Multi-marker Analysis Of GenoMic Annotation Research Articles

Multi-marker analysis of genomic annotation on gastric cancer GWAS data from Chinese populations.

Gastric cancer (GC) is one of the high-incidence and high-mortality cancers all over the world. Though genome-wide association studies (GWASs) have found some genetic loci related to GC, they could only explain a small fraction of the potential pathogenesis for GC. We used multi-marker analysis of genomic annotation (MAGMA) to analyze pathways from four public pathway databases based on Chinese GWAS data including 2631 GC cases and 4373 controls. The differential expressions of selected genes in certain pathways were assessed on the basis of The Cancer Genome Atlas database. Immunohistochemistry was also conducted on 55 GC and paired normal tissues of Chinese patients to localize the expression of genes and further validate the differential expression. We identified three pathways including chemokine signaling pathway, potassium ion import pathway, and interleukin-7 (IL7) pathway, all of which were associated with GC risk. NMI in IL7 pathway and RAC1 in chemokine signaling pathway might be two new candidate genes involved in GC pathogenesis. Additionally, NMI and RAC1 were overexpressed in GC tissues than normal tissues. Immune and inflammatory associated processes and potassium transporting might participate in the development of GC. Besides, NMI and RAC1 might represent two new key genes related to GC. Our findings might give new insight into the biological mechanism and immunotherapy for GC.

Comprehensive pathway analyses of schizophrenia risk loci point to dysfunctional postsynaptic signaling

Large-scale genome-wide association studies (GWAS) have implicated many low-penetrance loci in schizophrenia. However, its pathological mechanisms are poorly understood, which in turn hampers the development of novel pharmacological treatments. Pathway and gene set analyses carry the potential to generate hypotheses about disease mechanisms and have provided biological context to genome-wide data of schizophrenia. We aimed to examine which biological processes are likely candidates to underlie schizophrenia by integrating novel and powerful pathway analysis tools using data from the largest Psychiatric Genomics Consortium schizophrenia GWAS (N=79,845) and the most recent 2018 schizophrenia GWAS (N=105,318). By applying a primary unbiased analysis (Multi-marker Analysis of GenoMic Annotation; MAGMA) to weigh the role of biological processes from the Molecular Signatures Database (MSigDB), we identified enrichment of common variants in synaptic plasticity and neuron differentiation gene sets. We supported these findings using MAGMA, Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA) and Interval Enrichment Analysis (INRICH) on detailed synaptic signaling pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and found enrichment in mainly the dopaminergic and cholinergic synapses. Moreover, shared genes involved in these neurotransmitter systems had a large contribution to the observed enrichment, protein products of top genes in these pathways showed more direct and indirect interactions than expected by chance, and expression profiles of these genes were largely similar among brain tissues. In conclusion, we provide strong and consistent genetics and protein-interaction informed evidence for the role of postsynaptic signaling processes in schizophrenia, opening avenues for future translational and psychopharmacological studies.