Genome-Wide Association Studies of Complex Diseases and Related Endophenotypes and Biomarkers
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Genetics and Genomics".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 6
Special Issue Editor
Special Issue Information
Dear Colleagues,
A genome-wide association (GWA) study is a hypothesis-free approach that conceptually captures the full spectrum of genetic variation responsible for common and complex human diseases. The first GWA study was reported in 2005, and since then, a plethora of GWA studies have identified thousands of new genes for several common and complex diseases. However, the genetic variance associated with the identified variants for these diseases is small relative to their heritability estimates, indicating that additional underlying genetic factors need to be identified by performing more GWA studies. A significant drop in the cost for performing whole-genome sequencing (WGS) is enabling the discovery of novel genes based on ultra-rare variants and copy number variants that were not possible using GWA arrays. Some of the missing heritability may also be discovered using alternative approaches to the case–control design. Studies focusing on disease-related endophenotypes and biomarkers provide a powerful alternative approach to identify not only additional variants/genes, but they may also help to uncover underlying disease mechanisms that cannot be obtained from case–control studies.
To date, the attention of the vast majority of GWA studies has been on European or European-derived White populations, with little focus on non-White populations. The purpose of this call is for papers on additional GWA studies on complex diseases and related endophenotypes/biomarkers using common, rare, and copy number variants on well-powered samples from diverse population groups.
Prof. Dr. M. Ilyas Kamboh
Guest Editor
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Keywords
- genome-wide association studies
- complex diseases
- endophenotypes
- biomarkers
- rare variants
- copy number variants
- diverse populations
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