Multitrait genomic prediction of methane emissions in Danish Holstein cattle

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 CH₄ records are scarce, the use of information on routinely recorded and highly correlated traits with CH₄ 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 CH₄ by including or omitting CH₄, 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 CH₄ 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 CH₄ information; (2) without CH₄, but with information from BW, ECM, or BW+ECM only in reference population; (3) without CH₄, but with information from BW, ECM, or BW+ECM in both validation and reference population; (4) with CH₄ information and BW, ECM, or BW+ECM information only in the reference population; and (5) with CH₄ 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 CH₄ 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 CH₄ 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 CH₄ in the reference population had the lowest correlations (0.17–0.33) with single-trait CH₄ GEBV, and scenarios with CH₄ in the reference population had the highest correlations (0.41–0.81). Thus, failure to include CH₄ in future reference populations results in predicted CH₄ GEBV, which cannot be used in practical selection. Therefore, recording CH₄ 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 CH₄ and is a viable solution for increasing the accuracies of GEBV for scarcely recorded CH₄ in practice.