Genomic regions affecting perinatal and early life survival in dairy calves
Bibliographic record
Abstract
Calves that survive and thrive are important to the productivity of dairy herds through their role as potential herd replacements or as a source of livestock trading income. Conversely, calf losses are costly, leading to poorer farm productivity and welfare outcomes. Stillbirths (SB) are calvings where the calf dies near birth. Preweaning mortality (PWM) describes calves that are born alive but die before weaning. Improving SB and PWM can be achieved through conventional quantitative trait selection strategies, but these traits, especially SB, can sometimes occur because of a single large-effect recessive deleterious mutation. Identifying the causal variant enables carrier screening, improves predictions by reducing the risk of eroding linkage disequilibrium between tagging SNP markers and the variant, and enables the variant to be added to new genotyping panels. The dataset used in this study is ideally suited to exploring deleterious loci because both dead and alive calves are included, so if there were early life deaths because of a single deleterious variant, we may be able to detect them. Furthermore, because these diseases generally arise due to recent inbreeding, identification of runs of homozygosity around these loci could be useful to identify recessive lethal haplotypes and their common ancestral origin. The objective of this study was to identify regions of the genome that were associated with SB and PWM, as well as runs of homozygosity (ROH), in order to reveal whether region-specific inbreeding was negatively affecting these traits. We first conducted a GWAS using SB and PWM adjusted phenotypes for 11,525 Holstein and 1,272 Jersey calves and their genotypes imputed to the whole genome sequence. Phenotypes were adjusted for herd-year-season, sex, parity and calving ease. Our GWAS results suggest that SB and PWM are polygenic traits, as 198 significant variants were detected in many independent regions. The maximum effect sizes resulted in 14% more SB in Holstein, 20% more SB in Jersey, and 4% higher PWM in Holstein calves. To explore the impact of homozygous genomic regions that could arise through inbreeding (ROH) on SB and PWM, we used Haplofinder to scan for unfavorable haplotypes. We found 243 haplotypes of about ∼2 Mbp in length that were associated with increased SB and PWM when found in the homozygous state. Of these, 4 haplotypes also contained a significant GWAS variant, with nearby genes including the myostatin gene (MSTN), BIN1, and FER1L5. We found regions on BTA4, BTA19, and BTA22 that were previously reported to be associated with SB and a new region of BTA2 where ROH had deleterious consequences. Genes of interest that are located in these regions include MKS1 (linked to lethal malformation in infants), MYH3 (linked to growth, carcass traits, and embryonic development), FHIT (important for energy transfer, signaling, and stress response) and THSD7A (linked to cytoskeletal organization). These results contribute to the growing understanding of the genetic controls influencing early life survival in dairy calves.
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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 | 0.001 |
| 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".