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

Narita Akira, Sasaki, Sasaki Yoshiyuki

Description

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.

Sujets

• Bayesian inference
• Epistasis

Informations

• Publié par: biomed
• Publié le: 01 janvier 2004
• Nombre de lectures: 5
• Langue: English

Extrait

Genet. Sel. Evol. 36 (2004) 415–433 c  INRA, EDP Sciences, 2004 DOI: 10.1051 / gse:2004009

415 Original article Detection of multiple QTL with epistatic e ff ects under a mixed inheritance model in an outbred population Akira N  , Yoshiyuki S  ∗ Laboratory of Animal Breeding and Genetics, Division of Applied Biosciences, Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan (Received 11 August 2003; accepted 12 February 2004)

Abstract – A quantitative trait depends on multiple quantitative trait loci (QTL) and on the in- teraction between two or more QTL, named epist asis. 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 su ffi ciently. The ob- jective of the study was to propose an integrated method to de tect 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 simultane- ously 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 e ff ects and they have epistatic e ff ects be- tween each other, but also in cases where neither of the two QTL has main e ff ects but they have epistatic e ff ects. The method will help ascertain the complicated structure of quantitative traits. Bayesian inference / multiple QTL / epistasis / outbred population / mixed inheritance model

1. INTRODUCTION

It may be more realistic that interlocus interactions (epistasis) between two or more quantitative trait loci (QTL), as well as the main e ff ects of QTL them- selves, play an important role in expressing a quantitative trait, and the im- portance of exploring epistatic e ff ects has been discussed recently [17, 30]. ∗ Corresponding author: [e-mail]