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

Manzanilla-Pech, C.I.V.; Difford, Gareth Frank; Sahana, Goutam; Romé, Hélène; Løvendahl, Peter; Lassen, Jan

doi:10.3168/jds.2021-20410

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…As stated before, many countries have opted to use sniffers to record methane emissions, given the practical ease of installation in automated milking stations, bringing an additional phenotype called CH 4 breath concentration (MeC) ( Madsen et al, 2010 ; Calderón-Chagoya et al, 2019 ; Sypniewski et al, 2019 ; Difford et al, 2020 ). However, to calculate MeP from CH 4 (and CO 2 ), ECM and BW are needed ( Madsen et al, 2010 ), leading to an artificially induced covariance structure between traits resulting in high correlations between MeP and ECM and BW ( Manzanilla-Pech et al, 2020 ; Manzanilla-Pech et al, 2022 ). This induced covariance could be removed by the calculation of adjusted traits as residual traits.…”

Section: Resultsmentioning

confidence: 99%

“…These methods, though, measure CH 4 as concentration (in ppm; MeC) not MeP. For this reason, some authors have used methane concentration as trait ( López-Paredes et al, 2020 ; Sypniewski et al, 2021 ; Manzanilla-Pech et al, 2022 ) as it is a direct measurement from some recording instrumentation such as sniffers. However, this raw phenotype does not account for the size (weight), production or feed intake of the animal.…”

Section: Introductionmentioning

confidence: 99%

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Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions

et al. 2022

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In the last decade, several countries have included feed efficiency (as residual feed intake; RFI) in their breeding goal. Recent studies showed that RFI is favorably correlated with methane emissions. Thus, selecting for lower emitting animals indirectly through RFI could be a short-term strategy in order to achieve the intended reduction set by the EU Commission (-55% for 2030). The objectives were to 1) estimate genetic parameters for six methane traits, including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the impact of reducing methane emissions in dairy cattle using the Danish Holstein population as an example. A total of 26,664 CH4 breath records from 647 Danish Holstein cows measured over 7 years in a research farm were analyzed. Records on dry matter intake (DMI), body weight (BW), and energy corrected milk (ECM) were also available. Methane traits were methane concentration (MeC, ppm), methane production (MeP; g/d), methane yield (MeY; g CH4/kg DMI), methane intensity (MeI; g CH4/kg ECM), residual methane concentration (RMeC), residual methane production (RMeP, g/d), and two definitions of residual feed intake with or without including body weight change (RFI1, RFI2). The estimated heritability of MeC was 0.20 ± 0.05 and for MeP, it was 0.21 ± 0.05, whereas heritability estimates for MeY and MeI were 0.22 ± 0.05 and 0.18 ± 0.04, and for the RMeC and RMeP, they were 0.23 ± 0.06 and 0.16 ± 0.02, respectively. Genetic correlations between methane traits ranged from moderate to highly correlated (0.48 ± 0.16–0.98 ± 0.01). Genetic correlations between methane traits and feed efficiency were all positive, ranging from 0.05 ± 0.20 (MeI-RFI2) to 0.76 ± 0.09 (MeP-RFI2). Selection index calculations showed that selecting for feed efficiency has a positive impact on reducing methane emissions’ expected response, independently of the trait used (MeP, RMeP, or MeI). Nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane in the breeding goal seems to be a faster way to achieve the desired methane emission reductions in dairy cattle.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions

et al. 2022

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“…Feed-efficient animals consume less food to produce the same amount of meat 7 , and emit less methane per unit of output 8,9 , thereby leading to increased system productivity and profitability, and reduced resource usage and environmental footprint. Moreover, utilizing high-throughput genomic technologies, such as genomewide association studies (GWAS) and genomic selection (GS), enables the identification of genetic markers associated with feed, methane emission and production efficiency traits [10][11][12][13][14] . These markers are powerful tools for predicting genetic potential 15 , allowing for more informed breeding decisions, ultimately expediting genetic progress, and enabling faster dissemination of superior genetic traits across cattle populations.…”

mentioning

confidence: 99%

Transcriptional response to an alternative diet on liver, muscle, and rumen of beef cattle

Fernandes,

Reverter,

Keogh

et al. 2024

Sci Rep

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Feed cost represents a major economic determinant within cattle production, amounting to an estimated 75% of the total variable costs. Consequently, comprehensive approaches such as optimizing feed utilization through alternative feed sources, alongside the selection of feed-efficient animals, are of great significance. Here, we investigate the effect of two diets, traditional corn-grain fed and alternative by-product based, on 14 phenotypes related to feed, methane emission and production efficiency and on multi-tissue transcriptomics data from liver, muscle, and rumen wall, derived from 52 Nellore bulls, 26 on each diet. To this end, diets were contrasted at the level of phenotype, gene expression, and gene-phenotype network connectivity. As regards the phenotypic level, at a P value < 0.05, significant differences were found in favour of the alternative diet for average daily weight gain at finishing, dry matter intake at finishing, methane emission, carcass yield and subcutaneous fat thickness at the rib-eye muscle area. In terms of the transcriptional level of the 14,776 genes expressed across the examined tissues, we found 487, 484, and 499 genes differentially expressed due to diet in liver, muscle, and rumen, respectively (P value < 0.01). To explore differentially connected phenotypes across both diet-based networks, we focused on the phenotypes with the largest change in average number of connections within diets and tissues, namely methane emission and carcass yield, highlighting, in particular, gene expression changes involving SREBF2, and revealing the largest differential connectivity in rumen and muscle, respectively. Similarly, from examination of differentially connected genes across diets, the top-ranked most differentially connected regulators within each tissue were MEOX1, PTTG1, and BASP1 in liver, muscle, and rumen, respectively. Changes in gene co-expression patterns suggest activation or suppression of specific biological processes and pathways in response to dietary interventions, consequently impacting the phenotype. The identification of genes that respond differently to diets and their associated phenotypic effects serves as a crucial stepping stone for further investigations, aiming to build upon our discoveries. Ultimately, such advancements hold the promise of improving animal welfare, productivity, and sustainability in livestock farming.

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