Feasibility of multi-trait scenarios Based on the accuracies obtained in this study across scenarios, three scenarios (scenario 3, 4, and 5) stood out.


Scenario 3 In this scenario without CH4


, all the genetic variation of CH4


comes from ECM and BW, implying that a reduction in CH4 will in practice come from selecting smaller animals that pro-


duce less milk, which is counterintuitive to the aim of profitable dairy production – making the scenario not ideal.


Scenario 4 Conversely, scenario 4 with CH4


information and predictor


traits included only in the reference population has the full ge- netic variation of CH4


, including the fraction that is not solely


explained by ECM and BW. “In principle, in scenario 4 with CH4 information included, using all traits in a selection index can


achieve selection for lower emitting animals when increasing or maintaining milk production (i.e. improved methane inten- sity [CH4


/L of ECM]). This scenario is the most ideal multi-trait


combination since it is the most closely aligned to genomic selection schemes in practice and has the highest correlations (0.72–0.81) with the base scenario,” said the scientists.


Scenario 5 It is interesting to note that scenario 5, which has CH4


informa-


tion and predictor traits (ECM and BW) in both the reference and validation populations, achieved the highest prediction


accuracies. However, because GEBV are usually predicted for young animals before they have their own phenotypes, sce- nario 5 is not closely aligned with genomic selection schemes in practice. The researchers suggested that this scenario could be important when trying to predict CH4


in second or later lactation cows (with ECM and BW information available).


Concluding remarks This study demonstrated that a multi-trait genomic prediction leads to higher prediction accuracy than traditional single-trait genomic prediction, particularly when predictor traits are high- ly genetically correlated with the goal trait. It can be concluded that the most feasible multi-trait scenario in terms of feasibility when predicting CH4


for young animals is the scenario with


CH4, ECM and BW information in the reference population. This scenario also proved to be the one most genetically correlated


with the base scenario. Although the scenario which has CH4 information and predictor traits (ECM and BW) in both the ref- erence and validation populations achieved the highest predic- tion accuracies, the researchers think it more suitable when predicting CH4


in second or later lactation cows (with ECM and BW information available).


This article is based on the original article by Manzanil- la-Pech C.I.V., D. Gordo, G.F. Difford, P. Lovendahl and J. Las- sen. 2020. Multitrait genomic prediction of methane emis- sions in Danish Holstein cattle, Journal of Dairy Science Vol 103, 2020. https://doi.org/10.3168/jds.2019-17857.


Figure 1 - Accuracies of prediction of genomic EBV for methane, averaged across 10 validation groups per sub-scenario


for BLUP and single-step genomic BLUP (SSGBLUP). CH4 = methane concentration, OR = only reference, VR = validation + reference.


1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00


BLUP SSGBLUP


▶ DAIRY GLOBAL | Volume 8, No. 1, 2021


21


ACCURACY


1. CH4 2a. BW_OR 2b. ECM_OR 2c. BW+ECM_OR 3a. BW_VR 3b. ECM_VR 3c. BW+ECM_VR 4a. CH4+BW_OR 4b. CH4+ECM_OR 4c. CH4+BW+ECM_OR 5a. CH4+BW_VR 5b. CH4+ECM_VR 5c. CH4+BW+ECM_VR


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