137 Longitudinal Heritability of Ocular Microbiota in Preweaned Beef Cattle
Bibliographic record
Abstract
Abstract The bovine ocular microbiome is of interest because of its potential role in ocular disease, such as infectious bovine keratoconjunctivitis. The ocular bacterial community of 223 pre-weaned beef calves from a single cohort were sampled four times; day 0, day 21, day 41, and day 139 (mean calf ages 65, 86, 99, and 204 days, respectively). The bacterial community was phenotyped using the V4 region of the 16S rRNA gene and were grouped into amplicon sequence variants (ASV) which were taxonomically classified at the phylum and family level. Heritability was estimated for the log transformed relative abundance of each phylum, family, and ASV at each time point using ASReml v. 4.2. The model included calf age at time of sampling, calf sex, pinkeye vaccination treatment group, breed fractions, and retained heterosis as fixed effects. The random effect of animal had a (co)variance structure given by an H matrix comprised of 3,025 animals of which 1,207 were genotyped including all of the sampled animals. Heritability estimates ranged from 0.16 to 0.95 at the ASV level, 0.18 to 0.91 at the family level, and 0.18 to 0.81 at the phylum level. Standard errors were approximately 0.20 on average. The number of features (ASV, family, or phylum) with estimates greater than their standard error changed across time points (Table 1). Overall, time point 1 had the largest number of features with estimates greater than their standard error, followed by time point 4. Time point 3 had a greater number ASV with estimates greater than their standard error than time point 2; however, time point 2 had greater numbers of families and phyla with estimates greater than their standard errors. This trend likely stems from the fact that time points 1 and 4 had the greatest diversity of features while time point 2 had the least diversity. Average Chao1 index diversity estimates of ASV were 144.6, 28.1, 64.7, and 194.6, for time points 1 through 4, respectively. We hypothesize this is due to the perturbation of the ocular microbiome at sampling and the fact that overall diversity in the microbiome recovered by the final sampling, although, species composition differed compared with time point 1. While no ASV, family, or phylum had a heritability estimate greater than its standard error across all time points, certain features had heritabilities greater than their standard errors at multiple time points. The phylum Armatimonadota had heritability estimates greater than their standard error at time points 1, 2, and 4 (0.52, 0.18, and 0.37, respectively). Additionally, an ASV of the Streptococcus genus had estimates of 0.26, 0.45, and 0.26 at time points 1, 2, and 3, respectively. These estimates demonstrate that certain features of the ocular microbiome are influenced by host genetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".