ArticleJournal of applied genetics2026
Identification of variants from gene expression data of opioid-addicted patients.
Article in Journal of applied genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Deciphering the impact of genetic variants on vulnerability to Opioid Use Disorder.Frontiers in cellular neuroscience · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
NGS (next-generation sequencing) has become a rapid advance in discovering the variants in the genomic data for disease diagnosis, prognosis, and therapeutic decision. However, the scope of detecting single nucleotide polymorphisms (SNPs) becomes limited by the availability of reliable high-throughput data. OUD (opioid use disorder) is a chronic condition marked by prolonged opioid misuse, leading to cycles of relapse and remission. The discovery of genetic variants associated with OUD is constrained by limited genomic data, making it crucial to identify these variants and their genetic factors. The identification of variants from RNA-seq (RNA-sequencing) data can become the representative of the SNP analysis that is generally preferred from the whole genome or exome sequencing data. This study aimed to identify variants from gene expression data downloaded from NCBI GEO with accession PRJNA492904 in postmortem ventral midbrain specimens of chronic opioid users. We hypothesized that the NRXN3 gene would exhibit the highest number of variants due to its significant role in neuronal synapse function and its association with opioid addiction and impulsivity. We utilized RNA-Seq data from OUD patients (PRJNA492904, NCBI SRA) to detect variants in expressed RNA, which can indicate functional protein changes. Eight genes were analyzed: BDNF, DRD2, DRD3, NRXN3, OPRD1, OPRM1, and NGFB, with a primary focus on NRXN3. Our findings revealed the highest number of variants in NRXN3 compared to the other genes, highlighting its potential importance in OUD and the robustness of RNA-Seq in variant detection.
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