ArticleG3 (Bethesda, Md.)2017
Genotype Calling from Population-Genomic Sequencing Data.
Article in G3 (Bethesda, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Unified Multi-Caller Ensemble (UME) Generates an Unbiased Maize Haplotype Map for Variable Coverage Whole Genome Data.Molecular ecology resources · 2026Article
- Exceedingly low genetic diversity in snow leopards due to persistently small population size.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Assessing Genetic Variation inInternational journal of molecular sciences · 2025Article
- Recent Trends in Food Quality and Authentication: The Role of Omics Technologies in Dairy and Meat Production.International journal of molecular sciences · 2025Review
- Reduced-Representation Sequencing Detects Trans-Arctic Connectivity and Local Adaptation in Polar Cod (Boreogadus saida).Molecular ecology · 2025Article
- vcfgl: a flexible genotype likelihood simulator for VCF/BCF files.Bioinformatics (Oxford, England) · 2025Article
- Enhanced risk assessment framework integrating distribution dynamics, genetically inferred populations, and morphological traits ofZoological research · 2025Article
- Modeling Biases from Low-Pass Genome Sequencing to Enable Accurate Population Genetic Inferences.Molecular biology and evolution · 2025Article
- What can we infer about mutation calling by using time-series mutation accumulation data and a Bayesian Mutation Finder?Ecology and evolution · 2024Article
- Adaptive introgression reveals the genetic basis of a sexually selected syndrome in wall lizards.Science advances · 2024Article
- Genome-wide diversity, population structure and signatures of inbreeding in the African buffalo in Mozambique.BMC ecology and evolution · 2024Article
- Exploring Genomics and Microbial Ecology: Analysis ofPlants (Basel, Switzerland) · 2024Article
- Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids.The plant genome · 2023Article
- Evolutionary Insights from a Large-Scale Survey of Population-Genomic Variation.Molecular biology and evolution · 2023Article
- Evolutionary Genomics of Sister Species Differing in Effective Population Sizes and Recombination Rates.Genome biology and evolution · 2023Article
- Evolutionary Insights from a Large-scale Survey of Population-genomic Variation.bioRxiv : the preprint server for biology · 2023Article
- The Linkage-Disequilibrium and Recombinational Landscape in Daphnia pulex.Genome biology and evolution · 2022Article
- Evolutionary Genomics of a Subdivided Species.Molecular biology and evolution · 2022Article
- Demographic changes in Pleistocene sea turtles were driven by past sea level fluctuations affecting feeding habitat availability.Molecular ecology · 2022Article
- Population Genomics of Wall Lizards Reflects the Dynamic History of the Mediterranean Basin.Molecular biology and evolution · 2022Article
Corrections and comments
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2 authors.
Funding
Abstract
Genotype calling plays important roles in population-genomic studies, which have been greatly accelerated by sequencing technologies. To take full advantage of the resultant information, we have developed maximum-likelihood (ML) methods for calling genotypes from high-throughput sequencing data. As the statistical uncertainties associated with sequencing data depend on depths of coverage, we have developed two types of genotype callers. One approach is appropriate for low-coverage sequencing data, and incorporates population-level information on genotype frequencies and error rates pre-estimated by an ML method. Performance evaluation using computer simulations and human data shows that the proposed framework yields less biased estimates of allele frequencies and more accurate genotype calls than current widely used methods. Another type of genotype caller applies to high-coverage sequencing data, requires no prior genotype-frequency estimates, and makes no assumption on the number of alleles at a polymorphic site. Using computer simulations, we determine the depth of coverage necessary to accurately characterize polymorphisms using this second method. We applied the proposed method to high-coverage (mean 18×) sequencing data of 83 clones from a population of
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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.