Whole genome fine mapping of quantitative trait loci for ultrasound and carcass merit traits in beef cattle
Bibliographic record
Abstract
Nalaila, S. M., Stothard, P., Moore, S. S., Wang, Z. and Li, C. 2011. Whole genome fine mapping of quantitative trait loci for ultrasound and carcass merit traits in beef cattle. Can. J. Anim. Sci. 91: 61-73. Quantitative trait loci (QTL) mapped to large chromosomal regions have limited utility as DNA markers for marker-assisted selection (MAS) and are less informative as a reference for the identification of the underlying causative quantitative trait nucleotides (QTN). The objective of this study was to conduct a whole genome fine mapping of QTL for ultrasound and carcass merit traits in beef cattle using a greater density of single nucleotide polymorphism (SNP) markers, and to identify SNP markers within the QTL regions that are associated with the traits. A total of 418 steers from 28 sires were used in this study with nine ultrasound and seven carcass merit traits that were collected as part of a feedlot trial conducted from 2003 to 2005 at the University of Alberta Kinsella ranch. Sires and their progeny were genotyped for a total of 4592 SNP markers distributed across all 29 bovine autosomes (BTA). Across-family analyses detected 12 QTL for five ultrasound traits on nine chromosomes and 18 QTL for six carcass merit traits on 10 chromosomes (P<0.05). Within-family analyses identified 78 significant QTL for nine ultrasound and seven carcass merit traits (P<0.01). The use of a denser panel of SNP markers allowed fine mapping of QTL to smaller chromosomal regions ranging from 0.6 to 11 cM compared with relatively larger QTL regions of 4 to 24 cM reported in previous studies. Furthermore, single SNP marker association analyses identified 22 SNPs that were significantly associated with three ultrasound and four carcass merit traits under 12 QTL regions (P<0.05). These identified SNP markers significantly associated with the traits under the fine mapped QTL regions provide genomic tools for potential application of MAS and a reference to assist with the identification of QTN causing variations in ultrasound and carcass merit traits in beef cattle.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".