Rasmus Bak Stephansen

Methane emissions from ruminants significantly contribute to greenhouse gases, making it crucial for sustainable livestock breeding to understand how both genetic and microbial factors influence methane production. We compared the heritability and microbiability for enteric methane in cows using microbial features derived from 16S rRNA amplicon data and shotgun metagenomics data, together with genome-wide marker data. The features derived from 16S rRNA data were 16s genus (16s-G), 16s species (16s-S), 16s Predicted microbial genes (16s-PMG) and 16s Predicted metabolic pathways (16s-PMP). The features derived from metagenomics data were metagenomic species (M-S) and metagenomic genus (M-G) considering 3 different databases (MGnify, GTDB, and NCBI). The heritability of methane ranged from 0.08 to 0.14. The 16s-G explained 28% of phenotypic variation in methane, and contributed the most to the heritability estimate for methane among other features. For the same feature data sets, we estimated the heritability of each microbial feature. Most microbial features had low heritability, while a subset had high values (up to 0.8). The highest heritabilities were observed for M-S MGnify feature RUG592 sp902767285 (0.95) and M-G NCBI genus feature Leadbettera (0.98). We found that the microbiota in the rumen is primarily determined by environmental factors, whereas host genetics has a significant impact on the abundance of certain functionally important microbes. To the best of our knowledge, this study presents the first comparison of methane heritability in dairy cattle incorporating microbial data (1) from multiple techniques such as 16S rRNA amplicon sequencing and shotgun metagenomic sequencing, and (2) from multiple levels of microbial features such as 16s-G, 16s-S, 16s-PMG, 16s-PMP, and M-S and M-G. Our results highlight heritable microbial species/genus as potential targets for microbiome-informed breeding strategies to reduce methane emissions in dairy cattle.

This study aimed to quantify the effect of the methane-mitigating feed additive 3-NOP on genetic parameters and potential genotype-by-environment (G × E) interaction for gas emission traits, i.e., methane (CH₄) and, carbon dioxide (CO₂) breath concentrations, as well as milk production traits in Danish Holstein cows. Weekly gas emission phenotypes on individual cow level, recorded with sniffers, and monthly test day records from 2,404 cows in 5 commercial herds were analyzed using a reaction norm model in which additive genetic and permanent environmental effects were modeled as linear functions of 3-NOP supplementation. Mean CH₄ concentration decreased from 398 to 214 ppm when 3-NOP was supplied to the cows, accompanied by reductions in CO₂ concentrations as well as the CH₄:CO₂ ratio, and small changes in milk production traits. 3-NOP substantially reduced additive genetic, permanent environmental, and residual variances for the CH₄ concentration, but did not significantly change the heritability and repeatability for CH₄, CO₂ and CH₄:CO₂ ratio. For milk production traits, no effect was observed on the estimated variance components. Genetic correlations between environments of 3-NOP were high and near unity most traits, indicating no G × E reranking. Our findings suggest that CH₄ do not require separate trait definition across 3-NOP environments; however, accounting for the reduced variance structure is recommended to ensure unbiased genetic evaluation of CH₄ phenotypes.

Summary: Enhancing feed efficiency in dairy cattle is essential for a more sustainable milk production. However, understanding how genetic selection for feed efficiency affects other important traits (e.g., production, health, and fertility) is not well explored. This study explored that for the feed efficiency traits: body weight (BW), genetic residual feed intake (gRFI), and genetic Feed Saved (gFS). Using data from >5,100 cows in commercial herds, we found that BW was slightly unfavorably associated with productive lifetime and showed no benefit for 305-day yield, suggesting that selecting smaller cows may enhance lifespan. Both gRFI and gFS showed low correlations with 305-day yield and functional traits, indicating minimal trade-offs. However, early lactation gRFI in multiparous cows had a slight negative correlation with longevity, warranting further investigation. The gFS index showed strong alignment with the current Nordic Saved Feed index and selection for gFS is unlikely to negatively affect other traits in the breeding goal.

Genetic enhancement of feed efficiency can improve the economic sustainability and environmental responsibility of dairy farming. Although genetic selection holds promise for improving feed efficiency across the lifespan of dairy cows, comprehensive data spanning whole lactations or even a productive lifetime are currently limited. To address this, we used production data and data from a camera-based feed intake and BW recording system, along with records of production, feed intake, and weight on Holstein cows from a research herd. We aimed to estimate variance components for a multivariate, multiparity model of production, feed intake, and BW data to calculate genetic residual feed intake (gRFI) for each of the Nordic breeds (Holstein, Jersey, and Red Dairy Cattle). Our approach included investigating a new definition of energy balance (EB_body) calculated from changes in body reserves, serving as an energy sink in gRFI. The data in our analysis consisted of 4,751 Holstein cows (7,851 lactations), 2,068 Jersey cows (3,486 lactations), and 3,235 Red Dairy Cattle cows (5,419 lactations). We used Gibbs sampling to estimate posterior means and SD for all model parameters. Our findings revealed moderate lactation-wise heritability of gRFI (0.15-0.38) across all breeds and parities. Moreover, gRFI genetic correlations varied (-0.2 to 0.4) between early- and mid- to late-lactation stages across all breeds, and for lactation-wise gRFI, there were moderately high genetic correlations (0.39-0.59) between primi- and multiparous lactations across the 3 breeds. Those results suggest the importance of recording phenotypes in most time periods within and across lactations. Our analysis indicated that improving gRFI with one genetic SD unit corresponded to a 2% to 3% gain in net return profit per cow-year, with no or minimal impact on production and body reserve management. We demonstrated the feasibility of incorporating EB_body into gRFI. Comparing gRFI calculated with EB_body or changes in BW as an energy sink trait for body reserve management were highly genetically correlated (>0.95). This result shows that the choice of the energy sink trait for body reserve management in gRFI will yield limited reranking among cows and sires when based on BW records only. However, EB_body offers an opportunity to incorporate BCS information without increasing the number of genetic parameters to be estimated, but it relies on parameters estimated in experimental settings. In conclusion, our study demonstrates the feasibility of developing a model for gRFI over most of the productive lifetime of dairy cattle, offering significant economic benefits without compromising productivity or body reserve management. Moving forward, comprehensive recording schemes covering whole lactations and productive lifetimes are advantageous for accurate selection indices of gRFI.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH₄]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH₄. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH₄ records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH₄ phenotypes in genomic analysis.

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.

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 GenTORE and 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.

Recording complex phenotypes on a large scale is becoming possible with the incorporation of recently developed new technologies. One of these new technologies is the use of 3D cameras on commercial farms to measure feed intake and body weight (BW) daily. Residual feed intake (RFI) has been proposed as a proxy for feed efficiency in several species, including cattle, pigs, and poultry. Dry matter intake and BW records are required to calculate RFI, and the use of this new technology will help increase the number of individual records more efficiently. The aim of this study was to estimate genetic parameters (including genetic correlations) for DMI and BW obtained by 3D cameras from 6,000 cows in commercial farms from the breeds Danish Holstein, Jersey, and Nordic Red. Additionally, heritabilities per parity and genetic correlations among parities were estimated for DMI and BW in the 3 breeds. Data included 158k weekly records of DMI and BW obtained between 2019 and 2022 on 17 commercial farms. Estimated heritability for DMI ranged from 0.17 to 0.25, whereas for BW they ranged from 0.44 to 0.58. The genetic correlations between DMI and BW were moderately positive (0.58-0.65). Genetic correlations among parities in both traits were highly correlated in the 3 breeds, except for DMI between first parity and late parities in Holstein where they were down to 0.62. Based on these results, we conclude that DMI and BW phenotypes measured by 3D cameras are heritable for the 3 dairy breeds and their heritabilities are comparable to those obtained by traditional methods (scales and feed bins). The high heritabilities and correlations of 3D measurements with the true trait in previous studies demonstrate the potential of this new technology for measuring feed intake and body weight in real-time. In conclusion, 3D camera technology has the potential to become a valuable tool for automatic and continuous recording of feed intake and body weight on commercial farms.