2022-03-23

054_ Assessment of miRNA-eQTLs in the PsyCourse Study

Research Question and Aims

Micro-RNAs are small, single-stranded RNA molecules that have mainly been shown to downregulate protein-coding genes through a variety of mechanisms including mRNA cleavage and degradation, translational inhibition, and shortening of the mRNA poly tail (reviewed in PMID 18197166).
There is mounting evidence that miRNA expression levels are altered in the central nervous system and the peripheral tissues of individuals with severe psychiatric disorders such as bipolar disorder (BD) or schizophrenia (SCZ) and they have been hypothesized to play an important role in disease etiology through their functional regulation of gene expression (PMID: 17326821). More evidence was provided by large GWAS studies in schizophrenic patients that found strong association signals in miRNA loci. (PMID: 23974872) A recent study performed miRNA-eQTL analysis in post-mortem brains. It identified 32 eQTLs that influenced miRNA expression levels and showed significant overlap between SZ and BD (PMID 25817407).
Comprehensive analysis of micro-RNA profiles and micro-RNA expression quantitative trait loci (miRNA-eQTL) can yield insights into regulatory effects and dose-response relationships of microRNA transcription. Further, it can help elucidate the role of microRNA as mediators of complex traits. Additionally, findings from plasma studies might help identify readily measurable biomarkers of BD and SZ in the future.
Therefore, we propose i) to perform mircoRNA-eQTL analysis from whole blood samples to identify genetic variants, that regulate microRNA expression level and ii) to assess the enrichment/overlap of the identified miRNA-QTLs in different psychiatric traits.

Analytic Plan

First step: QC and quantification of miRNAs from .fastq files in PsyCourse.
Whole blood microRNA measurements from microRNome sequencing are already available for 1541 PsyCourse participants (359 controls, 1182 patients) through a collaboration with the laboratory of André Fischer (DZNE Göttingen). We will use measurements from visit 1. Surrogate variable analysis (SVA) and principal component analysis (PCA) will be performed to address potential confounders.
Second step: miRNA-eQTL analysis. Identification of functional SNPs in expression quantitative trait loci will be conducted using PLINK1.9 using the microRNA levels as target phenotypes. GSA-imputed data will be used in these analyses. A tailored set of covariates for each microRNA species will be selected using the R package MASS. Third step: overlap of identified microRNA-eQTLs with psychiatric disorders.
We will run formal enrichment analyses with MAGMA and LDSC methodologies to assess the overlap between miRNA-eQTLs and risk loci for major psychiatric disorders.

Resources needed

V1_stat
v1_age
v1_yob
v1_sex
v1_center
v1_cur_psy_trm
v1_age_1st_out_trm
v1_age_1st_inpat_trm
v1_dur_illness
raw medication data sets (v1_med_clin_orig, v1_med_con_orig)
v1_med_pst_wk
v1_ever_smkd
v1_fam_hist
v1_scid_dsm_dx
v1_scid_dsm_dx_cat

gsa_id (genotyping/imputed data)
v1_smRNAome_id (smallRNAome)

Small non-coding sequencing data:
Fastq files for all samples available in PsyCourse for visit1

Genetic data:
GSA raw genotypes to calculate PCAs hg19
GSA imputed genotypes for microRNA-QTL analyses