Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort

Objective: To determine whether there is a correlation between obesity-related variants and regional brain volumes. Methods: Based on a mixed linear model (MLM), we analyzed the association between 1,498 obesity-related SNPs in the GWAS Catalog and 164 regional brain volumes from 29,420 participants...

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Autores principales:	Xingchen Pan, Miaoran Zhang, Aowen Tian, Lanlan Chen, Zewen Sun, Liying Wang, Peng Chen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Obesity Brain image-derived phenotypes MLM-based association analysis Genetic correlation Computer applications to medicine. Medical informatics R858-859.7 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/b58b4d6d2ddd4233979b6dd7965c2fb0
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spelling	oai:doaj.org-article:b58b4d6d2ddd4233979b6dd7965c2fb02021-12-04T04:33:59ZExploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort2213-158210.1016/j.nicl.2021.102870https://doaj.org/article/b58b4d6d2ddd4233979b6dd7965c2fb02022-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2213158221003144https://doaj.org/toc/2213-1582Objective: To determine whether there is a correlation between obesity-related variants and regional brain volumes. Methods: Based on a mixed linear model (MLM), we analyzed the association between 1,498 obesity-related SNPs in the GWAS Catalog and 164 regional brain volumes from 29,420 participants (discovery cohort N = 19,997, validation cohort N = 9,423) in UK Biobank. The statistically significant brain regions in association analysis were classified into 6 major neural networks (dopamine (DA) motive system, central autonomic network (CAN), cognitive emotion regulation, visual object recognition network, auditory object recognition network, and sensorimotor system). We summarized the association between obesity-related variants (metabolically healthy obesity variants, metabolically unhealthy obesity variants, and unclassified obesity-related variants) and neural networks. Results: From association analysis, we determined that 17 obesity-related SNPs were associated with 51 regional brain volumes. Several single SNPs (e.g., rs13107325-T (SLC39A8), rs1876829-C (CRHR1), and rs1538170-T (CENPW)) were associated with multiple regional brain volumes. In addition, several single brain regions (e.g., the white matter, the grey matter in the putamen, subcallosal cortex, and insular cortex) were associated with multiple obesity-related variants. The metabolically healthy obesity variants were mainly associated with the regional brain volumes in the DA motive system, sensorimotor system and cognitive emotion regulation neural networks, while metabolically unhealthy obesity variants were mainly associated with regional brain volumes in the CAN and total tissue volumes. In addition, unclassified obesity-related variants were mainly associated with auditory object recognition network and total tissue volumes. The results of MeSH (medical subject headings) enrichment analysis showed that obesity genes associated with brain structure pointed to the functional relatedness with 5-Hydroxytryptamine receptor 4 (5-HT4), growth differentiation factor 5 (GDF5), and high mobility group protein AT-hook 2 (HMGA2 protein). Conclusion: In summary, we found that obesity-related variants were associated with different brain volume measures. On the basis of the multiple SNPs, we found that metabolically healthy and unhealthy obesity-related SNPs were associated with different brain neural networks. Based on our enrichment analysis, modifications of the 5-HT4 pathway might be a promising therapeutic strategy for obesity.Xingchen PanMiaoran ZhangAowen TianLanlan ChenZewen SunLiying WangPeng ChenElsevierarticleObesityBrain image-derived phenotypesMLM-based association analysisGenetic correlationComputer applications to medicine. Medical informaticsR858-859.7Neurology. Diseases of the nervous systemRC346-429ENNeuroImage: Clinical, Vol 33, Iss , Pp 102870- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Obesity Brain image-derived phenotypes MLM-based association analysis Genetic correlation Computer applications to medicine. Medical informatics R858-859.7 Neurology. Diseases of the nervous system RC346-429
spellingShingle	Obesity Brain image-derived phenotypes MLM-based association analysis Genetic correlation Computer applications to medicine. Medical informatics R858-859.7 Neurology. Diseases of the nervous system RC346-429 Xingchen Pan Miaoran Zhang Aowen Tian Lanlan Chen Zewen Sun Liying Wang Peng Chen Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort
description	Objective: To determine whether there is a correlation between obesity-related variants and regional brain volumes. Methods: Based on a mixed linear model (MLM), we analyzed the association between 1,498 obesity-related SNPs in the GWAS Catalog and 164 regional brain volumes from 29,420 participants (discovery cohort N = 19,997, validation cohort N = 9,423) in UK Biobank. The statistically significant brain regions in association analysis were classified into 6 major neural networks (dopamine (DA) motive system, central autonomic network (CAN), cognitive emotion regulation, visual object recognition network, auditory object recognition network, and sensorimotor system). We summarized the association between obesity-related variants (metabolically healthy obesity variants, metabolically unhealthy obesity variants, and unclassified obesity-related variants) and neural networks. Results: From association analysis, we determined that 17 obesity-related SNPs were associated with 51 regional brain volumes. Several single SNPs (e.g., rs13107325-T (SLC39A8), rs1876829-C (CRHR1), and rs1538170-T (CENPW)) were associated with multiple regional brain volumes. In addition, several single brain regions (e.g., the white matter, the grey matter in the putamen, subcallosal cortex, and insular cortex) were associated with multiple obesity-related variants. The metabolically healthy obesity variants were mainly associated with the regional brain volumes in the DA motive system, sensorimotor system and cognitive emotion regulation neural networks, while metabolically unhealthy obesity variants were mainly associated with regional brain volumes in the CAN and total tissue volumes. In addition, unclassified obesity-related variants were mainly associated with auditory object recognition network and total tissue volumes. The results of MeSH (medical subject headings) enrichment analysis showed that obesity genes associated with brain structure pointed to the functional relatedness with 5-Hydroxytryptamine receptor 4 (5-HT4), growth differentiation factor 5 (GDF5), and high mobility group protein AT-hook 2 (HMGA2 protein). Conclusion: In summary, we found that obesity-related variants were associated with different brain volume measures. On the basis of the multiple SNPs, we found that metabolically healthy and unhealthy obesity-related SNPs were associated with different brain neural networks. Based on our enrichment analysis, modifications of the 5-HT4 pathway might be a promising therapeutic strategy for obesity.
format	article
author	Xingchen Pan Miaoran Zhang Aowen Tian Lanlan Chen Zewen Sun Liying Wang Peng Chen
author_facet	Xingchen Pan Miaoran Zhang Aowen Tian Lanlan Chen Zewen Sun Liying Wang Peng Chen
author_sort	Xingchen Pan
title	Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort
title_short	Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort
title_full	Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort
title_fullStr	Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort
title_full_unstemmed	Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort
title_sort	exploring the genetic correlation between obesity-related traits and regional brain volumes: evidence from uk biobank cohort
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/b58b4d6d2ddd4233979b6dd7965c2fb0
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Exploring the genetic correlation between obesity-related traits and regional brain volumes: Evidence from UK Biobank cohort

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