<i>Stacks</i>: Building and Genotyping Loci <i>De Novo</i> From Short-Read Sequences

TLDR

Sequencing advances enable rapid, cost‑effective genotyping, yet software to automate calling tens of thousands of genotypes across many individuals remains lacking. This study presents the algorithms of Stacks and demonstrates their effectiveness by building loci from simulated RAD‑tags of stickleback and improving a zebrafish genetic map. Stacks processes short‑read Illumina data, either de novo or reference‑based, to identify and genotype loci, recovering thousands of SNP markers from RAD‑tags for population and cross analyses. The software produces markers for ultra‑dense linkage maps, supports population phylogeography, aids reference genome assembly, and was validated by reconstructing stickleback loci and refining a zebrafish map.

Abstract

Advances in sequencing technology provide special opportunities for genotyping individuals with speed and thrift, but the lack of software to automate the calling of tens of thousands of genotypes over hundreds of individuals has hindered progress. Stacks is a software system that uses short-read sequence data to identify and genotype loci in a set of individuals either de novo or by comparison to a reference genome. From reduced representation Illumina sequence data, such as RAD-tags, Stacks can recover thousands of single nucleotide polymorphism (SNP) markers useful for the genetic analysis of crosses or populations. Stacks can generate markers for ultra-dense genetic linkage maps, facilitate the examination of population phylogeography, and help in reference genome assembly. We report here the algorithms implemented in Stacks and demonstrate their efficacy by constructing loci from simulated RAD-tags taken from the stickleback reference genome and by recapitulating and improving a genetic map of the zebrafish, Danio rerio.