Multitrait genomic prediction of methane emissions in Danish Holstein cattle

Abstract

In dairy cattle, selecting for lower methane-emitting animals is one of the new challenges of this decade. However, genetic selection requires a large number of animals with records to get accurate estimated breeding values (EBV). Given that CH 4 records are scarce, the use of information on routinely recorded and highly correlated traits with CH 4 has been suggested to increase the accuracy of genomic EBV (GEBV) through multitrait (genomic) prediction. Therefore, the objective of this study was to evaluate accuracies of prediction of GEBV for CH 4 by including or omitting CH 4 , energy-corrected milk (ECM), and body weight (BW) as well as genotypic information in multitrait analyses across 2 methods: BLUP and single-step genomic BLUP (SSGBLUP). A total of 2,725 cows with CH 4 concentration in breath (14,125 records), BW (61,667 records), and ECM (61,610 records) were included in the analyses. Approximately 2,000 of these cows were genotyped or imputed to 50K. Ten cross-validation groups were formed by randomly grouping paternal half-sibs. Five scenarios were performed: (1) base scenario with only CH 4 information; (2) without CH 4 , but with information from BW, ECM, or BW+ECM only in reference population; (3) without CH 4 , but with information from BW, ECM, or BW+ECM in both validation and reference population; (4) with CH 4 information and BW, ECM, or BW+ECM information only in the reference population; and (5) with CH 4 information and BW, ECM, or BW+ECM information in both validation and reference population. As a result, for each method (BLUP, SSGBLUP), 13 sub-scenarios were performed, 1 from scenario 1, and 3 for each of the subsequent 4 scenarios. The average accuracy of GEBV for CH 4 in the base scenario was 0.32 for BLUP and 0.42 for SSGBLUP, and it ranged from 0.10 in scenario 2 to 0.78 in scenario 5 across methods. In terms of bias, the base scenario 1 was unbiased for SSGBLUP; similar results were achieved with scenario 5. Including information on ECM increased the accuracy of GEBV for CH 4 by up to 61%, whereas adding information on both traits (BW and ECM) increased the accuracy by up to 90%. Scenarios that did not include CH 4 in the reference population had the lowest correlations (0.17-0.33) with single-trait CH 4 GEBV, and scenarios with CH 4 in the reference population had the highest correlations (0.41-0.81). Thus, failure to include CH 4 in future reference populations results in predicted CH 4 GEBV, which cannot be used in practical selection. Therefore, recording CH 4 in more animals remains a priority. Finally, multiple-trait genomic prediction using routinely recorded BW and ECM leads to higher prediction accuracies than traditional single-trait genomic prediction for CH 4 and is a viable solution for increasing the accuracies of GEBV for scarcely recorded CH 4 in practice.

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

“…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.…”

“…Measurements of CH 4 and CO 2 on 650 Holstein cows recorded between 2013 and 2020 at the Danish Cattle Research Center (DCRC, Tjele, Denmark) were available. Data have been partially described previously by Zetouni et al ( 2018 ), Difford et al ( 2020 ), and Manzanilla-Pech et al ( 2020 ). Methane breath concentration (CH 4 in parts per million, ppm, referred to as MeC) was measured by the non-dispersive infrared CH 4 sensor (Guardian NG, Edinburgh Instruments Ltd., Livingston, United Kingdom), and in parallel, CO 2 was measured using the same technique (Gascard, Edinburgh Instruments Ltd., Livingston, United Kingdom) installed in each of the three automatic milking stations (AMS).…”

Investigation of β‐hydroxybutyrate in early lactation of Simmental cows: Genetic parameters and genomic predictions

“…The inclusion of genomic information in the single‐trait model led to an increase of 0.07 (Table 4 ) in predictive ability. Predictive ability was largely used as validation strategy in livestock species (Bengtsson et al., 2020 ; Lourenco et al., 2015 ; Magalhães et al., 2019 ; Manzanilla‐Pech et al., 2020 ).…”

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

“…A total of 1,962 Danish Holstein cows with weekly records on CH 4 breath concentration (7,227 records), BW (7,295 records), milk yield (7,311 records), and DMI (4,785 records) were available from the Danish Cattle Research Center (DCRC, Tjele, Denmark) and 10 commercial farms in Denmark. Data were collected between 2011 and 2016, and were previously described by Zetouni et al (2018), Difford et al (2020), andManzanilla-Pech et al (2020). Methane concentration was measured by 2 sniffer methods (Garnsworthy et al, 2012;Lassen et al, 2012): the nondispersive infrared CH 4 sensor (Guardian NG, Edinburgh Instruments Ltd.) in the research farms and some commercial farms and the portable Fourier transform infrared Gasmet DX-4000 (Gasmet; Gasmet Technologies Oy) in the commercial farms.…”

“…Somewhat counter-intuitively, researchers are then required to remove the covariance with ECM and BW through calculating the ratio or residual traits discussed above. For this reason, some authors have used MeC directly in genetic parameter estimations (Difford et al, 2020;Manzanilla-Pech et al, 2020).…”