The study of neurogenetics and genomics has transformed our understanding of how genetic variation influences brain development, cognitive function, and susceptibility to neurological and psychiatric disorders. This research aims to integrate genomic data with neurobiological phenotypes to identify critical genetic variants, regulatory elements, and gene networks involved in neural function. Using genome-wide association studies (GWAS), whole-exome sequencing, and gene expression profiling from postmortem brain tissue, we evaluated the genetic landscape across multiple cohorts diagnosed with major neurological conditions. Our findings indicate strong associations between specific single nucleotide polymorphisms (SNPs) and gene expression changes in regions linked to neurodevelopment and synaptic plasticity. This study supports the growing evidence that integrative genomics is essential for deciphering complex neural mechanisms and offers new targets for therapeutic development

Neurogenetics is a rapidly evolving field that investigates how genes influence the development and functioning of the nervous system. Paired with advances in genomics, it offers unprecedented insights into the genetic architecture of brain function and the etiology of neurological disorders. Genetic studies have revealed that complex traits such as memory, cognition, and mood regulation have a substantial heritable component, often involving multiple genes with small effect sizes. Technological advances such as next-generation sequencing (NGS), transcriptomics, and epigenomics have expanded our capacity to explore the genome's role in neurobiology with high resolution. Numerous studies have linked genetic variation to neurodevelopmental and neurodegenerative disorders, including schizophrenia, autism spectrum disorder (ASD), Alzheimer's disease, and epilepsy. However, the complexity of gene-environment interactions and the pleiotropic nature of genetic variants necessitate integrative approaches combining genetic, transcriptomic, and phenotypic data. This study aims to map the connections between genetic variants and neurological phenotypes through genomic analyses in well-characterized clinical cohorts.

Study Population

The study included three cohorts totaling 1,200 individuals: 400 with schizophrenia, 400 with Alzheimer’s disease, and 400 healthy controls. Subjects were recruited from three tertiary research hospitals with ethical approvals and informed consent.

Genomic Data Acquisition

Peripheral blood samples were collected for DNA extraction. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) were performed using Illumina NovaSeq 6000 platforms. Read alignment and variant calling were performed using the BWA-GATK pipeline.

Transcriptome Analysis

Postmortem brain tissues (frontal cortex) from a subset of 100 individuals were used for RNA sequencing. Transcript quantification was performed using STAR aligner and RSEM. Differential gene expression was analyzed using DESeq2.

GWAS and SNP Analysis

GWAS was conducted using PLINK 2.0, and quality control was applied to exclude SNPs with minor allele frequency < 1>

Pathway and Network Analysis

Gene Ontology (GO) and pathway enrichment analyses were performed using DAVID and GSEA. Protein-protein interaction networks were constructed using STRING database.

Our genomic analysis identified over 50,000 SNPs, with 112 SNPs significantly associated (p < 5>CACNA1C, GRIN2B, and DISC1, genes previously implicated in synaptic signaling and neurodevelopment. Transcriptomic analysis revealed 316 differentially expressed genes (DEGs) in the schizophrenia cohort and 287 DEGs in the Alzheimer’s cohort, compared to controls. Notably, BDNF, MAPT, and SYN1 were among the top dysregulated genes. Pathway analysis highlighted disruptions in synaptic vesicle transport, axon guidance, and calcium signaling pathways. Integrative network analysis identified key regulatory hubs, including  CREB1 and FOXP2 , linked to cognition and language. 

The findings underscore the polygenic and network-based nature of brain function and neurological disorders. Variants in calcium channel genes (CACNA1C) and glutamate receptors (GRIN2B) align with previous studies emphasizing their role in synaptic plasticity and cognitive processes. Dysregulation of neurotrophic and cytoskeletal genes suggests common pathogenic pathways across distinct brain diseases. Importantly, the overlap of risk loci between schizophrenia and Alzheimer’s disease points to shared genetic mechanisms, possibly involving neuroinflammation and mitochondrial function. Transcriptomic data support the notion that gene expression changes may mediate the effects of risk variants, offering insight into downstream effects of genomic alterations. The application of multi-omics approaches enhances our understanding of complex brain phenotypes. However, the interpretation of genomic data remains challenging due to heterogeneity, linkage disequilibrium, and incomplete annotation of non-coding elements. Future work should focus on functional validation using CRISPR-based models and single-cell transcriptomics to refine gene-function relationships.

This study contributes to the growing body of evidence supporting the role of genetic and transcriptomic variation in the etiology of neurological disorders. Our integrative approach combining GWAS, transcriptomics, and network analysis identifies key genes and pathways involved in brain function and dysfunction. These insights may inform biomarker development and targeted therapies in neuropsychiatric and neurodegenerative diseases. Continued investment in large-scale neurogenomic studies and cross-disciplinary collaboration will be critical to translate these findings into clinical practice.