In recent years, the advantages of RNA-sequencing (RNA-Seq) have made it the platform of choice for measuring gene expression over traditional microarrays. However, RNA-Seq comes with bioinformatical challenges and higher computational costs. Therefore, researchers from the University of Sydney set out to assess whether the increased depth of transcriptomic information facilitated by RNA-Seq is worth the increased computation over microarrays, specifically at three levels:
- absolute expression levels
- differentially expressed genes identification
- expression QTL (eQTL) mapping in regions of the human brain
Using the United Kingdom Brain Expression Consortium (UKBEC) dataset, there is high agreement of gene expression levels measured by microarrays and RNA-seq when quantifying absolute expression levels and when identifying differentially expressed genes. These findings suggest that depending on the aims of a study, the relative ease of working with microarray data may outweigh the computational time and costs of RNA-Seq pipelines. On the other, there was low agreement when mapping eQTLs. However, a number of eQTLs associated with genes that play important roles in the brain were found in both platforms. For example, a trans-eQTL was mapped that is associated with the MPZ gene in the substantia nigra. These eQTLs that we have highlighted are extremely promising candidates that merit further investigation.
(A) Significant eQTLs (FDR ≤ 0.01) identified by microarrays against RNA-Seq in PUTM and SNIG. There is a much higher number of eQTLs in common in PUTM than SNIG. Overall, RNA-Seq data identified more significant eQTLs than microarray for both regions. (B) Boxplots of the effect of SNP rs5760176 on GSTT1 expression levels in PUTM. The AA homozygous genotype is associated with an increase in GSTT1 expression for both microarray and RNA-Seq data. Note that this eQTL was significant in PUTM only (C) Boxplots of the effect of SNP rs11002001 on MPZ expression levels in SNIG. The AA homozygous genotype is associated with a decrease in MPZ expression for both microarray and RNA-Seq data. Note that this eQTL was significant in SNIG only and because of sample subsetting for platform comparison, there were no samples with the GG genotype. It is worth mentioning that the GTEx dataset had only one sample with the GG genotype out of 483 samples and in the 1000 Genome dataset, the G allele has a frequency of 0.0136).