Genetic mapping and QTL analysis - JoinMap and QTLNetwork - 2016. 3. 29 Genetic mapping and QTL analysis - JoinMap and QTLNetwork - Crop Molecular Breeding lab., SNU Jeonghwan Seo

What is a QTL? • Quantitative Trait Locus • Locus, meaning region of the genome – not necessarily a single gene, could be several linked genes. • Allelic variation at a QTL region causes phenotypic variation in a quantitative trait.

Objectives of QTL Analysis 1. Identify regions of the genome containing QTL 2. Estimate the effects of the QTL on the quantitative trait: – how much of the variation for the trait is caused by a specific region? – what is the gene action associated with the QTL - additive effect? Dominant effect? – which allele is associated with the favorable effect? 3. Use QTL estimates and genotypic information to assign breeding values to lines

Requirements of QTL analysis - An appropriate population - Informative markers - Framework map with complete genome coverage - Good phenotypes - Statistical methods (software) for establishing significant associations between markers and traits - Verification

Populations for QTL analysis <N. Manikanda Boopathi, Genetic mapping & marker assisted selection, 2013>

Genotype analysis and scoring 1 2 3 genotypes Example for manual marker coding scheme in F2 population using STS marker.

Genotype analysis and scoring Scatter plot and call map of F2 population using Fluidigm 96-plex indica/japonica SNP set. -> Automatic genotype calling

Linkage analysis and map construction Genetic mapping : determination of the relative position of genetic loci on chromosomes in eukaryotic organisms Recombinant frequency is used to determine the order of loci along a chromosome and give estimates of the relative distances between the loci - Genetic recombinants result from crossing-over takes place during meiosis Recombination frequency depends on the physical distance between two loci Genetic mapping software - Mapmaker : Maximum likelihood algorithm - JoinMap 3.0 : Stepwise algorithm - JoinMap 4.0 : Regression mapping algorithm, Maximum likelihood algorithm

The statistical methodology of QTL analysis Single point analysis (SPA) Multiple linear regression (MLG) Simple interval mapping (SIM) Composite interval mapping (CIM) Mixed linear model approaches (MCIM)

List of QTL mapping software QGENE : http://www.qgene.org/ QTL EXPRESS : http://qtl.cap.ed.ac.uk/ MAPMAKER/EXP : http://www.broad.mit.edu/ftp/distribution/software/mapmaker3/ QTL Cartographer : http://statgen.ncsu.edu/qtlcart/cartographer.html - Composite interval mapping PLABQTL version: 1.2bic (2010) : https://www.uni-hohenheim.de/plantbreeding/software/ - Composite interval mapping, Epistatic effect JoinMap 4.1 & MapQTL 6 : http://www.kyazma.nl/index.php/mc.JoinMap/ QTLNetwork 2.1 : http://ibi.zju.edu.cn/software/qtlnetwork - MCIM, Epistatic effect, QE interaction effect QTL IciMapping : https://www.integratedbreeding.net/supplementary-toolbox/genetic- mapping-and-qtl/icimapping - Composite interval mapping, Epistatic effect, QE interaction effect - QTL mapping in nested association mapping populations

Objectives of practice Construction of genetic linkage map using JoinMap 4.0 QTL analysis for four traits using QTLNetwork 2.1 * We use genotypes of two chromosomes and four traits in rice F2 population.

Sample data Marker name Individual number Trait name Individual number HWC-line/Dasan F2 population 190 F2 progeny 20 markers on chromosome 3 & 5 4 traits : grain length (GL), grain width (GW), grain shape (GS), 100 grain weight (100GW) genotype sheet phenotype sheet

Genotype conversion for JoinMap NAME=population name POPT=population type NLOC=number of locus (marker) NIND=number of individual (population size) 1 (P1) -> a 2 (P2) -> b 3 (hetero) -> h - (missing data) -> . Copy genotype data to JM input sheet. Delete gray section Change numeric genotype to genotype code

Genotype conversion for JoinMap Copy all converted genotype data, and paste on notepad and save. Or, Save as text file (separated by tab) : JoinMap input file

Genetic mapping using JoinMap 4.0 Click this icon (New project) Open JoinMap 4.0 Click file -> New project (ctrl+N) Save a new project file

Genetic mapping using JoinMap 4.0 Click this icon (Load data) Click file -> Load data (F4) Select the JoinMap input file

Genetic mapping using JoinMap 4.0 Click this icon (Calculation option) Click Options -> Calculation options Population -> Grouping -> Select linkage LOD Group -> Mapping algorithm -> Select Regression mapping Regression Mapping -> Mapping function -> Select Kosambi’s Other option is default and click OK

Genetic mapping using JoinMap 4.0 Click this icon (Calculate) Markers in group Click Calculate -> Calculate (F9) in each tab Select (right click or space) appropriate groups according to marker information We select 10.0/1(11) and 4.0/2(9)

Click this

Click Group -> Calculate Map Check the Map Chart. Click this Click Group -> Calculate Map Check the Map Chart. Select proper map, if there are more than two maps. Copy the map chart (Ctrl+C) and paste (Ctrl+V) on map sheet in sample data file.

Genetic map construction Click Map -> Drag map table or Ctrl+A -> Paste on map sheet in sample data file

Genetic map construction _MapFunction : mapping function (K=Kosambi, H=Haldane) _DistanceUnit : map unit between adjacent markers (cM=centiMorgan) _Chromosomes : number of chromosomes (linkage groups) _MarkerNumbers : number of markers in each chromosomes Calculate genetic interval. Make map contents according to format. (map sheet in sample data file) Drag map contents and paste on notepad. Save the map file as *.txt file and convert to *.map.

Data file preparation for QTLNetwork 2.1 Copy genotype data in JM input sheet and paste on data sheet. Fill data information and add semicolon on end of each row.

Data file preparation for QTLNetwork 2.1 Copy trait data in phenotype sheet and paste on data sheet. Add semicolon on end of each row. Copy all contents in data sheet, and paste on notepad and save. Or, Save as text file (separated by tab) : QTLNetwork data file

QTL analysis using QTLNetwork 2.1 Click this (New project) Open QTLNetwork 2.1 Click Project -> New (ctrl+N) Input Map file and Data file.

QTL analysis using QTLNetwork 2.1 Click run and start with default setting.

QTL analysis using QTLNetwork 2.1 - Check result of QTL analysis : QTLNetwork tab in left side, shows interaction among each QTLs. In this result, there are no interaction between two QTLs for trait 2 (GW).

QTL analysis using QTLNetwork 2.1 - QTL tab in left tab, shows F-value graph with threshold.

QTL analysis using QTLNetwork 2.1 - Report in left tab, provides detail result of QTL analysis. We can find “data file name”.pre and “data file name”.qnk in folder where data file exist.

QTL mapping on genetic map The list of main-effect QTLs for four grain related traits on chromosome 3 and 5 in HWC-line/Dasan F2 population. Trait QTL Chr. Interval Position Range A F-value PVE(%) GL qGL5 5 S05066-S05077A 73.4 64.4-81.4 0.15 8.3 (6.2) 8.0 GW qGW3 3 RM15007-S03076B 104.2 84.2-113.2 0.08 13.5 (6.5) 6.4 qGW5 S05030A-S05048 49.5 44.5-59.8 -0.17 47.1 (6.5) 34.0 GS qGS3 S03099-S03115 125.4 118.4-137.7 -0.09 14.6 (6.7) 7.4 qGS5 S05054-S05066 63.4 60.4-67.4 0.17 49.1 (6.7) 30.5 100GW q100GW 51.5 43.5-58.8 -0.15 18.9 (6.3) 17.0 A represents additive effect of HWC-line allele. F-value represents F-static value and number in parenthesis indicates threshold value. GL GW 100GW GS

Q-TARO (http://qtaro.abr.affrc.go.jp/)

- The candidate gene for qGW5 and q100GW is GW5/qSW5.