Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of neurological function and disease

Charlie Hatcher, Caroline L. Relton, Tom R. Gaunt, Tom G. Richardson
doi: https://doi.org/10.1101/429134
Charlie Hatcher
1MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Caroline L. Relton
1MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tom R. Gaunt
1MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tom G. Richardson
1MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: Tom.G.Richardson@bristol.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Integrative approaches which harness large-scale molecular datasets can help develop mechanistic insight into findings from genome-wide association studies (GWAS). We have performed extensive analyses to uncover transcriptional and epigenetic processes which may play a role in neurological trait variation.

This was undertaken by applying Bayesian multiple-trait colocalization systematically across the genome to identify genetic variants responsible for influencing intermediate molecular phenotypes as well as neurological traits. In this analysis we leveraged high dimensional quantitative trait loci data derived from prefrontal cortex tissue (concerning gene expression, DNA methylation and histone acetylation) and GWAS findings for 5 neurological traits (Neuroticism, Schizophrenia, Educational Attainment, Insomnia and Alzheimer’s disease).

There was evidence of colocalization for 118 associations suggesting that the same underlying genetic variant influenced both nearby gene expression as well as neurological trait variation. Of these, 73 associations provided evidence that the genetic variant also influenced proximal DNA methylation and/or histone acetylation. These findings support previous evidence at loci where epigenetic mechanisms may putatively mediate effects of genetic variants on traits, such as KLC1 and schizophrenia. We also uncovered evidence implicating novel loci in neurological disease susceptibility, including genes expressed predominantly in brain tissue such as MDGA1, KIRREL3 and SLC12A5.

An inverse relationship between DNA methylation and gene expression was observed more than can be accounted for by chance, supporting previous findings implicating DNA methylation as a transcriptional repressor. Our study should prove valuable in helping future studies prioritise candidate genes and epigenetic mechanisms for in-depth functional follow-up analyses.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted October 02, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of neurological function and disease
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of neurological function and disease
Charlie Hatcher, Caroline L. Relton, Tom R. Gaunt, Tom G. Richardson
bioRxiv 429134; doi: https://doi.org/10.1101/429134
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of neurological function and disease
Charlie Hatcher, Caroline L. Relton, Tom R. Gaunt, Tom G. Richardson
bioRxiv 429134; doi: https://doi.org/10.1101/429134

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genetics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4382)
  • Biochemistry (9591)
  • Bioengineering (7090)
  • Bioinformatics (24856)
  • Biophysics (12600)
  • Cancer Biology (9956)
  • Cell Biology (14349)
  • Clinical Trials (138)
  • Developmental Biology (7948)
  • Ecology (12105)
  • Epidemiology (2067)
  • Evolutionary Biology (15988)
  • Genetics (10925)
  • Genomics (14738)
  • Immunology (9869)
  • Microbiology (23660)
  • Molecular Biology (9484)
  • Neuroscience (50857)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2681)
  • Physiology (4013)
  • Plant Biology (8657)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2394)
  • Systems Biology (6433)
  • Zoology (1346)