Cited by 438Genomics and Phylogenetic StudiesGenetic Associations and EpidemiologyGenetic diversity and population structure

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

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Nielsen, Rasmus, Korneliussen, Thorfinn, Albrechtsen, Anders, Li, Yingrui, Wang, Jun·2012·PLoS ONE

DOI 10.1371/journal.pone.0037558PMC PMC3404070

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Abstract

We present a statistical framework for estimation and application of sample allele frequency spectra from New-Generation Sequencing (NGS) data. In this method, we first estimate the allele frequency spectrum using maximum likelihood. In contrast to previous methods, the likelihood function is calculated using a dynamic programming algorithm and numerically optimized using analytical derivatives. We then use a bayesian method for estimating the sample allele frequency in a single site, and show how the method can be used for genotype calling and SNP calling. We also show how the method can be extended to various other cases including cases with deviations from Hardy-Weinberg equilibrium. We evaluate the statistical properties of the methods using simulations and by application to a real data set.

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ESTIMATING F -STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE
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“In fact, imputation based genotype calling will lead to a substantial increase in accuracy over other methods [7].”
BackgroundMethodInfluential
A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data
Li, H. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr509 · Cited by 6,454

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“Then, the realSFS function was used to optimize the saf and estimate the unfolded site frequency spectrum (SFS; [72]).”
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“We used ANGSD to estimate site allele frequency (SAF) likelihoods for each individual mapped to the impala reference genome, from which we estimated the 2DSFS between each pair using realSFS (Nielsen et al. 2012).”
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“After SNP and genetic variant calling (27,28), the genotype at one specific locus is represented by AA, Aa and aa for one homozygote, the heterozygote and the other homozygote, respectively.”
Background
A hidden Markov approach for ascertaining cSNP genotypes from RNA sequence data in the presence of allelic imbalance by exploiting linkage disequilibrium
Steibel, J. P., Wang, H., & Zhong, P. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0479-2 · Cited by 1
“…of genes or transcripts is tested between treatments or tissues [2], recently, RNAseq has been increasingly utilized for nucleotide-centric inferences such as, for coding SNP (cSNP) discovery [3], for cSNP genotyping to estimate population parameters [4] or for allelic specific expression [5,6].”
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“First, the genetic statistics of populations that depended on the inferred site‐frequency spectrum (SFS) were estimated directly from the genotype likelihoods rather than by calling the genotype (Nielsen et al., 2012) in ANGSD (Korneliussen et al.”
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“The demographic history of populations was inferred by Stairway Plot 2 software (Liu and Fu 2020) using the Site Frequency Spectrum (SFS) approach described by Nielsen et al. (2012).”
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“We used methods based on the site frequency spectrum (SFS) (Korneliussen, Moltke, Albrechtsen, & Nielsen, 2013; Nielsen, Korneliussen, Albrechtsen, Li, & Wang, 2012) to estimate nucleotide diversity, the neutrality test statistic Tajima’s D and genome-wide fixation index (F ST ) values.”
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“S3), which is a phenomena well described in the literature (Lin et al., 2022; Nielsen et al., 2012; Yao et al., 2020).”
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“Using the folded SFS calculated in realSFS (Nielsen et al. 2012), we determined genome-wide Tajima’s D and nucleotide diversity ( π ) across all samples.”
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“The folded site-frequency spectrum (SFS, Fisher 1931 and Wright, 1938) was estimated as described by Nielsen et al. (2012) using ANGSD with the following parameters: - min-MapQ 10 - minQ 20 - minInd 24 - uniqueOnl y 1.”
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“931 (Nielsen et al. 2012).”
Retracted
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“To enhance the analysis, we developed an advanced multi-SNP-based summary data-based Mendelian randomization (SMR) method (--smr-multi), which incorporates multiple single nucleotide polymorphisms (SNPs) within QTL regions [27].”
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“Challenged samples often have low call rates and reduced accuracy, which has obvious impacts on downstream analyses [e.g. kinship classification (Nielsen et al. 2011, 2012, de Vries et al. 2022, Turner et al. 2022)].”
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“…software. the maximum proportion of SnPs with allele frequency differences allowed is set to the default value of 0.05. the study applied a multi-SnP SMR method that considers multiple SnPs at Qtl loci [20]. the HEidi test used a p_HEidi threshold of >0.01 to distinguish pleiotropy from linkage.”
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“To quantify heterozygosity, we used ANGSD: estimating genotype likelihoods using the SAMtools model, setting base and mapping quality thresholds of 20 and 30 and calculating site allele frequency likelihood (-dosaf 1), before estimating site frequency spectrum with realSFS (Nielsen et al. 2012).”
Method
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Johnson, A., Zipfel, K. J., Smith, D. M., & Welsh, A. B. (2025) · DNA · doi:10.3390/dna5010014 · Cited by 1
“Filtering of datasets typically consists of using a minor allele frequency (MAF) filter that assists in discarding errant genotypes, removing singletons, and using more common SNPs in quantifying genomic diversity and population structuring [29–31].”
A probabilistic approach to visualize the effect of missing data on PCA in ancient human genomics
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ngsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language
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“, 2014), which estimates the SFS using an expectation–maximization method (Li, 2011; Nielsen et al., 2012).”
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“Allele frequencies for the selection scan were estimated directly from the site allele frequency (saf) likelihoods based on individual genotype likelihoods assuming HWE [72] in ANGSD.”
Method
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“700 (Korneliussen et al. 2014; Nielsen et al. 2012).”
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BackgroundMethodInfluential
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“8 (SFS) were estimated directly from the genotype likelihoods rather than by calling the genotype (Nielsen et al., 2012), in ANGSD (Korneliussen et al.”
Method
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Tian, Y. M., Liu, S., Ingvarsson, P. K., Zhao, D., Wang, L., Abuduhamiti, B., Wú, Z., Zhang, J., & Wang, Z. (2021) · doi:10.22541/au.161251070.07552202/v1
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Moon, S., Kim, T.-H., Lee, K., Kim, M.-J., Kim, N., Chung, W., Kim, Y., & Kim, H. (2015)
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Simon, H., & Huttley, G. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.23.453461
“The SFS has been widely used as a summary statistic in population genetic inference (Nielsen et al., 2012; Han et al., 2013).”
Method
Report on Work-In-Progress Session of the 9th ACM Symposium on Operating Systems Principles.
Levin, R., & Redell, D. D. (1984) · ACM SIGOPS Operating Systems Review
ngsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language
Mas-Sandoval, À., Jin, C., Fracassetti, M., & Fumagalli, M. (2022) · F1000Research · doi:10.12688/f1000research.104368.1
Heterozygosity
Knapp, L. A. (2018) · The International Encyclopedia of Biological Anthropology · doi:10.1002/9781118584538.ieba0235
Barriers to gene flow play an important role in the speciation of two closely related Populus (Salicaceae) speciesPreprint
Tian, Y., Liu, S., Ingvarsson, P. K., Zhao, D., Wang, L., Abuduhamiti, B., Wu, Z., ZHANG, J. G., & Wang, Z. (2020) · doi:10.22541/au.160133572.27075825
Migratory beekeeping facilitates genetic admixture in populations of the honeybee parasite Varroa destructorPreprint
Ryals, D. K., de Medeiros, B., & Farrell, B. (2022) · doi:10.22541/au.166079895.54947645/v1
Migratory beekeeping facilitates genetic admixture in populations of the honeybee parasite Varroa destructorPreprint
Ryals, D. K., de Medeiros, B. A. S., & Farrell, B. (2022) · doi:10.22541/au.166079895.54947645/v2
Constitutive and variable patterns of genome-wide DNA methylation in populations from spatial-environmental range extremes of the bumble bee Bombus vosnesenskiiPreprint
Rahman, S. R., & Lozier, J. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.02.539175
ngsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language
Mas-Sandoval, À., Jin, C., Fracassetti, M., & Fumagalli, M. (2023) · F1000Research · doi:10.12688/f1000research.104368.3
Comparative Phylogeography of Tree Species in the Hyrcanian Forests, Iran by Genome-wide SNPsPreprint
Vatanparast, M., Madsen, P., Sagheb‐Talebi, K., Larsen, J. B., Ayan, S., & Hansen, O. K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.25.554899
Computational Genomics Approaches for Livestock Improvement and Management
Singh, K., Iquebal, M. A., Jaiswal, S., Rai, A. K., & Kumar, D. (2023) · Livestock diseases and management · doi:10.1007/978-981-99-2209-3_19
High genetic diversification in a symbiotic marine annelid is driven by microgeography and glaciationPreprint
Sato, Y., Wilkins, L. G. E., Gruhl, A., Gruber‐Vodicka, H. R., & Dubilier, N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.13.562162
“Pairwise global F ST between populations were estimated from the site frequency spectrum (SFS) based on the SAF likelihoods instead of genotype calling using ANGSD programs realSFS (Nielsen et al. 2012; Korneliussen et al. 2013).”
Method
Low-ABC: a robust demographic inference from low-coverage whole-genome data through ABCPreprint
Vizzari, M. T., Ghirotto, S., Agostini, R. B., Delser, P. M., Cassidy, L. M., Manica, A., & Benazzo, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.01.606209
“Whilst this assumption is not problematic for high coverage data, the level of uncertainty associated with the genotype calling increases with decreasing sequencing coverage levels (Nielsen et al. 2012).”
Background
Distinct intraspecific diversification dynamics in Neotropical montane versus lowland birds revealed by whole-genome comparative phylogeographyPreprint
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“We also created a dataset of all sites (variant and invariant) within individual libraries per species, following best practice for population genetic inference from site frequency spectra (Nielsen et al. 2012, Korunes and Samuk 2021).”
Method
Population structure and domestication history of the Javan banteng ( Bos javanicus javanicus )Preprint
Wang, X., Aninta, S. G., Garcia‐Erill, G., Li, Z., Khan, A., Liu, X., Bertola, L. D., Dharmayanthi, A. B., Yulianto, Y., Yonathan, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.01.646613
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A Genomic Framework for Climate-Resilient ConservationPreprint
Michell, C., Schmidt‐Roach, S., Micke, P., Anlauf, H., Alguthmi, S., Hawley, C. B., Herrera, M., Barreto, M. M., Hung, S.-H., Howells, E. J., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7572772/v1
Additional file 30 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945748
Additional file 22 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945724
Additional file 1 of Ecological and biogeographic features shaped the complex evolutionary history of an iconic apex predator (Galeocerdo cuvier)
Lesturgie, P., Laine, H., Suwalski, A., Chifflet‐Belle, P., Delser, P. M., Clua, É., Jaquemet, S., Magalon, H., & Mona, S. (2024) · Figshare · doi:10.6084/m9.figshare.26555207
Additional file 33 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945757
Additional file 7 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945685
Additional file 2 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Васильевич, Б. В., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945670
Additional file 24 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945730
Additional file 13 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945703
Additional file 21 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945721
Additional file 1 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Васильевич, Б. В., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945667
Additional file 6 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945682
Additional file 25 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945733
Additional file 20 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945718
Additional file 12 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Васильевич, Б. В., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945700
Additional file 19 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945715
Additional file 26 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Belkin, V. V., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945736
Additional file 10 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Васильевич, Б. В., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945694
Additional file 14 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Васильевич, Б. В., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945706
Additional file 17 of Signals of positive selection in genomes of palearctic Myotis-bats coexisting with a fungal pathogen
Twort, V., Laine, V. N., Field, K. A., Whiting-Fawcett, F., Ito, F., Reiman, M., Bartonicka, T., Fritze, M., Ilyukha, V. A., Васильевич, Б. В., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26945709

Authors: Nielsen, R., Korneliussen, T., Albrechtsen, A., Li, Y., & Wang, J.
Affiliations: BGI Group (China) (CN)
University of Copenhagen (DK)
Corresponding author: Nielsen, Rasmus
Wang, Jun
ORCID: Nielsen, Rasmus
Korneliussen, Thorfinn
Albrechtsen, Anders
Li, Yingrui
Wang, Jun
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2012-07-24
Volume: 7
Issue: 7
Pages: e37558
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0037558
PMC: PMC3404070
OpenAlex: W2005287518
Cited by: 424
References: 39
Citations by year: 2026: 24 · 2025: 18 · 2024: 43 · 2023: 33 · 2022: 38 · 2021: 40 · 2020: 25 · 2019: 19 · 2018: 37 · 2017: 30 · 2016: 39 · 2015: 30 · 2014: 25 · 2013: 19 · 2012: 1
FWCI: 9.58
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS ONE (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by)
Figshare (submittedVersion, cc-by)
Funders: Villum Fonden (00007171)
National Institutes of Health
National Human Genome Research Institute (R01HG003229)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Keywords: allele frequency estimation, allele frequency spectrum, SNP calling, genotype calling, next-generation sequencing, maximum likelihood estimation, Hardy-Weinberg equilibrium, genetic variation
MeSH terms: Algorithms, Bayes Theorem, Computer Simulation, Denmark, Gene Frequency, Genotype, Humans, ROC Curve, Software, Likelihood Functions, Computational Biology, Internet, Polymorphism, Single Nucleotide, White People, High-Throughput Nucleotide Sequencing, Exome

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

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