Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows

Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions

Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.

Comparison of miR-106b, miR-191, and miR-30d expression dynamics in milk with regard to its composition in Holstein and Ayrshire cows

OBJECTIVE

Milk composition varies considerably and depends on paratypical, genetic, and epigenetic factors. MiRNAs belong to the class of small non-coding RNAs; they are one of the key tools of epigenetic control because of their ability to regulate gene expression at the post-transcriptional level. We compared the relative expression levels of miR-106b, miR-191, and miR-30d in milk to demonstrate the relationship between the content of these miRNAs with protein and fat components of milk in Holstein and Ayrshire cattle.

METHODS

Milk fat, protein, and casein contents were determined in the obtained samples, as well as the content of the main fatty acids (g/100 g milk), including: saturated acids, such as myristic (C14:0), palmitic (C16:0), and stearic (C18:0) acids; monounsaturated acids, including oleic (C18:1) acid; as well as long-, medium- and short-chain, polyunsaturated, and trans fatty acids. Real-time stem-loop one-tube reverse transcription polymerase chain reaction with TaqMan probes was used to measure the miRNA expression levels.

RESULTS

The miRNA expression levels in milk samples were found to be decreased in the first two months in Holstein breed, and in the first four months in Ayrshire breed. Correlation analysis did not reveal any dependence between changes in the expression level of miRNA and milk fat content, but showed a multidirectional relationship with individual milk fatty acids. Positive associations between the expression levels of miR-106b and miR-30d and protein and casein content were found in the Ayrshire breed. Receiver operating characteristic curve analysis showed that miR-106b and miR-30d expression levels can cause changes in fatty acid and protein composition of milk in Ayrshire cows, whereas miR-106b expression level determines the fatty acid composition in Holsteins.

CONCLUSION

The data obtained in this study showed that miR-106b, miR-191, and miR-30d expression levels in milk samples have peculiarities associated with breed affiliation and the lactation period.

Recent Advances in Enteric Methane Mitigation and the Long Road to Sustainable Ruminant Production

Mitigation of methane emission, a potent greenhouse gas, is a worldwide priority to limit global warming. A substantial part of anthropogenic methane is emitted by the livestock sector, as methane is a normal product of ruminant digestion. We present the latest developments and challenges ahead of the main efficient mitigation strategies of enteric methane production in ruminants. Numerous mitigation strategies have been developed in the last decades, from dietary manipulation and breeding to targeting of methanogens, the microbes that produce methane. The most recent advances focus on specific inhibition of key enzymes involved in methanogenesis. But these inhibitors, although efficient, are not affordable and not adapted to the extensive farming systems prevalent in low- and middle-income countries. Effective global mitigation of methane emissions from livestock should be based not only on scientific progress but also on the feasibility and accessibility of mitigation strategies.

Heritability estimates and genome-wide association study of methane emission traits in Nellore cattle

The objectives of the present study were to estimate the heritability for daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to perform genome-wide association studies (GWAS) to identify genomic regions and candidate genes influencing the genetic variation of CH4 and CH4res. Methane emission phenotypes of 743 Nellore animals belonging to 3 breeding programs were evaluated. CH4 was measured using the sulfur hexafluoride (SF6) tracer technique (which involves an SF6 permeation tube introduced into the rumen, and an appropriate apparatus on each animal), and CH4res was obtained as the difference between observed CH4 and CH4 adjusted for dry matter intake. A total of 6,252 genotyped individuals were used for genomic analyses. Data were analyzed with a univariate animal model by the single-step GBLUP method using the average information restricted maximum likelihood (AIREML) algorithm. The effects of single nucleotide polymorphisms (SNPs) were obtained using a single-step GWAS approach. Candidate genes were identified based on genomic windows associated with quantitative trait loci (QTLs) related to the 2 traits. Annotation of QTLs and identification of candidate genes were based on the initial and final coordinates of each genomic window considering the bovine genome ARS-UCD1.2 assembly. Heritability estimates were of moderate to high magnitude, being 0.42 ± 0.09 for CH4 and 0.21 ± 0.09 for CH4res, indicating that these traits will respond rapidly to genetic selection. GWAS revealed 11 and 15 SNPs that were significantly associated (P < 10-6) with genetic variation of CH4 and CH4res, respectively. QTLs associated with feed efficiency, residual feed intake, body weight, and height overlapped with significant markers for the traits evaluated. Ten candidate genes were present in the regions of significant SNPs; 3 were associated with CH4 and 7 with CH4res. The identified genes are related to different functions such as modulation of the rumen microbiota, fatty acid production, and lipid metabolism. CH4 and CH4res presented sufficient genetic variation and may respond rapidly to selection. Therefore, these traits can be included in animal breeding programs aimed at reducing enteric methane emissions across generations.

Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis

The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits [methane yield (METY), methane intensity (METINT), and methane production (METP)] in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative (- 0.433 and - 0.262, respectively). Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans.

Host genetics associated with gut microbiota and methane emission in cattle

In livestock sector, dairy animals alone produce 18% of the total greenhouse gas emissions globally as methane (CH₄). This Enteric methane is the largest component of total carbon footprints produced by livestock production system and its reduction is today's new challenge to make livestock farming sustainable for earth's environment. The production of enteric methane in ruminants is a complex phenomena involving different host factors like host genotype, rumen microbiome, host physiology along with dietary factors. Efforts have been made to reduce methane emissions largely through nutritional interventions and dietary supplements, but permanent reductions can be obtained through genetic means by selecting and breeding of low methane emitting animals. From genome-wide association studies, many important genomic QTL regions and single nucleotide polymorphisms involved in shaping the composition of the ruminal microbiome and thus their carbon footprints have been recognised, implying that methane emission traits are quantitative traits. The major bottleneck in implementation of reduced methane emission traits in the breeding programs is wide variation at phenotypic level, lack of precise methane measurements at individual level. Overall, the heritability for CH₄ production traits is moderate, and it can be used in breeding programmes to target changes in microbial composition to reduce CH₄ emission in the dairy industry for far-reaching environmental benefits at the cost of a minor reduction in genetic gain in production traits.

Stool and Ruminal Microbiome Components Associated With Methane Emission and Feed Efficiency in Nelore Beef Cattle

Background: The impact of extreme changes in weather patterns on the economy and human welfare is one of the biggest challenges our civilization faces. From anthropogenic contributions to climate change, reducing the impact of farming activities is a priority since it is responsible for up to 18% of global greenhouse gas emissions. To this end, we tested whether ruminal and stool microbiome components could be used as biomarkers for methane emission and feed efficiency in bovine by studying 52 Brazilian Nelore bulls belonging to two feed intervention treatment groups, that is, conventional and by-product-based diets.

Results: We identified a total of 5,693 amplicon sequence variants (ASVs) in the Nelore bulls’ microbiomes. A Differential abundance analysis with the ANCOM approach identified 30 bacterial and 15 archaeal ASVs as differentially abundant (DA) among treatment groups. An association analysis using Maaslin2 software and a linear mixed model indicated that bacterial ASVs are linked to the host’s residual methane emission (RCH₄) and residual feed intake (RFI) phenotype variation, suggesting their potential as targets for interventions or biomarkers.

Conclusion: The feed composition induced significant differences in both abundance and richness of ruminal and stool microbial populations in ruminants of the Nelore breed. The industrial by-product-based dietary treatment applied to our experimental groups influenced the microbiome diversity of bacteria and archaea but not of protozoa. ASVs were associated with RCH₄ emission and RFI in ruminal and stool microbiomes. While ruminal ASVs were expected to influence CH₄ emission and RFI, the relationship of stool taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits was not reported before and might be associated with host health due to their link to anti-inflammatory compounds. Overall, the ASVs associated here have the potential to be used as biomarkers for these complex phenotypes.

Bovine host genome acts on rumen microbiome function linked to methane emissions

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

Board Invited Review: Enteric methane mitigation interventions

Mitigation of enteric methane (CH₄) presents a feasible approach to curbing agriculture’s contribution to climate change. One intervention for reduction is dietary reformulation, which manipulates the composition of feedstuffs in ruminant diets to redirect fermentation processes toward low CH₄ emissions. Examples include reducing the relative proportion of forages to concentrates, determining the rate of digestibility and passage rate from the rumen, and dietary lipid inclusion. Feed additives present another intervention for CH₄ abatement and are classified based on their mode of action. Through inhibition of key enzymes, 3-nitrooxypropanol (3-NOP) and halogenated compounds directly target the methanogenesis pathway. Rumen environment modifiers, including nitrates, essential oils, and tannins, act on the conditions that affect methanogens and remove the accessibility of fermentation products needed for CH₄ formation. Low CH₄-emitting animals can also be directly or indirectly selected through breeding interventions, and genome-wide association studies are expected to provide efficient selection decisions. Overall, dietary reformulation and feed additive inclusion provide immediate and reversible effects, while selective breeding produces lasting, cumulative CH₄ emission reductions.

Genome-wide association study for methane emission traits in Danish Holstein cattle

Selecting for lower methane emitting cows requires insight into the most biologically relevant phenotypes for methane emission, which are close to the breeding goal. Several methane phenotypes have been suggested over the last decade. However, the (dis)similarity of their underlying genetic architecture and correlation structures are poorly understood. Therefore, the objective of this study was to test association of SNP and genomic regions through GWAS on 8 CH₄ emission traits in Danish Holstein cattle. The traits studied were methane concentration (MeC; ppm), methane production (MeP ; g/d), 2 definitions of residual methane (RMETc and RMETp: MeC and MeP regressed on metabolic body weight and energy-corrected milk, respectively), 2 definitions of methane intensity (MeI; MeIc = MeC/ECM and MeIp = MeP/ECM); 2 definitions of methane yield per kilogram of dry matter intake (MeY; MeYc = MeC/dry matter intake and MeYp = MeP/dry matter intake). A total of 1,962 cows with genotypes (Illumina BovineSNP50 Chip or Eurogenomic custom SNP chip) and repeated records of the above-mentioned 8 methane traits were analyzed. Strong associations were found with 3 traits (MeC, MeP, and MeYc) on chromosome 13 and with 5 traits (MeC, MeP, MeIp, MeYp, and MeYc) on chromosome 26. For MeIc, MeIp, RMETc, MeYc, and MeYp, some suggestive association signals were identified on chromosome 1. Genomic segments of 1 Mbp (n = 2,525) were tested for their association with these traits, which identified between 33 to 54 significantly associated regions. In a pairwise comparison, MeC and MeP were the traits that shared the highest number of significant segments (17). The same trend was observed when comparing SNP significantly associated with the traits MeC and MeP shared from 23 to 25 SNP (most of which were located in chromosomes 11, 13, and 26). Based on our results on GWAS and genetic correlations, we conclude that MeC is (genetically) more closely linked to MeP than any of the other methane traits analyzed.

Genetic Variability of Methane Production and Concentration Measured in the Breath of Polish Holstein-Friesian Cattle

The genetic architecture of methane (CH₄) production remains largely unknown. We aimed to estimate its heritability and to perform genome-wide association studies (GWAS) for the identification of candidate genes associated with two phenotypes: CH₄ in parts per million/day (CH₄ ppm/d) and CH₄ in grams/day (CH₄ g/d). We studied 483 Polish Holstein-Friesian cows kept on two commercial farms in Poland. Measurements of CH₄ and carbon dioxide (CO₂) concentrations exhaled by cows during milking were obtained using gas analyzers installed in the automated milking system on the farms. Genomic analyses were performed using a single-step BLUP approach. The percentage of genetic variance explained by SNPs was calculated for each SNP separately and then for the windows of neighbouring SNPs. The heritability of CH₄ ppm/d ranged from 0 to 0.14, with an average of 0.085. The heritability of CH₄ g/d ranged from 0.13 to 0.26, with an average of 0.22. The GWAS detected potential candidate SNPs on BTA 14 which explained ~0.9% of genetic variance for CH₄ ppm/d and ~1% of genetic variance for CH₄ g/d. All identified SNPs were located in the TRPS1 gene. We showed that methane traits are partially controlled by genes; however, the detected SNPs explained only a small part of genetic variation-implying that both CH₄ ppm/d and CH₄ g/d are highly polygenic traits.

Genome-wise engineering of ruminant nutrition- nutrigenomics: applications, challenges, and future perspectives – a review

Use of genomic information in ruminant production systems can help relieve concerns related to food security and sustainability of production. Nutritional genomics (i.e., Nutrigenomics) is a field of research that is interested in all types of reciprocal interactions between nutrients and genomes of organisms, i.e., variable patterns of gene expression and effect of genetic variations on the nutritional environment. Devising a revolutionizing analytical approach to traditional ruminant nutrition research, the relatively novel area of ruminant nutrigenomics has several studies concerning different aspects of animal production systems. This paper aims to review the current nutrigenomics research in the frame of how nutrition of ruminants can be modified accounting for individual genetic backgrounds and gene/diet relationships behind productivity, quality, efficiency, disease resistance, fertility, and GHG emissions. Furthermore, current challenges facing ruminant nutrigenomics are evaluated and future directions for the novel area are strongly argued by this review.

Genotyping and Whole-Genome Resequencing of Welsh Sheep Breeds Reveal Candidate Genes and Variants for Adaptation to Local Environment and Socioeconomic Traits

Background

Advances in genetic tools applied to livestock breeding has prompted research into the previously neglected breeds adapted to harsh local environments. One such group is the Welsh mountain sheep breeds, which can be farmed at altitudes of 300 m above sea level but are considered to have a low productive value because of their poor wool quality and small carcass size. This is contrary to the lowland breeds which are more suited to wool and meat production qualities, but do not fare well on upland pasture. Herein, medium-density genotyping data from 317 individuals representing 15 Welsh sheep breeds were used alongside the whole-genome resequencing data of 14 breeds from the same set to scan for the signatures of selection and candidate genetic variants using haplotype- and SNP-based approaches.

Results

Haplotype-based selection scan performed on the genotyping data pointed to a strong selection in the regions of GBA3, PPARGC1A, APOB, and PPP1R16B genes in the upland breeds, and RNF24, PANK2, and MUC15 in the lowland breeds. SNP-based selection scan performed on the resequencing data pointed to the missense mutations under putative selection relating to a local adaptation in the upland breeds with functions such as angiogenesis (VASH1), anti-oxidation (RWDD1), cell stress (HSPA5), membrane transport (ABCA13 and SLC22A7), and insulin signaling (PTPN1 and GIGFY1). By contrast, genes containing candidate missense mutations in the lowland breeds are related to cell cycle (CDK5RAP2), cell adhesion (CDHR3), and coat color (MC1R).

Conclusion

We found new variants in genes with potentially functional consequences to the adaptation of local sheep to their environments in Wales. Knowledge of these variations is important for improving the adaptative qualities of UK and world sheep breeds through a marker-assisted selection.

Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

Background

Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle.

Results

Based on the significant-association threshold (p < 5 × 10^− 8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n = 2), PME per kg fat (n = 14), and valeric acid (n = 17). Besides, 69 genes were identified for valeric acid (n = 18), PME per kg milk (n = 4) and PME per kg fat (n = 47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10^− 2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10^− 10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs.

Conclusions

Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to feed intake and preference. Identified SNPs were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of reference population required in genome-wide association studies and genomic selection.

Short communication: Genetic correlations between methane and milk production, conformation, and functional traits

Livestock produce CH₄, contributing to the global warming effect. One of the currently investigated solutions to reduce CH₄ production is selective breeding. The goal of this study was to estimate the genetic correlations between CH₄ and milk production, conformation, and functional traits used in the selection index for Polish-Holstein cows. In total, 34,429 daily CH₄ production observations collected from 483 cows were available, out of which 281 cows were genotyped. The CH₄ was measured using a so-called sniffer device installed in an automated milking system. Breeding values for CH₄ were estimated with the use of single-step genomic BLUP, and breeding values for remaining traits were obtained from the Polish national genomic evaluation. Genetic correlations between CH₄ production and remaining traits were estimated using bivariate analyses. The estimated genetic correlations were in general low. The highest values were estimated for fat yield (0.21), milk yield (0.15), chest width (0.15), size (0.15), dairy strength (0.11), and somatic cell count (0.11). These estimates, as opposed to estimates for the remaining traits, were significantly different from zero.