ArticlePLoS genetics2016
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning.
Article in PLoS genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
126 citing papers in PubMed, 219 citations in OpenAlex.
- Allelic gene conversion softens selective sweeps.Genetics · 2026Article
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Genomic insights into natural selection in recent human history.Nature reviews. Genetics · 2026Review
- Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.BMC genomics · 2026Article
- What has population genomics told us about the dynamics of selection and plant adaptation?Molecular biology and evolution · 2026Review
- Haplotype and diversity signatures of ultra-soft selective sweeps in HIV-1.bioRxiv : the preprint server for biology · 2026Article
- Neural posterior estimation for population genetics.Genetics · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Omics Technologies in Molecular Biology.International journal of molecular sciences · 2026Article
- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
- Soft Selective Sweeps Predominate in the Yellow Fever Mosquito Aedes aegypti.Molecular biology and evolution · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Multiple-testing corrections in case-control studies using identity-by-descent segments.American journal of human genetics · 2026Article
- Editorial: Utilizing machine learning with phenotypic and genotypic data to enhance effective breeding in agricultural and horticultural crops.Frontiers in plant science · 2026Article
- Article
- Multiple-testing corrections in selection scans using identity-by-descent segments.American journal of human genetics · 2025Article
- Global impact of micronutrients in modern human evolution.American journal of human genetics · 2025Article
- Signatures of soft selective sweeps predominate in the yellow fever mosquitobioRxiv : the preprint server for biology · 2025Article
- Multiple-testing corrections in case-control studies using identity-by-descent segments.bioRxiv : the preprint server for biology · 2025Article
- Article
66 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
2 authors at 1 institution in 1 country.
Funding
Abstract
Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over the past decade there has been a renewed interest in determining the importance of selection from standing variation in adaptation of natural populations, yet very few methods for inferring this model of adaptation at the genome scale have been introduced. Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and distinguishing sweeps from linked as well as neutrally evolving regions. Moreover, we show that S/HIC is uniquely robust among its competitors to model misspecification. Thus, even if the true demographic model of a population differs catastrophically from that specified by the user, S/HIC still retains impressive discriminatory power. Finally, we apply S/HIC to the case of resequencing data from human chromosome 18 in a European population sample, and demonstrate that we can reliably recover selective sweeps that have been identified earlier using less specific and sensitive methods.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.