Morten Kargo

The value of cows in reference populations for genomic selection of new functional traits

Today, almost all reference populations consist of progeny tested bulls. However, older progeny tested bulls do not have reliable estimated breeding values (EBV) for new traits. Thus, to be able to select for these new traits, it is necessary to build a reference population. We used a deterministic prediction model to test the hypothesis that the value of cows in reference populations depends on the availability of phenotypic records. To test the hypothesis, we investigated different strategies of building a reference population for a new functional trait over a 10-year period. The trait was either recorded on a large scale (30 000 cows per year) or on a small scale (2000 cows per year). For large-scale recording, we compared four scenarios where the reference population consisted of 30 sires; 30 sires and 170 test bulls; 30 sires and 2000 cows; or 30 sires, 2000 cows and 170 test bulls in the first year with measurements of the new functional trait. In addition to varying the make-up of the reference population, we also varied the heritability of the trait (h2 = 0.05 v. 0.15). The results showed that a reference population of test bulls, cows and sires results in the highest accuracy of the direct genomic values (DGV) for a new functional trait, regardless of its heritability. For small-scale recording, we compared two scenarios where the reference population consisted of the 2000 cows with phenotypic records or the 30 sires of these cows in the first year with measurements of the new functional trait. The results showed that a reference population of cows results in the highest accuracy of the DGV whether the heritability is 0.05 or 0.15, because variation is lost when phenotypic data on cows are summarized in EBV of their sires. The main conclusions from this study are: (i) the fewer phenotypic records, the larger effect of including cows in the reference population; (ii) for small-scale recording, the accuracy of the DGV will continue to increase for several years, whereas the increases in the accuracy of the DGV quickly decrease with large-scale recording; (iii) it is possible to achieve accuracies of the DGV that enable selection for new functional traits recorded on a large scale within 3 years from commencement of recording; and (iv) a higher heritability benefits a reference population of cows more than a reference population of bulls.

Short communication: Is crossbreeding only beneficial in herds with low management level?

The economic benefit of crossbreeding has been well known for many years within dairy production. However, in most countries with an intensive dairy production, an extended use of systematic crossbreeding has not occurred. This may be due to the myth that heterosis is expressed mainly in low-producing herds. The aim of the study was to investigate the effect of heterosis with different management levels in Danish Jersey herds. More than 300,000 records of 305-d milk, fat, and protein yield from first-lactation Danish Jersey cows with different contributions from original Danish and US Jersey were analyzed using an animal model. The herds were distributed in 5 management groups based on production level. First, the results showed a large increase in additive genetic variance from the herds with lowest production level to the high-producing ones, and second, heterosis for all 3 production traits were lowest within the low-intensity management group and tended to be highest in the intermediate management groups. The results, therefore, support that crossbreeding is a breeding system that should be considered valuable for all management levels.

Genomic selection strategies in dairy cattle breeding programmes: Sexed semen cannot replace multiple ovulation and embryo transfer as superior reproductive technology.

The aim of this study was to test whether the use of X-semen in a dairy cattle population using genomic selection (GS) and multiple ovulation and embryo transfer (MOET) increases the selection intensity on cow dams and thereby the genetic gain in the entire population. Also, the dynamics of using different types of sexed semen (X, Y or conventional) in the nucleus were investigated. The stochastic simulation study partly supported the hypothesis as the genetic gain in the entire population was elevated when X-semen was used in the production population as GS exploited the higher selection intensity among heifers with great accuracy. However, when MOET was applied, the effect was considerably diminished as was the exchange of females between the nucleus and the production population, thus causing modest genetic profit from using X-sorted semen in the production population. In addition, the effect of using sexed semen on the genetic gain was very small compared with the effect of MOET and highly dependent on whether cow dams or bull dams were inseminated with sexed semen and on what type of semen that was used for the bull dams. The rate of inbreeding was seldom affected by the use of sexed semen. However, when all young bull candidates were born following MOET, the results showed that the use of Y-semen in the breeding nucleus tended to decrease the rate of inbreeding as it enabled GS to increase within-family selection. This implies that the benefit from using sexed semen in a modern dairy cattle breeding scheme applying both GS and MOET may be found in its beneficial effect on the rate of inbreeding.

Estrus traits derived from activity measurements are heritable and closely related to the time from calving to first insemination

The aim of this study was to estimate genetic parameters for estrus-related traits that could improve selection for increased fertility due to improved ability of the cow to return to cycling and go into heat after calving. We compared the time from calving to first insemination (CFI) to 3 physical activity traits: the interval from calving to first high activity (CFHA), estrus duration (ED), and estrus strength (ES). We calculated CFI based on data from commercial Holstein herds that included the insemination dates for 11,363 cows. The CFHA, ED, and ES traits were derived from electronic activity tags for 3,533 Holstein cows. Estimates of heritability were 0.07 for CFI, 0.16 for CFHA, 0.02 for ED, and 0.05 for ES. We found a strong genetic correlation between CFI and CFHA (0.96). Genetic correlations between ED and CFI and CFHA were -0.37 and -0.68, respectively. Genetic correlations between ES and CFI and CFHA were -0.50 and -0.58, respectively. The heritability of CFHA and its strong genetic correlation with CFI suggest that including CFHA in the genetic evaluation of female cow fertility could improve the effectiveness of selection, because CFHA reflects the ability to return to cyclicity and go into heat after calving.

Genomic testing interacts with reproductive surplus in reducing genetic lag and increasing economic net return

Until now, genomic information has mainly been used to improve the accuracy of genomic breeding values for breeding animals at a population level. However, we hypothesize that the use of information from genotyped females also opens up the possibility of reducing genetic lag in a dairy herd, especially if genomic tests are used in combination with sexed semen or a high management level for reproductive performance, because both factors provide the opportunity for generating a reproductive surplus in the herd. In this study, sexed semen is used in combination with beef semen to produce high-value crossbred beef calves. Thus, on average there is no surplus of and selection among replacement heifers whether to go into the herd or to be sold. In this situation, the selection opportunities arise when deciding which cows to inseminate with sexed semen, conventional semen, or beef semen. We tested the hypothesis by combining the results of 2 stochastic simulation programs, SimHerd and ADAM. SimHerd estimates the economic effect of different strategies for use of sexed semen and beef semen at 3 levels of reproductive performance in a dairy herd. Besides simulating the operational return, SimHerd also simulates the parity distribution of the dams of heifer calves. The ADAM program estimates genetic merit per year in a herd under different strategies for use of sexed semen and genomic tests. The annual net return per slot was calculated as the sum of operational return and value of genetic lag minus costs of genomic tests divided by the total number of slots. Our results showed that the use of genomic tests for decision making decreases genetic lag by as much as 0.14 genetic standard deviation units of the breeding goal and that genetic lag decreases even more (up to 0.30 genetic standard deviation units) when genomic tests are used in combination with strategies for increasing and using a reproductive surplus. Thus, our hypothesis was supported. We also observed that genomic tests are used most efficiently to decrease genetic lag when the genomic information is used more than once in the lifetime of an animal and when as many selection decisions as possible are based on genomic information. However, all breakeven prices were lower than or equal to €50, which is the current price of low-density chip genotyping in Denmark, Finland, and Sweden, so in the vast majority of cases, it is not profitable to genotype routinely for management purposes under the present price assumptions.

Economic basis for the Nordic Total Merit Index.

Within a group of cooperating countries, all breeding animals are judged according to the same criteria if a joint breeding goal is applied in these countries. This makes it easier for dairy farmers to compare national and foreign elite bulls and may lead to more selection across borders. However, a joint breeding goal is only an advantage if the countries share the same production environment. In this study, we investigated whether the development of a joint breeding goal for each of the major dairy cattle breeds across Denmark, Finland, and Sweden would be an advantage compared with national breeding goals. For that purpose, economic values for all breeding goal traits in the 3 countries were derived, and estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were compared. The economic values within country were derived by means of an objective bio-economic model, and the basic situation in each of the 3 production environments was based on an average dairy cattle herd with regard to production system, production level, and management strategy. The common Nordic economic values for each trait were calculated as the average of that specific trait in each of the 3 production environments. Balanced breeding goals were obtained in all situations because the derived economic values for traits related to health, fertility, milk production, and longevity were sizeable. For both Nordic Red Dairy Cattle and Nordic Holstein, the estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were very high. Thus, a joint breeding goal within breed is feasible for Denmark, Finland, and Sweden.

Genomic selection using indicator traits to reduce the environmental impact of milk production

The aim of this simulation study was to test the hypothesis that phenotype information of specific indicator traits of environmental importance recorded on a small-scale can be implemented in breeding schemes with genomic selection to reduce the environmental impact of milk production. A stochastic simulation was undertaken to test alternative breeding strategies. The breeding goal consisted of milk production, a functional trait, and environmental impact (EI). The indicator traits (IT) for EI were categorized as large-, medium-, or small-scale, depending on how the traits were recorded. The large-scale traits were stayability and stature; the medium-scale traits were live weight and methane in the breath of the cow measured during milking; and the small-scale traits were residual feed intake and methane recorded in a respiration chamber. Simulated scenarios considered information for just one IT in addition to information for milk production and functional traits. The annual monetary genetic gain was highest in the large-scale scenario that included stayability as IT. The annual monetary gain in the scenarios with medium- or small-scale IT varied from €50.5 to 47.5. The genetic gain improvement in EI was, however, best in the scenarios where the genetic correlation between IT and EI was ≥0.30 and the accuracy of direct genomic value was ≥0.40. The genetic gain in EI was 26 to 34% higher when indicator traits such as greenhouse gases in the breath of the cow and methane recorded in respiration chamber were used compared with a scenario where no indicator trait was included. It is possible to achieve increased genetic gain in EI by using a highly correlated indicator trait, but it requires that the established reference population for the indicator trait is large enough so that the accuracy of direct genomic values will be reasonably high.

Genotype by environment interaction for the interval from calving to first insemination with regard to calving month and geographic location in Holstein cows in Denmark and Sweden.

The objectives of this study were to investigate genotype by environment interaction effects, with environments defined as calving month and geographic location, on the interval from calving to first insemination (CFI) of Holstein cows in Denmark and Sweden. The data set included 811,285 records on CFI for first-parity cows from January 2010 to January 2014 housed in 7,458 herds. The longest mean CFI was 84.7 d for cows calving in April and the shortest was 76.3 d for cows calving in September. The longest mean CFI of 87.1 d was recorded at the northernmost location (LOC-8), whereas the shortest mean CFI of 73.5 d was recorded at the southernmost location (LOC-1). The multiple trait approach, in which CFI values in different calving months and different geographic locations were treated as different traits, was used to estimate the variance components and genetic correlations for CFI by using the average information (AI)-REML procedure in a bivariate sire model. Estimates of genetic variance and heritability were highest for January calvings and 3 times smaller for June calvings. Location 2 had the highest heritability and LOC-8 the lowest, with heritability estimates decreasing from LOC-2 to LOC-8. Genetic correlations of CFI between calving months were weakest between cold months (December and January) and warm months (June, August, and September); the lowest estimate was found between January and September calvings. Genetic correlations of CFI between the different geographic locations were generally strong, and the weakest correlation was between LOC-3 and LOC-8. These results indicate a genotype by environment interaction for CFI primarily regarding seasons described by calving months. The effect of geographic location was less important, mostly producing a scaling effect of CFI in different locations. We concluded that CFI is more sensitive to seasonal effects than geographic locations in Denmark and Sweden.

Breakeven prices for recording of indicator traits to reduce the environmental impact of milk production.

A breeding scheme using genomic selection and an indicator trait for environmental impact (EI) was studied to find the most effective recording strategy in terms of annual monetary genetic gain and breakeven price for the recording of indicator traits. The breakeven price shows the investment space for developing a recording system for an indicator trait. The breeding goal consisted of three traits – milk production, functional trait and environmental impact – with economic values of €83, €82 and €-83, respectively. The first scenario included only breeding goal traits and no indicator traits (NoIT). The other scenarios included all three breeding goal traits and one indicator trait (IT) for EI. The indicator traits were recorded on a large scale (stayability after first lactation and stature), medium scale (live weight and greenhouse gases (GHG) measured in the breath of the cow during milking) or small scale (residual feed intake and total enteric methane measured in a respiration chamber). In the scenario with stayability, the genetic gain in EI was over 11% higher than it was in NoIT. The breakeven price of recording stayability was €8 per record. Stayability is easy to record in the national milk recording system, and its use as an indicator trait for EI would not generate any additional recording costs. Therefore, stayability would be a good indicator trait to use to mitigate EI. The highest genetic gain in EI (23% higher compared to NoIT) was achieved when the GHG measured in the breath of the cow was used as indicator trait. The breakeven price for this indicator trait was €29 per record in the reference population. Ideally the recording of a specific indicator trait for EI would take place when: (i) the genetic correlation between the IT and EI is high; and (ii) the number of phenotypic records for the indicator trait is high enough to achieve a moderately high reliability of direct genomic values.

Short communication: Heterosis by environment and genotype by environment interactions for protein yield in Danish Jerseys

Crossing of lines or strains within and between breeds has been demonstrated to be beneficial for dairy cattle performance. However, even within breed, differences between strains may also give rise to heterosis. A key question is whether an interaction exists between heterosis and environment (H × E) that is independent of genotype by environment (G × E) interactions. In this study, H × E and G × E interactions were estimated in a population of approximately 300,000 Danish Jersey cows. The cows were a mixture of pure Danish Jerseys and crosses of US and Danish Jerseys. The phenotype studied was protein yield. A reaction norm model where the unknown environmental covariates are inferred simultaneously with the other parameters in the model was used to analyze the data. When H × E, but not G × E, was included in the model, heterosis was estimated to be 3.8% for the intermediate environmental level. However, when both H × E and G × E were included in the model, heterosis was estimated to be 4.1% for the intermediate environmental level. Furthermore, when only H × E was included in the model, the regression on the unknown environmental covariate was estimated to be 0.15, interpreted as meaning that an increase of average herd-year protein yield by 1 kg of protein led to an increase in heterosis of 0.15 kg above the average heterosis for a first-cross cow. When both H × E and G × E were included in the model, the regression on the unknown environmental covariate was not significantly different from zero, meaning that heterosis was similar in all environments investigated. The genetic correlation of protein yields for different environmental levels ranged from 0.72 to 0.93, which was significantly different from unity, indicating that G × E exist for protein yield.