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