Linkage maps are used to identify the location of genes responsible for
traits and diseases. New sequencing techniques have created opportunities to
substantially increase the density of genetic markers. Such revolutionary
advances in technology have given rise to new challenges, such as creating
high-density linkage maps. Current multiple testing approaches based on
pairwise recombination fractions are underpowered in the high-dimensional
setting and do not extend easily to polyploid species. We propose to construct
linkage maps using graphical models either via a sparse Gaussian copula or a
nonparanormal skeptic approach. Linkage groups (LGs), typically chromosomes,
and the order of markers in each LG are determined by inferring the conditional
independence relationships among large numbers of markers in the genome.
Through simulations, we illustrate the utility of our map construction method
and compare its performance with other available methods, both when the data
are clean and contain no missing observations and when data contain genotyping
errors and are incomplete. We apply the proposed method to two genotype
datasets: barley and potato from diploid and polypoid populations,
respectively. Our comprehensive map construction method makes full use of the
dosage SNP data to reconstruct linkage map for any bi-parental diploid and
polyploid species. We have implemented the method in the R package netgwas.Comment: 25 pages, 7 figure