Pedigree-based inbreeding coefficient explains more variation in fitness than heterozygosity at 160 microsatellites in a wild bird population
Bibliographic record
Abstract
Although the pedigree-based inbreeding coefficientFpredicts theexpectedproportion of an individual's genome that is identical-by-descent (IBD), heterozygosity at genetic markers captures Mendelian sampling variation and thereby provides an estimate ofrealizedIBD. Realized IBD should hence explain more variation in fitness than their pedigree-based expectations, but how many markers are required to achieve this in practice remains poorly understood. We use extensive pedigree and life-history data from an island population of song sparrows (Melospiza melodia) to show that the number of genetic markers and pedigree depth affected the explanatory power of heterozygosity andF, respectively, but that heterozygosity measured at 160 microsatellites did not explain more variation in fitness thanF. This is in contrast with other studies that found heterozygosity based on far fewer markers to explain more variation in fitness thanF. Thus, the relative performance of marker- and pedigree-based estimates of IBD depends on the quality of the pedigree, the number, variability and location of the markers employed, and the species-specific recombination landscape, and expectations based on detailed and deep pedigrees remain valuable until we can routinely afford genotyping hundreds of phenotyped wild individuals of genetic non-model species for thousands of genetic markers.
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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".