Resolving microsatellite genotype ambiguity in populations of allopolyploid and diploidized autopolyploid organisms using negative correlations between allelic variables

A major limitation in the analysis of genetic marker data from polyploid organisms is non-Mendelian segregation, particularly when a single marker yields allelic signals from multiple, independently segregating loci (isoloci). However, with markers such as microsatellites that detect more than two alleles, it is sometimes possible to deduce which alleles belong to which isoloci. Here we describe a novel mathematical property of codominant marker data when it is recoded as binary (presence/absence) allelic variables: under random mating in an infinite population, two allelic variables will be negatively correlated if they belong to the same locus, but uncorrelated if they belong to different loci. We present an algorithm to take advantage of this mathematical property, sorting alleles into isoloci based on correlations, then refining the allele assignments after checking for consistency with individual genotypes. We demonstrate the utility of our method on simulated data, as well as a real microsatellite dataset from a natural population of octoploid white sturgeon (Acipenser transmontanus). Our methodology is implemented in the R package O_SCPLOWPOLYSATC_SCPLOW version 1.5.