Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

Multiple-trait random regression modeling of feed efficiency in US Holsteins.

Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple-trait random regression (MTRR) modeling developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change in metabolic body weight (MBW). The MTRR analyses, using daily data on the core traits of DMI, MBW, and milk energy (MilkE), were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and change in MBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05-0.07 and 0.11-0.17, respectively) and multiparous (0.03-0.13 and 0.10-0.17, respectively) cows. Genetic correlations of RFI across DIM varied (>0.05) widely compared with FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg0.75 for primiparous and 0.12 to 0.14 kg/kg0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency.

Prediction of fatty acid composition using milk spectral data and its associations with various mid-infrared spectral regions in Michigan Holsteins.

Fatty acid composition in milk is not only reflective of nutritional quality but also potentially predictive of other attributes (e. g. including the cow's energy balance and its relative output of methane emissions). Furthermore, a higher ratio of long-chain to short-chain fatty acids or mean carbon number has been associated with negative energy balance in dairy cows, whereas enhanced nutritional properties have been generally associated with higher levels of unsaturation. We set out to directly compare Bayesian regression strategies with partial least squares for the prediction of various milk fatty acids using Fourier-transform infrared spectrum data on 777 milk samples taken from 579 cows on 4 Michigan dairy herds between 5 and 90 d in milk. We also set out to identify those spectral regions that might be associated with fatty acids and whether carbon number or level of unsaturation might contribute to the strength of these associations. These associations were based on adaptively clustered windows of wavenumbers to mitigate the distorting effects of severe multicollinearity on marginal associations involving individual wavenumbers. In general, Bayesian regression methods, particularly the variable selection method BayesB, outperformed partial least squares regression for cross-validation prediction accuracy for both individual fatty acids and fatty acid groups. Strong signals for wavenumber associations using BayesB were well distributed throughout the mid-infrared spectrum, particularly between 910 and 3,998 cm-1. Carbon number appeared to be linearly related to strength of wavenumber associations for 38 moderately to highly predicted fatty acids within the spectral regions of 2,286 to 2,376 and 2,984 to 3,100 cm-1, whereas nonlinear associations were determined within 1,141 to 1,205; 1,570 to 1,630; and 1,727 to 1,768 cm-1. However, no such associations were detected with level of unsaturation. Spectral regions where there were significant relationships between strength of association and carbon number may be useful targets for inferring the relative proportion of long-chain to short-chain fatty acids, and hence energy balance.

Association between days open and milk spectral data in dairy cows.

Data on 19,489 Brown Swiss cows reared in northeastern Italy were used to associate absorbances of individual wavenumbers within the mid-infrared range with days open (DO). Different postcalving days in milk (DIM) intervals were studied to determine the most informative milk sampling periods for predicting DO. Milk samples were analyzed using a MilkoScan (Foss Electric, Hillerød, Denmark) Fourier-transform infrared (FTIR) spectrometer for 1,060 wavenumbers (wn) ranging from 5,011 to 925 cm-1. To determine DO, we considered an insemination to lead to conception when there was no return of heat (i.e., no successive insemination) and the cow had a subsequent calving date whereby gestation length was required to be within ±30 d of 290 d. Only milk records within the first 90 DIM were considered. Associations were inferred by (1) fitting linear regression models between the DO and each individual wavenumber or milk component, and (2) fitting a Bayesian regression model that included the complete FTIR spectral data. The effects of including systematic effects (parity number, year-season, herd) in the model on these associations were also studied. These analyses were performed for the complete data (5-90 DIM) and for data stratified by DIM period (5 to 30, 31 to 60, and 61 to 90 DIM). Overall, regions of wavenumbers of the milk FTIR spectra that were associated with DO included wn 2,973 to 2,830 cm-1 [related to fat-B (C-H stretch)], wn 2,217 to 1,769 cm-1 [related to fat-A (C = O stretch)], wn 1,546 cm-1 (related to protein), wn 1,465 cm-1 (related to urea and fat), wn 1,399 to 1,245 cm-1 (related to acetone), and wn 1,110 cm-1 (related to lactose). Estimated effects depended on the DIM period, with milk samples drawn during DIM intervals 31 to 60 d and 61 to 90 d being most strongly associated with DO. These DIM intervals are also typically most associated with negative energy balance and peak lactation.

Symposium review: Genetic relationships between different measures of feed efficiency and the implications for dairy cattle selection indexes.

A greater number of dairy economic selection indexes are incorporating a measure of feed efficiency (FE) as a key trait. Definitions of FE traits have ranged from dry matter intake (DMI) to residual feed intake (RFI), noting that RFI is effectively DMI adjusted for various energy sink traits such as body weight (BW) and milk energy (MilkE). Other definitions of FE fall between these 2 extremes such as feed saved (FS), which combines RFI and the portion of DMI required to maintain BW. The choice between different FE traits can create confusion as to how to meaningfully compare their heritabilities, estimated breeding values (EBV) and their corresponding reliabilities, and how to differentially incorporate these EBV into selection indexes. If RFI and FS are merely linear functions of DMI, BW, and MilkE with known genetic variances and covariances between these 3 traits, there may be no need to directly compute RFI or FS phenotypes to determine their heritabilities, genetic correlations, EBV, and respective reliabilities for individual animals. We demonstrate how the estimated total genetic merit is invariant to the specification of a FE trait within a selection index. That is, economic weights for a selection index involving one particular FE trait readily convert into the economic weights for a selection index involving a different linear function of that FE trait. We use these different specifications of FE to provide insight as to the effect of the degree of missingness (e.g., paucity of DMI relative to milk yield records) on the EBV accuracies of the various derivative FE traits. We particularly highlight that the generally observed higher EBV accuracies for DMI, then for FS, and lastly for RFI are partly driven by the greater genetic correlations of DMI with BW and MilkE and of FS with BW. Finally, we advocate a genetic regression approach to deriving FS and RFI recognizing that genetic versus residual relationships between FE component traits may differ substantially from each other.

Genomic prediction of residual feed intake in US Holstein dairy cattle.

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.

High-density genome-wide association study for residual feed intake in Holstein dairy cattle.

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.

Updating predictions of dry matter intake of lactating dairy cows.

Our objective was to model dry matter intake (DMI) by Holstein dairy cows based on milk energy (MilkE), body weight (BW), change in BW (ΔBW), body condition score (BCS), height, days in milk (DIM), and parity (primiparous and multiparous). Our database included 31,631 weekly observations on 2,791 cows enrolled in 52 studies from 8 states of the United States, mostly in the Upper Midwest. The means ± standard deviations of these variables were 24 ± 5 kg of DMI, 30 ± 6 Mcal of MilkE/d, 624 ± 83 kg of BW, 0.24 ± 1.50 kg of ΔBW/d, 3.0 ± 0.5 BCS, 149 ± 6 cm height, and 102 ± 45 DIM. Data analysis was performed using a mixed-effects model containing location, study within location, diet within study, and location and cow within study as random effects, whereas the fixed effects included the linear effects of the covariates described previously and all possible 2-way interactions between parity and the other covariates. A nonlinear (NLIN) mixed model analysis was developed using a 2-step approach for computational tractability. In the first step, we used a linear (LIN) model component of the NLIN model to predict DMI using only data from mid-lactation dairy cows (76-175 DIM) without including information on DIM. In the second step, a nonlinear adjustment for DIM using all data from 0 to 368 DIM was estimated. Additionally, this NLIN model was compared with an LIN model containing a fourth-order polynomial for DIM using data throughout the entire lactation (0-368 DIM) to assess the utility of an NLIN model for the prediction of DMI. In summary, a total of 8 candidate models were evaluated as follows: 4 ways to express energy required for maintenance (BW, BW0.75, BW adjusted for a BCS of 3, and BW0.75 adjusted for a BCS of 3) × 2 modeling strategies (LIN vs. NLIN). The candidate models were compared using a 5-fold across-studies cross-validation approach repeated 20 times with the best-fitting model chosen as the proposed model. The metrics used for evaluation were the mean bias, slope bias, concordance correlation coefficient (CCC), and root mean squared error of prediction (RMSEP). The proposed prediction equation was DMI (kg/d) = [(3.7 + parity × 5.7) + 0.305 × MilkE (Mcal/d) + 0.022 × BW (kg) + (-0.689 + parity × -1.87) × BCS] × [1 - (0.212 + parity × 0.136) × exp(-0.053 × DIM)] (mean bias = 0.021 kg, slope bias = 0.059, CCC = 0.72, and RMSEP = 2.89 kg), where parity is equal to 1 if the animal is multiparous and 0 otherwise. Finally, the proposed model was compared against the Nutrient Requirements of Dairy Cattle (2001) prediction equation for DMI using an independent data set of 9,050 weekly observations on 1,804 Holstein cows. The proposed model had smaller mean bias and RMSEP and higher CCC than the Nutrient Requirements of Dairy Cattle equation to predict DMI and has potential to improve diet formulation for lactating dairy cows.

Laying hen production and welfare in enriched colony cages at different stocking densities.

Many laying hen companies in the United States are pledging to move away from intensive conventional cages to extensive housing systems. Enriched colony cages (ECC) are a practical alternative to conventional cage systems. Scientific research is limited on the effects of ECC on hen production and welfare. Therefore, the objective of this study was to evaluate the effects of stocking density on welfare and performance with the overall outcome to provide guidance on stocking density for ECC. At 16 wk, W-36 pullets were placed into 2 commercial ECC housing systems. Within each ECC enclosure, hens were allocated into 1 of 6 stocking densities: A) 465 to 484 cm2/bird, B) 581 to 606 cm2/bird, C) 652 to 677 cm2/bird, D) 754 to 780 cm2/bird, E) 799 to 832 cm2/bird, and F) 923 to 955 cm2/bird. Body weight, egg production, mortality, and Welfare Quality data were collected each 28 d period from 17 to 68 wk. The 6 ECC stocking densities had several transient effects on production measures within age periods with no difference in hen-day production (P > 0.05). Body weight was affected by stocking density (P < 0.05) where hens raised at stocking density A (465 to 484 cm2/bird) weighed at least 25 g less than hens from other stocking densities. Stocking density differences for Welfare Quality assessments were only apparent for feather coverage. Hens raised at stocking density A (465 to 484 cm2) consistently had the poorest (P < 0.05) crop, keel, belly, back, and rump feather coverage. The keel, neck, and back body regions had poorer feather coverage when hens were raised at stocking densities B (581 to 606 cm2) and C (652 to 677 cm2) compared to hens from lower stocking densities (P < 0.05). Therefore, the minimum area per hen housed in commercial ECC systems should be 754 cm2 per bird for greater feather coverage.

A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows.

Fourier-transform near- and mid-infrared (FTIR) milk spectral data are routinely collected in many countries worldwide. Establishing an optimal strategy to use spectral data in genetic evaluations requires knowledge of the heritabilities of individual FTIR wavelength absorbances. Previous FTIR heritability estimates have been based on relatively small sample sizes and have not considered the possibility that heritability may vary across parities and stages of the lactation. We used data from ∼370,000 test-day records of Canadian Holstein cows to produce a landscape of the heritability of FTIR spectra, 1,060 wavelengths in the near- and mid-infrared spectrum (5,011-925 cm-1), by parity and month of the lactation (mo 1 to 3 and mo 1 to 6, respectively). The 2 regions of the spectrum associated with absorption of electromagnetic energy by water molecules were estimated to have very high phenotypic variances, very low heritabilities, and very low proportion of variance explained by herd-year-season (HYS) subclasses. The near- or short-wavelength infrared (SWIR: 5,066-3,672 cm-1) region was also characterized by low heritability estimates, whereas the estimated proportion of the variance explained by HYS was high. The mid-wavelength infrared region (MWIR: 3,000-2,500 cm-1) and the transition between mid and long-wavelength infrared region (MWIR-LWIR: 1,500-925 cm-1) harbor several waves characterized by moderately high (≥0.4) heritabilities. Most of the high-heritability regions contained wavelengths that are reported to be associated with important milk metabolites and components. Interestingly, these 2 same regions tended to show more variability in heritabilities between parity and lactation stage. Second parity showed heritability patterns that were distinctly different from those of the first and third parities, whereas the first 2 mo of the lactation had clearly distinct heritability patterns compared with mo 3 to 6.

Genomic heritability and genome-wide association analysis of anti-Müllerian hormone in Holstein dairy heifers.

Anti-Müllerian hormone (AMH) is an ovarian growth factor that plays an important role in regulation of ovarian follicle growth. The objectives of this study were to estimate the genomic heritability of AMH and identify genomic regions associated with AMH production in a genome-wide association (GWA) analysis. Concentrations of AMH were determined in 2,905 dairy Holstein heifers genotyped using the Zoetis medium density panel (Zoetis Inclusions, Kalamazoo, MI) with 54,519 single nucleotide polymorphism (SNP) markers remaining after standard genotype quality control edits. A linear mixed model was used to model the random effects of sampling day and genomics on the logarithm of AMH. The genomic heritability (± standard error of the mean) of AMH was estimated to be 0.36 ± 0.03. Our GWA analysis inferred significant associations between AMH and 11 SNP markers on chromosome 11 and 1 SNP marker on chromosome 20. Annotated genes with significant associations were identified using the Ensembl genome database (version 88) of the cow genome (version UMD 3.1; https://www.ensembl.org/biomart). Gene set enrichment analysis revealed that 2 gene ontology (GO) terms were significantly enriched in the list of candidate genes: G-protein coupled receptor signaling pathway (GO:0007186) and the detection of chemical stimulus involved in sensory perception (GO:0050907). The estimated high heritability and previously established associations between AMH and ovarian follicular reserve, fertility, longevity, and superovulatory response in cattle implies that AMH could be used as a biomarker for genetic improvement of reproductive potential.

Effects of different litter substrates and induced molt on production performance and welfare quality parameters of white Leghorn hens housed in multi-tiered aviary system.

More than 90% of the commercial egg production in the United States is pledged to be in cage-free systems by 2025. Management practices like induced molting and litter area management have come under scrutiny because of the housing system change. The aim of this study was to determine the welfare and production implications of different litter substrates and also evaluate induced molting of hens in a cage-free system. Bovan White hens were housed in a multi-tier aviary system with daily access to open litter area of either Astroturf (AT), wood shavings (SH), or straw (ST) and bare concrete floor (CO) serving as control. At 68 wk of age, molt was induced in half of the hens whereas the other half continued without molting to 116 wk. Production and welfare parameters were measured periodically throughout first and second cycles. Litter substrate did not influence hen-day production and case-weight measurements. However, CO had the lowest total number of eggs produced during the first cycle (P < 0.05). Hen-day percentage was approximately 14% greater in molted hens during the second cycle with egg case weight being heavier in non-molt hens toward the end of second cycle (P < 0.05). The only welfare parameter influenced by litter substrate during the first cycle was a greater crop feather loss in AT than ST at mid-lay (P < 0.05). Keel deformations increased with age irrespective of the litter substrate with 91.5% of palpated hens having keel deformations at the end of first cycle (P < 0.05). Molting did not influence the keel palpation and footpad scores whereas frequency of moderate comb wound was greater in molt hens during molt (P < 0.05). Severe feather loss was seen in non-molt hens during the second cycle (P < 0.05). Litter substrate does not affect production and physical parameters of welfare of hens in a multi-tier aviary system. Additionally, induced molting can be successfully carried out in the multi-tier cage-free system.

Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency.

Genome-wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with one based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and nongenotyped cows. Estimated effects for single SNP markers were small and not statistically important but virtually identical for either FE measure (RFI vs. DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within nonoverlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of Bos taurus autosomes BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of orthogonal relationships clearly defined between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.

Analyses of reaction norms reveal new chromosome regions associated with tick resistance in cattle.

Despite single nucleotide polymorphism (SNP) availability and frequent cost reduction has allowed genome-wide association studies even in complex traits as tick resistance, the use of this information source in SNP by environment interaction context is unknown for many economically important traits in cattle. We aimed at identifying putative genomic regions explaining differences in tick resistance in Hereford and Braford cattle under SNP by environment point of view as well as to identify candidate genes derived from outliers/significant markers. The environment was defined as contemporary group means of tick counts, since they seemed to be the most appropriate entities to describe the environmental gradient in beef cattle. A total of 4363 animals having tick counts (n=10 673) originated from 197 sires and 3966 dams were used. Genotypes were acquired on 3591 of these cattle. From top 1% SNPs (410) having the greatest effects in each environment, 75 were consistently relevant in all environments, which indicated SNP by environment interaction. The outliers/significant SNPs were mapped on chromosomes 1, 2, 5, 6, 7, 9, 11, 13, 14, 15, 16, 18, 21, 23, 24, 26 and 28, and potential candidate genes were detected across environments. The presence of SNP by environment interaction for tick resistance indicates that genetic expression of resistance depends upon tick burden. Markers with major portion of genetic variance explained across environments appeared to be close to genes with different direct or indirect functions related to immune system, inflammatory process and mechanisms of tissue destruction/repair, such as energy metabolism and cell differentiation.

Predicting nutrient digestibility in high-producing dairy cows.

Our objective was to determine the effects of dry matter intake (DMI), body weight (BW), and diet characteristics on total tract digestibilities of dry matter, neutral detergent fiber, and starch (DMD, NDFD, and StarchD, respectively) in high-producing dairy cows. Our database was composed of 1,942 observations from 662 cows in 54 studies from Michigan, Ohio, and Georgia. On average, cows ate 23 ± 4.5 kg of dry matter/d, weighed 669 ± 79 kg, and produced 38 ± 10 kg of milk/d. Diets were 31 ± 5% neutral detergent fiber, 27 ± 6% starch, 2.6 ± 1.2% fatty acids, and 17 ± 1.4% crude protein. Digestibility means were 66 ± 6, 42 ± 11, and 93 ± 5% for DMD, NDFD, and StarchD, respectively. Forage sources included corn silage, alfalfa, and grasses. Corn source was classified by its ruminal fermentability. Data were analyzed using a mixed effects model, including diet chemical composition, forage source, and corn source, all expressed as percentage of dry matter, except for DMI, which was expressed as percentage of BW (DMI%BW); location and 2-way interactions were fixed effects. Cow, block, period, treatment, and study were included as random effects. Best fitting candidate models were generated using backward and stepwise regression methods. Additionally, the simplest model was generated using only DMI and location as fixed effects and all random effects. Candidate models were cross-validated across studies, and the resulting predictive correlation coefficients across studies (PC) and root mean square error of prediction (RMSEP) were compared by t-test. For each nutrient, the digestibility model that resulted in the highest PC and lowest RMSEP was determined to be the best fitting model. We observed heterogeneous coefficients among the different locations, suggesting that specific location factors influenced digestibilities. The overall location-averaged best fitting prediction equations were: DMD = 69 - 0.83 × DMI%BW (PC = 0.22, RMSEP = 5.39); NDFD = 53 + 0.26 × grass %DM - 0.59 × starch %DM + 3.06 × DMI%BW - 0.46 × DMI%BW2 (PC = 0.53, RMSEP = 9.70); and StarchD = 96 + 0.19 × HFERM%DM - 0.12 × starch %DM - 1.13 × DMI%BW (PC = 0.34, RMSEP = 4.77); where HFERM%DM is highly-fermentable corn source as percentage of DM. Our results confirm that digestibility is reduced as DMI increases, albeit at a lower rate than that reported in National Research Council. Furthermore, dietary starch depresses NDFD. Whereas DMD can be predicted based on DMI only, the best predictions for NDFD and StarchD require diet characteristics in addition to DMI.

The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows.

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain ß-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output.

Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle.

Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across-environment models.

Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors.

Feed efficiency (FE), characterized as the fraction of feed nutrients converted into salable milk or meat, is of increasing economic importance in the dairy industry. We conjecture that FE is a complex trait whose variation and relationships or partial efficiencies (PE) involving the conversion of dry matter intake to milk energy and metabolic body weight may be highly heterogeneous across environments or management scenarios. In this study, a hierarchical Bayesian multivariate mixed model was proposed to jointly infer upon such heterogeneity at both genetic and nongenetic levels on PE and variance components (VC). The heterogeneity was modeled by embedding mixed effects specifications on PE and VC in addition to those directly specified on the component traits. We validated the model by simulation and applied it to a joint analysis of a dairy FE consortium data set with 5,088 Holstein cows from 13 research stations in Canada, the Netherlands, the United Kingdom, and the United States. Although no differences were detected among research stations for PE at the genetic level, some evidence was found of heterogeneity in residual PE. Furthermore, substantial heterogeneity in VC across stations, parities, and ration was observed with heritability estimates of FE ranging from 0.16 to 0.46 across stations.