A recessive haplotype resulting in elevated calf mortality but with apparent incomplete penetrance was previously linked to the end of chromosome 16 (78.7 to 80.7Mbp). Genotype analysis of 5.6 million Holsteins indicated that the haplotype was common and traced back to 1952 with a key ancestor born in 1984 (HOUSA1964484, Southwind) identified from chip genotypes as homozygous for the suspect haplotype. Sequence data from Southwind, an affected calf, and the sire of the affected calf was scanned for candidate mutations. A missense mutation with a deleterious projected impact at 79,613,592 bp was homozygous in the affected calf and heterozygous in the calf's sire and Southwind. Sequence data available from the Cooperative Dairy DNA Repository for 299 other Holsteins indicated a 97% concordance with the haplotype and an 89% call rate. Exon amino acid sequence appears to be broadly conserved in the CACNA1S gene, and mutations in humans and mice can cause phenotypes of temporary or permanent paralysis analogous to those in calves suffering from muscle weakness (HMW). Improved methods for using pedigree to track new mutations within existing haplotypes were developed and applied to the haplotypes for both muscle weakness and Holstein cholesterol deficiency (HCD). For HCD, concordance of the gene test with its haplotype status was greatly improved. For both defects, haplotype status was matched to heifer livability records for 558K calves. For HMW, only 46 heifers with livability records were homozygous and traced only to Southwind on both sides. Of those, 52% of those died before 18 mo at an average age of 1.7 ± 1.6 mo, but that death rate may be underestimated if only healthier calves were genotyped. The death rate was 2.4% for noncarriers. Different reporting methods or dominance effects may be needed to include HMW and other partially lethal effects in selection and mating. Direct tests are needed for new mutations within existing common haplotypes because tracking can be difficult even with accurate pedigrees when the original haplotype has high frequency.

Genotype imputation has enabled selecting and using many more loci in research and prediction for much less cost than genotyping all individuals with the same array density or technology. Genotyping arrays are updated often to include new QTLs or higher-effect markers and applied to new animals without regenotyping all previous or reference animals. That may require imputing the new loci from less-dense to higher-density arrays and from descendant genotypes to ancestors in the opposite direction of normal reference and target populations. Routine expansion and recycling of haplotype libraries and updating only individuals expected to change can speed imputation for routine predictions. This study compared imputation strategies for 78,964 loci of 4.6 million Holsteins. Sequence imputation is more challenging due to higher error rates and more complex variants, but new techniques could make sequencing costcompetitive with arrays for routine predictions.

The goal of this study was to identify potential quantitative trait loci (QTL) for 27 production, fitness, and conformation traits of Guernsey cattle through genome-wide association (GWA) analyses, with extra emphasis on BTA19, where major QTL were observed for several traits. Animals' de-regressed predicted transmitting abilities (PTA) from the December 2018 traditional US evaluation were used as phenotypes. All of the Guernsey cattle included in the QTL analyses were predictor animals in the reference population, ranging from 1,077 to 1,685 animals for different traits. Single-trait GWA analyses were carried out by a mixed-model approach for all 27 traits using imputed high-density genotypes. A major QTL was detected on BTA19, influencing several milk production traits, conformation traits, and livability of Guernsey cattle, and the most significant SNP lie in the region of 26.2 to 28.3 Mb. The myosin heavy chain 10 (MYH10) gene residing within this region was found to be highly associated with milk production and body conformation traits of dairy cattle. After the initial GWA analyses, which suggested that many significant SNP are in linkage with one another, conditional analyses were used for fine mapping. The top significant SNP on BTA19 were fixed as covariables in the model, one at a time, until no more significant SNP were detected on BTA19. After this fine-mapping approach was applied, only 1 significant SNP was detected on BTA19 for most traits, but multiple, independent significant SNP were found for protein yield, dairy form, and stature. In addition, the haplotype that hosts the major QTL on BTA19 was traced to a US Guernsey born in 1954. The haplotype is common in the breed, indicating a long-term influence of this QTL on the US Guernsey population.

Marker sets used in US dairy genomic predictions were previously expanded by including high-density (HD) or sequence markers with the largest effects for Holstein breed only. Other non-Holstein breeds lacked enough HD genotyped animals to be used as a reference population at that time, and thus were not included in the genomic prediction. Recently, numbers of non-Holstein breeds genotyped using HD panels reached an acceptable level for imputation and marker selection, allowing HD genomic prediction and HD marker selection for Holstein plus 4 other breeds. Genotypes for 351,461 Holsteins, 347,570 Jerseys, 42,346 Brown Swiss, 9,364 Ayrshires (including Red dairy cattle), and 4,599 Guernseys were imputed to the HD marker list that included 643,059 SNP. The separate HD reference populations included Illumina BovineHD (San Diego, CA) genotypes for 4,012 Holsteins, 407 Jerseys, 181 Brown Swiss, 527 Ayrshires, and 147 Guernseys. The 643,059 variants included the HD SNP and all 79,254 (80K) genetic markers and QTL used in routine national genomic evaluations. Before imputation, approximately 91 to 97% of genotypes were unknown for each breed; after imputation, 1.1% of Holstein, 3.2% of Jersey, 6.7% of Brown Swiss, 4.8% of Ayrshire, and 4.2% of Guernsey alleles remained unknown due to lower density haplotypes that had no matching HD haplotype. The higher remaining missing rates in non-Holstein breeds are mainly due to fewer HD genotyped animals in the imputation reference populations. Allele effects for up to 39 traits were estimated separately within each breed using phenotypic reference populations that included up to 6,157 Jersey males and 110,130 Jersey females. Correlations of HD with 80K genomic predictions for young animals averaged 0.986, 0.989, 0.985, 0.992, and 0.978 for Jersey, Ayrshire, Brown Swiss, Guernsey, and Holstein breeds, respectively. Correlations were highest for yield traits (about 0.991) and lowest for foot angle and rear legs-side view (0.981and 0.982, respectively). Some HD effects were more than twice as large as the largest 80K SNP effect, and HD markers had larger effects than nearby 80K markers for many breed-trait combinations. Previous studies selected and included markers with large effects for Holstein traits; the newly selected HD markers should also improve non-Holstein and crossbred genomic predictions and were added to official US genomic predictions in April 2020.

Genomic evaluations of crossbred dairy cattle were first released in the United States in April 2019 by the Council on Dairy Cattle Breeding (Bowie, MD). Each crossbred animal has an evaluation calculated for each of 5 breeds: Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey. The evaluations are expressed on an all-breed base and combined by weighting the breed proportions — called breed base representation (BBR) — for each animal. The BBRs are estimated with the same process used for other traits. The breed reference populations are updated annually and are composed of AI progeny-tested bulls (≥10 daughters) with at least 4 generations of same breed ancestry. A blending procedure is applied to animals that have a highest BBR of <90%. The SNP effects are estimated from animals with a BBR of ≥94%, which are considered to be purebreds. Evaluations are reported on individual breed bases. In most cases, the breed with the highest BBR is used. However, to facilitate comparisons within herd, the breed associated with an animal’s identification record is used if the BBR is ≥40%. For weekly evaluations, an initial approximate BBR is calculated based on only the SNPs present on the genotyping chip so that imputation is not required. Currently, 5 groups of chips are defined, and SNP effects for breed determination are estimated for each group. To minimize the portion of animals that are incorrectly designated for blended evaluations based on the approximate BBR, animals with a BBR of ≥85% are processed with purebreds until a BBR based on an imputed genotype is calculated; then animals with a new BBR of <90% are moved to the blended group. A breed error is declared if the highest BBR is ≥85% for a breed other than that from the identification record. The blending process is not applied to type traits, which are not comparable across breeds, or to feed saved and calving traits, which are not calculated for all breeds. Recessive haplotypes are not reported for crossbreds because the largely within-breed nature of those conditions leads to less accuracy in detecting crossbred carriers. Crossbred evaluations have lower reliability than those for purebreds; however, by processing crossbreds, parentage discovery and BBRs are provided. In July 2021, 154,670 animals had blended evaluations. Of those animals, 48% were born in 2019 or later, an indication of recent increases in crossbreeding and genotyping of crossbreds.

A new undesirable genetic factor, neuropathy with splayed forelimbs (JNS), has been identified recently in the Jersey breed. Calves affected with JNS are unable to stand on splayed forelimbs that exhibit significant extensor rigidity and excessive lateral abduction at birth. Affected calves generally are alert at birth but exhibit neurologic symptoms, including spasticity of head and neck and convulsive behavior. Other symptoms reported include dislocated shoulders, congenital craniofacial anomalies, and degenerative myelopathy. Inheritance of an undesirable genetic factor was determined from a study of 16 affected calves reported by Jersey breeders across the United States. All of their pedigrees traced back on both paternal and maternal sides to a common ancestor born in 1995. Genotypes revealed that JNS is attributable to a specific haplotype on Bos taurus autosome 6. Currently 8.2% of the genotyped US Jersey population are carriers of the haplotype. Sequencing of the region of shared homozygosity revealed missense variant rs1116058914 at base 60,158,901 of the ARS-UCD1.2 reference map as the most concordant with the genetic condition and the most likely cause. The single-base G to A substitution is in the coding region of the last exon of UCHL1, which is conserved across species. Mutations in humans and gene knockouts in mice cause similar recessive symptoms and muscular degeneration. Since December 2020, carrier status has been tracked using the identified haplotype and reported for all 459,784 genotyped Jersey animals. With random mating, about 2,200 affected calves per year with losses of about $250,000 would result from the 1.3 million US Jersey cows in the national population. Selection and mating programs can reduce numbers of JNS-affected births using either the haplotype status or a direct gene test in the future. Breeders should report calf abnormalities to their breed association to help discover new defects such as JNS.

The addition of cattle health and immunity traits to genomic selection indices holds promise to increase individual animal longevity and productivity, and decrease economic losses from disease. However, highly variable genomic loci that contain multiple immune-related genes were poorly assembled in the first iterations of the cattle reference genome assembly and underrepresented during the development of most commercial genotyping platforms. As a consequence, there is a paucity of genetic markers within these loci that may track haplotypes related to disease susceptibility. By using hierarchical assembly of bacterial artificial chromosome inserts spanning 3 of these immune-related gene regions, we were able to assemble multiple full-length haplotypes of the major histocompatibility complex, the leukocyte receptor complex, and the natural killer cell complex. Using these new assemblies and the recently released ARS-UCD1.2 reference, we aligned whole-genome shotgun reads from 125 sequenced Holstein bulls to discover candidate variants for genetic marker development. We selected 124 SNPs, using heuristic and statistical models to develop a custom genotyping panel. In a proof-of-principle study, we used this custom panel to genotype 1,797 Holstein cows exposed to bovine tuberculosis (bTB) that were the subject of a previous GWAS study using the Illumina BovineHD array. Although we did not identify any significant association of bTB phenotypes with these new genetic markers, 2 markers exhibited substantial effects on bTB phenotypic prediction. The models and parameters trained in this study serve as a guide for future marker discovery surveys particularly in previously unassembled regions of the cattle genome.

Genomic selection is an important tool to introduce feed efficiency into dairy cattle breeding. The goals of the current research are to estimate genomic breeding values of residual feed intake (RFI) and to assess the prediction reliability for RFI in the US Holstein population. The RFI data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States, and were pre-adjusted to remove phenotypic correlations with milk energy, metabolic body weight, body weight change, and for several environmental effects. In the current analyses, genomic predicted transmitting abilities of milk energy and of body weight composite were included into the RFI model to further remove the genetic correlations that remained between RFI and these energy sinks. In the first part of the analyses, a national genomic evaluation for RFI was conducted for all the Holsteins in the national database using a standard multi-step genomic evaluation method and 60,671 SNP list. In the second part of the study, a single-step genomic prediction method was applied to estimate genomic breeding values of RFI for all cows with phenotypes, 5,252 elite young bulls, 4,029 young heifers, as well as their ancestors in the pedigree, using a high-density genotype chip. Theoretical prediction reliabilities were calculated for all the studied animals in the single-step genomic prediction by direct inversion of the mixed model equations. In the results, breeding values were estimated for 1.6 million genotyped Holsteins and 60 million ungenotyped Holsteins, The genomic predicted transmitting ability correlations between RFI and other traits in the index (e.g., fertility) are generally low, indicating minor correlated responses on other index traits when selecting for RFI. Genomic prediction reliabilities for RFI averaged 34% for all phenotyped animals and 13% for all 1.6 million genotyped animals. Including genomic information increased the prediction reliabilities for RFI compared with using only pedigree information. All bulls had low reliabilities, and averaged to only 16% for the top 100 net merit progeny-tested bulls. Analyses using single-step genomic prediction and high-density genotypes gave similar results to those obtained from the national evaluation. The average theoretical reliability for RFI was 18% among the elite young bulls under 5 yr old, being lower in the younger generations of elite bulls compared with older bulls. To conclude, the size of the reference population and its relationship to the predicted population remain as the limiting factors in the genomic prediction for RFI. Continued collection of feed intake data is necessary so that reliabilities can be maintained due to close relationships of phenotyped animals with breeding stock. Considering the currently low prediction reliability and high cost of data collection, focusing RFI data collection on relatives of elite bulls that will have the greatest genetic contribution to the next generation will give more gains and profit.

In 2013, the Council on Dairy Cattle Breeding (CDCB) started calculating and releasing U.S. genetic and genomic evaluations, which historically had been totally managed by USDA. The role of USDA in the U.S. dairy genetics industry is still extremely important because USDA’s Animal Genomic and Improvement Laboratory conducts most of the research for developing the cutting-edge methodologies applied by CDCB. This presentation reviews the latest enhancements to U.S. evaluations during 2018 and 2019, all of them result of the interaction between public service and private U.S. industry. In April 2018, CDCB introduced six new health traits for Holsteins; these traits were included in lifetime net merit in August 2018. Early first calving, another new trait, was introduced in April 2019. In April 2018, CDCB also extended genomic evaluation to an all-breed system, which had been used for traditional evaluations since 2007. This, together with further methodology developments to determine an animal’s breed composition (breed base representation, or BBR), resulted in the first release of a multi-breed genomic evaluation for crossbred animals in April 2019. In December 2018, the reference genome assembly was updated from the UMD3 version to the latest ARS-UCD1 version. At the same time, the number of markers used in genomic evaluations was increased from 60k to 80k. The new set of markers included more exact gene tests, removed poorer performers, added new variants with larger effects on traits, and changed the marker order based on the new assembly. The latest discovered recessive haplotype in Holsteins (HH6, BTAU-16) also was released in December 2018, and haplotypes BH1 (Brown Swiss) and JH2 (Jersey) were discontinued. Further enhancements were applied to productive life and fertility traits in 2018. Future projects include the release of evaluations for Jersey health traits, further improvements to the marker set, and enhancements of crossbred evaluation.

Phenotypes from the December 2018 US national genetic evaluations were used to compute effects of the polled haplotype in US Brown Swiss (BS), Holstein (HO), and Jersey (JE) cattle on milk, fat, and protein yields, somatic cell score, single-trait productive life, daughter pregnancy rate, heifer conception rate, and cow conception rate. Lactation records pre-adjusted for nongenetic factors and direct genomic values were used to estimate phenotypic and genetic effects of the polled haplotype, respectively. No phenotypic or direct genomic values effects were different from zero for any trait in any breed. Genomic PTA (gPTA) for the lifetime net merit (NM$) selection index of bulls born since January 1, 2012, that received a marketing code from the National Association of Animal Breeders (Madison, WI), and cows born on or after January 1, 2015, were compared to determine whether there was a systematic benefit to polled or horned genetics. Horned bulls had the highest average gPTA for NM$ in all 3 breeds, but that difference was significant only in HO and JE (HO: 615.4 ± 1.9, JE: 402.3 ± 3.4). Homozygous polled BS cows had significantly higher average gPTA for NM$ than their heterozygous polled or horned contemporaries (PP = 261.4 ± 43.5, Pp = 166.1 ± 13.7, pp = 174.1 ± 1.8), but the sample size was very small (n = 9). In HO and JE, horned cows had higher gPTA for NM$ (HO = 378.3 ± 0.2, JE = 283.3 ± 0.3). Selection for polled cattle should not have a detrimental effect on yield, fertility, or longevity, but these differences show that, in the short term, selection for polled over horned cattle will result in lower rates of genetic gain.

Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sum-based marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.

The U.S. genomic evaluation began using a new reference map in December 2018 to improve genotype imputation and lethal carrier detection. Additional selected markers and gene tests also were included at the same time to improve reliability of genomic predictions. After edits removed some markers from both the old and new maps that were potentially mismapped, haplotype inheritance was better with the new map for all five breeds tested. The new map and the inclusion of more gene tests detected more carrier animals for 18 of the 24 individual variants tracked, thereby improving the accuracy of imputing carrier status. A further benefit of using the new map for national genetic evaluation was to allow simpler merging with international sequence data from run 7 of the 1000 Bull Genomes Project that also aligned variants to the new map.

Pregnancy-associated glycoproteins (PAG) are secreted by the trophoblast and are detectable in maternal circulation around the time of attachment of the fetal placenta, as well as in blood and milk throughout gestation. The understanding of the genetic mechanisms controlling PAG levels can confer advantages for livestock breeding programs given the precocity and the ease of obtaining this phenotype from routine pregnancy diagnosis. The aim of this study was to characterize PAG levels by estimating genetic parameters and correlations with other dairy traits, and to identify genomic regions and candidate genes associated with PAG levels in milk. The PAG data consisted of pregnancy diagnoses using commercial assays from 2012 to 2017, and genotype data consisted of 54,123 SNP markers for 2,352 individuals (embryos and dams). The model included contemporary group (herd, year, and season) and embryo age as fixed effects, and random embryonic (direct) and maternal (indirect) genetic effects. Using genomic data, the estimated heritability for direct and maternal genetic effects (± standard deviations) were 0.23 ± 0.05 and 0.11 ± 0.05, respectively. The genetic correlation between these effects was almost zero (0.001 ± 0.06). A preliminary analysis revealed low correlations between milk PAG levels and other dairy traits. The genome-wide association analysis was performed using 2 approaches: single-marker and single-step using all markers. Four genomic regions with direct genetic effects were detected on Bos taurus autosome (BTA) 6, BTA7, BTA19, and BTA29 of the embryonic genome. The BTA29 locus was within the bovine PAG gene cluster, suggesting a cis-regulatory quantitative trait locus on the PAG expression. However, other associations, without an obvious link to PAG expression, could be related to the transportation of PAG and their concentration in milk. Only 1 region from the maternal genome, on BTA4, had a significant indirect effect, where WNT2 is a candidate gene related to placenta vascularization and gestation health. Collectively, our results suggest a moderate genetic control of milk PAG levels from both maternal and fetal genomes, but larger studies are needed to fully evaluate the usefulness of milk PAG in the genetic evaluation of fetal growth and cow fertility.

Incidence of ketosis, metritis, mastitis, and retained placenta were studied on Israeli Holstein cows calving between 2008 and 2017. These diseases were selected based on their economic impact. Ketosis, metritis, and retained placenta were scored dichotomously. Mastitis was scored as absent, a single occurrence during the lactation or more than once. Ketosis and metritis were recorded during the first 21 d after calving, retained placenta during the first 5 d after calving, and mastitis up to 305 d in milk. The effects of herd-year-season, calving age, month of calving, gestation length and occurrence of dystocia were included in the first parity analysis models. All effects were significant (P < 0.001) for metritis and retained placenta. For ketosis all effects were significant, except for gestation length. For mastitis, only the effects of herd-yearseason and calving age were significant. First-parity heritabilities and genetic and environmental correlations among these diseases and the traits included in the Israeli breeding index were computed by the MTC REML individual animal model program. Heritabilities and genetic and environmental correlations among the disease traits are in Table 1. All correlations were positive, but the highest correlation, between ketosis and metritis, was only 0.26. Genetic correlations between the disease traits and milk production traits were all “positive,” but all correlations were <0.25. Since mastitis is farmer recorded, a truncated data set was analyzed including only herd-year with >50 cows and >4% mastitis. Genetic correlations between mastitis and lactation somatic cell score were higher in second and third parities, and heritabilities for mastitis were slightly higher.