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Updated: Dec 26, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Estimating and accounting for genotyping errors in RAD-seq experiments.

Luisa Bresadola1, Vivian Link1,2, C Alex Buerkle3

  • 1Department of Biology, University of Fribourg, Fribourg, Switzerland.

Molecular Ecology Resources
|March 7, 2020
PubMed
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Reduced representation sequencing, like Restriction site Associated DNA sequencing (RAD-seq), can have high genotyping error rates. New methods accurately assess these errors, enabling reliable evolutionary studies in non-model organisms.

Area of Science:

  • Population Genetics
  • Genomics
  • Evolutionary Biology

Background:

  • Reduced representation sequencing techniques are widely used for evolutionary studies in non-model organisms due to cost-effectiveness and minimal genomic resource requirements.
  • Accumulating evidence suggests potential biases in these methods, raising concerns about genotype accuracy and their utility in evolutionary research.

Purpose of the Study:

  • To introduce and validate three novel strategies for estimating genotyping error rates in reduced representation sequencing data.
  • To assess the impact of genotyping errors on evolutionary analyses, specifically ancestry inference in Populus hybrids.
  • To demonstrate how accounting for genotyping errors can improve the robustness of evolutionary conclusions.

Main Methods:

  • Developed three methods for genotyping error rate estimation: comparison with high-quality genotypes, analysis of individual replicates, and use of population samples under Hardy-Weinberg equilibrium.
Keywords:
PopulusRAD-seqgenotype likelihoodsgenotypinggenotyping errors

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  • Applied these methods to Restriction site Associated DNA sequencing (RAD-seq) data.
  • Inferred genome-wide and local ancestry in well-characterized Populus hybrids.

    • Main Results:

    • Genotyping error rates in RAD-seq data were significantly higher than sequencing error rates, with a notable bias towards misidentifying heterozygous sites as homozygous.
    • High error rates led to inaccurate biological conclusions regarding ancestry inference in Populus hybrids.
    • Incorporating error rate assessment and recalibrating genotype likelihoods enabled robust and biologically meaningful ancestry inferences.

    Conclusions:

    • Genotyping errors in reduced representation sequencing data can be substantial and impact evolutionary inferences.
    • Accurate estimation and accounting for genotyping error rates are crucial for reliable evolutionary studies using these techniques.
    • The presented strategies and tools facilitate robust downstream analyses, supporting biologically meaningful conclusions in non-model organisms.