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SNP Calling, Genotype Calling, and Sample Allele Frequency Estimation from New-Generation Sequencing Data
Open accessAbstract
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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“due to the error rate of sequencing, the mixture of nonparenchymal cells and polyploidy of hepatocytes, this approach could not detect low-frequency single-nucleotide polymorphisms in hepatocytes11.”
From Genome editing with Cas9 in adult mice corrects a disease mutation and phenotype (2014) · cites this paper as [11]
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ANGSD: Analysis of Next Generation Sequencing Data
Korneliussen, T. S., Albrechtsen, A., & Nielsen, R. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0356-4 · Cited by 2,982
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Genome editing with Cas9 in adult mice corrects a disease mutation and phenotype
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Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data
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Self-citationThe genomic landscape underlying phenotypic integrity in the face of gene flow in crows
Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., Müller, I., Baglione, V., Unneberg, P., Wikelski, M. C., Grabherr, M. G., et al. (2014) · Science · doi:10.1126/science.1253226 · Cited by 551
A field guide to whole‐genome sequencing, assembly and annotation
Ekblom, R., & Wolf, J. B. W. (2014) · Evolutionary Applications · doi:10.1111/eva.12178 · Cited by 466
“If not, alternative approaches should be considered rather than producing a poor, low coverage, assembly ● Familiarization with data handling pipelines and file formats (see below) ● High-quality DNA sample (with individual metadata) ● Plan for analyses and publication Some useful resources Internet forums for discuss…”
BackgroundInfluentialPopulation genomics reveal recent speciation and rapid evolutionary adaptation in polar bears
Liu, S., Lorenzen, E. D., Fumagalli, M., Li, B., Harris, K., Xiong, Z., Zhou, L., Korneliussen, T. S., Somel, M., Babbitt, C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.03.054 · Cited by 411
“We (1) computed a homogeneity test statistic to identify genes with a low polymorphism-to-divergence ratio in polar bears relative to brown bears; (2) used the Hudson-Aguade-Kreitman (HKA) test to verify that selection had acted specifically on the polar bear lineage and not on the brown bear lineage; (3) estimated Fs…”
MethodSelf-citationBurri, R., Nater, A., Kawakami, T., Mugal, C. F., Olason, P. I., Smeds, L., Suh, A., Dutoit, L., Bureš, S., Garamszegi, L. Z., et al. (2015) · Genome Research · doi:10.1101/gr.196485.115 · Cited by 418
“ANGSD(Korneliussenetal.2014)wasusedtoestimateallelefrequencylikelihoods,obtainamaximumlikelihoodesti-mate of the unfolded site frequency spectrum (SFS) (Nielsen et al. 2012), and estimate diversity and SFS statistics including Tajima ’ s D , and Fay and Wu ’ s H (Fay and Wu 2000).”
Fuentes‐Pardo, A. P., & Ruzzante, D. E. (2017) · Molecular Ecology · doi:10.1111/mec.14264 · Cited by 417
“…data analysis, which can exceed (2–39) the size of the final data file (e.g., for Atlantic herring, SAM file ~150 GB). across sites, a sample allele frequency per site is calculated from which other statistics are inferred, including the SFS (Nielsen et al., 2011, 2012; Korneliussen et al., 2014).”
BackgroundMethodInfluentialLinck, E. B., & Battey, C. J. (2019) · Molecular Ecology Resources · doi:10.1111/1755-0998.12995 · Cited by 402
The genomes of two key bumblebee species with primitive eusocial organization
Sadd, B. M., Barribeau, S. M., Bloch, G., de Graaf, D. C., Dearden, P. K., Elsik, C. G., Gadau, J. R., Grimmelikhuijzen, C. J. P., Hasselmann, M., Lozier, J. D., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0623-3 · Cited by 390
“113 × 10(6)) using a genotype-likelihood based approach that does not rely on SNP calling [224,225].”
Unlocking the vault: next‐generation museum population genomics
Bi, K., Linderoth, T. P., Vanderpool, D., Good, J. M., Nielsen, R., & Moritz, C. C. (2013) · Molecular Ecology · doi:10.1111/mec.12516 · Cited by 380
“A detailed description of the algorithms employed by ANGSD can be found at http://popgen.dk/wiki/index. php/ANGSD and in Nielsen et al. (2012).”
BackgroundMethodInfluentialSelf-citationBrowning, S. R., & Browning, B. L. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.07.012 · Cited by 338
Ancient genomes show social and reproductive behavior of early Upper Paleolithic foragers
Sikora, M., Seguin-Orlando, A., Sousa, V. C., Albrechtsen, A., Korneliussen, T., Ko, A., Rasmussen, S., Dupanloup, I., Nigst, P. R., Bosch, M. D., et al. (2017) · Science · doi:10.1126/science.aao1807 · Cited by 312
Self-citationYang, J. H., Li, W., Lv, F., He, S., Tian, S., Peng, W., Sun, Y., Zhao, Y., Tu, X.-L., Zhang, M., et al. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw129 · Cited by 349
“This strategy accounts for genotype uncertainty by using GL and an ML estimate of the site frequency spectrum (SFS; Nielsen et al. 2012) as a prior information and thus can facilitate unbiased interpretations of low-coverage data.”
MethodInfluentialShafer, A. B. A., Peart, C., Tusso, S., Maayan, I., Brelsford, A., Wheat, C. W., & Wolf, J. B. W. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12700 · Cited by 363
“Sequencing errors in particular will result in an excess of singletons and a biased SFS (Johnson & Slatkin 2008); for lower coverage data sets the choice of SNP calling algorithm reflects a trade-off between SNP number and undercalling true heterozygotes (Nielsen et al. 2012).”
BackgroundA beginner's guide to low‐coverage whole genome sequencing for population genomics
Lou, R. N., Jacobs, A., Wilder, A. P., & Therkildsen, N. O. (2021) · Molecular Ecology · doi:10.1111/mec.16077 · Cited by 331
“This method corrects for the bias caused by low-coverage data, and can be generalized to estimate the SFS jointly for up to four populations (Nielsen et al., 2012).”
BackgroundMethodInfluentialAdvances and limits of using population genetics to understand local adaptation
Tiffin, P., & Ross‐Ibarra, J. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.10.004 · Cited by 346
Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions
Qiu, Q., Wang, L., Wang, K., Yang, Y. P., Ma, T., Wang, Z., Zhang, X., Ni, Z., Hou, F., Long, R., et al. (2015) · Nature Communications · doi:10.1038/ncomms10283 · Cited by 307
“46) programme in order the estimate the SFS. Genotypes were called using the full set of genotype likelihoods data.”
Zenger, K. R., Khatkar, M. S., Jones, D. B., Samani, N. K., Jerry, D. R., & Raadsma, H. W. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2018.00693 · Cited by 292
“Further efficiency of breeding programs can be captured through structured management of genotype by environment interactions and incorporating dominance and epistatic effects (Dupont-Nivet et al., 2008; Nilsson et al., 2016).”
BackgroundGenome-culture coevolution promotes rapid divergence of killer whale ecotypes
Foote, A. D., Vijay, N., Ávila‐Arcos, M. C., Baird, R. W., Durban, J. W., Fumagalli, M., Gibbs, R. A., Hanson, M. B., Korneliussen, T. S., Martin, M. D., et al. (2016) · Nature Communications · doi:10.1038/ncomms11693 · Cited by 299
Self-citationKorneliussen, T. S., Moltke, I., Albrechtsen, A., & Nielsen, R. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-289 · Cited by 269
“…will explore for calculating neutrality statistics ( T ) for a region, is to estimate the SFS, η = ( η k ) k =0, … , n , using one of these methods, in this case the maximum likelihood method by [35] ( ^ η ML ), and then calculate T using estimated values of η instead of the (unknown) true values.”
MethodInfluentialSelf-citationPrince, D. J., O'Rourke, S. M., Thompson, T., Ali, O. A., Lyman, H. S., Saglam, I. K., Hotaling, T. J., Spidle, A., & Miller, M. R. (2016) · Science Advances · doi:10.1126/sciadv.1603198 · Cited by 221
Sequence Capture versus Restriction Site Associated DNA Sequencing for Shallow Systematics
Harvey, M. G., Smith, B. T., Glenn, T. C., Faircloth, B. C., & Brumfield, R. T. (2016) · Systematic Biology · doi:10.1093/sysbio/syw036 · Cited by 263
“However, in low-coverage datasets, such as those we analyzed, discriminating between errors and rare alleles is challenging (Nielsen et al. 2012; Fumagalli et al. 2013).”
BackgroundngsTools: methods for population genetics analyses from next-generation sequencing data
Fumagalli, M., Vieira, F. G., Linderoth, T. P., & Nielsen, R. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu041 · Cited by 252
“Recently, probabilistic models that take these errors into account have been proposed to accurately assign genotypes and estimate allele frequencies (e.g. Nielsen et al., 2012).”
BackgroundSelf-citationUsing Genomic Data to Infer Historic Population Dynamics of Nonmodel Organisms
Beichman, A. C., Huerta‐Sánchez, E., & Lohmueller, K. E. (2018) · Annual Review of Ecology Evolution and Systematics · doi:10.1146/annurev-ecolsys-110617-062431 · Cited by 212
Next-generation biology: Sequencing and data analysis approaches for non-model organisms
da Fonseca, R. R., Albrechtsen, A., Themudo, G. E., Ramos‐Madrigal, J., Sibbesen, J. A., Maretty, L., Mendoza, M. L. Z., Campos, P. F., Heller, R., & Pereira, R. J. (2016) · Marine Genomics · doi:10.1016/j.margen.2016.04.012 · Cited by 220
Self-citationPopulation genomics of post-glacial western Eurasia
Allentoft, M. E., Sikora, M., Refoyo-Martínez, A., Irving-Pease, E. K., Fischer, A., Barrie, W., Ingason, A., Stenderup, J. T., Sjögren, K.-G., Pearson, A., et al. (2024) · Nature · doi:10.1038/s41586-023-06865-0 · Cited by 281
Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data
Fumagalli, M., Vieira, F. G., Korneliussen, T. S., Linderoth, T. P., Huerta‐Sánchez, E., Albrechtsen, A., & Nielsen, R. (2013) · Genetics · doi:10.1534/genetics.113.154740 · Cited by 209
“…and Bayesian methods have been developed for estimating the allele frequency at any given site (Lynch, 2009; Keightley and Halligan, 2011; Kim et al., 2011) or the entire distribution of allele frequencies jointly across multiple sites (Nielsen et al., 2012; Li, 2011; Keightley and Halligan, 2011).”
BackgroundMethodInfluentialSelf-citationGenomic legacy of the African cheetah, Acinonyx jubatus
Dobrynin, P., Liu, S., Tamazian, G., Xiong, Z., Yurchenko, A. A., Krasheninnikova, K., Kliver, S., Schmidt-Küntzel, A., Koepfli, K.-P., Johnson, W., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0837-4 · Cited by 204
“The method described in [65] is a robust and high-precision method for SNV calling at low depth based on the methodology of the site frequency spectrum (SFS).”
MethodSelf-citationWhole genome re-sequencing of date palms yields insights into diversification of a fruit tree crop
Hazzouri, K. M., Flowers, J. M., Visser, H. J., Khierallah, H. S. M., Rosas, U., Pham, G. M., Meyer, R. S., Johansen, C. K., Fresquez, Z. A., Masmoudi, K., et al. (2015) · Nature Communications · doi:10.1038/ncomms9824 · Cited by 200
Global urban environmental change drives adaptation in white clover
Santangelo, J. S., Ness, R. W., Cohan, B., Fitzpatrick, C. R., Innes, S. G., Koch, S., Miles, L. S., Munim, S., Peres‐Neto, P. R., Prashad, C. M., et al. (2022) · Science · doi:10.1126/science.abk0989 · Cited by 196
Historical Genomes Reveal the Genomic Consequences of Recent Population Decline in Eastern Gorillas
van der Valk, T., Díez-del-Molino, D., Marques-Bonet, T., Guschanski, K., & Dalén, L. (2019) · Current Biology · doi:10.1016/j.cub.2018.11.055 · Cited by 188
“We observed slight biases on diversity estimates related to differential coverage (Figure S1C), and therefore all genomic comparisons were subsequently based on the modern genomes downsampled to 4X coverage. e4 Current Biology 29, 165–170.e1–e6, January 7, 2019”
MethodInfluentialBoitard, S., Rodríguez, W., Jay, F., Mona, S., & Austerlitz, F. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005877 · Cited by 190
“Such algorithms are available both for AFS [55] and LD statistics [56], which is another advantage of using these standard summary statistics.”
MethodVijay, N., Bossu, C. M., Poelstra, J. W., Weissensteiner, M. H., Suh, A., Kryukov, A. P., & Wolf, J. B. W. (2016) · Nature Communications · doi:10.1038/ncomms13195 · Cited by 191
The origin of domestication genes in goats
Zheng, Z., Wang, X., Li, M., Li, Y., Yang, Z., Wang, X., Pan, X., Gong, M., Zhang, Y., Guo, Y., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz5216 · Cited by 184
Self-citationRecent novel approaches for population genomics data analysis
Andrews, K. R., & Luikart, G. (2014) · Molecular Ecology · doi:10.1111/mec.12686 · Cited by 172
“2013), as long as data are available for multiple individuals per population (Nielsen et al. 2012).”
BackgroundMethodAssessing the Effect of Sequencing Depth and Sample Size in Population Genetics Inferences
Fumagalli, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079667 · Cited by 168
“Similarly, empirical Bayes methods have been proposed to calculate the posterior probability P(fsDX ) of the sample allele frequency f at site s [11,12].”
InfluentialAthrey, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817052-6.00018-5 · Cited by 164
Monovar: single-nucleotide variant detection in single cells
Zafar, H., Wang, Y., Nakhleh, L. K., Navin, N. E., & Chen, K. (2016) · Nature Methods · doi:10.1038/nmeth.3835 · Cited by 157
A time transect of exomes from a Native American population before and after European contact
Lindo, J., Huerta‐Sánchez, E., Nakagome, S., Rasmussen, M., Petzelt, B., Mitchell, J., Cybulski, J. S., Willerslev, E., DeGiorgio, M., & Malhi, R. S. (2016) · Nature Communications · doi:10.1038/ncomms13175 · Cited by 158
“52) to compute allele frequencies for the modern Tsimshian, the PRH Ancient, the Han Chinese (CHB) and the Great Britain (GBR) populations directly from the raw sequencing reads, accounting for the uncertainty in genotype calling.”
MethodThe impact of whole-genome sequencing on the reconstruction of human population history
Veeramah, K. R., & Hammer, M. F. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3625 · Cited by 151
Evolutionary Patterns and Processes: Lessons from Ancient DNA
Leonardi, M., Librado, P., Sarkissian, C. S. I. D., Schubert, M., Alfarhan, A., Alquraishi, S. A., Al‐Rasheid, K. A. S., Gamba, C., Willerslev, E., & Orlando, L. (2016) · Systematic Biology · doi:10.1093/sysbio/syw059 · Cited by 156
“With lower coverage comes greater statistical uncertainty associated with genotype calling, which can impact downstream inferences (Nielsen et al. 2012; Fumagalli et al. 2013).”
BackgroundInfluentialCrawford, J. E., Alves, J. M., Palmer, W. J., Day, J. P., Sylla, M., Ramasamy, R., Surendran, S. N., Black, W. C., Pain, A., & Jiggins, F. M. (2017) · BMC Biology · doi:10.1186/s12915-017-0351-0 · Cited by 146
“We estimated one- and two-dimensional site frequency spectra (SFS) using the doSaf function within ANGSD to estimate per-site allele frequencies combined with the realSFS program [39] to optimize the genomewide SFS.”
MethodMeleshko, O., Martin, M. D., Korneliussen, T. S., Schröck, C., Lamkowski, P., Schmutz, J., Healey, A. L., Piatkowski, B. T., Shaw, A. J., Weston, D. J., et al. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab063 · Cited by 150
Self-citationCorbett-Detig, R. B., & Nielsen, R. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006529 · Cited by 134
“A growing number of next-generation sequencing projects produce low coverage data that 629 cannot be used to unambiguously assign individual genotypes, but which can be analyzed 630 probabilistically to account for uncertainty in individual genotypes [70-72].”
BackgroundSelf-citationMianné, J., Chessum, L., Kumar, S., Aguilar, C., Codner, G. F., Hutchison, M., Parker, A. J., Mallon, A., Wells, S. E., Simon, M. M., et al. (2016) · Genome Medicine · doi:10.1186/s13073-016-0273-4 · Cited by 138
“SNV annotations were done using NGS-SNP [14].”
A Comparison of Variant Calling Pipelines Using Genome in a Bottle as a Reference
Cornish, A. S., & Guda, C. (2015) · BioMed Research International · doi:10.1155/2015/456479 · Cited by 139
“generating simulated data so as to create a set of known true positives (TP) and true negatives (TN) [8–10].”
BackgroundImproving the estimation of genetic distances from Next-Generation Sequencing data
Vieira, F. G., Lassalle, F., Korneliussen, T. S., & Fumagalli, M. (2015) · Biological Journal of the Linnean Society · doi:10.1111/bij.12511 · Cited by 136
Self-citationTowards the identification of the loci of adaptive evolution
Pardo‐Díaz, C., Salazar, C. A., & Jiggins, C. D. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12324 · Cited by 141
“Technical replicates with sufficient coverage may also be needed in order to assess the reproducibility of the genotyping technique and the quality of SNP calling, both of which can affect the proper calling of heterozygous sites and the reliability of GWAS (Hong et al. 2012; Nielsen et al. 2012).”
BackgroundAssociation mapping from sequencing reads using k-mers
Rahman, A. H., Hallgrímsdóttir, I. B., Eisen, M. B., & Pachter, L. S. (2018) · eLife · doi:10.7554/elife.32920 · Cited by 137
Melo, A. T. O., Bartaula, R., & Hale, I. L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0879-y · Cited by 129
“[47] discussed Fig.”
Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data
Lipatov, M., Sanjeev, K., Patro, R., & Veeramah, K. R. (2015) · doi:10.1101/023374 · Cited by 108
“Our approach is similar to other recent methods that attempt to infer population genetic parameters from low coverage data by utilizing genotype likelihoods rather than assuming a single best genotype [29, 20, 37, 19].”
ResultInfluentialLohmueller, K. E., Sparsø, T., Li, Q., Galijatovic, E. A. A., Korneliussen, T. S., Albrechtsen, A., Banasik, K., Grarup, N., Hallgrímsdóttir, I. B., Kiil, K., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.11.005 · Cited by 123
“were done with ANGSD (Analysis of Next Generation Sequencing Data) software.(45) On the basis of the genotype likelihoods calculated from”
MethodSelf-citationPatton, A. H., Margres, M. J., Stahlke, A. R., Hendricks, S. A., Lewallen, K., Hamede, R., Ruiz‐Aravena, M., Ryder, O. A., McCallum, H. I., Jones, M. E., et al. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz191 · Cited by 125
“ANGSD implements a twostage procedure to infer the SFS. Briefly, ANGSD first calculates individual genotype likelihoods from the.bam assemblies assuming Hardy–Weinberg Equilibrium (HWE: option -doSaf 1) and uses these to calculate the sample allele frequencies at each site.”
MethodInfluentialFrom next-generation resequencing reads to a high-quality variant data set
Pfeifer, S. P. (2016) · Heredity · doi:10.1038/hdy.2016.102 · Cited by 126
“…and Halligan, 2011; Kim et al. , 2011) or jointly across many loci (Keightley and Halligan, 2011; Li, 2011a; Nielsen et al. , 2012), mutation rates (Kang and Marjoram, 2011) as well as other summary statistics (Gompert and Buerkle, 2011; Li, 2011a; Nielsen et al. , 2012; Fumagalli et al. , 2013).”
BackgroundHigh evolutionary potential of marine zooplankton
Peijnenburg, K. T. C. A., & Goetze, E. (2013) · Ecology and Evolution · doi:10.1002/ece3.644 · Cited by 129
“…to compare genetic results across marker types in order to obtain rigorous estimates of dispersal through Bayesian, Maximum Likelihood, or coalescent analytical techniques (e.g., Pritchard et al. 2000; Hey and Nielsen 2004; Zhang 2008; Beerli and Palczewski 2010; Hey 2010; Nielsen et al. 2012).”
BackgroundMethodWang, H., Xu, X., Vieira, F. G., Xiao, Y., Li, Z., Wang, J., Nielsen, R., & Chu, C. (2016) · Molecular Plant · doi:10.1016/j.molp.2016.04.018 · Cited by 123
“These methods account for the uncertainty regarding genotypes inherent in low-coverage data (Nielsen et al., 2011).”
MethodSelf-citationWaples, R. K., Albrechtsen, A., & Moltke, I. (2018) · Molecular Ecology · doi:10.1111/mec.14954 · Cited by 130
“The second approach, which we denote the SFS‐based ap‐ proach, consists of performing ML estimation of the two‐dimen‐ sional site‐frequency spectrum (SFS) as in realSFS (Nielsen et al., 2012).”
Self-citationReproductive isolation between phylogeographic lineages scales with divergence
Singhal, S., & Moritz, C. C. (2013) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2013.2246 · Cited by 121
“Here, we only used UTR sequence because UTRs are more likely to evolve neutrally than coding sequence [47], and we restricted our analysis to high-coverage regions (≥20×) where we had greater confidence in genotype calling [25].”
MethodThe Genetic Basis of Adaptation following Plastic Changes in Coloration in a Novel Environment
Corl, A. B., Bi, K., Luke, C., Challa, A. S., Stern, A. J., Sinervo, B., & Nielsen, R. (2018) · Current Biology · doi:10.1016/j.cub.2018.06.075 · Cited by 116
Self-citationMa, T., Wang, K., Hu, Q., Xi, Z., Wan, D., Wang, Q., Feng, J., Jiang, D., Ahani, H., Abbott, R. J., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1713288114 · Cited by 122
“euphratica (43), and population genetic statistics based on the site frequency spectrum were then estimated using methods that accounted for uncertainty in the assignment of genotypes (44, 45).”
MethodDetecting Polygenic Adaptation in Admixture Graphs
Racimo, F., Berg, J. J., & Pickrell, J. K. (2018) · Genetics · doi:10.1534/genetics.117.300489 · Cited by 118
“[63] Rasmus Nielsen, Thorfinn Korneliussen, Anders Albrechtsen, Yingrui Li, and Jun Wang.”
BackgroundVieira, F. G., Fumagalli, M., Albrechtsen, A., & Nielsen, R. (2013) · Genome Research · doi:10.1101/gr.157388.113 · Cited by 107
Self-citationWit, P. D., Pespeni, M. H., & Palumbi, S. R. (2015) · Molecular Ecology · doi:10.1111/mec.13165 · Cited by 110
“InDels are particularly troublesome in highdiversity species with large population sizes, for example, in many fish and marine invertebrates, InDels are so common in introns that traditional sequencing of amplified loci long ago shifted to exons.”
BackgroundMethodUncovering the Genetic History of the Present-Day Greenlandic Population
Moltke, I., Fumagalli, M., Korneliussen, T. S., Crawford, J. E., Bjerregaard, P., Jørgensen, M. E., Grarup, N., Gulløv, H. C., Linneberg, A. R., Pedersen, O. B., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.11.012 · Cited by 114
“We estimated the SFS from sequencing data from five populations (Greenlanders, CEU, CHB, JPT, and YRI) by using the full maximum-likelihood method from Nielsen et al. 27 as implemented in ANGSD (Analysis of Next Generation Sequencing Data).”
MethodSelf-citationRuns of homozygosity have utility in mammalian conservation and evolutionary studies
Brüniche–Olsen, A., Kellner, K. F., Anderson, C. J., & DeWoody, J. A. (2018) · Conservation Genetics · doi:10.1007/s10592-018-1099-y · Cited by 108
Population genomics of postglacial western eurasiaPreprint
Allentoft, M. E., Sikora, M., Refoyo-Martínez, A., Irving-Pease, E. K., Fischer, A., Barrie, W., Ingason, A., Stenderup, J. T., Sjögren, K.-G., Pearson, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.04.490594 · Cited by 103
Lozier, J. D. (2013) · Molecular Ecology · doi:10.1111/mec.12636 · Cited by 96
“525 (Nielsen et al. 2012) was developed to estimate population genetic parameters from low-coverage NGS data.”
BackgroundMethodResultInfluentialOn the Extent of Linkage Disequilibrium in the Genome of Farm Animals
Qanbari, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01304 · Cited by 107
Sampling strategies for frequency spectrum-based population genomic inference
Robinson, J. D., Coffman, A. J., Hickerson, M. J., & Gutenkunst, R. N. (2014) · BMC Evolutionary Biology · doi:10.1186/s12862-014-0254-4 · Cited by 95
“Nonetheless, methods have recently been developed to directly infer the AFS, without the need for SNP or genotype calling, from next-generation sequence data [34].”
MethodAncient nuclear genomes enable repatriation of Indigenous human remains
Wright, J. L., Wasef, S., Heupink, T. H., Westaway, M., Rasmussen, S., Pardoe, C., Fourmile, G. G., Young, M., Johnson, T., Slade, J., et al. (2018) · Science Advances · doi:10.1126/sciadv.aau5064 · Cited by 99
“Low levels of contamination from a European source were observed in some of the low-coverage ancient samples such as PA109, although it has been reported that using ADMIXTURE on low-coverage samples can result in statistical uncertainty associated with SNP and genotype calling, resulting in high error rates due to sam…”
BackgroundWright, A. E., Fumagalli, M., Cooney, C. R., Bloch, N. I., Vieira, F. G., Buechel, S. D., Kolm, N., & Mank, J. E. (2018) · Evolution Letters · doi:10.1002/evl3.39 · Cited by 97
Wang, J., Street, N. R., Scofield, D. G., & Ingvarsson, P. K. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw051 · Cited by 95
“S1, Supplementary Material online): (1) For those population genetic statistics that relied on inferred sitefrequency-spectrum (SFS), estimation was performed directly from genotype likelihoods without calling genotypes (Nielsen et al. 2011) as implemented in ANGSD (Korneliussen et al.”
Self-citationBrandenburg, J., Mary‐Huard, T., Rigaill, G. J., Hearne, S., Corti, H., Joets, J., Vitte, C., Charcosset, A., Nicolas, S., & Tenaillon, M. I. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006666 · Cited by 94
“Heterozygotes are much more challenging to call than homozygotes particularly with coverage <20x [35].”
BackgroundSequencing consolidates molecular markers with plant breeding practice
Yang, H., Li, C., Lam, H., Clements, J. C., Yan, G., & Zhao, S. (2015) · Theoretical and Applied Genetics · doi:10.1007/s00122-015-2499-8 · Cited by 96
“A series of statistical methods have been developed to improve the accuracy of variation calling, which is decisive for subsequent analysis (Nielsen et al. 2011, 2012).”
MethodCharacterizing bias in population genetic inferences from low-coverage sequencing data
Han, E., Sinsheimer, J. S., & Novembre, J. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/mst229 · Cited by 94
“We used the freely available program ANGSD (version 0.522) with an EM algorithm option to obtain the MLE of the SFS (Nielsen et al. 2012).”
InfluentialGenotype Calling from Population-Genomic Sequencing Data
Maruki, T., & Lynch, M. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.039008 · Cited by 94
“…the population-level information on genotype frequencies and error rates into the genotype-calling process using Bayes ’ theorem [see Martin et al. (2010); Nielsen et al. (2012); Vieira et al. (2013) for similar methods using the expectation-maximization algorithm for estimating priors].”
MethodWang, J., Street, N. R., Scofield, D. G., & Ingvarsson, P. K. (2015) · Genetics · doi:10.1534/genetics.115.183152 · Cited by 88
Self-citationNunziata, S. O., Lance, S. L., Scott, D. E., Lemmon, E. M., & Weisrock, D. W. (2016) · Molecular Ecology · doi:10.1111/mec.13988 · Cited by 87
“700 (Nielsen et al. 2012; Korneliussen et al. 2014).”
Conservation Genetics and Genomics of Amphibians and Reptiles
Shaffer, H. B., Gidiş, M., McCartney‐Melstad, E., Neal, K. M., Oyamaguchi, H. M., Tellez, M., & Toffelmier, E. M. (2015) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-022114-110920 · Cited by 87
A high-throughput SNP discovery strategy for RNA-seq data
Zhao, Y., Wang, K., Wang, W., Yin, T., Dong, W., & Xu, C. (2019) · BMC Genomics · doi:10.1186/s12864-019-5533-4 · Cited by 88
Experimental evidence for rapid genomic adaptation to a new niche in an adaptive radiation
Marques, D. A., Jones, F. C., Palma, F. D., Kingsley, D. M., & Reimchen, T. E. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0581-8 · Cited by 86
Głowacka, K., Clark, L. V., Adhikari, S., Peng, J. H., Stewart, J. R., Nishiwaki, A., Yamada, T., Jørgensen, U., Hodkinson, T. R., Gifford, J. M., et al. (2013) · GCB Bioenergy · doi:10.1111/gcbb.12166 · Cited by 82
“(2012) was within typical error rates based on other sequencing studies (Hedges et al., 2009; Dewey et al., 2012; Nielsen et al., 2012), and that our study found no nuclear or cytoplasmic genetic differences between ‘Illinois’ and ‘Freedom’, it is likely that the variation observed by Chouvarine et al.”
BackgroundResultInfluentialRADseq approaches and applications for forest tree genetics
Parchman, T. L., Jahner, J. P., Uckele, K. A., Galland, L. M., & Eckert, A. J. (2018) · Tree Genetics & Genomes · doi:10.1007/s11295-018-1251-3 · Cited by 82
“Recent studies have described and used such models to estimate allele frequencies and genotype probabilities (Gompert et al. 2012; Nielsen et al. 2012), to quantify population structure (Fumagalli et al. 2013), and to estimate admixture coefficients (Skotte et al. 2013; Gompert et al. 2014; Lindtke…”
BackgroundInfluentialThe genomic basis of environmental adaptation in house mice
Phifer‐Rixey, M., Bi, K., Ferris, K. G., Sheehan, M. J., Lin, D., Mack, K. L., Keeble, S. M., Suzuki, T. A., Good, J. M., & Nachman, M. W. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007672 · Cited by 80
“We called SNPs and estimated allele frequencies at variable sites using the software ANGSD [73], a package that uses a Bayesian framework to address biases that result from calling variant sites and genotypes with low to moderate coverage sequence data [74].”
MethodScarcelli, N., Mariac, C., Couvreur, T. L. P., Faye, A., Richard, D., Sabot, F., Berthouly‐Salazar, C., & Vigouroux, Y. (2015) · Molecular Ecology Resources · doi:10.1111/1755-0998.12462 · Cited by 78
“…the original work is properly cited and is not used for commercial purposes. if the calling errors can be handled, for example by filtering on base quality, the error rate is low but will still be significant compared to the amount of data generated (at least 0.1% according to Nielsen et al. 2012).”
ResultInfluentialFantastic Beasts and How To Sequence Them: Ecological Genomics for Obscure Model Organisms
Matz, M. V. (2017) · Trends in Genetics · doi:10.1016/j.tig.2017.11.002 · Cited by 87
Sex-Specific Selection Drives the Evolution of Alternative Splicing in Birds
Rogers, T. F., Droguett, D. H. P., & Wright, A. E. (2020) · Molecular Biology and Evolution · doi:10.1093/molbev/msaa242 · Cited by 80
“Next, we estimated the overall unfolded site frequency spectrum for each species (Nielsen et al. 2012).”
BackgroundSeqMule: automated pipeline for analysis of human exome/genome sequencing data
Guo, Y., Ding, X., Shen, Y., Lyon, G. J., & Wang, K. (2015) · Scientific Reports · doi:10.1038/srep14283 · Cited by 76
Next-generation data filtering in the genomics era
Hemstrom, W., Grummer, J. A., Luikart, G., & Christie, M. R. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00738-6 · Cited by 77
“For example, methods based on the SFS of a population may be biased if correlated allele frequencies in a variant-rich region differ from the genome-wide average, and failure to remove non-independent”
InfluentialSNP genotyping and parameter estimation in polyploids using low-coverage sequencing data
Blischak, P. D., Kubatko, L. S., & Wolfe, A. D. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx587 · Cited by 73
“…the uncertainty of inferring genotypes when estimating other parameters (allele frequencies, inbreeding coefficients, population differentiation, etc.) (e.g. Fumagalli et al., 2013; Huang et al., 2016; Li, 2011; Martin et al., 2010; Nielsen et al., 2011, 2012; Vieira et al., 2013, among others).”
BackgroundBee conservation in the age of genomics
Lozier, J. D., & Zayed, A. (2016) · Conservation Genetics · doi:10.1007/s10592-016-0893-7 · Cited by 74
“Nonetheless, HTS-based approaches offer their own sets of biases in diversity estimation, including heterozygote dropout in low coverage datasets or loss of restriction cut sites through mutation in genome reduction approaches such as RADseq (Nielsen et al. 2012; Arnold et al. 2013).”
Principles and approaches of association mapping in plant breeding
Ibrahim, A., Zhang, L., Niyitanga, S., Afzal, M. Z., Xu, Y., Zhang, L., Zhang, L., & Qi, J. (2020) · Tropical Plant Biology · doi:10.1007/s12042-020-09261-4 · Cited by 79
“Moreover, genotype calling will be complicated when sequencing depth is low (Nielsen et al. 2012) due to the sequencing errors and repetitive regions.”
BackgroundModel‐based genotype and ancestry estimation for potential hybrids with mixed‐ploidy
Shastry, V., Adams, P. E., Lindtke, D., Mandeville, E. G., Parchman, T. L., Gompert, Z., & Buerkle, C. A. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13330 · Cited by 69
Singhal, S. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12077 · Cited by 73
“Despite the only moderate levels of concordance in SNP calls, I find that the SFS is nearly identical across all the different approaches, but VARSCAN (Fig.”
BackgroundMethodInfluentialMarques, D. A., Taylor, J. S., Jones, F. C., Palma, F. D., Kingsley, D. M., & Reimchen, T. E. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2001627 · Cited by 73
Metadata
- 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
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-07-24
- Volume
- 7
- Issue
- 7
- Pages
- e37558
- ISSN
- 1932-6203 (ISSN-L)
- 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
- 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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