For decades dairy production systems have faced the challenge of attaining adequate fertility levels. Insufficient reproductive performance will result on reductions on the proportion of cows at their peak production period, increments in insemination costs, and delayed genetic progress. Moreover, impaired fertility is one of the most frequent reasons for culling and increased days open are associated with a greater risk of death or culling in the subsequent lactation.

An historical trend for declining dairy fertility has likely resulted from high prevalence of anovulation, reduced fertilization, and embryonic survival. Contributing factors to this condition include changes in cow physiology associated with greater milk production, challenges for optimal nutritional management, housing, increased herd size, reduced estrus expression, and current genetic makeup. In addition, the level of inbreeding has increased in the Holstein population, with present average values greater than 5%.

An uneventful and timely calving is a desired trait for improved fertility of dairy cows.

Relevant to this problem, starting in the sixties, breeding programs selecting for milk production have been very successful and current trends indicate increments on milk yield per cow of 1 to 2% per year. However, some unfavorable genetic correlations between production and other traits may exist, resulting in undesirable side effects, such as a higher risk for behavioral, physiological, and immunological problems.

However, although these negative associations between production and fertility traits are probable, it is possible to select for improved milk yield and fitness traits, including fertility. This fact is evidenced by increments in reproductive performance occurring in Holsteins after the implementation of genetic evaluations for daughter pregnancy rate (DPR) in 2003. The trend for DPR indicate a partial recovery in dairy fertility, despite no apparent slowing down in the rate of increase of milk production per cow.

Genetic Selection and Improved Fertility: It’s Not That Simple

Fertility traits are multi-factorial in nature, which makes it difficult to determine the degree of involvement of genetics on reproductive outcomes. It has been established that reproductive traits are largely influenced by the environment and may be affected by multiple genes with small individual effects. Consequently, genetic progress for fertility, by way of conventional breeding strategies is hindered by low heritability, which represents the proportion of visible variation attributable to genetic differences among animals.

From the biological perspective, genetic variation affecting fertility may be directly involved in the physiology of reproductive processes. However, genetics may also determine, to some extent, the behavior of other related traits that have an impact on fertility. Among others, these comprise factors such as the ability to maintain adequate body condition and feed intake during the transition period, the potential for adequate immune responses resulting in adequate health, and the capacity to retain early pregnancy.

Some significant obstacles can be anticipated when the logistics of selection for fertility are planned. High costs of reliable data collection, the long time period required for validation, and biased phenotypes, such as non-return rates and DPR, are a few of these challenges. Adding to these limitations, the influence of factors unrelated to fertility, such as breeding policy and voluntary waiting period, is a constant difficulty for precise reproductive estimations.

A New Hope: Genomic Tools are Here to Stay

Although small heritabilities for reproductive performance traits have been reported, when more objective measures of fertility were evaluated (interval to first ovulation, anovulation, and pregnancy loss), heritabilities were moderate to high (0.15 to 0.40). For reproductive disorders, such as metritis and retained placenta, heritability estimates were close to 0.20. Notably, genetic variation is manifest when DPR is considered, as daughters of the highest and lowest sires for DPR differ by 29 days open per lactation.

With the arrival of low cost genotyping, which is the ability to read the DNA, the use of marker analysis (single nucleotide polymorphisms; SNP) in the evaluation of dairy cattle genetics has become a reality. The use of genomic analyses allows for estimation of breeding values at birth, which reduces the costs of proving bulls and increases the genetic gain because of shorter generation intervals.  In addition, genotyping platforms commercially available from several companies have become widely used in research, as well as at the farm level, where genotyping of females is gaining momentum.

As with genetic evaluations, genomic selection has extended to multiple traits of economic interest, including more specific health problems. In the US, indirect health predictions are available from the Council on Dairy Cattle Breeding and recent data indicate that these traits result in genetic improvement for resistance to adverse health events. Producer-recorded health events have been successfully used to identify genetic differences between individuals regarding susceptibility to common health disorders including retained placenta, metritis, displaced abomasum, ketosis, lameness, and mastitis.

Matching Fertility and Genomics?

Specific reproductive traits that are currently evaluated by genomic analyses in the US include daughter pregnancy rate, sire calving ease, daughter calving ease, sire stillbirth rate, daughter stillbirth rate, heifer conception rate, and cow conception rate.

New research exploring genomic variation related to novel fertility traits is in progress. As a result, multiple genomic regions associated with variation in cattle reproductive traits have been mapped. More recently, genome-wide association studies (GWAS) performed with thousands of SNP markers have facilitated the resolution of associated regions and the discovery of candidate genes.

Interestingly, genomic markers have been identified for many reproductive traits including ovulation rate, pregnancy rate, DPR, non-return rate, and estrus intensity. Genetic variation has also been identified for gestation length, dystocia and stillbirth, and postpartum fertility. In addition, genomic analyses have offered the capability for locating lethal recessive genes affecting fertility outcomes. As an example, five recessive defects on fertility were recently identified by examining haplotypes that had a high population frequency but were never homozygous. These lethal effects may result in conception, gestation, and stillbirth losses.

As indicated previously, some physiological measures of fertility, such as resumption of ovarian cyclicity, have moderate heritabilities. What is interesting is that cows resuming estrous cyclicity soon after calving are more likely to show estrus and to become pregnant in a timely manner. Therefore, decomposing aggregate reproductive phenotypes into their detailed components could result into an effective tool for selection. For example, calving interval could be decomposed into several reproductive components such as the postpartum interval to commencement of estrus cyclicity, expression of estrus, conception, maintenance of pregnancy, and gestation length.

Presently, a major goal for advancing in genomic selection for fertility is the collection of high numbers of accurate fertility phenotypes associated with the corresponding genotypes, coupled to large scale evaluations of the association between direct measures of fertility. These fertility measures include uterine health, resumption of postpartum ovulation, detection of estrus, pregnancy per A.I., and maintenance of pregnancy. Collecting accurate data represents another challenge and potential strategies may include using DHI resources and data recorded within on-farm herd management software programs.

Finally, selection for traits with low heritabilities could be integrated into new reproductive technologies, such as in vitro fertilization (IVF) and embryo transplant, that allow for higher rates of genetic improvement by increasing the reproduction of superior females. It is reported this strategy could increase genetic gain by 10 to 20% compared with traditional breeding schemes.

Our Research Effort

Our team of researchers from multiple United States institutions was awarded a 5-year grant to explore genomic variation associated with reproductive traits in dairy cattle (Genomic Selection for Improved Fertility of Dairy Cows with Emphases on Cyclicity and Pregnancy; Grant no. 2013-68004-20361 from the USDA NIFA). The overall objective was to develop a fertility database with genotypes and phenotypes based on objective and direct measures of fertility in Holstein cows. The subsequent goal was to identify SNPs and haplotypes significantly associated with fertility traits by use of genome-wide analyses and to consider this information to obtain genomic-estimated breeding values that can be applied in selection of dairy cattle for improved fertility.

Consequently, our approach was to test a significant number of cows (approximately 12,000 individuals from 7 states in the USA) that were enrolled at calving and monitored weekly on farm until pregnancy confirmation. The evaluations included uterine health, metabolic status during transition, resumption of postpartum ovulation, estrus, pregnancy per AI, and pregnancy loss, under different management practices and environments.

Our initial analyses indicated that overall, 71% of the population resumed ovarian cyclicity by 50 DIM. Conception rates at first and second A.I. were 32.8% and 33.7%, respectively. Pregnancy loss between 32 and 60 days after A.I. were 10% and 8.7% for first and second A.I, respectively. Overall, 19.7% and 4% of the population was sold or died before 305 DIM.

Using this population, a reproductive index (RI) calculating the predicted probability of pregnancy at first A.I. was developed. The RI considered logistic regression models that included cow-level variables that were thought to have a genetic component (diseases, anovulation, BCS, milk yield, etc.). Interestingly, when the index from this population of cows was categorized as low, medium, and high, there was a consistent agreement between categories of the predicted RI and the measures of fertility collected from dairy cows.

By means of the developed RI, our population of cows was ranked as highly-fertile pregnant (850 cows) and a lowly-fertile non-pregnant (1,750 cows) for subsequent DNA analysis. At this point, preliminary genome-wide analyses with our high- and low-fertility subpopulations are confirming that there is potential for genomic selection in the traits of interest. We are evaluating genomic variation for dichotomous variables (uterine disease, anovulation, detection of estrus, pregnancy per AI, pregnancy loss) and for a continuous variable (predicted probability of pregnancy based on the RI, services per conception) to maximize opportunities for prediction accuracy.

Our initial analyses have estimated the heritability and the marker effects for lameness, metritis, mastitis, resumption of cyclicity, pregnancy after first A.I., and the RI. Significant markers have been associated with genes in chromosomal regions previously reported as contributing to variation in fertility and health traits in dairy cattle. In addition, causal associations among multiple traits, including retained fetal membranes, metritis, clinical endometritis, resumption of cyclicity by 50 days in milk, pregnancy after first A.I., and lameness early in lactation were investigated. Finally, selection models using significant markers are going through checking and further validation. This large scale evaluation will eventually be combined with current selection traits to further refine genomic selection of cattle by dairy producers.

Conclusions

Fertility is a key component of modern dairy production systems. However, a trend for declining dairy fertility has been evident in diverse production systems. Although fertility traits are strongly influenced by the environment, there is evidence for genotypic variation providing opportunity for selection, as suggested by a partial recovery in dairy fertility since the incorporation of daughter pregnancy rate into bull genetic evaluations. There are current efforts placed in collection of high numbers of accurate fertility phenotypes associated with the corresponding genotypes, coupled with large scale evaluations of the association between direct measures of fertility (uterine health, resumption of postpartum ovulation, detection of estrus, pregnancy per A.I., and maintenance of pregnancy) and genomic variation on dairy cows under different management practices and environments. As the cost of genotyping is decreasing, the number of animals subject to genomic evaluations is expected to continue increasing. If adequate markers and causal variants for fertility traits are identified, molecular breeding value could be estimated for each trait enabling selection to proceed population-wide.

Source: dairy-cattle.extension.org

Dramatic changes have occurred in dairy sire selection practices in recent years. These changes have been facilitated by the sequencing of the bovine genome, which led to the discovery of thousands of DNA markers, known as single nucleotide polymorphisms or SNPs. The development of predicted breeding values based on marker data alone (Meuwissen et al., 2001), coupled with a reduction in the cost of genotyping, has allowed for accurate genomic selection of dairy sires by AI centers. As described by Hayes et al. (2009) genomic selection refers to selection decisions based on genomic breeding values (GEBV). Before continuing the discussion on genomic selection, however, it is important to understand traditional progeny testing. Historically, progeny testing was key to genetic improvement of dairy cattle (Sattler, 2013), via identification of the best bulls for widespread use and as sires of bulls for the next generation.

Progeny Testing

As described by Sattler (2013), progeny testing has a three-fold purpose to: 1) provide information for AI sire selection decisions, 2) provide a critical mass of data needed for effective genetic evaluations, and 3) develop AI bulls with accurate evaluations for wide scale AI use. Elite cows were identified, mated to the best sires, and sons were included in a progeny test program to identify, based on daughter performance records, which bulls inherited desirable genes. Progeny test data were the basis for a bull to graduate from a young sire to a proven sire, and have semen made available for widespread use by producers. Furthermore, proven sires (sires with progeny) have long been used as sires of bulls for the next generation. Although progeny testing has been a successful AI center management strategy, it takes more than 3 years, is very expensive, and limits the number of bulls evaluated (Sattler, 2013).

Genomic Selection

Currently, selecting dairy sires for use in AI has shifted from progeny testing to reliance on genomic predictions of daughter health, milk production, and fertility. With genomic selection, the sire and dam of a future sire (next generation) are selected on the basis of DNA marker profiles and mated soon after puberty, with the next generation sire being marker-analyzed for desirable (and undesirable) traits soon after birth, or as an in vitro fertilization (IVF) embryo (Dalton et al., 2017). According to Sattler (2013) this allows AI companies to “test tens of thousands” of bulls rather than a few thousand bulls through a traditional progeny testing program. In addition, rather than wait for progeny test results, today many bulls are used as sire fathers and are widely marketed based on genomic evaluations (Sattler, 2013).

Use of genomic markers has reduced the generation interval, defined as the average age of parents when their offspring are born. Both males and females are contributing to the reduced generation interval, as elite genomic heifers 6 to 8 months of age routinely undergo ultrasound-guided trans-vaginal follicular aspiration (commonly known as ovum pick-up or OPU), followed by in vitro fertilization (IVF) and embryo transfer (ET). According to Garcia-Ruiz et al. (2016) the generation interval of sires of bulls has decreased from approximately 7 to 2.5 years, while dams of bulls has decreased from 4 to 2.5 years. Furthermore, genomic selection has increased the rate of improvement in economically important traits such as daughter pregnancy rate (DPR), productive life (PL), and somatic cell score (SCS) (Garcia- Ruiz et al., 2016). Consequently, Amann and DeJarnette (2012) argue that producers should get more value from each pregnancy resulting from sperm produced by a young marker-selected sire than by a mature progeny-tested sire.

Genomic selection presents many hurdles for AI centers. Embryos or newborn calves with desirable genomic predictions have a much higher acquisition cost than a calf with a good pedigree only (Amann and DeJarnette, 2012). Many sires will be replaced by better bulls before their daughters complete their first lactation; however, young bulls produce 35 to 50% of the sperm of a mature sire (>5 years of age). Consequently, AI centers will require additional facilities (housing, isolation and quarantine, semen collection) to collect semen from nearly twice as many bulls to produce the same total number of marketable straws (Dalton et al., 2017).

An ongoing challenge for the dairy industry is maintenance of data collection infrastructure necessary to support genomic evaluations (Sattler, 2013). A common misconception is that genomics will replace DHIA milk testing. This is not true, as genomics is based on the relationship of the phenotype and genotype. Therefore, genomics requires accurate data collection to establish the reference population to calibrate genomic results and to continually update the reference population (National DHIA, 2011). The flow of data (including milk recording, type classification, and health traits) into the system is critical as the size of the reference population impacts the accuracy of genomic predictions. The Council on Dairy Cattle Breeding (CDCB) currently performs genetic evaluations, which were previously done by the USDA. In the event that progeny test programs were discontinued, the CDCB would be able to assure data continues to be acquired for use in genomic evaluations (Sattler, 2013).

Will progeny testing continue?

According to Sattler (2013) genomic evaluations provide more accurate genetic information on young bulls than what was previously available. Genomic evaluations have, in fact, replaced progeny testing in regard to information for key sire selection decisions. Consequently, AI companies have reduced progeny testing efforts by approximately 20%.

Although the percentage of semen sold from bulls without progeny data has risen from approximately 10% in 2008 to greater than 60% in 2018, progeny-tested sires remain in demand as they continue to compete favorably with young genomic bulls. This is likely a result of the critical mass of data generated via progeny test, which facilitates the accurate evaluation necessary for continued widespread use.

Genomic evaluations continue to be a work in progress, and increased accuracy of genomic evaluations will likely occur in the future (Sattler, 2013). Taken together with increased rates of genetic improvement, the competiveness of young genomic sires will likely improve. For the foreseeable future, though, progeny testing will likely continue to provide enough progeny-proven sires to satisfy market demand (Sattler, 2013).

Source: dairy-cattle.extension.org

In the coming weeks, Irish dairy farmers will be sitting down to select sires to breed the next generation of cows on their farms.

AgriLand recently caught up with Mark Ryder, LIC Europe general manager, to find out why the New Zealand industry has moved away from genomic selection after initially embracing it.

Ryder began: “From a New Zealand perspective, we were the first to get into genomics and we saw a lot of excitement in the New Zealand industry about the benefits that can be gained by shortening the generation interval.

“We got into it on a large scale initially and up to 40% of our 4.5 million inseminations were actually genomic.

“But, what we actually found, as we monitored the performance of the daughters of those bulls as they started milking in New Zealand, we found that we were getting quite a large variation.

We were creating some very elite females, but we were also creating some quite poor ones.

Ryder admitted that a large team of bulls was used and the average BW (breeding worth) that was expected was achieved. But, there was quite a large variation in the performance of the cows produced.

“Farmers were very unhappy about young cows leaving the herd early and they were getting quite a bit of wastage.

“In Ireland, it costs €1,400-1,500 to get a heifer into milk. If only 25% of those heifers coming in are any good, it’s not economic so we’ve changed our approach.”

Given this, the New Zealand based co-operative is now using genomics to select young bulls before proving them for the future. The process of proving a bull, he said, costs in the region of NZ$40,000-50,000.

Given the co-op’s stance, wholesale genomic semen sales now only equate to about 5-6% of all of the inseminations made on New Zealand farms and 94-95% of farmers are choosing straws from bulls with high reliability.

“We are encouraging farmers here to use teams of genomic bulls. But, you’re still going to get the same result.

It will deliver the average, but you are going to create heifers that aren’t going to last in the herd.

Due to this, Ryder encouraged farmers to focus on the use of high-reliability sires when it comes to breeding the next generation of their cows for their herds.

“LIC’s New Zealand bred bull team is backed up by daughter proven data, which includes at least one full lactation in New Zealand.

“The reliability of this information gives LIC the confidence that LIC sires have outstanding genetic merit to meet the individual needs of pasture-based farmers.”

From speaking to Ryder, it’s clear that LIC is not totally against genomics. But, instead, the co-op is focused on supplying its dairy farmer customers with reliable genetics that will go one to deliver in the parlour.

Risks with genomics

Along with creating “a large variation” in the off-spring produced, the LIC representative also touched on the dangers of genomics-on-genomics breeding.

“There’s a huge risk there and we see it a lot in this country. We are seeing it start to play out now as some of those genomic proofs aren’t delivering.”

This, he said, is been proven by the daughters of some bulls actually passing through the parlour.

Some of the indexes of those genomic bulls are dropping significantly. And, the way that the industry has been using it here, there’s a lot of genomics-on-genomics.

“There’s a lot of AI companies that have got genomic sires – bull calves that they’ve paid good money for –  and all of a sudden they are not worth a hell of a lot because their sire – who was genomic – has now dropped. Correspondingly, those bulls have dropped as well,” he said.

Source: AgriLand

We’re well into the genomic era. If you’re like most producers, you’re now comfortable incorporating genomic-proven bulls as part of your balanced breeding program.

Yet, you might still have questions about the difference you can expect between a bull’s first genomic proof and his daughter proof. To answer your questions, we’ve done an in-depth proof analysis of all industry bulls. Our goal was to find out how genomic proofs hold up. Do they become more or less accurate with time?

What did we learn?

Graph 1 shows the average change in TPI from initial genomic proofs to April 2017 daughter proofs. The TPI change from genomic to daughter proof is the amount of space that separates the blue and orange lines.

Graph 1. Change in TPI from genomic release to April 2017 daughter proof

This graph includes all industry bulls released between January 2010 and April 2014. As you can see, the bulls released in January 2010 had an average change of 171 TPI points from their first genomic release to their April 2017 daughter proof.

Now, fast forward a few years. Bulls released as genomic sires in April 2014 saw only a 52 point TPI difference from their initial genomic proof to their April 2017 daughter proof.

This means the stability in GTPI from genomic release until daughter proofs has improved by more than 100 TPI points! As a bonus, it’s clear to see that the genetic levels of bulls continue to rise!

The same goes for Net Merit $. You can discover those results in Graph 2.

Industry bulls first released as genomic-proven sires in January 2010 dropped, on average, 151 NM$ from their first release until their April 2017 daughter proof. Whereas, the bulls first released as genomic sires in April 2014 only changed 77 NM$ from their initial release.

Graph 2. Change in Net Merit $ from genomic release to April 2017 daughter proof

So, even though genomic numbers are still slightly inflated, the gap between genomic and daughter proofs changes less with each passing proof round.

Want more details?

Let’s look at the facts and figures in a different light. We’ll focus in on all 1,078 industry bulls released in 2013. We use this group because all bulls released in 2013 should now have a daughter proof for production, health and conformation traits.

The bell-shaped curve of Graph 3 shows the mean and standard deviation change in TPI on the 1,078 industry bulls released as genomic-proven sires in 2013.

Graph 3. Histogram of difference in TPI from genomic release in 2013 to April 2017 daughter proof

As you can see, on average, these bulls changed less than 100 points from their initial release in 2013 to their daughter proof in April 2017. One hundred of these bulls have a daughter-proven TPI within just twenty points of their original genomic TPI. Only about 40 bulls from the entire group of 1,078 lost more than 300 TPI points – that’s less than 4%.

We see the same trend for NM$. Graph 4 shows the average NM$ change and standard deviation of the same 1,078 industry bulls released in 2013. These sires changed about -103 NM$ from their initial genomic proof in 2013 to their daughter proof in April 2017.

Ninety-five bulls held steady within the small 20 point swing from genomic to daughter-proven NM$. Less than 20 bulls changed more than 300 NM$.

Graph 4. Histogram of difference in NM$ from genomic release in 2013 to April 2017 daughter proof

What are your options today?

Still debating whether daughter-proven or genomic-proven sire groups are your best option? Take a look at the top 10 TPI sires available from Alta today.

You’ve likely heard the statement: “Genomics has doubled the rate of genetic progress”. In this article, we find out if this claim is true by looking at genetic gain for indexes and individual traits before and after the implementation of genomic evaluations.

Female Genetic Trend for LPI and Pro$

Figure 1 shows the genetic trend for LPI and Pro$ for Canadian Holstein females based on year of birth. The trends for LPI and Pro$ are essentially identical but since the scales for the two indexes are different they are plotted on different lines.

Prior to genomics, which began in 2009 for Holsteins, the average LPI for females born during the 5-year period from 2004 to 2009 was increasing by 50 LPI points per year. Since the introduction of genomics (vertical line in Figure 1), this rate of genetic progress has increased substantially. For females born in the past five years, from 2011 to 2016, the average LPI has increased gains of 107 points per year – over twice the amount of annual gain prior to genomics.

During the same time periods, the annual genetic trend for Pro$ increased from $79 before genomics to $176 per year during the past five years. This has happened without selection for Pro$ as the index was only introduced in August 2015. However, since Pro$ is made up of many of the same traits as LPI and the two indexes are correlated by 96%, selection for LPI was leading to an associated progress for Pro$ before this national index existed.

Genetic Gain for Individual Traits

Like for overall indexes, genetic gain for individual traits can be assessed before and after genomics. Figure 2 shows the relative genetic gain by trait realized during the five years before genomics and during the most recent five years with genomics. Genetic gain is expressed in standardized units so that gain can be comparable across traits with different units of expression.

When comparing the gains achieved during the two 5-year periods, we can see that:

• Gains achieved with genomics have been positive for all traits, including lower heritability functional traits. Prior to genomics, gains for most functional traits were slow and even negative for some traits, such as Daughter Fertility. The impressive gain for these low heritability traits is a direct result of the increase in the accuracy of genetic evaluations due to genomics. Overall, the largest gains relative to the five years before genomics are seen for functional traits.
• Gains achieved with genomics were doubled for Fat and Protein Yields while also making considerably higher genetic gains for deviations.
• Gains achieved with genomics were the highest for overall type traits, especially Conformation and Mammary System, but these traits were also achieving the highest rates of genetic progress prior to genomics. These past and continued gains are partially due to the high selection intensity for these traits.
• Gains achieved with genomics were doubled for Feet and Legs, which is the least heritable among the five overall type traits. Since genomics significantly increases the accuracy of published evaluations, the genetic gain realized for this trait has been large.

Another way to compare the genetic gain achieved pre- and post-genomics is presented in Table 1. Here, gains are expressed in the same units as proofs for each given trait. For example, yields are expressed as EBV based on kilograms while most functional traits are expressed as Relative Breeding Value (RBV) points.

In Figure 2 we saw that with genomics, there has been twice the amount of genetic gain realized for Protein yield in terms of standard units.  Referring to Table 1, we can see that the combined gain for Protein yield during the five years prior to genomics was 11.8 kg, or around 2.4 kg per year. In the past five years with genomics, the combined gain was 24 kg, or 4.8 kg per year. This means that females born in the last 5 years in a herd with average management are expected to have 305 day lactation yields that increase roughly 5 kg per year, on average. In this case, double the genetic gain translates into double the performance increase for protein yield.

Likewise, when looking at the functional traits, the combined gain for Herd Life during the five years prior to genomics was 1.12 RBV points, or 0.22 RBV per year. Over the past five years, however, with genomics, the combined genetic gain for this trait was tripled to 3.36, or two-thirds of an RBV point of gain per year. Significant gains with genomics have also been observed for other functional traits, meaning that Holsteins today can be expected to last longer in the herd due to improved calving and reproductive performance along with higher resistance to disease.

Genetic gains have increased substantially across the board since the implementation of genomics in Canada. While all traits have benefitted from the increased accuracy the technology provides, this is particularly true for lower heritability functional traits. The increased gain for individual traits translates into a rate of progress that has doubled for both of Canada’s national genetic indexes.

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

Download a PDF copy of this article

A University of Wisconsin research professor is nearly finished with a study on genomic selection for feed efficiency in dairy cows.

Dr. Kent Weigel is the chairman of Dairy Science department, specializing in breeding and genetics.  He says, “We’re trying to do 8,000 cows, we’re over 6,000 so far, and form a genomic reference population that then we can use to make predictions for feed efficiency.  You know, using those data (sets), we can predict the feed efficiency of any heifer calf or the feed efficiency daughters of any bull at any breeding company.”

Weigel tells Brownfield the study has required a lot of labor over the past five years, making detailed measurements of each cow.  “Specifically, how efficiently does each cow utilize the energy that she consumes, and in order to do that you measure every individual cow’s feed intake for a couple of months in the middle of lactation.”

Weigel says the results of the research will be available to the industry soon, saying, “Within the next month or later this year.”

Weigel says measuring feed efficiency on this scale wouldn’t have been possible a few years ago, but genomics technology has reduced the expense and labor.

Scotland may be best known for bagpipes, but it’s the data derived from Scottish dairy cows that is music to the ears of genomic researchers.

As it seeks to enhance feed efficiency and reduce methane emissions with the power of genomics, a Genome Alberta project is part of a global initiative. The effort includes a dairy research herd at Scotland’s Rural College (SRUC) Dairy Research Centre on the outskirts of Dumfries, South West Scotland, supported primarily by funding from the Scottish government.

The project aims to use milk mid-infrared (MIR) spectral data to estimate feed efficiency and methane emissions as the data correlates well with both traits. Compared to the current method of collecting phenotypes to predict these traits, MIR data is easier and less expensive.

Best of all, this data is already being collected. In order to use it for their purposes, however, researchers must increase the amount of data they compile from cows for which they already have phenotype information on the targeted traits.

As it turns out, the SRUC research herd fits the bill.

Numbers game

“We’ve been running 200 cows for 45 years now,” said Eileen Wall, Team Leader of Integrative Animal Sciences at SRUC.

“We phenotype the heck out of our research herd animals and follow them from birth through three lactations of production data, gathering weekly information on key aspects like feeding behavior and feed intake, including regular milk MIR profiles.”

Having gathered hundreds of thousands of rows of research phenotypes, Ms Wall and her team tie into a national evaluation system and obtain data on every milk-producing cow in the country. In collaboration with National Milk Records, they have collected MIR data on 1.5 million cows over the last year in what she calls a “really stunning example of the research community working with industry,” assisted by funding from Innovate UK and BBSRC (British Biological Sciences Research Council).

One product of all this data is a powerful set of genetic prediction tools for traits of interest to dairy producers.

“Our dairy research herd serves as an excellent resource for reference phenotypes. The prediction tools developed to date were based on more than half a million records taken from over 900 Holstein Friesian cows from 2003 – 2014 on a range of phenotypes and dietary components. As we continue to collect records on farm and spectral data we can routinely update and improve tools as well as test and develop tools to predict new phenotypes.”

In turn, the Scottish results are helping the Efficient Dairy Genome Project develop a similar set of robust prediction equations using MIR data.

“Based on our experience, we can share information with the Canadian researchers and help them avoid any mistakes that we made along the way; so it’s a combination of pooled data and shared learning.”

Also, since North America and the UK have different production systems, pooling data should produce better genomic predictions for a wider range of cows and systems.

Feeding progress

Motivating research in both Canada and the United Kingdom is the potential benefit for producers.

“Feed is the highest intake cost for dairy farmers. If we can increase the efficiency of milk production, it will help farmers save money and stay competitive. Right now our government is stressing the need for research to have real-life applications so that results don’t stay on paper but are translated to the field.”

For the UK dairy industry, reducing feed waste has the potential to cumulatively increase profitability by 17 million GBP (British pounds) per year. The hope is that Canada can realize similar benefits by making active selection decisions for critical traits. Who knows, maybe Canadian farmers will even don kilts as a show of solidarity.

Source: The Cattle Site

In light of some recent election results, “giving the people what they want” isn’t always the way to go. When those people are the beneficiaries of research efforts, however, it’s not a bad idea.

That’s certainly the case with a Genome Alberta project to develop and apply more accurate genomically-enhanced breeding values for traits of importance to the commercial cattle industry. Researchers may have started with a theory, but practical results for industry are a top priority.

“One thing we talked about at a recent team meeting was user tools being generated by the project and how best to roll them out,” said John Crowley, a co-lead on the project and a research geneticist with Livestock Gentec at the University of Alberta.

“We need to pinpoint the most effective way for industry to use the information on breed composition. It can be a huge aid in guiding your decisions on what breeds you cross with or what animals within a breed will be of interest to you.”

Ultimately their goal is a decision tool to maximize hybrid vigor, what Dr Crowley calls “that extra bump in performance you get when crossing two unrelated breeds or animals.”

Begin with the end user in mind

In regard to breeding values, the team is looking at how to package and communicate the findings and encapsulate them in one or two easy-to-read figures.

“You can sum up genetic scores in different ways. It may be that a maternal and all around index would be best for the end user, where the economic value of each trait is given its own weighting.”

Since he also serves as Director of Scientific and Industry Advancement with the Canadian Beef Breeds Council (CBBC), Dr Crowley is sensitive to the needs of industry and is closely following their reaction.

“People seem pretty happy with our progress. Research participants are excited about the information they have received so far and glad they had a chance to dip their toes in genomics, so to speak.”

One of their main research partners is Cow Calf Health and Management Solutions (CCHMS). Genome Alberta researchers are working with 10 CCHMS clients and gathering valuable feedback to inform the project.

Breeding success

Another end user for the project is breeding associations for whom phenotypic data and genotypes can be quite valuable.

“It’s helpful for these associations to see how bulls are performing on a commercial farm instead of just being circulated around the seed stock sector. This gives them the opportunity to grab more of the data.”

As Dr Crowley explained, often what go into the genetic evaluation of an animal are its own physical measurements and those of immediate relatives, sire or dam.

“To get a proper evaluation you need to see a lot of progeny perform. Unless an animal becomes the sire for many seed stock progeny, he doesn’t get the chance to show what he can do as there is a disconnection between commercial farms and seed stock farms; this project is trying to close that gap a bit.”

How the end user perceives this project depends on their particular interests. For example, Cargill’s focus is more on immediate benefits and what is being done to improve certain traits.

“They’re looking at the most economically relevant traits such as carcass quality, feed efficiency and female traits. The Cargill representative who saw our findings was quite interested and planned to share them with his staff and colleagues. He liked that we are looking at this from a whole value chain perspective and also appreciated the chance to learn more about this field of study.”

Though there is still a long way to go on the project, Dr Crowley and his colleagues feel they’re on the right track. Given recent events, it’s reassuring to know that listening to the people isn’t always a bad thing; you just have to choose the right people.

Source: The Cattle Site

Click to enlarge

Click to enlarge

Click to enlarge

Download a PDF copy of this article

Advances in genomic testing create new opportunities for commercial dairy producers to have more control over their herds’ profit potential. With an abundance of available replacement heifers, producers have the ability to replace less profitable cows with genetically superior heifers, shortening the generation interval and accelerating the genetic progress.

Genomic testing offers a powerful screening tool to predict what heifer will be a top performer and which will be below average. The payoff can be big, as investing money toward feeding and housing heifers that are not going to contribute to the bottom line only narrows profit margins.

Technology advancements make testing affordable
Once a tool only affordable for dairy producers owning elite dairy herds to identify the best bull mothers, genomic testing is now cost effective on a large-scale basis. Costs have fallen dramatically since genomic testing first hit the market in 2008, and it is now possible to test for as low as $28 per head or approximately 1 percent of the cost to raise a heifer.

Managing genomic information to find genetically inferior calves is quite simple. Dairy producers select a key indicator, such as the predicted transmitting ability (PTA) of net merit dollars (NM$) which estimates the expected lifetime profit of a cow compared to the breed base of an average cow born and raised in the same environment. NM$ include traits that capture an economic impact, such as milk yield, health, longevity, fertility and calving ease. If a heifer does not meet the minimum requirements for NM$, she could be sold prior to breeding.

Another option is to breed the bottom-end replacements to a beef sire and sell the resulting crossbred calves as higher-dollar beef animals. Depending on market conditions, it may be possible to pay for testing costs for all of the replacements just from the increased value of the crossbred calves.

Conversely, a heifer that ranks high in NM$ might be a candidate for sexed semen. Keeping the calves with the best genetics and applying sexed semen, while selling calves with the poorest genetics, can deliver substantial cost savings. Ultimately, producers realize value by collecting the NM$ information at an early age, allowing them to gain an understanding of how profitable the animal will be as a lactating cow.

How it works
Genomics got its start in 2004 with the sequencing of the bovine genome, which is made up of approximately 25,000 useful genes. Sequencing allowed basic information about genetic coding to be used to improve how genetic values of cattle are estimated.

For genomic selection, researchers look for markers or single nucleotide polymorphisms (SNPs). A SNP is a place in a chromosome where the DNA sequence can differ among individuals. SNPs are most useful when they occur close to a gene that contributes to an important trait. Most traits are controlled by many genes, making this a very complex process. Significant progress was made when a genotyping computer chip was developed that could identify more than 50,000 SNPs (50K test) on the genome.

Genomics has rapidly advanced in the past decade. Today most laboratories use custom chips that are able to use fewer SNPs through the use of imputation. Imputation is a method that uses knowledge of the parental genome in calculations to predict the genotype of the calf.

The Council on Dairy Cattle Breeding (CDCB) maintains the genomic database and produces a genomic evaluation report on a weekly basis containing data on every animal. Producers can take a tissue sample from a calf and submit it to a laboratory, which then sends the genotype to the CDCB. The CDCB then calculates PTAs that are equal to progeny tests on many daughters of sires.

While the reports contain extensive data, most dairy producers prefer to use simple rankings, such as Net Merit, Cheese Merit or Fluid Merit. For those who like more details, reports can be run to work with custom-made indexes.

Testing for commercial operations
Neogen Corp. recently introduced Igenity-Essential in response to requests by commercial dairymen for a more convenient and cost-effective genomic assessment of their animals that can be done during times of low milk prices.

An alternative to traditional USDA-CDCB evaluations, Igenity-Essential can be performed soon after a calf is born, and test results are received within two to three weeks after tissue submission. Ideal for simple heifer sorting, Igenity-Essential is powered by a low-density 7K chip with custom content to examine 15 traits essential for improved milk production and reproduction. For the commercial dairy producer looking for a simple but effective sorting tool, this is an excellent option. It is available for Holsteins and Jerseys and contains the key content needed for an accurate genomic evaluation to make management decisions.

Find out more information at www.neogen.com or contact your local Merck Animal Health representative.

Source: Merck Animal Health

Seven years into it, genomics has become nearly as common of a term as AI. We’re now used to the genetic technology and feel confident using genomic-proven bulls as part of a balanced breeding program.

However, you may still have questions about the difference to expect between a genomic proof and a daughter proof. Or maybe you’re looking for comparisons on the genetic merit between genomic-proven and daughter-proven sires.

Peace of mind from increasing proof stability

Closer analysis of proof stability shows the ever-increasing stability we see with each passing proof round.

Graph 1 below illustrates the change in TPI for all industry genomic-proven Holstein bulls first released between January 2010 and December 2012. As the graph shows, the bulls released in January 2010 had an average change of 164 TPI points from their first genomic release to their December 2015 daughter proof (the difference shown as the amount of space between the blue line and the orange line).

The decreasing change in TPI from first genomic release to Dec ’15 dtr proofs for industry bulls released between 2010 and 2012.

Fast forward to December 2012, and the bulls released at that time saw only a 67 point TPI difference from their initial genomic proof to their December 2015 daughter proof.

This means that the stability in GTPI from the time of genomic release until daughter proofs has improved by nearly 100 TPI points!

The same goes for Net Merit $, shown below in Graph 2. Industry bulls first released as genomic-proven sires in January 2010 experienced an average change of -135 NM$. That change from first genomic proof to December 2015 daughter proof decreased to just -63 NM$ for the group of bulls first released to genomic line-ups in December 2012.

The decreasing change in NM$ from first genomic proof to Dec ’15 dtr proof for industry bulls released between 2010 and 2012.

This shows that while genomic proofs are still slightly inflated, the gap between genomic and daughter proofs changes less with each passing proof round.

If we take a look at the facts and figures in a different light, and zoom in on just the bulls released in 2012, the stability remains. The bell curve below shows the average change in TPI for the 1,060 industry bulls released as genomic-proven sires throughout 2012 that now have milking daughter proofs.

As you can see, the average change in TPI was -89 points. Nearly 70 bulls have a daughter-proven TPI within ten points of their original genomic proof, and only 40 bulls of that entire group gained or lost more than 300 TPI points.

And for NM$, the same trend holds true. As you can see below, the average change in NM$ from the group of all industry bulls released throughout 2012 was at -92 NM$ from initial genomic proof to December 2015 daughter proof. Sixty-three bulls held steady within the small 10 point swing from genomic to daughter-proven NM$.

If you are still debating whether daughter-proven or genomic-proven sire groups are your best option, take a look at the top 10 TPI sires available for you from Alta today.

Compare your top daughter-proven sire choices with your top genomic-proven options.

Despite the solid 2364 TPI average from the top daughter-proven sire options, the genomic-proven group provides a 220 point TPI advantage! Your odds are nearly zero that every single sire on the genomic-proven list would drop in production, health and conformation traits after receiving a daughter proof to rank them lower than the current daughter-proven stars.

As you make your genetic selection decisions, keep in mind:

1. While genomic proofs are slightly inflated, they continue to become more accurate and see less change in TPI and NM$ with each passing proof round because of model adjustments made along the way.
2. Even though the average change in TPI and NM$ for bulls released in 2012 is about -90 from genomic proof to daughter proof, the currently available genomic sires will still make much faster genetic progress than the current daughter-proven bulls available.
3. Make sure the genetic progress you make is in the direction of your goals. Use a group of genomic-proven sires selected based on your customized genetic plan with emphasis only on the production, health or conformation traits that matter most to you.

Please Click HERE to download a PDF of this article.

Written by Gerbrand van Burgsteden, Global Product Development Analyst, Alta Genetics

Source: Alta Genetics

The dairy industry needs to “wake up” to the dangers of using genomic bulls on maiden heifers, according to a breeding specialist.

Munster AI’s Doreen Corridan claimed that more heifers are experiencing difficult calvings this year because both farmers and farm advisors ignored best practice by mating low reliability bulls with maiden heifers.

“I’m hearing about a lot of calving difficulties and Caesareans in heifers that were carrying calves by some genomic bulls that have since topped the latest spring AI list,” said Ms Corridan.

Among the farms that are facing culling first calvers due to problems this spring was the Greenfield demonstration farm in Kilkenny. Management there decided to use two genomic bulls on maiden heifers in an effort to accelerate EBI gain in their 2015 calf crop.

“It was our own mistake and we definitely won’t be doing it again,” said Teagasc specialist on the Greenfield project, Abigail Ryan.

She added that the risks associated with using genomic bulls on maiden heifers were considered worthwhile last year because of the sires’ very high EBIs and low calving difficulty scores. However, the plan has backfired, with two heifers subjected to Caesareans likely to be culled this year.

“We ended up with two Caesareans out of the 89 heifers that have calved down so far this spring. They’re barely milking now and I’d say they’ll be culled, even though they were smashing heifers. We jacked a good few of them too, but these animals are all doing fine now,” she said.

Teagasc research has shown that every Caesarean can end up costing the farmer over €700 in dead calves, higher culling rates, lower milk yields, labour, and veterinary costs.

“We cost Caesareans at six hours in labour for the farmer and a minimum of €160 for the vet,” said Teagasc geneticist, Donagh Berry.

“We also assume that the cow has a 5pc higher chance of dying, and 25pc more likely to be barren. There’s also the cost of the calf and the loss of 600kg of milk.”

Even calving interventions that don’t require a vet cost farmers dearly.

“What we class as severe assistance by the farmer will result in a 5pc increase in barreness, 50kg less milk and additional hours of labour. That’s going to be close to €150 per cow,” said Mr Berry.

Workload

Doreen Corridan believes that genomic proofs are not as accurate on calving difficulty as other production traits. This may be partly due to the subjective nature of scoring calving difficulty for farmers at a time when they are under severe workload stress.

“Gene Ireland is the only way that we can get accurate information on crucial issues such as calving difficulty and congenital defects,” said Mr Berry.

“It ensures that we have a minimum number of animals in commercial situations where the farmer has no vested interest in playing with the figures.”

Ms Corridan’s advice for farmers is never to use a bull on maiden heifers that has a calving difficulty index of more than 2pc, or a calving index reliability that is lower than 90pc.

“Yes, you are compromising your genetic gain, but at least you are guaranteeing that your heifers survive into cows.

“A lot of Teagasc advisors were mating genomic bulls with maiden heifers. Both they, and some farmers, need to cool the jets on the chase for elite EBI calves,” she said.

Source: Independent.ie

Over the past 5 years, genomic evaluations have been the hot topic for dairy cattle genetics.  Yes, the technology is exciting.  However, any new technology typically receives undue hype after its launch and unrealistic predictions of impact are promoted.  The initial period of unwarranted hype about a new technology is usually followed by a time of reflection, which in turn is followed by periods of disillusionment and, eventually, proper implementation of the new technology.

Definitely, genomic evaluations of dairy cattle have been the topic of considerable hype and, perhaps, unrealistic expectations have been nurtured by some.  Let’s face it – the potential positive impact of a new technology is likely to be emphasized and the potential downsides are likely to be glossed over.  So, where are we on the hype/reflection/disillusionment/proper adoption path in regard to genomic evaluations?  Probably, somewhere between reflection and disillusionment, but proper adoption will be the next step.

Reliability versus Accuracy

An overriding problem is interpretation of the “accuracy” of genomic evaluations.  How is “accuracy” measured?  Well, most would say by the reliability published alongside each genomic PTA (transmitting ability) for an individual trait or for an index that combines traits into a single value (such as Net Merit).  However, are reliability and accuracy the same thing?  No, they are not!

Accuracy has a specific statistical (scientific) meaning in addition to an intuitive meaning.  Accuracy is a measure of the confidence range around a PTA – say, plus or minus 200 lb of milk or plus or minus $50 for Net Merit.  Mathematically, accuracy for genetic evaluations is calculated as the square root of reliability.  To put this into perspective, we will compare reliability versus accuracy alongside their corresponding confidence range for Net Merit for the four types of genetic evaluations of bulls – Parent Average (average of the PTA of his two parents), genomic test, first-crop daughters, fully proven (thousands of daughters in hundreds of herds).  We will use a confidence range of 68% (which means there is about a two-thirds chance the eventual Net Merit of a bull will fall within the range).

The reliability of the genetic evaluations for Net Merit of most Holstein A.I. bulls is about 38% (Parent Average), 70% (genomic test), 85% (first-crop daughters), 99% (fully proven).  These reliabilities suggest the result of a genomic test almost doubles the genetic knowledge known about a young bull without daughters.  However, that is NOT the case.  The accuracy of the genetic evaluations for Net Merit associated with those four levels of reliability are 62% (Parent Average), 84% (genomic test), 92% (first-crop daughters), and 99% (fully proven).

Use of accuracy instead of reliability demonstrates the gain in genetic knowledge when going from Parent Average to genomic testing isn’t as large as reliability leads one to conclude.  The truth is Parent Average is a fairly good predictor (but far from perfect) of eventual PTA for traits of bulls or females.  Yes, genomic testing provides improvement in accuracy over Parent Average, but it doesn’t provide the extent of improvement that some have suggested – certainly not, “a doubling of knowledge”.  Most people in the dairy industry are likely unaware that reliability and accuracy are different measures and reporting of reliability exaggerates the increase of genetic knowledge when moving from Parent Average to fully proven status for bulls.

Over-Evaluation of Highest Ranking Individuals

The greatest frustration has been the over-evaluation of highest-ranking young bulls by genomic testing.  This upward bias of PTA for highest-ranking bulls is now well-documented by both researchers and A.I. organizations.  The extent of this upward bias has been estimated by some to be at least $150 for the Net Merit index and 200 for the TPI index of Holstein USA.  The upward bias is more pronounced if the PTA of the sire of a young bull is also genomic-only (no daughters contributing to the sire’s PTA).

Consequently, most in the dairy industry recommend separate rankings of proven bulls (with daughters) versus genomic-only bulls (no daughters), because the genomic-only bulls are more likely to drop for PTA than bulls with daughters contributing to PTA.  Definitely, when genomic-only bulls are selected for use in a herd, a group of them should be used to spread the risk over a larger number of bulls.  Proven bulls can be used with more confidence in regard to potential changes of PTA.

Genomic testing of heifers

Some that provide advice to dairy producers recommend genomic testing of all heifers to determine which surplus heifers to sell or to breed to beef bulls.  However, routine genomic testing of all heifers in commercial settings should be given very careful consideration prior to implementation.  It’s important to keep in mind environmental influences, in addition to genetics, are large on the eventual performance of heifers as cows.  For example, differences of heifers due to respiratory illness that causes lung damage, the lack of growth, and structural deficiencies could easily overcome differences for genomic test results versus Parent Average.  Also, daughters of genomic-only bulls are more prone to changes of their genomic test results over time compared to daughters of proven bulls with daughters contributing to their genetic evaluations.                      

Source:      University of Minnesoa

The dairy industry has greatly evolved over the years with necessity-driven innovation. In order to be efficient in feeding a growing world, it has to.

Dairy producers have to find ways to excel faster and further than they ever have before. Genomic testing, one of the fastest and most powerful breeding tools we have today, does just that.

Genomic testing is no longer a new tool in the dairy industry. However, the innovations for applying this tool are continuing to be discovered at a rapid pace. The practicality and economic logic for utilizing genomic evaluations in commercial female settings is real.

We know more than we ever have about the bovine genome. Even better, we know how to utilize the data for improved profitability and efficiency. Genomic testing is a powerful management tool for realizing maximum gain for return on investment of herd replacements.

The need to increase production efficiency with technology innovation
The agriculture industry needs to continue striving for innovation to meet the nutritional needs of a growing population and increase the average life span with fewer natural resources.

If producers can’t grow larger due to limited space and resources within an operation, they need to strive for efficiency. Growing profitability from a genetics standpoint in replacement heifer groups is one of the most efficient places to experience this gain.

Genetic innovation for increased efficiency within the dairy industry will continue to grow in importance. Genomic-testing calves before making a large initial investment to raise them for replacements reduces resource spending on predicted unproductive cows. Genomic evaluations are a predictive tool for screening herd productivity for the type of cows who will be entering a farmer’s parlor as 2-year-olds.

Applications today in large herds
Since the introduction of genomic evaluations, the Holstein breed has seen an increase of about $80 per year in average net merit value. According to Dr. Paul VanRaden of USDA/AIPL, at the 2014 Advancing Dairy Cattle Genetics: Genomics and Beyond Conference, an increase of $90 per year is realistic.

This expeditious advancement is an example of how powerful the shorter generation interval is at creating higher-profit animals. The female side of genetics has truly had the most benefit from genomic testing technology.

Producers are now able to be more selective for genetics with higher reliability in their replacement heifers, instead of primarily relying on sire selection for genetic advancement.

Producers who are expanding through purchasing large groups of replacement heifers with limited record availability on the group can use the predictive power of genomic testing to obtain a more accurate idea of the type of heifers they are bringing home.

Even if heifers have some pedigree information available, genomic evaluations combined with pedigree information is a much higher reliability prediction of the group’s performance. In addition, a farmer can receive parentage verification for more accurate parent average ranking, genomic inbreeding values and identification of expensive genetic recessive carriers.

Accelerate genetic progress from the female side of your herd with genomic evaluations for breeding program decisions. Depending on your annual needed replacement rate, the cost of testing all or the majority of heifers born can be economically justified from the gain in genetic value and lifetime profitability.

The heifers in a herd are going to be the most genetically valuable animals in a herd because they are one generation further along in progress.

Pre-screening for future high-performing cows gives the ability to shorten the generation interval in your herd with heifer donor selection for ET or IVF procedures. Creating more replacements from the highest heifers in the herd will drastically increase the average genetic value of replacements in just one generation.

Other breeding program strategies include using sexed semen on the top genetically elite heifers to increase the number of future replacement heifers from these higher heifers, culling heifers with lower predicted performance or breeding them with beef semen.

Mating decisions for heifers can be made more accurately using genomic evaluations in sire selection. This data will be more reliable and predictive than solely using pedigree information.

It will also be a stronger line of defense for protecting against potential inbreeding losses. The genomic inbreeding value will be a true measure of how much gene relatedness is in the animal’s DNA.

Testing animals as carriers for expensive recessives and fertility haplotypes will give farmers a better tool to manage the frequency of these genetics in their herd.

It is more genetically efficient to manage the use of carrier sires who do offer profitable mating matches in females than to completely avoid these sires. Genetics can advance aggressively but still protect for recessive dangers.

Managing recessive carriers through a mating program that will protect against two known carriers allows safe use of elite sires who are carriers of the fertility haplotypes.

Herds do not need to completely avoid profitable matings with a particular sire because he is a carrier – but simply to identify and eliminate matings between two carriers. The benefit from reducing losses in abortions and shortening days open intervals would be a high-realized gain from genomic evaluations on replacement heifers.

Ancestor recovery will help producers with data management, which in turn will help them to more correctly identify heifers they believe to be elite based off of pedigree and dam performance compared to herdmates.

The larger a herd, the more calves being born each day and the greater the opportunity for calves to be misidentified at birth. According to VanRaden, more than 50,000 misidentified sires were discovered in genomic sample submissions in 2013.

Applications tomorrow in novel traits
Researchers and academia are pushing forward with hopes of discovering genetic correlation to dairy cattle health traits that will be applicable in everyday herd management. The subjectivity of categorizing health events is the major challenge in advancing these efforts. In order to collect and utilize data from on-farm health events, systems for standardization across the industry need to be established.

The health traits currently in the midst of being researched include a high health immune response for reliability in disease immunity and hoof health traits. Other novel health traits being developed for genetic correlation include a feed efficiency index, heat stress measurement and methane emissions.

As with all new technologies, it takes time to adapt management styles to utilize that information for increased overall herd profitability. The tool will continue to be developed upon and more unrealized potentials for utilizing genomic evaluations on modern, commercial operations will be released.

The dream cow can easily become reality
Writing up a wish list for a producer’s made-to-order ideal cow is easy. A group of moderate-statured, highly productive, feed-efficient cows with sound feet and legs, as well as trouble-free health, paints a general picture of a profitable barn in any region of the U.S.

However, actually, making that dream cow a reality takes a little longer than ordering your next piece of custom equipment. However, thanks to genomics and research efforts, the dairy industry will continue making steps in the right direction at a faster pace with a focus on the commercial producer’s ideal barn cow.

Written By: Mandy Brazil, Program Services Coordinator at Accelerated Genetics

Leading sires are RICKLAND PREDESTINE 669-ET (22), SEAGULL-BAY SUPERSIRE-ET (21), and LADYS-MANOR MAN-O-SHAN-ET (19).  Top owners are De Su Holsteins LLC (26), Genesis Cooperative Herd (15) and Sandy-Valley (13).

Source: Holstein USA

Top sires include CASHCOIN (27), SUPERSIRE (27), and MCCUTCHEN (17).  Top dams include DE-SU 1438-ET(7), DE-SU 1642-ET (5), SEAGULL-BAY MISS AMERICA-ET (5).