The use of polygenic scores (also known polygenic risk score or PRS) has become widespread in personalized medicine. Polygenic scores aim to quantify the cumulative effects of a number of genes, which may individually have a very small effect on susceptibility. There is therefore considerable interest in polygenic scores as a biomarker for earlier identification of those at increased risk prior to manifestation of traditional indicators of clinical diseases. Polygenic scores aren’t new, but as they rely on large databases of genome sequences with accompanying phenotypic data, they have become more prevalent and more powerful as sequencing has become cheaper and faster. Traditionally, Genome Wide Association Study (GWAS) of Mutations (MUT or SNP) or of Copy Number Variations (CNV or CN) have been used to build PRS models. However, it has been shown that the gene expression data (EXPR) has much more predictability power compare to gene copy number (CN) or gene mutation (MU) data.
Bioada platform supports all three types of omic data (EXPR, CN and MUT).
