Bibliographic record
Abstract
The dairy sector has a big impact on food production for the growing world population and contributes substantially to the world economy. In order to produce food in a sustainable way, dairy cows need to be able to produce milk without problems and as long as possible. Therefore, breeding programs focuses on improvement of important traits for dairy cows. In order to improve desirable traits and obtain genetic gain there is a constant need for optimization of breeding programs and search for useful parameters to include within breeding programs. Over the last several decades, breeding in dairy cattle mainly focused on production and fertility traits, with less emphasis on health traits. Health problems, however, can cause substantial economic losses to the dairy industry. The economic losses, together with the rising awareness of animal welfare, increased herd size, and less attention for individual animals, have led to an increased need to focus more on health traits. Longevity is strongly related to disease resistance, since a more healthy cow will live a longer productive life (longevity). The identification of biomarkers and the detection of genes controlling health and longevity, would not only greatly enhance the understanding of such traits but also offer the opportunity to improve breeding schemes. The objectives of this thesis therefore were 1) to find an easy measurable disease resistance related biomarker in dairy cows, 2) identify the relation between antibodies and longevity, 3) identify genomic regions that are involved with antibody production/expression. In this thesis antibodies are investigated as parameter for longevity. Antibodies might be a novel parameter that enables selection of cows with an improved ability to stay healthy and to remain productive over a longer period of time. In this thesis antibodies bindiging the naive antigen keyhole limpet hemocyanin (KLH) were assumed to be natural antibodies. Antibodies binding bacteria-derived antigens lipoteichoic acid (LTA), lipopolysaccharide (LPS) and peptidoglycan (PGN) were assumed to be specific antibodies. In chapter 2 it was shown that levels of antibodies are heritable (up to h2 = 0.23). Additionally, antibody levels measured in milk and blood are genetically highly correlated (± 0.80) for the two studied isotypes (IgG and IgM). On the other hand, phenotypically, natural antibodies (from both IgG and IgM isotype) measured in milk cannot be interpreted as the same trait (phenotypic correlation = ± 0.40). In chapter 3 and 4 it was shown that levels of antibodies (both natural-and specific antibodies) showed a negative relation with longevity: first lactation cows with low IgM or IgG levels were found to have a longer productive life. When using estimated breeding values for longevity, only a significant relation was found between natural antibody level (IgM binding KLH) and longevity. Lastly chapter 5 reports on a genome-wide-association study (GWAS), to detect genes contributing to genetic variation in natural antibody level. For natural antibody isotype IgG, genomic regions with a significant association were found on chromosome 21 (BTA). These regions included genes have impact on in isotype class switching (from IgM to IgG). The gained knowledge on relations between antibodies and longevity and the gained insight on genes responsible for natural antibodies level make antibodies potential interesting biomarkers for longevity.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".