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Enregistrement W2550664066 · doi:10.1534/genetics.116.196170

Charlesworth <i>et al.</i> on Background Selection and Neutral Diversity

2016· article· en· W2550664066 sur OpenAlexaff
Stephen Wright

Notice bibliographique

RevueGenetics · 2016
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueEvolution and Genetic Dynamics
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésBiologyDiversity (politics)Evolutionary biologyGenomeGeneticsSelection (genetic algorithm)Genetic diversityNeutral theory of molecular evolutionGenetic variationPopulationVariation (astronomy)Background selectionGeneDemography

Résumé

récupéré en direct d'OpenAlex

A significant fraction of genetic diversity across the genome may be of little fitness consequence. But such neutral variation is profoundly informative about the evolutionary forces experienced by a population, and understanding what shapes this diversity across the genome is a key question in population genetics. In their landmark GENETICS article, Charlesworth et al. (1993) revealed an important effect structuring neutral genetic variation—a phenomenon called “background selection.” Their work forced the reinterpretation of influential empirical results and stimulated a major research program on how to distinguish background selection from other influences on neutral diversity. The question of what shapes neutral variation is closely linked to crucial questions about positive selection. How frequently does positive selection of adaptive variants occur, and how does this process influence different parts of the genome? In 1974, Maynard Smith and Haigh showed that positive selection will reduce neutral variation linked to the selected locus, an effect known as genetic “hitchhiking” (Maynard Smith and Haigh 1974). They predicted that, if positive selection is frequent enough, genetic diversity should be lower in regions of low recombination rates because, in these locations, beneficial mutations will be linked to a greater number of sites. This prediction set in motion empirical work that examined the distribution of neutral diversity in natural populations. Stephan and Langley (1989), Aguade et al. (1989), Begun and Aquadro (1992), and others showed that variation in the genomes of wild Drosophila species is indeed lower in regions of low recombination. This striking confirmation of Maynard Smith and Haigh’s predictions was widely taken as evidence for the action of frequent positive selection. But there was an alternative explanation. Charlesworth et al. described, for the first time, another effect that could produce lower neutral diversity in regions of low recombination: negative selection against deleterious mutations. Naming the phenomenon “background selection” they demonstrated that the effect could plausibly produce many of the observed patterns. Using classic results from the theory of mutation-selection balance, coupled with computer simulations that relax some of the simplifying assumptions, they showed that diversity in a nonrecombining region will be reduced as a function of the fraction of copies of the region that contain deleterious mutations. This is because copies that bear deleterious mutations are destined to be eliminated rapidly from the population, limiting possibilities for them to contribute to neutral diversity. In a large nonrecombining region, such as those surrounding centromeric regions, such a diversity loss can be substantial. While their analytical work assumed no recombination, their simulations explored the effects of partial recombination, and they were able to recover a correlation between recombination and diversity. Subsequent work by Hudson and Kaplan (1995), Nordborg et al. (1996), and others demonstrated how recombination rates can be incorporated into the equations predicting the amount of neutral diversity under background selection, quantifying how regions of very low recombination can be strongly affected by this process. This presented a new puzzle for population geneticists; negative and positive selection have very different implications for the evolutionary process, but seemed to have inconveniently similar effects on genetic diversity. Distinguishing between background selection and genetic hitchhiking became the focus of significant research and a vigorous debate that continues today. Unlike with positive selection, we have at least some direct experimental insights into the deleterious mutation rate, and the strength of selection against harmful mutations. These estimates mean we can attempt to predict and control for background selection in order to evaluate the evidence for an additional role for positive selection, as first shown by Charlesworth et al. (1993). As this study first demonstrated, both positive and negative selection are likely to jointly contribute to the structuring of neutral variation in Drosophila. Background selection is now widely acknowledged as a major force structuring genetic variation in many species, including humans, where it likely plays a major role in reducing variation near functional sites (e.g., Cai et al. 2009; McVicker et al. 2009; Lohmueller et al. 2011). It is also likely a major contributor to low genetic diversity on Y chromosomes, as well in asexual and selfing species (Glémin 2007; Agrawal and Hartfield 2016). Since background selection can increase the probability of fixing slightly deleterious mutations, this process can also contribute to the degeneration of the Y chromosome, and cause a decline in fitness of selfing and asexual lineages. The important advance made by Charlesworth et al. (1993) represents a remarkable case of feedback between theoretical and empirical population genetics, in which predictions from theory stimulated empirical tests, providing observations that motivated new theoretical work, forcing a rethink of the original observations and models, and prompting yet further advances in the continuing quest to understand the balance of evolutionary forces in natural populations.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,063
Score d'incertitude au seuil0,210

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,009
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,003
Communication savante0,0030,003
Science ouverte0,0020,002
Intégrité de la recherche0,0050,006
Charge utile insuffisante (le modèle a refusé de juger)0,0630,020

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,244
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2016
Routes d'admission1
Résumé présentoui

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