The most significant change for the April 2, 2024, triannual evaluations is an adjustment in the trait model for six CDCB health evaluations – Resistance to Milk Fever (MFEV), Displaced Abomasum (DA), Ketosis (KETO), Mastitis (MAST), Metritis (METR) and Retained Placenta (RETP). 
 
Since these traits debuted six years ago, the number of health records in the National Cooperator Database has tripled or quadrupled – depending on the trait. Detail here. With this data surge, the trait model has been adjusted with new variance component estimates and adjusted weights, effective with April 2024 evaluations. This evolution follows the typical progression of newer traits.
 
These CDCB evaluations for disease resistance were first launched for Holstein in April 2018, Jersey in April 2020, and Brown Swiss in August 2022. Variance components were originally estimated in 2018, when Holstein records available ranged from 1.2 to 2.2 million per trait. Current volume ranges from 4.3 to 7.7 million for the three breeds, with Mastitis having the most records in CDCB’s database.
 
In a test run comparing the previous and updated model, correlations of genomic estimated breeding values (GEBV) for five of the traits were ≥0.96 for Holstein, ≥0.90 for Jersey and ≥0.92 for Brown Swiss. For Displaced Abomasum, lower correlations were observed (≥0.95 HO, ≥0.82 JE and ≥0.81 BS) due to the largest change in heritability.
 
With the model adjustment, variation in Predicted Transmitting Ability (PTA) for some individual animals, particularly Jersey and Brown Swiss, was expected. The impact on Net Merit is very small, given the weighting of these traits in the index.
 
Read detail on health records, model effects, new variance component estimates, adjusted weights and correlations between old and new model in the triannual change documentby CDCB and USDA AGIL.

Highlights

• The number of genotypes of crossbred animals is increasing in US dairy farms.
• Including crossbred data in genomic evaluations is possible.
• This study analyzed purebred and crossbred data together.
• Single-step genomic predictions for crossbred cows were more accurate than predictions based on SNP effects and breed proportions.

The number of crossbred genotypes in the dairy cattle sector has increased, necessitating the inclusion of crossbred animals in genomic evaluations. This study aimed to investigate the feasibility of including crossbred genotypes in multibreed, single-step genomic BLUP (ssGBLUP) evaluations. The Council of Dairy Cattle Breeding provided over 47 million lactation records registered between 2000 and 2021 in purebred Holstein and Jersey and their crosses. A total of 27 million animals were included in the analysis, of which 1.4 million were genotyped. Milk, fat, and protein yields were analyzed in a 3-trait repeatability model using BLUP or ssGBLUP. The two models were validated using prediction bias and accuracy computed for genotyped cows with no records in the truncated dataset and at least one lactation in the complete dataset.

The genomic predictions of crossbred genotyped cows were slightly more accurate than purebred cows. Multistep evaluations are still the official route to obtaining genomic predictions for dairy cattle in the United States, which comprises a multibreed best linear unbiased predictor (BLUP) followed by a single-breed estimation of single nucleotide polymorphism (SNP) effects. After estimating single-breed SNP effects, direct genomic values (DGV) are computed for genotyped animals as a sum of SNP effects weighted by the genotype content. Genomic PTA are then calculated as a linear combination of DGV and parent average (PA).

However, routine genomic evaluations for dairy cattle do not consider crossbreds and are typically made separately by breed. There are several studies about genetic and genomic predictions for crossbred cattle, such as breed composition (BC) or proportion. In the United States, the number of available genotypes of crossbred cattle quickly increased to 150,000 in 2021. New concepts were proposed in the genomic era: genomic BC (Hulsegge et al., 2013) and breed base representation (BBR; VanRaden and Cooper, 2015). Both methods partition the genotype of a crossbred animal according to the proportion of the genome originating from each breed, and the genomic predictions of the purebreds are usually proportionally combined to evaluate the crossbred animals.

Computing SNP effects based on crossbred reference populations in multistep methods could help increase reliabilities, but this option becomes less straightforward when the breed proportion varies within the population and there are no clear boundaries between classes to create proper training sets. A different approach to obtaining genomic predictions for crossbred animals is to include their genotypes in the single-step GBLUP (ssGBLUP) method, which relies on the use of the inverse of a modified relationship matrix (H), combining the numerator relationship matrix (A) and the genomic relationship matrix (G).

Cesarani et al., 2022, conducted a multibreed ssGBLUP evaluation for Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey cattle. The authors found that reliabilities from the multibreed model were similar to those from single-breed models, which was surprising due to the unbalanced number of genotyped animals within each breed. However, proper modeling of genetic differences among breeds helped to avoid loss of predictive power when using only purebred animals.

As the number of genotyped crossbred animals in US dairy cattle is rapidly increasing, it would make sense to consider them in the evaluation together with their purebred ancestors. Some studies reported increased reliabilities of this approach in dairy cattle using less than 10k genotyped individuals in ssGBLUP and less than 50k in GBLUP and BayesR. This study aims to expand on the research findings of Cesarani et al., 2022, and include genotypes for crossbreds in a large-scale, joint Holstein-Jersey ssGBLUP evaluation in the United States.

Data used in the official multibreed genomic evaluations for US dairy cattle breeds were provided by the Council on Dairy Cattle Breeding. The analyses considered 305-d milk (MY), fat (FY), and protein (PY) yields for the first 5 lactations recorded from January 1, 2000, to August 2021. All data were preadjusted to have the genetic variance equal across time, breed, and herd and to have the same heritability of 0.20.

Animals were genotyped with 48 different arrays ranging from less than 3k to more than 600k SNPs. Genotypes were imputed, within each breed, to a common set of 79,294 selected SNPs using Findhap v3. Crossbreds were imputed separately, and genotypes for the purebred parents of all breeds were included to improve imputation.

Two evaluation methods were considered: (1) traditional BLUP and (2) ssGBLUP with unknown parent groups (UPG) for A and A22. A total of 16 UPG were considered and defined based on breed (HO or JE), sex, and year of birth. The algorithm for proven and young (APY) was used for ssGBLUP with 45,000 randomly selected animals as the core.

The data were analyzed with a 3-trait repeatability animal model that included herd management, age-parity, inbreeding coefficient, and heterosis as fixed effects; UPG as fixed effect; and herd-sire, animal, and permanent environment as random effects. Heterosis was calculated from the full pedigrees going back as many generations as recorded. For ssGBLUP, all the genotyped animals were used simultaneously in the construction of G, which was blended with 5% of A22 to avoid singularity and include a residual polygenic effect.

The study aimed to validate the predictive ability of a genomic model for crossbred cattle using BLUP and single-step genomic BLUP (ssGBLUP). Three sets were created: purebred Holstein (n = 688,985), purebred Jersey (n = 119,743), and CROSS animals (n = 3,235). The CROSS group only had cows because most of the crossbred animals are genotyped to accelerate commercial herd management. Two datasets were considered: complete (with phenotypes recorded from January 2000 to August 2021) and reduced (up to August 2017). Genotyped cows with phenotypes in the complete but not in the reduced dataset were included in the validation set.

Average predictive abilities across traits estimated with BLUP were 0.33, 0.30, and 0.26 for HO, JE, and CROSS groups, respectively. As expected, genomic information improved the predictability for all traits and groups. The breeding values estimated in the present paper for purebred HO and JE cows were compared with those estimated in Cesarani et al., 2022 to investigate the impact of including crossbred animals in the analysis. A total of 17.6 million and 1.7 million HO and JE animals were shared between the two analyses, and correlations between BV estimated in the two studies ranged from 0.98 (MY for JE) to 1.00. The correlation for young bulls was also larger than 0.99.

In terms of regression coefficients of YADJ on EBV from BLUP, the inclusion of crossbred phenotypes led to poorer results compared with Cesarani et al., 2022. However, values calculated for the two purebreds using ssGBLUP were almost the same with or without the crossbred data, suggesting greater stability of the genomic model.

The average predictive ability and stability computed using BLUP for crossbred animals were lower than for the two purebreds, but the predictive ability computed for MY in the CROSS group was larger than the values for HO (0.30) and JE (0.33). Under ssGBLUP, average values for predictive ability and stability were slightly higher in CROSS than in HO (0.55 and 0.95) and JE (0.50 and 0.93) cows. Predictive abilities consider adjusted phenotypes, which remove fixed effects from the phenotypes. In the present study, using genomic information within the ssGBLUP model could have partially overcome the absence of breed as a fixed effect. Assuming that accuracies are inflated for crossbreds due to incomplete accounting for BC, the inflation can be reduced by better accounting for this effect (Misztal et al., 2022).

The higher accuracies for crossbreds in MY could be explained by the larger phenotypic difference between HO and JE, reflecting a greater genetic difference between the two originating breeds. These breed differences, which can be easily predicted from the genotyped animals, can contribute to larger reliabilities in the crossbred population in a scenario where the genomic predictions of crossbred animals are weighted according to each breed’s DNA proportion (VanRaden et al., 2020).

Higher accuracy reported for crossbred animals is not uncommon in dairy cattle (Winkelman et al., 2015; Khansefid et al., 2020), and other species (Hidalgo et al., 2016). In their study, predictions for crosses were consistently more accurate than for Jersey, except for longevity. Crossbred dairy cattle had higher accuracy when their data were considered in the reference population (Khansefid et al., 2020).

In the present study, the benefits from directly including the genomic information in a single step exceeded any initial disadvantage in pedigree modeling. The average improvement with genomics varied according to the BBR of the crossbred cows: the largest increase was observed for cows with BBR between 75% and 89%. The average improvement using genomics reported by VanRaden et al., 2020, is much lower than the improvements found in the present study.

For dairy cattle, inflation values of 1 ± 0.15 are still acceptable (Tsuruta et al., 2011). According to the Interbull validation, the b1 values estimated with ssGBLUP were all within 1 ± 0.1. The average value was 1.02 ± 0.06, ranging from 0.90 to 1.09, whereas for BLUP, the EBVs were more inflated (0.81 ± 0.09) and with a more extensive range (0.72–0.91). In ssGBLUP, all validation groups showed nonbiased average predictions. The number of genotyped animals considered in the present study was very similar to VanRaden et al., 2020, but larger than other studies.

The genomic era has revolutionized the process of assigning the proportion of a crossbred individual’s genotype to the originating breeds. However, identifying a specific breed origin for each SNP can be challenging. In this study, genotypes of purebred and crossbred were considered together, and G accounted for the relationship among them. Genomic predictions of less numerous breeds and crossbred animals from ssGBLUP could be worsened if there is an imbalanced number of genotypes among breeds.

In the present study, crossbred animals represented less than 1% of the genotyped animals, and most (about 80%) were considered validation animals. However, including crossbred and purebred data in a ssGBLUP model could enhance the prediction of crossbred animals through the H matrix. The impact of including a fixed number of purebred and crossbred animals in the core for APY deserves further investigation.

The genomic setup took about 10 hours, while the EBV computation took around 4 hours. The solving process for ssGBLUP took 3 more hours, resulting in a genomic process carried out in less than one day for these three traits. Further computational improvements could be achieved by indirectly predicting young genotyped animals or using solutions from previous runs.

Crossbred data can be included in multibreed US dairy cattle single-step evaluations without reducing accuracy or increasing inflation of genomic EBV for purebred animals. This evaluation system allows similar gains in accuracy for purebreds and crossbreds, simplifying genetic evaluation pipelines and increasing computing efficiency while delivering predictions for managing commercial crossbred herds.

Read more Single-step genomic predictions for crossbred Holstein and Jersey cattle in the United States

Genosource Captain stays at the top of the International GTPI daughterproven ranking, with +3287 GTPI (+34 for GTPI). Gameday comes in second with +3163 GTPI (+125 for GTPI), with Westcoast Lambeau rounding out the stage at +3147. Ripcord is the top GTPI sire over 12 months with NAAB-code, with +3390 GTPI and +1507 NM$. Darth Vader comes in second at +3342 GTPI, with +1482 NM$ and +2458kgM, while Genosource Bonjour rounds out the stage with +3314 GTPI. SHG Lego remains the world’s leading PTAT sire, with +4.69 PTAT, he is a Siemers Fitters Choice kid from the #1 PTAT cow (>2 years).The #1 PTAT Red Carrier bull is SHG Lazer *RC with +4.24 PTAT.

• USA – GTPI – >12 months & NAAB-code – 04/24
• USA – GTPI – Top 100 Int. GTPI bulls – 04/24
• USA – Net Merit – >12 months & NAAB-code – 04/24
• USA – PTAT Bulls = >12 Months – 04/24
• USA | Top *RC GTPI >12 months & NAAB-code 04/24
• USA | Top R&W GTPI >12 months & NAAB-code 04/24
• USA | Top R&W & *RC PTAT Bulls >12 Mths – 04/24 

Beyond HI-Power now leads the Canadian LPI index with +4002 gLPI. He is followed by Kenyon-Hill Ltchwrth Oli, who has +3958 gLPI. The stage concluded with T-Spruce Ethan, the #1 gLPI sire of the December ’23 run, with +3956 gLPI. In the top LPI Domestic daughter proven list, Genosource Captain has the highest gLPI at +3761. The Genomic sire Progenesis Aneesh is now the #1 TYPE bull, with a nog with less than +18 Conformation. Hyden Limited P is the #1 daughter confirmed TYPE bull with a +17 conformation.

• CANADA – GLPI – Genomics – Rel. in Canada – 04/24
• CANADA – Top LPI – Domestic – Dtr proven – 04/24
• CANADA – Top LPI – Mace – Dtr proven – 04/24
• CANADA – Conformation Genomic Bulls – 04/24
• CANADA – Conf. – Domestic – Dtr. Proven 04/24 

There have been no changes to the top three B&W RZG Interbull Genomic rankings. The B&W RZG Interbull Genomic rating is topped by a Rover son, Real Syn, who has +166 RZG (-5 for RZG)! He is followed by Vivify at +161 RZG, who completes the stage with Rome at +160 RZG! Skill Red leads the R&W Interbull Genomic ranking with +161 RZG. CR7 P, Redford, and Handout P finished second with +158 RZG, while Koepon Redbull, Pringle-Red, and Kretos-Red finished third with +157 RZG. Genosource Captain remains the top B&W Interbull Dtr proven sire, with +153 RZG, followed by Ginetta at +150 RZG and Madboy, AltaZarek, Pursuit, and Commitment in a tie for third place at +148 RZG. Zoom Red and Freestyle-Red are the #1 R&W Interbull Dtr proven sires, with +148 RZG.

• GERMANY – B&W – Interbull Genomics – 04/24
• GERMANY – B&W – Interbull Dtr Proven – 04/24
• GERMANY – B&W – Domestic Genomics – 04/24
• GERMANY – B&W – Dtr Proven – 04/24
• GERMANY – B&W – Domestic 99% R Bulls – 04/24
• GERMANY – R&W – Interbull Genomics – 04/24
• GERMANY – R&W – Interbull Dtr Proven – 04/24
• GERMANY – R&W – Domestic Genomics – 04/24
• GERMANY – R&W – Dtr Proven – 04/24
• GERMANY – R&W – Domestic 99% R Bulls – 04/24 

Due to a base adjustment, the breeding values for all bulls having a gNVI breeding value have decreased by roughly 20 NVI points this run. The publishing criteria for the conformation breeding values of imported bulls have also been updated. Delta Boyan (s. Warren P *RC) is the #1 NVI B&W Genomic sire this run, with +391 gNVI, followed by Tigerwoods De La Vigne at +386 gNVI and Sitron at +379 gNVI rounding out the top three. Furthermore, we discover in this top 20 DG Dr. No @ AI-Total at +328 gNVI and +1950kgM. Delta Cream P Red is this run’s #1 NVI R&W Genomic sire, with +375 gNVI. At the fourth slot, we discover NH Skyliner-Red (s. Sputnik *RC) at +358 gNVI, +3739 kgM, and +532 INET.

• Netherlands – B&W – Genomic Bulls – 04/24
• Netherlands – B&W – Dtr proven bulls – 04/24
• Netherlands – B&W – Dtr proven INTERBULL – 04/24
• Netherlands – R&W – Genomic Bulls – 04/24
• Netherlands – R&W – Dtr proven bulls – 04/24
• Netherlands – R&W – Dtr proven INTERBULL – 04/24 

Ecbert (s. Gladius) is the new leader in the Italian gPFT genomic (domestic) list, with +5123 gPFT. Alanzo’s son Al.Co.Bia Essence comes in second at +5048 gPFT, while Al.Co.Bia Soproni, a Zingler x Mojo, rounds out the top three with +5002 gPFT. Yoox leads the Italian daughter proven ranking with +4545 gPFT, followed by Aristocrat son Wilder Holocron at +4524 gPFT and Isolabella Inseme Distefano at +4501 gPFT.

• ITALY – Dtr proven PFT (Domestic) – 04/24
• ITALY – Genomic PFT (Domestic) – 04/24
• ITALY – Foreign Genomic PFT – 04/24 

Diamond Genetics bred the top three with PLI Genomics bulls for the April 24 run. DG Peace leads this list with +908 PLI (+22). He is the Captain son of Paessens Jezebel VG-86-NL, a 2-year-old cow from the Meier-Madows EL Jezebel EX-92-USA herd. He is followed by another Captain son, DG Space of the Ladys-Manor Ruby D cow family, who has +873 PLI (+13). DG Dillon, bred by Diamond Genetics and sold to Cogent, rounds out the top three with +868 PLI. Genosource Captain remains at the top of the PLI Daughter Proven list, with +874 PLI, followed by Westcoast River at +778 PLI and FB Kenobi Targaryen at +710 PLI.

op index bulls in the United Kingdom:

• UK – PLI Young Genomic Bulls – 04/24
• UK – PLI Daughter Proven Bulls – 04/24
• UK – TYPE Merit Young Genomic Bulls – 04/24
• UK – TYPE Merit Daughter Proven – 04/24 

The Scandinavian nations’ indices (Denmark, Sweden, and Finland) are now accessible online. There have been no changes to the top three with NTM genomics bulls this run. Mecanico remains the top NTM genomic sire, with +46 NTM, followed by VH Karat *RC at +43 NTM and Dixon at +42 NTM. VH Deco *RC, VH Fillman, Yoda, and Youngster tied for the top place in the NTM daughter-proven ranking with +28 NTM each.

• SCANDINAVIA – NTM Genomic bulls 04/24
• SCANDINAVIA – NTM Proven bulls 04/24 

We begin today with the first indices arriving from Switzerland. Blakely’s son Swissgen Enrico is the new leader on the Swiss chart, with +1667 ISET. He is followed by the #1 ISET sire of the December ’23 run, TGD-Holstein Beautyman (+1647 ISET), and Swissgen Empire (s. Blakely) (+1633 ISET). S-S-I Hodedoe Montley remains at the top of the Interbull daughter-proven index, with +1572 ISET. He is followed by Wilra SSI Rivet Genuine at +1552 ISET, while Larcrest Commitment comes in third with +1532 ISET.

All top ranks may be seen by clicking on this link.

• SWITZERLAND – ISET – Genomic 04/24
• SWITZERLAND – ISET – Interbull Dtr proof – 04/24
• SWITZERLAND – ISET – Domestic dtr proven 04/24
• SWITZERLAND – Top 100 ISET heifers 04/24
• SWITZERLAND – Top 100 ISET cows 04/24
• SWITZERLAND – Top 100 RC RED ISET heifers 04/24 

Lactanet is collaborating with Angus Genetics Incorporated (AGI) to share genotyping of Angus bulls from Canada, the U.S., and Australia to assist in beef-on-dairy breeding decisions. This move comes as dairy sector stakeholders call for better information on crossbred calves, which are a significant potential income stream on Canadian dairy farms. The “perfect trifecta” of conditions led to calls for better beef-on-dairy information, with genomic testing of dairy animals allowing for accurate rankings within a herd, regardless of age, of the best dams from which to build bloodlines.

The movement to breed the rest of the animals to beef sires has increased the chance of getting a heifer calf from those top ranking animals to 95%. Nearly 40% of Holstein breeders and 30% of Jersey and Ayrshire breeders across Canada use some form of this strategy, with producers now looking at other traits such as calving ease when selecting beef bulls for lower-ranking dams. To aid beef-on-dairy decisions, Lactanet can collect on-farm data it already performs on participating Dairy Herd Improvement and DairyComp herds.

Lactanet also has access to data on calf move-ins and move-outs through its leadership of the DairyTrace national traceability program, but generally knows little on the beef sire side. Genotyping the crossbred calves could grow and strengthen the database, but it is unlikely any dairy producer will pay to genotype animals that will leave the farm within a few weeks.

A key difference between the beef-on-dairy strategy and a genetic program to build dairy strengths over the long term is that the beef strategy typically doesn’t aim to build long-lasting bloodlines. Lactanet will not invest in a beef-on-dairy genotyping initiative, as it would require a huge investment of money, people, and time with little chance of a return on investment. Instead, a collaboration has begun with Missouri-based Angus Genetics Incorporated (AGI) for access to genomic data from Angus bulls in the U.S., Australia, and Canada. The organization will soon include a “Beef on Dairy Query” on its website, alongside other “Query” options for searching bull or cow information.

Breed breed associations could eventually share their information about carcass weights or carcass quality if they’re collecting such data. For the most reliable information, more research is necessary in North America into beef-on-dairy breeding.

The purpose of this research was to determine whether or not utilizing a cow’s parents’ genotypes for imputing single nucleotide polymorphisms (SNPs) affected the calculation of genomic inbreeding coefficients. The imputation genotypes of 68,127 Italian Holstein dairy cows were examined using a variety of genotyping methods. Genomic inbreeding coefficients were calculated using four PLINK v1.9 estimators, two genomic relationship matrix (grm)-based estimators, and one run of homozygosity (ROH; FROH) estimator. When at least one of the parents was genotyped, the findings revealed consistently high genomic inbreeding coefficients. However, skewed genomic inbreeding coefficients were seen in cows genotyped with MD SNP panels whose SNPs were poorly represented in the chosen imputation SNP data set and did not have their parents genotyped, in contrast to what was predicted based on actual genotype data. For cows genotyped with MD, the estimators Fhat1, Fhat2, and Fgrm gave greater genomic inbreeding coefficients, even when both parents and the maternal grandsire were genotyped. Overall, FROH was the strongest estimator, followed by F and Fhat3.

Word genome single nucleotide polymorphisms (SNPs) are frequently employed in cattle breeding programs throughout the globe to estimate genomic breeding values, as well as genome-wide association studies, population genetics, and determining realized homozygosity and inbreeding. Breeding businesses may cut genotyping costs by genotyping a small number of core animals with HD SNP panels and a large number of LD/HD animals, then projecting them to HD genotypes or a set of predetermined SNPs.

Imputation success in dairy cattle breeding is determined by three major factors: the relationship between core animals genotyped in HD and those to be imputed from LD/MD to HD, the distribution along the genome and the number of SNPs in the LD/MD panels, and the linkage disequilibrium between SNPs in the LD/MD and HD. Although there are strategies for achieving high imputation accuracy, variability in genomic estimations based on imputed SNP data is to be anticipated.

This work expands on recent findings on whole genome imputation SNP-based genomic inbreeding coefficients (FSNP) in dairy cattle. Extreme genomic inbreeding coefficients might be the outcome of imputation, particularly in cows genotyped with MD SNP panels with just a handful of their SNPs included in the final imputation SNP data. The goal of this research was to see whether significant genomic inbreeding coefficients in cows genotyped with MD SNP panels that had few of their SNPs included in the final imputation SNP data might be attributed to not having their parents genotyped during the imputation process.

Whole genome SNP data is frequently imputed in dairy cow breeding, which may significantly decrease genotyping costs. However, there may be cows with incorrect imputation owing to the lack of genotyped parents. The findings revealed that whole genome SNP inbreeding coefficients might be skewed for cows who did not have parents or maternal grandparents genotyped and were also genotyped with an SNP panel with low representation of its SNPs on the chosen imputation SNP data.

Artificial insemination businesses provide top genetics for dairy cow genetic improvement projects, with the most marketable bulls having the most genetic value. This extensive within-family selection results in higher degrees of connection, which leads to increased inbreeding. Reducing or even restricting inbreeding often results in the selection of lower-index bulls. As improved reproductive methods become more widely used and long-term technologies like as in vitro breeding evolve, selection intensities on bull and cow dams may skyrocket, worsening the situation even further.

The dairy genetics community must now decide who is prepared to decrease the pace of genetic gain in order to effectively regulate inbreeding. The literature has several instances of the negative impacts of inbreeding, however it is not true that all inbreeding is equal, and a distinct boundary exists between safe and risky. There is likely to be a tipping point beyond which we do not want to go, but its position is uncertain. Lush (1945) proposed that generations of high-intensity selection may necessarily result in large percentages of homozygosity.

To analyze the present situation, we may compare changes in phenotypic performance during inbreeding to rates of genetic gain. In all but one situation, annual genetic benefits outweigh inbreeding depression: the daughter pregnancy rate, which decreases by 0.02 PTA units each year. This is not to say that there is nothing to be worried about; it just means that we are not yet losing ground. As the phrase goes, “When you’re in a hole, stop digging,” but we need to act first.

The desire for elite genetics contributes to the continual loss of genetic variety in dairy cow herds, making it harder to preserve heterozygosity. Elite bulls are no longer enough; they must also have good breeding values for milk, components, fertility, and other attributes. Most marketed bulls cannot hit all of these criteria, but it is often assumed that they will.

Some measures may be used to reduce inbreeding rates in dairy cow herds. Refine PTA Adjustments: Genetic assessments in the United States are altered to account for the potential implications of future inbreeding. Predicted transmitting abilities (PTA) for bulls closely related to the population are reduced to account for inbreeding depression, while PTA for bulls less related to the population than average are increased to account for advantageous heterosis. However, the problem with this strategy is that the top AI bulls causing genetic change in the population are often severe outliers, and post-hoc modifications lack theoretical support.

Use the Right Metric: Genomic inbreeding assesses both identity-in-state and identity-by-descent, while pedigree inbreeding solely considers the latter. One technique to lower apparent inbreeding rates is to use pedigree inbreeding instead of genomic inbreeding. A more tempting strategy is to shift away from crude measurements of inbreeding and toward more accurate measures of diversity, such as runs of homozygosity (ROH) or direct measures of identity-by-descent (IBD). Timing is important, and older inbreeding is less concerning than current inbreeding since older haplotypes have been cleared of possible recessive genes.

Trim Pedigrees: Pedigree information is simple and inexpensive to record, but it has numerous limitations, including incompleteness, greater than expected genuine inbreeding, and underestimation of real ties among people. Trimming pedigrees to just a specific number of generations is a simple technique to minimize inbreeding. A similar alternative is to use a changing base year for inbreeding rather than a set reference year (the US uses 1960 as its base population).

Trimming pedigrees does not directly address rates of inbreeding in the population, either pedigree or genomic, but it is proposed because it may drive selection decisions away from families with the highest rates of recent inbreeding, which are the lineages most likely to contain genetic load that has not yet been purged.

The area of genetic selection has various obstacles, including the issue of bulls with the highest PTA being in high demand, independent culling levels, and implementing optimum contribution selection. To remedy this, it is proposed that instead of publicizing PTA for specific qualities, bulls be randomly assigned to the cow population. Bulls might also be given red, yellow, or green badges for each characteristic to signify their PTA, although this may not be desirable.

Homomorphic encryption may be used to hide the PTA provided to software from end users, although this is not a usual practice, and implementation is difficult. Hiding PTA would be a significant departure from existing methods, and most consumers or salesmen may not find this approach desirable.

Additional measures of genetic variety might be added to selection indices as a trait, exerting direct selection pressure on heterozygosity. This implies that choosing high-index bulls would entail some selection for increased heterozygosity, rather than trusting that inbreeding is taken into account during the mating process. However, this is a less efficient method of achieving optimum contribution selection, and it has been largely neglected in the United States since its inception.

Varona et al. (2019) recommended that an artificial purging system be built around a selection index that includes both breeding values and inbreeding burdens. This is a more attractive approach than include an ad hoc measure of variety in the index, and there is a strong theoretical underpinning to support its usage.

The implementation of optimum contribution selection in certain places, such as the United States, is not especially contentious owing to its complexity and the impression that the advantages do not outweigh the increased complexity. Many genetics end users are unwilling to accept the trade-off between genetic trend and heterozygosity preservation, and in a competitive market, they may just buy their sperm and embryos from another provider.

Finally, genetic protection procedures that limit germplasm interchange are allowing breeding projects run by several AI corporations to develop into distinct subpopulations within breeds. While it is unclear if these groupings are distinct enough to result in widespread outbreeding, current study indicates that bigger variances between families within a breed may exist than previously thought.

Genetics corporations may concentrate on selling embryos that represent perfect terminal dairy cows, necessitating a large increase in the scope of dairy embryo transfer. This would result in a system that is more like modern swine production than ancient dairy cow rearing. Some genetics businesses may begin selling embryos into smaller-scale specialist markets, such as those seeking high-genetic-merit polled animals.

Gene editing technologies have advanced, leading in increased efficiency and the capacity to stack numerous alterations. This might be a technique to sustain rates of genetic gain without experiencing negative repercussions or relying on natural purging. However, this strategy has several obstacles, including a lack of understanding about editing targets, an inability to edit dozens or hundreds of targets at the same time, and an ever-changing regulatory environment.

To effectively address the challenges posed by inbreeding, a collaboration between AI companies and the scientific community must persuade farmers that there is a real problem to be solved, that our proposed solutions will work, and that they are not being asked to jeopardize their livelihoods by participating in this process. The size of the issue is important, since many farmers are worried about the long-term impacts of inbreeding but do not necessarily agree that vigorous action must be done now. There is also common belief that the individuals who created the issue cannot be trusted to repair it, and that things function differently in the actual world than they do on paper.

Inbreeding is expected to attract greater attention in the future because of its impact on social license rather than production economics. Most laypeople are unaware of the fundamental distinction between inbreeding with stringent performance selection, as is used in animal breeding programs, and inbreeding with no performance selection. Increased genetic load also impairs an animal’s capacity to adapt to changes, which is concerning as the environment shifts.

Dairy cow breeding is a loosely connected method that allows for efficient management across the production chain. Effective population control will be challenging and will need adjustments on the side of AI businesses and farmers.

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• The U.S. dairy herd is 1% larger today but produces 19.2% more milk and 32.2% more butterfat.
• The industry’s Net Merit index, a proxy for cattle genetic progress on sustainability, shows that a cow produced by combining the best-known genes would be three times more sustainable based on improved genetics than the top Holstein bull on the market today.
• The introduction of genomics in 2008 has significantly improved milk, butterfat, and protein production, cow health, and cow longevity.
• The average genetic gain from 2000 to 2004 was $13.50 per year for marketed Holstein bulls.
• By 2010, genetic improvement leapt forward, more than doubling from $36.90 per year from 2004 to 2009 to the $83.33 annual genetic gain from 2010 to 2022.
• This six-fold improvement equates to a $329 million annual net gain when extrapolating this $70 annual gain per female across the annual U.S. dairy heifer calf crop.
• This development greatly enhances sustainability by generating more nutrition with less inputs.
• The aggregate gain since 2010 would be $4.28 billion when calculating the full genomic impact.
• This means the U.S. dairy industry needs fewer dairy cows each passing year to supply the same amount of milk to serve the domestic market or has transformed new growth in milk, butterfat, and protein into dairy products to deliver nutrition to international customers.
• ABS Global and Genus recently built a solar project at one of the world’s largest AI facilities, a bull barn in Wisconsin, housing 480 elite AI bulls.
• The complex includes a lab for processing bull semen, which accounts for nearly half of all sales in the U.S.
• This sexed semen product allows dairy farmers to use beef semen on dairy cows, reducing the number of replacements and generating more immediate revenue from dairy-beef cross calves.
• Research indicates that beef-dairy crossed calves are approaching the same efficiency as beef cattle and are 30% more efficient at converting feed to beef than Holstein steers.
• The combined amount of carbon saved by the solar panels’ production of electricity for the facility over 10 years is less than the carbon savings from just one month of genetic progress from future offspring of the facility’s 480 bulls.
• The genetic gain on offspring is measured by more production, better health, and longer cow life via the Net Merit Index.
• The impact for dairy processors is significant, with butterfat and protein production from Holstein cows improving annually by 0.46% and 1.23% annually in the genomic era.
• Genomic testing is making significant inroads as more females get tested each year, leading to less methane production, a reduced carbon footprint, and less feed for each additional unit of milk.
• By August 2023, CDCB received its 8 millionth genomic test, with 93% of genomic tests run on female dairy animals.
• The genetic super cow is not in sight, with the dairy community only 24% of the way to breeding the perfect Holstein cow.
• Genomic science can identify new traits that will help reduce methane production and carbon footprint, with the “feed saved” trait being the tip of the iceberg.

In the past 15 years, dairy farmers have been using genomic science to select cattle traits that improve productivity and support sustainability. The U.S. dairy herd today is 1% larger than in 2008 but produces 19.2% more pounds of milk and 32.2% more pounds of butterfat. The dairy industry still has room to grow more efficient through genomics. The industry’s Net Merit Index (NM$) shows that a cow produced by combining the best-known genes would be three times more sustainable based on improved genetics than the top Holstein bull on the market today.

Genomics has been the most important reason for improvements in milk, butterfat, and protein production, cow health, and cow longevity. The average genetic gain from 2000 to 2004 was $13.50 per year for marketed Holstein bulls. By 2010, genomic testing became commercially viable in the United States, genetic improvement leapt forward, more than doubling from $36.90 per year from 2004 to 2009 to $83.33 annual genetic gain from 2010 to 2022. This profound development greatly enhances sustainability, as new Holstein bulls entering active A.I. service father offspring that generate $83.33 in combined gains that enhance sustainability-related metrics.

Genetics has become a significant factor in the economic development of dairy farming, with research showing that beef-dairy crossed calves are approaching the same efficiency as beef cattle and are 30% more efficient at converting feed to beef than Holstein steers. This improvement in feed conversion and fewer days on feed prior to harvest yields a net carbon footprint reduction and reduced methane emissions. ABS Global found that the combined amount of carbon saved by the solar panels’ production of electricity for the facility over 10 years is less than the carbon savings from just one month of genetic progress from future offspring of the facility’s 480 bulls.

The impact of genomics on dairy processors has been significant, with butterfat and protein production improving annually by 0.46% and 1.75% annually in the genomic era. Commercial dairy farms have started to test more heifer calves at birth to determine those animals that would make the best replacements by making the best use of resources. Since 2020, it has taken less than 12 months to accumulate an additional 1 million genomic tests, with estimates indicating that 20% to 25% of the U.S. dairy heifer calf population is being tested annually.

However, genetic improvement is not yet reaching its ceiling, with only 24% of the dairy community being close to breeding the perfect Holstein cow. Genomic science can identify new traits that will help reduce methane production and carbon footprint, with the “feed saved” trait being the tip of the iceberg.

For the full story visit: Dairy Cattle Genomics is Quietly Improving Sustainability