Application of Genome-Wide Association Studies in Rare Diseases Research
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".
Deadline for manuscript submissions: 10 October 2025 | Viewed by 110
Special Issue Editors
Interests: Bayesian hierarchical model; dynamic prediction; environmental health; statistical learning; prediction model
Interests: genetic polymorphism; association analysis; molecular cardiology; molecular nephrology; pharmacogenetics; monogenic diseases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Genome-wide association studies (GWASs) have revolutionized our understanding of genetic contributions to complex traits and diseases. While GWASs have been widely applied to common diseases, their application in rare disease research presents unique challenges and opportunities. Rare diseases collectively affect millions worldwide but individually have low prevalence, making traditional GWAS approaches difficult to implement due to limited sample sizes and statistical power.
This Special Issue aims to highlight innovative methodological approaches and applications of GWASs in rare disease contexts. We welcome original research articles and reviews addressing novel statistical frameworks, meta-analytic approaches, integration of multi-omics data, and innovative study designs that overcome sample size limitations. This Special Issue will showcase advances in polygenic risk scores for rare conditions, cross-ancestry GWAS applications, and gene–gene interaction (epistasis) and gene–environment interaction findings that may have outsized effects in rare disease etiology. We particularly encourage submissions exploring how environmental factors may modify genetic risk in rare diseases and methodological advances for detecting epistatic effects despite limited sample sizes. Contributions demonstrating successful translation of GWAS findings into clinical applications for rare disease diagnosis, prognosis, and treatment are also highly welcomed.
I look forward to receiving your contributions.
Dr. Menglu Liang
Prof. Dr. Andrzej Ciechanowicz
Guest Editors
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Keywords
- rare disease genetics
- statistical methods
- genomic association
- small sample inference
- polygenic risk scores
- cross-ancestry GWAS
- gene–gene interaction
- gene–environment interaction
- disease biomarkers
- precision medicine
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