Flavio Schramm Schenkel

DMI is a key component of feed efficiency in dairy cattle, yet direct measurement requires costly, specialized equipment that is rarely available on commercial farms. The aim of this study was to develop prediction equations for DMI using routinely collected milk information represented either by mid-infrared spectral (MIRS) data or by conventional milk, fat, and protein yields (MFP), and to estimate genetic parameters for measured and predicted DMI in primiparous Canadian Holsteins. Weekly average DMI records (n = 21,363) were available from 1,521 cows in 3 herds. Partial least squares regression (PLSR) models were fitted separately to predict DMI for 2 lactation periods (5-60 and 61-305 d in milk) using either milk MIRS components (512 wavenumbers) or MFP. All predictor sets also included days in milk and age of calving. In early lactation, MIRS-based predictions showed lower animal-level testing-set error than MFP-based predictions, with mean absolute error (MAE) = 2.15 ± 0.08 kg/d and a correlation (r) = 0.70 ± 0.02. In mid-to-late lactation, MIRS also showed slightly lower animal-level testing-set error than MFP (MAE = 2.48 ± 0.08 kg/d; r = 0.61 ± 0.02). These descriptive results suggest that milk MIRS may contain predictive information not fully captured by conventional milk production traits, particularly in early lactation. Held-out herd validation resulted in low to moderate prediction performance for MIRS-based models, whereas temporal validation using the most recent birth year cohort as the testing set yielded stronger prediction performance in both lactation periods (r ≥0.65; MAE ≤2.61). Heritability estimates for MFP-based predicted DMI were moderate (0.27 ± 0.05 in early lactation and 0.36 ± 0.05 in mid-to-late lactation) and comparable to, or higher than, those for measured DMI (0.26 ± 0.05 and 0.27 ± 0.04, respectively), whereas heritabilities for MIRS-based predicted DMI were lower (0.17 ± 0.04 and 0.21 ± 0.04, respectively). MFP-predicted DMI had a moderate genetic correlation with measured DMI (0.52), but also very strong positive genetic correlation with milk production traits (up to 0.99), suggesting that these predictions largely reflect production-related patterns. In contrast, MIRS-predicted DMI showed moderate genetic correlations with measured DMI (0.46-0.55), while showing weak or near-zero correlations with milk production traits. This suggests that MIRS-based predictions may capture additional biological signals of feed intake that are only weakly related to production. Future research should assess the suitability of these proxies for selection by comparing breeding values derived from predicted DMI with official sire evaluations for measured DMI.

In the livestock industry, feed is the largest expense, driving interest in breeding more feed-efficient animals. Residual feed intake (RFI) is a widely used measure of feed efficiency, accounting for actual feed intake versus expected requirements for maintenance and production. Because feed efficiency is highly polygenic, associated genes may have pleiotropic effects on traits such as immunity, complicating breeding strategies. One way to study pleiotropy is through -OMICs technologies, including transcriptomics, which uses RNA-Sequencing (RNA-Seq) to analyze the transcriptome at specific tissue levels and time points. This study used RNA-Seq to identify differentially expressed (DE) genes between Low RFI (n = 22) and High RFI (n = 17) Holstein cows. A total of 36 genes were DE between groups, enriched in 28 significant metabolic pathways, most of which were immune-related, including the RIG-I-like receptor signaling pathway. QTL annotation and enrichment analyses were also performed for the DEG. Within these gene regions, 646 previously annotated QTL were identified, of which 26.7% were related to milk traits. Notably, 10 QTL were associated with feed efficiency, while 4 were linked to host immunity. QTL enrichment further revealed 387 significantly enriched QTL, with the majority of the top associations related to health. In summary, this research identified candidate genes and QTL regions suggesting potential pleiotropic relationships between feed efficiency and immunity in Holstein dairy cows. These findings highlight the need for further investigation to balance productivity and health in genetic improvement programs.

Genetic selection for calf feed efficiency could lower rearing costs and environmental impact by breeding animals that meet growth requirements with fewer feed resources. Here we estimated genetic parameters for residual metabolizable energy intake (RMEI) as a calf feed efficiency trait using multiple-trait model estimates for energy sink traits and recursive modeling. Data comprised 4,662 weekly average records for metabolizable energy intake (MEI), average daily gain (ADG), and metabolic body weight (MBW) from 938 Holstein dairy calves collected in one research herd from 2016 to 2024. Multi-trait repeatability models including MEI, ADG, and MBW were fit separately for the pre-weaning (PreW; d 5 to 28 of life) and peri-weaning period (PeriW; d 46 to 60 of life), with age, year-season of birth, scour incidence, trial participation, and dam parity as fixed effects. Heritability estimates for PreW-RMEI and PeriW-RMEI were 0.11 and 0.33. In the PreW period heritability estimates for MEI, ADG, and MBW were 0.32 ± 0.05, 0.20 ± 0.04, and 0.54 ± 0.06 respectively. In the PeriW period heritability estimates for MEI, ADG, and MBW were 0.37 ± 0.06, 0.20 ± 0.04, and 0.51 ± 0.07 respectively. Genetic correlations among MEI, ADG, and MBW were moderate to high in the PreW (0.72 ± 0.06 to 0.86 ± 0.06) and PeriW (0.44 ± 0.12 to 0.72 ± 0.06) periods. PreW-RMEI relative breeding value correlations were favorable with heifer fertility traits, while PeriW-RMEI relative breeding value correlations were moderately favorable with selection indices (Pro$ and LPI) and estimated breeding values for protein and fat yield. These findings highlight that genetic selection could be implemented for PreW-RMEI and PeriW-RMEI as distinct early-life feed efficiency traits in dairy cattle. Future studies should be used to collect more phenotypic data across multiple herds and at different stages of life and estimate proper genetic correlations using multi-trait models.

Calf health is becoming an increasingly important consideration for genetic selection in modern dairy cattle. However, the impact of calf diseases on other economically important traits and the genetic architecture underlying these traits remain poorly understood. This study aimed to (1) determine the impact of including calf disease traits into Canadian national genetic evaluation by estimating genetic and phenotypic correlations with other economically important traits, and (2) conduct a GWAS and functional analysis to identify and understand genomic regions associated with calf disease traits in the Holstein breed. The majority of genetic correlation estimates between calf diseases and other economically important traits were close to zero. However, notable correlations were found between respiratory problems and both fertility traits and clinical mastitis, which showed a favorable relationship. Similarly, estimated phenotypic correlations were generally small, suggesting limited phenotypic impact of calf disease on later life performance. These results highlight the importance of including calf disease traits within national evaluations to ensure improvements can be made on the genetic level. From the GWAS, 17 SNPs across 5 chromosomes were significantly associated with diarrhea, and 20 SNPs across 5 chromosomes were significantly associated with respiratory problems. The enrichment analysis revealed 7 candidate genes that were colocalized with the SNP significantly associated with diarrhea (ACER3, CAPN5, ILK, LIPT2, PGM2L1, PPME1, and TNP2). For respiratory problems, the enrichment analysis revealed 4 candidate genes that were colocalized with the significant SNP (SYNPO, DCTN4, ANXA6, and MYOZ3). Several potential candidate genes were identified, although further work is required to determine the exact association between identified genes and calf disease traits.

Feed efficiency (FE) in dairy cattle is influenced by both genetic and environmental factors, including diet composition. Differential expression (DE) of mRNA isoforms was analyzed using publicly available RNA-Seq data to investigate alternative splicing associated with residual feed intake (RFI) under control and High-Concentrate (HC) diets in Holstein and Jersey cattle. Low-RFI (efficient) animals exhibited more extensive transcriptomic responses than their high-RFI counterparts (207 vs. 103 DE isoforms in Holstein; 183 vs. 90 in Jersey), reflecting greater isoform-specific regulation. Under the control diet, DE isoforms were enriched for transcriptional regulation and cellular function pathways, while the HC diet showed a balanced pattern of up- and downregulation affecting growth, signaling, and metabolic pathways. Overlap between DE isoforms and milk production QTLs was characterized based on functional annotations. In Holsteins, overlap decreased from 66.87% under control to 49.69% under HC, while in Jersey, it increased from 45.02% under control to 64.64% under HC. Comparison between low- and high-RFI animals showed a reduction in reproductive QTL overlap in both breeds: Holstein decreased from 29.65% to 18.69%, respectively, and Jersey decreased from 13.27% to 3.50%, respectively. In Holsteins, the mRNA isoforms WDR33_6 and PRKAR2A_3 were identified as DE transcripts associated with dietary treatment and RFI status suggesting potential roles in FE and metabolic adaptation. As an exploratory study, these results indicate mRNA isoform-level differences associated with RFI and dietary responses in Holstein and Jersey cattle. The observed patterns may reflect contrasts between reproductive and milk production-related biological processes, providing initial insights into candidate features associated with productivity and sustainability.

The future direction of the dairy industry is breeding cattle that have low environmental impact and are resilient when faced with environmental perturbations. In Canada, genetic selection for reduced environmental impact is partly based on methane production predicted from mid-infrared spectroscopy data (MP_MIR). While relationships between MP_MIR and production have been previously reported, correlations between heat tolerance of production traits and MP_MIR have not been studied. The objectives of this study were to 1) estimate genetic correlations between MP_MIR and heat tolerance defined by milk yield (MY), fat yield (FY), and protein yield (PY), and 2) evaluate how these relationships change across the lactation. Data comprised 7,714,324 MY, 7,128,396 FY, 7,177,657 PY, and 1,017,436 MP_MIR records collected on first parity Canadian Holsteins. A random sample of 1,000 herds (approximately 15% of all data) was selected and 6 ranges of days in milk (DIM) within the subset were generated: 5 to 305, 5 to 185, 30 to 185, 60 to 185, 90 to 185, and 120 to 185 DIM. Hourly ambient temperature and relative humidity were extracted from the NASA POWER database (https://power.larc.nasa.gov/; accessed 2025-08-05) and average temperature and humidity index (THI) values across each test-day and the 2 preceding days (d-2 to d0) were matched to each production record. Repeatability reaction norm models were fit for production traits using THI thresholds of 65 (MY), 55 (FY), and 60 (PY) and a repeatability model was fit for MP_MIR. Pairwise models were run between MP_MIR and each production trait across the 6 DIM windows. While magnitude varied, relationships between MP_MIR and production traits were generally consistent across DIM windows. Genetic correlations between MP_MIR and heat tolerance were negligible for MY (-0.01 ± 0.05 to 0.26 ± 0.69) and PY (-0.03 ± 0.04 to 0.09 ± 0.09), and lowly favorable for FY (-0.05 ± 0.07 to -0.16 ± 0.04). Correlations for MY and PY were not significant across any DIM window evaluated while correlations for FY were significant across 2 of the 6 windows (5 to 305 and 5 to 185). Results indicate little to no change in heat tolerance of MY and PY and marginal improvements in heat tolerance of FY with selection for lower methane production. However, interpretation of these results should be made with caution, as methane production was derived from a mid-infrared predicted phenotype rather than direct measurements.

Breeding feed-efficient animals has become a key focus of the dairy industry, yet most research on its genetic potential has focused on adult animals, resulting in a dearth of understanding for feed efficiency in youngstock. Metabolizable energy intake (MEI) of milk replacer and solid feed could provide information on early-life feed efficiency, as it encompasses both forms of the diet fed to young calves. The objective of this study was to estimate the variance components and genetic parameters of MEI in dairy calves over the rearing period (from 5 to 60 d of life). A total of 44,919 daily feed intake records from 913 Holstein calves collected from July 2016 to June 2024 were analyzed. A univariate random regression model was used to estimate genetic parameters of daily MEI. Average heritability and repeatability (SE) for daily MEI were 0.17 (SE = 0.03) and 0.50 (SE = 0.03), respectively, with the highest values for both genetic parameters between d 5 and 18 of life and d 47 to 60 of life. Genetic parameters for daily MEI were lowest during the weaning transition from d 28 to 32 of life. Genetic correlations among daily MEI were highly variable, ranging from -0.46 to 0.99, likely reflecting the physiological changes in digestion as calves transitioned from a liquid to solid diet. We conclude that daily MEI of calves in the rearing period shows potential for genetic selection, with genetic parameters being greatest in the first (wk 1 and 2) and last 2 wk (wk 7 and 8) of life. Further studies with larger datasets and additional herds are needed to confirm these findings.

Feed efficiency is a critical trait in dairy cattle production, with profound implications on both economic profitability and environmental sustainability. The objective of this study was to assess genetic relationships between feed intake and feed efficiency traits of calves and first-lactation Holstein cows. Data from 795 calves and 720 primiparous cows were analyzed, with feed intake measured as metabolizable energy intake (MEI) in calves and DMI in cows. Calf MEI was summarized as weekly averages across 2 key developmental phases: wk 1 to 3 (MEI1) and wk 7 to 9 (MEI2). Dry matter intake was recorded weekly from lactation wk 9 to 44. Feed efficiency was defined as residual feed intake (RFI) in primiparous cows and residual metabolizable energy intake (RMEI) in calves, estimated for the same 2 developmental periods as MEI (RMEI1 and RMEI2). In both cases, efficiency was expressed as the deviation of observed intake from expected intake based on energy sinks. Using a multitrait repeatability model, genetic correlations were estimated among the 6 traits (MEI1, MEI2, RMEI1, RMEI2, DMI, and RFI). Notably, genetic correlations (±SE) were moderate and positive between MEI2 and DMI at 0.49 ± 0.15 and RFI at 0.44 ± 0.21. In contrast, the genetic correlations of MEI1 with DMI was 0.15 ± 0.15 and RFI was 0.12 ± 0.21, which were close to zero and not significant. The genetic correlation between RFI and RMEI1 was estimated at -0.35 ± 0.26, indicating a possible negative relationship, although not significant. A moderately high positive genetic correlation was estimated between RFI and RMEI2 at 0.71 ± 0.36. These findings suggest that RMEI2 seems to be favorably genetically correlated with RFI, whereas RMEI1 shows no or perhaps a relatively weak unfavorable association with RFI. The results likely reflect physiological differences in calf rumen development, but support the potential for early selection based on RMEI2 to enhance cow feed efficiency.

Genetic improvement of fertility in dairy cattle remains a significant challenge due to the complex interplay between reproductive and environmental factors resulting in low heritability traits. Recent advancements in sensor technologies have allowed for the assessment of estrus-related traits as indicators of fertility. Three sensor-derived estrus traits previously associated with fertility outcomes include estrus duration (ED), maximum estrus intensity (MEI), and the interval from calving to first estrus (CTFE). The objectives of this study were to estimate the genetic parameters of ED, MEI, and CTFE in Holsteins, to assess the reliability of EBVs, to evaluate the inclusion of genomic information, and to estimate breeding value correlations with economically important traits. Phenotypes were derived from activity-monitoring collars (CowScout, GEA) between 2020 and 2023 from a single farm in Slovakia. After editing, 8,363, 8,092, and 2,909 records on 2,390, 2,360, and 2,147 cows were available for analysis of ED, MEI, and CTFE, respectively. Heritability estimates for the traits were low to moderate, with values of 0.18 ± 0.02 for MEI, 0.16 ± 0.02 for ED, and 0.07 ± 0.03 for CTFE. Repeatability across lactations was 0.34 ± 0.03 for CTFE, but no significant permanent environmental effect was found across lactation for MEI and ED. Repeatability within lactation was low for MEI (0.25 ± 0.02) and ED (0.19 ± 0.01). The average reliability of EBVs was moderate for phenotyped cows, with values of 0.32 ± 0.05 for CTFE and 0.48 ± 0.09 for MEI and ED. For sires with at least 10 daughters, EBV reliability ranged from 0.56 ± 0.11 for CTFE to 0.79 ± 0.13 for MEI and ED. Inclusion of genomic information did not greatly affect genetic parameters and resulted in marginal improvements to reliability of breeding values. Genetic correlations among estrus traits were moderate and favorable, indicating that better performance of 1 trait will correspond to improved performance in the other 2 traits. Breeding value correlations between estrus traits and economically important traits were generally favorable. These results suggest that sensor-derived estrus traits are heritable, with potential for more rapid progress in reproduction than currently evaluated traits. Future research should estimate proper genetic correlations between estrus traits and other economically important traits and expand datasets to enhance the genetic evaluations of these sensor-derived estrus traits.