Predicting novel genomic regions linked to genetic disorders using GWAS and chromosome conformation data - a case study of schizophrenia

Buxton, D.S., Batten, D.J., Crofts, J.J. ORCID: 0000-0001-7751-9984 and Chuzhanova, N. ORCID: 0000-0002-4655-3618, 2019. Predicting novel genomic regions linked to genetic disorders using GWAS and chromosome conformation data - a case study of schizophrenia. Scientific Reports, 9: 17940. ISSN 2045-2322

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Abstract

Genome-wide association studies identified numerous loci harbouring single nucleotide polymorphisms (SNPs) associated with various human diseases, although the causal role of many of them remains unknown. In this paper, we postulate that co-location and shared biological function of novel genes with genes known to associate with a specific phenotype make them potential candidates linked to the same phenotype ("guilt-by-proxy"). We propose a novel network-based approach for predicting candidate genes/genomic regions utilising the knowledge of the 3D architecture of the human genome and GWAS data. As a case study we used a well-studied polygenic disorder ‒ schizophrenia ‒ for which we compiled a comprehensive dataset of SNPs. Our approach revealed 634 novel regions covering ~398 Mb of the human genome and harbouring ~9000 genes. Using various network measures and enrichment analysis, we identified subsets of genes and investigated the plausibility of these genes/regions having an association with schizophrenia using literature search and bioinformatics resources. We identified several genes/regions with previously reported associations with schizophrenia, thus providing proof-of-concept, as well as novel candidates with no prior known associations. This approach has the potential to identify novel genes/genomic regions linked to other polygenic disorders and provide means of aggregating genes/SNPs for further investigation.

Item Type: Journal article
Publication Title: Scientific Reports
Creators: Buxton, D.S., Batten, D.J., Crofts, J.J. and Chuzhanova, N.
Publisher: Nature Publishing Group
Date: 29 November 2019
Volume: 9
ISSN: 2045-2322
Identifiers:
NumberType
1237190Other
10.1038/s41598-019-54514-2DOI
Rights: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 18 Nov 2019 16:11
Last Modified: 09 Dec 2019 11:22
URI: http://irep.ntu.ac.uk/id/eprint/38355

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