (cross.inp).
Source data will be stored in .mcd format. Click Finish.
Save source dataStep 8. Save source data file. Click OK
Verify source dataStep 9. Verify map, genotype and phenotype info. in Data Pane
6. Data Pane
Working with source dataStep 10. Click Dsum in toolbar. Check phenotypic data summary in
Data Pane. *.txt result file stored in working directory.
Working with source dataStep 11. Click DrawChr in toolbar to check genetic linkage map
Working with source dataStep 12. Click TraitView in Form Pane. Identify trait or traits that
you would want to analyze. Four traits marked.
Working with source dataStep 13. Delete traits that are not of interest by clicking Trait in
Source data manipulation. Remove traits 3,4,7-42.
Working with source dataStep 14. Confirm deletion. Individuals, markers, and chromosomes
can also be removed from Source data manipulation
Single Marker Analysis (SMA)Step 15. Proceed with Single marker analysis by clicking GO in
Analysis section of Foam Pane.
5. Form Pane
SMAStep 16. Once complete, View Info for individual traits to check
significant associations (just scroll and check). Click Close.
Result of SMAStep 17. Single marker analysis results are stored in working
directory folder. Check for *-singleAna.txtCopy and save the SMA text file in excel format, keep significant marker-trait associations (* 0.05, ** 0.01, *** 0.001, and ****0.0001)
Example:
Quickly scan SMA result for: a. number and nature of significant associations b. significant associations at contiguous markers along linkage groups
Trait Chrom. Marker b0 b1 -2ln(L0/L1) F(1,n-2) pr(F)MFLW_WSM1 1 11 97.993 0.303 3.964 3.963 0.0477 *
MFLW_WSM1 1 15 97.941 0.446 8.915 9.008 0.0030 **
MFLW_WSM1 1 16 98.064 0.667 19.861 20.545 0.0000 ****
MFLW_WSM1 1 17 98.023 0.651 19.183 19.814 0.0000 ****
MFLW_WSM1 1 18 98.025 0.639 18.342 18.912 0.0000 ****
MFLW_WSM1 1 19 98.007 0.524 11.956 12.16 0.0006 ***
MFLW_WSM1 1 20 97.991 0.329 4.746 4.754 0.0302 *
MFLW_WSM1 1 22 97.971 0.325 4.53 4.535 0.0343 *
phi0560.0bnl5.6210.2umc104121.4umc157a47.2bnlg117854.2bnlg142959.2bnlg162764.3umc11a81.1bnlg43992.3bnlg2238108.4
bnlg2086138.2umc177a158.2csu61b160.4bnlg1057167.2umc1122185.9umc1128214.2umc128219.0umc166b221.7dupssr12231.9
phi011265.3
bnlg1720286.6umc106a296.6umc147b306.5
bnlg2331347.1bnlg2123362.5bnl6.32372.1
Ch1
umc32a0.0phi1041279.7
bnlg132523.8
bnlg144742.6umc15455.3umc92a57.8bnlg1019a68.2phi05383.3bnlg42089.7umc130792.2
bnl10.24a151.1
umc7173.5umc3b179.5
umc16a199.4
umc63a226.6
bnlg1182243.7csu36c250.5bnlg1754253.6
Ch3
umc10170.0
umc129421.4phi02131.3umc155039.0
umc165255.9bnlg49058.5
csu10073.1umc156a79.2
bnlg229199.7umc19107.5
mmc0341126.0
umc133a140.6umc15a148.9
csu11b161.9npi593a172.2bnlg589176.3
bnlg1337198.9phi019207.5phi006213.1
Ch4
umc85a0.0bnlg4268.1
umc36c18.0
bnlg215129.7
umc188751.4umc65a56.7umc101464.8
bnlg192282.4
mmc0241111.9bnlg1732116.4
umc36144.4umc39146.7
bnlg1740168.8
umc2059186.4
Ch6
phi0560.0bnl5.6210.2umc104121.4umc157a47.2bnlg117854.2bnlg142959.2bnlg162764.3umc11a81.1bnlg43992.3bnlg2238108.4
bnlg2086138.2umc177a158.2csu61b160.4bnlg1057167.2umc1122185.9umc1128214.2umc128219.0umc166b221.7dupssr12231.9
phi011265.3
bnlg1720286.6umc106a296.6umc147b306.5
bnlg2331347.1bnlg2123362.5bnl6.32372.1
Ch1
umc32a0.0phi1041279.7
bnlg132523.8
bnlg144742.6umc15455.3umc92a57.8bnlg1019a68.2phi05383.3bnlg42089.7umc130792.2
bnl10.24a151.1
umc7173.5umc3b179.5
umc16a199.4
umc63a226.6
bnlg1182243.7csu36c250.5bnlg1754253.6
Ch3
umc10170.0
umc129421.4phi02131.3umc155039.0
umc165255.9bnlg49058.5
csu10073.1umc156a79.2
bnlg229199.7umc19107.5
mmc0341126.0
umc133a140.6umc15a148.9
csu11b161.9npi593a172.2bnlg589176.3
bnlg1337198.9phi019207.5phi006213.1
Ch4umc10170.0
umc129421.4phi02131.3umc155039.0
umc165255.9bnlg49058.5
csu10073.1umc156a79.2
bnlg229199.7umc19107.5
mmc0341126.0
umc133a140.6umc15a148.9
csu11b161.9npi593a172.2bnlg589176.3
bnlg1337198.9phi019207.5phi006213.1
Ch4
umc85a0.0bnlg4268.1
umc36c18.0
bnlg215129.7
umc188751.4umc65a56.7umc101464.8
bnlg192282.4
mmc0241111.9bnlg1732116.4
umc36144.4umc39146.7
bnlg1740168.8
umc2059186.4
Ch6
phi4028930.0
bnlg129713.8
bnlg204241.9
umc44b66.8
csu4088.0
umc135100.4
umc8g114.4csu54a119.5umc55a128.2umc152131.2umc14b135.4
csu154a156.2dupssr25163.7umc150b167.7umc1551177.2
csu109a196.9umc36a199.1
Ch2
phi4028930.0
bnlg129713.8
bnlg204241.9
umc44b66.8
csu4088.0
umc135100.4
umc8g114.4csu54a119.5umc55a128.2umc152131.2umc14b135.4
csu154a156.2dupssr25163.7umc150b167.7umc1551177.2
csu109a196.9umc36a199.1
Ch2
bnl8.330.0npi4095.1
umc147a30.8umc9038.3umc107b46.0
bnlg104671.7
umc166a87.5bnl6.2298.1csu36b109.8
bnl5.71a134.5umc48b147.0npi237154.0umc54164.8
bnlg1346199.4
bnlg118216.4
umc1225230.2umc104b237.9bnlg1885244.8
Ch5
bnl8.330.0npi4095.1
umc147a30.8umc9038.3umc107b46.0
bnlg104671.7
umc166a87.5bnl6.2298.1csu36b109.8
bnl5.71a134.5umc48b147.0npi237154.0umc54164.8
bnlg1346199.4
bnlg118216.4
umc1225230.2umc104b237.9bnlg1885244.8
Ch5phi4028930.0
bnlg129713.8
bnlg204241.9
umc44b66.8
csu4088.0
umc135100.4
umc8g114.4csu54a119.5umc55a128.2umc152131.2umc14b135.4
csu154a156.2dupssr25163.7umc150b167.7umc1551177.2
csu109a196.9umc36a199.1
Ch2
npi114a0.0
umc132716.0
npi110a33.6
umc103a51.3
bnlg66964.1
umc185884.7
umc2c114.9
umc48a130.2asg52a133.2umc150a136.6
umc1384155.4
umc7166.6bnlg1056169.9
umc39b181.1
Ch8
npi114a0.0
umc132716.0
npi110a33.6
umc103a51.3
bnlg66964.1
umc185884.7
umc2c114.9
umc48a130.2asg52a133.2umc150a136.6
umc1384155.4
umc7166.6bnlg1056169.9
umc39b181.1
Ch8
bnlg12720.0umc1095.0
umc113a26.2
umc105a53.7
umc8174.0
bnl8.17110.9
umc1231118.6
bnlg1588140.4umc1733145.5
Ch9bnlg12720.0umc1095.0
umc113a26.2
umc105a53.7
umc8174.0
bnl8.17110.9
umc1231118.6
bnlg1588140.4umc1733145.5
Ch9
phi1180.0
npi285a17.0
umc13049.3
bnlg107961.8
umc111586.4npi232a93.1
umc44a105.1umc182112.9bnlg236119.2
bnl7.49a135.5
bnlg1450151.6
umc1038175.6
Ch10phi1180.0
npi285a17.0
umc13049.3
bnlg107961.8
umc111586.4npi232a93.1
umc44a105.1umc182112.9bnlg236119.2
bnl7.49a135.5
bnlg1450151.6
umc1038175.6
Ch10
MFLW_WSM1MFLW_WSM2MFLW_WWM1MFLW_WWM2
Result of SMAStep 18. Compare with pre-analyzed data (P ≤ 0.01)
Interval mapping (IM)Step 19. Select Interval mapping, click GO in Analysis section of
Foam Pane.
IMStep 20. Usually ran with Permutation Times. (1,000) at genome-
wide Significance Level of 0.05 and Walk speed (cM) of 2 cM.
However, it will take hours to complete analysis under aforementioned settings (only use these settings for homework exercise)
IM
Here, interval mapping running at -1,000 permutations- 0.05 level of significance- 1.0 walk speed- for all chromosomes- for all traits - clicked OK For All Traits under Threshold Value Settings- ran overnight and crashed at the end!- To find permutation based LOD thresholds, run individual traits (NOT all traits) in Trait Selection and click OK in Threshold Value Setting
IMStep 21. Instead, proceed directly to interval mapping using All
Chromosomes, All Traits, Walk speed (cM) of 1. Click START
Should be finished within 10 minutes for 4 traits.
IM Graph WindowStep 22. Once complete, graph window pops-up. To check IM
results, maximize the graph widow
IM Graph WindowStep 23. Check graphs using graph window menu tools
Show one or more chromosomes
Show one or more traits
Show QTL InformationStep 24. Show QTL information using Automatic locating QTLs with
Min 20 cM between QTLs and Min 1 LOD from top to valley and save information in excel
Save QTL info. in excel
Composite interval mapping (CIM)Step 25. Select Composite Interval mapping, click GO in Analysis
section of Foam Pane.
CIMStep 26. Usually Permutation Thres. (1,000) at genome-wide
Significance Level of 0.05 and Walk speed (cM) of 2 cM.
However, it will take hours to complete analysis under aforementioned settings (only use these settings for homework exercise)
Step 27. Instead, proceed directly to composite interval mapping (as with interval mapping).
CIM
- Set model by clicking control- CIM Model 6 is standard- Use default values; click START- Graph window pops-up, proceed as in Step 24
Understanding IM and CIM Output FilesStep 28. IM and CIM results are saved in the destination folder as
*In_i.qrt and *In_c.qrt that can be opened with WinQLTCart . Excel files are saved as *in-i.xls and *in-c.xls. Open *in-c.xls file. Check the files.
TraitChromosome
Marker #Position of QTL
Likelihood-ratio test statistic
R2 value
Additive effectTest statistic, S
Understanding IM and CIM Output FilesStep 28. IM and CIM results are saved in the destination folder as
*In_i.qrt and *In_c.qrt that can be opened with WinQLTCart . Excel files are saved as *in-i.xls and *in-c.xls. Open *in-c.xls file. Check the files.
TraitChromosome
Position of QTL Likelihood-ratio
test statistic
Additive effectR2 value One LOD support
interval
Two LOD support interval
phi0560.0bnl5.6210.2umc104121.4umc157a47.2bnlg117854.2bnlg142959.2bnlg162764.3umc11a81.1bnlg43992.3bnlg2238108.4
bnlg2086138.2umc177a158.2csu61b160.4bnlg1057167.2umc1122185.9umc1128214.2umc128219.0umc166b221.7dupssr12231.9
phi011265.3
bnlg1720286.6umc106a296.6umc147b306.5
bnlg2331347.1bnlg2123362.5bnl6.32372.1
Ch1
umc32a0.0phi1041279.7
bnlg132523.8
bnlg144742.6umc15455.3umc92a57.8bnlg1019a68.2phi05383.3bnlg42089.7umc130792.2
bnl10.24a151.1
umc7173.5umc3b179.5
umc16a199.4
umc63a226.6
bnlg1182243.7csu36c250.5bnlg1754253.6
Ch3
umc10170.0
umc129421.4phi02131.3umc155039.0
umc165255.9bnlg49058.5
csu10073.1umc156a79.2
bnlg229199.7umc19107.5
mmc0341126.0
umc133a140.6umc15a148.9
csu11b161.9npi593a172.2bnlg589176.3
bnlg1337198.9phi019207.5phi006213.1
Ch4
umc85a0.0bnlg4268.1
umc36c18.0
bnlg215129.7
umc188751.4umc65a56.7umc101464.8
bnlg192282.4
mmc0241111.9bnlg1732116.4
umc36144.4umc39146.7
bnlg1740168.8
umc2059186.4
Ch6
npi114a0.0
umc132716.0
npi110a33.6
umc103a51.3
bnlg66964.1
umc185884.7
umc2c114.9
umc48a130.2asg52a133.2umc150a136.6
umc1384155.4
umc7166.6bnlg1056169.9
umc39b181.1
Ch8
phi0560.0bnl5.6210.2umc104121.4umc157a47.2bnlg117854.2bnlg142959.2bnlg162764.3umc11a81.1bnlg43992.3bnlg2238108.4
bnlg2086138.2umc177a158.2csu61b160.4bnlg1057167.2umc1122185.9umc1128214.2umc128219.0umc166b221.7dupssr12231.9
phi011265.3
bnlg1720286.6umc106a296.6umc147b306.5
bnlg2331347.1bnlg2123362.5bnl6.32372.1
Ch1
umc32a0.0phi1041279.7
bnlg132523.8
bnlg144742.6umc15455.3umc92a57.8bnlg1019a68.2phi05383.3bnlg42089.7umc130792.2
bnl10.24a151.1
umc7173.5umc3b179.5
umc16a199.4
umc63a226.6
bnlg1182243.7csu36c250.5bnlg1754253.6
Ch3
umc10170.0
umc129421.4phi02131.3umc155039.0
umc165255.9bnlg49058.5
csu10073.1umc156a79.2
bnlg229199.7umc19107.5
mmc0341126.0
umc133a140.6umc15a148.9
csu11b161.9npi593a172.2bnlg589176.3
bnlg1337198.9phi019207.5phi006213.1
Ch4
umc85a0.0bnlg4268.1
umc36c18.0
bnlg215129.7
umc188751.4umc65a56.7umc101464.8
bnlg192282.4
mmc0241111.9bnlg1732116.4
umc36144.4umc39146.7
bnlg1740168.8
umc2059186.4
Ch6
phi4028930.0
bnlg129713.8
bnlg204241.9
umc44b66.8
csu4088.0
umc135100.4
umc8g114.4csu54a119.5umc55a128.2umc152131.2umc14b135.4
csu154a156.2dupssr25163.7umc150b167.7umc1551177.2
csu109a196.9umc36a199.1
Ch2
npi114a0.0
umc132716.0
npi110a33.6
umc103a51.3
bnlg66964.1
umc185884.7
umc2c114.9
umc48a130.2asg52a133.2umc150a136.6
umc1384155.4
umc7166.6bnlg1056169.9
umc39b181.1
Ch8
phi1180.0
npi285a17.0
umc13049.3
bnlg107961.8
umc111586.4npi232a93.1
umc44a105.1umc182112.9bnlg236119.2
bnl7.49a135.5
bnlg1450151.6
umc1038175.6
Ch10
MFLW_WSM1MFLW_WSM2MFLW_WWM1MFLW_WWM2
Results of IM and CIM analyses
IM
CIM
CIM Permutations
Here, composite interval mapping finished running at:- 500 permutations- 0.05 level of significance- 2.0 walk speed- for all chromosomes- for first trait- click START to begin mapping analysis- Resulting graph will have permutation based LOD threshold instead of regular threshold (LOD = 2.5) for the first trait
phi0560.0bnl5.6210.2umc104121.4umc157a47.2bnlg117854.2bnlg142959.2bnlg162764.3umc11a81.1bnlg43992.3bnlg2238108.4
bnlg2086138.2umc177a158.2csu61b160.4bnlg1057167.2umc1122185.9umc1128214.2umc128219.0umc166b221.7dupssr12231.9
phi011265.3
bnlg1720286.6umc106a296.6umc147b306.5
bnlg2331347.1bnlg2123362.5bnl6.32372.1
Ch1
umc32a0.0phi1041279.7
bnlg132523.8
bnlg144742.6umc15455.3umc92a57.8bnlg1019a68.2phi05383.3bnlg42089.7umc130792.2
bnl10.24a151.1
umc7173.5umc3b179.5
umc16a199.4
umc63a226.6
bnlg1182243.7csu36c250.5bnlg1754253.6
Ch3
umc10170.0
umc129421.4phi02131.3umc155039.0
umc165255.9bnlg49058.5
csu10073.1umc156a79.2
bnlg229199.7umc19107.5
mmc0341126.0
umc133a140.6umc15a148.9
csu11b161.9npi593a172.2bnlg589176.3
bnlg1337198.9phi019207.5phi006213.1
Ch4
umc85a0.0bnlg4268.1
umc36c18.0
bnlg215129.7
umc188751.4umc65a56.7umc101464.8
bnlg192282.4
mmc0241111.9bnlg1732116.4
umc36144.4umc39146.7
bnlg1740168.8
umc2059186.4
Ch6
phi4028930.0
bnlg129713.8
bnlg204241.9
umc44b66.8
csu4088.0
umc135100.4
umc8g114.4csu54a119.5umc55a128.2umc152131.2umc14b135.4
csu154a156.2dupssr25163.7umc150b167.7umc1551177.2
csu109a196.9umc36a199.1
Ch2
npi114a0.0
umc132716.0
npi110a33.6
umc103a51.3
bnlg66964.1
umc185884.7
umc2c114.9
umc48a130.2asg52a133.2umc150a136.6
umc1384155.4
umc7166.6bnlg1056169.9
umc39b181.1
Ch8
phi1180.0
npi285a17.0
umc13049.3
bnlg107961.8
umc111586.4npi232a93.1
umc44a105.1umc182112.9bnlg236119.2
bnl7.49a135.5
bnlg1450151.6
umc1038175.6
Ch10
MFLW_WSM1; permutation based threshold 2.9
Result of CIM Permutations
CIM without permutation
MFLW_WSM2; permutation based threshold 3.1
MFLW_WWM1; permutation based threshold 3.0
MFLW_WWM1; permutation based threshold 2.9
Do not meet permutation thresholds
Report results of CIM
R 2
Chr Env Mark Peak Interval LOD Add (%)1 WSM1 umc128 221 219-221 7.5 0.71 12.8
WSM2 umc128 221 207-227 3.21 0.44 4.9WWM1 umc1128 215 213-221 3.3 0.49 5.0
2 WSM2 csu54a 119 116-127 3.5 -0.45 4.93 WSM1 umc92a 62 58-79 5.1 0.86 10.2
WSM2 umc92a 61 57-67 5.8 0.71 10.1WWM1 bnlg1019a 78 67-88 4.9 0.65 9.8
4 WSM2 csu11b 162 154-170 6.8 0.65 9.6WWM1 csu11b 160 153-170 6.5 0.62 9.8WWM2 csu11b 161 152-162 4.6 0.61 7.8
8 WWM1 bnlg669 61 52-78 4.4 -0.55 7.810 WSM1 bnlg1079 61 49-86 2.9 -0.49 5.1
Distance (cM)
Thanks
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