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Deriving genotypes from RAD-seq short-read data using Stacks

Nature Protocols volume 12, pages 2640–2659 (2017)Cite this article

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Abstract

Restriction site-associated DNA sequencing (RAD-seq) allows for the genome-wide discovery and genotyping of single-nucleotide polymorphisms in hundreds of individuals at a time in model and nonmodel species alike. However, converting short-read sequencing data into reliable genotype data remains a nontrivial task, especially as RAD-seq is used in systems that have very diverse genomic properties. Here, we present a protocol to analyze RAD-seq data using the Stacks pipeline. This protocol will be of use in areas such as ecology and population genetics. It covers the assessment and demultiplexing of the sequencing data, read mapping, inference of RAD loci, genotype calling, and filtering of the output data, as well as providing two simple examples of downstream biological analyses. We place special emphasis on checking the soundness of the procedure and choosing the main parameters, given the properties of the data. The procedure can be completed in 1 week, but determining definitive methodological choices will typically take up to 1 month.

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Figure 1: Genotype calling greatly depends on coverage.
Figure 2: Selection of assembly parameters in a de novo analysis.
Figure 3: Evolution of the catalog as more samples are added.
Figure 4: Transitioning to populations genetics.

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Acknowledgements

We thank J. Paris and N. Rayamajhi for their help in testing the procedure and for discussion of the manuscript.

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Authors and Affiliations

  1. Department of Animal Biology, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA

    Nicolas C Rochette & Julian M Catchen

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  1. Nicolas C Rochette
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  2. Julian M Catchen
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Contributions

N.C.R. and J.M.C. designed the protocol, performed experiments, and wrote the manuscript.

Corresponding author

Correspondence to Julian M Catchen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Observed per-sample coverages.

The median number of reads for the 78 samples of the demonstration dataset is 1,209,782. The last two samples, ‘sj_1483.05’ and ‘sj_181931’, have almost no reads, likely because of a mistake at the bench at the multiplexing step that caused them not to be represented in the DNA library.

Supplementary Figure 2 Comparison between the reference-based and de novo approaches.

The two approaches yield very similar overall results, yet differ in their treatment of specific subsets of the data.

(A) Number of loci (solid lines) and polymorphic loci (dashed lines) shared by 80% of samples in the reference-based (blue) and de novo (black, same numbers as in Figure 2) approaches, for 12 representative samples. The reference-based analysis yields 35,792 loci shared by 80% samples while the de novo one yields (with M=n=4) 35,277 such loci. Furthermore, mapping the consensus sequences of these de novo loci to the reference genome using BWA (not shown) shows that just 32,843 of them (93.1%) have a one-to-one relationship with loci in the reference-based analysis, while 96 (0.3%) appear under-merged (pairs of loci map to the same genomic location) and 1,748 (5.0%) can’t be mapped to the reference. The remaining 590 de novo loci (1.7%) correspond to in-between cases in which the de novo loci partly exist in the reference-based analysis but are not part of the filtered set of loci present in 80% of samples. Conversely, 2,903 of the filtered loci of the reference-based analysis are missing from the filtered de novo set.

(B) Distribution of the number of SNPs per locus for the reference-based (blue) and de novo (yellow-red, same numbers as in Figure 2) approaches. We note that the total number of SNPs is higher in the reference-based analysis than in the de novo analysis with M=n=4 (57,872 vs. 53,051), but that the rate of SNPs with implausibly high heterozygosities (>60%) is also slightly higher (2.0% vs. 1.8%).

(C) PCA of 76 individuals, computed in the same way as figure 4B, but using the genotypes resulting from the reference-based analysis. For comparability with Figure 4B, the Y-axis is PC3, not PC2. Insert: Percentage of the variance explained by the first ten components.

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Supplementary Figures 1 and 2. (PDF 376 kb)

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Rochette, N., Catchen, J. Deriving genotypes from RAD-seq short-read data using Stacks. Nat Protoc 12, 2640–2659 (2017). https://doi.org/10.1038/nprot.2017.123

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