Genotyping-in-Thousands by sequencing (GT-seq) is a method that uses next-generation sequencing of multiplexed PCR products to generate genotypes from relatively small panels (50-500) of targeted single-nucleotide polymorphisms (SNPs) for thousands of individuals in a single Illumina HiSeq lane. This method uses only unlabelled oligos and PCR master mix in two thermal cycling steps for amplification of targeted SNP loci. During this process, sequencing adapters and dual barcode sequence tags are incorporated into the amplicons enabling thousands of individuals to be pooled into a single sequencing library. Post sequencing, reads from individual samples are split into individual files using their unique combination of barcode sequences. Genotyping is performed with a simple perl script which counts amplicon-specific sequences for each allele, and allele ratios are used to determine the genotypes. We demonstrate this technique by genotyping 2068 individual steelhead trout (Oncorhynchus mykiss) samples with a set of 192 SNP markers in a single library sequenced in a single Illumina HiSeq lane. Genotype data were 99.9% concordant to previously collected TaqMan TM genotypes at the same 192 loci, but call rates were slightly lower with GT-seq (96.4%) relative to Taqman (99.0%). Of the 192 SNPs, 187 were genotyped in ≥90% of the individual samples and only 3 SNPs were genotyped in <70% of samples. This study demonstrates amplicon sequencing with GTseq greatly reduces the cost of genotyping hundreds of targeted SNPs relative to existing methods by utilizing a simple library preparation method and massive efficiency of scale.

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Genome scans with many genetic markers provide the opportunity to investigate local adaptation in natural populations and identify candidate genes under selection. In particular, SNPs are dense throughout the genome of most organisms and are commonly observed in functional genes making them ideal markers to study adaptive molecular variation. This approach has become commonly employed in ecological and population genetics studies to detect outlier loci that are putatively under selection. However, there are several challenges to address with outlier approaches including genotyping errors, underlying population structure and false positives, variation in mutation rate and limited sensitivity (false negatives). In this study, we evaluated multiple outlier tests and their type I (false positive) and type II (false negative) error rates in a series of simulated data sets. Comparisons included simulation procedures (FDIST2, ARLEQUIN v.3.5 and BAYESCAN) as well as more conventional tools such as global F(ST) histograms. Of the three simulation methods, FDIST2 and BAYESCAN typically had the lowest type II error, BAYESCAN had the least type I error and Arlequin had highest type I and II error. High error rates in Arlequin with a hierarchical approach were partially because of confounding scenarios where patterns of adaptive variation were contrary to neutral structure; however, Arlequin consistently had highest type I and type II error in all four simulation scenarios tested in this study. Given the results provided here, it is important that outlier loci are interpreted cautiously and error rates of various methods are taken into consideration in studies of adaptive molecular variation, especially when hierarchical structure is included.

In the example for the B-H FDR procedure, four incorrectly printed values create confusion for the reader. Corrections in the Methods section, second paragraph, third and fourth sentences: ''The first p-value to satisfy p i £ i/k • a is p (10) since p (10) = 0.032 £ 10/15 • 0.05 = 0.0333. Thus pairwise tests in the experiment with p-values less than or equal to 0.0333 reject the null hypothesis. The gain in power with the B-H method FDR over the Bonferroni procedure (critical values of 0.033 and 0.003 respectively in this example) are substantial.'' Corrections in the Methods section, last paragraph, last sentence: ''This critical value is intermediate relative to those calculated from Bonferroni (0.0033) and B-H method FDR (0.