Next Article in Journal
Functional SNPs in the Human Autoimmunity-Associated Locus 17q12-21
Next Article in Special Issue
Machine Learning and Integrative Analysis of Biomedical Big Data
Previous Article in Journal
A Human Skin Model Recapitulates Systemic Sclerosis Dermal Fibrosis and Identifies COL22A1 as a TGFβ Early Response Gene that Mediates Fibroblast to Myofibroblast Transition
Previous Article in Special Issue
Artificial Intelligence and Integrated Genotype–Phenotype Identification
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessReview
Genes 2019, 10(2), 76; https://doi.org/10.3390/genes10020076

From Genotype to Phenotype: Through Chromatin

1
Department of Global Public Health and Primary Care, University of Bergen, 5018 Bergen, Norway
2
Computational Biology Unit, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
*
Author to whom correspondence should be addressed.
Received: 19 December 2018 / Revised: 16 January 2019 / Accepted: 21 January 2019 / Published: 23 January 2019
(This article belongs to the Special Issue Systems Analytics and Integration of Big Omics Data)
  |  
PDF [1012 KB, uploaded 2 February 2019]
  |  

Abstract

Advances in sequencing technologies have enabled the exploration of the genetic basis for several clinical disorders by allowing identification of causal mutations in rare genetic diseases. Sequencing technology has also facilitated genome-wide association studies to gather single nucleotide polymorphisms in common diseases including cancer and diabetes. Sequencing has therefore become common in the clinic for both prognostics and diagnostics. The success in follow-up steps, i.e., mapping mutations to causal genes and therapeutic targets to further the development of novel therapies, has nevertheless been very limited. This is because most mutations associated with diseases lie in inter-genic regions including the so-called regulatory genome. Additionally, no genetic causes are apparent for many diseases including neurodegenerative disorders. A complementary approach is therefore gaining interest, namely to focus on epigenetic control of the disease to generate more complete functional genomic maps. To this end, several recent studies have generated large-scale epigenetic datasets in a disease context to form a link between genotype and phenotype. We focus DNA methylation and important histone marks, where recent advances have been made thanks to technology improvements, cost effectiveness, and large meta-scale epigenome consortia efforts. We summarize recent studies unravelling the mechanistic understanding of epigenetic processes in disease development and progression. Moreover, we show how methodology advancements enable causal relationships to be established, and we pinpoint the most important issues to be addressed by future research. View Full-Text
Keywords: epigenetics; chromatin modification; sequencing; regulatory genomics; disease variants epigenetics; chromatin modification; sequencing; regulatory genomics; disease variants
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Romanowska, J.; Joshi, A. From Genotype to Phenotype: Through Chromatin. Genes 2019, 10, 76.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Genes EISSN 2073-4425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top