PSIV-29 Identifying candidate genes and QTL associated with enteric methane emission-related traits in Nellore cattle using sequencing data.
Bibliographic record
Abstract
Abstract Developing strategies to mitigate methane emissions without compromising animal productivity is crucial for promoting sustainable agricultural practices. However, enteric methane measurements at the individual animal level are expensive and labor-intensive. Therefore, using genomic approaches combined with whole-genome information may be an alternative to overcome these challenges. This study aimed to use sequencing data to carry out a genome-wide association study (GWAS) to identify genomic regions and candidate genes involved in biological processes and metabolic pathways of enteric methane emission-related traits (ME: daily methane emission, RME: residual methane emission, MY: methane yield, and MI: methane intensity). For this, 1,042 Nellore animals with phenotypic information and 2,744 imputed sequence genotypes belonging to three breeding programs from Brazil were used. After quality control filtering, a total of 2,591,217 SNPs and all 2,744 samples were retained for further analyses. In the GWAS analyses, single-trait models were fit using the single-step GBLUP approach to identify significant SNPs associated with each trait by back-solving for SNP effects. Significant associations were determined using a genome-wide Bonferroni correction based on the number of independent chromosomal segments (p < 3.55 × 10⁻⁶). For ME, a total of 27 SNPs were deemed significant, surrounding 89 positional candidate genes (within 250 kb up- and down-stream from the SNPs). For RME, 21 SNP were significant, close to 48 positional candidate genes. Regarding MY, 20 SNPs were significant, near to 76 positional candidate genes. For MI, 5 significant SNPs were located close to 15 positional candidate genes. Significant SNPs on BTA 5, 6, 8, 10, 11, 13, 19, and 27 were shared between methane emission-related traits. Mapping QTL harboring ±500 kb from the SNPs associated with methane-related traits identified overlapping genomic regions with previously reported QTL for feed efficiency, growth, and enteric methane emission. The potential candidate genes in these regions were DUOX1, DUOX2, FRMD4A, NOS2, CHRNB3, CHRNA6, CALM2, EPCAM, MSH2, MSH6, KCNK12, MUC4, MUC20, LDHAL6B, SLC20A2, LIPC, EDNRA, ACOXL, MAP4K4, IL1R1, and IL1R2. In general, these genes are involved in several biological processes and signaling pathways related to gastrointestinal motility, salivary secretion, the enteric nervous system, mucosal barrier integrity, epithelial transport, lipid metabolism, oxidative stress, cAMP, cGMP-PKG, MAPK cascade, among others. Our results highlight the complexity of methane emission as a polygenic phenotype, suggesting that bovine genetics can modulate enteric methane emissions by controlling the ruminal ecosystem. These findings could lead to advancements in sustainable beef cattle production, assisting in the development of selection strategies to mitigate greenhouse gas emissions. This work was supported by the Foundation for Research Support of the State of São Paulo (FAPESP - Grant #2017/10630–2, #2018/20002–6, #2023/17818-8, and #2024/16663-3).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".