Abstract
With the rapid advancement of high throughput sequencing, large numbers of genetic markers can be readily and cheaply acquired, but most current software packages for genetic map construction cannot handle such dense input. Modern computer architectures and server farms represent untapped resources that can be used to enable higher marker densities to be processed in tractable time. Here we present a pipeline using a modified version of OneMap that parallelizes over bottleneck functions and achieves substantial speedups for producing a high density linkage map (N = 20,000). Using simulated data we show that the outcome is as accurate as the traditional pipeline. We further demonstrate that there is a direct relationship between the number of markers used and the level of deviation between true and estimated order, which in turn impacts the final size of a genetic map.
Highlights
Genetic linkage maps are constructed to determine the relative order and distance between loci on a chromosome
BatchMap is significantly faster than OneMap
By using a simulated dataset containing over 1,000 markers per chromosome and over 700 markers for each pseudo-testcross we show that the BatchMap is as accurate as the original version of OneMap but substantially faster for high-density maps
Summary
Genetic linkage maps are constructed to determine the relative order and distance between loci on a chromosome. These maps can, among other things, be used for association genetics and marker assisted breeding by linking phenotypic traits to regions and genes in the map (QTL mapping), to improve fragmented genome assemblies by ordering and orienting scaffolds along chromosomes [1, 2], or to analyze genome synteny between related species [2]. Constructing a genetic map is often a great challenge due to the computational effort involved and, until recently, the developmental cost and time investment required to identify reliable markers.
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