Advances in Statistical Genetics and Its Applications

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 933

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College of Health Solutions, Arizona State University, Phoenix, AZ, USA
Interests: statistical genetics; bioinformatics; biostatistics; statistical data analysis
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Special Issue Information

Dear Colleagues,

Statistical methods play a critical role in genetic studies, and advanced statistical methods and applications can enable researchers to expand upon existing results in the field.

This Special Issue aims to highlight new statistical approaches or novel applications of existing statistical methods to studies related to genetics.

We welcome original research and review papers demonstrating cutting-edge research on machine learning methods, causal inference, etc.

Prof. Dr. Zhongxue Chen
Guest Editor

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Keywords

  • statistics genetics
  • causal inference
  • meta-analysis
  • Mendelian randomization
  • machine learning
  • genotype
  • phenotype
  • GWAS

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Published Papers (1 paper)

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Research

16 pages, 1542 KB  
Article
Large-Scale Analyses of GWAS Identify Five Key Pleiotropic Genes Involved in Complex Diseases
by Imene Mahdi, Ghazi Chabchoub, Najla Kharrat and Ahmed Rebai
Genes 2026, 17(7), 766; https://doi.org/10.3390/genes17070766 - 30 Jun 2026
Viewed by 473
Abstract
Background/Objectives: Genome-Wide Association Studies (GWASs) have revealed numerous Single Nucleotide Polymorphisms (SNPs) linked to diverse diseases and traits, highlighting extensive pleiotropy. This study aimed to identify functionally relevant pleiotropic genes through large-scale analysis of the GWAS catalog and variant annotation. Methods: [...] Read more.
Background/Objectives: Genome-Wide Association Studies (GWASs) have revealed numerous Single Nucleotide Polymorphisms (SNPs) linked to diverse diseases and traits, highlighting extensive pleiotropy. This study aimed to identify functionally relevant pleiotropic genes through large-scale analysis of the GWAS catalog and variant annotation. Methods: An initial set of 494 putative pleiotropic genes across 223 phenotypes was refined using stringent criteria, yielding 343 SNP–trait associations corresponding to 53 SNPs mapped to 16 genes. Results: Five top-ranked genes were prioritized using a composite score, and their variants were fully annotated for genetic and clinical features. Approximately 70% of the SNPs were intronic, with five located in UTRs, and 11 in coding regions. ABO emerged as the highest-scoring pleiotropic gene, with six SNPs in strong linkage disequilibrium, implicating rs8176719 (c.261delG in exon 6) as the likely causal variant. ALDH2 showed 20 strong associations across two SNPs, with rs671 (E504K) identified as a key missense variant linked to multiple diseases. GCKR exhibited 29 associations across three SNPs, with rs1260326 (P446L) reducing glucokinase inhibition and enhancing hepatic glycolysis and triglyceride production. In HLA-DQA1, three of four SNPs located in UTRs suggested regulatory roles in gene expression. TERT displayed 59 GWAS signals, primarily cancer-related across seven organs, involving eight SNPs. Among these, rs10069690 in intron 4 has been associated with altered gene expression, while the functional impact of other variants remains to be clarified. Conclusions: This work highlights key pleiotropic genes and variants, offering insights into the genetic architecture and mechanisms underlying complex human diseases. Full article
(This article belongs to the Special Issue Advances in Statistical Genetics and Its Applications)
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