Research Topic · Peer-Reviewed

Genome-wide Association Studies

Genome-wide association studies are observational genetic investigations that scan markers, typically single-nucleotide polymorphisms, across the entire genomes of many individuals to identify statistical associations between genetic variants and a trait or disease. By comparing allele frequencies between cases and …

Curated from this journal's research 📚 9 peer-reviewed articles cited Cited 57× across the literature 🗓 Reviewed July 2026

Overview

Genome-wide association studies are observational genetic investigations that scan markers, typically single-nucleotide polymorphisms, across the entire genomes of many individuals to identify statistical associations between genetic variants and a trait or disease. By comparing allele frequencies between cases and controls, or by testing variants against quantitative phenotypes, they locate regions of the genome that contribute to complex, polygenic conditions in which many variants each exert a small effect. Methodologically they depend on high-density genotyping or sequencing, stringent correction for multiple testing, control for population structure, imputation, and replication in independent cohorts, with results often summarised as polygenic risk scores. Findings inform understanding of disease biology, the discovery of candidate genes and pathways, and the development of diagnostics and personalised treatment, though much heritability remains unexplained. The articles collected here engage closely with these themes: bioinformatic analysis of coronary-disease-associated SNPs and genes implicated in atherosclerosis, computational analysis of regulatory SNPs and transcription-factor binding sites in relation to disease and high-altitude adaptation, association studies of gene polymorphisms in type 2 diabetes and obesity, and explicit discussion of missing heritability and co-heritability in genomic studies. Recurring concerns include functional interpretation of associated variants, gene-environment interaction, and the translation of association signals into mechanistic and clinical insight within personalised medicine.

Research published in this journal

9 peer-reviewed articles, ranked by relevance. Each links to its DOI.

2014

Bioinformatics of Metabolomics in Diabetes Mellitus Type 2

Ahmad Sliem HamdyCorresponding author
Biochemistry and internal Medicine*, Basic oral and medical sciences, College of dentistry, Qassim University, Saudi Arabia
Exact topic Bioinformatics And Diabetes Cited by 2 doi:10.14302/issn.2374-9431.jbd-13-212
2013

Bioinformatic Resources for Diabetic Nephropathy

Jayne McKnight AmyCorresponding author
Nephrology Research, Centre for Public Health, Queen’s University of Belfast
Exact topic Bioinformatics And Diabetes Cited by 4 doi:10.14302/issn.2374-9431.jbd-13-226

How this research is being cited

The 9 articles above have been cited 57 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Genome-wide Association Studies, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in International Journal of Personalized Medicine.

Journal editorial board
David-Paul Minde · United Kingdom Tarek Magdy Mohamed · United States Bridget Bax · United Kingdom

This page summarises published research for orientation; it is not medical or professional advice.