SNP calling, genotype calling, and sample allele frequency estimation from New-Generation Sequencing data.
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.
BGI-Shenzhen, Shenzhen, China. rasmus_nielsen@berkeley.edu
Rasmus
R
Nielsen
Thorfinn
T
Korneliussen
Anders
A
Albrechtsen
Yingrui
Y
Li
Jun
J
Wang
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D003718:Denmark; D059472:Exome; D005787:Gene Frequency; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D020407:Internet; D016013:Likelihood Functions; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D012984:Software; D044465:White People / Q000235:genetics