Identification of Pathogenic Regulatory Variants in Mendelian Diseases

Mendelian disease, also known as monogenic disease, is caused by variation in a single gene and are inherited according to Mendel’s laws. The identification of pathogenic regulatory variants is essential for understanding the genetic underpinnings of Mendelian diseases. Although coding variants have traditionally received the primary focus, noncoding regulatory variants are also increasingly being recognized as pivotal contributors to different diseases. Regulatory variants cause Mendelian diseases by disrupting gene regulation elements and have been classified according to their functional impact on diseases. The identification of pathogenic regulatory variants is significant for understanding the genetic contribution of noncoding DNA to diseases.This chapter provides an overview of computational methods used to find and annotate the function of regulatory variants linked to Mendelian disorders. We discuss significant approaches such as next generation sequencing technologies, genome-wide association studies, chromatin accessibility profiling, and expression quantitative trait loci analysis that make it possible to find noncoding regulatory elements. Furthermore, we also highlight advanced computational tools that are used to predict the functional impact of regulatory variants and their potential contribution in disease onset and severity. We also emphasize how the advancement in computational methods and large-scale genomic data can be combined to help identify new pathogenic variants and improve our understanding of disease mechanisms. Adopting integrative multi-omics approaches powered by machine learning methods would accelerate the identification of pathogenic regulatory variants, paving the way for more precise and individualized approaches to diagnose and treat Mendelian diseases.