ArticlePloS one2009
Correcting the site frequency spectrum for divergence-based ascertainment.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Intraspecific sequence variation and complete genomes refine the identification of rapidly evolved regions in humans.Cell genomics · 2026Article
- Robustness in population-structure and demographic-inference results derived from the Aedes aegypti genotyping chip and whole-genome sequencing data.G3 (Bethesda, Md.) · 2024Article
- Article
- Inferring Selective Constraint from Population Genomic Data Suggests Recent Regulatory Turnover in the Human Brain.Genome biology and evolution · 2015Article
- Highly constrained intergenic Drosophila ultraconserved elements are candidate ncRNAs.Genome biology and evolution · 2015Article
- The evolutionary genetics of the genes underlying phenotypic associations for loblolly pine (Pinus taeda, Pinaceae).Genetics · 2013Article
- Computational analysis and characterization of UCE-like elements (ULEs) in plant genomes.Genome research · 2012Article
- The application and performance of single nucleotide polymorphism markers for population genetic analyses of lepidoptera.Frontiers in genetics · 2011Article
- A population genetic hidden Markov model for detecting genomic regions under selection.Molecular biology and evolution · 2010Article
- GC-biased evolution near human accelerated regions.PLoS genetics · 2010Article
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1 author.
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Abstract
Comparative genomics based on sequenced referenced genomes is essential to hypothesis generation and testing within population genetics. However, selection of candidate regions for further study on the basis of elevated or depressed divergence between species leads to a divergence-based ascertainment bias in the site frequency spectrum within selected candidate loci. Here, a method to correct this problem is developed that obtains maximum-likelihood estimates of the unascertained allele frequency distribution using numerical optimization. I show how divergence-based ascertainment may mimic the effects of natural selection and offer correction formulae for performing proper estimation into the strength of selection in candidate regions in a maximum-likelihood setting.
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