Dataset Information

Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.

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.

SUBMITTER: Li Y

PROVIDER: S-EPMC10979906 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.

Li Yujue Y Xue Fei F Li Bingxuan B Yang Yilin Y Fan Zirui Z Shu Juan J Yang Xiaochen X Wang Xiyao X Lin Jinjie J Copana Carlos C Zhao Bingxin B

bioRxiv : the preprint server for biology 20240316

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 ...[more]

PMID: 38559152

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Project description:BackgroundGenome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms.ResultsWe developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher's Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted pFET < 1 × 10- 3 (pFET < 0.01) among the 25 traits. Our trait-trait association network revealed not only many relationships among the traits within the same group but also novel relationships among traits from different groups, which warrants further investigation in future.ConclusionsOur study revealed that risk variants for 25 different traits aggregated in particular biological pathways and that these pathways were frequently shared among traits. Our results confirmed known mechanisms and also suggested several novel insights into the etiology of multi-traits.

Dataset Information

Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.

Publications

Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.

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