BREEDING ▶▶▶


Selecting for low emission cows


Methane emission is a heritable trait, which makes selection for lower emitting animals possible. Recent research with Danish Holstein dairy cattle shows the impact of a multi-trait approach to achieve high selection accuracy of lower methane-emitting animals.


BY MATTHEW WEDZERAI S


tudies show that due to the limited number of cows with methane (CH4


) records, the accuracies of


genomic estimated breeding values (GEBV) are low. CH4


require a considerable number of cows with CH4 records in


the reference population to accurately estimate GEBV. Re- searchers from Aarhus University carried out a study on the use of information of routinely recorded and highly correlat- ed traits with CH4


multi-trait (genomic) prediction approach.


The study They focused their study on evaluating the 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 multi-trait analyses across two methods: BLUP and single-step genomic BLUP (SSGBLUP). A total of 2,725 Danish Holstein cows with CH4


concentration


in breath (14,125 records), BW (61,667 records) and ECM (61,610 records) were included in the analyses. Approxi- mately 2,000 of these cows were genotyped. To find the best combination of traits in both the reference and validation populations, the following five scenarios were performed:


• Scenario 1: base scenario with only CH4 • Scenario 2: without CH4


information , but with information from BW, ECM


or BW+ECM in reference population only • Scenario 3: without CH4


, but with information from BW, ECM


or BW+ECM in both validation and reference populations • Scenario 4: with CH4


information in the reference population only • Scenario 5: with CH4


information and BW, ECM or BW+ECM information and BW, ECM or BW+ECM information in both validation and reference populations.


Consistency of predictor traits To determine the benefit of including genotypic information in the prediction of the EBV and the consistency of the


20 ▶ DAIRY GLOBAL | Volume 8, No. 1, 2021 is a scarcely recorded trait, which means it would


predictor traits across methods, two methods were tested: (1) the BLUP, which used pedigree-derived additive genetic rela- tionships to estimate an EBV for each animal in the pedigree, and (2) the SSGBLUP, which allowed the addition of pheno- typic information of non-genotyped animals into the genom- ic BLUP method by combining in a single step the genomic relationship matrix (GRM) with the pedigree relationship ma- trix into a new relationship matrix used to obtain the GEBV. The researchers found that the average accuracy of GEBV for CH4


in the base scenario was 0.32 for the BLUP method and


0.42 for the SSGBLUP method. Comparing across multi-trait scenarios, the accuracy ranged from 0.10 to 0.72 for the BLUP method and from 0.12 to 0.75 for the SSGBLUP method. They concluded that multi-trait prediction performs similarly (or consistently) across the two methods and on average better than the single-trait scenario.


to increase the accuracy of GEBV through a


Prediction accuracy of GEBV “Multiple-trait genomic prediction using routinely recorded BW and ECM leads to higher prediction accuracies than tradi- tional single-trait genomic prediction for CH4


mation on ECM increases the accuracy of GEBV for CH4 by up


to 61%, whereas adding information on both traits (BW and ECM) increases the accuracy by up to 90%,” remarked the researchers. On the other hand, scenarios that did not include CH4


in the reference population had the lowest correlations (0.17–0.33) with single-trait CH4


GEBV (base scenario), and


est correlations (0.41–0.81). Thus, failure to include CH4 ture reference populations results in predicted CH4 GEBV,


which cannot be used in practical selection. This means recording CH4


in more animals is a priority.


Which predictor trait is better? Across sub-scenarios it was found that adding information on ECM better improved the accuracy of prediction of CH4


com-


pared with adding BW. This was attributed to the relatively higher genetic correlation of ECM to CH4


compared to BW. It


was clear that the magnitude of genetic correlations between the traits is the key factor determining the increase in accura- cy. The two traits were reported as being good predictor traits for CH4


. In addition, it was also observed that sub-scenarios


with BW and ECM information in both reference and valida- tion populations had higher prediction accuracies than the scenarios having the two traits on the reference population only.


scenarios with CH4 in the reference population had the high- in fu-


. Including infor-


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