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Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Updated: Jan 16, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics.

Hanbyul Lee¹, Haochen Sun¹, Xuewei Cao^1,2

¹Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Biorxiv : the Preprint Server for Biology

|October 3, 2025

Summary

This summary is machine-generated.

Spacelink enhances the detection of spatially variable genes (SVGs) across multiple resolutions. This framework links spatial gene expression to complex traits and diseases, offering new insights into tissue organization and neurodegeneration.

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Last Updated: Jan 16, 2026

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Spatial gene expression is crucial for tissue organization, development, and disease.
Existing methods for detecting spatially variable genes (SVGs) have limitations in flexibility, robustness, and integration with genetic data.

Purpose of the Study:

To introduce Spacelink, a unified framework for modeling and quantifying spatial gene variability at multiple resolutions.
To assess the utility of Spacelink in connecting spatial gene expression patterns to complex traits, diseases, and developmental processes.

Main Methods:

Spacelink models spatial variability using an adaptive mixture of data-driven spatial kernels.
It calculates an Effective Spatial Variability (ESV) metric for genes.
The framework was validated on diverse datasets, including Visium, CosMx, and Stereo-seq, and applied to human tissues and mouse organogenesis atlases.

Main Results:

Spacelink demonstrated superior detection power (up to 3.2x) and False Discovery Rate (FDR) control compared to existing SVG methods.
It showed higher cross-platform concordance and revealed that SVGs are highly informative for 113 complex traits and diseases.
Analysis of mouse development and Alzheimer's disease (AD) pathology identified stage-associated and pathology-associated SVGs, linking them to relevant pathways and genetic risk factors.

Conclusions:

Spacelink provides a flexible and robust framework for analyzing spatial gene expression at multiple scales.
The method effectively links spatial gene patterns to complex traits, developmental biology, and neurodegenerative diseases.
Spacelink facilitates the discovery of spatially structured gene programs relevant to disease genetics and progression.