Evolution of Large Polymorphic Inversions in a Panmictic Songbird
Bibliographic record
Abstract
Chromosomal inversions have long been appreciated as an important source of genetic diversity, local adaptation, and speciation. However, selection pressures maintaining ancestral and derived alleles at high frequency over extended periods of time remain poorly characterized. Using genome-wide single-nucleotide polymorphism markers and shared barcodes of linked-read sequences from 20 wild and 7 captive zebra finches Taeniopygia guttata, we systematically scanned a high-quality zebra finch reference genome and identified all large polymorphic inversions that segregate at high minor allele frequencies. Apart from the known polymorphic inversions on chromosomes Tgu5, Tug11, Tgu13, and TguZ, we characterized two inversions on microchromosomes Tgu26 and Tgu27 and identified another eight putative inversions, located mostly on microchromosomes and ranging in size from 0.42 to 65.22 Mb. Population genomic analyses show that most of the six bona fide inversions are complex, containing short nested inversions. The early inversions emerged an estimated 0.6 to 2.2 million years ago and segregate at relatively high frequencies in the wild (minor haplotype frequency range: 0.289 to 0.429). Based on fitness-related measures of about 5,000 captive zebra finches, we conclude that three of the inversion polymorphisms (Tgu11, Tgu27, and TguZ) may be maintained by net heterosis. In the youngest of the six inversions (Tgu13), the derived haplotype showed weak positive additive effects on various fitness components. In combination with previous discoveries, we provide a comprehensive overview of the genomic distribution and evolutionary dynamics of large polymorphic inversions in the panmictic zebra finch. Our findings highlight (i) that microchromosomes may harbor quite a few additional inversion polymorphisms, (ii) that most of the inversions contain smaller nested or overlapping inversions, and (iii) that inversions were most likely maintained by weak heterosis with small fitness effects requiring large sample sizes to be detected.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".