WASP: allele-specific software for robust molecular quantitative trait locus discovery
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
WASP calibration was performed on raw BAM files to eliminate allelic bias towards reference alleles during alignment.
“To further eliminate allelic bias towards reference alleles during the alignment step, we performed WASP (v.0.3.4) calibration on the generated raw BAM files68.”
From PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia (2025) · cites this paper as [68]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
Streaming fragment assignment for real-time analysis of sequencing experiments
Roberts, A., & Pachter, L. (2013) · Nature Methods · doi:10.1038/nmeth.2251 · Cited by 1,063 · see citing works
Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data.
Degner, J. F., Marioni, J. C., Pai, A. A., Pickrell, J. K., Nkadori, E., Gilad, Y., & Pritchard, J. K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp579 · Cited by 502 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 19
- [1]Degner JF Nature 482 390 394 2012 22307276 PMC3501342
- [2]Montgomery SB Nature 464 773 777 2010 20220756 PMC3836232
- [3]Pickrell JK Nature 464 768 2010 20220758 PMC3089435
- [4]Skelly DA Johansson M Madeoy J Wakefield J Akey JM Genome research 21 1728 1737 2011 21873452 PMC3202289
- [5]Harvey CT Bioinformatics 31 1235 1242 2015 25480375 PMC4393517
- [6]Sun W Biometrics 68 1 11 2012 21838806 PMC3218220
- [7]Degner JF Bioinformatics 25 3207 3212 2009 19808877 PMC2788925
- [8]Panousis NI Gutierrez-Arcelus M Dermitzakis ET Lappalainen T Genome biology 15 467 2014 25239376 PMC4212091
- [9]Anders S Huber W Genome biology 11 R106 2010 20979621 PMC3218662
- [10]Rozowsky J Mol Syst Biol 7 522 2011 21811232 PMC3208341
- [11]Liu Z Genet Epidemiol 38 591 598 2014 25183311
- [12]Roberts A Pachter L Nat Methods 10 71 73 2013 23160280 PMC3880119
- [13]Turro E Genome biology 12 R13 2011 21310039 PMC3188795
- [14]Li H Bioinformatics 25 2078 2079 2009 19505943 PMC2723002
- [15]Benjamini Y Speed TP Nucleic Acids Res 40 e72 2012 22323520 PMC3378858
- [16]McVicker G Science 342 747 749 2013 24136359 PMC3947669
- [17]Lappalainen T Nature 501 506 511 2013 24037378 PMC3918453
- [18]Katz Y Wang ET Airoldi EM Burge CB Nat Methods 7 1009 1015 2010 21057496 PMC3037023
- [19]Trapnell C Nat Biotechnol 31 46 53 2013 23222703 PMC3869392
Cited by
Sorted by citations
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Annotation-free quantification of RNA splicing using LeafCutter
Li, Y. I., Knowles, D. A., Humphrey, J., Barbeira, A. N., Dickinson, S. P., Im, H. K., & Pritchard, J. K. (2018) · Nature Genetics · doi:10.1038/s41588-017-0004-9 · Cited by 765 · see citing works
Self-citationGenetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.026 · Cited by 679 · see citing works
Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0092-1 · Cited by 491 · see citing works
Genetic Ancestry and Natural Selection Drive Population Differences in Immune Responses to Pathogens
Nédélec, Y., Sanz, J., Baharian, G., Szpiech, Z. A., Pacis, A., Dumaine, A., Grenier, J.-C., Freiman, A., Sams, A. J., Hebert, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.025 · Cited by 512 · see citing works
Genetic adaptation and Neandertal admixture shaped the immune system of human populations
Quach, H., Rotival, M., Pothlichet, J., Loh, Y.-H. E., Dannemann, M., Zidane, N., Laval, G., Patin, E., Harmant, C., Lopez, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.024 · Cited by 475 · see citing works
Integrative detection and analysis of structural variation in cancer genomes
Dixon, J. R., Xu, J., Dileep, V., Zhan, Y., Song, F., Le, V. T., Yardımcı, G. G., Chakraborty, A., Bann, D. V., Wang, Y., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0195-8 · Cited by 452 · see citing works
Histone Acetylome-wide Association Study of Autism Spectrum Disorder
Sun, W., Poschmann, J., Cruz-Herrera del Rosario, R., Parikshak, N. N., Hajan, H. S., Kumar, V., Ramasamy, R., Belgard, T. G., Elanggovan, B., Wong, C. C. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.031 · Cited by 306 · see citing works
Dynamic genetic regulation of gene expression during cellular differentiation
Strober, B. J., Elorbany, R., Rhodes, K., Krishnan, N., Tayeb, K., Battle, A., & Gilad, Y. (2019) · Science · doi:10.1126/science.aaw0040 · Cited by 219 · see citing works
Self-citationDynamic Rewiring of Promoter-Anchored Chromatin Loops during Adipocyte Differentiation
Siersbæk, R., Madsen, J. G. S., Javierre, B. M., Nielsen, R., Bagge, E. K., Cairns, J., Wingett, S. W., Traynor, S., Spivakov, M., Fraser, P., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.04.010 · Cited by 243 · see citing works
Impacts of Neanderthal-Introgressed Sequences on the Landscape of Human Gene Expression
McCoy, R. C., Wakefield, J., & Akey, J. M. (2017) · Cell · doi:10.1016/j.cell.2017.01.038 · Cited by 200 · see citing works
Huang, H., Zhu, Q., Jussila, A., Han, Y., Bintu, B., Kern, C., Conte, M., Zhang, Y., Bianco, S., Chiariello, A. M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00863-6 · Cited by 187 · see citing works
Genetics of the human microglia regulome refines Alzheimer’s disease risk loci
Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01149-1 · Cited by 168 · see citing works
SNPsplit: Allele-specific splitting of alignments between genomes with known SNP genotypes
Krueger, F., & Andrews, S. R. (2016) · F1000Research · doi:10.12688/f1000research.9037.2 · Cited by 163 · see citing works
Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms
Rusu, V., Hoch, E., Mercader, J. M., Tenen, D. E., Gymrek, M., Hartigan, C. R., DeRan, M., von Grotthuss, M., Fontanillas, P., Spooner, A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.011 · Cited by 152 · see citing works
Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants
Zhang, S., Zhang, H., Zhou, Y., Qiao, M., Zhao, S., Kozlova, A., Shi, J., Sanders, A. R., Wang, G., Luo, K., et al. (2020) · Science · doi:10.1126/science.aay3983 · Cited by 127 · see citing works
Mosquera, J. V., Auguste, G., Wong, D., Turner, A. W., Hodonsky, C. J., Alvarez-Yela, A. C., Song, Y., Cheng, Q., Lino Cardenas, C. L., Theofilatos, K., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113380 · Cited by 123 · see citing works
Genetic Regulatory Mechanisms of Smooth Muscle Cells Map to Coronary Artery Disease Risk Loci
Liu, B., Pjanic, M., Wang, T., Nguyen, T., Gloudemans, M., Rao, A., Castano, V. G., Nurnberg, S., Rader, D. J., Elwyn, S., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.001 · Cited by 94 · see citing works
Haplotype-aware pantranscriptome analyses using spliced pangenome graphs
Sibbesen, J. A., Eizenga, J. M., Novak, A. M., Sirén, J., Chang, X., Garrison, E., & Paten, B. (2023) · Nature Methods · doi:10.1038/s41592-022-01731-9 · Cited by 83 · see citing works
Functional characterization of Alzheimer’s disease genetic variants in microglia
Yang, X., Wen, J., Yang, H., Jones, I. R., Zhu, X., Liu, W., Li, B., Clelland, C. D., Luo, W., Wong, M. Y., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01506-8 · Cited by 71 · see citing works
PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia
Kozlova, A., Zhang, S., Sudwarts, A., Zhang, H., Smirnou, S., Byeon, S. K., Thapa, C., Sun, X., Stephenson, K., Zhao, X., et al. (2025) · Nature · doi:10.1038/s41586-025-09486-x · Cited by 53 · see citing works
“To further eliminate allelic bias towards reference alleles during the alignment step, we performed WASP (v.0.3.4) calibration on the generated raw BAM files 68 .”
InfluentialDai, X., Bian, P., Hu, D., Luo, F., Huang, Y., Jiao, S., Wang, X., Gong, M., Li, R., Cai, Y., et al. (2023) · Genome Research · doi:10.1101/gr.277481.122 · Cited by 43 · see citing works
Chiou, J., Zeng, C., Cheng, Z., Han, J. Y., Schlichting, M., Huang, S., Wang, J., Sui, Y., Deogaygay, A., Okino, M.-L., et al. (2019) · doi:10.1101/693671 · Cited by 189 · see citing works
- Authors
- van de Geijn, B., McVicker, G., Gilad, Y., & Pritchard, J. K.
- Affiliations
- Stanford University (US)
- University of Chicago (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Pritchard, Jonathan K
- Venue
- Nature Methods
- Published
- 2015-09-14
- Volume
- 12
- Issue
- 11
- Pages
- 1061-1063
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4626402
- OpenAlex
- W2221356560
- Cited by
- 575
- References
- 4
- Citations by year
- 2026: 42 · 2025: 70 · 2024: 75 · 2023: 70 · 2022: 48 · 2021: 70 · 2020: 53 · 2019: 56 · 2018: 29 · 2017: 30 · 2016: 27 · 2015: 5
- FWCI
- 13.32
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (0638477)
- Howard Hughes Medical Institute
- National Institutes of Health (MH101825)
- National Institute of Mental Health (MH101825, R01MH101825)
- National Human Genome Research Institute (R01HG006123, HG006123, HG007036, U01HG007036)
- National Institute of General Medical Sciences (GM007197, T32GM007197)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- RNA-seq, ChIP-seq, technical artifacts, open-source bioinformatics software, simulated reads
- MeSH terms
- Alleles, Genotype, Haplotypes, Heterozygote, Humans, Software, Reproducibility of Results, Likelihood Functions, Artifacts, Genome, Sequence Analysis, DNA, Sequence Analysis, RNA, Computational Biology, Quantitative Trait Loci, Chromatin Immunoprecipitation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review