Insights into the genetic architecture of morphological and sexually selected traits in two passerine bird species
Bibliographic record
Abstract
Knowledge about the underlying genetic architecture of phenotypic traits is needed to understand and predict their evolutionary dynamics. The number of causal loci underlying most phenotypic traits, the magnitude of their effects and their location in the genome is still largely unknown. Here we use genome-wide SNP data from two large-scale datasets on house sparrows and collared flycatchers to examine the genetic architecture of morphological (tarsus length, wing length, body mass, bill depth and bill length) and sexually selected traits (total badge size and visible badge size in house sparrows and white wing patches in collared flycatchers). Heritabilities were estimated using kinship coefficients calculated from the SNP data. The proportion of variance captured by the SNPs (SNP-based heritability) was lower in house sparrows compared to collared flycatchers, as expected given marker density (6,348 SNPs in house sparrows versus 38,689 SNPs in collared flycatchers). Chromosome partitioning analyses demonstrated that the proportion of variance explained by each chromosome was significantly positively related to the chromosome size for most traits. Finally, we only found two genome-wide significant associations with very small effect sizes; one SNP on chromosome 20 associated with bill length in house sparrows and one SNP on chromosome 4 for tarsus length in collared flycatchers. Although we cannot exclude the possibility of undetected large-effect QTL, our results support the hypothesis that variation in morphological and sexually selected traits is typically determined by many loci with small effect.
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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 | 0.000 | 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 | 0.001 |
| 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".