GWAS genetic variant data and their integration in the context of network biology

Abstract

Regardless of the success of Genome Wide Association Studies (GWAS) to identify genetic variants associated with human diseases, investigating the molecular mechanisms and disease-associated genes linked to those genetic variants, is a very complex task. Specifically, where intergenic genetic variants are linked to the adjacent neighbouring genes. Consequently, the inference for the mechanistic connection between diseases and its susceptible genetic variants becomes more challenging. Functional genomics studies can support to reveal the significance of variants via intermediate molecular traits. Moreover, approaches like computational and bioinformatics predictions based on the variants location and its sequence attributes can assist to propose the candidate genes. As, the spectrum of potential functional consequences of variants is much broader; it still requires new methodologies to predict any molecular level perturbation. Thus, specialized algorithms and computable modelling approaches are essential, for the modelling and simulation of genetic regulatory networks. In this review, we are briefly summarizing all the existing methodologies for genome wide association studies, currently available algorithms and computable modelling approaches; moreover also emphasizing the required new approaches for modelling and simulations of genetic regulatory networks to predict the functional consequences of disease-associated genetic variants.