119 Pedigree and Population-Based Genomic Inbreeding Trends Over Time in Five Commercial Swine Breeding Populations
Bibliographic record
Abstract
Abstract Controlling inbreeding within a population is critical to maintain additive genetic variance and reduce negative consequences such as inbreeding depression in small, closed herds. Some breeding programs may utilize subjective standard operating procedures (SOPs) such as setting the minimum number of boars, limiting the number of matings per boar, and limiting the relationships among active boars. Software programs such as Matesel can control inbreeding within a population more precisely using evolutionary algorithms by optimizing the mating and selection process to constrain inbreeding at a given target degree or other metric specified (e.g., coancestry). The main problem has been the choice of what relationship matrix to use in constraining inbreeding in these programs. Furthermore, there are many different genomic inbreeding metrics that can be used to quantify inbreeding rates over time. The objective of the current research was to investigate the trends in the pedigree [PED, (A diagonal – 1) * 100] and two population-based genomic inbreeding metrics. One based on expected homozygosity (2pq, Ghom) and one based on drift (squared allele frequency changes, Gdrift). Data included nucleus animals born from 2017 through 2022 from five different genetic lines (lines A-E). Most parents and male selection candidates were genotyped using the commercial 50k SNP panel from GeneSeek. Pedigree was traced back at least 3 generations. A total of 80k animals were sampled from the total number of genotyped animals for computational reasons across line and birth year. Results show that in general, the maternal lines (A and B) showed the least rate of inbreeding over time (0.30 and 0.43% / year for A and B, respectively), while terminal lines were greater, especially line D (0.36 to 0.76% / year). Pedigree inbreeding in 2022 was between 1.48% (line A) to 3.80% (line D) after subtracting the average pedigree inbreeding in 2017. Population-based genomic measures of inbreeding showed a very small difference in line A (0.09%), larger Gdrift than Ghom in line B (1.37%), and larger Ghom than Gdrift in lines C, D, and E (2.87% to 4.38%). In general, the trend for PED and Gdrift was very similar for all lines, while Ghom tended to increase rapidly out of control in the three terminal lines (C-E) in later years. Future research will focus on integrating results from the diagonals of a variety of G matrices on an individual basis (e.g., VanRaden 1 and 2, runs-of-homozygosity, and a linkage-analysis matrix). Results showed the pedigree and genomic drift trends over time were similar, however, the genomic homozygosity trend was much greater in terminal lines.
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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 | 0.001 | 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) | 0.000 | 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".