Bayesian inference on major loci in related multigeneration selection lines of laying hens

Analysis of the existence of major genes affecting alpaca fiber traits

The aim of this study was to determine the presence of major genes for fiber diameter (FD), SD of FD (SDFD), CV of FD, and comfort factor (CF) in Huacaya (HU) and Suri (SU) Peruvian alpaca breeds. Bayesian segregation analyses with relaxed transmission probabilities were performed using 1,906 and 6,592 available records for SU and HU breeds. Evidence for the presence of major genes was statistically supported when the 95% posterior density did not include zero. Significant major genes were found associated with decreased FD, SDFD, CV values, and increased CF values. Additive effects of the major genes were 4.18 and 4.23 μm for FD, 1.67 and 1.61 μm for SDFD, 3.32 and 3.76% for CV, and 15.03 and 14.90% for CF in HU and SU breeds, respectively. Dominance effects were -1.98 and -2.03 μm for FD, -0.88 and -1.11 μm for SDFD, -1.37 and -2.17% for CV, and 13.0 and 11.8% for CF in HU and SU breeds, respectively. Major gene variance was larger than the polygenic variance for all traits. Major gene allelic frequencies for FD, SDFD, and CV ranged from 0.81 to 0.86 for HU breed and from 0.70 to 0.77 for the SU breed and were 0.24 and 0.36, respectively, for CF. It can be concluded that a major gene affecting these traits could be segregating. Then, molecular identification and monitoring of animals carrying favorable genes throughout the worldwide alpaca population would allow for a quick genetic improvement.

Investigation of major gene for milk yield, milking speed, dry matter intake, and body weight in dairy cattle

The main aim of this study was to determine if there exist any major gene for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) recorded at various stages of lactation in first-lactation dairy cows (2543 observations from 320 cows) kept at the research farm of the Swiss Federal Institute of Technology between April 1994 and April 2004. Data were modelled based a simple repeatability covariance structure and analysed by using Bayesian segregation analyses. Gibbs sampling was used to make statistical inferences on posterior distributions; inferences were based on a single run of the Markov chain for each trait with 500,000 samples, with each 10th sample collected because of the high correlation among the samples. The posterior mean (+/-SD) of major gene variance was 2.61 (+/-2.46) for MY, 0.83 (+/-1.26) for MS, 4.37 (+/-2.34) for DMI, and 2056.43 (+/-665.67) for BW. Highest posterior density regions for 3 of the 4 traits did not include 0 (except MS), which supported the evidence for major gene. With additional tests for agreement with Mendelian transmission probabilities, we could only confirm the existence of a major gene for MY, but not for MS, DMI, and BW. Expected Mendelian transmission probabilities and their model fits were also compared.

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

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 (sigmau2). 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 sigmau2, 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.