Restriction site‐associated <scp>DNA</scp> sequencing, genotyping error estimation and <i>de novo</i> assembly optimization for population genetic inference

TLDR

RADseq enables genome‑wide polymorphism discovery in nonmodel organisms, yet its genotyping errors—often overlooked—can bias population genetic inferences. The study aims to quantify genotyping error using replicate samples without a reference genome. By analyzing replicate samples, the authors optimized Stacks assembly parameters to reduce error and increase informative loci, and measured error rates for loci, alleles, and SNPs in a double‑digest RAD dataset from *Berberis alpina*.

Abstract

Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.