Seqbayes: An Adaptive Bayesian Framework for Calling Genotypes from Next-Generation Sequence Data

• Activity Number: 424
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Daniel D. Kinnamon*+ and Eric H. Powell and Michael A. Schmidt and Eden R. Martin
• Companies: University of Miami Miller School of Medicine and University of Miami Miller School of Medicine and University of Miami Miller School of Medicine and University of Miami Miller School of Medicine
• Keywords: genotype calling ; next-generation sequencing ; Markov chain Monte Carlo

Abstract

Calling individual genotypes from next-generation sequence data requires estimating their posterior probabilities using a joint model for the observed nucleotide read data and latent genotypes. Existing approaches estimate posterior probabilities by substituting either fixed parameter values based on prior knowledge or MLEs obtained using the current sample of independent individuals into this model. While substituting MLEs yields lower average genotype-call error rates, some small samples with sparse information may have higher genotype-call error rates due to non-identifiability. We propose a Bayesian genotype-calling approach that solves this problem by assigning priors to all parameters in the model. These priors can improve identifiability by combining existing knowledge with current sample information in an adaptive manner. We also propose an MCMC algorithm for estimating marginal posterior genotype probabilities under our Bayesian model. In simulations, we show that our MCMC algorithm has favorable empirical properties. We also demonstrate that our approach yields lower average and worst-case genotype-call error rates than using MLEs in small samples with sparse information.