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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deep sequencing could not detect low-frequency single-nucleotide polymorphisms in hepatocytes due to sequencing error rate, mixture of nonparenchymal cells, and polyploidy of hepatocytes.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ESTIMATING F -STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE
Weir, B. S., & Cockerham, C. C. (1984) · Evolution · doi:10.1111/j.1558-5646.1984.tb05657.x · Cited by 18,909 · cited 1× · see citing works
“, [21]) also proceed from estimates of the SFS.”
BackgroundProceedings of the National Academy of Sciences of the United States of America
Dowling, J. E., & Wald, G. (2009) · Nutrition Reviews · doi:10.1111/j.1753-4887.1981.tb06752.x · Cited by 17,937 · see citing works
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933 · see citing works
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411 · see citing works
Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.
Tajima, F. (1989) · Genetics · doi:10.1093/genetics/123.3.585 · Cited by 13,991 · see citing works
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · cited 1× · see citing works
“, [5]), in population genetic studies (e.”
Estimating F-Statistics for the Analysis of Population Structure
Weir, B. S., & Cockerham, C. C. (1984) · Evolution · doi:10.2307/2408641 · Cited by 11,023 · see citing works
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877 · cited 4× · see citing works
“In fact, imputation based genotype calling will lead to a substantial increase in accuracy over other methods [7].”
BackgroundMethodInfluentialLi, H. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr509 · Cited by 6,283 · see citing works
On the number of segregating sites in genetical models without recombination
Watterson, G. A. (1975) · Theoretical Population Biology · doi:10.1016/0040-5809(75)90020-9 · Cited by 3,968 · see citing works
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928 · cited 1× · see citing works
“, [30,31,32,33,34]) as in the 1000 Genomes project [7].”
BackgroundBrowning, S. R., & Browning, B. L. (2007) · The American Journal of Human Genetics · doi:10.1086/521987 · Cited by 3,138 · cited 1× · see citing works
“, [30,31,32,33,34]) as in the 1000 Genomes project [7].”
BackgroundMapping short DNA sequencing reads and calling variants using mapping quality scores
Li, H., Ruan, J., & Durbin, R. (2008) · Genome Research · doi:10.1101/gr.078212.108 · Cited by 2,638 · cited 2× · see citing works
“, [10,11,12,13]), but if the coverage is low, they will be sharing the basic features outlined here: a trade-off between including too many SNPs and under-calling true heterozygotes.”
BackgroundA new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609 · cited 1× · see citing works
“, [30,31,32,33,34]) as in the 1000 Genomes project [7].”
BackgroundGutenkunst, R. N., Hernandez, R. D., Williamson, S. H., & Bustamante, C. D. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000695 · Cited by 1,906 · cited 1× · see citing works
“[20]) and quantifying population subdivision using FST (e.”
MethodNext-generation sequencing transforms today's biology
Schuster, S. C. (2007) · Nature Methods · doi:10.1038/nmeth1156 · Cited by 1,813 · see citing works
Sequencing of 50 Human Exomes Reveals Adaptation to High Altitude
Yi, X., Liang, Y., Huerta-Sanchez, E., Jin, X., Cuo, Z. X. P., Pool, J. E., Xu, X., Jiang, H., Vinckenbosch, N., Korneliussen, T. S., et al. (2010) · Science · doi:10.1126/science.1190371 · Cited by 1,611 · cited 5× · see citing works
“For simulation purposes we will occasionally also use the estimator by [8], which is faster to calculate but may not be as accurate as the ML estimator.”
BackgroundMethodInfluentialSelf-citationNumerical Recipes in C: The Art of Scientific Computing
Seiler, M. C., & Seiler, F. A. (1989) · Risk Analysis · doi:10.1111/j.1539-6924.1989.tb01007.x · Cited by 1,356 · see citing works
The sequence and de novo assembly of the giant panda genome
Li, R., Fan, W., Tian, G., Zhu, H., He, L., Cai, J., Huang, Q., Cai, Q., Li, B., Bai, Y., et al. (2010) · Nature · doi:10.1038/nature08696 · Cited by 1,180 · cited 1× · see citing works
“, [2]), in human disease genetics and diagnostics (e.”
Self-citationSNP detection for massively parallel whole-genome resequencing
Li, R., Li, Y., Fang, X., Yang, H., Wang, J., Kristiansen, K., & Wang, J. (2009) · Genome Research · doi:10.1101/gr.088013.108 · Cited by 944 · see citing works
Self-citationNext-Generation Sequencing: From Basic Research to Diagnostics
Voelkerding, K. V., Dames, S. A., & Durtschi, J. (2009) · Clinical Chemistry · doi:10.1373/clinchem.2008.112789 · Cited by 846 · cited 1× · see citing works
“, [3,4]), in gene expression analyses (e.”
Demographic history and rare allele sharing among human populations
Gravel, S., Henn, B. M., Gutenkunst, R. N., Indap, A. R., Marth, G., Clark, A. G., Yu, F., Gibbs, R. A., The 1000 Genomes Project, Bustamante, C. D., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019276108 · Cited by 625 · cited 1× · see citing works
“[35] provided an alternative approach.”
Self-citationEvaluation of next generation sequencing platforms for population targeted sequencing studies
Harismendy, O., Ng, P. C., Strausberg, R. L., Wang, X., Stockwell, T. B., Beeson, K. Y., Schork, N. J., Murray, S. S., Topol, E. J., Levy, S., et al. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r32 · Cited by 614 · cited 1× · see citing works
“, [10,11,12,13]), but if the coverage is low, they will be sharing the basic features outlined here: a trade-off between including too many SNPs and under-calling true heterozygotes.”
BackgroundAn Introduction to Empirical Bayes Data Analysis
Casella, G. (1985) · The American Statistician · doi:10.1080/00031305.1985.10479400 · Cited by 524 · cited 1× · see citing works
“, [28]) as a large set of data points is being used to define a prior that subsequently can be applied to each data point.”
BackgroundMassively Parallel Sequencing: The Next Big Thing in Genetic Medicine
Tucker, T., Marra, M. A., & Friedman, J. M. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.06.022 · Cited by 384 · cited 1× · see citing works
“, [3,4]), in gene expression analyses (e.”
Li, Y., Vinckenbosch, N., Tian, G., Huerta‐Sánchez, E., Jiang, T., Jiang, H., Albrechtsen, A., Andersen, G., Cao, H., Korneliussen, T. S., et al. (2010) · Nature Genetics · doi:10.1038/ng.680 · Cited by 307 · cited 4× · see citing works
“Many NGS studies (e.g., [6,7,8] are based on medium to low coverage, i.e. coverage at , 20X.”
BackgroundMethodInfluentialSelf-citationEstimation of allele frequency and association mapping using next-generation sequencing data
Kim, S. Y., Lohmueller, K. E., Albrechtsen, A., Li, Y., Korneliussen, T. S., Tian, G., Grarup, N., Jiang, T., Andersen, G., Witte, D. R., et al. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-231 · Cited by 216 · cited 2× · see citing works
“The latter method is based on the likelihood function described in [11,22,23,29].”
BackgroundMethodSelf-citationAn Introduction to Empirical Bayes Data Analysis
Casella, G. (1985) · The American Statistician · doi:10.2307/2682801 · Cited by 194 · see citing works
Benchmarking next-generation transcriptome sequencing for functional and evolutionary genomics.
Gibbons, J., Janson, E. M., Hittinger, C., Johnston, M., Abbot, P., & Rokas, A. (2009) · Molecular biology and evolution · doi:10.1093/molbev/msp188 · Cited by 163 · cited 1× · see citing works
“In most NGS, the error rate is at least 0.1% even after stringent filtering based on quality scores (e.g., [9]).”
BackgroundInfluentialSNP detection and genotyping from low-coverage sequencing data on multiple diploid samplesPreprint
Le, S. Q., & Durbin, R. M. (2010) · Genome Research · doi:10.1101/gr.113084.110 · Cited by 149 · cited 3× · see citing works
“Similar dynamic programming algorithm has previously been used in [8] and [26] for single site inferences.”
BackgroundMethodLynch, M. (2008) · Molecular Biology and Evolution · doi:10.1093/molbev/msn185 · Cited by 123 · cited 1× · see citing works
“As a result, low coverage and medium coverage NGS data tends to provide biased estimates of the distribution of allele frequencies ([14,15,16,17] In this paper, we will explore the implications of this for population genetic inferences.”
BackgroundMapping Trait Loci by Use of Inferred Ancestral Recombination Graphs
Minichiello, M. J., & Durbin, R. (2006) · The American Journal of Human Genetics · doi:10.1086/508901 · Cited by 115 · cited 1× · see citing works
“, [30,31,32,33,34]) as in the 1000 Genomes project [7].”
BackgroundPopulation genetic analysis of shotgun assemblies of genomic sequences from multiple individualsPreprint
Hellmann, I., Mang, Y., Gu, Z., Li, P., Vega, F. M. D. L., Clark, A. G., & Nielsen, R. (2008) · Genome Research · doi:10.1101/gr.074187.107 · Cited by 95 · cited 1× · see citing works
“As a result, low coverage and medium coverage NGS data tends to provide biased estimates of the distribution of allele frequencies ([14,15,16,17] In this paper, we will explore the implications of this for population genetic inferences.”
BackgroundSelf-citationAccounting for Bias from Sequencing Error in Population Genetic Estimates
Johnson, P. L. F., & Slatkin, M. (2007) · Molecular Biology and Evolution · doi:10.1093/molbev/msm239 · Cited by 94 · cited 3× · see citing works
“This bias will affect different allele frequency categories differentially and lead to biases in the estimate of the SFS. [15,16] argue that methods for estimating h should instead take the inherent uncertainty in the data into account.”
BackgroundMethodDesign of association studies with pooled or un‐pooled next‐generation sequencing data
Kim, S. Y., Li, Y., Guo, Y., Li, R., Holmkvist, J., Hansen, T. F., Pedersen, O. B., Wang, J., & Nielsen, R. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20501 · Cited by 88 · cited 4× · see citing works
“We will use the maximum likelihood estimator of population allele frequency (not to be confused with sample allele frequency) described by [22] in any data applications in this paper.”
BackgroundMethodInfluentialSelf-citationExome Sequencing of a Multigenerational Human Pedigree
Hedges, D., Burges, D., Powell, E., Almonte, C., Huang, J., Young, S., Boese, B., Schmidt, M., Pericak-Vance, M. A., Martin, E., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008232 · Cited by 76 · cited 1× · see citing works
“, [10,11,12,13]), but if the coverage is low, they will be sharing the basic features outlined here: a trade-off between including too many SNPs and under-calling true heterozygotes.”
BackgroundInference of population genetic parameters in metagenomics: A clean look at messy dataPreprint
Johnson, P. L. F., & Slatkin, M. (2006) · Genome Research · doi:10.1101/gr.5431206 · Cited by 75 · cited 3× · see citing works
“This bias will affect different allele frequency categories differentially and lead to biases in the estimate of the SFS. [15,16] argue that methods for estimating h should instead take the inherent uncertainty in the data into account.”
BackgroundMethodSeqEM: an adaptive genotype-calling approach for next-generation sequencing studies
Martin, E. R., Kinnamon, D. D., Schmidt, M. A., Powell, E. H., Zuchner, S. L., & Morris, R. W. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq526 · Cited by 70 · cited 1× · see citing works
“The latter method is based on the likelihood function described in [11,22,23,29].”
MethodKeightley, P. D., & Halligan, D. L. (2011) · Genetics · doi:10.1534/genetics.111.128355 · Cited by 59 · see citing works
Imputation methods to improve inference in SNP association studies
Dai, J. Y., Ruczinski, I., LeBlanc, M. L., & Kooperberg, C. L. (2006) · Genetic Epidemiology · doi:10.1002/gepi.20180 · Cited by 50 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
“[9].”
BackgroundMethodInfluentialSelf-citationHarnessing the power of RADseq for ecological and evolutionary genomics
Andrews, K. R., Good, J. M., Miller, M. R., Luikart, G., & Hohenlohe, P. A. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.28 · Cited by 1,502 · see citing works
Robust Demographic Inference from Genomic and SNP Data
Excoffier, L., Dupanloup, I., Huerta‐Sánchez, E., Sousa, V. C., & Foll, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003905 · Cited by 1,434 · see citing works
“Several approaches have been proposed to correct for this bias [32,35], either during the process of genotype calling itself [e.g. 31,36,37] or later by applying quality filters on called genotypes [e.g. 38].”
MethodGenomics and the origin of species
D, O. S. C., Butlin, R. K., Keller, I., Wagner, C. E., BOUGHMAN, J. W., Hohenlohe, P. A., Peichel, C. L., Sætre, G., Bank, C., Brännström, Å., et al. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3644 · Cited by 1,007 · see citing works
Genome editing with Cas9 in adult mice corrects a disease mutation and phenotype
Yin, H., Xue, W., Chen, S., Bogorad, R. L., Benedetti, E., Grompe, M., Koteliansky, V., Sharp, P. A., Jacks, T., & Anderson, D. G. (2014) · Nature Biotechnology · doi:10.1038/nbt.2884 · Cited by 951 · see citing works
Genome sequencing and population genomics in non-model organisms
Ellegren, H. (2013) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2013.09.008 · Cited by 760 · see citing works
Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data
Meisner, J., & Albrechtsen, A. (2018) · Genetics · doi:10.1534/genetics.118.301336 · Cited by 673 · see citing works
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 542 · see citing works
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 436 · see citing works
“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 · see citing works
“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 398 · see citing works
“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 396 · see citing works
“…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 395 · see citing works
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 381 · see citing works
“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 · see citing works
“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 353 · see citing works
Ancient genomes show social and reproductive behavior of early Upper Paleolithic foragers
Sikora, M., Seguin‐Orlando, A., Sousa, V. C., Albrechtsen, A., Korneliussen, T. S., Ko, A., Rasmussen, S., Dupanloup, I., Nigst, P. R., Bosch, M. D., et al. (2017) · Science · doi:10.1126/science.aao1807 · Cited by 333 · see citing works
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 342 · see citing works
“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 343 · see citing works
“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 · see citing works
“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 331 · see citing works
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 304 · see citing works
“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 · see citing works
“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 288 · see citing works
Self-citationKorneliussen, T. S., Moltke, I., Albrechtsen, A., & Nielsen, R. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-289 · Cited by 278 · see citing works
“…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 · see citing works
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 250 · see citing works
“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 · see citing works
“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 207 · see citing works
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 207 · see citing works
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 263 · see citing works
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 201 · see citing works
“…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 · see citing works
“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 · see citing works
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 · see citing works
Boitard, S., Rodríguez, W., Jay, F., Mona, S., & Austerlitz, F. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005877 · Cited by 190 · see citing works
“Such algorithms are available both for AFS [55] and LD statistics [56], which is another advantage of using these standard summary statistics.”
MethodHistorical 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 · see citing works
“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”
MethodInfluentialVijay, 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 184 · see citing works
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 · see citing works
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 · see citing works
“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 161 · see citing works
“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 163 · see citing works
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 153 · see citing works
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 154 · see citing works
“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 148 · see citing works
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 151 · see citing works
“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 · see citing works
“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 148 · see citing works
Self-citationCorbett-Detig, R. B., & Nielsen, R. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006529 · Cited by 134 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
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 131 · see citing works
“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 128 · see citing works
Melo, A. T. O., Bartaula, R., & Hale, I. L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0879-y · Cited by 129 · see citing works
“[47] discussed Fig.”
Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing DataPreprint
Lipatov, M., Sanjeev, K., Patro, R., & Veeramah, K. R. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/023374 · Cited by 118 · see citing works
“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 125 · see citing works
“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., Stahlke, A. R., Hendricks, S. A., Lewallen, K., Hamede, R., Ruiz-Aravena, M., Ryder, O., McCallum, H., Jones, M. E., et al. (2019) · Molecular biology and evolution · doi:10.1093/molbev/msz191 · Cited by 117 · see citing works
“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 122 · see citing works
“…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 121 · see citing works
“…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 · see citing works
“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 123 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
“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 115 · see citing works
“[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 · see citing works
Self-citationWit, P. D., Pespeni, M. H., & Palumbi, S. R. (2015) · Molecular Ecology · doi:10.1111/mec.13165 · Cited by 110 · see citing works
“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 113 · see citing works
“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 · see citing works
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 · see citing works
Lozier, J. D. (2013) · Molecular Ecology · doi:10.1111/mec.12636 · Cited by 96 · see citing works
“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 99 · see citing works
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 94 · see citing works
“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 97 · see citing works
“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 · see citing works
Wang, J., Street, N. R., Scofield, D. G., & Ingvarsson, P. K. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw051 · Cited by 95 · see citing works
“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 90 · see citing works
“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 95 · see citing works
“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 · see citing works
“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 89 · see citing works
“…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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
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 82 · see citing works
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 · see citing works
“(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 81 · see citing works
“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 · see citing works
“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 · see citing works
“…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 83 · see citing works
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 77 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“…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 · see citing works
“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 73 · see citing works
“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 · see citing works
Singhal, S. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12077 · Cited by 73 · see citing works
“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 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
