ArticleNature communications2018
Localization of adaptive variants in human genomes using averaged one-dependence estimation.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
64 citing papers in PubMed.
- Genomic insights into natural selection in recent human history.Nature reviews. Genetics · 2026Review
- AI solutions for evolutionary genomics of nonmodel species.Evolution letters · 2026Review
- Identifying Adaptive Footprints in the Presence of Demographic Uncertainty.Genome biology and evolution · 2026Article
- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
- Selection scans and downstream analysis with selscan.Human population genetics and genomics · 2026Article
- Fast and Memory-Efficient Dynamic Programming Approach for Large-Scale EHH-Based Selection Scans.Molecular biology and evolution · 2025Article
- Selection scans and downstream analysis with selscan.bioRxiv : the preprint server for biology · 2025Article
- Fast and Memory-Efficient Dynamic Programming Approach for Large-Scale EHH-Based Selection Scans.bioRxiv : the preprint server for biology · 2025Article
- How Everything Is Connected to Everything Else - Population-Specific Connections between Adaptive Evolution, Disease Susceptibility, and Drug Responsiveness.Advanced genetics (Hoboken, N.J.) · 2025Article
- Article
- Sweeps in Space: Leveraging Geographic Data to Identify Beneficial Alleles in Anopheles gambiae.Molecular biology and evolution · 2025Article
- Parameter Scaling in Population Genetics Simulations may Introduce Unintended Background Selection: Considerations for Scaled Simulation Design.Genome biology and evolution · 2025Article
- Digital Image Processing to Detect Adaptive Evolution.Molecular biology and evolution · 2024Article
- Population Genomic Scans for Natural Selection and Demography.Annual review of genetics · 2024Review
- The multi-tissue gene expression and physiological responses of water deprived Peromyscus eremicus.BMC genomics · 2024Article
- Interpreting generative adversarial networks to infer natural selection from genetic data.Genetics · 2024Article
- IntroUNET: Identifying introgressed alleles via semantic segmentation.PLoS genetics · 2024Article
- When the tap runs dry: The multi-tissue gene expression and physiological responses of water deprivedbioRxiv : the preprint server for biology · 2024Article
- IntroUNET: identifying introgressed alleles via semantic segmentation.bioRxiv : the preprint server for biology · 2024Article
- Hunter-gatherer genetics research: Importance and avenues.Evolutionary human sciences · 2024Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.