- D.P. Berry (2)
- P. Boettcher (1)
- S. Brotherstone (1)
- F. Canavesi (1)
- D.P. Casper (1)
- M.P. Coffey (2)
- N. Dana (1)
- J. Dijkstra (1)
- P. Dillon (1)
- J.G. Fadel (1)
- J. France (1)
- Egbert Frank Knol (1)
- A.F. Groen (1)
- Y. Haas de (1)
- B.J. Hayes (1)
- J.M. Hickey (1)
- J. Jamrozik (1)
- E. Kebreab (1)
- L.E. Moraes (1)
- J.E. Pryce (1)
- A. Samore (1)
- Ewa Sell-Kubiak (1)
- A.B. Strathe (1)
- R.F. Veerkamp (5)
- E.H. Waaij van der (1)
- W.J. Wales (1)
- E. Wall (1)
- J.J. Windig (1)
Selecting for changes in average “parity curve” pattern of litter size in Large White pigs
Sell-Kubiak, Ewa ; Knol, Egbert Frank ; Mulder, Herman Arend - \ 2019
Journal of Animal Breeding and Genetics 136 (2019)2. - ISSN 0931-2668 - p. 134 - 148.
random regression - reproduction traits - sows - total number born - within-sow variation
This study aimed to analyse genetic background of variation in reproductive performance between parities of a sow and to investigate selection strategies to change the “parity curve”. Total number born (TNB) recorded in Large White sows was provided by Topigs Norsvin. Analysis with basic (BM) and random regression (RRM) models was done in ASReml 4.1. The BM included only a fixed “parity curve”, while RRM included 3rd order polynomials for additive genetic and permanent sow effects. Parameters from RRM were used in simulations in SelAction 2.1. Based on Akaike information criterion, RRM was a better model for TNB data. Genetic variance and heritability estimates of TNB from BM and RRM were increasing with parity from parity 2. Genetically, parity 1 is the most different from parities 7 to 10, whereas most similar to parities 2 and 3. This indicates presence of genetic variation to change the “parity curve”. Based on simulations, the selection to increase litter size in parity 1 only increases TNB in all parities, but does not change the observed shape of “parity curve”, whereas selection for increased TNB in parity 1 and reduced TNB in parity 5 decreases differences between parities, but also reduces overall TNB in all parities. Changing the “parity curve” will be difficult as the genetic and phenotypic relationships between the parities are hard to overcome even when selecting for one parity.
Mutrivariate and univariate analysis of energy balance data from lactating dairy cows
Moraes, L.E. ; Kebreab, E. ; Strathe, A.B. ; Dijkstra, J. ; France, J. ; Casper, D.P. ; Fadel, J.G. - \ 2015
Journal of Dairy Science 98 (2015)6. - ISSN 0022-0302 - p. 4012 - 4029.
body tissue mobilization - random regression - equation models - milk-production - major advances - genetic merit - growing pigs - net energy - efficiency - expression
The objectives of the study were to develop a multivariate framework for analyzing energy balance data from lactating cows and investigate potential changes in maintenance requirements and partial efficiencies of energy utilization by lactating cows over the years. The proposed model accounted for the fact that metabolizable energy intake, milk energy output, and tissue energy balance are random variables that interact mutually. The model was specified through structural equations implemented in a Bayesian framework. The structural equations, along with a model traditionally used to estimate energetic parameters, were fitted to a large database of indirect calorimetry records from lactating cows. Maintenance requirements and partial efficiencies for both models were similar to values reported in the literature. In particular, the estimated parameters (with 95% credible interval in parentheses) for the proposed model were: net energy requirement for maintenance equal to 0.36 (0.34, 0.38) MJ/kg of metabolic body weight·day; the efficiency of utilizing dietary energy for milk production and tissue gain were 0.63 (0.61, 0.64) and 0.70 (0.68, 0.72), respectively; the efficiency of utilizing body stores for milk production was 0.89 (0.87, 0.91). Furthermore, additional analyses were conducted for which energetic parameters were allowed to depend on the decade in which studies were conducted. These models investigated potential changes in maintenance requirements and partial efficiencies over the years. Canonical correlation analysis was used to investigate the association between changes in energetic parameters with additional dietary and animal characteristics available in the database. For both models, net energy requirement for maintenance and the efficiency of utilizing dietary energy for milk production and tissue gain increased in the more recent decades, whereas the efficiency of utilizing body stores for milk production remained unchanged. The increase in maintenance requirements in modern milk production systems is consistent with the literature that describes increased fasting heat production in cows of higher genetic merit. The increase in utilization of dietary energy for milk production and tissue gain was partially attributed to the changes in dietary composition, in particular to the increase in dietary ether extract to levels closer to currently observed in modern milk production systems. Therefore, the estimated energetic parameters from this study can be used to update maintenance requirements and partial efficiencies of energy utilization in North American feeding systems for lactating cows.
Genomic selection for feed efficiency in dairy cattle
Pryce, J.E. ; Wales, W.J. ; Haas, Y. de; Veerkamp, R.F. ; Hayes, B.J. - \ 2014
Animal 8 (2014)01. - ISSN 1751-7311 - p. 1 - 10.
body condition score - dry-matter intake - 1st 3 lactations - genetic-parameters - beef-cattle - methane production - production traits - energy-balance - live weight - random regression
Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.
Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countries
Banos, G. ; Coffey, M.P. ; Veerkamp, R.F. ; Berry, D.P. ; Wall, E. - \ 2012
Animal 6 (2012)7. - ISSN 1751-7311 - p. 1040 - 1048.
body condition score - daily energy-balance - dry-matter intake - milk-production - genetic merit - feed-intake - random regression - grass intake - cows - yield
This study set out to demonstrate the feasibility of merging data from different experimental resource dairy populations for joint genetic analyses. Data from four experimental herds located in three different countries (Scotland, Ireland and the Netherlands) were used for this purpose. Animals were first lactation Holstein cows that participated in ongoing or previously completed selection and feeding experiments. Data included a total of 60 058 weekly records from 1630 cows across the four herds; number of cows per herd ranged from 90 to 563. Weekly records were extracted from the individual herd databases and included seven traits: milk, fat and protein yield, milk somatic cell count, liveweight, dry matter intake and energy intake. Missing records were predicted with the use of random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. A total of 23 different lactation traits were derived from these records: total milk, fat and protein yield, average fat and protein percentage, average fat-to-protein ratio, total dry matter and energy intake and average dry matter intake-to-milk yield ratio in lactation weeks 1 to 44 and 1 to 15; average milk somatic cell count in lactation weeks 1 to 15 and 16 to 44; average liveweight in lactation weeks 1 to 44; and average energy balance in lactation weeks 1 to 44 and 1 to 15. Data were subsequently merged across the four herds into a single dataset, which was analysed with mixed linear models. Genetic variance and heritability estimates were greater (P <0.05) than zero for all traits except for average milk somatic cell count in weeks 16 to 44. Proportion of total phenotypic variance due to genotype-by-environment (sire-by-herd) interaction was not different (P > 0.05) from zero. When estimable, the genetic correlation between herds ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase power of detection in a genetic analysis and augment the potential reference population in genome-wide association studies, especially of difficult-to-record traits
Genetic and phenotypic parameter estimates for body weights and egg production in Horro chicken of Ethiopia
Dana, N. ; Waaij, E.H. van der; Arendonk, J.A.M. van - \ 2011
Tropical Animal Health and Production 43 (2011)1. - ISSN 0049-4747 - p. 21 - 28.
white-leghorn hens - laying hens - random regression - economic traits - native fowl - selection - performance - poultry - models - breeds
A breeding program has been established in 2008 to improve productivity of Horro chicken, an indigenous population in the western highlands of Ethiopia. The pedigree descended from 26 sires and 260 dams. Body weights were measured every 2 weeks from hatch to 8 weeks then every 4 weeks for the next 8 weeks. Egg production was recorded to 44 weeks of age for one generation. Genetic parameters were estimated using animal model fitted with common environmental effects for growth traits and ignoring common environment for egg production traits. Direct heritabilities ranged from low (0.15¿±¿0.08), for body weight at 6 weeks, to moderate (0.40¿±¿0.23), for hatch weight. Heritabilities of common environmental effects on growth were high at hatch (0.39¿±¿0.10) and remained low afterwards. Age at first egg showed a very low heritability (0.06¿±¿0.15). Heritabilities of egg numbers in the first, second, third, and fourth months of laying were 0.32 (±0.13), 0.20 (±0.16), 0.56 (±0.15), and 0.25 (±0.14), respectively. Heritabilities of cumulative of monthly records of egg numbers were from 0.24¿±¿0.16 (for the first 2 months, EP12) to 0.35¿±¿0.16 (over the 6 months, EP16). Body weight at 16 weeks of age (BW16) has a strong genetic correlation with the cumulative of monthly records: 0.92 (with EP12), 0.69 (with EP36), and 0.73 (with EP16). Besides their strong association, BW16 and EP16 showed higher heritability, relative to their respective trait categories. These two traits seemed to have common genes and utilizing them as selection traits would be expected to improve both egg production and growth performance of local chicken. However, the standard errors of estimates in this study were mostly high indicating that the estimates have low precision. Parameter estimations based on more data are needed before applying the current results in breeding programs
Genetic correlation patterns between somatic cell score and protein yield in the Italian Holstein-Friesian Population
Samore, A. ; Groen, A.F. ; Boettcher, P. ; Jamrozik, J. ; Canavesi, F. ; Bagnato, A. - \ 2008
Journal of Dairy Science 91 (2008). - ISSN 0022-0302 - p. 4013 - 4021.
1st 3 lactations - test-day model - clinical mastitis - milk-yield - dairy-cattle - environmental correlations - phenotypic relationships - functional longevity - random regression - survival analysis
Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.
Relationships between Milk Progesterone Profiles and Genetic Merit for Milk Production, Milking Frequency, and Feeding Regimen in Dairy Cattle
Windig, J.J. ; Beerda, B. ; Veerkamp, R.F. - \ 2008
Journal of Dairy Science 91 (2008). - ISSN 0022-0302 - p. 2874 - 2884.
body condition score - energy-balance - luteal activity - reproductive-performance - endocrine parameters - random regression - production traits - health disorders - herd environment - fertility traits
Milk progesterone profiles were determined from samples obtained twice weekly for 100 d postpartum in 100 Holstein primiparous cows at a Dutch experimental farm. Three treatments were applied in a 2 x 2 x 2 factorial arrangement with high-low genetic merit for overall production, high-low caloric density diet, and 23 times milking/ day as factors. Milk progesterone profiles were characterized by start of first ovarian cyclical activity (commencement of luteal activity, C-LA), length and peak milk progesterone concentration of first ovarian cycle, and number of ovarian cycles in first 100 d postpartum, as well as classified into normal, delayed, prolonged, and interrupted ovarian cyclical activity. Cows with a greater milk production had lower peak progesterone concentrations, especially if the high milk production was caused by milking 3 times a day. A more negative energy and protein balance was associated with later C-LA and less ovarian cycles within 100 d postpartum. Relationships between protein balance and C-LA differed between cows with a high genetic merit and a low genetic merit. Cows with a high genetic merit for production showed delayed C-LA with more negative protein balances, whereas this association was not observed among cows with a low genetic merit. Cows in negative energy balance had greater risk for prolonged ovarian cycles when there was no delay in CLA than when C-LA was delayed.
Genetics and genomics to improve fertility in high producing dairy cows
Veerkamp, R.F. ; Beerda, B. - \ 2007
Theriogenology 68 (2007)suppl. 1. - ISSN 0093-691X - p. S266 - S273.
body condition score - quantitative trait loci - linear type traits - milk-production - energy-balance - random regression - herd environment - calving interval - holstein cattle - luteal activity
Improving dairy cow fertility by means of genetic selection is likely to become increasingly important, since it is now well established that declining fertility cannot only be arrested by improved management. Profit margins per kg milk produced are decreasing, therefore farmers need to reduce cost and increase herd size. This restricts the labor input per cow and the disposable cost of getting a cow pregnant, whilst at the same time hormone treatments have become less acceptable. This makes it unlikely that additional management interventions will maintain fertility at acceptable levels in the near future. Genetic improvement seems the obvious solution. Effective selection tools are available in most Western countries using traditional breeding value estimation procedures. Also, in addition to gene assisted selection using individual genes or QTL, high throughput Single Nucleotide Polymorphism (SNP) technology allows genetic improvement of fertility based on information from the whole genome (tens of thousands SNP per animal), i.e. genomic selection. Simulation studies have shown that genomic selection improves the accuracy of selecting juvenile animals compared with traditional breeding methods and compared with selection using information from a few genes or QTL only. Research in the areas genomics and proteomics promise to make genetic selection even more effective. The genomic and proteomics technologies combined with the bioinformatics tools that support the interpretation of gene functioning and protein expression facilitate an exciting starting point for the development of new management strategies and tools for the improvement of reproductive performance.
Phenotypic profiles for body weight, body condition score, energy intake, and energy balance across different parities and concentrate feeding levels
Berry, D.P. ; Veerkamp, R.F. ; Dillon, P. - \ 2006
Livestock Science 104 (2006)1-2. - ISSN 1871-1413 - p. 1 - 12.
1st 3 lactations - dairy-cows - random regression - milk-yield - reproductive-performance - genetic-parameters - holstein cows - live weight - postpartum - cattle
The objective of this study was to investigate potential differences in lactation profiles for body weight (BW), body condition score (BCS), net energy intake (NEI) and energy balance (EB) across different parities and concentrate feeding levels. Records collected from the research farm in southern Ireland from 1995 to 2002 were analysed using random regression methodology. A total of 27,126 and 8212 records were available for BW and BCS, respectively; 1861 and 1835 records were available for NEI and EB, respectively. Significantly different lactation profiles existed for BW, BCS, NEI and EB across parities one to three. First parity cows were lighter, lost more BCS in early lactation, had lower NEI and were in negative energy balance for longer compared to later parity cows. Average NEI across the first 225 days of lactation was 15.4, 19.5 and 21.0 UFLs/day for first, second and third parity cows, respectively. Energy balance turned positive at days 71, 60 and 73 of lactation for first, second and third parity cows, respectively. Cows on the higher feeding levels were heavier, mobilised less body condition in early lactation and had higher NEI than cows on the lower feeding level. No significant difference in height of the EB profile existed between the cows on the low and high feeding level indicating that the duration and extent of NEB was not influenced by the concentrate feeding levels adopted in this study.
Genetic Aspects of Growth of Holstein-Friesian Dairy Cows from Birth to Maturity
Coffey, M.P. ; Hickey, J.M. ; Brotherstone, S. - \ 2006
Journal of Dairy Science 89 (2006)1. - ISSN 0022-0302 - p. 322 - 329.
quantitative trait loci - energy-balance profiles - body-weight - random regression - murine growth - live-weight - feed-intake - heifers - parameters - protein
In general, genetic selection is applied after first calving to traits that manifest themselves during the animal¿s productive life, mostly during the early part of productive life. This selection policy has had undesirable correlated responses in other economically important traits, such as health and fertility, and may also have had an effect on the growth of animals both during productive life and before first calving. In this study, we analyzed the growth trajectory of dairy heifers that had been selected for maximum production of combined fat and protein (measured in kg; select line) or for average production (control line) in the United Kingdom. Before first calving, these divergent lines were managed as a single group. Select line heifers grew faster than did control line heifers. They were also heavier at first calving, but by the end of 3 lactations, the lines were not significantly different in live weight. Selection primarily for yield and for other traits has led to heifers that grow faster and reach higher growth rates earlier in life. A genetic analysis of birth, weaning, and calving weights yielded heritability estimates of 0.53 (birth weight), 0.45 (weaning weight), and 0.75 (calving weight). Confidence intervals for the genetic correlations between the traits indicated that these BW traits are not under the same genetic control.