TITLE:
Bayesian genetic analysis of milk yield in Tunisian Holstein dairy cattle population
AUTHORS:
Houcine Ilahi, Mohamed Ben Hammouda, Mohamed Houcine Othmane
KEYWORDS:
Bayesian Analysis; Segregation; Major Gene; Genetic Parameters; Milk Yield; Holstein Dairy Cattle
JOURNAL NAME:
Open Journal of Genetics,
Vol.2 No.2,
June
28,
2012
ABSTRACT: The genetic determinism of 305-d milk yield and its genetic parameters were investigated in Tunisian Holstein dairy cattle population through Bayesian segregation analyses using a Monte Carlo Markov Chains (MCMC) method. Data included 49,709 records of 305-d milk yield collected between 1996 and 2003 from 114 dairy herds. The postulated major gene was assumed to be additive biallelic locus with Mendelian transmission probabilities and priors used for variance components were uniform. Gibbs sampling was used to generate a chain of 500,000 samples, which were used to obtain posterior means of genetic parameters. Estimated marginal posterior means ± posterior standard deviations of variance components of milk yield were 402866.28 ± 23629.97, 271256.66 ± 34477.83, 68276.83 ± 233027.62 and 1098855.75 ± 10009.52 for polygenic variance (σ2u), permanent environmental variance (σ2ne), major gene variance (σ2G) and error variance (σ2e), respectively. The main finding of this paper showed the postulated major locus was not significant, since the 95% highest posterior density regions (HPDs95%) of most major gene parameters included 0, and particularly for the major gene variance. Estimated transmission probabilities for the 95% highest posterior density regions (HPDs95%) were overlapped. Genetic parameters of 305-d milk yield were very similar under both mixed inheritance and polygenic models. These results indicated that the genetic determinism of milk yield in Tunisian Holstein dairy cattle population is purely polygenic. Based on 50,000 Gibbs samples, heritability and repeatability estimates using polygenic model were h2 = 0.22 ± 0.012 and r = 0.38 ± 0.006, respectively.