Estimation of allele frequency and association mapping using next-generation sequencing data

Su Yeon Kim; Kirk E Lohmueller; Anders Albrechtsen; Yingrui Li; Thorfinn Sand Korneliussen; Geng Tian; Niels Grarup; Tao Jiang; Gitte Andersen; Daniel Witte; Torben Jorgensen; Torben Hansen; Oluf Pedersen; Jun Wang; Rasmus Nielsen

doi:10.1186/1471-2105-12-231

Estimation of allele frequency and association mapping using next-generation sequencing data

Su Yeon Kim, Kirk E Lohmueller, Anders Albrechtsen, Yingrui Li, Thorfinn Sand Korneliussen, Geng Tian, Niels Grarup, Tao Jiang, Gitte Andersen, Daniel Witte, Torben Jorgensen, Torben Hansen, Oluf Pedersen, Jun Wang, Rasmus Nielsen

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Abstract

Background: Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.Results: We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.Conclusions: Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.

Original language	English
Journal	B M C Bioinformatics
Volume	12
Pages (from-to)	231
Number of pages	16
ISSN	1471-2105
DOIs	https://doi.org/10.1186/1471-2105-12-231
Publication status	Published - 1 Jun 2011

Keywords

Gene Frequency
Genetics, Population
Genotype
High-Throughput Nucleotide Sequencing
Humans
Likelihood Functions
Polymorphism, Single Nucleotide
Sequence Analysis, DNA

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Kim, S. Y., Lohmueller, K. E., Albrechtsen, A., Li, Y., Korneliussen, T. S., Tian, G., Grarup, N., Jiang, T., Andersen, G., Witte, D., Jorgensen, T., Hansen, T., Pedersen, O., Wang, J., & Nielsen, R. (2011). Estimation of allele frequency and association mapping using next-generation sequencing data. B M C Bioinformatics, 12, 231. https://doi.org/10.1186/1471-2105-12-231

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title = "Estimation of allele frequency and association mapping using next-generation sequencing data",

abstract = "Background: Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.Results: We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.Conclusions: Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.",

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author = "Kim, {Su Yeon} and Lohmueller, {Kirk E} and Anders Albrechtsen and Yingrui Li and Korneliussen, {Thorfinn Sand} and Geng Tian and Niels Grarup and Tao Jiang and Gitte Andersen and Daniel Witte and Torben Jorgensen and Torben Hansen and Oluf Pedersen and Jun Wang and Rasmus Nielsen",

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AU - Kim, Su Yeon

AU - Lohmueller, Kirk E

AU - Albrechtsen, Anders

AU - Li, Yingrui

AU - Korneliussen, Thorfinn Sand

AU - Tian, Geng

AU - Grarup, Niels

AU - Jiang, Tao

AU - Andersen, Gitte

AU - Witte, Daniel

AU - Jorgensen, Torben

AU - Hansen, Torben

AU - Pedersen, Oluf

AU - Wang, Jun

AU - Nielsen, Rasmus

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N2 - Background: Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.Results: We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.Conclusions: Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.

AB - Background: Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.Results: We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.Conclusions: Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.

KW - Gene Frequency

KW - Genetics, Population

KW - Genotype

KW - High-Throughput Nucleotide Sequencing

KW - Humans

KW - Likelihood Functions

KW - Polymorphism, Single Nucleotide

KW - Sequence Analysis, DNA

U2 - 10.1186/1471-2105-12-231

DO - 10.1186/1471-2105-12-231

M3 - Journal article

C2 - 21663684

SN - 1471-2105

VL - 12

SP - 231

JO - B M C Bioinformatics

JF - B M C Bioinformatics

ER -

Estimation of allele frequency and association mapping using next-generation sequencing data

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Keywords

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