Author response: An integrative genomic analysis of the Longshanks selection experiment for longer limbs in mice
Bibliographic record
Abstract
Humans have been making use of artificial selection for thousands of years. Much of what we eat, for example, from beef to poultry to cereals, comes from a collection of organisms with genomes that have been completely reshaped by the actions of generations of farmers and breeders. Yet, despite decades of research in evolutionary biology, it remains difficult to predict what will happen to an organism’s genes when selective pressure is applied. Traits that at first seem simple often arise from layers upon layers of complexity. It can take hundreds if not thousands of tiny changes to many genes, plus just the right alterations to a few key ones, to have a desired effect on a single trait. Also, if you consider that often the genomes of the starting population are unknown and that many traits are under simultaneous selection in wild populations, it becomes clear why many questions remain unanswered. Castro, Yancoskie, et al. have analyzed an on-going laboratory experiment dubbed “the Longshanks experiment” to explore how an animal’s genome changes under strong selection. Over five years, two independent populations of mice were selectively bred to have longer legs. In each generation, the mice were measured and those with the longest tibia – a bone in the shin – relative to their body mass were allowed to breed. Genetic data were also recorded. Now, Castro, Yancoskie, et al. have analyzed the genetic data up to the first 17 generations in the Longshanks experiment to find out what kind of genes may be relevant to the 13% increase in leg length seen in the mice so far. This analysis uncovered many genes, possibly thousands, all acting in concert to increase tibia length. But the gene with the largest effect by far was a key developmental gene called Nkx3-2. Mutations in this gene cause a disease called spondylo-megaepiphyseal-metaphyseal dysplasia in people, which can lead to long limbs and a short trunk. Although inactivating this gene completely in mice is lethal, among the founding group of mice in the Longshanks experiment was a rare copy of Nkx3-2. This copy of the gene worked perfectly in all tissues with the exception of the legs, where a genetic switch that controls it was left in the “off” state. Mice inheriting this short stretch of DNA ended up having longer tibia. In effect, these mice held the winning ticket in the genetic lottery that was the Longshanks experiment. Even in highly controlled experiments that record a great deal of information about the organisms involved, predicting how the genome will change and which genes will be involved is not a straightforward question. Finding out how the genome may change in response to selection is important because scientists can build better models to help with breeding farm animals or crops, or with predicting the consequences of climate change. As a result, experiments such as these may have broad applications in conservation, genomic medicine and agriculture.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.138 | 0.030 |
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".