A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.

ABSTRACT: Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the "missing heritability" problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1)

SUBMITTER: Silva PP 

PROVIDER: S-EPMC9499949 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature