52. Transferability of Methane Emission SNP Effects from Dairy to Beef Cattle Populations.

Clicks: 13
ID: 330114
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #427 of 847 articles by views in italian journal of animal science

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 847 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Transferring single-nucleotide polymorphism (SNP) effects across cattle breeds has often proven to be unreliable for a range of traits, even when using higher-density SNP panels. However, methane production (MeP) may exhibit greater portability across breeds because it has not been subjected to the prolonged, intense selection applied to many economically important traits. The objective of this study was to predict direct genomic values (DGVs) for MeP in a crossbred beef population using SNP effects estimated in a dairy population and to assess correspondence between DGVbased groupings and observed emission levels. The dairy training set comprised 648 Canadian firstparity Holstein cows between 110 and 210 days in milk, measured with a GreenFeed system from 2017 to 2025 for 5 consecutive days, 3-4 times per day. Outlier records 3 standard deviations from the mean were removed, and records were averaged by day and then by week to yield a single record per cow. The pedigree contained 6,687 animals across 12 generations, out of which 2,787 animals were genotyped using the Illumina 50k SNP panel, including the 648 cows recorded for MeP. Genomic breeding values were estimated by fitting a linear animal model using single-step GBLUP, and SNP effects were back-solved using the software POSTGSF90. The target beef population consisted of 77 Angus x Simmental cows in their final trimester of gestation (average parity = 3). Beef cow MeP was measured using two voluntary GreenFeed systems over two 7-day collection periods. After excluding cows with insufficient MeP records (< 30) and those>3 standard deviations from the mean, 73 animals remained. Records were averaged by day and then across all days to create 1 record per cow. Cows were genotyped using the Illumina 100k SNP panel; 29,434 SNPs common to both panels were used for DGV prediction. DGVs were computed as the sum over loci of genotype dosages multiplied by their corresponding SNPeffect estimates from the Holstein population and partitioned into three tertiles (lowest, middle, highest values). When comparing DGV tertiles to MeP tertiles, out of the low-emitting MeP cows, 12/24 (50%) cows were correctly classified as low-emitters by their DGVs, while 6/24 (25%) were misclassified as high-emitters. Out of the high-emitting MeP cows, 9/24 cows (37.5%) were correctly classified as high-emitters, while 4/24 (16.7%) were misclassified as low-emitters. The study shows that there is moderate ability to phenotypically classify the 1/3 lowest or 1/3 highest MeP Angus x Simmental cows using about 30k SNPs from a Holstein population. However, the beef dataset size used in this investigation is small and limits broader inferences. Ongoing work will compare DGVs to adjusted MePs for known sources of environmental variation and will use larger datasets to validate and refine these preliminary findings.
Reference Key
openalex_W7214703189 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Timothy Houghton, Samla Marques Freire Cunha, Madeline J McLennan, Katie M Wood, Ángela Cánovas, F. Miglior, Christine F. Baes, Flávio Schramm Schenkel
Are you one of these authors? Claim your profile
Journal italian journal of animal science
Year 2026
DOI
10.1093/jas/skag272.090
URL
https://doi.org/10.1093/jas/skag272.090
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.