Using mid-infrared spectroscopy to increase GWAS power to detect QTL associated with blood urea nitrogen
Bibliographic record
Abstract
Blood urea nitrogen (BUN) is an indicator trait for urinary nitrogen excretion. Measuring BUN level requires a blood sample, which limits the number of records that can be obtained. Alternatively, BUN can be predicted using mid-infrared (MIR) spectroscopy of a milk sample and thus records become available on many more cows through routine milk recording processes. The genetic correlation between MIR predicted BUN (MBUN) and BUN is 0.90. Hence, genetically, BUN and MBUN can be considered as the same trait. The objective of our study was to perform genome-wide association studies (GWAS) for BUN and MBUN, compare these two GWAS and detect quantitative trait loci (QTL) for both traits, and compare the detected QTL with previously reported QTL for milk urea nitrogen (MUN). The dataset used for our analyses included 2098 and 18,120 phenotypes for BUN and MBUN, respectively, and imputed whole-genome sequence data. The GWAS for MBUN was carried out using either the full dataset, the 2098 cows with records for BUN, or 2000 randomly selected cows, so that the dataset size is comparable to that for BUN. The GWAS results for BUN and MBUN were very different, in spite of the strong genetic correlation between the two traits. We detected 12 QTL for MBUN, on bovine chromosomes 2, 3, 9, 11, 12, 14 and X, and one QTL for BUN on chromosome 13. The QTL detected on chromosomes 11, 14 and X overlapped with QTL detected for MUN. The GWAS results were highly sensitive to the subset of records used. Hence, caution is warranted when interpreting GWAS based on small datasets, such as for BUN. MBUN may provide an attractive alternative to perform a more powerful GWAS to detect QTL for BUN.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".