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Genome-wide association analysis of insomnia using data from Partners Biobank
Insomnia is one of the most prevalent and burdensome mental disorders worldwide, affecting between 10–20% of adults and up to 48% of the geriatric population. It is further associated with substance usage and dependence, as well other psychiatric disorders. In this study, we combined electronic heal...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7181749/ https://www.ncbi.nlm.nih.gov/pubmed/32332799 http://dx.doi.org/10.1038/s41598-020-63792-0 |
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author | Song, Wenyu Torous, John Kossowsky, Joe Chen, Chia-Yen Huang, Hailiang Wright, Adam |
author_facet | Song, Wenyu Torous, John Kossowsky, Joe Chen, Chia-Yen Huang, Hailiang Wright, Adam |
author_sort | Song, Wenyu |
collection | PubMed |
description | Insomnia is one of the most prevalent and burdensome mental disorders worldwide, affecting between 10–20% of adults and up to 48% of the geriatric population. It is further associated with substance usage and dependence, as well other psychiatric disorders. In this study, we combined electronic health record (EHR) derived phenotypes and genotype information to conduct a genome wide analysis of insomnia in a 18,055 patient cohort. Diagnostic codes were used to identify 3,135 patients with insomnia. Our genome-wide association study (GWAS) identified one novel genomic risk locus on chromosome 8 (lead SNP rs17052966, p = 4.53 × 10(−9), odds ratio = 1.28, se = 0.04). The heritability analysis indicated that common SNPs accounts for 7% (se = 0.02, p = 0.015) of phenotypic variation. We further conducted a large-scale meta-analysis of our results and summary statistics of two recent insomnia GWAS and 13 significant loci were identified. The genetic correlation analysis yielded a strong positive genetic correlation between insomnia and alcohol use (rG = 0.56, se = 0.14, p < 0.001), nicotine use (rG = 0.50, se = 0.12, p < 0.001) and opioid use (rG = 0.43, se = 0.18, p = 0.02) disorders, suggesting a significant common genetic risk factors between insomnia and substance use. |
format | Online Article Text |
id | pubmed-7181749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-71817492020-04-29 Genome-wide association analysis of insomnia using data from Partners Biobank Song, Wenyu Torous, John Kossowsky, Joe Chen, Chia-Yen Huang, Hailiang Wright, Adam Sci Rep Article Insomnia is one of the most prevalent and burdensome mental disorders worldwide, affecting between 10–20% of adults and up to 48% of the geriatric population. It is further associated with substance usage and dependence, as well other psychiatric disorders. In this study, we combined electronic health record (EHR) derived phenotypes and genotype information to conduct a genome wide analysis of insomnia in a 18,055 patient cohort. Diagnostic codes were used to identify 3,135 patients with insomnia. Our genome-wide association study (GWAS) identified one novel genomic risk locus on chromosome 8 (lead SNP rs17052966, p = 4.53 × 10(−9), odds ratio = 1.28, se = 0.04). The heritability analysis indicated that common SNPs accounts for 7% (se = 0.02, p = 0.015) of phenotypic variation. We further conducted a large-scale meta-analysis of our results and summary statistics of two recent insomnia GWAS and 13 significant loci were identified. The genetic correlation analysis yielded a strong positive genetic correlation between insomnia and alcohol use (rG = 0.56, se = 0.14, p < 0.001), nicotine use (rG = 0.50, se = 0.12, p < 0.001) and opioid use (rG = 0.43, se = 0.18, p = 0.02) disorders, suggesting a significant common genetic risk factors between insomnia and substance use. Nature Publishing Group UK 2020-04-24 /pmc/articles/PMC7181749/ /pubmed/32332799 http://dx.doi.org/10.1038/s41598-020-63792-0 Text en © The Author(s) 2020 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 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/. |
spellingShingle | Article Song, Wenyu Torous, John Kossowsky, Joe Chen, Chia-Yen Huang, Hailiang Wright, Adam Genome-wide association analysis of insomnia using data from Partners Biobank |
title | Genome-wide association analysis of insomnia using data from Partners Biobank |
title_full | Genome-wide association analysis of insomnia using data from Partners Biobank |
title_fullStr | Genome-wide association analysis of insomnia using data from Partners Biobank |
title_full_unstemmed | Genome-wide association analysis of insomnia using data from Partners Biobank |
title_short | Genome-wide association analysis of insomnia using data from Partners Biobank |
title_sort | genome-wide association analysis of insomnia using data from partners biobank |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7181749/ https://www.ncbi.nlm.nih.gov/pubmed/32332799 http://dx.doi.org/10.1038/s41598-020-63792-0 |
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