B-264 Identification of Potential Biomarkers Related to Major Depressive Disorder Using a Meta-Analysis Approach
Authors: de Carvalho A H , de Lima A B , Dias H D , Zauli D A 
Published: September 2023

Abstract

Background
Major depressive disorder (MDD), also known as depression, has become the most prevalent mood disorder today. Currently, the diagnosis remains a major challenge given the disease heterogeneity and have been made by associating signs and symptoms with other diagnostic criteria. Several treatment options are available for MDD. However, less than 35% of patients achieve remission while 60% may show some degree of resistance to treatment even when receiving antidepressant treatment according to consensus guidelines and using measurement-based care. Between difficulties associated with the management of MDD, the lack of consensus on classification, diagnosis, and treatment stands out. Because of the incomplete understanding of MDD pathogenesis, it is urgent to develop studies that can help in the elucidation of the mechanisms related to the disease and the identification of biomarkers that allow its correct classification. Thus, this work aimed to perform a meta-analysis study to identify potential molecular biomarkers from different NGS databases.

Methods
Datasets from seven association studies stored in the IEU Open GWAS Project Database were selected. The meta-analysis was performed using the METAL tool (https://genome.sph.umich.edu/wiki/METAL_Documentation), which combines the relevance of the p-values, sample size, and the direction of the effect size. The criteria adopted for selecting the datasets were: (1) Specification of “depression”, “major depressive disorder” or “major depression” as the only phenotype; and (2) description of allele frequency data. The threshold for results significance was defined as P-value ≤5 × 10−8. After performing the analyses, only the SNPs whose direction of effect was the same in all original studies were evaluated, considering only the set of SNPs that were not significant in the original studies.

Results
After performing the meta-analysis, 348 new SNPs with P-value ≤5 × 10−8 were identified and, of those, 133 SNPs were mapped next to 17 genes. None of these genes was previously associated with depression. However, the GABBR1 gene, which encodes a type B GABA receptor subunit, has been associated with alcoholism, while the SORCS3 gene has been associated with attention deficit/hyperactivity disorder and Alzheimer’s disease.

Conclusions
The meta-analysis findings could help elucidate genes and polymorphisms involved in MDD pathogenesis. Due to MDD heterogeneity is nearly impossible to restrict and select a single disease biomarker. It is worth noting that only the identification of genetic polymorphisms involved in MDD development cannot be the only source of potential biomarkers for MDD. Indeed, it is necessary to extend the analysis to other components, such as regulatory regions, epigenetics, metabolites, etc. However, this work provide new insights into the identification of MDD potential biomarkers that can be better investigated in association to others factors related to the disease.