Quantifying the Power and Precision of QTL Analysis in Autopolyploids Under Bivalent and Multivalent Genetic Models.
New genotyping technologies, offering the possibility of high genetic resolution at low cost, have helped fuel a surge in interest in the genetic analysis of polyploid species. Nevertheless, autopolyploid species present extra challenges not encountered in diploids and allopolyploids, such as polysomic inheritance or double reduction. Here we investigate the power and precision of quantitative trait locus (QTL) analysis in outcrossing autopolyploids, comparing the results of a model that assumes random bivalent chromosomal pairing during meiosis to one that also allows for multivalents and double reduction. Through a series of simulation studies we found that marginal gains in QTL detection power are achieved using the double reduction model when multivalent pairing occurs. However, when exploring the effect of variable genotypic information across parental homologs, we found that both QTL detection power and precision require high and uniform genotypic information contents. This effect far outweighed considerations regarding bivalent or multivalent pairing (and double reduction) during meiosis. We propose that autopolyploid QTL studies be accompanied by both marker coverage information and per-homolog genotypic information coefficients (GIC). Application of these methods to an autotetraploid potato (Solanum tuberosum L.) mapping population confirmed our ability to locate and dissect QTL in highly heterozygous outcrossing autotetraploid populations.
['Bourke|Peter M|PM|0000-0002-0665-6508', 'Hackett|Christine A|CA|', 'Voorrips|Roeland E|RE|0000-0003-4388-9244', 'Visser|Richard G F|RGF|', 'Maliepaard|Chris|C|']
D000465:Algorithms D000483:Alleles D002874:Chromosome Mapping D005838:Genotype D008957:Models, Genetic D011123:Polyploidy D040641:Quantitative Trait Loci D015203:Reproducibility of Results
autopolyploid double reduction Quantitative Trait Locus (QTL) analysis genotypic information coefficient (GIC) Bayesian Information Criterion (BIC)