The current challenges for using GWAS to prioritize variants for functional follow-up experiments are described and a multi-modal approach for resolving GWAS loci to a focused set of high-confidence variants for functional exploration is suggested.
Background Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits and diseases, yet translating these signals into biological insight remains challenging. Most associated variants are non-coding and reside in linkage disequilibrium (LD) blocks, where multiple correlated variants jointly contribute to association signals. These clusters, or haplotypes, may capture shared regulatory and functional contexts. Interpreting GWAS signals thus requires approaches that integrate regulatory, functional, and cross-trait evidence, while preserving the broader haplotypic context of disease-associated loci. At the same time, the rapid growth of publicly available GWAS summary statistics has enabled large-scale cross-trait analyses, but also introduced redundancy across closely related phenotypes. Efficient interpretation of GWAS data therefore requires tools that integrate heterogeneous data sources while preserving genomic and biological contexts. Results We present snpXplorer, an interactive web platform for haplotype-aware exploration and annotation of GWAS data. The platform incorporates >10,000 GWAS datasets from OpenGWAS and enables multi-scale analysis across variants, haplotypes, genes, and traits. Key features include (i) a haplotype-based representation of association signals derived from LD structure, (ii) a unified variant annotation framework integrating clinical annotations (ClinVar), allele frequencies (gnomAD), functional predictions (CADD, AlphaGenome), quantitative trait loci (GTEx), structural variation, and GWAS associations, and (iii) cross-trait exploration using semantic similarity-based clustering of phenotypes. Use cases centered on Alzheimer’s disease illustrate this utility: for example, at the TMEM106B locus, snpXplorer identified a haplotype linked to eleven distinct traits, revealing synergistic pleiotropy across neurological and behavioral phenotypes alongside antagonistic pleiotropy with height. Conclusions snpXplorer allows users to browse, filter, and inspect variant-, haplotype-, gene- and trait-level evidence, lowering the barrier to biological interpretation of GWAS results. Compared with existing tools that focus on specific aspects of GWAS interpretation, the strength of snpXplorer is that it reduces the need for fragmented queries across databases.
N. Tesi, G. Green, A. Salazar et al.· bioRxiv· 0 citations
This review describes the main characteristics and limitations of standard statistical approaches for GWAS, the main uses of AI methods in computational genomics, and recent attempts to leverage AI strategies in GWAS and presents 30 methods designed to leverage AI in GWAS.
S. D’Antona, Mawada Elmagboul Abdalla Abakar, Daniele Ramazzotti et al.· BioData Mining· 0 citations
ABSTRACT Genome‐wide association studies (GWAS) have shown that pleiotropy, whereby a single genetic variant or gene influences multiple traits, is common in complex human diseases. Detecting cross‐phenotype associations from GWAS summary statistics remains challenging because of small effect sizes, extensive multiple testing, heterogeneous effects, and possible differences in effect direction across traits. Methods that jointly analyze multiple traits can improve the ability to detect pleiotropic signals while retaining the practical advantages of summary statistic‐based analyses. Although a range of statistical approaches has been developed for this purpose, practical guidance on their application, assumptions, and interpretation remains limited. This tutorial reviews several widely used methods for pleiotropy detection from GWAS summary statistics, including ASSET, PLACO, GPA, CPBayes, and GCPBayes, and demonstrates their application using breast and thyroid cancer datasets. We also highlight the importance of accounting for effect heterogeneity, correlation, and biological group structure at the gene and pathway levels in the detection and interpretation of pleiotropic association signals.
Christina Y. Feng, P. Sugier, Nan Zou et al.· Statistics in Medicine· 0 citations
SUMMARY
Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation.
AVAILABILITY AND IMPLEMENTATION
The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Alireza Ani, I. Nolte, Zoha Kamali et al.· Bioinformatics· 0 citations
Alzheimer's Disease (AD) is a complex neurodegenerative disorder with a strong genetic architecture. Genome-Wide Association Studies (GWAS) have identified numerous susceptibility loci. However, the majority of associated variants reside in non-coding regions, making it difficult to resolve their functional consequences and identify causal genes. To address this limitation, integration of GWAS with expression Quantitative Trait Loci (eQTL) and protein Quantitative Trait Loci (pQTL) data has emerged as a key strategy for linking genetic variation to downstream molecular phenotypes. This review discusses statistical frameworks for multi-omics integration in AD research, with a focus on approaches that enable causal inference and gene prioritization. Major methods include colocalization analysis for detecting shared causal variants, Mendelian Randomization (MR) for assessing putative causal relationships, and Transcriptome-Wide Association Studies (TWAS) for linking genetically predicted gene expression to disease risk. Applications of these frameworks have facilitated the identification of candidate causal genes and proteins, thereby improving the mechanistic interpretation of AD-associated loci. However, challenges remain, including tissue specificity and cell-type specificity, limited ancestral diversity in available datasets, and constraints in causal inference. Emerging single-cell and spatial multi-omics approaches are expected to provide a more detailed characterization of AD-associated molecular mechanisms while supporting therapeutic target discovery.
Zhuolan Li· Journal of Clinical Technolo...· 0 citations
Genome-wide association studies (GWAS) use statistical models to correlate single nucleotide polymorphisms (SNPs) to a phenotype of interest. This scan of the entire genome identifies regions of association with a phenotype, but due to linkage disequilibrium (LD), GWAS on their own cannot identify single genes responsible for phenotypic variation. Rather, fine-mapping of GWAS regions is required, necessitating the use of additional tools and software. With the introduction of more pangenomic resources in a number of crops (Guo et al. 2025; Hufford et al. 2021), the fidelity of these fine-mapping efforts is growing, presenting the opportunity to leverage new information about allelic variation towards gene discovery (Shi et al. 2023; Della Coletta et al. 2021). Panvar is a tool developed to integrate existing software and resources to perform GWAS and fine-mapping in one seamless step. For each identified GWAS peak, panvaR outputs information about LD and SNP effect prediction for each SNP and by layering locations of nearby genes, creates a refined list of possible candidate genes. We have implemented Panvar as an R package, “panvaR”, which runs the analysis functions, creates interactive and static visualizations, and outputs results tables. This tool seeks to bridge the gap between GWAS and gene speeding up an important step of quantitative genetic studies.
Collin Luebbert, Rijan R. Dhakal, Phillip Ozersky et al.· bioRxiv· 0 citations