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Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL

Nature Computational Science volume 1, pages 421–432 (2021)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Detecting genetic variants associated with traits (quantitative trait loci, QTL) requires genotyped study individuals. Here we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads (eQTL for gene expression) even when no genotypes are available. When used with genotypes to map eQTL, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100 kB window that could be tested contained 26% of eQTL detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in the absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific eQTL for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.

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Fig. 1: Schematic representation of BaseQTL.
Fig. 2: Reference mapping bias correction.
Fig. 3: Benchmarking BaseQTL with observed genotypes.
Fig. 4: eQTL effects estimated with BaseQTL with observed or hidden genotypes.
Fig. 5: eQTLs in skin.
Fig. 6: Disentangling condition-specific eQTLs.

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Data availability

Geuvadis samples were accessed from E-GEUV-1, ftp://ftp.sra.ebi.ac.uk/vol1/fastq, on 16 April 2017 or 23 January 2018 as indicated in Supplementary Table 1. Psoriasis and normal skin samples were accessed from E-GEOD-54456, ftp://ftp.sra.ebi.ac.uk/vol1/fastq, on 2 November 2018. GTEx associations for skin, blood and lymphoblastic cell lines corresponding to Analysis V7 were downloaded from https://gtexportal.org/home/datasets on 21 June 2019. Differentially regulated genes between psoriasis and normal skin were downloaded from https://ars.els-cdn.com/content/image/1-s2.0-S0022202X15368834-mmc2.xls on the 21 November 2018. We downloaded RNA-seq data from 86 Geuvadis samples with EUR ancestry (GBR code) from ArrayExpress (E-GEUV-1, Supplementary Table 1). We also analyzed 94 and 90 RNA-seq normal and psoriasis skin samples13 obtained from ArrayExpress (E-GEOD-54456). For the analysis of psoriasis eQTL we selected 51 upregulated genes in psoriasis versus normal skin (P ≤ 10−6 corresponding to family-wise error rate <0.025) and with a median expression of at least 500 RPKM in psoriasis samples (data extracted from https://ars.els-cdn.com/content/image/1-s2.0-S0022202X15368834-mmc2.xls13, and/or within 100 kB of a psoriasis GWAS hit 25 (380 genes). Datasets to reproduce figures in this paper were uploaded into Zenodo 41.

Code availability

The source code and documentation for BaseQTL are available at https://gitlab.com/evigorito/baseqtl. We also provide a pipeline to process RNA fastq files and genotypes, if available, to prepare for running BaseQTL at https://gitlab.com/evigorito/baseqtl_pipeline (Supplementary Fig. 12 and Supplementary Section 3). The code to reproduce the figures is available at https://gitlab.com/evigorito/baseqtl_paper. The three repositories have been uploaded to Zenodo41.

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Acknowledgements

This work was co-funded by the Wellcome Trust (WT107881), the MRC (MC_UU_00002/2, MC_UU_00002/4, MC_UU_00002/13, MR/R013926/1 (to the CLUSTER Consortium)) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). S.R.W. was supported by the NIHR Cambridge Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This research was funded in whole, or in part, by the Wellcome Trust (WT107881). For the purpose of open access, the author has applied a CC BY public copyright licence to any author accepted manuscript version arising from this submission.

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Authors and Affiliations

  1. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

    Elena Vigorito, Wei-Yu Lin, Colin Starr, Paul D. W. Kirk, Simon R. White & Chris Wallace

  2. Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK

    Paul D. W. Kirk & Chris Wallace

  3. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Simon R. White

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Contributions

C.W. conceived of the project. E.V., C.W. and S.R.W. developed the model. E.V. wrote the software and performed analyses. W.-Y.L. and C.S. performed analyses and implemented the software. P.D.W.K. and S.R.W. contributed to the design of statistical analysis. E.V. and C.W. wrote the manuscript with input from all authors. C.W. directed the project.

Corresponding authors

Correspondence to Elena Vigorito or Chris Wallace.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Eric Gamazon, Wei Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Tables 1–17, Figs. 1–21, Sections 1–4 and References.

Supplementary Data 1

Geuvadis samples used in this study.

Supplementary Data 2

eQTL estimates for psoriasis and normal skin running individual models.

Supplementary Data 3

eQTL estimates for psoriasis and normal skin running a joint model.

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Vigorito, E., Lin, WY., Starr, C. et al. Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL. Nat Comput Sci 1, 421–432 (2021). https://doi.org/10.1038/s43588-021-00087-y

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