Understanding the Function of a Locus Using the Knowledge Available at Single-Nucleotide Polymorphisms

Understanding the function of a locus is a challenge in molecular biology. Although numerous molecular data have been generated in the last decades, it remains difficult to grasp, how these data are related at a locus? In this study, we describe an analytical workflow that can solve this problem using the knowledge available at single-nucleotide polymorphisms (SNPs) level. The underlying algorithm uses SNPs as connectors to link omics data and identify correlation between them through a joint bioinformatical/statistical approach. We describe its application in finding the mechanism whereby a mutation causes a phenotype and in revealing the path whereby a gene is being regulated and impacts the phenotypes. We translated our workflow into freely available shell scripts that carry out the analyses. Our approach provides a basic framework to solve the information overload problem in biology.