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Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study

BACKGROUND: To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups. METHODS: Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and incl...

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Autores principales: Wan, Jia Y., Goodman, Deborah L., Willems, Emileigh L., Freedland, Alexis R., Norden-Krichmar, Trina M., Santorico, Stephanie A., Edwards, Karen L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170963/
https://www.ncbi.nlm.nih.gov/pubmed/34074324
http://dx.doi.org/10.1186/s13098-021-00670-3
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author Wan, Jia Y.
Goodman, Deborah L.
Willems, Emileigh L.
Freedland, Alexis R.
Norden-Krichmar, Trina M.
Santorico, Stephanie A.
Edwards, Karen L.
author_facet Wan, Jia Y.
Goodman, Deborah L.
Willems, Emileigh L.
Freedland, Alexis R.
Norden-Krichmar, Trina M.
Santorico, Stephanie A.
Edwards, Karen L.
author_sort Wan, Jia Y.
collection PubMed
description BACKGROUND: To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups. METHODS: Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina’s Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping. RESULTS: Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects. CONCLUSIONS: This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-021-00670-3.
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spelling pubmed-81709632021-06-03 Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study Wan, Jia Y. Goodman, Deborah L. Willems, Emileigh L. Freedland, Alexis R. Norden-Krichmar, Trina M. Santorico, Stephanie A. Edwards, Karen L. Diabetol Metab Syndr Research BACKGROUND: To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups. METHODS: Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina’s Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping. RESULTS: Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects. CONCLUSIONS: This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-021-00670-3. BioMed Central 2021-06-01 /pmc/articles/PMC8170963/ /pubmed/34074324 http://dx.doi.org/10.1186/s13098-021-00670-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wan, Jia Y.
Goodman, Deborah L.
Willems, Emileigh L.
Freedland, Alexis R.
Norden-Krichmar, Trina M.
Santorico, Stephanie A.
Edwards, Karen L.
Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study
title Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study
title_full Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study
title_fullStr Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study
title_full_unstemmed Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study
title_short Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study
title_sort genome-wide association analysis of metabolic syndrome quantitative traits in the gennid multiethnic family study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170963/
https://www.ncbi.nlm.nih.gov/pubmed/34074324
http://dx.doi.org/10.1186/s13098-021-00670-3
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