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...
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
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.
Christina Y. Feng, P. Sugier, Nan Zou et al.· Statistics in Medicine· 0 citations
The SNPannotator package is introduced, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings and provide a practical framework for efficiently deriving biologically meaningful insights from GWAS data.
Alireza Ani, I. Nolte, Zoha Kamali et al.· Bioinformatics· 1 citation
Analysis of statistical frameworks for multi-omics integration in AD research focuses on approaches that enable causal inference and gene prioritization, with a focus on approaches that enable causal inference and gene prioritization.
Zhuolan Li· Journal of Clinical Technolo...· 0 citations
Panvar is a tool developed to integrate existing software and resources to perform GWAS and fine-mapping in one seamless step and 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
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