ArticleGenome biology and evolution2015
Inferring Selective Constraint from Population Genomic Data Suggests Recent Regulatory Turnover in the Human Brain.
Article in Genome biology and evolution, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 29 citations in OpenAlex.
- Neural posterior estimation for population genetics.Genetics · 2026Article
- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- The performance of genetic-constraint metrics varies significantly across the human noncoding genome.bioRxiv : the preprint server for biology · 2026Article
- Novelty versus innovation of gene regulatory elements in human evolution and disease.Current opinion in genetics & development · 2025Review
- Bringing CAM photosynthesis to the table: Paving the way for resilient and productive agricultural systems in a changing climate.Plant communications · 2024Review
- This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks.Genetics · 2023Article
- Article
- Identification of natural selection in genomic data with deep convolutional neural network.BioData mining · 2021Article
- Human-lineage-specific genomic elements are associated with neurodegenerative disease and APOE transcript usage.Nature communications · 2021Article
- Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution.PLoS genetics · 2020Article
- An inferred fitness consequence map of the rice genome.Nature plants · 2020Article
- The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.Molecular biology and evolution · 2019Article
- A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks.Advances in neural information processing systems · 2018Article
- Supervised Machine Learning for Population Genetics: A New Paradigm.Trends in genetics : TIG · 2018Review
- The human noncoding genome defined by genetic diversity.Nature genetics · 2018Article
- Soft Sweeps Are the Dominant Mode of Adaptation in the Human Genome.Molecular biology and evolution · 2017Article
- Differential Gene Expression in the Human Brain Is Associated with Conserved, but Not Accelerated, Noncoding Sequences.Molecular biology and evolution · 2017Article
- Determining the Effect of Natural Selection on Linked Neutral Divergence across Species.PLoS genetics · 2016Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
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Abstract
The comparative genomics revolution of the past decade has enabled the discovery of functional elements in the human genome via sequence comparison. While that is so, an important class of elements, those specific to humans, is entirely missed by searching for sequence conservation across species. Here we present an analysis based on variation data among human genomes that utilizes a supervised machine learning approach for the identification of human-specific purifying selection in the genome. Using only allele frequency information from the complete low-coverage 1000 Genomes Project data set in conjunction with a support vector machine trained from known functional and nonfunctional portions of the genome, we are able to accurately identify portions of the genome constrained by purifying selection. Our method identifies previously known human-specific gains or losses of function and uncovers many novel candidates. Candidate targets for gain and loss of function along the human lineage include numerous putative regulatory regions of genes essential for normal development of the central nervous system, including a significant enrichment of gain of function events near neurotransmitter receptor genes. These results are consistent with regulatory turnover being a key mechanism in the evolution of human-specific characteristics of brain development. Finally, we show that the majority of the genome is unconstrained by natural selection currently, in agreement with what has been estimated from phylogenetic methods but in sharp contrast to estimates based on transcriptomics or other high-throughput functional methods.
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