SNP Calling, Genotype Calling, and Sample Allele Frequency Estimation from New-Generation Sequencing Data

By Rasmus Nielsen, Thorfinn Sand Korneliussen, Anders Albrechtsen, Yingrui Li, Jun Wang. PLoS ONE, 2012

390 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cc6f0cf546d016e4f24a726d6f663c6a51616e06

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Laurent Excoffier
2. Matthieu Foll
3. Anders Albrechtsen
4. Vitor C. Sousa
5. Isabelle Dupanloup
6. E. Huerta-Sánchez
7. Rasmus Nielsen
8. Thorfinn Sand Korneliussen

In Molecular Biology

1. Anders Albrechtsen
2. Thorfinn Sand Korneliussen
3. Laurent Excoffier
4. Matthieu Foll
5. Rasmus Nielsen
6. Vitor C. Sousa
7. E. Huerta-Sánchez
8. Isabelle Dupanloup

Citations in the corpus

• ANGSD: Analysis of Next Generation Sequencing Data (2014, methodology): “[9].”
• Robust Demographic Inference from Genomic and SNP Data (2013, methodology): “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].”
• Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data (2015, result): “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].”