De novo construction of polyploid linkage maps using discrete graphical models

TL;DR

This paper introduces a novel method for constructing high-density linkage maps in polyploid species using graphical models, addressing limitations of traditional approaches in high-dimensional genetic data analysis.

Contribution

It presents a new graphical model-based approach for linkage map construction applicable to both diploid and polyploid species, implemented in the R package netgwas.

Findings

Effective in high-dimensional settings with large marker data

Performs well with data containing errors or missing values

Successfully applied to barley and potato datasets

Abstract

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.