The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data
Open accessAbstract
Over the past few years, new high-throughput DNA sequencing technologies have dramatically increased speed and reduced sequencing costs. However, the use of these sequencing technologies is often challenged by errors and biases associated with the bioinformatical methods used for analyzing the data. In particular, the use of naïve methods to identify polymorphic sites and infer genotypes can inflate downstream analyses. Recently, explicit modeling of genotype probability distributions has been proposed as a method for taking genotype call uncertainty into account. Based on this idea, we propose a novel method for quantifying population genetic differentiation from next-generation sequencing data. In addition, we present a strategy for investigating population structure via principal components analysis. Through extensive simulations, we compare the new method herein proposed to approaches based on genotype calling and demonstrate a marked improvement in estimation accuracy for a wide range of conditions. We apply the method to a large-scale genomic data set of domesticated and wild silkworms sequenced at low coverage. We find that we can infer the fine-scale genetic structure of the sampled individuals, suggesting that employing this new method is useful for investigating the genetic relationships of populations sampled at low coverage.
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Cites
Principal components analysis corrects for stratification in genome-wide association studies
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Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Population Structure and Eigenanalysis
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Sequencing of 50 Human Exomes Reveals Adaptation to High Altitude
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Self-citationGenotype and SNP calling from next-generation sequencing data
Nielsen, R., Paul, J. S., Albrechtsen, A., & Song, Y. S. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2986 · Cited by 1,396
Self-citationWhole-genome resequencing reveals loci under selection during chicken domestication
Rubin, C.-J., Zody, M. C., Eriksson, J., Meadows, J. R. S., Sherwood, E., Webster, M. T., Jiang, L., Ingman, M., Sharpe, T., Ka, S., et al. (2010) · Nature · doi:10.1038/nature08832 · Cited by 1,138
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“…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).”
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Cited by
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
Self-citationInferring 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
Self-citationRAD Capture (Rapture): Flexible and Efficient Sequence-Based Genotyping
Ali, O. A., O’Rourke, S. M., Amish, S. J., Meek, M. H., Luikart, G., Jeffres, C., & Miller, M. R. (2016) · Genetics · doi:10.1534/genetics.115.183665 · Cited by 482
Genomic basis of geographical adaptation to soil nitrogen in rice
Liu, Y., Wang, H., Jiang, Z., Wang, W., Xu, R., Wang, Q., Zhang, Z., Li, A., Liang, Y., Ou, S., et al. (2021) · Nature · doi:10.1038/s41586-020-03091-w · Cited by 468
Population 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
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Metadata
- Authors
- Fumagalli, M., Vieira, F. G., Korneliussen, T. S., Linderoth, T., Huerta-Sánchez, E., Albrechtsen, A., & Nielsen, R.
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-11-01
- Volume
- 195
- Issue
- 3
- Pages
- 979-992
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W2112902627
- Cited by
- 207
- References
- 9
- FWCI
- 9.31
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Fumagalli, M., Vieira, F. G., Korneliussen, T. S., Linderoth, T., Huerta-Sánchez, E., Albrechtsen, A., & Nielsen, R. (2013). Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data. Genetics. https://doi.org/10.1534/genetics.113.154740
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