Characterization of milk fatty acids based on genetic and herd parameters

Heck, J.M.L.; Valenberg, H.J.F. van; Bovenhuis, H.; Dijkstra, J.; Hooijdonk, Toon C.M. van

doi:10.1017/s0022029911000641

Cited by 31 publications

(28 citation statements)

References 36 publications

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“…A total of 47 individual fatty acids, 13 groups of fatty acids, and 5 desaturation indices were evaluated. The phenotypic variations observed for the milk yield, milk fat content, and relative proportions of individual fatty acids (Table 1) are in line with the results from previous studies in Holstein cows (Moate et al, 2007;Bobe et al 2008;Heck et al, 2012) and other breeds, including Brown Swiss (Kelsey et al, 2003;Soyeurt et al, 2007;Vlaeminck et al, 2006). Likewise, the relative proportions of the SFA, MUFA, PUFA, and stearoyl-CoA desaturase (SCD) indices (Table 2) were consistent with data previously reported by Soyeurt et al (2006) and Garnsworthy et al (2010).…”

Section: Milk Fatty Acid Profile Of Brown Swiss Cowssupporting

confidence: 92%

“…High herd-date effects and low heritability estimates were also found for odd-chain fatty acids and BCFA (Table 3), which is consistent with the existing literature (Vlaeminck et al, 2006;Patel et al, 2013). A previous study showed that the herd-date also strongly influenced LCFA, trans fatty acids, C18:1 trans-11 (VA), and C18:2 cis-9,trans-11 (RA), likely reflecting the effect of dietary factors on the contents of these fatty acids in milk fat (Heck et al, 2012). In Holstein cattle, high herd-date effects were reported for VA and RA (Stoop et al, 2008;Duchemin et al 2013), and the heritability estimates for VA and RA ranged from null to bigger than 0.4 (Bobe et al, 2008, Stoop et al, 2008Mele et al, 2009).…”

Section: Heritability and Herd-date Effect For Milk Fatty Acid Profilesupporting

confidence: 90%

“…Gas chromatography has some major advantages over FTIR, including higher precision and accuracy in measuring a large number of fatty acids, even those present at low concentrations in milk fat (Rutten et al, 2010). Few studies have used GC to examine how genetic and herd factors affect the milk profile of 23 to 26 fatty acid traits: namely Stoop et al (2008), Garnsworthy et al (2010), and Heck et al (2012). Only Bilal et al (2014) reported data on 33 fatty acids and also evaluated all the genetic parameters based on GC analysis for the Holstein Friesian breed.…”

Section: Genetic and Environmental Relationships Of Detailed Milk Fatmentioning

confidence: 98%

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Genetic and environmental relationships of detailed milk fatty acids profile determined by gas chromatography in Brown Swiss cows

Pegolo

Cecchinato

Casellas

et al. 2016

Journal of Dairy Science

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The aim of this study was to characterize the profile of 47 fatty acids, including conjugated linoleic acid (CLA), 13 fatty acid groups, and 5 Δ(9)-desaturation indices in milk samples from Brown Swiss cows. The genetic variation was assessed and the statistical relevance of the genetic background for each trait was evaluated using the Bayes factor test. The additive genetic, herd-date, and residual relationships were also estimated among all single fatty acids and groups of fatty acids. Individual milk samples were collected from 1,158 Italian Brown Swiss cows and a detailed analysis of fat percentages and milk fatty acid compositions was performed by gas chromatography. Bayesian animal models were used for (co)variance components estimation. Exploitable genetic variation was observed for most of the de novo synthesized fatty acids and saturated fatty acids, except for C4:0 and C6:0, whereas long-chain fatty acids and unsaturated fatty acids (including CLA) were mainly influenced by herd-date effects. Herd-date effect explained large portions of the total phenotypic variance for C18:2 cis-9,cis-12 (0.668), C18:3 cis-9,cis-12,cis-15 (0.631), and the biohydrogenation and elongation products of these fatty acids. The desaturation ratios showed higher heritability estimates than the individual fatty acids, except for CLA desaturation index (0.098). Among the medium-chain fatty acids, C12:0 had greater heritability than C14:0 (0.243 vs. 0.097, respectively). Both C14:0 and C16:0 showed negative additive genetic correlations with the main monounsaturated and polyunsaturated fatty acids of milk fat, suggesting that their synthesis in the mammary gland may be influenced by the presence of unsaturated fatty acids. No correlation was observed between C4:0 and the other short-chain fatty acids (except for C6:0), confirming the independence of C4:0 from de novo mammary fatty acid synthesis. Among the genetic correlations dealing with potentially beneficial fatty acids, C18:0 was positively correlated with vaccenic and rumenic acids and negatively with linoleic acid. Finally, fatty acids C6:0 through C14:0 showed relevant correlations due to unknown environmental effects, suggesting the potential existence of genetic variances in micro-environmental sensitivity. This study allowed us to acquire new knowledge about the genetic and the environmental relationships among fatty acids. Likewise, the existence of genetic variation for most of de novo synthetized fatty acids and saturated fatty acids was also observed. Overall, these results provide useful information to combine feeding with genetic selection strategies for obtaining a desirable milk fatty acids profile, depending on the origin of fatty acids in milk.

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Section: Milk Fatty Acid Profile Of Brown Swiss Cowssupporting

confidence: 92%

Section: Heritability and Herd-date Effect For Milk Fatty Acid Profilesupporting

confidence: 90%

Section: Genetic and Environmental Relationships Of Detailed Milk Fatmentioning

confidence: 98%

See 1 more Smart Citation

Genetic and environmental relationships of detailed milk fatty acids profile determined by gas chromatography in Brown Swiss cows

Pegolo

Cecchinato

Casellas

et al. 2016

Journal of Dairy Science

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show abstract

“…But Heck et al (2012) suggested that a part of these two FAs derives from the blood, meaning that the influence of animal’s diet also is of importance. Similarities between IC and RCC also agree with this as they were on the same diet.…”

Section: Discussionmentioning

confidence: 99%

Principal Milk Components in Buffalo, Holstein Cross, Indigenous Cattle and Red Chittagong Cattle from Bangladesh

Islam

Alam

Islam

et al. 2014

Asian Australas. J. Anim. Sci

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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, β-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 αs2-, κ-CN and α-lactalbumin, free amino acids, unsaturated fatty acids, Ca and Ca:P. The B milk was also lower in β-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 β-lactoglobulin and naturally occuring peptides. Considering the results obtained including the ratio of αs1-, αs2-, β- and κ-CN, B and RCC milk showed best data both from nutritional and technological aspects.

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“…Based on genetic and herd clustering of milk FA, Heck et al (2012) found that C4:0 and C12:0 differed from the general pattern of the other FA in the group of de novo synthesized even-chain FA. Milk FA C12:0 and C14:0 are not only synthesized de novo in the mammary gland, but can also originate from dietary C12:0 and C14:0 (e.g., van Zijderveld et al, 2011b).…”

Section: Random-effects Model Analysis Correlation Per Unit Of Feedmentioning

confidence: 96%

Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle

Lingen

Crompton

Hendriks

et al. 2014

Journal of Dairy Science

121

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Various studies have indicated a relationship between enteric methane (CH4) production and milk fatty acid (FA) profiles of dairy cattle. However, the number of studies investigating such a relationship is limited and the direct relationships reported are mainly obtained by variation in CH4 production and milk FA concentration induced by dietary lipid supplements. The aim of this study was to perform a meta-analysis to quantify relationships between CH4 yield (per unit of feed and unit of milk) and milk FA profile in dairy cattle and to develop equations to predict CH4 yield based on milk FA profile of cows fed a wide variety of diets. Data from 8 experiments encompassing 30 different dietary treatments and 146 observations were included. Yield of CH4 measured in these experiments was 21.5 ± 2.46 g/kg of dry matter intake (DMI) and 13.9 ± 2.30 g/kg of fat- and protein-corrected milk (FPCM). Correlation coefficients were chosen as effect size of the relationship between CH4 yield and individual milk FA concentration (g/100g of FA). Average true correlation coefficients were estimated by a random-effects model. Milk FA concentrations of C6:0, C8:0, C10:0, C16:0, and C16:0-iso were significantly or tended to be positively related to CH4 yield per unit of feed. Concentrations of trans-6+7+8+9 C18:1, trans-10+11 C18:1, cis-11 C18:1, cis-12 C18:1, cis-13 C18:1, trans-16+cis-14 C18:1, and cis-9,12 C18:2 in milk fat were significantly or tended to be negatively related to CH4 yield per unit of feed. Milk FA concentrations of C10:0, C12:0, C14:0-iso, C14:0, cis-9 C14:1, C15:0, and C16:0 were significantly or tended to be positively related to CH4 yield per unit of milk. Concentrations of C4:0, C18:0, trans-10+11 C18:1, cis-9 C18:1, cis-11 C18:1, and cis-9,12 C18:2 in milk fat were significantly or tended to be negatively related to CH4 yield per unit of milk. Mixed model multiple regression and a stepwise selection procedure of milk FA based on the Bayesian information criterion to predict CH4 yield with milk FA as input (g/100g of FA) resulted in the following prediction equations: CH4 (g/kg of DMI)=23.39 + 9.74 × C16:0-iso - 1.06 × trans-10+11 C18:1 - 1.75 × cis-9,12 C18:2 (R(2) = 0.54), and CH4 (g/kg of FPCM) = 21.13 - 1.38 × C4:0 + 8.53 × C16:0-iso - 0.22 × cis-9 C18:1 - 0.59 × trans-10+11 C18:1 (R(2) = 0.47). This indicated that milk FA profile has a moderate potential for predicting CH4 yield per unit of feed and a slightly lower potential for predicting CH4 yield per unit of milk.

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Characterization of milk fatty acids based on genetic and herd parameters

Cited by 31 publications

References 36 publications

Genetic and environmental relationships of detailed milk fatty acids profile determined by gas chromatography in Brown Swiss cows

Genetic and environmental relationships of detailed milk fatty acids profile determined by gas chromatography in Brown Swiss cows

Principal Milk Components in Buffalo, Holstein Cross, Indigenous Cattle and Red Chittagong Cattle from Bangladesh

Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle

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