Bridging heterogeneous mutation data to enhance disease gene discovery

Jingbo Xia

Abstract: Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations that link genotype and phenotype. However, due to the differences of size and quality between GWAS studies, not all de facto vital variations are able to pass the multiple testing. In the meantime, mutation events widely reported in literature unveil typical functional biological process, including mutation types like gain of function and loss of function. To bring together the heterogeneous mutation data, we propose a ‘Gene–Disease Association prediction by Mutation Data Bridging (GDAMDB)’ pipeline with a statistic generative model. The model learns the distribution parameters of mutation associations and mutation types and recovers false-negative GWAS mutations that fail to pass …