The molecular signature of selection underlying human adaptations
Bibliographic record
Abstract
In the last decade, advances in human population genetics and comparative genomics have resulted in important contributions to our understanding of human genetic diversity and genetic adaptation. For the first time, we are able to reliably detect the signature of natural selection from patterns of DNA polymorphism. Identifying the effects of natural selection in this way provides a crucial piece of evidence needed to support hypotheses of human adaptation. This review provides a detailed description of the theory and analytical approaches used to detect signatures of natural selection in the human genome. We discuss these methods in relation to four classic human traits--skin color, the Duffy blood group, bitter-taste sensation, and lactase persistence. By highlighting these four traits we are able to discuss the ways in which analyses of DNA polymorphism can lead to inferences regarding past histories of selection. Specifically, we can infer the importance of specific regimes of selection (i.e. directional selection, balancing selection, and purifying selection) in the evolution of a trait because these different types of selection leave different patterns of DNA polymorphism. In addition, we demonstrate how these types of data can be used to estimate the time frame in which selection operated on a trait. As the field has advanced, a general issue that has come to the forefront is how specific demographic events in human history, such as population expansions, bottlenecks, and subdivision of populations, have also left a signature across the genome that can interfere with our detection of the footprint of selection at particular genes. Therefore, we discuss this general problem with respect to the four traits reviewed here, and describe the ways in which the signature of selection can be teased from a background signature of demographic history. Finally, we move from a discussion of analyses of selection motivated by a "candidate-gene" approach, in which a priori information led to the analysis of specific gene, to discussion of "genome-scanning" approaches that are directed at discovering new genes that have been under positive selection. Such scans can be designed to detect those genes that have been positively selected in our divergence from chimpanzees, as well as those genes that have been under selection as human populations have migrated, differentiated, and adapted to specific geographic environments. We predict that both approaches will be applied in the future, enabling a greater insight into human species-wide adaptations, as well as the specific adaptations of human populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".