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  • Review Article
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Genotype and SNP calling from next-generation sequencing data

Nature Reviews Genetics volume 12, pages 443–451 (2011)Cite this article

Subjects

Key Points

  • Converting next-generation sequencing (NGS) image files into a set of called SNPs involves a number of steps including image analysis, alignment and assembly, SNP calling and genotype calling.

  • Genotype probabilities for a single individual can be calculated from alignments using recalibrated quality scores.

  • SNP calling and genotype calling is best done using information from multiple individuals simultaneously. The pattern of linkage disequilibrium should be used to call SNPs and genotypes when possible.

  • Analyses of low coverage data can proceed by taking uncertainty in the genotype calls into account, rather than assuming any particular genotype call is correct.

  • The methods used for calling SNPs and for taking uncertainty in SNP genotypes into account can have a strong effect on downstream analyses, including association mapping analyses.

Abstract

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.

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Figure 1: Steps for converting raw next-generation sequencing data into a final set of SNP or genotype calls.
Figure 2: A comparison of three genotype callers.
Figure 3: The power of association mapping for next-generation sequencing data.
Figure 4: The site frequency spectrum in next-generation sequencing data.

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Acknowledgements

This work was supported in part by NIH grants NIGMS R01-HG003229–05 and R01-HG003229–0551 to R.N., an NSF CAREER grant DBI-0846015 to Y.S.S. and an NIH National Research Service Award Trainee appointment on T32-HG00047 to J.S.P.

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Authors and Affiliations

  1. Department of Integrative Biology, University of California, Berkeley, 94720, California, USA

    Rasmus Nielsen

  2. Centre for Bioinformatics, University of Copenhagen, Universitetsparken 15, 2100, Copenhagen Ø, Denmark

    Rasmus Nielsen & Anders Albrechtsen

  3. Department of Statistics, University of California, Berkeley, 94720, California, USA

    Rasmus Nielsen & Yun S. Song

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, 94720, California, USA

    Joshua S. Paul & Yun S. Song

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Correspondence to Rasmus Nielsen or Yun S. Song.

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FURTHER INFORMATION

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1000 Genomes Project

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Glossary

Likelihoods

Functions expressing the probability of observing the data — for example, next-generation sequencing data — given a parameter, such as a genotype or an allele frequency.

Posterior probabilities

In this context, these are the probabilities of a particular genotype given observed data: they are calculated by incorporating the information from the next-generation sequencing data as well as some prior information.

Hashing

A procedure of creating a data structure that helps to accelerate alignment. It stores information about which reads or where in the reference genome a particular substring or subsequence occurs. Some hash-based aligners hash the reads, while others hash the reference genome.

Paired-end reads

Sequencing of both the forward and reverse template of a DNA molecule, which is possible by inserting a primer sequence between the two ends of the read. The use of this technique greatly helps to increase assembly and alignment accuracy.

CEU individuals

One of the 11 populations in HapMap phase three. It consists of Utah residents with Northern and Western European ancestry from the Centre d'Etude du Polymorphisme Humain (CEPH) collection.

Bayes' formula

A mathematical expression showing that a posterior probability can be found as the prior probability multiplied by the likelihood divided by a constant.

Correlated errors

Errors that do not occur independently of each other. An error that is observed in one position might increase the chance of observing another error in a neighbouring position.

Prior probability

In the context of this Review, the probability of a genotype calculated without incorporating information from the next-generation sequencing data. Prior probabilities can be obtained from a set of reference data.

Maximum likelihood

The statistical principle of estimating a parameter by finding the value of the parameters that maximizes the likelihood function.

Imputation

The substitution of some value for a missing data point. In this context, it is the use of a set of reference haplotypes to infer a genotype for an individual, when data are missing or incomplete.

Likelihood ratio test

A method for testing statistical hypotheses based on comparing the maximum likelihood under two different models. In this context, the allele frequency in one model equals zero, whereas the frequency in the second model might be larger than zero.

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Nielsen, R., Paul, J., Albrechtsen, A. et al. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet 12, 443–451 (2011). https://doi.org/10.1038/nrg2986

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