PSVI-11 Genome-Wide Association Study for 4 Behavior Traits in a Population of Labrador Retrievers Bred as Guide Dogs
Bibliographic record
Abstract
Abstract The objective of this study was to assess the genetic architecture and investigate genomic regions associated with Body Handling, Harness sensitivity, Self-modulation, and Noise sensitivity in Labrador Retrievers used as guide dogs. A single-step genome-wide association study (ssGWAS) was used for this purpose. Phenotypic data were collected in 4,841 Labrador Retrievers with ages ranging from 3 months to 2.5 years, for all four traits. Phenotypes were selected from the Behavior Checklist, which is a scoring tool used by a skilled scorer to describe aspects of behavior observed during a variety of assessments including formal tests and while training or observing another handler working with the dog. The pedigree file contained 23,593 animals with birth years ranging from 1991 to 2019 from two populations with related ancestors. Genomic data were available for 457 individuals and were obtained by selecting 250K SNPs from whole genome sequences. Associations were calculated as the percentage of variance explained by windows of 80 adjacent SNPs. SNP variances were calculated based on SNP effects that were obtained by back-solving genomic estimated breeding values obtained with the single-step GBLUP method in a genomic selection program with the same population. Additionally, the genomic relationship matrix was modified under a weighted ssGBLUP (wssGBLUP) approach that allowed SNPs to have different distributions. Manhattan plots were generated with the variance explained by SNP windows for 3 iterations of wssGBLUP. After visual inspection of the Manhattan plots, the R package GALLO was used to annotate the genes located within the peaks that explained 0.6% or more of the genetic variance. The maximum amount of variance explained by a window of 80 SNPs was 1.1% for Body Handling. This trait presented three other regions explaining more than 0.6% of the genetic variance. Harness sensitivity presented four peaks above the threshold, with the major one explaining 0.75% of the genetic variance. Self-modulation and Noise sensitivity both had their major peak explaining 0.95% of the genetic variance and presented four and three regions above the threshold, respectively. The genes annotated in the candidate regions were not specifically related to behavior, nor were they involved in any pathway with biological relevance to the phenotypes. Moreover, some of the regions were not annotated, and no associated genes were found. The traits studied in this project were shown to be polygenic and complex, and that explains the lack of major genes. It is expected that the quality of associations will improve as the sample size increases. The next steps of this project are to increase the number of genotyped animals, increase the number of markers, and adopt alternative annotation and enrichment tools for the post-GWAS analysis.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".