Comparison of quantitative trait loci methods: Total expression and allelic imbalance method in brain RNA-seq

Research output: Contribution to journalJournal articleResearchpeer-review

  • Jesper R Gådin
  • Alfonso Buil
  • Carlo Colantuoni
  • Andrew E Jaffe
  • Jacob Nielsen
  • Joo-Heon Shin
  • Thomas M Hyde
  • Joel E Kleinman
  • Niels Plath
  • Per Eriksson
  • Brunak, Søren
  • Michael Didriksen
  • Daniel R Weinberger
  • Lasse Folkersen
  • BrainSeq Consortium

BACKGROUND: Of the 108 Schizophrenia (SZ) risk-loci discovered through genome-wide association studies (GWAS), 96 are not altering the sequence of any protein. Evidence linking non-coding risk-SNPs and genes may be established using expression quantitative trait loci (eQTL). However, other approaches such allelic expression quantitative trait loci (aeQTL) also may be of use.

METHODS: We applied both the eQTL and aeQTL analysis to a biobank of deeply sequenced RNA from 680 dorso-lateral pre-frontal cortex (DLPFC) samples. For each of 340 genes proximal to the SZ risk-SNPs, we asked how much SNP-genotype affected total expression (eQTL), as well as how much the expression ratio between the two alleles differed from 1:1 as a consequence of the risk-SNP genotype (aeQTL).

RESULTS: We analyzed overlap with comparable eQTL-findings: 16 of the 30 risk-SNPs known to have gene-level eQTL also had gene-level aeQTL effects. 6 of 21 risk-SNPs with known splice-eQTL had exon-aeQTL effects. 12 novel potential risk genes were identified with the aeQTL approach, while 55 tested SNP-pairs were found as eQTL but not aeQTL. Of the tested 108 loci we could find at least one gene to be associated with 21 of the risk-SNPs using gene-level aeQTL, and with an additional 18 risk-SNPs using exon-level aeQTL.

CONCLUSION: Our results suggest that the aeQTL strategy complements the eQTL approach to susceptibility gene identification.

Original languageEnglish
Article numbere0217765
JournalPLoS ONE
Issue number6
Number of pages16
Publication statusPublished - 2019

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