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Updated: Jun 29, 2025
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
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Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing
Yujue Li1, Fei Xue1, Bingxuan Li2
1Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Biorxiv : the Preprint Server for Biology
|April 1, 2024
Summary
BIGA is a new website that simplifies analyzing genome-wide association study (GWAS) summary statistics. It provides unified tools and data resources for researchers to explore genetic links between complex traits and diseases.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Large-scale biobanks increasingly offer deep phenotyping and genomic data.
- Genome-wide association studies (GWAS) are crucial for understanding complex trait and disease genetics.
- Publicly available GWAS summary statistics enable cross-trait analyses but pose analytical challenges.
Conclusions:
- BIGA addresses the logistical and computational challenges of analyzing large-scale GWAS summary statistics.
- The website serves as a valuable resource for the research community, promoting efficient data consolidation and discovery.
- BIGA facilitates deeper exploration of genetic overlaps between diverse phenotypes.
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