title: Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels.
abstract: BACKGROUND
  RNA-sequencing (RNA-seq) is a powerful technique for the identification of genetic variants that affect gene-expression levels, either through expression quantitative trait locus (eQTL) mapping or through allele-specific expression (ASE) analysis. Given increasing numbers of RNA-seq samples in the public domain, we here studied to what extent eQTLs and ASE effects can be identified when using public RNA-seq data while deriving the genotypes from the RNA-sequencing reads themselves.
  
  
  METHODS
  We downloaded the raw reads for all available human RNA-seq datasets. Using these reads we performed gene expression quantification. All samples were jointly normalized and subjected to a strict quality control. We also derived genotypes using the RNA-seq reads and used imputation to infer non-coding variants. This allowed us to perform eQTL mapping and ASE analyses jointly on all samples that passed quality control. Our results were validated using samples for which DNA-seq genotypes were available.
  
  
  RESULTS
  4,978 public human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though these data originated from many different laboratories, samples reflecting the same cell type clustered together, suggesting that technical biases due to different sequencing protocols are limited. In a joint analysis on the 1,262 samples with high quality genotypes, we identified cis-eQTLs effects for 8,034 unique genes (at a false discovery rate ≤0.05). eQTL mapping on individual tissues revealed that a limited number of samples already suffice to identify tissue-specific eQTLs for known disease-associated genetic variants. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels.
  
  
  CONCLUSIONS
  By deriving and imputing genotypes from RNA-seq data, it is possible to identify both eQTLs and ASE effects. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become especially relevant for studying the effects of tissue-specific and rare pathogenic genetic variants to aid clinical interpretation of exome and genome sequencing.
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands ; University of Groningen, University Medical Center Groningen, Genomics Coordination Center, 9700 RB Groningen, The Netherlands.
authors.forename: Patrick
authors.initials: P
authors.lastname: Deelen
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Daria V
authors.initials: DV
authors.lastname: Zhernakova
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands ; University of Groningen, University Medical Center Groningen, Genomics Coordination Center, 9700 RB Groningen, The Netherlands.
authors.forename: Mark
authors.initials: M
authors.lastname: de Haan
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Marijke
authors.initials: M
authors.lastname: van der Sijde
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Marc Jan
authors.initials: MJ
authors.lastname: Bonder
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Juha
authors.initials: J
authors.lastname: Karjalainen
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands ; University of Groningen, University Medical Center Groningen, Genomics Coordination Center, 9700 RB Groningen, The Netherlands.
authors.forename: K Joeri
authors.initials: KJ
authors.lastname: van der Velde
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Kristin M
authors.initials: KM
authors.lastname: Abbott
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Jingyuan
authors.initials: J
authors.lastname: Fu
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Cisca
authors.initials: C
authors.lastname: Wijmenga
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Sinke
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands ; University of Groningen, University Medical Center Groningen, Genomics Coordination Center, 9700 RB Groningen, The Netherlands.
authors.forename: Morris A
authors.initials: MA
authors.lastname: Swertz
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, The Netherlands.
authors.forename: Lude
authors.initials: L
authors.lastname: Franke