Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis

• Published in: Scientific reports Vol. 12; no. 1; pp. 12352 - 10
• Main Author: Ghavi Hossein-Zadeh, Navid
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19-07-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/208/1348; 631/208/729; Animals; Cattle - genetics; Dairy cattle; Diet - veterinary; Emissions; Estimates; Female; Genetic diversity; Heritability; Humanities and Social Sciences; Lactation - genetics; Meta-analysis; Methane; Methane - metabolism; Milk; Milk - chemistry; Milk Proteins - metabolism; multidisciplinary; Oils & fats; Phenotype; Proteins; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-16778-z

The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits [methane yield (METY), methane intensity (METINT), and methane production (METP)] in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative (− 0.433 and − 0.262, respectively). Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans.