QTL mapping in autotetraploids using SNP dosage information

Christine A. Hackett (Lead / Corresponding author), John E. Bradshaw, Glenn J. Bryan

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    Abstract

    Dense linkage maps derived by analysing SNP dosage in autotetraploids provide detailed information about the location of, and genetic model at, quantitative trait loci. Recent developments in sequencing and genotyping technologies enable researchers to generate high-density single nucleotide polymorphism (SNP) genotype data for mapping studies. For polyploid species, the SNP genotypes are informative about allele dosage, and Hackett et al. (PLoS ONE 8:e63939, 2013) presented theory about how dosage information can be used in linkage map construction and quantitative trait locus (QTL) mapping for an F1 population in an autotetraploid species. Here, QTL mapping using dosage information is explored for simulated phenotypic traits of moderate heritability and possibly non-additive effects. Different mapping strategies are compared, looking at additive and more complicated models, and model fitting as a single step or by iteratively re-weighted modelling. We recommend fitting an additive model without iterative re-weighting, and then exploring non-additive models for the genotype means estimated at the most likely position. We apply this strategy to re-analyse traits of high heritability from a potato population of 190 F1 individuals: flower colour, maturity, height and resistance to late blight (Phytophthora infestans (Mont.) de Bary) and potato cyst nematode (Globodera pallida), using a map of 3839 SNPs. The approximate confidence intervals for QTL locations have been improved by the detailed linkage map, and more information about the genetic model at each QTL has been revealed. For several of the reported QTLs, candidate SNPs can be identified, and used to propose candidate trait genes. We conclude that the high marker density is informative about the genetic model at loci of large effects, but that larger populations are needed to detect smaller QTLs.
    Original languageEnglish
    Number of pages20
    JournalTheoretical and Applied Genetics
    Early online date1 Jul 2014
    DOIs
    Publication statusPublished - 2014

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