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This indicates that BST has a strong correlation with AT and RH. Expression equations were estimated as Y (THI) = 31.54 + 0.1085 × X (BST of eyes) and Y (THI) = 30.48 + 0.1147 × X (BST of hindquarters) by simple linear regression analysis in this experiment. Conclusion As a consequence, the upper bound for heat stress estimation can be specified ranging from THI of 65 (eyes) to 70 (hindquarters). From this we can expect a precise feeding system for Korean native cattle in the field.Objective The objective of this study was to characterize the number of loci affecting growth traits and the distribution of single nucleotide polymorphism (SNP) effects on growth traits, and to understand the genetic architecture for growth traits in Hanwoo (Korean cattle) using genome-wide association study (GWAS), genomic partitioning, and hierarchical Bayesian mixture models. Methods GWAS A single-marker regression-based mixed model was used to test the association between SNPs and causal variants. A genotype relationship matrix (GRM) was fitted as a random effect in this linear mixed model to correct the genetic structure of a sire family. Genomic restricted Maximum Likelihood (GREML) and BayesR A priori information included setting the fixed additive genetic variance to a pre-specified value; the first mixture component was set to zero, the second to 0.0001 × σ_g^2 , the third 0.001 × σ_g^2, and the fourth to 0.01 × σ_g^2. BayesR fixed a priori information was not more than 1% of the genetic variance for each of the SNPs affecting the mixed distribution. Results The GWAS revealed common genomic regions of 2 Mb on bovine chromosome 14 (BTA14) and 3 had a moderate effect that may contain causal variants for body weight at 6, 12, 18, and 24 months. This genomic region explained approximately 10% of the variance against total additive genetic variance and body weight heritability at 12, 18, and 24 months. BayesR identified the exact genomic region containing causal SNPs on BTA14, 3, and 22. However, the genetic variance explained by each chromosome or SNP was estimated to be very small compared to the total additive genetic variance. Causal SNPs for growth trait on BTA14 explained only 0.04-0.5% of the genetic variance. Conclusion Segregating mutations have a moderate effect on BTA14, 3, and 19; many other loci with small effects on growth traits at different ages were also identified.Objective The aim of the study was to evaluate the influence of polymorphic loci and other factors on milk performance and the technological properties of milk. Methods The analysis was performed on Simmental and Holstein cows in field conditions. Milk yield in kg, fat and protein percentage and yield were evaluated. Technological properties were evaluated by milk fermentation ability, renneting, and an alcohol test. Polymorphisms in the DGAT1, LEP, FASN, SCD1, CSN2, CSN3 and LGB genes were genotyped, and association analysis was performed. Results The DGAT1 AA genotype was associated with higher milk, protein and fat yields (p0.05). The MM genotype in the LEP gene was associated with a lower protein percentage and the W allele with a higher protein percentage (p0.05). In cows with the FASN GG genotype, the protein percentage was higher, but the A allele was associated with higher milk, protein and fat yields than the G allele. The TT genotype in SCD1 was associated with the lowest milk, protein and fat yields and with the highest milk protein percentage (p0.01). The T allele had higher values than the C allele (p0.05) except for fat percentage. The genotype CSN3 AA was associated with a significantly heightened milk yield; BB was associated with a high protein percentage. The effect of the alleles on the technological properties was not significant. The CSN2 BB genotype was associated with the best alcohol test (p0.01), and the renneting order was inverse. Milk from cows with the CSN2 A1A1 genotype was best in the milk fermentation ability. CSN3 significantly affected the technological properties. Conclusion The findings revealed the potential of some polymorphic loci for use in dairy cattle breeding and for the management of milk quality. In field research, the pivotal role of farms in milk yield, composition and technological properties was confirmed.Objective The aim of this study was to compare overfeeding performance, fatty acid composition, blood chemistry, enzymes and genes expression overfed Xupu and Landes geese. find more Methods Sixty male Xupu geese (80 d) and Landes geese (80 d) were selected. After a period of one-week of pre-overfeeding, Xupu and Landes geese were overfed three meals of 550 g/d and 350 g/d, respectively, of a high-carbohydrate diet in the first week of the overfeeding period. The next week, geese were given four meals of 1,200 g/d and 850 g/d, respectively, over 8-14 d. Finally, geese were given five meals of 1,600 g/d and 1,350 g/d, respectively, for the last two weeks. Results After overfeeding for 28 d Compared with Landes geese, Xupu geese liver weight and liver-to-body weight ratio decreased (P less then 0.05), while final weight, slaughter weight, total weight gain, abdominal fat weight, and feed-to-liver weight ratio increased (P less then 0.05). The levels of methyl elaidate, methyl oleate, eicosenoic, and arachidonic in the liver of Xupu geese significantly increased (P less then 0.05), and the levels of myristic and stearic significantly decreased (P less then 0.05), while methyleicosanoate significantly increased (P less then 0.05). Xupu geese had higher plasma concentrations of TG and VLDL-C (P less then 0.05), and decreased activities of ALT, AST, and LPS (P less then 0.05). Landes geese had higher LPS activity (P less then 0.05), but lower CHE activity (P less then 0.05) when compared with Xupu geese. The mRNA expression levels of FADS, ELOVL1, ELOVL5, and ACAT2 were significantly upregulated (P less then 0.05) in Landes goose when compared with Xupu geese. Conclusions This study demonstrates that the liver production performance of Landes geese was better than that of Xupu geese to some extent, which may be closely related to LPS activity, as well as the expression of FADS, ELOVL1, ELOVL5, and ACAT2.