020_ Weighted-gene correlation network analysis of lithium response
Research Question and Aims
Response to lithium in Bipolar Disorder (BD) is both heritable and variable between individuals. This project investigates smallRNAome correlates of lithium response in 100 PsyCourse BD patients that suffer from BD I or II, and 277 control participants using weighted gene correlation network analysis (WGCNA).
Briefly, WGCNA is a systems biology approach that leverages pairwise correlation coefficients to reconstruct gene expression (or other 'omic data) networks. By hierarchically clustering gene expression data into modules of co-regulated genes and then assigning each module an"eigengene" (i.e. the first principal component of the expression profile of a given module) to enable association testing at the level of the module rather than individual transcripts, the multiple testing burden is significantly reduced. WGCNA is therefore more powerful than traditional transcriptome-wide approaches (e.g. differential expression analyses), normally resulting in dozens of candidate genes. Results can then be annotated and interpreted using e.g. interfaces to gene ontology software.
We have started to analyze lithium response using WGCNA before PsyCourse proposal were mandatory, our primary dependent variables are the continuous and the dichotomous lithium response phenotype as defined in PMID: 26806518.
A We will pre-process the collected small RNAome seq data from PsyCourse patients using standard and well-documented tools e.g. FastQC and Cutadapt (for quality and adapter trimming of reads). We will then apply miRDeep2 to produce count data for known miRNAs.
B We will run the above-mentioned miRNA count data through the WGCNA pipeline to identify relevant modules and candidate smallRNAs for lithium response, using only BD patients:
1. Construct a gene co-expression network
2. Identify modules
3. Relate modules to external information (here: both continuous and dichotomous lithium response phenotypes and covariates)
4. Study module relationships
5. Find key drivers in interesting modules
6. Annotation of results
C We may repeat the analyses described in A using control participants and good responders to lithium, to identify characteristics of this disorder subtype (see. e.g. PMID: 26503763).
D Depending on the result of the WGCNA analyses, key drivers in interesting modules may be further investigated using cell culture or mouse experiments.
We have used the following variables from the PsyCourse3.0 dataset:
v1_panss_X (all PANSS items of V1 and V4)
v1_idsc_itm_X (all IDSC-items)
v1_ymrs_X (all IDSC-items)
Note that some variables were extracted from the original secuTrial phenotype database, because phenotype information from some individuals was not contained in the PsyCourse 3.0 dataset.
We will use the smallRNAome seq data of the PsyCourse participants in question (clinical: 100 individuals, healthy controls: 277 individuals)