Enhancers are non-coding regulatory elements that are distant from their target gene. Their characterization still remains elusive especially due to challenges in achieving a comprehensive pairing of enhancers and target genes. A number of computational biology solutions have been proposed to address this problem leveraging the increasing availability of functional genomics data and the improved mechanistic understanding of enhancer action. In this review we focus on computational methods for genome-wide definition of enhancer-target gene pairs. We outline the different classes of methods, as well as their main advantages and limitations. The types of information integrated by each method, along with details on their applicability are presented and discussed. We especially highlight the technical challenges that are still unresolved and hamper the effective achievement of a satisfactory and comprehensive solution. We expect this field will keep evolving in the coming years due to the ever-growing availability of data and increasing insights into enhancers crucial role in regulating genome functionality.

This is the first and largest genetic map the DPYD variants associated with adverse drug reaction to 5-FU in south Asian population. Our study highlights ethnic differences in allelic frequencies.

South Asia is home to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\sim $\end{document}20% of the world population and characterized by distinct ethnic, linguistic, cultural and genetic lineages. Only limited representative samples from the region have found its place in large population-scale international genome projects. The recent availability of genome scale data from multiple populations and datasets from South Asian countries in public domain motivated us to integrate the data into a comprehensive resource. In the present study, we have integrated a total of six datasets encompassing 1213 human exomes and genomes to create a compendium of 154 814 557 genetic variants and adding a total of 69 059 255 novel variants. The variants were systematically annotated using public resources and along with the allele frequencies are available as a browsable-online resource South Asian genomes and exomes. As a proof of principle application of the data and resource for genetic epidemiology, we have analyzed the pathogenic genetic variants causing retinitis pigmentosa. Our analysis reveals the genetic landscape of the disease and suggests subset of genetic variants to be highly prevalent in South Asia.

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer–target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.