Identification of disease-related genetic variants and imaging factors leveraging summary statistics

Brain imaging genetics offers insights into the genetic basis of brain structure and function by exploring the relationships between genetic variations and neuroimaging features, with canonical correlation association learning as a vital and effective tool. However, imaging large cohorts affected by specific brain diseases entails significant costs. To tackle this challenge, we introduced a novel bi-multivariate sparse canonical correlation association method based on summary statistics from large GWAS (S-SCCA). S-SCCA leverages effect sizes obtained from these datasets to identify genetic variants associated with complex traits, including those influenced by pleiotropy, while simultaneously identifying imaging factors related to the disease under study. Moreover, we have implemented a rapid optimization strategy to circumvent computational burdens while identifying disease-associated risk factors within genetic variations across the entire chromosome. We assessed S-SCCA against conventional SCCA using a neuroimaging genetic dataset from the Alzheimer’s Disease Neuroimaging Initiative. Results showed that S-SCCA demonstrated comparable or superior modeling performance and feature selection capabilities. Furthermore, we applied S-SCCA to two summary statistics datasets from two large GWAS, where original imaging and genetic data were inaccessible. S-SCCA replicated the genetic loci identified by GWAS and additional meaningful variants. Additionally, it revealed bi-multivariate relationships between imaging QTs and SNPs, indicating its powerful modeling capability. These findings highlight the promise of S-SCCA as a practical bi-multivariate learning technique in brain imaging genetics, circumventing the need for sensitive individual-level imaging and genetic data, thereby enhancing its potential for broader applicability and accessibility in biomedical studies.