A meta-analysis of heritability estimates for milk fatty acids and their genetic relationship with milk production traits in dairy cows using a random-effects model

Evidence of additive genetic variation for major milk proteins in dairy cows: A meta-analysis

In the past, there have been reports of genetic parameters for milk proteins in various dairy cattle populations. The high variability among genetic parameter estimates has been caused by this. This study aimed to use a random-effects meta-analysis model to compile published estimates of genetic parameter for major milk proteins of α-lactalbumin, β-lactoglobulin, sum of whey proteins, casein, αs1-casein, αs2-casein, β-casein, and κ-casein in dairy cows. The study used a total of 140 heritability and 256 genetic correlation estimates from 23 papers published between 2004 and 2022. The estimated range of milk protein heritability is from 0.284 (for α-lactalbumin in milk) to 0.596 (for sum of whey proteins). The genetic correlation estimates between casein and milk yield, milk fat and protein percentages were -0.461, 0.693, and 0.976, respectively (p < 0.05). The genetic correlation estimates between milk proteins expressed as a percentage of milk were significant and varied from 0.177 (between β-lactoglobulin and κ-casein) to 0.892 (between αs1-casein and αs2-casein). Moderate-to-high heritability estimates for milk proteins and their low genetic associations with milk yield and composition indicated the possibility for improving milk proteins in a genetic selection plan with negligible correlated effects on production traits in dairy cows.

Milk coagulation properties are moderately heritable in dairy cows: a meta-analysis using the random-effects model

This study aimed to conduct a meta-analysis using the random-effects model to merge published genetic parameter estimates for milk coagulation properties (MCP: comprising rennet coagulation time (RCT), curd-firming time (k20), curd firmness 30 min after rennet addition (a30), titrable acidity (TA) and milk acidity or pH) in dairy cows. Overall, 80 heritability estimates and 157 genetic correlations from 23 papers published between 1999 and 2020 were used. The heritability estimates for RCT, a30, k20, TA, and pH were 0.273, 0.303, 0.278, 0.189 and 0.276, respectively. The genetic correlation estimates between RCT-a30, RCT-pH, and RCT-TA were 0.842, 0.549 and -0.565, respectively. Genetic correlation estimates between RCT and production traits were generally low and ranged from -0.142 (between RCT and casein content) to 0.094 (between RCT and somatic cell score). Moderate and significant genetic correlations were observed between a30-pH (-0.396) and a30-TA (0.662). Also, the genetic correlation estimates between a30 and production traits were low to moderate and varied from -0.165 (between a30 and milk yield) to 0.481 (between a30 and casein content). Genetic correlation estimates between pH and production traits were low and varied from -0.190 (between pH and milk protein percentage) to 0.254 (between pH and somatic cell score). The results of this meta-analysis indicated the existence of additive genetic variation for MCP that could be used in genetic selection programs for dairy cows. Because of the moderate heritability of MCP and small genetic correlations with production traits, it could be possible to improve MCP with negligible correlated effects on production traits.

A meta-analysis of the genetic contribution to greenhouse gas emission in sheep

The objective of this study was to use a random-effects model of meta-analysis to merge various heritability estimates of different gas emission traits (methane yield [METY], methane production [METP], carbon dioxide production [CO₂ ], the sum of carbon dioxide and methane production [METP + CO₂ ], METP METP + CO 2 ratio, and oxygen consumption [O₂ ]) and their genetic association with growth and partial efficiency traits in sheep. A total of 53 genetic correlations and 47 heritability estimates from 13 scientific articles were used in the meta-analysis. The included papers were published between 2010 and 2022. To measure heterogeneity, Chi-square (Q) test was performed, and the I² statistic was determined. The average heritability estimates for the studied traits were low to moderate and ranged from 0.137 (for METY) to 0.250 (for METP + CO₂ ). The heterogeneity test of heritability estimates indicated that heritability estimates for METY, O₂ consumption, and METP METP + CO 2 had low Q values and non-significant heterogeneity (p > 0.10). However, the average heritability estimates for other traits experienced significant heterogeneities (p < 0.10). The genetic correlation estimate between METP with O₂ was -0.597 (p < 0.05), but its genetic correlations with other gas traits ranged from 0.593 (with METP + CO₂ ) to 0.653 (CO₂ ; p < 0.05). Also, mean estimates of genetic correlation between METP with live weight (LW), feed intake (FI), and residual feed intake (RFI) were 0.719, 0.598, and 0.408, respectively. The genetic correlations of CO₂ with performance traits varied from 0.641 (with RFI) to 0.833 (with FI; p < 0.05). This meta-analysis showed gas emission traits in sheep are under low-to-moderate genetic control. The average genetic parameter estimates obtained in this study could be considered in the genetic selection programmes for sheep, especially when there is no access to accurate phenotypic records or genetic parameter estimates for gas emission traits.

A meta-analysis of the genetic contribution estimates to major indicators for ketosis in dairy cows

The present study aimed to perform a meta-analysis using the random-effects model to merge published genetic parameter estimates for major indicators of ketosis [milk concentrations of acetone (ACETm) and β-hydroxybutyrate (BHBAm), and blood concentration of β-hydroxybutyrate (BHBAb)] in dairy cows. Overall, 51 heritability estimates and 130 genetic correlations from 19 papers published between 2012 and 2022 were used in this study. The average heritability estimates for ACETm, BHBAm, and BHBAb were 0.164, 0.123, and 0.141, respectively. The genetic correlation estimates between BHBAm and milk yield (MY), milk protein percentage (PP), and body condition score (BCS) were negative and moderate (-0.252, -0.200, and - 0.314, respectively). Genetic correlation estimates between BHBAm and milk fat percentage (FP), milk fat to protein ratio (FPR), and ketosis (KET) were moderate to high (0.411, 0.512, and 0.614, respectively). The genetic correlation estimates between BHBAb and MY and FP were low and equal to 0.128 and 0.035, respectively. The genetic correlation estimates between ACETm-MY and ACETm-PP were negative and moderate (-0.374 and - 0.398, respectively). Estimates of genetic correlation between ACETm and FP, FPR, and KET were moderate to high (0.455, 0.626, and 0.876, respectively). The results of this meta-analysis indicated the existence of additive genetic variation for ketosis indicator metabolites which could be exploited in genetic selection programs to reduce ketosis in dairy cows. Moreover, the results propose that selection for lower concentrations of indicator traits could be an effective plan for indirect improvement of production and reproduction performance, and health in dairy cows.

A meta-analysis of genetic parameter estimates for milk and serum minerals in dairy cows

This study aimed to conduct a meta-analysis based on a random-effects model to combine different published heritability estimates and genetic correlations for milk and serum minerals in dairy cows. In total, 59 heritability and 25 genetic correlation estimates from 12 articles published between 2009 and 2021 were used. The heritability estimates for milk macro-minerals were moderate to high and ranged from 0.311 (for Na) to 0.420 (for Ca). On the other hand, milk micro-minerals had lower heritabilities with a range from 0.013 (for Fe) to 0.373 (for Zn). The heritability estimates for serum macro-minerals were generally low and varied from 0.126 (for K) to 0.206 (for Mg). The estimates of genetic correlation between milk macro-minerals varied from -0.024 (between Na and K) to 0.625 (between Mg and P). The genetic correlations of milk Ca and P with milk yield were -0.171 and -0.211, respectively. The estimates of genetic parameters reported in this meta-analysis study are appropriate to utilize in breeding plans when valid estimates are not available for milk minerals in dairy cow populations.

Associations among Farm, Breed, Lactation Stage and Parity, Gene Polymorphisms and the Fatty Acid Profile of Milk from Holstein, Simmental and Their Crosses

This study aimed to analyze the factors affecting the fatty acid (FA) profile in cow's milk. The effects of a farm, lactation parity and stage, breed and polymorphisms in the AGPAT6, DGAT1, LEP, FASN and SCD1 genes were evaluated. A total of 196 Holstein cows, 226 Simmental cows and seven crosses were sampled 751 times. The cows were kept at five farms and were in the first up to the sixth lactation, and 49 individual FAs and 11 groups were analyzed. The farm significantly affected the proportion of all FAs except for C16:1n-7c and isoC14:0. Additionally, the lactation stage was significant for most FAs, and the opposite was true for lactation parity. The effect of the breed was negligible. For the gene polymorphisms, the SCD1&nbsp;TT genotype exceeded the CC in C10:0, C12:0, C14:0, C16:1n-7c and C18:2, and the opposite was true for C10:1, C12:1, C14:1n-5c, isoC17:0, C16:1 and C18:1, i.e., the TT genotype was higher for saturated FAs, and the CT genotype was higher for monounsaturated FAs. The results hint at the intermediary heredity of the SCD1 gene. The FASN gene was strongly associated with four FAs and branched-chain FAs, and genotype AG was better than GG. LEP was significant for five individual FAs and branched-chain FAs. The differences in FA composition among genotypes were rather small, which could lead to overestimation of the effect and needs to be considered in the next research.