Exploring cross-trait genetic architectures: statistical models, computational challenges, and the BIGA platform

About this event

Numerous statistical models have been proposed to analyze cross-trait genetic architectures utilizing summary statistics from genome-wide association studies (GWAS). However, systematically analyzing high-dimensional GWAS summary statistics presents logistical and computational challenges. In this talk, we introduce the BIGA platform (http://bigagwas.org/), a website that offers unified data analysis pipelines and centralized data resources. We have developed a framework that implements statistical genetics tools on a cloud computing platform, integrated with extensive curated GWAS datasets. Furthermore, we discuss our recent theoretical analyses of the LD score regression (LDSC), a widely-used method for inferring heritability and genetic correlation using GWAS summary statistics. We provide theoretical guarantees for LDSC-based estimators by explicitly modeling the block-wise dependence pattern of high-dimensional GWAS data. These analyses are joint work with Fei Xue and Yujue Li.