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Emerging Trends for Genome-Wide Association Studies in Complex Disease Genetics
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Emerging Trends for Genome-Wide Association Studies in Complex Disease Genetics

A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 3951

Special Issue Editor

Dr. Xiaowei Wu

E-Mail Website
Guest Editor
Department of Statistics, Virginia Tech, 250 Drillfield Dr, Blacksburg, VA 24061, USA
Interests: statistical genetics; bioinformatics; Bayesian and computational statistics; branching processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Genome-wide association studies (GWASs) have revolutionized the study of complex diseases by identifying thousands of genetic loci linked to human traits and diseases. Despite these advances, significant methodological challenges remain, including translating statistical associations into biological mechanisms, enhancing polygenic risk prediction across diverse populations, and effectively capturing rare and structural variants. Recent developments in GWAS methodology—such as sophisticated statistical modeling, multi-omics integration, and cutting-edge machine learning techniques—are beginning to address these limitations, offering deeper insights into disease etiology and paving the way for personalized medicine. Moreover, the growth of large-scale biobanks, advances in long-read sequencing, and AI-driven data interpretation are further accelerating genetic discovery.

We would like to invite you to contribute to this Special Issue, “Emerging Trends for Genome-Wide Association Studies in Complex Disease Genetics”. It highlights cutting-edge approaches that enhance the power, interpretability, and clinical applicability of GWASs, fostering innovation in research on complex disease genetics. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Methodological innovations (e.g., rare variant analysis, cross-ancestry polygenic risk scores, AI/ML applications).
  • Functional and causal inference (fine-mapping and Mendelian randomization).
  • Clinical and translational applications (precision medicine and drug discovery).
  • Emerging technologies (long-read sequencing, federated GWAS, and quantum computing).

We look forward to receiving your contributions.

Dr. Xiaowei Wu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Current Issues in Molecular Biology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GWAS
  • polygenic risk scores
  • rare variants
  • structural variants
  • multi-omics
  • Mendelian randomization
  • population structure and relatedness
  • machine learning
  • Bayesian models
  • stochastic models

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Published Papers (3 papers)

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Research

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23 pages, 1293 KB  
Article
Family-Based GWAS of Cognitive Endophenotypes Reveals Genetic Architecture of Memory and Executive Function in Alzheimer’s Disease
by Kesheng Wang, Xueying Yang, Gayenell Magwood, Chun Xu, R. Osvaldo Navia, Jean Neils-Strunjas and Xiaoming Li
Curr. Issues Mol. Biol. 2026, 48(5), 442; https://doi.org/10.3390/cimb48050442 - 24 Apr 2026
Viewed by 545
Abstract
Alzheimer’s disease (AD), the most common cause of dementia, is characterized by progressive memory and cognitive decline. Conventional genome-wide association studies (GWAS) comparing AD cases and controls may miss genetic influences that act along a continuum of cognitive function. Using data from 3007 [...] Read more.
Alzheimer’s disease (AD), the most common cause of dementia, is characterized by progressive memory and cognitive decline. Conventional genome-wide association studies (GWAS) comparing AD cases and controls may miss genetic influences that act along a continuum of cognitive function. Using data from 3007 participants in the National Institute on Aging Late-Onset Alzheimer’s Disease Family Study (NIA-LOAD GWAS), we conducted a family-based GWAS of eight quantitative cognitive phenotypes encompassing episodic memory (Logical Memory IA and IIA), working memory (Digit Span Forward, Backward, and Ordering), and semantic fluency (Animal, Fruit and Vegetable, and Vegetable Fluency). Family-based association testing in PLINK v1.9 identified numerous single nucleotide polymorphisms (SNPs) associated with cognitive phenotypes at genome-wide significant (p < 5 × 10−8) levels. Notably, genome-wide significant variants with cognatic functions were localized to genes implicated in synaptic function, neurodevelopment, and neurodegeneration, including TOMM40 (rs2075650), ERBB4 (rs1521543), APLP2 (rs12281267, rs959354), PTPRD (rs1353983, rs970347, rs1392511), NCAM2 (rs2826728), GRM7 (rs6788201), PAX5 (rs2988003, rs2381595), NRG1 (rs16875655), and NRG3 (rs1937957). Furthermore, the TOMM40 (rs2075650) was significantly associated with AD as a binary outcome (p = 4.60 × 10−24) and APLP2 (rs12281267, rs959354), APOE (rs405509), PTPRD (rs1353983, rs970347, rs1392511) were associated with AD (p < 0.001). Additionally, several pathways including the ERBB4 signaling pathway (adjusted p = 2.82 × 10−3), driven by ERBB4, NRG1, and NRG3 may contribute to cognitive impairments. This study provides a comprehensive resource of cognitive endophenotype associations in AD families, advancing understanding of the genetic architecture underlying memory, executive function, and cognitive aging, and highlights new therapeutic targets for replication and functional follow-up. Full article
(This article belongs to the Special Issue Emerging Trends for Genome-Wide Association Studies in Complex Disease Genetics)
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19 pages, 1719 KB  
Article
Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals
by Xiaowei Wu
Curr. Issues Mol. Biol. 2026, 48(3), 273; https://doi.org/10.3390/cimb48030273 - 4 Mar 2026
Viewed by 465
Abstract
Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded [...] Read more.
Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded by enabling joint modeling, nonlinear effects, and integrative analysis. However, deep learning approaches remain underutilized in augmenting traditional GWAS frameworks, particularly in the presence of cryptic relatedness among sampled individuals. In this paper, we propose a deep neural network (DNN)-based machine learning method to assist genetic association testing in samples with related individuals. By approximating the phenotype–genotype relationships in classical association tests and combining approximations across multiple tests, the proposed method aims to improve predictive performance in the identification of associated variants. Simulation studies demonstrate that our approach effectively complements conventional statistical methods and generally achieves increased power for detecting genetic associations. We further apply the method to data from the Framingham Heart Study, illustrating how DNN-based machine learning can facilitate the identification of genome-wide SNPs associated with average systolic blood pressure. Full article
(This article belongs to the Special Issue Emerging Trends for Genome-Wide Association Studies in Complex Disease Genetics)
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Review

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27 pages, 573 KB  
Review
From GWAS Signals to Causal Genes in Chronic Kidney Disease
by Charlotte Delrue, Reinhart Speeckaert and Marijn M. Speeckaert
Curr. Issues Mol. Biol. 2026, 48(2), 148; https://doi.org/10.3390/cimb48020148 - 28 Jan 2026
Viewed by 2271
Abstract
Genome-wide association studies (GWAS) have transformed the study of chronic kidney disease (CKD) by identifying hundreds of genetic loci associated with multiple aspects of kidney function, including albuminuria and CKD risk factors, in diverse populations. A major challenge is translating statistically significant signals [...] Read more.
Genome-wide association studies (GWAS) have transformed the study of chronic kidney disease (CKD) by identifying hundreds of genetic loci associated with multiple aspects of kidney function, including albuminuria and CKD risk factors, in diverse populations. A major challenge is translating statistically significant signals into causal genes and mechanisms, as most CKD-associated variants lie in non-coding regulatory regions and often act in a cell type- and context-specific manner. In this review, we provide an overview of the current strategies for moving from GWAS signals toward the identification of causal genes for CKD. We discuss advances in four areas: statistical and functional fine-mapping, molecular quantitative trait locus (QTL) mapping, colocalization, and transcriptome-wide associations, highlighting the advantages and disadvantages of each. We further examined how emerging kidney-specific single-cell, single-nucleus, and spatial transcriptomic atlases have enabled the mapping of genetic risk to specific renal cell types and microanatomical niches. By combining these approaches with chromatin interaction data, multi-omics analytics, and clustered regularly interspaced short palindromic repeats (CRISPR)-based studies, the process of generating causal relationships and mechanistic understanding has been further refined. Importantly, this review provides a unifying framework that synthesizes cross-sectional and longitudinal GWAS with kidney-specific functional genomics to distinguish genetic determinants of CKD susceptibility from modifiers of disease progression, thereby highlighting how regulatory variation and disease trajectories inform precision nephrology. As a result, we can provide insights into the role of genetically informed gene prioritization for experimentation, therapeutic target discovery, and the development of a framework for precision nephrology. Together, these advancements highlight how human genetics, in conjunction with functional genomics and experimental biology, can link an association signal to a clinically relevant interpretation of CKD. Full article
(This article belongs to the Special Issue Emerging Trends for Genome-Wide Association Studies in Complex Disease Genetics)
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