The pregnane X receptor (PXR; NR1I2) and the constitutive androstane receptor (CAR; NR1I3) are members of the nuclear receptor superfamily that act as transcription factors. They regulate the expression of several important genes that encode enzymes responsible for the metabolism of endogenous hormones and exogenous substances. We determined the nucleotide sequences of porcine CARand PXR genes bp using cDNA clones derived from porcine cDNA libraries. The porcine CAR gene has a 1407 bp mRNA, which contains a 216 bp 5'-untranslated region (UTR), a 144 bp 3'-UTR, and a 1047 bp coding region that encodes 348 amino acids. The mRNA sequence of porcine PXR is composed of 2567 bp that contains a 273 bp 5'-UTR, a 1266 bp putative coding region encoding 421 amino acids, and a 1028 bp 3'-UTR. The porcine CAR and porcine PXR proteins showed a high degree of sequence identity in their DNA-binding domain (DBD) and ligand-binding domain (LBD) with 80% - 90% amino acid identity in comparison with those of humans. However, the AB domain of the porcine CAR and PXR proteins is considerably different in comparison with that of humans. Eighty six percentage and 43 % sequence identity is found between porcine and human CAR proteins and porcine and human PXR proteins, respectively. The mRNA expression of both porcine CAR and PXR was detected in liver, small intestine, and kidney bp using reverse-transcription (RT) -PCR.
The effects of initial solution and order of observations on the solving of mixed model equations (MME) by traditional iterative algorithms-Gauss-Seidel, successive over-relaxation (SOR), and second-order Jacobi-and by preconditioned conjugate gradient algorithms-scaled conjugate gradient (SCG) and incomplete Cholesky conjugate gradient (ICCG) simulation were investigated by using data in a computer. With SOR, the use of adjusted initial solutions based on phenotype and heritability of the trait and order of the animal within the trait in the MME was effective for fast convergence. Adjustment of initial solutions was not an advantage in preconditioned conjugate gradient algorithms. Of the five algorithms, ICCG required the lowest number of iterations until convergence. However, ICCG needed the largest central processing unit (CPU) time to calculate one round of iteration. The SCG algorithm was an attractive alternative for solving MME because it required the second fewest rounds of iteration and had the shortest CPU time per round of iteration until convergence.
To predict breeding values in dairy cattle, preconditioned conjugate gradient (PCG) algorithm has become of interest lately as a means of solving large sets of mixed model equations (MME) . Thereby, the effects of data structure on the solving of MME by PCG algorithms were investigated by using data in a computer simulation. A large number of animals did not cause slow convergence if the data structure (e.g. number of generations and mating ratio) was fixed. The numbers of breeding animals and their progeny affected the structure of the A-inverse matrix, but selection methods did not. An increasing number of iterations seemed to be due to an imbalance of nonzero elements in the A-inverse matrix, which resulted in the imbalance of nonzero elements in the MME. The number of iterations until convergence was roughly constant for two-trait animal model when the heritability of a trait was constant regardless of the heritability of another trait, and it was the smallest when the heritabilities of both traits were about 0.5 to 0.6. Increasing absolute values of genetic and environmental correlations resulted in slow convergence. In particular, genetic correlation affected convergence strongly.