SNP Calling, Genotype Calling, and Sample Allele Frequency Estimation from New-Generation Sequencing Data

Nielsen, Rasmus; Korneliussen, Thorfinn Sand; Albrechtsen, Anders; Li, Yingrui; Wang, Jun

doi:10.1371/journal.pone.0037558

Cited by 361 publications

(466 citation statements)

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“…Not surprisingly, the probabilistic method performs overall better than using called genotypes. However, the difference between using called genotypes and the probabilistic approach is much smaller here than observed in other studies (Kim et al 2011;Nielsen et al 2012), because only true SNPs are included in these simulations, alleviating the problem of an excess of false singletons (and to a lesser degree doubletons) in methods based on genotype calling.…”

Section: Effect Of Inbreeding On Sfsmentioning

confidence: 91%

“…We used both the standard approach based on called genotypes (see Methods) and a recent probabilistic method by Nielsen et al (2012). High inbreeding coefficients have a marked effect on SFS estimation, and can increase the RMSD in the estimate of the SFS many fold ( Fig.…”

Section: Effect Of Inbreeding On Sfsmentioning

confidence: 99%

“…As for the cultivated accessions, they are thought to be almost totally inbred with self-crossing rates close to 95%, although O. s. indica has been described as having slightly lower rates (Oka 1988 To assess the impact of explicitly assuming inbreeding on SFS estimation, we estimated it for each of the four rice species/ subspecies. We used two different priors (random mating and estimated inbreeding coefficients) over two different methods (the probabilistic method by Nielsen et al 2012 and using calling genotypes). Figure 5 shows that even for high coverage data (;103) (O. s. indica and O. s. japonica in Fig.…”

Section: Estimating Inbreeding Coefficients From Ngs Datamentioning

confidence: 99%

“…Recent methods rely on probabilistic frameworks to account for these errors and accurately call SNPs and genotypes, even at low coverage (Martin et al 2010;Li 2011;Nielsen et al 2012). These methods integrate the base quality score together with other error sources (e.g., mapping or sequencing errors) to calculate an overall ''genotype likelihood.''…”

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Estimating inbreeding coefficients from NGS data: Impact on genotype calling and allele frequency estimation

et al. 2013

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Most methods for next-generation sequencing (NGS) data analyses incorporate information regarding allele frequencies using the assumption of Hardy–Weinberg equilibrium (HWE) as a prior. However, many organisms including those that are domesticated, partially selfing, or with asexual life cycles show strong deviations from HWE. For such species, and specially for low-coverage data, it is necessary to obtain estimates of inbreeding coefficients (F) for each individual before calling genotypes. Here, we present two methods for estimating inbreeding coefficients from NGS data based on an expectation-maximization (EM) algorithm. We assess the impact of taking inbreeding into account when calling genotypes or estimating the site frequency spectrum (SFS), and demonstrate a marked increase in accuracy on low-coverage highly inbred samples. We demonstrate the applicability and efficacy of these methods in both simulated and real data sets.

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Section: Effect Of Inbreeding On Sfsmentioning

confidence: 91%

Section: Effect Of Inbreeding On Sfsmentioning

confidence: 99%

Section: Estimating Inbreeding Coefficients From Ngs Datamentioning

confidence: 99%

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confidence: 99%

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Estimating inbreeding coefficients from NGS data: Impact on genotype calling and allele frequency estimation

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“…A growing number of next-generation sequencing projects produce low coverage data that cannot be used to unambiguously assign individual genotypes, but which can be analyzed probabilistically to account for uncertainty in individual genotypes [82][83][84]. However, most existing LAI methods require genotype data derived from diploid individuals.…”

Section: Resultsmentioning

confidence: 99%

A hidden Markov model approach for simultaneously estimating local ancestry and admixture time using next generation sequence data in samples of arbitrary ploidy

Corbett-Detig

Nielsen

2016

Preprint

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Admixture-the mixing of genomes from divergent populations-is increasingly appreciated as a central process in evolution. To characterize and quantify patterns of admixture across the genome, a number of methods have been developed for local ancestry inference. However, existing approaches have a number of shortcomings. First, all local ancestry inference methods require some prior assumption about the expected ancestry tract lengths. Second, existing methods generally require genotypes, which is not feasible to obtain for many nextgeneration sequencing projects. Third, many methods assume samples are diploid, however a wide variety of sequencing applications will fail to meet this assumption. To address these issues, we introduce a novel hidden Markov model for estimating local ancestry that models the read pileup data, rather than genotypes, is generalized to arbitrary ploidy, and can estimate the time since admixture during local ancestry inference. We demonstrate that our method can simultaneously estimate the time since admixture and local ancestry with good accuracy, and that it performs well on samples of high ploidy-i.e. 100 or more chromosomes. As this method is very general, we expect it will be useful for local ancestry inference in a wider variety of populations than what previously has been possible. We then applied our method to pooled sequencing data derived from populations of Drosophila melanogaster on an ancestry cline on the east coast of North America. We find that regions of local recombination rates are negatively correlated with the proportion of African ancestry, suggesting that selection against foreign ancestry is the least efficient in low recombination regions. Finally we show that clinal outlier loci are enriched for genes associated with gene regulatory functions, consistent with a role of regulatory evolution in ecological adaptation of admixed D. melanogaster populations. Our results illustrate the potential of local ancestry inference for elucidating fundamental evolutionary processes. Data Availability Statement:No novel data were produced for this work. Short read data analyzed in this work are all available through the NCBI short read trace archive under project accession number PRJNA256231.Funding: RCD was supported by a Chancellor's Postdoctoral Fellowship during this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author SummaryWhen divergent populations hybridize, their offspring obtain portions of their genomes from each parent population. Although the average ancestry proportion in each descendant is equal to the proportion of ancestors from each of the ancestral populations, the contribution of each ancestry type is variable across the genome. Estimating local ancestry within admixed individuals is a fundamental goal for evolutionary genetics, and here we develop a method for doing this that circumvents many of the problems associated with existing methods. Briefly, our method c...

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Heterozygosity

Knapp

2018

The International Encyclopedia of Biological Anthropology

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Heterozygosity refers to the amount of diversity found at one or more genetic loci in a population. Heterozygosity is often measured by genotyping microsatellites and single nucleotide polymorphisms in the genome. Population geneticists use the Hardy–Weinberg equilibrium (HWE) equation to compare observed and expected levels of heterozygosity. If heterozygosity is higher than expected, evolutionary forces such as natural selection may be contributing to the diversity in the study population.

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SNP Calling, Genotype Calling, and Sample Allele Frequency Estimation from New-Generation Sequencing Data

Cited by 361 publications

References 36 publications

Estimating inbreeding coefficients from NGS data: Impact on genotype calling and allele frequency estimation

Estimating inbreeding coefficients from NGS data: Impact on genotype calling and allele frequency estimation

A hidden Markov model approach for simultaneously estimating local ancestry and admixture time using next generation sequence data in samples of arbitrary ploidy

Heterozygosity

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