ArticleBMC bioinformatics2013
Calculation of Tajima's D and other neutrality test statistics from low depth next-generation sequencing data.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.
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131 citing papers in PubMed.
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- Genetic diversity and selection signatures in sheep breeds.Journal of applied genetics · 2025Article
- Gone With the Wind: Exploring a Vanished Rock Dove,Ecology and evolution · 2025Article
- Translocations spur population growth but fail to prevent genetic erosion in imperiled Florida Scrub-Jays.Current biology : CB · 2025Article
- Ocean-Wide Conservation Genomics of Blue Whales Suggest New Northern Hemisphere Subspecies.Molecular ecology · 2025Article
- Genome-wide selection analysis identifies genes associated with mandibular defects in Duolang sheep.Frontiers in veterinary science · 2025Article
- Persistent Gene Flow Suggests an Absence of Reproductive Isolation in an African Antelope Speciation Model.Systematic biology · 2024Article
- Linked Selection and Gene Density Shape Genome-Wide Patterns of Diversification in Peatmosses.Evolutionary applications · 2024Article
71 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
backgroundA number of different statistics are used for detecting natural selection using DNA sequencing data, including statistics that are summaries of the frequency spectrum, such as Tajima's D. These statistics are now often being applied in the analysis of Next Generation Sequencing (NGS) data. However, estimates of frequency spectra from NGS data are strongly affected by low sequencing coverage; the inherent technology dependent variation in sequencing depth causes systematic differences in the value of the statistic among genomic regions.
resultsWe have developed an approach that accommodates the uncertainty of the data when calculating site frequency based neutrality test statistics. A salient feature of this approach is that it implicitly solves the problems of varying sequencing depth, missing data and avoids the need to infer variable sites for the analysis and thereby avoids ascertainment problems introduced by a SNP discovery process.
conclusionUsing an empirical Bayes approach for fast computations, we show that this method produces results for low-coverage NGS data comparable to those achieved when the genotypes are known without uncertainty. We also validate the method in an analysis of data from the 1000 genomes project. The method is implemented in a fast framework which enables researchers to perform these neutrality tests on a genome-wide scale.
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