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WASP: allele-specific software for robust molecular quantitative trait locus discovery

Nature Methods volume 12, pages 1061–1063 (2015)Cite this article

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Abstract

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), but they are challenging to analyze and are prone to technical artifacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and chromatin immunoprecipitation sequencing (ChIP-seq) reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL-mapping approaches.

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Figure 1: Mapping of allele-specific reads.
Figure 2: The combined haplotype test and its performance.

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Acknowledgements

We thank members of the Liu, Pritchard, Stephens and Gilad labs for helpful discussions. We thank X.S. Liu's lab for hosting G.M. as a visitor in the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute while this work was conducted. We thank many early users of WASP, particularly C. DeBoever, who contributed bug fixes and code improvements. This work was supported by the Howard Hughes Medical Institute, the US National Institutes of Health (NIH grants HG007036, HG006123, MH101825 and GM007197) and the US National Science Foundation (NSF Graduate Research Fellowship DGE-0638477 to B.v.d.G.).

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Author notes

  1. Bryce van de Geijn and Graham McVicker: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Human Genetics, University of Chicago, Chicago, Illinois, USA

    Bryce van de Geijn & Yoav Gilad

  2. Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA

    Bryce van de Geijn

  3. Department of Genetics, Stanford University, Stanford, California, USA

    Graham McVicker & Jonathan K Pritchard

  4. Department of Biology, Stanford University, Stanford, California, USA

    Jonathan K Pritchard

  5. Howard Hughes Medical Institute, Stanford University, Stanford, California, USA

    Jonathan K Pritchard

Authors
  1. Bryce van de Geijn
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  2. Graham McVicker
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  3. Yoav Gilad
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  4. Jonathan K Pritchard
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Contributions

B.v.d.G., G.M., J.K.P. and Y.G. conceived of the project. B.v.d.G. and G.M. performed the analyses and implemented the software. G.M. and B.v.d.G. wrote the manuscript with input from all authors. J.K.P. and Y.G. directed the project.

Corresponding author

Correspondence to Jonathan K Pritchard.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 WASP mapping errors at heterozygous sites.

WASP mapping errors at heterozygous sites as a function of the rate of unknown single-nucleotide variants (SNVs).

Supplementary Figure 2 Quantile-quantile plots of ranked –log10 P values from the combined haplotype test.

(a) Ranked –log10 P values from running the combined haplotype test on H3K27ac ChIP-seq data from ten lymphoblastoid cell lines compared to P values expected under the null hypothesis. The permuted points are for same data set, but with the genotypes of each SNP shuffled. (b) Ranked –log10 P values from running the combined haplotype test on RNA-seq data from 69 YRI cell lines. The test was run only on eQTLs that were previously identified in cell lines derived from European individuals1. The permuted points are for the same data set, but with the genotypes of each SNP shuffled. 1. Lappalainen, T. et al. Nature 501, 506–511 (2013).

Supplementary Figure 3 Receiver operating characteristic (ROC) curves showing the performance of five methods for QTL identification on simulated data.

Performance for different numbers of individuals and effect sizes. The simulations are described in Supplementary Note 6.

Supplementary Figure 4 The WASP mapping pipeline.

Reads are first mapped to the genome using a mapping tool of the user’s choice. The aligned reads are provided to WASP in SAM (sequence alignment/map) or BAM (binary alignment/map) format, along with a list of known polymorphisms. WASP identifies reads that overlap known polymorphisms, flips the alleles in the reads, and remaps them to the genome. Reads that map to a different location than the original read are then discarded. Finally, WASP can optionally remove reads that map to the same genomic location (‘duplicate reads’) without introducing a reference bias.

Supplementary Figure 5 The WASP combined haplotype test pipeline.

Mapped reads (in BAM or SAM format) for each individual, genotypes for known SNPs, and a list of regions and SNPs to test are provided to WASP. WASP extracts read counts for the target regions as well as allele-specific read counts. Read counts from multiple sources can be used to update heterozygous probabilities. Expected read counts for each region are adjusted through modeling of the relationships between read counts and GC content and between read counts and total read counts for each sample. Dispersion parameters are estimated from the data and provided to the combined haplotype test along with the read counts. Principal components can optionally be used as covariates by the test.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Table 1 and Supplementary Notes 1–8 (PDF 3390 kb)

Supplementary Software

WASP code and documentation. Updated files are maintained at https://github.com/bmvdgeijn/WASP (ZIP 688 kb)

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van de Geijn, B., McVicker, G., Gilad, Y. et al. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat Methods 12, 1061–1063 (2015). https://doi.org/10.1038/nmeth.3582

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