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Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

View ORCID ProfilePatrick Deelen, View ORCID ProfileDaria V. Zhernakova, View ORCID ProfileMark de Haan, View ORCID ProfileMarijke van der Sijde, View ORCID ProfileMarc Jan Bonder, View ORCID ProfileJuha Karjalainen, View ORCID ProfileK. Joeri van der Velde, View ORCID ProfileKristin M. Abbott, View ORCID ProfileJingyuan Fu, View ORCID ProfileCisca Wijmenga, View ORCID ProfileRichard J. Sinke, View ORCID ProfileMorris A. Swertz, View ORCID ProfileLude Franke
doi: https://doi.org/10.1101/007633
Patrick Deelen
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
2University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
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Daria V. Zhernakova
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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  • ORCID record for Daria V. Zhernakova
Mark de Haan
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
2University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
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Marijke van der Sijde
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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  • ORCID record for Marijke van der Sijde
Marc Jan Bonder
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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  • ORCID record for Marc Jan Bonder
Juha Karjalainen
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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K. Joeri van der Velde
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
2University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
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Kristin M. Abbott
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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Jingyuan Fu
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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Cisca Wijmenga
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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Richard J. Sinke
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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Morris A. Swertz
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
2University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
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Lude Franke
1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
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  • ORCID record for Lude Franke
  • For correspondence: lude@ludesign.nl
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Abstract

Given increasing numbers of RNA-seq samples in the public domain, we studied to what extent expression quantitative trait loci (eQTLs) and allele-specific expression (ASE) can be identified in public RNA-seq data while also deriving the genotypes from the RNA-seq reads. 4,978 human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though this data originated from many different laboratories, samples reflecting the same cell-type clustered together, suggesting that technical biases due to different sequencing protocols were limited. We derived genotypes from the RNA-seq reads and imputed non-coding variants. In a joint analysis on 1,262 samples combined, we identified cis-eQTLs effects for 8,034 unique genes. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become relevant for studying tissue-specific effects of rare pathogenic genetic variants.

  • List of abbreviations

    eQTL
    Expression quantitative trait locus
    ASE
    Allele-specific expression
    ENA
    European nucleotide archive
    MAF
    Minor allele frequency
    RNA-seq
    RNA-sequencing
    PCA
    Principal component analysis
    QC
    Quality control
    LCL
    Lymphoblastoid cell-line
    FDR
    False discovery rate
    GoNL
    Genome of the Netherlands
    GQ
    Phred-scaled genotype quality
    DR2
    Estimated dosage r2 after imputation
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted August 01, 2014.
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    Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels
    Patrick Deelen, Daria V. Zhernakova, Mark de Haan, Marijke van der Sijde, Marc Jan Bonder, Juha Karjalainen, K. Joeri van der Velde, Kristin M. Abbott, Jingyuan Fu, Cisca Wijmenga, Richard J. Sinke, Morris A. Swertz, Lude Franke
    bioRxiv 007633; doi: https://doi.org/10.1101/007633
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    Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels
    Patrick Deelen, Daria V. Zhernakova, Mark de Haan, Marijke van der Sijde, Marc Jan Bonder, Juha Karjalainen, K. Joeri van der Velde, Kristin M. Abbott, Jingyuan Fu, Cisca Wijmenga, Richard J. Sinke, Morris A. Swertz, Lude Franke
    bioRxiv 007633; doi: https://doi.org/10.1101/007633

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