Linkage maps are important tools for genetic research . 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. Whereas most tetraploid potato linkage maps until now have been created either from diploid populations or from a subset of marker types, our comprehensive map construction method will be able to deal with realistic data settings for any biparental diploid and polyploid species containing arbitrary marker types . We have implemented the method in the R package netgwas which is freely available at URL
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 .
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important tools for genetic research
used to identify the location of genes responsible for traits and diseases
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Whereas most tetraploid potato linkage maps until now have been created either from diploid populations or from a subset of marker types, our
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will be able to deal with realistic data settings for any biparental
makes full use of the dosage SNP data to reconstruct linkage map for any bi-parental
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containing arbitrary marker types
R
which is freely available at URL
.