distAngsd: Fast and Accurate Inference of Genetic Distances for Next-Generation Sequencing Data

Abstract

Commonly used methods for inferring phylogenies were designed before the emergence of high-throughput sequencing and can generally not accommodate the challenges associated with noisy, diploid sequencing data. In many applications, diploid genomes are still treated as haploid through the use of ambiguity characters; while the uncertainty in genotype calling-arising as a consequence of the sequencing technology-is ignored. In order to address this problem, we describe two new probabilistic approaches for estimating genetic distances: distAngsd-geno and distAngsd-nuc, both implemented in a software suite named distAngsd. These methods are specifically designed for next-generation sequencing data, utilize the full information from the data, and take uncertainty in genotype calling into account. Through extensive simulations, we show that these new methods are markedly more accurate and have more stable statistical behaviors than other currently available methods for estimating genetic distances-even for very low depth data with high error rates.

Keywords: expectation maximization; genetic distance; genotype likelihood; high-throughput sequencing; maximum likelihood; molecular evolution; next-generation sequencing; phylogeny reconstruction.