Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform
Li, Y.; Xue, F.; Li, B.; Yang, Y.; Fan, Z.; Shu, J.; Wang, X.; Lin, J.; Copana, C.; Zhao, B.
Show abstract
As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Variational Autoencoder-based Model Improves Polygenic Prediction in Blood Cell Traits 92%
- Scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications to studying Alzheimer’s disease 92%
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations 92%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Utilizing Non-Invasive Prenatal Test Sequencing Data Resource for Human Genetic Investigation 95%
- Streamlining Large-Scale Genomic Data Management: Insights from the UK Biobank Whole-Genome Sequencing Data 93%
- MUSSEL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.