Análises genética e genômica de características longitudinais em gado de leite
Bibliographic record
Abstract
Traits with multiple phenotypic values taking over time are termed longitudinal traits, e.g., milk production. Despite of the great importance of analyzing these traits taking into account their time- dependent nature, the majority of studies on longitudinal traits have converted the repeated records for each animal into a single measure (e.g., average over all time points or accumulated yield), which does not allow any inference about the trait over time. Therefore, the general objective of this thesis was to better understand the genetic and genomic aspects of longitudinal traits over time in dairy cattle. Simulated and real datasets (from Brazilian Gyr and Canadian Ayrshire, Holstein and Jersey dairy cattle breeds) were used in this research. First, breeding values were predicted (EBVs) using a multiple-trait random regression model (RRM) combining Legendre orthogonal polynomials and linear B-splines to simultaneously describe the first and second lactation of Gyr Dairy cattle. Subsequently, genomic predictions, genome-wide association analyses were performed for milk, fat and protein yields, and somatic cell score from the first three lactations of the Canadian dairy breeds using different methodologies, including two-step and single-step genomic best linear unbiased prediction (GBLUP). The performance of the most used deregression methods for non-longitudinal traits for the deregression of cows’ and bulls’ EBVs for using in genomic evaluation of longitudinal traits was also evaluated, using RRMs and the Canadian Jersey data. In addition, the impact of including information from bulls and their daughters in the training population of multiple-step genomic evaluations was investigated using a simulated population. Combining different functions to model the fixed and random effects in multiple-trait RRMs seems to be a good alternative (based on the goodness-of-fit of model, breeding values and variance component estimates) for genetic modeling of lactation curves in dairy cattle, as shown here for Gyr cattle. Deregressed longitudinal EBVs obtained using well established methods of deregression for non-longitudinal traits can be used for genomic prediction of longitudinal traits. Furthermore, removing the parent average and the genotyped daughters’ average from the deregressed EBVs can increase the reliability of genomic estimated breeding values (GEBVs). In Holstein, the reliability of GEBVs predicted using the RRM was in general lower than the reliability from the accumulated 305-d model when using the two-step GBLUP method, however, the RRM provided less biased GEBVs compared to the accumulated 305-d model. The use of single-step GBLUP to predict GEBVs for longitudinal traits based on RRMs increased the reliability and reduced bias of GEBVs compared to traditional parent average, in the Canadian Ayrshire, Holstein, and Jersey breeds. Different genomic regions associated with the analyzed traits were identified for different lactation stages, supporting differential gene control across lactation stages. For all Canadian breeds, the pattern of the effect of several single nucleotide polymorphisms associated with the analyzed longitudinal traits changed over time. In addition, prospective candidate genes with potential different patterns of expression over time were identified in putative chromosomal regions. The findings described in this thesis will contribute to advance the knowledge on the genomic expression and prediction of breeding values for longitudinal traits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".