7 Transcriptomic studies

The aim of transcriptome-wide association studies is to identify genes whose transcription volume associates with phenotypic differences among individuals. Transcription is measured from tRNA content in biological samples. The transcriptomic data are therefore high-dimensional measurements of the transcriptome under given conditions, such as tissue, age, health status or intervention stage. The transcriptome can be assessed using microarray data or RNA-sequencing (RNA-seq). Microarray data depends on the probes used to detect the amount of transcription of a gene. While microarrays probe gene exons, initial data contains one probe per gene. The amount or intensity of hybridization to a gene’s probe measures the transcription output of the gene. If more than one probe is available for a gene, a winsorized mean across probes is taken as the transcription measure of the gene. By contrast, RNA-seq is independent of the probes as it massively sequences short tRNA reads that are then mapped to known gene transcripts. The quantity of reads mapped to a gene is a measure of the transcription of the gene. In this chapter, we will analyze publicly available transcriptomic data based on microarray and RNA-seq experiments. We will discuss how to normalize both types of data and the methods to infer transcriptome-wide association studies.

The R code to reproduce the results and figures of this chapter can be found here.