Genomic Analysis of Inbreeding and Coancestry in Nordic Jersey and Holstein Dairy Cattle Populations.

In recent years, Genomic Selection (GS) has accelerated genetic gain in dairy cattle breeds worldwide. Despite the evident genetic progress, several dairy populations have also encountered challenges such as heightened inbreeding rates and reduced effective population sizes. The challenge has been to find a balance between achieving substantial genetic gain while managing genetic diversity within the population, thereby mitigating the negative effects of inbreeding depression. This study aims to elucidate the impact of GS on pedigree and genomic rates of inbreeding (ΔF) and coancestry (ΔC) in Nordic Jersey (NJ) and Holstein (NH) cattle populations. Furthermore, key genetic metrics including the generation interval (L), effective population size (Ne), and future effective population size (FNe) were assessed between 2 time periods, before and after GS, and across distinct animal cohorts in both breeds: females, bulls, and approved semen-producing bulls (AI-sires). Analysis of ΔF and ΔC revealed distinct trends across the studied periods and animal groups. Notably, there was a consistent increase in yearly ΔF for most animal groups in both breeds. An exception was observed in NH AI-sires, which demonstrated a slight decrease in yearly ΔF. Moreover, NJ displayed minimal changes in yearly ΔC between the periods, whereas NH exhibited elevated ΔC values across all animal groups. Particularly striking was the substantial increase in yearly ΔC within the NH female population, surging from 0.02% to 0.39% between the periods. Implementation of GS resulted in a reduction of the generation interval across all animal cohorts in both NJ and NH breeds. However, the extent of reduction was more pronounced in males compared with females. This reduction in generation interval influenced generational changes in ΔF and ΔC. Bulls and AI-sires of both breeds exhibited reduced generational ΔF between periods, in contrast to females that demonstrated an opposing pattern. Between the periods, NJ maintained a relatively stable Ne, 29.4 before and 30.3 after GS, while NH experienced a notable decline from 54.3 to 42.8. Female groups in both breeds displayed a negative Ne trend, while males demonstrated either neutral or positive Ne developments. Regarding FNe, NJ exhibited positive FNe development with an increase from 40.7 to 57.2. The opposite was observed in NH, where FNe decreased from 198.8 to 42.7. In summary, it was evident that the genomic methods could detect differences between the populations and changes in ΔF and ΔC more efficiently than pedigree methods. GS implementation yielded positive outcomes within the NJ population regarding the rate of coancestry but the opposite was observed with NH. Moreover, analysis of ΔC data hints at the potential to decrease future ΔF through informed mating strategies. Conversely, NH faces more pressing concerns, even though ΔF remains comparatively modest in contrast to what has been observed in other Holstein populations. These findings underscore the necessity of genomic control of inbreeding and coancestry with strategic changes in the Nordic breeding schemes for dairy to ensure long-term sustainability in the forthcoming years.

Combined mechanistic and genetic modelling to benchmark body reserve traits as proxies of dairy cows' lifetime efficiency in grass-based production systems.

Improving lifetime lactation efficiency of dairy cows by selection is difficult due to the complexity of this trait and the existence of genotype-by-environment interactions. This study aimed at assessing the relevance of traits derived from body reserves as lifetime efficiency indicators under contrasting nutritional environments. Given the absence of large-scale datasets covering a panel of feeding regimes, phenotypes were simulated for populations of 20 000 dairy cows using a mechanistic bioenergetic model. Ten phenotypes were computed for third-lactation cows. Analysed phenotypes comprised total milk production, lactation efficiency, BW at calving (BWcalv), DM intake (DMI) and interval between first insemination and conception. Five traits described levels and changes of body reserves at different periods during lactation. Lifetime lactation efficiency was computed for all cows (Life_Eff). Three nutritional environments were defined considering a grass-based production system with seasonal calving: a high non-limiting scenario (HS) mimicking ad libitum access to feed and two limiting environments with moderate (MS) and low (LS) feed offer. Variance components were estimated for all traits within and between environments using REML. Heritabilities estimated for milk production, lactation efficiency, BWcalv and DMI were moderate in the different environments (0.27-0.35 ± 0.04). The heritability of body reserve levels and dynamics were moderate in the HS and MS scenarios (0.23-0.30 ± 0.03) and lower in the LS scenario (0.14-0.25 ± 0.03). The heritability of Life_Eff was low in the HS environment (0.07 ± 0.01) and slightly increased in the limiting environments. All genetic correlations estimated between environments were moderate to high (≥0.66 ± 0.07), suggesting low to moderate genotype-by-environment interactions. Estimated genetic correlations were moderate between Life_Eff and body reserve levels (from 0.39 to 0.51 ± 0.08) and moderate but negative between Life_Eff and change in body reserves traits (-0.27 to -0.37 ± 0.09) in the HS environment. The genetic correlations between Life_Eff and body reserve levels increased to higher values in the limiting environments. In contrast, genetic correlations between Life_Eff and the changes in body reserves were closer to zero. In conclusion, this study showed that body reserve levels were relevant proxies of lifetime irrespective of the environment. In contrast, changes in body reserves that reflected energy mobilisation in early lactation were less informative about lifetime efficiency in environments with severe feed restrictions.

Repeatabilities of individual measures of feed intake and body weight on in-house commercial dairy cattle using a 3-dimensional camera system.

In this study a 3-dimensional (3D) camera system was set up to measure individual feed intake of dairy cows in a commercial in-house setting. The system was developed to identify the cows while eating, predict body weight based on the curvature of the back of the cow, and quantify the amount of feed eaten by the cow at each visit of eating. The identification of the cow was based on recognizing the patterns, colors, and curvatures of the back from a reference database obtained in a corridor after milking, where images were taken of all cows with a simultaneous reading of the electronic ear tag. Body weight is predicted using the curvatures of the back of the cow. Feed intake is quantified as the difference in surface of the feed a cow can reach before and after a visit is initiated. This estimate is in liters but converted to kilograms, using the density of the feed in the specific herd. A total of 9,142 cows were measured in 19 herds across 3 breeds: Jersey (2,513 cows), Red Dairy Cattle (2,813 cows), and Holstein (3,816 cows). Mean daily feed intake was higher for Red Dairy Cattle (61.72 kg) and Holstein (64.59 kg) than for Jersey (55.74 kg). Repeatability estimates for daily feed intake as a weekly average was 0.62, 0.65, and 0.63 for Jersey, Red Dairy, and Holstein cattle, respectively. Mean body weight was higher for Red Dairy (647.9 kg) and Holstein (683.8 kg) than for Jersey (469.6 kg). Repeatability estimates for body weight as a weekly average was 0.83, 0.85, and 0.88 for Jersey, Red Dairy, and Holstein, respectively. The perspectives in having such records available is huge both for the farmer and for the dairy industry. The records can both be used for improving management in farms on an individual cow level and herd level, but also for genetic evaluation and selection as well as testing feeding regimens. Feed intake can be measured on an individual level using a 3D camera system.

Mating allocations in Holstein combining genomic information and linear programming optimization at the herd level.

In this study, we explored mating allocation in Holstein using genomic information for 24,333 Holstein females born in Denmark, Finland, and Sweden. We used 2 data sets of bulls: the top 50 genotyped bulls and the top 25 polled genotyped bulls on the Nordic total merit scale. We used linear programming to optimize economic scores within each herd, considering genetic level, genetic relationship, semen cost, the economic impact of genetic defects, polledness, and ß-casein. We found that it was possible to reduce genetic relationships and eliminate expression of genetic defects with minimal effect on the genetic level in total merit index. Compared with maximizing only Nordic total merit index, the relative frequency of polled offspring increased from 13.5 to 22.5%, and that of offspring homozygous for ß-casein (A2A2) from 66.7 to 75.0% in one generation, without any substantial negative impact on other comparison criteria. Using only semen from polled bulls, which might become necessary if dehorning is banned, considerably reduced the genetic level. We also found that animals carrying the polled allele were less likely to be homozygous for ß-casein (A2A2) and more likely to be carriers of the genetic defect HH1. Hence, adding economic value to a monogenic trait in the economic score used for mating allocation sometimes negatively affected another monogenetic trait. We recommend that the comparison criteria used in this study be monitored in a modern genomic mating program.

Emphasis on resilience in dairy cattle breeding: Possibilities and consequences.

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Mating allocations in Nordic Red Dairy Cattle using genomic information.

In this study, we compared mating allocations in Nordic Red Dairy Cattle using genomic information. We used linear programming to optimize different economic scores within each herd, considering genetic level, semen cost, the economic impact of recessive genetic defects, and genetic relationships. We selected 9,841 genotyped females born in Denmark, Finland, or Sweden in 2019 for mating allocations. We used 2 different pedigree relationship coefficients, the first tracing the pedigree 3 generations back from the parents of the potential mating and the second based on all available pedigree information. We used 3 different genomic relationship coefficients, 1 SNP-by-SNP genomic relationship and 2 based on shared genomic segments. We found high correlations (≥0.83) between the pedigree and genomic relationship measures. The mating results showed that it was possible to reduce the different genetic relationships between parents with minimal effect on genetic level. Including the cost of known recessive genetic defects eliminated expression of genetic defects. It was possible to reduce genomic relationships between parents with pedigree measures, but it was best done with genomic measures. Linear programming maximized the economic score for all herds studied within seconds, which means that it is suitable for implementation in mating software to be used by advisors and farmers.

Possibilities for a specific breeding program for organic dairy production.

Organic dairy production differs from conventional dairy production in many aspects. However, breeding programs for the 2 production systems are the same in most countries. Breeding goals (BG) might be different for the 2 production systems and genotype × environment interaction may exist between organic and conventional dairy production, both of which have an effect on genetic gain in different breeding strategies. Other aspects also need to be considered, such as the application of multiple ovulation and embryo transfer (MOET), which is not allowed in organic dairy production. The general aim of this research was to assess different environment-specific breeding strategies for organic dairy production. The specific aim was to study differences in BG weights and include the effect of genotype × environment interaction, MOET, and the selection of breeding bulls from the conventional environment. Different scenarios were simulated. In the current scenario, the present-day situation for dairy production in Denmark was emulated as much as possible. The BG was based on a conventional dairy production system, MOET was applied in both environments, and conventional bulls could be selected as breeding bulls in the organic environment. Four alternative scenarios were simulated, all with a specific organic BG in the organic breeding program but differences in the usage of MOET and the selection of conventional bulls as breeding bulls. Implementation of a specific BG in organic dairy production slightly increased genetic gain in the aggregate genotype compared with the breeding program that is currently implemented in organic dairy production. Not using embryo transfer or only selecting breeding bulls from the organic environment decreased genetic gain in the aggregate genotype by as much as 24%. However, the use of embryo transfer is debatable because this is not allowed according to current regulations for organic dairy production. Assessing genetic gain on trait levels showed that a significant increase for functional traits was possible compared with the current breeding program in the organic environment without a decrease in genetic gain in the aggregate genotype. This difference on trait level was even more present when selection of conventional bulls as breeding bulls in the organic environment was not possible. This finding is very relevant when breeding for the desired cow in organic dairy production.

Genotyping more cows increases genetic gain and reduces rate of true inbreeding in a dairy cattle breeding scheme using female reproductive technologies.

Both small dairy cattle populations and dairy cattle populations with a low level of linkage disequilibrium (LD) suffer from low reliability of genomic prediction. In this study, we investigated whether adding more genotyped cows to the reference population influences the rate of genetic gain and rate of inbreeding by affecting the reliability. A standard breeding program with a large reference population and high LD, which mimicked a breeding program for Danish Holstein population, was simulated as a reference. A Danish Jersey population with a small reference population and high LD and a Red Dairy Cattle population with a large reference population and low LD were also simulated. Two additional breeding programs were simulated for Danish Jersey and Red Dairy Cattle populations, where 2,000 additional genotyped cows were included in the population for genomic selection. All 5 simulated breeding programs were initiated by a founder population to generate LD resembling the real LD pattern, followed by a 20-yr conventional progeny-testing scheme with 1,000 or 10,000 genotyped progeny-tested bulls and a 10-yr genomic selection scheme with or without 2,000 additional genotyped cows. Evaluation criteria were annual monetary genetic gain and rate of true inbreeding. Our results showed that adding more genotyped cows to the reference in dairy cattle populations has the potential to increase genetic gain and reduce the rate of inbreeding, regardless of reference population size and level of LD. However, it is still not possible to reach the same genetic gain as in the simulated Danish Holstein population with either a small reference population or low LD. Our results also showed that in a small reference population with high LD, it is difficult to manage inbreeding because of lower accuracy compared with the simulated Danish Holstein population and a smaller number of relevant families to select from. Therefore, breeding strategies need to be chosen to match population size and structure. The rate of true inbreeding is always underestimated by pedigree inbreeding and even more in genomic breeding programs, indicating that some forms of genome-wide inbreeding, instead of pedigree-based inbreeding, should be used to monitor inbreeding when genomic selection is implemented.

Genomic selection improves the possibility of applying multiple breeding programs in different environments.

One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (rg). The break-even correlation (rb) refers to the specific rg where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if rg is higher than rb, whereas 2 environment-specific BP have higher genetic gain if rg is lower than rb. Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find rb when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on rb and genetic gain; and (4) to find rb from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The rb was 0.65 and not different from rb with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on rb was not large. A different size in 2 environments decreased rb by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high rg. When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when rg between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP.

Simulating consequences of choosing a breeding goal for organic dairy production.

In Denmark, Finland, and Sweden, the Nordic Total Merit index is used as the breeding selection tool for both organic and conventional dairy farmers based on common economic models for conventional dairy farming. Organic farming is based on the principles of organic agriculture (POA) defined by the International Federation of Organic Agriculture Movements. These principles are not set up with an economic point of view, and therefore it may be questionable to use a breeding goal (BG) for organic dairy production based on economic models. In addition to economics and the principles of organic agriculture, it is important to look at farmers' preferences for improving BG traits when setting up a BG for organic farming. The aim of this research was to set up, simulate, and compare long-term effects of different BG for organic and conventional dairy production systems based on economic models, farmers' preferences, and POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency. The BG based on economic models and on farmers' preferences were taken from previous studies. The other BG were desired gains indices, set up by means of a questionnaire about relatedness between the POA and BG traits. Each BG was simulated in the stochastic simulation program ADAM. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, caused favorable genetic gain in all 12 traits included in this study compared with 6 traits for the other BG. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, were very different from BG for organic and conventional production based on economic models and farmers' preferences in both simulated genetic change and correlations between BG. The BG that was created based on the principles of organic agriculture could be used as a specific index for organic dairy farming in Denmark, but this index was economically not very sustainable. Hence, an intermediate breeding goal could be developed by breeding companies to address both economics and the principles of organic agriculture.

Economic opportunities for using sexed semen and semen of beef bulls in dairy herds.

Dairy farmers can increase the number of dairy heifer calves born in their herd by using sexed semen. They can reduce the number of both dairy bull and heifer calves by using beef semen. Long before sexed semen became commercially available, it was believed that it would provide opportunities for increasing genetic level in both herds and populations. In this study, we studied the potential for increasing the genetic level of a herd by using beef semen in combination with sexed semen. We tested the hypothesis that the potential of increasing the genetic level and the overall net return would depend on herd management. To test this hypothesis, we simulated 7 scenarios using beef semen and sexed semen in 5 herds at different management levels. We combined the results of 2 stochastic simulation models, SimHerd and ADAM. SimHerd simulated the effects of the scenarios and management levels on economic outcomes (i.e., operational return) and on technical outcomes such as the parity distribution of the dams of heifer calves, but it disregarded genetic progress. The ADAM model quantified genetic level by using the dams' parity distributions and the frequency of sexed and beef semen to estimate genetic return per year. We calculated the annual net return per slot as the sum of the operational return and the genetic return, divided by the total number of slots. Net return increased up to €18 per slot when using sexed semen in 75% genetically superior heifers and beef semen in 70% genetically inferior, multiparous cows. The assumed reliability of selection was 0.84. These findings were for a herd with overall high management for reproductive performance, longevity, and calf survival. The same breeding strategy reduced net return by €55 per slot when management levels were average. The main reason for the large reduction in net return was the heifer shortage that arose in this scenario. Our hypothesis that the potential for beef semen to increase genetic level would be herd-specific was supported. None of the scenarios were profitable under Danish circumstances when the value of the increased genetic level was not included. A comparable improvement in genetic level could be realized by selectively selling dairy heifer calves rather than using beef semen.

Organic dairy farmers put more emphasis on production traits than conventional farmers.

The overall aim of this research was to characterize the preferences of Danish dairy farmers for improvements in breeding goal traits. The specific aims were (1) to investigate the presence of heterogeneity in farmers' preferences by means of cluster analysis, and (2) to associate these clusters with herd characteristics and production systems (organic or conventional). We established a web-based survey to characterize the preferences of farmers for improvements in 10 traits, by means of pairwise rankings. We also collected a considerable number of herd characteristics. Overall, 106 organic farmers and 290 conventional farmers answered the survey, all with Holstein cows. The most preferred trait improvement was cow fertility, and the least preferred was calving difficulty. By means of cluster analysis, we identified 4 distinct clusters of farmers and named them according to the trait improvements that were most preferred: Health and Fertility, Production and Udder Health, Survival, and Fertility and Production. Some herd characteristics differed between clusters; for example, farmers in the Survival cluster had twice the percentage of dead cows in their herds compared with the other clusters, and farmers that gave the highest ranking to cow and heifer fertility had the lowest conception rate in their herds. This finding suggests that farmers prefer to improve traits that are more problematic in their herd. The proportion of organic and conventional farmers also differed between clusters; we found a higher proportion of organic farmers in the production-based clusters. When we analyzed organic and conventional data separately, we found that organic farmers ranked production traits higher than conventional farmers. The herds of organic farmers had lower milk yields and lower disease incidences, which might explain the high ranking of milk production and the low ranking of disease traits. This study shows that heterogeneity exists in farmers' preferences for improvements in breeding goal traits, that organic and conventional farmers differ in their preferences, and that herd characteristics can be linked to different farmer clusters. The results of this study could be used for the future development of breeding goals in Danish Holstein cows and for the development of customized total merit indices based on farmer preferences.

Fine mapping of a calving QTL on Bos taurus autosome 18 in Holstein cattle.

Decreased calving performance not only directly impacts the economic efficiency of dairy cattle farming but also influences public concern for animal welfare. Previous studies have revealed a QTL on Bos taurus autosome (BTA) 18 that has a large effect on calving traits in Holstein cattle. In this study, fine mapping of this QTL was performed using imputed high-density SNP chip (HD) genotypes followed by imputed next-generation sequencing (NGS) variants. BTA18 was scanned for seven direct calving traits in 6113 bulls with imputed HD genotypes. SNP rs136283363 (BTA18: 57 548 213) was consistently the most significantly associated SNP across all seven traits [e.g. p-value = 2.04 × 10(-59) for birth index (BI)]. To finely map the QTL region and to explore pleiotropic effects, we studied NGS variants within the targeted region (BTA18: 57 321 450-57 625 355) for associations with direct calving traits and with three conformation traits. Significant variants were prioritized, and their biological relevance to the traits was interpreted. Considering their functional relationships with direct calving traits, SIGLEC12, CD33 and CEACAM18 were proposed as candidate genes. In addition, pleiotropic effects of this QTL region on direct calving traits and conformation traits were observed. However, the extent of linkage disequilibrium combined with the lack of complete annotation and potential errors in the Bos taurus genome assembly hampered our efforts to pinpoint the causal mutation.

Reproductive technologies combine well with genomic selection in dairy breeding programs.

The objective of the present study was to examine whether genomic selection of females interacts with the use of reproductive technologies (RT) to increase annual monetary genetic gain (AMGG). This was tested using a factorial design with 3 factors: genomic selection of females (0 or 2,000 genotyped heifers per year), RT (0 or 50 donors selected at 14 mo of age for producing 10 offspring), and 2 reliabilities of genomic prediction. In addition, different strategies for use of RT and how strategies interact with the reliability of genomic prediction were investigated using stochastic simulation by varying (1) number of donors (25, 50, 100, 200), (2) number of calves born per donor (10 or 20), (3) age of donor (2 or 14 mo), and (4) number of sires (25, 50, 100, 200). In total, 72 different breeding schemes were investigated. The profitability of the different breeding strategies was evaluated by deterministic simulation by varying the costs of a born calf with reproductive technologies at levels of €500, €1,000, and €1,500. The results confirm our hypothesis that combining genomic selection of females with use of RT increases AMGG more than in a reference scheme without genomic selection in females. When the reliability of genomic prediction is high, the effect on rate of inbreeding (ΔF) is small. The study also demonstrates favorable interaction effects between the components of the breeder's equation (selection intensity, selection accuracy, generation interval) for the bull dam donor path, leading to higher AMGG. Increasing the donor program and number of born calves to achieve higher AMGG is associated with the undesirable effect of increased ΔF. This can be alleviated, however, by increasing the numbers of sires without compromising AMGG remarkably. For the major part of the investigated donor schemes, the investment in RT is profitable in dairy cattle populations, even at high levels of costs for RT.

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.

Adding cows to the reference population makes a small dairy population competitive.

Small dairy breeds are challenged by low reliabilities of genomic prediction. Therefore, we evaluated the effect of including cows in the reference population for small dairy cattle populations with a limited number of sires in the reference population. Using detailed simulations, 2 types of scenarios for maintaining and updating the reference population over a period of 15yr were investigated: a turbo scheme exclusively using genotyped young bulls and a hybrid scheme with mixed use of genotyped young bulls and progeny-tested bulls. Two types of modifications were investigated: (1) number of progeny-tested bulls per year was tested at 6 levels: 15, 40, 60, 100, 250, and 500; and (2) each year, 2,000 first-lactation cows were randomly selected from the cow population for genotyping or, alternatively, an additional 2,000 first-lactation cows were randomly selected and typed in the first 2yr. The effects were evaluated in the 2 main breeding schemes. The breeding schemes were chosen to mimic options for the Danish Jersey cattle population. Evaluation criteria were annual monetary genetic gain, rate of inbreeding, reliability of genomic predictions, and variance of response. Inclusion of cows in the reference population increased monetary genetic gain and decreased the rate of inbreeding. The increase in genetic gain was larger for the turbo schemes with shorter generation intervals. The variance of response was generally higher in turbo schemes than in schemes using progeny-tested bulls. However, the risk was reduced by adding cows to the reference population. The annual genetic gain and the reliability of genomic predictions were slightly higher with more cows in the reference population. Inclusion of cows in the reference population is a rapid way to increase reliabilities of genomic predictions and hence increase genetic gain in a small population. An economic evaluation shows that genotyping of cows is a profitable investment.

Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit.

The objective of this study was to evaluate a genomic breeding scheme in a small dairy cattle population that was intermediate in terms of using both young bulls (YB) and progeny-tested bulls (PB). This scheme was compared with a conventional progeny testing program without use of genomic information and, as the extreme case, a juvenile scheme with genomic information, where all bulls were used before progeny information was available. The population structure, cost, and breeding plan parameters were chosen to reflect the Danish Jersey cattle population, being representative for a small dairy cattle population. The population consisted of 68,000 registered cows. Annually, 1,500 bull dams were screened to produce the 500 genotyped bull calves from which 60 YB were selected to be progeny tested. Two unfavorably correlated traits were included in the breeding goal, a production trait (h(2)=0.30) and a functional trait (h(2)=0.04). An increase in reliability of 5 percentage points for each trait was used in the default genomic scenario. A deterministic approach was used to model the different breeding programs, where the primary evaluation criterion was annual monetary genetic gain (AMGG). Discounted profit was used as an indicator of the economic outcome. We investigated the effect of varying the following parameters: (1) increase in reliability due to genomic information, (2) number of genotyped bull calves, (3) proportion of bull dam sires that are young bulls, and (4) proportion of cow sires that are young bulls. The genomic breeding scheme was both genetically and economically superior to the conventional breeding scheme, even in a small dairy cattle population where genomic information causes a relatively low increase in reliability of breeding values. Assuming low reliabilities of genomic predictions, the optimal breeding scheme according to AMGG was characterized by mixed use of YB and PB as bull sires. Exclusive use of YB for production cows increased AMGG up to 3 percentage points. The results from this study supported our hypothesis that strong interaction effects exist. The strongest interaction effects were obtained between increased reliabilities of genomic estimated breeding values and more intensive use of YB. The juvenile scheme was genetically inferior when the increase in reliability was low (5 percentage points), but became genetically superior at higher reliabilities of genomic estimated breeding values. The juvenile scheme was always superior according to discounted profit because of the shorter generation interval and minimizing costs for housing and feeding waiting bulls.

The admixed population structure in Danish Jersey dairy cattle challenges accurate genomic predictions.

The main purpose of this study was to evaluate whether the population structure in Danish Jersey (DJ) known from the history of the breed also is reflected in its genomic structure. This is done by comparing the linkage disequilibrium and persistence of phase for subgroups of Jersey animals with high proportions of Danish (DNK) or United States (USJ) origin. Furthermore, it is investigated whether a model explicitly incorporating breed origin of animals, inferred either through the known pedigree or from SNP marker data, leads to improved genomic predictions compared with a model ignoring breed origin. The study of the population structure incorporated 1,730 genotyped Jersey animals. In total 39,542 SNP markers were included in the analysis. The 1,079 genotyped bulls with de-regressed proof for udder health were used in the analysis for the predictions of the genomic breeding values. A range of random regressions models that included the breed origin were analyzed and compared with a basic genomic model that assumes a homogeneous breed structure. The main finding in this study is that the importation of germplasm from the USJ population is readily reflected in the genomes of modern DJ animals. First, linkage disequilibrium in the group of admixed DJ animals is lower compared with the groups of the original DNK and USJ animals. Second, persistence of linkage disequilibrium phase is not conserved for longer marker distances between animals with mainly Danish or United States origin. Third, the STRUCTURE analysis could retrieve genomic-based breed proportions in alignment to the pedigree-based breed proportions. However, including this population structure in a random regression prediction model did not clearly improve the reliabilities of the genomic predictions compared with a basic genomic model.

Effects of Bos taurus autosome 9-located quantitative trait loci haplotypes on the disease phenotypes of dairy cows with experimentally induced Escherichia coli mastitis.

Several quantitative trait loci (QTL) affecting mastitis incidence and mastitis-related traits such as somatic cell score exist in dairy cows. Previously, QTL haplotypes associated with susceptibility to Escherichia coli mastitis in Nordic Holstein-Friesian (HF) cows were identified on Bos taurus autosome 9. In the present study, we induced experimental E. coli mastitis in Danish HF cows to investigate the effect of 2 E. coli mastitis-associated QTL haplotypes on the cows' disease phenotypes and recovery in early lactation. Thirty-two cows were divided in 2 groups bearing haplotypes with either low (HL) or high (HH) susceptibility to E. coli. In addition, biopsies (liver and udder) were collected from half of the cows (n=16), resulting in a 2 × 2 factorial design, with haplotype being one factor (HL vs. HH) and biopsy being the other factor (biopsies vs. no biopsies). Each cow was inoculated with a low E. coli dose (20 to 40 cfu) in one front quarter at time 0 h. Liver biopsies were collected at -144, 12, 24, and 192 h; udder biopsies were collected at 24h and 192 h post-E. coli inoculation. The clinical parameters: feed intake, milk yield, body temperature, heart rate, respiration rate, rumen motility; and the paraclinical parameters: bacterial counts, somatic cell count (SCC), and milk amyloid A levels in milk; and white blood cell count, polymorphonuclear neutrophilic leukocyte (PMNL) count, and serum amyloid A levels in blood were recorded at different time points post-E. coli inoculation. Escherichia coli inoculation changed the clinical and paraclinical parameters in all cows except one that was not infected. Clinically, the HH group tended to have higher body temperature and heart rate than the HL group did. Paraclinically, the HL group had faster PMNL recruitment and SCC recovery than the HH group did. However, we also found interactions between the effects of haplotype and biopsy for body temperature, heart rate, and PMNL. In conclusion, when challenged with E. coli mastitis, HF cows with the specific Bos taurus autosome 9-located QTL haplotypes were associated with differences in leukocyte kinetics, with low-susceptibility cows having faster blood PMNL recruitment and SCC recovery and a tendency for a milder clinical response than the high-susceptibility cows did.

Reliabilities of genomic estimated breeding values in Danish Jersey.

In order to optimize the use of genomic selection in breeding plans, it is essential to have reliable estimates of the genomic breeding values. This study investigated reliabilities of direct genomic values (DGVs) in the Jersey population estimated by three different methods. The validation methods were (i) fivefold cross-validation and (ii) validation on the most recent 3 years of bulls. The reliability of DGV was assessed using squared correlations between DGV and deregressed proofs (DRPs). In the recent 3-year validation model, estimated reliabilities were also used to assess the reliabilities of DGV. The data set consisted of 1003 Danish Jersey bulls with conventional estimated breeding values (EBVs) for 14 different traits included in the Nordic selection index. The bulls were genotyped for Single-nucleotide polymorphism (SNP) markers using the Illumina 54 K chip. A Bayesian method was used to estimate the SNP marker effects. The corrected squared correlations between DGV and DRP were on average across all traits 0.04 higher than the squared correlation between DRP and the pedigree index. This shows that there is a gain in accuracy due to incorporation of marker information compared with parent index pre-selection only. Averaged across traits, the estimates of reliability of DGVs ranged from 0.20 for validation on the most recent 3 years of bulls and up to 0.42 for expected reliabilities. Reliabilities from the cross-validation were on average 0.24. For the individual traits, the reliability varied from 0.12 (direct birth) to 0.39 (milk). Bulls whose sires were included in the reference group had an average reliability of 0.25, whereas the bulls whose sires were not included in the reference group had an average reliability that was 0.05 lower.