A tetrasomic inheritance model and likelihood-based method for mapping quantitative trait loci in autotetraploid species

Jing Chen, Lindsey Leach, Jixuan Yang, Fengjun Zhang, Qin Tao, Zhenyu Dang, Yue Chen, Zewei Luo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
141 Downloads (Pure)

Abstract

Dissecting the genetic architecture of quantitative traits in autotetraploid species is a methodologically challenging task, but a pivotally important goal for breeding globally important food crops, including potato and blueberry, and ornamental species such as rose. Mapping quantitative trait loci (QTLs) is now a routine practice in diploid species but is far less advanced in autotetraploids, largely due to a lack of analytical methods that account for the complexities of tetrasomic inheritance. We present a novel likelihood-based method for QTL mapping in outbred segregating populations of autotetraploid species. The method accounts properly for sophisticated features of gene segregation and recombination in an autotetraploid meiosis. It may model and analyse molecular marker data with or without allele dosage information, such as that from microarray or sequencing experiments. The method developed outperforms existing bivalent-based methods, which may fail to model and analyse the full spectrum of experimental data, in the statistical power of QTL detection, and accuracy of QTL location, as demonstrated by an intensive simulation study and analysis of data sets collected from a segregating population of potato (Solanum tuberosum). The study enables QTL mapping analysis to be conducted in autotetraploid species under a rigorous tetrasomic inheritance model.

Original languageEnglish
Number of pages12
JournalNew Phytologist
Early online date8 Jan 2020
DOIs
Publication statusE-pub ahead of print - 8 Jan 2020

Keywords

  • Solanum tuberosum
  • autotetraploids
  • double reduction
  • mixed chromosome pairing
  • quantitative trait locus (QTL)

ASJC Scopus subject areas

  • Physiology
  • Plant Science

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