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A Genome-Wide Association Study of Prediabetes Status Change

We conducted the first genome-wide association study of prediabetes status change (to diabetes or normal glycaemia) among 900 White participants of the Atherosclerosis Risk in Communities (ARIC) study. Single nucleotide polymorphism (SNP)-based analysis was performed by logistic regression models, c...

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Autores principales: Liu, Tingting, Li, Hongjin, Conley, Yvette P., Primack, Brian A., Wang, Jing, Lo, Wen-Juo, Li, Changwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234217/
https://www.ncbi.nlm.nih.gov/pubmed/35769078
http://dx.doi.org/10.3389/fendo.2022.881633
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author Liu, Tingting
Li, Hongjin
Conley, Yvette P.
Primack, Brian A.
Wang, Jing
Lo, Wen-Juo
Li, Changwei
author_facet Liu, Tingting
Li, Hongjin
Conley, Yvette P.
Primack, Brian A.
Wang, Jing
Lo, Wen-Juo
Li, Changwei
author_sort Liu, Tingting
collection PubMed
description We conducted the first genome-wide association study of prediabetes status change (to diabetes or normal glycaemia) among 900 White participants of the Atherosclerosis Risk in Communities (ARIC) study. Single nucleotide polymorphism (SNP)-based analysis was performed by logistic regression models, controlling for age, gender, body mass index, and the first 3 genetic principal components. Gene-based analysis was conducted by combining SNP-based p values using effective Chi-square test method. Promising SNPs (p < 1×10-5) and genes (p < 1×10-4) were further evaluated for replication among 514 White participants of the Framingham Heart Study (FHS). To accommodate familial correlations, generalized estimation equation models were applied for SNP-based analyses in the FHS. Analysis results across ARIC and FHS were combined using inverse-variance-weighted meta-analysis method for SNPs and Fisher’s method for genes. We robustly identified 5 novel genes that are associated with prediabetes status change using gene-based analyses, including SGCZ (ARIC p = 9.93×10-6, FHS p = 2.00×10-3, Meta p = 3.72×10-7) at 8p22, HPSE2 (ARIC p = 8.26×10-19, FHS p = 5.85×10-3, Meta p < 8.26×10-19) at 10q24.2, ADGRA1 (ARIC p = 1.34×10-5, FHS p = 1.13×10-3, Meta p = 2.88×10-7) at 10q26.3, GLB1L3 (ARIC p = 3.71×10-6, FHS p = 4.51×10-3, Meta p = 3.16×10-7) at 11q25, and PCSK6 (ARIC p = 6.51×10-6, FHS p = 1.10×10-2, Meta p = 1.25×10-6) at 15q26.3. eQTL analysis indicated that these genes were highly expressed in tissues related to diabetes development. However, we were not able to identify any novel locus in single SNP-based analysis. Future large scale genomic studies of prediabetes status change are warranted.
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spelling pubmed-92342172022-06-28 A Genome-Wide Association Study of Prediabetes Status Change Liu, Tingting Li, Hongjin Conley, Yvette P. Primack, Brian A. Wang, Jing Lo, Wen-Juo Li, Changwei Front Endocrinol (Lausanne) Endocrinology We conducted the first genome-wide association study of prediabetes status change (to diabetes or normal glycaemia) among 900 White participants of the Atherosclerosis Risk in Communities (ARIC) study. Single nucleotide polymorphism (SNP)-based analysis was performed by logistic regression models, controlling for age, gender, body mass index, and the first 3 genetic principal components. Gene-based analysis was conducted by combining SNP-based p values using effective Chi-square test method. Promising SNPs (p < 1×10-5) and genes (p < 1×10-4) were further evaluated for replication among 514 White participants of the Framingham Heart Study (FHS). To accommodate familial correlations, generalized estimation equation models were applied for SNP-based analyses in the FHS. Analysis results across ARIC and FHS were combined using inverse-variance-weighted meta-analysis method for SNPs and Fisher’s method for genes. We robustly identified 5 novel genes that are associated with prediabetes status change using gene-based analyses, including SGCZ (ARIC p = 9.93×10-6, FHS p = 2.00×10-3, Meta p = 3.72×10-7) at 8p22, HPSE2 (ARIC p = 8.26×10-19, FHS p = 5.85×10-3, Meta p < 8.26×10-19) at 10q24.2, ADGRA1 (ARIC p = 1.34×10-5, FHS p = 1.13×10-3, Meta p = 2.88×10-7) at 10q26.3, GLB1L3 (ARIC p = 3.71×10-6, FHS p = 4.51×10-3, Meta p = 3.16×10-7) at 11q25, and PCSK6 (ARIC p = 6.51×10-6, FHS p = 1.10×10-2, Meta p = 1.25×10-6) at 15q26.3. eQTL analysis indicated that these genes were highly expressed in tissues related to diabetes development. However, we were not able to identify any novel locus in single SNP-based analysis. Future large scale genomic studies of prediabetes status change are warranted. Frontiers Media S.A. 2022-06-13 /pmc/articles/PMC9234217/ /pubmed/35769078 http://dx.doi.org/10.3389/fendo.2022.881633 Text en Copyright © 2022 Liu, Li, Conley, Primack, Wang, Lo and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Liu, Tingting
Li, Hongjin
Conley, Yvette P.
Primack, Brian A.
Wang, Jing
Lo, Wen-Juo
Li, Changwei
A Genome-Wide Association Study of Prediabetes Status Change
title A Genome-Wide Association Study of Prediabetes Status Change
title_full A Genome-Wide Association Study of Prediabetes Status Change
title_fullStr A Genome-Wide Association Study of Prediabetes Status Change
title_full_unstemmed A Genome-Wide Association Study of Prediabetes Status Change
title_short A Genome-Wide Association Study of Prediabetes Status Change
title_sort genome-wide association study of prediabetes status change
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234217/
https://www.ncbi.nlm.nih.gov/pubmed/35769078
http://dx.doi.org/10.3389/fendo.2022.881633
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