Abstract
AbstractModern SNP genotyping technologies allow to measure the relative abundance of different alleles for a given locus and consequently to estimate their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels in a sequence of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters using the maximum-likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results demonstrate the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations.Author summaryIn this paper, we present a complete multilocus solution based on hidden Markov models to estimate recombination fractions and infer the linkage phase configuration in full-sib mapping populations with even ploidy levels under random chromosome segregation. We also present an efficient pairwise loci analysis to be used in cases were the multilocus analysis becomes compute-intensive.
Publisher
Cold Spring Harbor Laboratory
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