Introduction:
There are a number of psychiatric and neurological diseases that affect humans, and according to the world health organization (WHO), the majority of them are affected by depression, bipolar disorder (extreme mood swings with highs and lows), and schizophrenia (a mental disorder that affects the person’s ability to interpret reality), and these are associated with single nucleotide polymorphisms (SNPs, a genetic variation that affects only one base position in the DNA [deoxyribonucleic acid]). With the help of genome-wide association studies (GWAS), the genetic basis can be revealed. eQTLs help in understanding the mechanism of disease from the genetic signals, and with the help of cis eQTLs and trans eQTLs, interpretation of GWAS loci can be made. Cis eQTLs can provide a direct association between genes and phenotypes using approaches such as genetic co-localization analyses and Mendelian randomization. According to brain eQTL studies, schizophrenia, major depressive disorder, and bipolar disorder have similar genetic architecture.
What Is Brain Expression Quantitative Trait Locus (eQTL)?
Brain expression quantitative trait locus is a locus that explains the genetic variations of the gene expression in the brain and its associated disorders; it can vary with cell type, tissue, and other factors like a response to stimulation. If eQTLs are present on the gene of origin and SNPs are present within 1MB of the transcription start site, it is called local or cis eQTLs, and if the eQTLs are located far from the gene of origin and on another chromosome, it is called as distant or trans eQTLs. Disease-associated variants are more enriched for eQLTs; therefore eQTLs studies are helpful in understanding the pathological molecular processes.
What Are the Steps Involved in Brain Expression Quantitative Trait Locus?
The steps that are involved in brain eQTL are:
- In the study to analyze brain-related disorders, a midbrain resource is used, which contains RNA-seq samples (helps in the identification of gene expression in specific cell types and in associated isoforms) and its accompanying genotypes that are collected from psychENCODE consortium, AMP-AD consortium, and brainiac consortium. These samples are realigned, and duplicate samples are removed. The RNA-seq data defined seven tissue groups which are the cerebellum, cortex, basal ganglia, spinal cord, hypothalamus, hippocampus, and amygdala, and principal component analysis on these data showed clustering of these tissue groups.
- Another study to analyze the mental illness disorders like schizophrenia, major depressive disorder, and bipolar disorder; the study population consists of the UK brain expression consortium (UKBEC) and genotype-tissue expression (GTEx), this includes postmortem brain tissues from four regions. Molecular data such as transcriptome and genotype (consisting of disorder-associated SNPs) are collected. Expression data is collected from four brain regions: cerebellum, hippocampus, frontal cortex, and putamen.
- Schizophrenia, major depressive disorder, and bipolar disorder data from GWAS are selected for analysis, and SNPs are collected.
- Overlapping studies are conducted to increase the number of disorder-associated SNPs in the analysis. From these data, single region eQTLs analysis and multi eQTLs analysis are performed.
- R package Matrix eQTL is used to identify single region eQTLs analysis, and cis eQTLs are identified.
- Multi eQTLs analysis is done for multiple tissues, and meta-analysis of test statistics and hierarchical Bayesian mode is performed for this analysis. Multi-region eQTLs analysis indicates in tissue gene-SNP pair eQTLs are present. Permutation analysis is used to test the robustness of eQTLs in multi-region eQTLs. Cis eQTLs are identified from multi-region meta-analysis, and overlap with GTEx samples is assessed.
- The overlap between GWAS signals and eQTLs is assessed using Bayesian colocalization analysis.
What Are the Results of Brain Expression Quantitative Trait Locus and Brain Diseases?
This study showed that:
- In the case of schizophrenia, an eQTL that results in a missense mutation is the expression of the rs16969968 allele, and studies have shown that receptors that contain this missense mutation lead to a change in amino acid in CHRNA5 gene and are less responsive to the nicotinic agonist, this decreases cell-depolarization and cholinergic signaling. Therefore reduced cholinergic signaling is consistent with schizophrenia. Pharmaceutical modulation of cholinergic signaling can improve schizophrenia outcomes.
- Rs16969968 is an allele on CHRNA5 gene. CHRNA5 is associated with schizophrenia, and Rs16969968 is associated with increased tobacco use. Therefore cis-eQTLs for CHRNA5 can help in identifying variants that increase the risk of disease and also identify potential therapeutic targets.
- Rs12491598 allele is associated with the expression of RSRC1 on chromosome 3. Rs12491598 is involved in the developmental cause of schizophrenia.
- Schizophrenia, major depressive disorder, and bipolar disorder have similar genetic architecture.
- eQTL-effect databases are shared between tissues and ancestors, and opposite allelic effects are present.
- Increased expression of CYP24A1 (an enzyme) is related to the risk of multiple sclerosis (an autoimmune disease that affects the brain and spinal cord, and the immune system attacks the myelin sheath, therefore, causing communication between the body and brain). The excitatory neurons are susceptible to changes in CYP24A1 expression and vitamin D levels. Therefore CYP24A1 plays an important role in the cause of multiple sclerosis.
What Are the Limitations of the Brain Expression Quantitative Trait Locus Study?
The limitations of this study are:
- The analysis of eQTLs is limited to a single tissue and replicated RNA-seq is not included.
- GWAS overlap analysis is difficult to recognize previously identified genes due to differences in effect size, sample size, linkage disequilibrium (association of alleles) structure, variant density, and imputation quality.
- The use of GWAS overlap methods has drawbacks, such as multi-single nucleotide polymorphism cannot be used for the analysis of multiple sclerosis, and genes are affected by multiple indirect effects.
- Co-localization signals could not be detected in cis-eQTL genes.
- Bulk RNA-seq eQTL studies could have identified eQTL effects for genes that are not dosage sensitive and do not cause disruptive downstream consequences.
Conclusion:
According to these studies, schizophrenia, major depressive disorder, and bipolar disorder have similar genetic architecture. This study has identified robust cis-eQTLs that are associated with schizophrenia and other affective disorders. CHRNA5 and RSRC1 are associated with schizophrenia, and identification of Rs16969968 on the CHRNA5 gene is associated with increased tobacco use, therefore, associated with individuals with mental illness that smoke. These results help in understanding mechanisms related to the development of schizophrenia, major depressive disorder, and bipolar disorder and also help in identifying potential new therapeutic targets.
