Detection of multiple QTL with epistatic effects under a mixed inheritance model in an outbred population

Akira Narita, Yoshiyuki Sasaki

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


A quantitative trait depends on multiple quantitative trait loci (QTL) and on the interaction between two or more QTL, named epistasis. Several methods to detect multiple QTL in various types of design have been proposed, but most of these are based on the assumption that each QTL works independently and epistasis has not been explored sufficiently. The objective of the study was to propose an integrated method to detect multiple QTL with epistases using Bayesian inference via a Markov chain Monte Carlo (MCMC) algorithm. Since the mixed inheritance model is assumed and the deterministic algorithm to calculate the probabilities of QTL genotypes is incorporated in the method, this can be applied to an outbred population such as livestock. Additionally, we treated a pair of QTL as one variable in the Reversible jump Markov chain Monte Carlo (RJMCMC) algorithm so that two QTL were able to be simultaneously added into or deleted from a model. As a result, both of the QTL can be detected, not only in cases where either of the two QTL has main effects and they have epistatic effects between each other, but also in cases where neither of the two QTL has main effects but they have epistatic effects. The method will help ascertain the complicated structure of quantitative traits.

Original languageEnglish
Pages (from-to)415-433
Number of pages19
JournalGenetics Selection Evolution
Issue number4
Publication statusPublished - 2004 Jul
Externally publishedYes


  • Bayesian inference
  • Epistasis
  • Mixed inheritance model
  • Multiple QTL
  • Outbred population

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Animal Science and Zoology
  • Genetics


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