Learn about the new Holstein genetic evaluations for August 2024 and see how they’ll boost your farm’s fertility and productivity. Don’t miss out.

Come August 2024, the Holstein Association USA is shaking things up with an updated Fertility Index, rolled out alongside the official genetic evaluations. This game-changing move follows a recommendation from the Genetic Advancement Committee and has received the green light from the HAUSA Board of Directors. 

The revamped Fertility Index brings together multiple reproductive components into a unified measure, including: 

• The ability to conceive as a maiden heifer
• The ability to conceive as a lactating cow
• The cow’s overall ability to resume cycling, show heat, conceive, and sustain a pregnancy

The new Fertility Index formula is:

FI = (0.4 x Daughter Pregnancy Rate) + (0.4 x Cow Conception Rate) + (0.1 x Heifer Conception Rate) + (0.1 x Early First Calving) 

Significantly, this update involves a shift in weightings: The Daughter Pregnancy Rate has been scaled down from 0.7 to 0.4, while the Cow Conception Rate sees an increase from 0.1 to 0.4. This revamped Fertility Index is a crucial element of the Holstein Association USA’s Total Performance Index® (TPI®), a cornerstone for breeding decisions aimed at maximizing profit, efficiency, and fertility. 

By focusing on TPI, dairy farmers can dramatically improve their bottom lines; the genetic excellence expressed by these high-TPI cows isn’t just a short-term advantage—it’s a legacy for future generations. 

Curious to see the top-ranking animals, delve into sire summaries, or get the nitty-gritty on the TPI formula? Head over to www.holsteinusa.com/genetic_evaluations/GenUpdateMain.html for all the details.

Learn how errors in pedigrees affect the genetic evaluations. Do these errors distort breeding values and validation statistics? Discover more.

Accurate pedigrees are crucial for genetic evaluations, forming the backbone for understanding relatedness among individuals and guiding breeding decisions. They are vital for estimating breeding values, identifying superior genes, and enhancing livestock quality. 

However, pedigree errors, like misidentified parents or incorrect lineage records, are surprisingly common. These seemingly minor inaccuracies can have significant consequences, distorting the robustness of genetic models and leading to potentially detrimental breeding recommendations. 

These errors act as random exchanges, making individuals seem more or less related than they are. 

The single-step model, a promising solution, directly integrates genomic data into genetic evaluations. This method surpasses traditional models by providing greater accuracy through the combination of pedigree and genomic information, offering a comprehensive view of genetic potential. 

Using the single-step model, we examine how pedigree errors affect genetic evaluations. We’ll focus on the correlation between actual breeding values (TBV) and estimated breeding values (EBV) and the implications of these errors for validation studies with forward prediction. Understanding and addressing these errors is vital for robust genetic assessments. 

Pedigree Errors: An Often Overlooked but Critically Significant Issue 

Though often neglected, pedigree errors are critically significant as they misrepresent an animal’s genetic ancestry, leading to erroneous assumptions regarding genetic relationships. These errors can manifest in various ways, from incorrect parent recording to data entry mistakes. 

Familiar sources of pedigree errors include: 

• Misidentification of parents: Errors during breeding or registration processes can lead to incorrect sire or dam recordings.
• Recording mistakes: Clerical errors during data entry can misassign parents or offspring.
• Multiple sires: The presence of numerous potential sires without genetic testing can cause uncertainties in pedigree records.
• Errors in artificial insemination records: Mistakes in recording insemination details can significantly skew pedigree accuracy.

Previous research indicates that pedigree errors undermine genetic evaluations and impact breeding decisions. Traditional methods like the Animal Model or Parental Best Linear Unbiased Prediction (PBLUP), which often exclude genomic data, are particularly susceptible. These errors bias breeding values and hinder selection accuracy, making animals appear better or worse than they indeed are, thus distorting genetic evaluations and selection indices. 

Studies have shown that even a 5% error rate can reduce the accuracy of estimated breeding values (EBVs) by about 10%. Minor errors can also inflate early predictions in forward prediction models, creating a false sense of genetic progress. 

Traditionally, research focused on pedigree-based genetic evaluations, highlighting the detrimental effects of pedigree errors. This underscores the importance of the current investigation, which integrates genomic data to mitigate the negative impacts seen in traditional models. Looking ahead, future research should be inspired to refine methods that can detect and rectify pedigree errors, paving the way for more accurate genetic assessments.

Enhancing Breeding Precision Through Genomic Integration: An In-Depth Analysis 

This study, published in the Journal of Dairy Science, examined the impact of pedigree errors on genetic evaluations that incorporate both traditional and genomic information. These errors can significantly affect the accuracy of these evaluations, which are vital for breeding decisions. By understanding the influence of incorrect pedigree information, we can enhance precision, allowing farmers to make more informed breeding choices and ultimately improve their herds. 

This study analyzed the pedigrees and genetic data of 361,980 Fleckvieh cattle, with detailed genetic information on 25,950. This dataset provided a robust foundation to examine how errors in records might influence our findings. 

We simulated actual breeding values (TBV) and phenotypes by integrating genetic and environmental factors, with an assumed heritability of 25%. This approach ensured that our simulated data closely resembled real-life scenarios. 

Next, we examined the effect of pedigree errors on genetic evaluations using conventional (non-genomic) and single-step (genomic) models. We compared results using the correct pedigree against scenarios with 5%, 10%, and 20% incorrect records created by randomly reassigning sires to non-genotyped cows to replicate common recording mistakes.

Pedigree Errors: The Unseen Threat to Genetic Evaluation Integrity and Breeding Decisions

Our study reveals the practical implications of pedigree errors on genetic evaluations of cattle, particularly the link between True Breeding Values (TBV) and Estimated Breeding Values (EBV). As errors increase, this link weakens, impacting the reliability of genetic evaluations. This finding underscores the importance of accurate pedigree records in making informed breeding decisions. 

Along with this weak link came less variation in the predictions. This means pedigree errors made bulls look more similar in genetic quality than they are. This is much more obvious in bulls that have sired many calves, where such errors make it challenging to tell which bulls are the best. This blending effect in bulls with many offspring suggests that the system can’t differentiate well between high and low-quality bulls, potentially messing up your selection decisions. 

On the flip side, pedigree errors were not as damaging for young bulls that haven’t sired any calves yet. This happens because genetic evaluations for these young ones rely more on their DNA data than their offspring’s performance. This helps to buffer against the mistakes in their pedigree records. 

Moreover, in scenarios where future performance predictions are made, the errors in bulls with progeny tended to blow up early predictions. This makes early decisions potentially misleading and off-track. Thus, correcting pedigree errors is critical to keep genetic evaluations trustworthy and accurate, ensuring early predictions and overall breeding strategies stay on point.

Mitigating Pedigree Errors: Safeguarding the Future of Genetic Evaluations 

Understanding how pedigree errors impact genetic evaluations is crucial for dairy farmers. These errors, stemming from incorrect family tree data, lead to inaccurate breeding values (EBV) and poor selection decisions. 

As pedigree errors rise, the standard deviation of EBVs diminishes, making related animals, especially progeny-tested bulls, appear more alike than they are. This issue is less severe for younger animals but significantly affects bulls with many offspring. 

Reduced variation from pedigree errors causes overly optimistic early predictions, disrupting breeding programs. Inaccurate pedigrees weaken genetic evaluations, compromising effective selection. Ensuring accurate pedigrees through verification and genomic corrections is vital for precise EBV predictions, enhancing breeding programs, and strengthening your dairy herd.

The Bottom Line

When errors infiltrate cattle pedigrees, they severely disrupt genetic evaluations. A high frequency of mistakes weakens the correlation between a bull’s actual breeding value (TBV) and the estimated breeding value (EBV), reducing prediction reliability and consistency. This issue is particularly pronounced in progeny-tested bulls, where incorrect sire assignments inflate perceived similarities among bulls, skewing early predictions and undermining validation statistics. 

Maintaining precise pedigrees is fundamental for robust genetic evaluations. Accurate lineage information ensures the integrity of family relationships and sustains reliable breeding decisions. Implementing stringent checks, improving record-keeping, and leveraging advanced DNA testing are essential to minimize pedigree errors. DNA parentage tests significantly reduce the risk of misrecording sire-dam pairs. 

Future research should focus on refining methods to detect and rectify pedigree errors, assessing their impact across breeds, and seamlessly integrating genetic data into evaluation models. This approach will enhance the accuracy of genetic evaluations, ultimately fostering more reliable and efficient breeding programs.

Key Takeaways:

• Pedigree errors, including misidentified parents and incorrect lineage records, undermine the assumptions about relatedness in genetic evaluation models.
• The integration of genomic data using a single-step model enhances the precision of genetic evaluations, despite the presence of pedigree errors.
• Incorrect pedigrees lead to lower correlations between true breeding values (TBV) and estimated breeding values (EBV), particularly affecting progeny-tested bulls.
• Pedigree errors result in less variation among predictions, making genetically distinct animals appear more similar.
• In forward prediction validation scenarios, pedigree errors can cause an apparent inflation in the accuracy of early predictions for young animals.
• Implementation of stringent checks and advanced DNA testing can minimize pedigree errors, ensuring more robust genetic evaluations.
• Future research should focus on developing better methods for detecting and correcting pedigree errors to further enhance the accuracy and reliability of genetic evaluation models.

Summary: Accurate pedigrees are crucial for genetic evaluations, guiding breeding decisions and estimating breeding values. However, pedigree errors, such as misidentified parents or incorrect lineage records, can distort the robustness of genetic models and lead to detrimental breeding recommendations. A single-step model that integrates genomic data into genetic evaluations provides greater accuracy by examining the correlation between actual breeding values (TBV) and estimated breeding values (EBV). Traditional methods like the Animal Model or Parental Best Linear Unbiased Prediction (PBLUP) are particularly susceptible to these errors. Studies have shown that even a 5% error rate can reduce the accuracy of estimated breeding values (EBVs) by about 10%. Maintaining precise pedigrees is essential for robust genetic evaluations, and implementing stringent checks, improving record-keeping, and leveraging advanced DNA testing are essential to minimize pedigree errors. Future research should focus on refining methods to detect and rectify pedigree errors, assessing their impact across breeds, and seamlessly integrating genetic data into evaluation models to enhance genetic evaluation accuracy and foster more reliable and efficient breeding programs.

Unlike any other country, Direct Genomic Values (DGV) have been published in Canada for genotyped animals as part of its genomic evaluation system.  The intent of doing so was to provide producers and industry personnel a better insight into the “black box” of genomic evaluations when they were first introduced in 2009.  Ten years later, as of the December 2019 release, DGV will no longer be published or included in any outgoing data files associated with Canadian genetic evaluations.  Some breeders have expressed their disagreement with this decision and misunderstanding continues to be propagated.  This article provides further clarification regarding the decision to no longer publish DGV.

What Information Contributes to An Animal’s Genetic Evaluation?

After genomics was first introduced in Canada in 2009, Canadian Dairy Network (CDN) and industry partners launched an extensive education effort to help everyone better understand how the animal’s DNA analysis contributed to its genetic evaluation, resulting in the increased accuracy.  The information in Figure 1 was regularly used as part of this educational campaign.

Every young calf born in Canada and registered in the breed association herd book, whether it’s a heifer or bull, receives a Parent Average for each trait as its first official genetic evaluation, labelled as a PA. This estimate of its genetic potential is simply based on a formula that averages the genetic evaluation of its recorded parents.  In this sense, each animal’s pedigree serves as the first source of information for its genetic evaluation.

As a heifer calf ages and becomes a cow after first calving, her own performance data contributes to her genetic evaluation, labelled as an EBV (Estimated Breeding Value).  Performance data can include production data recorded through milk recording, classification data recorded by Holstein Canada and any other data that contributes to genetic evaluations for the various functional traits (i.e. health traits, fertility, longevity, etc.).  Including a cow’s own performance data to her genetic evaluation adds significant accuracy over and above the accuracy of its Parent Average from pedigree alone.  For cows that eventually have daughters old enough to have their own performance data, this also contributes to their genetic evaluation as a dam and further increases its accuracy.

For young bulls that enter A.I. and end up with many daughters with performance data, they end up reaching progeny proven status with an evaluation that is also labelled as an EBV.  While sires don’t have any of their own performance data included for dairy cattle traits, progeny proofs for sires end up with higher levels of accuracy (i.e.: Reliability) compared to cows, with their own data and with their daughter data, simply because of the volume of daughters that are included.

The significant difference that genomics has offered is the ability to genotype an animal at any stage of its life and have an analysis of its own DNA contribute to the estimate of its genetic potential. Doing so gives an increased accuracy of the resulting genetic evaluation with the greatest benefit occurring for young animals that otherwise would only have a Parent Average based on pedigree data alone.  In Figure 1, adding the contribution from the animal’s DNA is represented in red text and the resulting evaluation labels add the letter “G” to become either a GPA (for young animals) or a GEBV (for cows and progeny proven sires).

What is DGV and Why Stop Publication?

As shown in Figure 1, genotyping an animal means that an analysis of its DNA can contribute to the estimation of its genetic potential, which adds significant accuracy to that evaluation.  After introducing genomic evaluations in 2009, CDN coined the term “Direct Genomic Value”, or DGV, to represent this new source of contribution to genetic evaluations.  The terminology of Direct Genomic Value later became widespread around the world in the area of dairy cattle improvement.  The decision by CDN to publish DGV for each genotyped animal was simply to help everyone understand how genomics works.  The specific DGVs were not meant to be considered as an animal’s genetic evaluation and were never promoted to be used as a tool for selection or mating. 

Direct Genomic Values (DGV) are an intermediate step in the calculation of each animal’s most accurate genetic evaluation and serve as one of various sources of information that contribute to each animal’s estimate of genetic potential.

As an intermediate step in the process of estimating each animal’s most accurate genetic evaluation, it turns out that DGV are also not expressed on the same scale as the official evaluations of GPA.  The most elite animals of the breed have DGV that have a range that is higher than that for official GPA.  For this reason, it seems that various breeders and some A.I. organizations started to pay special attention to DGV and, on occasion, market their animals based on these higher values.

Once the Genetic Evaluation Board (GEB) of CDN, which includes breeders nominated by breed associations and other industry partners, announced its recommendation to the Board of Directors to no longer publish DGV, some of those breeders and A.I. companies that were marketing animals based on DGV made their opposition public and well known. As a consequence, senior staff at CDN and Holstein Canada met with some of the most vocal advocates of keeping DGV to listen to them and hear their perspectives on how DGV was important to their genetic selection and mating decisions.  Following extensive additional research into each of those perspectives presented to CDN and Holstein Canada, there remains no scientific evidence available that demonstrates that DGV provides any more information for good selection and mating decisions compared to using the official value of GPA.  It is based on this scientific evidence that the GEB and the Board of Directors of both CDN and Lactanet Canada have supported the direction to no longer publish Direct Genomic Values effective the December 2019 genetic evaluation release.

Since the initial research regarding the relative accuracy of Direct Genomic Values for selection decisions was openly presented in April 2018, there has much input from and consultation with producers as well as various breed associations, all of which was considered by the GEB when making the recommendation to no longer publish DGV as well as by the CDN and Lactanet Boards of Directors.

What About Transparency and Data Ownership?

The decision to no longer publish DGV does not reflect the position of CDN, or now Lactanet, as it relates to transparency of information and data ownership. The calculation of genetic and genomic evaluations is complex and uses advanced methods and models that involve several steps and sources of data contribution.  In addition to the contributions for domestic animals represented in Figure 1, there is also the use of data from international sources such as Interbull, CDCB in the United States and cow evaluations received from other countries. The genetic evaluation details on a trait by trait basis that are available via the former CDN web site, and the future Lactanet web site, are made available to help everyone understand the main traits presented on each animal’s Genetic Evaluation Summary page.

In terms of data ownership, Lactanet and other industry partners recognize that the raw data collected on dairy farms across the country belongs to the dairy producer.  Milk recording, classification, health recording, breeding data… all what is recorded and paid for by the producer belongs to them.  Even for genotyping, the use of the DNA sample provided by the producer, and the resulting genotype received from the laboratory, is treated seriously by Holstein Canada, CDN and now Lactanet Canada. The role and challenge of the various industry organizations is to take that raw data from the farm and transform it into valuable information for herd management decisions.  For genetic selection and mating decisions, the official genetic evaluation for genotyped animals, either GPA or GEBV, is the most accurate and valuable genetic decision tool, and not the intermediate value of DGV.

Author:                 Brian Van Doormaal, Chief Services Officer, Lactanet Canada

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Updates to crossbred evaluations

By Ezequiel Nicolazzi (CDCB), George Wiggans (CDCB), Leigh Walton (CDCB) and Paul VanRaden (USDA-ARS-AGIL)

Two important updates will be implemented in the crossbred evaluations in December 2019:

Change in F1’s threshold for the breed of evaluation to be the breed of the ID: Since the introduction of crossbred evaluations in April 2019, Breed Base Representation (BBR) is instrumental in determining the breed of evaluation of all animals. Until now, for F1 animals, the breed code of the animal ID determined the breed of evaluation even if the BBR value is only the second highest value if the 2 highest BBR breeds are in the range of 45-55% and the second breed is less than 10% from the first breed.

Effective with the December 2019 evaluation, the simplified rule will maintain the breed of the ID if its BBR is higher than 40%. In that case an animal with HO ID, will maintain the HO breed of evaluation if its HO BBR is higher than 40%. This change currently impacts less than 500 animals in
the evaluation.

Change in calculation of reliabilities for crossbred animals: The calculations of reliability (REL) and the inbreeding of future progeny (EFI) rely on the relationship between the animal and the predictor population. Since crossbred animals do not have a predictor population for PTA (purebred SNP effects are blended in based on BBR values), their reliability and EFI estimates have previously been obtained using a multi-breed reference population. This strategy results in nearly 30% lower
reliabilities for animals with BBR<90 compared to animals with BBR>90. A further concern was that crossbred animals with stronger links to the reference population (e.g. BBR close to the 90 threshold)
receive similar reliabilities than animals having a mixed-breed genetic makeup (e.g. F1’s, or animals with contributions from more than 2 breeds).

Effective December 2019, following a full review of the methodology and results, the most connected purebred reference population of each crossbred animal (determined by the breed of evaluation used) will be used instead. In addition, a differential weighting of the traditional and genomic
components will be applied, by giving more weight on the traditional component for animals with traditional reliability above 30% (animals with phenotypes). These changes make it even more
important to emphasize that genomic evaluations on crossbred animals are useful for animals being bred to animals of the same breed of evaluation (e.g. going towards “purity”), whereas are not an advisable tool for animals in a rotational crossbreeding program.

As expected, no effect will be observed on purebred animals, whereas reliabilities and PTAs of crossbred animals will be impacted. (Note that reliabilities are used to weight the traditional and genomic components of an evaluation.) The greatest changes will be observed on animals with BBR
close to the 90% threshold, as their reliabilities will be the ones mostly changing (upwards) with this new strategy. Reliabilities will be more closely linked to the BBR distribution, as animals having more purebred composition will receive a reliability and PTA estimates similar (with differences in weighting, obviously) to a purebred animal. Considering that animals with BBR close to the 90% threshold have most of their SNP effects based on that same breed on which relationships are obtained from, this change makes reliability estimates more accurate.

Correction to use of foreign fertility evaluations

By Ezequiel Nicolazzi (CDCB), Leigh Walton (CDCB) and Paul VanRaden (USDA-ARS-AGIL)

When extending the MACE information to include Mastitis into the evaluation in August 2019, an incorrect trait order when comparing reliabilities caused MACE fertility evaluations to be used incorrectly for some bulls. This resulted in more differences between traditional and genomic evaluations than expected, which mainly affected highly-reliable Holstein bulls and animals related to them. Test results on the error repair indicate the divergence between traditional and genomic PTAs for these bulls should be resolved and is expected to result in slightly different genomic PTAs propagating throughout the population, due to better SNP estimates.

We thank Ryan Starkenburg (ABS) for identifying the misalignment and the industry review committees for providing feedback.

It was ten years ago, in August 2009, that genomic evaluations were first officially published in Canada.  This started with the Holstein breed but the same technology was later also applied in the Jersey, Ayrshire, Brown Swiss and Guernsey breeds. Let’s take a quick look at how genomics has changed dairy cattle selection, its impact on genetic improvement and contemplate what’s next on the horizon.

Bull Selection and Usage

Almost immediately when genomics was introduced, the A.I. companies around the world were seemingly forced to embrace it.  Given the intense competition between organizations, as soon as any had decided to aggressively use genomic selection, they needed to as well to stay in business. The science showed that genomics was not a “fade” and technology had advanced to a point where DNA could finally be used for genetic selection in dairy cattle.  The biggest advantage that genomics provided to A.I. companies, was the increased accuracy of genetic information available prior to making any bull purchasing decisions. Also, genomics allowed for the use of younger sires and dams as the parents of the next generation of young bulls, without much sacrifice in accuracy.  Together, this translated to an unprecedented annual rate of increase in the average genetic merit of young bulls entering A.I. throughout North America, which now exceeds 120 LPI points and $200 Pro$ per year.  With such a continuous year over year boost in the genetic makeup of genomic young sires offered through A.I. companies, these bulls now represent two-thirds of the total semen market share in Canada.

Increased Genetic Progress

A direct and very significant outcome of having genomic evaluations for the past ten years is the impact on the increased rate of genetic progress.  Figure 1 shows this impact very clearly since the steady rate of annual gain before genomics, which was 46 LPI points and $79 Pro$ per year, suddenly switched after 2009.  During the past five years, the average rate of genetic gain has increased by 2.2 fold, reaching 102 LPI points and $180 Pro$ annually. The dashed lines since 2009 in Figure 1 reflect the expected genetic progress that would have been achieved for both LPI and Pro$ in Canadian Holsteins if genomics had not been introduced.

Of equal, or perhaps even greater, importance than these realized gains for LPI and Pro$ is the impact that genomics has had on genetic progress achieved for individual traits as shown in Figure 2. The first key point to notice is that positive genetic gain is now being realized for all of the major production, conformation and functional traits in addition to Pro$,  LPI and its three components. Before genomics, in addition to losing ground for Daughter Fertility, Persistency, Milking Temperament and the Health & Fertility component of LPI, very little genetic progress was being made for other traits including Fat and Protein Deviations, Milking Speed, Daughter Calving Ability and Metabolic Disease Resistance. For all of the other eleven traits in Figure 2, the average rate of genetic gain realized with genomics has increased two-fold. The truly amazing outcome now known is that genomics provides an unprecedented opportunity to realize selection objectives for lower heritability traits even if they have negative genetic correlations with traits of moderate or higher heritability.

Figure 1: Rate of Genetic Progress Achieved in Canadian Holsteins With Genomics

Figure 2: Genetic Gain Achieved in Canadian Holstein During the Past 5 Years Compared to 5 Years Before the Introduction of Genomics

Genotyping Adoption

Over the past ten years, over 3.2 million genotypes have now been accumulated in the genetic evaluation database at Lactanet.  This includes genotypes from animals all over the world, mainly the United States, since it was agreed at the onset that both countries would share all dairy cattle genotypes.  Figure 3 shows the evolution in the number of genotyped Canadian-born Holstein females since 2008. After an initial gradual growth period a level of plateau was seemingly reached during the years from 2015 to 2017. For various reasons, one of which was a 27% reduction in the cost of heifer genotyping in Canada, the adoption of female genotyping in Holsteins jumped to over 37,000 in 2018 and activity so far this year leads to a projected volume of 53,000 females for 2019. Figure 4 shows the similar information with genotyping adoption rates expressed in terms of the percentage of females registered by Holstein Canada by year of birth. This figure shows that the market penetration for heifer genotyping reached the 12% mark for registered Holsteins born in 2018.

Figure 3: Number of Canadian-Born Holstein Females Genotyped per Year

Figure 4: Adoption of Heifer Genotyping by Year of Birth for Registered Holsteins in Canada

A Crystal Ball

The implementation of genomic evaluations and the use of genomic selection have only just started to impact dairy cattle improvement strategies in Canada and globally. Given the experience with genomic selection over the past ten years, looking into a crystal ball towards the future, one can expect to see the following over the next ten years:

• The introduction of a vast array of new traits of economic and social importance, most of which have not yet even been considered by dairy producers
• Increased use of sexed semen, in-vitro fertilization and other advanced reproductive technologies, which also promote the increased use of beef semen to breed dairy cows
• Use of DNA genotypes for improved selection strategies balancing genetic gain with maintenance of genetic diversity, including the use of genome-based mating programs
• A significant restructuring and consolidation of the A.I. sector, leading to a handful of larger, multi-national breeding companies
• Significant value-added benefits from DNA genotyping including automated parentage discovery and recording as well as traceability of dairy animals and food products

Needless to say, we are still at the tip of the iceberg when it comes to the impact that genomics and DNA genotypes will ultimately have on the dairy cattle industry.

Source: LactaNet

Over the last couple of years, the noticeable seasonal fluctuation in trends in fertility traits has been difficult to understand. In particular, a large percentage of young bulls either increased or decreased, depending on the season of the triannual run (April, August, or December). It is no surprise that when bulls’ evaluations increase, it makes a lot of breeders happy. In contrast, when Predicted Transmitting Abilities decrease, many folks are disappointed. Understandably, producers hate to see bulls they’ve been using decline as it appears that their previous selection of service bulls was not optimum. These perceptions of the changes are unwarranted if it turns out the increases or decreases observed were due to shortcomings in the evaluations, i.e., changes occurred simply because the estimation procedures did not account for season appropriately. A key concern is that users may lose confidence in the usefulness of the results provided.

The good news is that after a complete revision of the fertility evaluations, which included several improvements over the last couple of years, the scientists at AGIL and CDCB have uncovered the reasons that caused seasonal fluctuations. The primary reason was that the seasonal grouping was derived previously from the heifers’ breeding dates instead of cows’ breeding dates. The less-than-exciting news is now that the issue has been discovered and rectified, there will be changes coming one more time, in the August 2019 run.

In order to understand the impact the revisions proposed (new seasonal grouping) would have on the stability of future evaluations, CDCB reran the past 4 evaluations using the revised seasonal groups. The results are shown below for daughter pregnancy rate (DPR) in Holstein. Trends from the same four runs were examined for cow conception rate (CCR) and heifer conception rate (HCR) as well, and also for Jerseys. Comparison of trends in the four official runs (Figure 1) and trends from improved seasonal grouping runs (Figure 2) gave reviewers’ confidence that this lingering problem is now resolved.

Figure 1:

Figure 2:

The results are extremely encouraging because almost all the seasonal variation disappeared when the model changes were applied. The seasonal variability observed in runs previously published (OFFICIAL) was gone in the test runs (TEST) using the revised procedure. These research runs give us confidence that the change introduced in the upcoming August 2019 evaluation will no longer produce the seasonal fluctuation experienced in the last couple of years. The actual changes in Predicted Transmitting Abilities (PTA) for the top 100 NM$ bulls in the previous evaluation are shown in Table 1 for 2 breeds.

     Table 1. Changes in PTAs in fertility traits of Holsteins and Jerseys between the April 2019 and August 2019 runs

There were a couple of other minor changes implemented that were obvious improvements that had little impact on the evaluations. For example, cow lactations initiated with an abortion were removed so they no longer biased Early First Calving (EFC). Abortions were previously entering that EFC calculation. Since the incidence of these cases was extremely low, the impact of this new edit is negligible on a population framework but could change EFC on single animals slightly.

In summary, the introduction of the new “stability package” will result in lower values for recent bulls, affecting most fertility traits. The table shows the changes for heifer conception rate (HCR), cow conception rate (CCR), and daughter pregnancy rate (DPR) that will be observed in the August run in comparison to the April 2019 evaluations. These changes will produce fertility evaluations that are considerably more accurate in future evaluations. We want to acknowledge the exceptional efforts of Paul VanRaden and Jana Hutchison for the research investigation to uncover causes of undesirable changes occurring and to Jay Megonigal for rerunning four consecutive test evaluations to confirm that improvements were incoming.

Source:  uscdcb.com

The Genetic Evaluation Board of CDN met on Wednesday, February 13, 2019 at the Holiday Inn in Guelph, Ontario following an Open Industry Session held the previous day. The following is a summary of the discussions and recommendations from the GEB, which will be considered by the Canadian Dairy Network (CDN) Board of Directors at its next meeting scheduled for March 7, 2019.

• Brian Anderson from Athlone Farms was re-elected as Chairman of the Genetic Evaluation Board for 2019.
• The GEB recommended that CDN proceed with the planned implementation of an updated Pro$ formula for the Holstein and Jersey breeds as well as the first introduction of Pro$ for Ayrshires, effective the April 2019 genetic evaluation release. Specific changes associated with the new Pro$ formula include:
  • Updated economic values for revenue based on current milk pricing across Canada.
  • Updated economic values on the expense side of the profit equation, which now include the cost of extra inseminations associated with poor reproduction as well as different daily maintenance costs based on each cow`s estimated relative body size
  • More accumulated cow lifetime profitability data since Pro$ was originally developed in2015.
  • A Pro$ formula for Jerseys based on cow lifetime profitability data specific to that breed.
• In addition, the GEB recommended that CDN implement automated procedures to develop and introduce an updated Pro$ formula annually for each breed, including an annual update to the genetic base used to express published Pro$ values.
• In conjunction with the update of the Pro$ formula, the GEB also recommended that CDN update the LPI formula in each breed, effective April 2019, based on the discussions held with the respective breed associations. Although all details of the updated LPI formula will be published separately by CDN, the key changes include:
  • For all breeds, an increased weight on fat such that it is at least equal to or greater than the weight on protein within the Production component, to reflect national changes in milk pricing in recent years
  • Changes to the Durability component in Holsteins by including Hoof Health alongside Feet & Legs as well as Rump
  • For Ayrshire, with the introduction of Pro$, the LPI formula will shift the relative emphasis on the three components to 46% Production, 32% Durability and 22% Health & Fertility, instead of the current 50:31:19, and also adjust the relative weights on the various traits within each of the three components
  • The Jersey LPI formula will increase emphasis on the Health & Fertility component relative to Production and Durability, and incorporate some adjustments within each of the three components including the removal of Dairy Strength from Durability
  • The LPI formula for the other coloured breeds will reflect current breed goals and the desired rate of genetic progress for key traits of importance

• In terms of future plans for genetic and genomic evaluation services, the GEB discussed and supported the strategy outlined by CDN to develop and introduce evaluations for additional traits including cystic ovaries, metritis and retained placenta, with April 2020 as the target release date, as well as feed efficiency with a target date of August 2020.

  The GEB discussed results from the ongoing work at CDN to develop an improved genetic and genomic evaluation model for calving performance traits, namely calving ease and calf survival. Based on the most recent analyses conducted by CDN, the GEB supported the current direction of implementing a single step genomic evaluation system, with a possible implementation date of December 2019 or April 2020. Results from genomic validation testing and final recommendations associated with the new calving performance system will be presented at the next Open Industry Session in October 2019.

  Given the current direction of industry partners to introduce new DHI service options to allow for the remote collection of on-farm production data electronically, without visiting the farm, the GEB discussed how such data could ultimately be included for genetic evaluation. It is expected that the inclusion of milk weights on each data collection date can easily be incorporated into the current Test Day Model used by CDN for production traits. The use of fat and protein components analysis from in-line sensors may also be possible. When discussing how resulting cow evaluations would be published and labelled, the GEB recommended that CDN examine the possibility of making publicly available all cow evaluations based on their own production data, including such evaluations that may completely be based on unsupervised and/or non-verified data. This topic will be openly discussed with industry partners and presented at Open Industry Sessions in the future.

  Another topic discussed by the GEB that is of broad interest to Canadian producers, breeders and industry partners is the current and future emphasis placed on stature, especially in the Holstein breed. In general, the GEB supports the recent changes implemented by Holstein Canada related to the assessment of stature and how it contributes to Dairy Strength and consequently to overall Final Score for Conformation. While such changes have an immediate impact on classification results, the GEB recognizes that they take several years of classification data before impacting genetic evaluations. For this reason, the GEB recommended that CDN examine the possibility of moving to composite indexes for the calculation and publication of genetic evaluations for Conformation, Mammary System, Feet & Legs, Dairy Strength and Rump. An expected advantage of this approach is the establishment of composite indexes that reflect the desired direction of selection for traits of intermediate optimums such as Stature, Rear Legs Side View, Teat Length and others. To allow for broad industry discussion and input on this topic, CDN will include it on the agenda for the Open Industry Sessions planned for October 2019 and March/April 2020.

  CDN continues to be closely involved in the international effort to address the potential downward bias of progeny proven sires resulting from genomic pre-selection applied by A.I. organizations prior to purchasing genomic young sires. Given the complexity of this issue, any advancement in methods and models to account for this effect is expected to take time.

  Following an industry request, the GEB discussed the current policy implemented by CDCB in the United States associated with the availability to CDN of haplotype results for genotyped animals. As a consequence, the GEB recommended that CDN initiate further discussions with CDCB to find a solution such that CDN is able to provide haplotype results for all genotyped animals.

The next Open Industry Session is scheduled to take place on Wednesday, October 9, 2019 in St. Hyacinthe, Québec with the Genetic Evaluation Board meeting the following day.If there are any questions, concerns or comments regarding the recommendations of the Genetic Evaluation Board, as outlined in this summary, please feel free to contact committee members listed at http://www.cdn.ca/committees-geb.php or by contacting Brian Van Doormaal directly at Canadian Dairy Network

There is no doubt that the arrival of genomics in Canada ten years ago has had a major impact on the entire dairy industry. It can be argued, however, that no segment of the industry has been more affected than the A.I. sector. In order to survive the competitive environment of that business, both nationally and internationally, A.I. companies needed to embrace and adapt to a new genetic selection scheme based on genomic selection. Canadian Dairy Network (CDN) recently looked at what has changed in terms of young bulls that were actively marketed to Canadian dairy producers in the years before the arrival of genomics (i.e.: 2004 to 2009) compared to those marketed in Canada more recently.

Figure 1 shows that the total number of young Holstein bulls with semen sold in Canada has not significantly changed since 2004, averaging 445.  That said, genomic evaluations make it easier for A.I. companies to market young bulls internationally, which means there are several more players offering genomic young bulls to Canadian producers.  Figure 1 also shows the increasing percentage of those bulls in A.I. that resulted from embryo transfer or manipulation such as embryo splitting, which now surpasses 90%.  This trend generally reflects the parallel increased adoption of new reproductive technologies such as in vitro fertilization (IVF). A reality of the new selection scheme based on genomics is the huge shift towards the use of young animals as parents of potential young bulls for entry into A.I.  This shift results from the significant gains in accuracy of genetic evaluations for young bulls and heifers due to genomics.

While the change towards the youngest parents possible and high selection intensity for A.I. purchases may be criticized by some, Figures 2 and 3 show the positive impact in terms of the average genetic merit of young Holstein bulls marketed in Canada since 2004 for LPI and Pro$, respectively. Prior to genomics, the average increase in LPI for young bulls with semen released between 2004 to 2009 was 84 points per year. For the most recent complete 5-year period from 2012 to 2017, this increased significantly to average 121 LPI points per year. For Pro$, these same averages were $142 and $206 annually, as shown in Figure 3.  This means that genomic young bulls released this year are expected to increase the average lifetime profit of their daughters by more than $200 compared to daughters of young bulls released a year earlier.

The race among A.I. companies to identify, purchase and offer to producers the highest genomic young bulls possible has also led to the adoption of strategies that put greater control in the hands of such companies. Essentially all leading A.I. organizations globally have implemented business plans that include the ownership of elite females, based on genomics, which serve as a primary source for producing the next generation of elite genomic young bulls.  In Canada, the five A.I. companies with the largest market shares all now have their own female multiplier herds and associated breeder prefixes including “Progenesis” for Semex Alliance, “S-S-I” for Select Sires, “Peak” for Alta Genetics, “Denovo” and “ABS” for ABS Global and “Co-op” for Genex/CRI.

The recent CDN analysis also examined the breeder prefix of the young Holstein bulls with semen released in Canada from 2004 to 2017. On average, there were over 220 breeder prefixes represented among those bulls with semen marketed in Canada.  Of these, two-thirds of the breeders provided only one bull during any given year and an average of 40 breeders were able to provide three or more bulls to A.I. that were marketed in Canada. In any given year, there were a handful of breeders that contributed at least 10 bulls that were offered in Canada. Figure 4 shows the trend in the percentage of young bulls marketed in Canada that were sourced from the ten most frequent prefixes within each year of semen release.  This proportion ranged between 19% and 28% for all years from 2004 to 2014 but has increased since then to 47.9% for genomic young bulls marketed in Canada in 2017.  This means that roughly half of all bulls entering A.I. for use by Canadian producers have been sourced from ten breeder prefixes. The main reason for this concentration of bulls sourced from fewer breeder prefixes is the introduction of female breeding programs by major A.I. companies in addition to contractual arrangements between A.I. organizations and specific breeders.

Summary

Genomic selection has significantly changed the design and structure of the typical genetic improvement scheme for dairy cattle.  Traditional young sire incentive programs have been replaced by strong demand for young sire semen to a point where it represents nearly 70% of the market share. The increased accuracy of evaluations for young males and heifers has moved sire selection schemes towards genomic testing for screening so that only the most elite are purchased for entry into A.I. In addition, the increased adoption of reproductive technologies such as IVF has significantly reduced the average age of the dams of young bull entries into A.I. The competitiveness of the A.I. sector globally has moved such global companies to strategies whereby they own their own females to have a greater control on sourcing the next generation of elite genomic young bulls.  In the end, this all translates to higher quality young sires offered to Canadian producers, which translates into faster rates of genetic progress for the breed.

Author:           
Brian Van Doormaal, General Manager, CDN
Lynsay Beavers, Industry Liaison, CDN

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2019 will mark Canada’s 10th anniversary of the introduction of official genomic evaluations. When they were first introduced by Canadian Dairy Network (CDN) in August 2009 for Holsteins, there was much hesitation and questioning about whether the technology was real and useful or just hype. Today, we know the truth and, as a consequence, breeds with genomic evaluations have rapidly increased the rate of genetic progress for essentially all traits.

Basically, genomic selection added another source of data to the genetic evaluation systems at CDN.  In addition to performance data and pedigree information, DNA became a new source of data for each genotyped animal.  To improve the understanding of how this new source of data was being used to produce published genomic evaluations, CDN decided to make Direct Genomic Values (DGV) public.  In recent months, there has been much discussion about the intent of CDN to no longer publish DGV. The strong interest and passion of Canadian breeders was clearly heard. For this reason, the CDN Board decided to delay the implementation of the GEB recommendation to be effective December 2019. Let’s take a closer look at why CDN will be moving forward with this direction.

What is Genomic Parent Average (GPA)?

For genotyped animals, there are three main sources of information that contribute to its official genomic evaluation.  These include the animal’s Parent Average (PA), any performance data (i.e.: such as lactation, classification, mastitis, fertility, etc. data) recorded on the animal and/or its progeny, and the DGV estimated from the animal’s DNA.  For young bulls and heifers, since no performance data exists, they receive a Genomic Parent Average (GPA), which combines its PA and its DGV into the single official genomic evaluation published by CDN, as shown in Figure 1.

Figure 1: Combining Parent Average (PA) and Direct Genomic Value (DGV) into the Official Genomic Parent Average (GPA)

Scale Differences

Since PA is simply the average of the evaluations for the animal’s sire and dam, the range for PA can never be wider than it is for evaluations of bulls and cows old enough to be parents.  Looking at Conformation as an example, the highest active sire in A.I. currently has a rating of +20, while the highest proven sire is +16 and the highest breeding age female born in Canada is +18.  This means that it is impossible for Canadian-bred animals to have a PA higher than +19. Looking at DGV for Conformation, however, the highest bulls are at +22.  This higher scale for DGV attracts extra attention to these values for marketing purposes. However due to their different scales, DGV cannot be directly compared to GPA values.  Further, since GPA results from a blending of PA and DGV, the most elite animals of the breed will almost always have a DGV higher than GPA.

Animal Rankings

Even though the scales for GPA and DGV are not exactly the same, the rankings for top animals of greatest interest for selection are essentially identical. In fact, regardless of the trait looked at (i.e.: LPI, Pro$, Conformation, etc.), over 90% of the highest genomic bulls would be the same if ranked by GPA versus DGV.  In this sense, DGV does not help identify the most elite animals for selection and mating compared to using GPA alone.

Prediction of Future Genetic Evaluations

In discussion with some breeders, there was the impression that DGV helped to better identify those genomic young bulls that would end up with the highest proofs once their progeny were milk recorded and type classified. This was the basis for the initial analysis conducted by CDN geneticists earlier this year.  The most appropriate way to assess this question is to look at sires that currently have an official progeny proof and see whether their GPA or DGV four years ago, when they were a genomic young sire in A.I., best predicted their current results.  The results of the analysis were clear. While GPA is not a perfect predictor of a young bull’s future progeny proof, using DGV was consistently a poorer predictor.  This can be explained by the fact that DGVs tend to be higher than GPA for elite genomic young bulls so a higher degree of over prediction is expected compared to GPA.

The same question can also be asked for females.  Does DGV for genotyped heifers provide a better prediction than GPA of their future performance as a lactating cow in the herd? CDN conducted a specific analysis to examine this question within several herds.  In the end, there was no practical difference in the correlation between GPA or DGV for heifers with their resulting 305-day lactation yields and classification scores during first lactation.

Looking at the Difference of DGV Versus GPA

Another strategy used by some breeders when assessing high end genomic bulls for semen purchase decisions has been to look at the difference of DGV minus GPA. The belief here has been that preference should be given to select genomic bulls for which the superiority of DGV over GPA is the highest. The CDN analysis looked at this hypothesis by focussing on the Top 100 GPA LPI genomic bulls in 2013, all of which now have an official progeny proof in 2018. The 25 genomic bulls with the highest difference of DGV minus GPA were compared to the 25 bulls with the lowest DGV superiority and results are presented in Figure 2. The 25 bulls with the biggest difference had an average DGV LPI of 3190 and an average GPA LPI of 3027.  As expected, this difference was much less at only 60 LPI points (i.e.: 3075 minus 3015) for the other group of 25 genomic bulls in 2013. Once proven, however, it was the 25 bulls with DGV and GPA being closest together that ended up with the higher average LPI, at 2929 compared to 2827 for the 25 bulls with the biggest difference of DGV minus GPA. This overall result stemmed from the fact that the bulls with the biggest difference had significantly lower Parent Average (PA) for LPI at 2622 points, compared to 2773 for the bulls for which DGV and GPA were quite similar.

Figure 2: Comparison of Average LPI Values for Two Groups of Genomic Bulls Among Top 100 for GPA LPI Based on the Degree of Difference Between DGV and GPA

Breeding for the Next Generation of Extreme Animals

Breeders aiming to produce young bulls for potential entry into A.I. and/or elite females for marketing and embryo sales tend to have navigated to using DGV as an important sire selection tool. The goal from this strategy is to use genomic bulls with the highest DGV for any given trait to increase the chance of producing progeny that also have an extreme DGV in the breed. CDN recently designed and conducted an analysis to assess this strategy compared to using GPA for achieving the same objective.  The conclusion from this study was that DGV was not superior to GPA in terms of identifying extreme genomic sires that will have higher chances of producing extreme progeny. 

Path Forward

Based on all scientific analysis conducted, no evidence has been found to show that DGV provides any information for improved sire selection and/or mating decisions, compared to using the official GPA itself. Based on these results, the Genetic Evaluation Board (GEB) of CDN approved a recommendation to no longer publish DGV in the future.  In terms of implementation of this recommendation, the CDN Board of Directors decided to delay it until December 2019.  In the meantime, CDN will work with the various breed associations and A.I. organizations to prepare and deliver an industry-wide communication plan related to this direction.

Author:           
Brian Van Doormaal, General Manager, CDN
Lynsay Beavers, Industry Liaison, CDN

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Canadian Dairy Network (CDN) held an Open Industry Session on Wednesday, October 24, 2018 at the St. Hyacinthe Congress Centre, which was followed, as usual, by a meeting of the Genetic Evaluation Board (GEB) the following day. The following is a summary of the discussions and recommendations resulting from that GEB meeting held the next day, which will be considered by the CDN Board of Directors for approval at its meeting on December 10, 2018.

• Following a comprehensive presentation related to the publication of Direct Genomic Values (DGV) and a lengthy discussion period during the Open Industry Session, the GEB members continued discussions during its meeting the following day. A summary of those discussions has been circulated by CDN in a separate document and is available upon request. In the end, given the technical nature of the GEB mandate, as an advisory committee to the CDN Board of Directors, the GEB felt there was no science-based reason to change the direction of its original recommendation made last April for CDN to no longer publish DGV for any animals or make them available in any data files. Consideration of non-technical points on this topic and an implementation plan fall within the responsibility of the CDN Board of Directors.
• The GEB discussed some final details associated with the transition of Somatic Cell Score expression to the Relative Breeding Value (RBV) scale used for all other functional traits, to be effective December 2018. As a consequence the GEB made the following recommendations:
  • That evaluations for the Mastitis Resistance (MR) index be labelled as official when the evaluation for Somatic Cell Score meets the minimum criteria for official status.
  • That CDN develop a Cow Genetic Evaluation Details page on its web site for providing genetic evaluations for traits with official status that do not otherwise appear on the animal’s Genetic Evaluation Summary page.
  • That CDN examine the use of single step methodology for calculating Clinical Mastitis genomic evaluations for possible implementation in the future. This approach is expected to increase the accuracy compared to current genomic evaluations and/or allow CDN to expand the availability of this trait to other breeds.
  • That CDN assess the current range of bull evaluations for Mastitis Resistance in the Ayrshire and Jersey breeds and consider making any required modifications given that these breeds do not have genomic evaluations for Clinical Mastitis, which is the case for Holstein.
• Also effective December 2018 are official genetic and genomic evaluations for additional hoof health traits in Holsteins, over and above Digital Dermatitis that was introduced in December 2017. On this topic, the GEB made the following recommendations:
  • That Holstein breed evaluations for eight hoof lesions, namely Digital Dermatitis (DD), Interdigital Dermatitis (ID), Heel Horn Erosion (HHE), Sole Ulcer (SU), Toe Ulcer (TU), White Line Lesion (WL), Sole Hemorrhage (SH) and Interdigital Hyperplasia (IH), be added to the “Health” page linked to each sire’s Genetic Evaluation Details page.
  • That the proposed formula for combining evaluations for the eight lesions into a single Hoof Health (HH) index be approved for implementation by CDN.
• That CDN continue its effort towards the development of a national strategy to have more hoof health data collected from herds across Canada with the aim of increasing the accuracy of genomic evaluations in Holsteins as well as expanding the availability of these traits to other breeds where possible.
• Given the intent of CDN to introduce updated formula for Pro$ and LPI in April 2019, the GEB reviewed the results of analysis conducted to date and made the following recommendations:
  • Following discussions with Ayrshire Canada, the Pro$ index will be introduced in the Ayrshire breed in addition to the Holstein and Jersey breeds.
  • That that genetic base used for Pro$ be defined such that published values are easily comparable over time and the units of expression reflect the expected difference in daughter profitability in dollar terms.
  • That CDN continue the ongoing research effort to account for different maintenance costs according to a cow’s body size when developing the updated Pro$ formula. To achieve this goal in a sustainable manner for the longer term, the industry should develop a national strategy for collecting body weight data on a routine basis.
  • That the Hoof Health (HH) index to be introduced in December 2018 be added in April 2019 to the Durability component of the Holstein LPI formula, along with Feet & Legs, in an effort to improve both the resistance to hoof lesions as well as mobility.
• That CDN continue the consultation process with breed associations to assess options of different LPI formula and present recommendations at the next Open Industry Session planned for February 2019.The GEB reviewed the results of work at CDN examining options for improving genetic and genomic evaluations for calving performance traits, namely calving ease and calf survival. Supporting the direction of this ongoing research effort, the GEB recommended the following:
  • That CDN verify that calvings resulting from reproductive technologies such as embryo transfer, where the dam giving birth to the calf is not the same as the genetic dam, be excluded from genetic evaluation calculations for calving performance.
  • Given that both Canada and United States are reviewing their current calving performance evaluation systems, that CDN and CDCB have technical discussions to improve the correlation of bull proofs for calving traits between the two countries.
  • With the increased accuracy from genomics to evaluate Direct Herd Life based on actual daughter survival data, the GEB discussed the value of continuing to also estimate an Indirect Herd Life evaluation based on a prediction formula that combines evaluations for several traits. After considering different options, the GEB recommended that CDN conduct an analysis to examine the impact of removing Indirect Herd Life as a predictor trait of daughter survival that is combined with Direct Herd Life to derive the officially published evaluations for Herd Life. Results of such an analysis will be presented at one or both of the Open Industry Sessions and GEB meetings to be held in 2019.
• The GEB discussed two topics related to the registration status and/or purity level of Jersey
  animals displayed on the CDN web site, with the following recommendations:
  • That CDN implement an automated check based on pedigree information available for A.I. sires such that only sires whose daughters qualify for herdbook registration by Jersey Canada can be designated as having “Active” status for semen marketed in Canada.
  • In response to correspondence received from Jersey Canada, that CDN examine ways to increase the visibility of purity levels and/or Breed Base Representation (BBR) values calculated by CDCB, especially for genomic young sires and heifers.

The next Open Industry Session will be held on Tuesday, February 12, 2019 at the Holiday Inn in Guelph, Ontario, with the Genetic Evaluation Board meeting the following day.

Darren and Sharon Parrish use Australian Breeding Values (ABVs) for workability and calving ease to help run a productive and profitable dairy business and make better decisions on their farm at Bodalla, on the New South Wales south coast.

The ABVs help them identify bulls whose progeny are quick and relaxed milkers, while reducing the need for assisted calvings.

While these ABVs are on the check list for any bulls used in the herd, the Parrishs also record the traits within their own herd so they have a measure on how each heifer and cow performs and how their herd is tracking.

Their herd’s Workability and Calving Ease records also go back to DataGene and are essential in building reliability in the proofs of the bulls they have used, a process which underpins ABVs and the genetic evaluation system.

“We’ve been keeping detailed cow records for a long time – it’s something I really like doing because the information we collect gives us feedback on our cows,” Sharon said. “While it does involve a bit of extra work, the information you get back shows you what your cows are doing, the gains we are making and helps us make better decisions.” The Parrishs milk 200 registered Holsteins cows year round under their Darradale prefix.

Workability
The Parrishs have recorded workability traits for all 2-yearold heifers in their first lactation for more than 20 years. Workability covers three traits that reflect how easy a cow is to have in the herd: milking speed, temperament and likeability. The Workability ABV is included in each of the three indices – Balanced Performance Index (BPI), Health Weighted Index (HWI) and Type Weighted Index (TWI), with the highest weighting in the HWI.

“We’ve always recorded the workability traits for our heifers but our on farm software program makes it really straight forward,” Sharon said. “Once a heifer’s calving date is recorded in the system, 30 days later Easy Dairy will automatically flag that the heifer needs to be assessed for the workability traits. “We calve 50-60 heifers at a time twice a year, so we will have two batches a year that need to be recorded as they come through the shed.

“Once we have assessed the heifers for their milking speed, temperament and likeability, those records are automatically sent to our herd recording service, Dairy Express. “It might seem like extra work but we are really interested in our cows and we want to know who is slow or nervous as they are not the types of cows we want in the herd.”

Calving ease
The Calving Ease ABV is an indicator of how easily a bull’s progeny will be born – bulls with a calving ease ABV of 100 or more produce easier calvings. The calving ease score has a range of code options from a normal birth, assisted and the level of assistance required up to surgical assistance.

“Every time a calf is born we record the date, its dam, its size, sex, calving ease score and fate,” Sharon said.

“When we select bulls to use over the herd we look at calving ease because we want to minimise calving problems.

“Heifers that have unassisted calvings reach peak production faster and get back in calf sooner than heifers that need assistance. “Heifers which have assisted calving also involve extra labour and often incur veterinary costs.

“It’s expensive to breed and grow out a heifer to the point of calving so we want our heifers to calve unassisted and come into production strongly and then get back in calf and stay in the herd.”

Record keeping
Sharon said having systems in places to record data, meant collecting cow records became second nature and fitted in with other farm activities.

“You can set things up so recording data is quick and easy,” she said. “I don’t see it as extra work, but more of an investment – the more reliable our records are, the more reliable the information is that we get back on our herd.

“Our record keeping was originally on paper but we now use Easy Dairy, although we still keep a paper diary in the dairy which everyone can refer to and use.”

“I’ve also recently downloaded the HerdData app which should make it easier to record data such as matings on our lease block and also calvings out in the paddock because I will able to use my mobile phone. “We do most of the milking – when you are hands on with the herd it is certainly makes recording data easier.”

Better decisions
Sharon said recording Workability and Calving Ease data on farm had a two-fold benefit.

“Recording Workability and Calving Ease traits gives us information on our herd, while contributing data back on the bulls we have used, which improves the reliability of their ABVs. If we want bulls to have meaningful and accurate proofs then we need to supply figures on their daughters’ traits back through the herd tests companies to get accurate bull ABVs.

“The figures also mean we can see the genetic progress we are making in our herd because we have accurate, objective data on the cows in our herd.

“The end result is that we know what we are breeding and can use the data from our herd and the bulls we use to make faster genetic gain.”

The Parrishs recently received genotypes on their heifers as well as estimates of the difference between the low and high genetic merit animals in terms of their contribution to farm profit as a result of being a genetic focus farm for the ImProving Herds Project.

Source: DataGene

Canadian Dairy Network (CDN) held an Open Industry Session on Wednesday, October 24, 2018 at the St. Hyacinthe Congress Centre, which was followed, as usual, by a meeting of the Genetic Evaluation Board (GEB) the following day. This communication specifically provides an update on the discussions regarding the publication of Direct Genomic Values (DGV) by CDN since it was a key topic discussed at length during the Open Industry Session. A complete executive summary including all actions and recommendations of the GEB will be circulated in the near future. The CDN Board of Directors will consider all such recommendations for approval at its meeting scheduled for Monday, December 10, 2018.

• The Open Industry Session was well attended with over 80 participants, including many breeders as well as industry personnel. CDN extends a special appreciation to those breeders who took the time to attend this meeting and share their thoughts.
• In advance of the meeting, CDN and Holstein Canada organized meetings and discussions with key advocates in favour of keeping the publication of Direct Genomic Values (DGV) to gain a better understanding of the various perspectives of Canadian breeders. As a result of these discussions, CDN conducted additional analysis to assess the potential benefits of DGV for breeders to make selection and mating decisions, compared to using the official Genomic Parent Averages (GPA) of young bulls and heifers.
• In brief, the CDN presentation of analysis results included the following key conclusions:
  • The DGV scale for any trait, including LPI and Pro$, is wider than the scale for GPA, which means they are not directly comparable. It also means that the most elite animals in the breed will have DGV higher than GPA for marketing purposes.
  • The group of highest genomic young bulls available in A.I. are almost identical with similar rankings based on either DGV or GPA. The same is also true for the ranking of heifers in herds of breeders that have been involved for decades in herdbook registration, milk recording, type classification and the use of A.I. sires.
  • In terms of predicting the future progeny proof of genomic young bulls, the scale of GPA is more appropriate and more accurate than DGV.
  • When considering high ranking genomic young bulls, the strategy of giving preference to those bulls with the highest difference of DGV minus GPA is not effective for identifying the most promising sires once they are progeny proven. In fact, such an approach ends up selecting genomic young bulls that have an lower Parent Average.
  • In terms of using a herd’s heifer genomic evaluations to predict future lactation and/or classification performance as a cow, both GPA and DGV have equal levels of accuracy, both being superior to using Parent Average (PA) alone.
  • Another strategy used by some breeders when making sire selection decisions is to identify genomic young bulls with the highest, most extreme, DGV with the goal of producing progeny that also have extreme genomic evaluations. Such extreme young bulls and heifers are necessary for breed improvement and provide important
    opportunities for impacting rates of genetic progress. The CDN analysis conducted to assess the benefits of this approach showed that extreme genomic young bulls based on GPA produced a higher proportion of extreme progeny compared to results based on extreme DGV.
  • Based on the discussions and opinions expressed, there was general recognition among those in attendance that no scientific evidence has been found to indicate that DGV offers any benefit over GPA for making selection and mating decisions. Given this conclusion, it is clearly understood by all industry partners, especially CDN, breed associations and A.I. organizations, that a concerted and collaborative communication effort is required across the country to breeders, producers and industry personnel.
  • Regardless of the technical results from the CDN analysis, those in favour of maintaining the publication of DGV stated that doing so (a) maintains the current transparency of data available publicly; (b) attracts international breeders to the CDN web site, which helps to promote LPI globally; and (c) leaves the choice of whether to use and how to use DGV in the hands of each individual breeder.

After giving due consideration to all of the above and recognizing that the GEB is an advisory committee to the CDN Board of Directors with the mandate of making science-based recommendations, a motion was duly passed to maintain the direction of the previous GEB recommendation to no longer publish Direct Genomic Values (DGV) and to exclude such data from all outgoing files. GEB members consider the timing for any implementation of this action
is the responsibility of the CDN Board of Directors but it should not be any earlier than the stated target date of April 2019.

If there are any questions, concerns or comments regarding the above recommendation of the Genetic Evaluation Board, please feel free to contact CDN Board Chairman, Norm McNaughton (Norm.McNaughton@gmail.com), GEB Chairman, Brian Anderson (athlone@cyg.net) and/or CDN General Manager, Brian Van Doormaal (Brian@cdn.ca).

Starting in December 2018, proofs for Somatic Cell Score (SCS) will be expressed as Relative Breeding Values (RBV) in order to improve interpretation and be consistent with all other functional trait expression. On the RBV scale, bull proofs for SCS will be expressed using a value of 100 as breed average and a standard deviation of 5. The most extreme bulls vary from the most undesirable at around 85 to the most desirable at 115.

In general, daughters of bulls with a better than average RBV for SCS will produce milk with a lower Somatic Cell Count (SCC) than daughters of bulls with an RBV for SCS that is average or poorer. In order to help with interpretation of this important trait, Canadian Dairy Network (CDN) has conducted an analysis relating sire RBV for SCS to the average daughter performance for SCC.

Sire RBV and Expected Daughter Performance

Bull proofs for SCS are calculated using the Canadian Test Day Model and each bull receives a separate proof for first, second and third lactation. These three values are combined into a single published proof. The CDN analysis compared each bull’s combined SCS RBV to the average somatic cell count (SCC) of their daughters on test day. Since the average SCC is expected to be different across lactations, the relationship between RBV and average daughter performance was performed separately for first, second and third lactation.

Figure 1 shows the average daughter SCC in each lactation relative to their sire’s overall SCS proof, expressed as an RBV, which is a combination of his genetic potential for each of the three lactations. Although not all bulls have exactly the same relationship between proof and daughter average performance, the three solid lines in Figure 1 show the general relationship within each lactation. This graph clearly demonstrates the trend of higher somatic cell counts associated with each successive lactation.

The actual results in Figure 1 can also be used to establish a table to help translate sire proofs for SCS into the expected average SCC for future daughters. Table 1 provides the difference in average SCC for daughters in first, second or third lactation according to the published SCS RBV of their sire. In addition to being influenced by genetics, actual SCC levels are significantly impacted by herd management. For this reason, expression of expected daughter performance differences in Table 1 are all relative to how daughters of an average sire with an RBV of 100 would perform. In this manner, Table 1 applies to expected performance in all herds regardless of herd management levels. Considering a herd with an average SCC of 140,000 in first lactation cows as an example, future daughters of a bull with an SCS RBV of 105 should have an average first lactation SCC of approximately 113,000 (140,000-27,000).

In general, the higher the sire RBV for SCS, the lower the average daughter SCC across all lactations. Research has shown that SCC generally increases with each lactation. This occurs more drastically in daughters of sires with poor RBVs for SCS. In other words, the more undesirable the sire RBV for SCS, the greater the increase in average daughter SCC from one lactation to the next. For example, daughters of sires with an SCS RBV of 85 have an average increase of nearly 107,400 SCC (223,300-115,900) between first and third lactation, while daughters of sires with an SCS RBV of 115 increase only half that amount with an average of 54,000 SCC (122,600-68,600) between first and third lactation.

Correlations Between SCS and Other Key Traits

Table 2 shows proof correlations between SCS and selected key traits derived using data from >4,000 domestically proven Holstein bulls.

In general, most traits are positively correlated with SCS meaning selection will be favorable toward reducing SCC. In particular, SCS is positively correlated with production yields, Herd Life, Mammary System and both national indexes. Milking Speed is one exception, where the negative correlation indicates that the higher the RBV for Milking Speed, the less desirable (lower) the RBV for SCS. This means that strong selection to improve somatic cell counts would indirectly lead to an increased frequency of slower milking cows in the herd. Not surprisingly, Mastitis Resistance is highly correlated with SCS at 87%.  Since Mastitis Resistance is an index that combines both Somatic Cell Score and Clinical Mastitis, it should be the primary trait considered when making selection and mating decisions to reduce the incidence of mastitis.

Summary

SCS on an RBV scale will lead to consistency of expression and interpretation of all functional traits, as well as allow producers to more easily monitor proof changes. With herd average SCC levels as a starting point, RBV for SCS can be related to the expected average daughter performance for SCC. In general, the higher the sire RBV for SCS, the better (lower) the daughter performance for SCC. Also, poor RBV for SCS are associated with more dramatic SCC increases with each consecutive lactation in daughters. As a trait, SCS has favorable correlations with many important traits including moderate correlations with both national indexes, which means that with selection, simultaneous improvements are made for both.  For breeds with Mastitis Resistance available, using this trait is the optimal way to genetically improve your herd for resistance to both clinical and sub-clinical mastitis.

Authors:          
Lynsay Beavers, Industry Liaison Coordinator, CDN
Brian Van Doormaal, General Manager, CDN

Download a PDF copy of this article

Many dairy producers are technologically savvy and seek out tools to help them better manage their herds. On the genetic front, the CDN website is one such tool, highly utilized by those keen on monitoring and querying genetic data. The inbreeding calculator, which provides inbreeding levels and Parent Averages (PA) for potential progeny from various matings, is one of the website’s most frequently used features. When looking to breed any given female, the inbreeding calculator can be accessed one of three ways:

• From the “Calculators” drop down found in the grey left-hand sidebar of the CDN website.
• From the “Inbreeding Calculator” link found above an Active List of females. An Active List of females can be generated by performing a Group Query, or you can target females with the same prefix, as covered in the example below.
• By clicking on the “Inbreeding” tab displayed at the top of any page for the female of interest, which then pre-populates the Inbreeding Calculator with the female’s registration number.

Using the Inbreeding Calculator for Females with Your Prefix

In the first Tips & Tricks article of this series, readers learned how to enter their prefix in the Individual Animal Query to bring up a list of animals they have bred. Using the Selection Refinement Filter, results can be further reduced to only include active females by clicking the “Active Only” option.  

Using the prefix “Ste Odile” – the highest LPI herd in August 2018 as an example – here are the steps to use the inbreeding calculator with a list of females with a common prefix and a male of interest:

• Select the Individual Animal Query.
• In the “Search by Name” box, select “Holstein” and “Female” and type “Ste Odile” into the empty field. Submit the query and you will be brought to the resulting Active List of females.
• To refine the list to only include Active females, select “Query Refinement Filter” and check the box next to “Active Only.” At this point you can also refine the female list by entering evaluation thresholds, as well as sort the list by a trait other than LPI by using the “Sort results by” dropdown at the bottom, if desired. Once you submit the Query Refinement Filter settings, you will be brought back to an updated Active List of females as seen below.

• From here, choose the red “Inbreeding Calculator” link. By default, “Use the active list” will be selected in the “Select Female(s)” section, as seen below. Under “Select Male(s)”, choose “Individual” and fill in the registration number for a sire of interest. Remember to change the country if the bull in question has a country code other than Canada as part of their registration number. In this case, the #1 proven sire for LPI and Pro$, Mr Mogul Delta-1427-ET, was used. Hit “Continue” to see the Inbreeding Calculator Report.

The top of the report shows the sire information and his genetic evaluations for a select number of traits. Below is a list of all of the potential female mates ranked in order of LPI. Accompanying these potential mates are the inbreeding levels and parent averages for potential progeny for a given female mated to the selected sire, Delta. Select “Download results to Excel” to find and sort traits by parent averages for additional traits beyond those listed in the Inbreeding Calculator Report.

Breeders can use this report to help them select a mate for the animal of interest. The inbreeding percentage (%INB) should be used to eliminate potential mates that lead to a %INB deemed too high by the breeder. While comfort levels for %INB may vary, most A.I. mating programs set a default threshold of 9% to eliminate mating suggestions that lead to a %INB greater than this level. After eliminating potential mates based on %INB, the Parent Averages for the resulting progeny from each potential mate should be considered. Ultimately, the combination of the highest Parent Averages and an acceptable level of inbreeding should lead to the selection of the most desirable mate.

The example illustrated in the screenshot above allows the user to determine which female would be the best mate for the bull Delta. The tool can also be used to easily look at results for various potential sires by clicking the button “Select Top Sire Group”, as an alternative under “Select Male(s)” mentioned in point 4 above, and then selecting from among the bull names listed. A third possible way to use the inbreeding calculator is to enter the registration numbers for a given female and male, and examine the values on an individual mating basis.

In the previous two Tips & Tricks articles the Animal Query, the Group Query and the Selection Refinement Filter were covered.  These tools, in combination with the Inbreeding Calculator described in this article, put genetic information at your fingertips in order to help facilitate the breeding decision process.

Source: CDN

With the August U.S. dairy genetic evaluations, Net Merit $ and the other lifetime profit indices have been revised to factor in disease resistance and to update the economic values used in calculations. Net Merit (NM$), Cheese Merit (CM$), Fluid Merit (FM$) and Grazing Merit (GM$) were revised for the triannual genetic evaluations released August 7 by the Council on Dairy Cattle Breeding (CDCB).

“It is exciting to incorporate these direct measures of disease resistance, so that Net Merit continues to evolve and provide the most relevant information for dairy producers as they work to breed and manage healthy, productive herds,” said João Dürr, CDCB chief executive officer.

In April 2018, evaluations for genetic resistance to six health disorders were launched by CDCB. For Holstein males and females, genetic and genomic evaluations then became available for six common and costly health events – Displaced Abomasum (DA), Hypocalcemia (MFEV), Ketosis (KETO), Mastitis (MAST), Metritis (METR) and Retained Placenta (RETP).

CDCB collaborates with the Animal Genomics and Improvement Laboratory (AGIL) to ensure that cutting-edge research is used to produce quality genetic evaluations. The research of AGIL, a division of the United States Department of Agriculture, was critical to establish appropriate economic values and weightings of the individual traits within the Net Merit index.

“Dairy producers can select for any combination of traits, but total genetic progress will be fastest using an index,” said Dr. Paul VanRaden, Research Geneticist at USDA AGIL. “Because many traits affect profitability, total profit usually increases when more traits are included in the selection index if the evaluations are accurate and correct economic values are used.”

Emphasis of Health Traits in Net Merit 

The six disease resistance traits were incorporated in NM$ through the new sub-index, Health Trait $ (HTH$), at a relative value of 2.3% for NM$, 1.9% for Cheese Merit (CM$), 2.3% for Fluid Merit (FM$) and 2.1% for Grazing Merit (GM$). The new Health Trait $ sub-index is not published separately, similar to the calving trait sub-index (CA$).

Relative emphasis on most other traits reduced slightly due to the addition of HTH$; however, yield trait emphasis increased slightly and somatic cell score (SCS) emphasis decreased greatly because of correlated health costs now assigned directly to HTH$.

“The actual benefits from adding health traits may not appear as large as some expect – because other traits such as productive life, SCS, fertility, livability and calving ease also directly or indirectly account for impacts on animal health,” stated VanRaden.

Additional Evaluation Changes

A handful of other changes were implemented by CDCB for the August evaluations, as part of the mission to apply current research and drive continuous improvement. These changes are described on the CDCB website. Most significantly, the model for female fertility traits was changed to address unexpected variability and heterosis procedures were updated to utilize exact Expected Future Inbreeding (EFI) as possible.

Access to Genetic Evaluations

The CDCB website includes a wealth of dairy genetic summaries, tables and lists, in addition to publicly-available queries on individual animals. The site is updated with lists for all sires, elite cows and heifers for Net Merit, and high-ranking grade cows and heifers, as well as comparative summaries. Further information will be available August 9 at 1 p.m. (EDT) to reflect the status of semen availability for sires in AI (artificial insemination). Additionally, the official CDCB evaluations will be published in various formats by breed associations, artificial insemination and genetic suppliers, dairy herd information (DHI), dairy magazines and other industry sources.

The next triannual evaluation will be December 4, 2018, and the 2019 release dates are April 2, August 13 and December 3. These triannual releases provide the genetic evaluations for individual animals used by dairy producers, genetic suppliers, breed associations and other dairy stakeholders.

Health traits in Net Merit $

By Paul VanRaden, John Cole, and Kristen Parker Gaddis

The August 2018 NM$ update includes genetic evaluations for six new direct health traits first introduced in April 2018 for Holsteins: displaced abomasum, hypocalcemia (milk fever), ketosis, mastitis, metritis and retained placenta. In Net Merit, the disease resistance traits are grouped into a health sub-index (HTH$) that is not published separately, similar to the calving ability sub-index (CA$).

Economic values of the six new traits were obtained as averages of two recent research studies plus additional yield losses not fully accounted for in published genetic evaluations for yield traits. Some yield losses associated with health conditions are not fully accounted for when 305-day lactation records include adjusted test days coded as sick or abnormal. The added weight of HTH$ on NM$ will lead to nearly the same progress for HTH$ because NM$ has been accounting indirectly for health effects for a long time. Addition of these six new traits to the index is counteracted by removal of indirect health costs previously assigned to other traits such as somatic cell score and yield.

Additional NM$ updates include new economic values for each unit of predicted transmitting ability (PTA) and the relative economic values of traits. Full details of the changes are provided in an updated format that documents the other indexes: https://aipl.arsusda.gov/reference/nmcalc-2018.htm.

Changes in fertility trait modeling

By Paul VanRaden and Jana Hutchison

Age-parity adjustment factors for daughter pregnancy rate (DPR) and cow conception rate (CCR) are revised for August to improve the stability of genetic trend estimates. During the April evaluation, recent genetic trends in traditional predicted transmitting ability (PTA) for DPR and CCR decreased when new age-parity groups were added by an automated process scheduled every five years. As a result, the fertility PTAs, NM$ and breed association indexes for recent animals declined by 1.7 DPR, 1.4 CCR and $22 NM$ in April.

Since 1995, age-parity effects for production have been estimated separately within five-year periods. Age and parity effects gradually changed across the decades, and more modern cows reached mature yield sooner (Norman et al., 1995). Different age-parity groups within each five-year period helped pass Interbull trend validation and had large effects on estimated genetic trend. These adjustments performed well for production, so were also used for SCS and fertility traits. However, because time groups are based on fresh dates, when the latest fertility group was formed, the least fertile daughters were partitioned into the new group whereas the most fertile daughters remained in the earlier group. To prevent abrupt changes in the future when new time groups are formed, the five-year groups are now redefined to instead gradually slide forward every four months. The April fertility PTAs were recomputed with this revised model, and for young animals the resulting trend returned about 60% of the way toward the December trend rather than maintaining the lower April trend . The age-parity definition change had a downward effect on the trend for older animals. The preliminary results in August indicate the trend for young animals is closer to December results in most breeds for DPR and CCR. As a general indication (since calculations are still ongoing), PTAs for recent birth years that had decreased in April are expected to be closer to the December values in August. In all cases, within-year rankings of animals were affected only a little.

Norman, H.D., Meinert, T.R., Schutz, M.M., and Wright, J.R. Age and seasonal effects on Holstein yield for four regions of the United States over time. J. Dairy Sci. 78(8):1855–1861. 1995.

EFI update and changing of heterosis procedure on genomic evaluations

by Ezequiel Nicolazzi, Gary Fok, Leigh Walton, Jay Megonigal and Paul VanRaden

Expected future inbreeding (EFI) is included in PTAs, but approximate adjustments were used in the all- breed weekly and monthly files after the April release until early May. Exact EFI is now used if both parents were in the pedigree file from the previous full run, and an approximate EFI is used only for new animals whose parents are also new since the last full release. Approximate methods were needed because reprocessing inbreeding for all 78 million animals takes nearly a day and is done only three times per year. Effective with the August 2018 genomic run, calculation of heterosis – previously reprocessed for all animals three times a year – will now be run on a monthly basis.

In light of the growing importance of heterosis and inbreeding values in the all-breed system introduced in April 2018, this critical change to the monthly processing – which required an extensive review – will better account for animals changing pedigree, especially those with changes of breed in their pedigrees (including own breed). Such enhanced procedure will also run during triannual genomic runs, so that all 78 million animals will undergo the procedure two times. The first heterosis run will be used exclusively for the traditional evaluation, and a second run will be used for the genomic evaluation and for reporting of final results. In the rare cases where progeny tested animals change pedigree, they could receive traditional and genomic PTAs with misaligned heterosis. However, the decision was to report PTAs reflecting the most current information available.

Exclusion of IDs from Interbull pedigree

by Jay Megonigal and Ezequiel Nicolazzi

Interbull pedigrees include dismissed IDs and non-standard IDs for some animals. For several years AGIL and CDCB have accepted these IDs as a way to track the past animal IDs. However, recently we discovered that such practice might create a misalignment that can cause old bulls to be submitted to Interbull with incorrect IDs. For August 2018 onwards, bulls with dismissed IDs (labeled as “X”) or that contain “_IMAG_” in their numeric IDs in the Interbull pedigree are now immediately excluded from the CDCB system.

Genomic mating file in HO – full implementation of rules

by Leigh Walton and George Wiggans

With the objective of reducing the dimension of the G-mating inbreeding file, and after discussions with National Association of Animal Breeders (NAAB) and two of its committee chairmen, an editing criteria was applied to females in the genomic mating inbreeding file in August 2017. The new criteria would include genotyped females with a usable genotype if any of the following conditions are met:

1. The last processing date received from the DRPC is within the past six months and the termination code does not indicate that they are dead.
2. If the DRPC does not indicate they are dead, they have a progeny born in the last 18 months.
3. If the DRPC does not indicate they are dead, they were born in the last five years.

These rules were intended to limit the growth of the file by eliminating cows that are not on DHI and are over five years old without progeny in the pedigree table, therefore not of interest for the industry. Applying these restrictions, the file included less than 800,000 animals from the nearly 1.3 million genotyped Holstein females.

After reviewing files distributed in April 2018, CDCB discovered the above criteria was not implemented in full as originally intended. The August 2018 inbreeding file and those in subsequent runs will contain such definition applied in full.

Changes in content of Format 38

No new changes in the formats were introduced, but a number of changes were introduced in the routine programs that generate format 38. All special characters are now routinely excluded from the file; sampling status, average standardized milk (protein) and DYD milk (protein) fields are now blanked. Daughter averages are not shown for traits with less than 10 daughters. As per the industry request, the strategy implemented in April 2018 of publishing daughter/herd information for all traits irrespective of the number of daughters available was reversed. Starting the August 2018, daughter/herd information for all traits, except HCR and GL (as data arrives before milk data), will be blanked for bulls having less than 10 daughters on milk yield.

Change in Jersey elite cow criteria

by Jay Megonigal and Ezequiel Nicolazzi
The elite cow criteria was edited to include the current registry code practices of the American Jersey Cattle Association (AJCA). The association’s current practice is to use numeric registry codes (01 to 06, indicating generation count number) and HR (Herd Register; animals with such status have seven or more unbroken generations of known Jersey ancestors recorded by AJCA). Such criteria was first implemented in March 2017, but never adopted on the elite cow criteria. In collaboration with AJCA, CDCB has modified the JE elite cow criteria to include cows having a numeric registry code greater than 02, or HR. The modification is in effect starting August 2018.

Guernsey phantom group reinstated

by Jana Hutchison, Jay Megonigal and Paul Vanraden.

The exception encountered in April 2018 involving the program that created the unknown parent groups (UPG) for the breeds during the traditional evaluation was edited, in order to allow the creation of the Guernsey UPG irrespectively of their low number of unknown parents in the last 15 years. All genomic breeds will receive their own UPG as was originally intended.

Many dairy producers are technologically savvy and seek out tools to help them better manage their herds. On the genetic front, the CDN website is one such tool, highly utilized by those keen on monitoring and querying genetic data. There are two ways to query animals on the CDN website: individually and by group. This article will cover tips and tricks for using the Group Query, while the previous article described the best ways to use the Individual Animal Query.

Group Query

There are two parts to the Group Query: the Quick Search, seen below, and the Advanced Search. Quick Search is used to easily query top male and Canadian-owned females for each breed simply by selecting the breed from the drop down list and then either Male or Female.  If desired, you can also use the list of countries provided to select animals born in any specific country of interest. You can also select specific groups of animals based on their Evaluation Type whereby EBV refers to sires with a domestic progeny proof or cows with Canadian lactation and classification data included, MACE refers to foreign animals with a MACE evaluation provided through Interbull, and PA refers to animals with a Parent Average for production and/or type traits. The search can also be narrowed to include only genotyped animals and/or only animals considered to be active in Canada.

The Advanced Search, on the other hand, includes the same options as the Quick Search but can be used to return more specific query results for top male and female lists for each breed. It can be used to limit the query to only return animals with certain recessive and/or haplotype carrier results, from certain A.I. Controllers (males), from specified parents and/or born within defined date ranges.

Recessives and Haplotypes

Users of the Advanced Search can limit output results by the following recessive or haplotype carrier results:

• Coat Colour – for Holstein males and females
• Beta Casein – for Holstein, Jersey, Ayrshire, Brown Swiss and Guernsey bulls with a known Beta Casein test result submitted to CDN
• Polled – for all breeds and both sexes
• Brachyspina – for Holstein males and females
• Haplotypes – Haplotypes affecting fertility including Holstein (HH1, HH2, HH3, HH4, HH5), Jersey (JH1 and JH2), Ayrshire (AH1 and AH2) and Brown Swiss (BH1 and BH2) males, as well as the Haplotype affecting Cholesterol Deficiency (HCD) in Holstein

A.I. Controllers

Using this filter, the query will return results including sires from only selected A.I. companies. There are nearly 20 A.I. companies listed that are members of CDN, which includes both major international companies as well as organizations unique to Canada. One or multiple A.I. companies can be selected, while the default is to display sires from all companies.

Province

When querying females, results can be limited by province, which is usually determined based on the province associated with a DHI herd number. Females that are not part of a herd enrolled on DHI are included in the group identified as “Unknown – Canadian Owned”.

Checking the “Non-Canadian” field will include foreign females in query results but, by default, the CDN queries only include Canadian-owned females.

Parentage

Both Females and Males of a certain parentage can be targeted in the Advanced Search. For example, perhaps the user would like to see if daughters of a certain sire are on the ground, or  search for sons of a certain dam x sire combination. Both of these example searches can be accomplished by using the parentage section of the Advanced Search. The appropriate fields for this selection are automatically filled in when you select “Group Query” at the top of the page when viewing the Progeny list of any given animal, as shown below using Comestar Lautrust as an example.

Date of Birth

Use this final part of the Advanced Search when targeting males or females born after or within a certain date range. This feature can be used on its own or in conjunction with any of the other search tools.

Putting it all Together

The true power of the Advanced Query tool is revealed when refining a search for either males or females using various combinations of the options described above. Looking to query genomic young sires from a particular A.I. company that are A2A2? Wanting to limit search results to only include red carrier sires free of HCD? Hoping to find out where your polled female out of a given sire ranks in the world? The Advanced Query can do all these things and more! Try it out and discover the hidden power of this popular feature of the CDN website.

Authors:          
Lynsay Beavers, Industry Liaison Coordinator, CDN
Brian Van Doormaal, General Manager, CDN

Download a PDF copy of this article

Source: CDN

Dairy producers are highly aware of the importance of good udder health on milk quality, animal health and the general profitability of the dairy herd. For decades now, milk recording services in Canada have included the analysis of milk samples for somatic cell count and this same data has been used to provide Somatic Cell Score (SCS) genetic evaluations for bulls and cows in all dairy breeds. In October 2017, the Genetic Evaluation Board (GEB) of Canadian Dairy Network (CDN) recommended that the expression of SCS genetic evaluations be changed to be consistent with all other functional traits.  Following approval of this recommendation by the CDN Board of Directors, an implementation plan has been established with an effective date of December 2018. Let’s take a closer look at the background and reasoning of this decision.

Genetic Selection for Improved Udder Health

In the 1990s, an overall Udder Health index was developed by Canadian researchers, which included Somatic Cell Score, Udder Depth and Milking Speed, for breeders and A.I. companies to make genetic selection decisions in this area. In August 2001, due to the increasing interest in genetic selection to improve udder health, these three traits were directly included in the LPI formula. In 2007, the dairy industry implemented a data collection system for health events recorded by producers enrolled on DHI and/or via the DSA program in Quebec. As a consequence, CDN later introduced official genetic evaluations for clinical mastitis as well as a Mastitis Resistance index for Holstein, Ayrshire and Jersey breeds in August 2014. One year later, modifications to the LPI formula for these three breeds included the addition of Mastitis Resistance as the optimized genetic selection index for improved udder health to replace Somatic Cell Score, Udder Depth and Milking Speed. At the same time, Pro$ was introduced as the new profit-based genetic selection index, which has a 40% correlation with Mastitis Resistance.

Availability of the Mastitis Resistance (MR) index provides producers with the opportunity to make genetic improvement to reduce the frequency of both clinical and subclinical mastitis in the herd.  Somatic cell count is a indicator of subclinical mastitis while clinical mastitis has a bigger negative impact on cow and herd profitability.

Proof Expression

In January 2008, the expression of genetic evaluations for all functional traits, with the exception of SCS, was changed to a Relative Breeding Value (RBV) scale with an average of 100 and a standard deviation of 5.  In general, this means that 99% of all bulls within each breed fall between 85 (poorest) and 115 (best), as presented in Figure 1.

• There are multiple reasons for the adoption of an RBV scale for functional traits but the key advantages include:
• The RBV scale is almost identical to the scale used over several decades for conformation traits, with the only difference being an average value of 100 for RBVs instead of 0 for type.
• The use of a consistent scale across all functional traits facilitates the understanding of how each bull ranks within the breed.
• The evaluations for all traits can be expressed in a common direction with the highest RBVs being most desirable.

Figure 1: Distribution of Bull Proofs as RBVs for Functional Traits

At the time when the RBV scale was introduced for all other functional traits, it was decided to exclude SCS in fear that it would create confusion at a time when producer interest in this trait was growing. Now, after ten years of using the RBV scale for many traits, it has been decided to move SCS to this scale as well. Some of the key reasons for this CDN decision include:

• The current scale for SCS, with an average of 3.00 and an approximate range from 2.25 to 3.75, is not well understood by producers other than the fact that values below the average are most desired.
• SCS is currently the only trait for which lower values are preferred so changing to the RBV scale allows the expression to become consistent across all functional traits, both in terms of range and direction of published values.
• Only three other countries involved in Interbull evaluations express SCS evaluations in the same manner as the current scale used in Canada.  These include Belgium, Slovakia and United States but, in reality, the scale used in the United States has about half the range (PTA) as the current scale in Canada (EBV). Such a scale difference between Canada and United States is not well known and therefore leads to misinterpretation when comparing evaluations from both countries.

Implementation Plan

There are several details associated with the implementation of this change, which explains the significant lead time before implementation in December 2018. The CDN web site will be modified starting the genetic evaluation release in August 2018 by removing Somatic Cell Score as a trait listed in the section of Functional traits on the Genetic Evaluation Summary page for all animals in the Holstein, Ayrshire and Jersey breeds. Focus should be shifted towards the Mastitis Resistance evaluations already available in this section. For bulls in these three breeds, evaluation details for Somatic Cell Score will continue to be available under the “Health” tab. For genetic evaluation data files provided by CDN for both bulls and cows, there will be no specific changes to the file formats and test files with SCS populated with RBV values can be requested from CDN. The Holstein, Ayrshire and Jersey breed associations will implement modifications to their respective web site queries, as well as official pedigrees and other official documents in advance of the December 2018 implementation. Similarly, prior to implementation, computerized mating programs offered by A.I. companies in Canada will require some modification to incorporate the new scale of expression and interpretation for Somatic Cell Score.

Provided by: Canadian Dairy Network

The lifetime net merit (NM$) index ranks dairy animals based on their combined genetic merit for economically important traits. Indexes are updated periodically to include new traits and to reflect prices expected in the next few years. The August 2018 update of NM$ includes genetic evaluations for 6 new health traits recorded by producers: clinical mastitis (MAST), ketosis (KETO), retained placenta (REPL), metritis (METR), displaced abomasum (DA), and milk fever (MFEV; hypocalcemia). Cows with genes that keep them healthy are more profitable than cows with health conditions that require extra farm labor, veterinary treatment, and medicine.

Economic values of the 6 new traits were obtained as averages of 2 recent research studies plus additional yield losses not fully accounted for in published genetic evaluations for yield traits. Liang et al. (2017) estimated direct treatment, labor, and discarded milk costs for health disorders from veterinary and producer survey responses, and Donnelly (2017) obtained health treatment costs from 8 cooperating herds in Minnesota. Some yield losses associated with health conditions are not fully accounted for when 305-day lactation records include adjusted test days that are coded as sick or abnormal. Total costs for the 6 traits are added to NM$ in the form of a health trait subindex (HTH$) that is not published separately. This is similar to the calving trait subindex (CA$) that combines 4 traits and is not published or to conformation traits, which are grouped into an udder composite, feet and leg composite, and body weight composite (BWC).

Relative emphasis on most other traits was slightly less because of the addition of HTH$. However, yield trait emphasis increased slightly and somatic cell score (SCS) emphasis decreased greatly because correlated health costs previously assigned indirectly to yield and SCS are now assigned directly to HTH$. Other economic values were updated very little. The 6 health traits are currently evaluated only for Holsteins. The 2018 and 2017 NM$ (VanRaden, 2017) indexes were correlated by 0.994 for recent Holstein bulls.

To read the full report, visit the USDA AIPL website HERE.

Information about the upcoming genetic evaluation changes for April 2018 has been released by the CDBC (Council for Dairy Cattle Breeding). Changes will include evaluations for resistance to six health traits, the all-breed system now being applied to genomic evaluations, and a correction in productive life. 

New health evaluations for Holsteins officially released

by Kristen Gaddis, Jay Megonigal, Leigh Walton, Duane Norman, John Cole, Paul VanRaden

Official genetic and genomic evaluations for resistance to six health events in Holsteins (Hypocalcemia, Displaced abomasum, Ketosis, Mastitis, Metritis, Retained placenta) will be first published in April 2018. These traits are six of the most common and costly health events impacting dairy herds. Preliminary results and test files were shared to the industry in December 2017. Positive predicted transmitting abilities (PTAs) measure resistance to these health disorders. For example, a mastitis PTA of +3 indicates that 3% fewer daughters will get mastitis during each lactation. Data from herds all over the country are included in April. Further research is ongoing to: i) extend health evaluations to other breeds; ii) further improve the evaluation model; iii) include these results in the international exchange of evaluations; iv) increase data consistency and sources. Please note that since this is the very first release of this new evaluation, reliabilities are expected to be lower than in the future, when more and more records will be included in the database. These traits are not yet included in the lifetime net merit (NM$) formula. For further information please refer to the content/uploads/2017/09/CDCB-Health-Traits-FAQs-10_2017.pdf

All-breed system extended to genomic evaluations

By Paul VanRaden, Gary Fok, Mel Tooker, Lillian Bacheller, Jay Megonigal, Leigh Walton 

The all-breed system used for traditional evaluations since 2007 is now also applied for genomic evaluations starting April 2018. This new system allows records from animals of all breeds to be analyzed together and expressed on the same scale. Relatives, regardless of breed composition, will now contribute to every animal’s parent average and its genomic evaluation. Previously, animals with pedigrees including ancestors of a different breed were not correctly accounting for the “out of breed” contribution (a generic unknown parent group was assigned, instead of using the full pedigree). Genomic evaluations are now calculated on an all-breed base and then are converted to within-breed genetic bases for release to the dairy industry. It is important to underline that crossbred animals will still not receive an evaluation. Genomic evaluations for purebreds will be slightly impacted (except for the revised PL calculations, see next topic), whereas a greater impact will be seen in animals with pedigrees containing ancestors from other breeds.

Productive Life (PL) correction

By Paul VanRaden, Gary Fok, Mel Tooker

The multiple-trait Productive Life (PL) processing for incoming Interbull data has been completely revised to prevent the emergency actions taken in April and August 2017. The new system no longer tries to forward the differences between single and multiple-trait PL from one generation to the next. This logic tended to inflate the resulting evaluation, affecting primarily foreign bulls. Since foreign bull evaluations were inflated, SNP effects used to estimate genomic evaluations were affected, extending the inflation to the general population (e.g., including domestic animals). The inflation was more evident in breeds dominated by foreign bulls, such as Ayshire and Brown Swiss, but outlier cases were observed in all breeds. The new multi-trait PL genomic model prevents this from happening. The evaluations obtained with the new system fit better to the Interbull evaluations for foreign bulls, as a result, reducing the inflation of SNP effects. To give an indication of the impact, the 1712 PL evaluation with the new methodology yielded an average (standard deviation) reduction in PL PTA from 6.10(2.46) to 4.21(1.66) for elite cows. Although averages in bulls remain fairly similar (-0.28 vs -0.34 for official and all-breed, respectively), standard deviations are lower (3.87 vs. 3.1) and the correlation between both systems is 96%, indicating some degree of variation for bulls.

Bulls with no daughters in their genomic proof for production or type.  No requirement for semen status.