Evidence of a major gene from Bayesian segregation analyses of liability to osteochondral diseases in pigs

H.N. Kadarmideen, L.L.G. Janss

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    Bayesian segregation analyses were used to investigate the mode of inheritance of osteochondral lesions (osteochondrosis, OC) in pigs. Data consisted of 1163 animals with OC and their pedigrees included 2891 animals. Mixed-inheritance threshold models (MITM) and several variants of MITM, in conjunction with Markov chain Monte Carlo methods, were developed for the analysis of these (categorical) data. Results showed major genes with significant and substantially higher variances (range 1.384¿37.81), compared to the polygenic variance (). Consequently, heritabilities for a mixed inheritance (range 0.65¿0.90) were much higher than the heritabilities from the polygenes. Disease allele frequencies range was 0.38¿0.88. Additional analyses estimating the transmission probabilities of the major gene showed clear evidence for Mendelian segregation of a major gene affecting osteochondrosis. The variants, MITM with informative prior on , showed significant improvement in marginal distributions and accuracy of parameters. MITM with a "reduced polygenic model" for parameterization of polygenic effects avoided convergence problems and poor mixing encountered in an "individual polygenic model." In all cases, "shrinkage estimators" for fixed effects avoided unidentifiability for these parameters. The mixed-inheritance linear model (MILM) was also applied to all OC lesions and compared with the MITM. This is the first study to report evidence of major genes for osteochondral lesions in pigs; these results may also form a basis for underpinning the genetic inheritance of this disease in other animals as well as in humans.
    Original languageEnglish
    Pages (from-to)1195-1206
    Issue number3
    Publication statusPublished - 2005


    • quantitative trait loci
    • binary traits
    • meat quality
    • dairy-cattle
    • regression
    • model
    • inheritance
    • parameters
    • inference
    • designs


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