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These aren’t the loci you’re looking for: principles of effective SNP filtering for molecular ecologists

Research output: Contribution to journalArticle



Shannon J. O'Leary, Jonathan B. Puritz, Stuart C. Willis, Christopher M. Hollenbeck, David S. Portnoy

School/Research organisations


Sequencing reduced-representation libraries of restriction site-associated DNA (RADseq) to identify single nucleotide polymorphisms (SNPs) is quickly becoming a standard methodology for molecular ecologists. Because of the scale of RADseq data sets, putative loci cannot be assessed individually, making the process of filtering noise and correctly identifying biologically meaningful signal more difficult. Artefacts introduced during library preparation and/or bioinformatic processing of SNP data can create patterns that are incorrectly interpreted as indicative of population structure or natural selection. Therefore, it is crucial to carefully consider types of errors that may be introduced during laboratory work and data processing, and how to minimize, detect and remove these errors. Here, we discuss issues inherent to RADseq methodologies that can result in artefacts during library preparation and locus reconstruction resulting in erroneous SNP calls and, ultimately, genotyping error. Further, we describe steps that can be implemented to create a rigorously filtered data set consisting of markers accurately representing independent loci and compare the effect of different combinations of filters on four RAD data sets. At last, we stress the importance of publishing raw sequence data along with final filtered data sets in addition to detailed documentation of filtering steps and quality control measures.


Original languageEnglish
Pages (from-to)3193-3206
Number of pages14
JournalMolecular Ecology
Issue number16
Early online date10 Jul 2018
Publication statusPublished - Aug 2018

    Research areas

  • Conservation genetics, Ecological genetics, Landscape genetics, Molecular evolution, Population ecology, Population genetics—empirical

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