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Data including 219 105 test day records of 22 569 first parity Holstein cows in 56 herds were analysed for milk yield, fat content and protein content. Legendre polynomials were used in a random regression model to explain traits curves, additive genetic and permanent environmental effects along days in milk (DIM). Legendre polynomials of order 3 were used to describe additive genetic effects on the traits. For permanent environmental effects, for milk the order of 6 and for fat and protein content the order of 4 were used. Heterogeneity of residual variance was considered. Restricted maximum likelihood (REML) methodology was used to estimate the required parameters. Variations in genetic, permanent environment and residual effects and heritability changes along DIM were computed and illustrated. Daily heritability estimates averaged as 0.22, 0.14 and 0.23 for milk, fat and protein contents, respectively. Minimum correlations between DIM for additive genetic effects were as 0.49, −0.01 and 0.34 for milk, fat and protein contents. There were higher genetic correlations between test day milk yield and protein content compared to fat content. The genetic trend of milk yield has increased over the years from 1971 to 2005, while the genetic trend for fat and protein content declined.

Genetic variation in three Iranian pelt sheep breeds namely: Gray Shiraz, Zandi and Karakul were investigated using fifteen microsatellite loci. Genomic DNA was extracted from 360 blood samples by extraction kits and salting-out procedure with some modifications. The total number of alleles ranged from 6 to12 in loci. The fifteen tested loci were all polymorphic in the three breeds. The average direct count of heterozygosity overall loci in each tested breed was more than the expected heterozygosity. Tests of genotype frequencies for deviation from the Hardy-Weinberg equilibrium (HWE) were performed at each locus of overall breeds and revealed significant departure from HWE (P < 0.001) due to heterozygote excess. Polymorphism information content value in Gray Shiraz, Zandi and Karakul were 0.815, 0.808 and 0.808, respectively. Rate of inbreeding within the three breeds was not noticeable (global Fis = - 0.19). Low genetic differentiation was detected by estimation of Fst index between all pairs of breeds. Results showed that high level of genetic diversity was observed in pelt sheep. The phylogenetic tree based on Nei distances were drown using the neighbor-joining (NJ) and unweighted pair-group method using an arithmetic average (UPGMA). With both methods, Gray Shiraz and Karakul sheep populations were located together at one cluster and Zandi sheep population at another. The results can be useful in the development of breeding strategy for genetic improvement of pelt sheep in Iran.Key words: Microsatellites, sheep, genetic, diversity.

Very low density apolipoprotein-II (apoVLDL-II) is a major polypeptide component of avian VLDL. The function of apoVLDL-II is the transport of neutral lipids (triacylglycerol) in the form of VLDL in the plasma. The apoVLDLII gene is dormant in embryos, chicks and roosters but can be activated by estrogen. The current study was designed to investigate the association of an apoVLDL-II gene polymorphism on chicken growth and body composition traits. Genomic DNAs were extracted from 400chickens from four different commercial broiler lines. Genotyping for the apoVLDL-II gene by using PCR-RFLP method and SfcI restriction endonuclease showed a mutation in 492-bp fragment located on the first intron. Polymorphism in apoVLDL-II gene was significantly (P < 0.05) associated with body weight at 6 week (BW6), carcass weight (CW), breast muscle weight (BMW), drumstick weight (DW) and wing weight (WINW). No significant difference was observed in the back weight (BAKW) and abdominal fat weight (AFW). This research suggests that apoVLDL-II gene could be a candidate gene that can affect some growth traits in chickens.

In this study, polymorphism of prolactin gene was analyzed as a candidate gene responsible for variation and genetic trends in milk yield and composition traits. Genomic DNAs were extracted from 268 semen samples belonged to Iranian Holstein bulls. Genotyping for the prolactin gene using PCRRFLP technique and RsaI restriction endonuclease showed a mutation in 294-bp fragment located in exon 4. The frequencies of A and G alleles were 0.069 and 0.931, respectively. The allelic substitution effect was significant for milk and protein yield (p < 0.05). The G allele was unfavorable for milk andprotein yield. Genetic trends for all analyzed traits were significant (p < 0.01) and that was progressive for milk, fat and protein yield, but diminishing for fat and protein percent. The effects of prolactin SNP on genetic trends and the difference between genetic trends produced by A and G alleles were notsignificant for all studied traits.

Transforming growth factor-β (TGF-β) polypeptides are members of a large superfamily of growth and differentiation factors that regulate the proliferation and differentiation of a great variety of cell types. The current study was designed to investigate the associations of TGF-β3 gene polymorphism on chicken growth and body composition traits. Genomic DNAs were extracted from 400 chickens from four different commercial broiler lines. Genotyping for the TGF-β3 gene using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method and BslI restriction endonuclease showed a mutation in 294-bp fragment located on the fourth intron of chromosome 5. Polymorphism in TGF-β3 gene was significantly (P < 0.1) associated with breast muscle weight (BMW), abdominal fat weight (AFW), wing weight (WINW), percentage of carcass weight (%CW), percentage of drumstick weight (%DW), percentage of breast muscle weight (%BMW), percentage of back weight (%BAKWT) and percentage of wing weight (%WINW). This research suggests that TGF-β3 gene could be a candidate gene that can affect some body composition traits in chicken.Key words: Broiler lines, body composition traits, transforming growth factor beta 3 (TGF-β3), PCR-RFLP.