Building genetic maps can be challenging and sometimes quite stressful, especially, when dealing with thousands or even millions of markers. In this post, I am hoping to help anyone who would like to get started to build a decent genetic map in an open software Lep-MAP3 , and finally, evaluating the accuarcy of the genetic map and plotting it.

Note If you have an amplicon sequencing (AmpSeq or rhAmpSeq) haplotype data, you can convert the data into a psuedo VCF file using Haplotype to VCF PERL script.

## Quality control analysis

Prior to building genetic maps - I strongly advise to perform QC analysis on your genetic data. There are two QC tests that i usually perform: 1) Multidimensional scaling (MDS) and 2) Check for Mendelian Error

## How to transfer files using FileZilla

Please watch below video to: download , install and configure FileZilla. It will show you how to upload files and folders to your server.

## Installing Lep-MAP3

The Lep-MAP3 software is built in Linux and one has to have some experience in working in command-line environment.

Downloand and install Lep-Map3 on your computer following below steps:

## Running Lep-MAP3

The steps invloved in the genetic mapping process in Lep-MAP3 are shown in the flow chart below.

### Step 1.1. File Preparation

Important - Correctly install the Lep-MAP3 software on your computer, and please make sure its the latest version. There are two files that are required as input files:

• (1) genotype file in a VCF format, and

• (2) pedigree file in .txt format. A snippet of the pedigree file showing relationship between all individuals in a family or population is shown below. It is important that the pedigree file is formatted exactly as shown in the below figure:

• ### Step 1.2. Parent Call

The parental genotypes are called using the ParentCall2 module, using the below command:

$java -cp [path]/Lep-MAP3/bin ParentCall2 data = pedigree.txt vcfFile = File.vcf > p.call  Note path is the directory where Lep-MAP3 is located on your computer. ### Step 1.3. Filtering This an optional step - However, One may use the Filtering2 module to remove non-informative markers (Markers that are monomorphic or homozygous in both parents), and distorted markers (markers segregating in a non-Mendelian fashion) using the below command line: $ java -cp /path/Lep-MAP3/bin Filtering2 data=p.call  removeNonInformative=1 dataTolerance=0.0000001  > p_fil.call


Note: Use the parameter removeNonInformative to remove markers that are homozygous/monomorphic, and dataTolerance to remove distorted markers at given p-value threshold.

### Step 1.4. Separate Chromosomes

In this step, SeparateChromosomes2 module is used to categorize markers into linkage groups (LGs) using the below command:

$java -cp /path/Lep-MAP3/bin SeparateChromosomes2 data=p_fil.call lodLimit=5 > map.txt  Note: One can use parameters such as lodLimit and theta to split the linkage groups. ### Step 1.5. Order Markers In this step, markers separated into their corresponding linkage groups are ordered using OrderMarkers2 module using the below command: $ java -cp /path/Lep-MAP3/bin  OrderMarkers2 data=p_fil.call map=map.txt > order.txt


One may use the parameter sexAveraged to calculate sex-averaged map distances (by default male and female genetic maps are curated), also numMergeIterations parameter can be used to adjust number of iterations (by deafault its 6 iterations per linkage group).

## 2.0 Checking the accuracy of the marker order

If the physical positons of the markers in the curated genetic map curation are known, then one may use that information to evaluate the quality of the marker order in the genetic map, especially markers that inflate the chromosome length, by making a correlation plot of the genetic and physical positions of the markers for each chromosome or linkage group.

Note: It is a common scenario to see the marker orders are flipped relative to their physical positions. There is nothing to panic about, one may fix it by manually sorting it.

Command to obtain the marker information using cut:

cut -f1,2 p.call > cut_pcall.txt



Please make sure to use the p.call file that you used in the ordering step

Follow the below steps to perform the correlation analysis:

## 3.0 Converting phased output data from OrderMarkers2 to genotypes

The phased data from OrderMarkers2 step can be converted to fully informative “genotype” data by using map2gentypes.awk script and command below: Download the map2gentypes.awk script here.

Next, run the map2genotypes.awk script by following the command shown below.

	\$ awk -vfullData=1 -f map2genotypes.awk order.txt > genotypes.txt


Snippet of the map2gentypes.awk output:

One may convert the genotypes in 1 1 => A, 2 2 => B, 1 2 or 2 1 => H format (See below figure) in MS Excel using Find and Replace function, which can be then be loaded in R/Qtl for QTL mapping.

LepMap3 imputes and phase the genotype calls, therefore, A and B allele represent major and minor allele frequencies and it will change from parent/phase. They do not represent one specific parent and information depends on the parent and the phase of the marker.

## 4.0 Validate the genetic map by conducting QTL analysis

It is a good QC step to perform a QTL analysis of a well studied trait to check if expected QTL region is observed in the curated genetic mmap