Genetic parameters of mid-infrared-predicted methane production and its relationship with production traits in...

Genetic selection to reduce methane production from dairy cows may be an efficient way for reducing the impact of dairy production on climate change. In this study genetic parameters and genomic regions associated with 2 commonly used daily methane features (predicted daily methane emission [PME; g/d]), and log-transformed predicted methane intensity (LMI = log [PME/milk yield (kg)]) were investigated. The PME (g/d) data, predicted using routinely recorded milk mid-infrared spectra, collected between 2007 and 2023 on 285,530 first-parity (1,920,130 test-day records), 224,643 second-parity (1,516,843 test-day records), and 160,226 third-parity (1,072,725 test-day records) Holstein cows distributed in 1,520 herds in the Walloon region of Belgium were used. Data of 565,049 SNPs, located on 29 Bos taurus autosomes (BTA), on 7,375 animals (1,798 bulls) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated through a single-step genomic BLUP approach. The proportion of genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼212 kb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Mean (SD) daily PME per cow was 324.3 (66.88) g/d, 355.0 (68.75) g/d, and 367.1 (71.42) g/d, whereas the mean daily LMI was 2.64 (0.36), 2.61 (0.39), and 2.58 (0.40) for the first, second, and third lactation, respectively. Mean (SD) h² estimates for PME were 0.22 (0.05), 0.20 (0.05), and 0.21 (0.05) and for LMI were 0.25 (0.05), 0.23 (0.05), and 0.22 (0.05) in the first, second and third lactation, respectively. Average genetic correlations (SD) estimated between PME and LMI were 0.53 (0.04), 0.46 (0.12), and 0.43 (0.16) in the first, second, and third lactation, respectively. The genetic correlations estimated between PME and production traits, including milk yield (MY), fat percentage (FP), protein percentage (PP), milk urea concentration (MU), and SCS, ranged from -0.12 (MY) to 0.42 (FP), -0.09 (MY) to 0.47 (FP), and -0.07 (MY) to 0.43 (FP) for the first, second, and third lactations, respectively. For LMI, the estimated genetic correlations ranged from -0.89 (MY) to 0.56 (FP), -0.91 (MY) to 0.55 (FP), and -0.90 (MY) to 0.50 (FP) for the first, second, and third lactations, respectively. Genome-wide association analyses identified 4 genomic regions (BAT1 144.38-144.47 Mb and BAT14 1.52-2.15 Mb, BAT14 2.19-2.57 Mb and BAT14 2.67-2.98 Mb) harboring genes including the SLC37A1 (BTA1), AHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, GML, LYPD2, and JPK (BTA14) that were associated with the studied methane features. The findings of this study help to unravel the genomic background of methane emissions and can be used for the future implementation of genomic evaluation of methane emissions in Walloon Holstein cows.