Cargando…
Analysis of overlapping genetic association in type 1 and type 2 diabetes
AIMS/HYPOTHESIS: Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, infere...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099827/ https://www.ncbi.nlm.nih.gov/pubmed/33830302 http://dx.doi.org/10.1007/s00125-021-05428-0 |
_version_ | 1783688655567585280 |
---|---|
author | Inshaw, Jamie R. J. Sidore, Carlo Cucca, Francesco Stefana, M. Irina Crouch, Daniel J. M. McCarthy, Mark I. Mahajan, Anubha Todd, John A. |
author_facet | Inshaw, Jamie R. J. Sidore, Carlo Cucca, Francesco Stefana, M. Irina Crouch, Daniel J. M. McCarthy, Mark I. Mahajan, Anubha Todd, John A. |
author_sort | Inshaw, Jamie R. J. |
collection | PubMed |
description | AIMS/HYPOTHESIS: Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. METHODS: Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases. RESULTS: Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect. CONCLUSIONS/INTERPRETATION: Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-021-05428-0. |
format | Online Article Text |
id | pubmed-8099827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-80998272021-05-11 Analysis of overlapping genetic association in type 1 and type 2 diabetes Inshaw, Jamie R. J. Sidore, Carlo Cucca, Francesco Stefana, M. Irina Crouch, Daniel J. M. McCarthy, Mark I. Mahajan, Anubha Todd, John A. Diabetologia Short Communication AIMS/HYPOTHESIS: Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. METHODS: Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases. RESULTS: Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect. CONCLUSIONS/INTERPRETATION: Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-021-05428-0. Springer Berlin Heidelberg 2021-04-08 2021 /pmc/articles/PMC8099827/ /pubmed/33830302 http://dx.doi.org/10.1007/s00125-021-05428-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Short Communication Inshaw, Jamie R. J. Sidore, Carlo Cucca, Francesco Stefana, M. Irina Crouch, Daniel J. M. McCarthy, Mark I. Mahajan, Anubha Todd, John A. Analysis of overlapping genetic association in type 1 and type 2 diabetes |
title | Analysis of overlapping genetic association in type 1 and type 2 diabetes |
title_full | Analysis of overlapping genetic association in type 1 and type 2 diabetes |
title_fullStr | Analysis of overlapping genetic association in type 1 and type 2 diabetes |
title_full_unstemmed | Analysis of overlapping genetic association in type 1 and type 2 diabetes |
title_short | Analysis of overlapping genetic association in type 1 and type 2 diabetes |
title_sort | analysis of overlapping genetic association in type 1 and type 2 diabetes |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099827/ https://www.ncbi.nlm.nih.gov/pubmed/33830302 http://dx.doi.org/10.1007/s00125-021-05428-0 |
work_keys_str_mv | AT inshawjamierj analysisofoverlappinggeneticassociationintype1andtype2diabetes AT sidorecarlo analysisofoverlappinggeneticassociationintype1andtype2diabetes AT cuccafrancesco analysisofoverlappinggeneticassociationintype1andtype2diabetes AT stefanamirina analysisofoverlappinggeneticassociationintype1andtype2diabetes AT crouchdanieljm analysisofoverlappinggeneticassociationintype1andtype2diabetes AT mccarthymarki analysisofoverlappinggeneticassociationintype1andtype2diabetes AT mahajananubha analysisofoverlappinggeneticassociationintype1andtype2diabetes AT toddjohna analysisofoverlappinggeneticassociationintype1andtype2diabetes |