Linkage mapping and QTL analysis_Lab
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Undergraduate level introductory laboratory exercise on linkage mapping and QTL analysis in experimental populations. Prepared in 2015.
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Linkage mapping and QTL analysis_Lab
- 1. Lab Manual
- 2. 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.
- 3. 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
- 4. Sample Data File for Linkage Mapping Mapping file information a: Total number of markers: 27 Mapping population: F2 intercross (Parent A x Parent B) Total number of individuals: 104 Marker 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
- 5. Sample Data File for Linkage Mapping Step 3. Check input file format: Source: http://pbgworks.org/sites/pbgworks.org/files/mapmakersampletextfile.txt Type of Population Population Size Number of Markers Defaults Genotype Score ScoresMarker Names
- 6. Start MapMaker Step 4. Double click MapMaker.exe and run the program
- 7. Set Working Directory Step 5. Change directory (cd command) to folder where your input file mapmakersampledataset.txt is located
- 8. Upload Input File Step 6. Upload the input file using prepare command Here, prepare mapmakersampledataset.txt
- 9. Saving work Step 7. Save occasionally to avoid loss of work. Use photo command. Here, saved as “output1.out”.
- 10. Specify data Step 8. Specify data to be used using sequence command. Here all marker data is selected
- 11. 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
- 12. 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
- 13. 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.
- 14. Ordering Markers in Group 1 Step 12. Linear order of markers in a specified group can be obtained using order command Automatic Ordering steps: 1. Finds most informative subset and map them 2. Adds remaining markers individually
- 15. Ordering Markers in Group 1 Step 12. Linear order of markers in a specified group can be obtained using order command Automatic Ordering steps: 1. Finds most informative subset and map them 2. Adds remaining markers individually 3. Tries unmapped ones at lower threshold
- 16. Ordering Markers in Group 1 Step 12. Linear order of markers in a specified group can be obtained using order command Automatic Ordering steps: 1. Finds most informative subset and maps them 2. Adds remaining markers individually 3. Tries unmapped ones at lower threshold 4. Reports markers that do not fit uniquely
- 17. 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
- 18. 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
- 19. Finalize Linkage Group 1 Step 15. Finally, map command is used to build genetic linkage map of the first group.
- 20. Linkage Group 2 Step 16. Repeat Steps 11 to 15 to build remaining linkage groups (here, second linkage group)
- 21. Genetic Linkage Maps MapChart a used for graphical presentation of genetic linkage map a Source: https://www.wageningenur.nl/en/show/Mapchart.htm
- 22. 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)
- 23. 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.
- 24. 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)
- 25. Open Windows QTL Cartographer Step 4. Double click WinQTLCart to open interface window. Familiarize yourselves to the interface. 1. Title Bar 2. Menu Bar 3. Toolbar 6. Data Pane 5. Form Pane 7. Status Bar 4. Tree Pane
- 26. Set working directory Step 5. Set working directory to folder where input files are located . Output files will be stored in the working directory.
- 27. Import input file (or files) Step 6. Import source data files from working directory folder. We have data in *.inp fomat. Click Next.
- 28. Upload input files Step 7. Upload Map File (map.inp) and Cross Data (cross.inp). Source data will be stored in .mcd format. Click Finish.
- 29. Save source data Step 8. Save source data file. Click OK
- 30. Verify source data Step 9. Verify map, genotype and phenotype info. in Data Pane 6. Data Pane
- 31. Working with source data Step 10. Click Dsum in toolbar. Check phenotypic data summary in Data Pane. *.txt result file stored in working directory.
- 32. Working with source data Step 11. Click DrawChr in toolbar to check genetic linkage map
- 33. Working with source data Step 12. Click TraitView in Form Pane. Identify trait or traits that you would want to analyze. Four traits marked.
- 34. Working with source data Step 13. Delete traits that are not of interest by clicking Trait in Source data manipulation. Remove traits 3,4,7-42.
- 35. Working with source data Step 14. Confirm deletion. Individuals, markers, and chromosomes can also be removed from Source data manipulation
- 36. Single Marker Analysis (SMA) Step 15. Proceed with Single marker analysis by clicking GO in Analysis section of Foam Pane. 5. Form Pane
- 37. SMA Step 16. Once complete, View Info for individual traits to check significant associations (just scroll and check). Click Close.
- 38. Result of SMA Step 17. Single marker analysis results are stored in working directory folder. Check for *-singleAna.txt Copy 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 *
- 40. Interval mapping (IM) Step 19. Select Interval mapping, click GO in Analysis section of Foam Pane.
- 41. IM Step 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)
- 42. 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
- 43. IM Step 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.
- 44. IM Graph Window Step 22. Once complete, graph window pops-up. To check IM results, maximize the graph widow
- 45. IM Graph Window Step 23. Check graphs using graph window menu tools Show one or more chromosomes Show one or more traits
- 46. Show QTL Information Step 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
- 47. Composite interval mapping (CIM) Step 25. Select Composite Interval mapping, click GO in Analysis section of Foam Pane.
- 48. CIM Step 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)
- 49. 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
- 50. Understanding IM and CIM Output Files Step 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. Trait Chromosome Marker # Position of QTL Likelihood-ratio test statistic R2 value Additive effect Test statistic, S
- 51. Understanding IM and CIM Output Files Step 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. Trait Chromosome Position of QTL Likelihood-ratio test statistic Additive effect R2 value One LOD support interval Two LOD support interval
- 52. phi0560.0 bnl5.6210.2 umc104121.4 umc157a47.2 bnlg117854.2 bnlg142959.2 bnlg162764.3 umc11a81.1 bnlg43992.3 bnlg2238108.4 bnlg2086138.2 umc177a158.2 csu61b160.4 bnlg1057167.2 umc1122185.9 umc1128214.2 umc128219.0 umc166b221.7 Ch1 umc32a0.0 phi1041279.7 bnlg132523.8 bnlg144742.6 umc15455.3 umc92a57.8 bnlg1019a68.2 phi05383.3 bnlg42089.7 umc130792.2 bnl10.24a151.1 Ch3 umc10170.0 umc129421.4 phi02131.3 umc155039.0 umc165255.9 bnlg49058.5 csu10073.1 umc156a79.2 bnlg229199.7 umc19107.5 mmc0341126.0 Ch4 umc85a0.0 bnlg4268.1 umc36c18.0 bnlg215129.7 umc188751.4 umc65a56.7 umc101464.8 bnlg192282.4 mmc0241111.9 Ch6 npi114a0.0 umc132716.0 npi110a33.6 umc103a51.3 bnlg66964.1 umc185884.7 Ch8 Ch1 Ch3 Ch4 Ch6Ch2 Ch8 Ch10 MFLW_WSM1 MFLW_WSM2 MFLW_WWM1 MFLW_WWM2 Results of IM and CIM analyses IM CIM
- 53. 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
- 54. Ch1 Ch3 Ch4 Ch6Ch2 Ch8 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
- 55. Report results of CIM R 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 4.9 WWM1 umc1128 215 213-221 3.3 0.49 5.0 2 WSM2 csu54a 119 116-127 3.5 -0.45 4.9 3 WSM1 umc92a 62 58-79 5.1 0.86 10.2 WSM2 umc92a 61 57-67 5.8 0.71 10.1 WWM1 bnlg1019a 78 67-88 4.9 0.65 9.8 4 WSM2 csu11b 162 154-170 6.8 0.65 9.6 WWM1 csu11b 160 153-170 6.5 0.62 9.8 WWM2 csu11b 161 152-162 4.6 0.61 7.8 8 WWM1 bnlg669 61 52-78 4.4 -0.55 7.8 10 WSM1 bnlg1079 61 49-86 2.9 -0.49 5.1 Distance (cM)