Introduction to Linkmapper

Hosted web app

The easiest way to use Linkmapper is through the hosted version on ShinyApps.io. No installation is required - open the URL in any modern browser and begin uploading your data immediately: https://project-genaxy.shinyapps.io/Linkmapper/

R package (local)

To run Linkmapper locally, install it from R-universe:

install.packages("linkmapper",
  repos = c('https://ebenogoe.r-universe.dev', 'https://cloud.r-project.org')
)

linkmapper::run_linkmapper()

This opens the app in your default browser. Running locally removes the session time limits of the hosted version and allows you to work with larger datasets.

File format

Linkmapper accepts genotype data in MAPMAKER .txt format. The file must begin with a header line specifying the data type, followed by a line declaring the number of individuals, markers, and phenotype columns:

data type f2 intercross
188 62 2

Supported data type strings:

• f2 intercross - F2 intercross population
• backcross - backcross population

Genotype encoding

Genotypes are encoded per MAPMAKER conventions:

Phenotype columns

Phenotype columns are optional for linkage mapping but are required for QTL analysis. If your file contains phenotype data, it must be declared in the header (the third number on the second line). Phenotype values should be numeric; missing phenotype data should be encoded as -.

Step 1 - Upload and prior analysis

Upload your MAPMAKER .txt file. Linkmapper validates the file format and reads the data using onemap::read_mapmaker(). The prior analysis panel then displays:

• A missing data plot showing the proportion of missing genotype calls per marker and per individual, helping identify low-quality markers or individuals to consider removing before proceeding.
• A segregation distortion test (chi-squared) for each marker. Markers that deviate significantly from expected Mendelian ratios are flagged. Users can choose whether to retain or exclude distorted markers before grouping.

At the end of this step, the number of individuals, markers, and phenotype columns is summarised, and a suggested LOD threshold is computed based on dataset size.

Step 2 - Marker grouping

Markers are grouped into putative linkage groups using two-point recombination frequency estimates (onemap::rf_2pts()). You set a LOD threshold and a maximum recombination frequency (max RF); markers that meet both criteria are placed in the same group.

The output is a table showing the number of markers assigned to each group and the number of unlinked markers. You can adjust the LOD and max RF parameters and re-run grouping until the result is biologically sensible (i.e. the number of groups matches the expected haploid chromosome number of your organism).

Step 3 - Marker ordering

Within each linkage group, markers are ordered to minimise the total map length. Linkmapper offers three ordering algorithms:

• RECORD - Recombination Counting and Ordering. Recommended for most F2 datasets; produces the most accurate order in simulation studies.
• RCD - Rapid Chain Delineation. Fast heuristic; suitable as a starting point or for very large groups.
• UG (Unidirectional Growth) - Alternative heuristic; useful when RECORD produces unexpected results.

Ordering is run on each linkage group in turn. The log-likelihood of the final order is reported for each group, allowing you to compare algorithms.

Step 4 - Linkage map generation

The ordered sequences are passed to onemap::map() to estimate inter-marker distances using the selected mapping function (Kosambi or Haldane). The final linkage map is displayed as:

• An interactive plot (plotly) where you can hover over markers to see their names and positions.
• A static PNG suitable for publication.

A summary table shows the number of markers and total length (cM) per linkage group. Both the plot and the summary table are available for download.

Step 5 - QTL analysis

If your data file contains phenotype columns, you can run a QTL scan on the completed map. Choose a phenotype, a scanning method (interval mapping or CIM), and a LOD significance threshold. The output is:

• A LOD profile plot showing the LOD score across the genome for the selected phenotype.
• A QTL summary table listing linkage group, position, and peak LOD score for all loci exceeding the threshold.

Both outputs are available for download.