Effect of polymorphisms in the leptin, leptin receptor and acyl-CoA: diacylglycerol acyltransferase 1 (DGAT1) genes and genetic polymorphism of milk proteins on bovine milk composition
ABSTRACT The relations between cow genetics and milk composition have gained a lot of attention during the past years, however, generally only a few compositional traits have been examined. The aim of this study was to determine if polymorphisms in the leptin (LEP), leptin receptor (LEPR) and acyl-CoA:diacylglycerol acyltransferase 1 (DGAT1) genes as well as genetic polymorphism of β-casein (β-CN), κ-CN and β-lactoglobulin (β-LG) impact several bovine milk composition traits. Individual milk samples from the Swedish Red and Swedish Holstein breeds were analyzed for components in the protein, lipid, carbohydrate and mineral profiles. Cow alleles were determined on the following SNP: A1457G, A252T, A59V and C963T on the LEP gene, T945M on the LEPR gene and Nt984+8(A-G) on the DGAT1 gene. Additionally, genetic variants of β-CN, κ-CN and β-LG were determined. For both the breeds, the same tendency of minor allele frequency was found for all SNPs and protein genes, except on LEPA1457G and LEPC963T. This study indicated significant (P<0·05) associations between the studied SNPs and several compositional parameters. Protein content was influenced by LEPA1457G (G>A) and LEPC963T (T>C), whereas total Ca, ionic Ca concentration and milk pH were affected by LEPA1457G, LEPA59V, LEPC963T and LEPRT945M. However, yields of milk, protein, CN, lactose, total Ca and P were mainly affected by β-CN (A2>A1) and κ-CN (A>B>E). β-LG was mainly associated with whey protein yield and ionic Ca concentration (A>B). Thus, this study shows possibilities of using these polymorphisms as markers within genetic selection programs to improve and adjust several compositional parameters.
- SourceAvailable from: Dag Ekeberg
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- "However, Patino et al. (2007) reported more P than Ca in different buffalo breeds. Genotypic variation may be a matter of concern as Glantz et al. (2012) found more Ca and P in milk with -CN A 2 than -CN A 1 , and the influence of -CN was A>B>E. Ariota et al. (2007) observed a strong correlation between fresh cheese yield and Ca and P content. "
ABSTRACT: The aim of the present study was to get a total physical and chemical characterization and comparison of the principal components in Bangladeshi buffalo (B), Holstein cross (HX), Indigenous cattle (IC) and Red Chittagong Cattle (RCC) milk. Protein and casein (CN) composition and type, casein micellar size (CMS), naturally occurring peptides, free amino acids, fat, milk fat globule size (MFGS), fatty acid composition, carbohydrates, total and individual minerals were analyzed. These components are related to technological and nutritional properties of milk. Consequently, they are important for the dairy industry and in the animal feeding and breeding strategies. Considerable variation in most of the principal components of milk were observed among the animals. The milk of RCC and IC contained higher protein, CN, beta-CN, whey protein, lactose, total mineral and P. They were more or less similar in most of the all other components. The B milk was found higher in CN number, in the content of alpha(s2)-, kappa-CN and alpha-lactalbumin, free amino acids, unsaturated fatty acids, Ca and Ca:P. The B milk was also lower in beta-lactoglobulin content and had the largest CMS and MFGS. Proportion of CN to whey protein was lower in HX milk and this milk was found higher in p-lactoglobulin and naturally occuring peptides. Considering the results obtained including the ratio of alpha(s1)-, alpha(s2)-, beta- and kappa-CN, B and RCC milk showed best data both from nutritional and technological aspects.Asian Australasian Journal of Animal Sciences 06/2014; 27(6):886-897. DOI:10.5713/ajas.2013.13586 · 0.56 Impact Factor
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ABSTRACT: ContentsDuring the last decades, genetic selection for milk production traits has led to increased fertility and health problems in dairy cattle. The aim of this study was to investigate the impact of three polymorphisms located in the ATP-binding cassette superfamily G member 2 transporter (ABCG2), stearoyl-CoA desaturase 1 (SCD1) and leptin receptor (LEPR) genes on reproductive traits and somatic cell count (SCC). The analysis was conducted on 408 randomly selected cows. The SNPs within the genes (LEPR, ABCG2 and SCD1) were genotyped using the PCR-RFLP method. All three possible genotypes were observed for SCD1-T878C and LEPR-T945M SNPs, but not for ABCG2-Y581S SNP. LEPR-T945M and ABCG2-Y581S SNPs had no statistically significant effect on the studied reproductive traits and SCC. However, SCD1-T878C SNP were negatively and significantly related to pregnancy length, dry days and open days (p < 0.05), which lead to decreased profitability in dairy herds. The results suggest that the T878C SNP of SCD1 might be useful as a DNA marker to decrease reproductive problems and improve production traits in Iranian Holstein dairy cows.Reproduction in Domestic Animals 08/2014; 49(5). DOI:10.1111/rda.12365 · 1.18 Impact Factor
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ABSTRACT: Optimisation of cheese yield is crucial for cheese production; a previous study showed large variations in chymosin-induced coagulation in milk from the second most common Swedish dairy breed, Swedish Red. In the present study, the effect of gross composition, protein composition, total and ionic calcium content, phosphorous content and casein micelle size on chymosin-induced gelation was determined in milk from 98 Swedish Red cows. The study showed that protein content and total calcium content, ionic calcium concentration and casein micelle size were the most important factors explaining the variation of gelation properties in this sample set. Non-coagulating milk was suggested to have lower ionic and total calcium content as well as lower relative concentrations of β-lactoglobulin than coagulating milk. The lower total calcium content in non-coagulating milk poses a problem as the difference was, theoretically, four times larger than the amount of calcium that is normally added in cheese processing.International Dairy Journal 11/2014; 39(1). DOI:10.1016/j.idairyj.2014.06.011 · 2.30 Impact Factor