TY - JOUR
T1 - Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study
AU - American Diabetes GENNID Study Group
AU - Wan, Jia Y.
AU - Goodman, Deborah L.
AU - Willems, Emileigh L.
AU - Freedland, Alexis R.
AU - Norden-Krichmar, Trina M.
AU - Santorico, Stephanie A.
AU - Edwards, Karen L.
AU - Boerwinkle, Eric
AU - Buse, John
AU - DeFronzo, Ralph
AU - Ehrmann, David
AU - Elbein, Steven C.
AU - Fujimoto, Wilfred
AU - Kahn, Steven E.
AU - Hanis, Craig L.
AU - Mulivor, Richard A.
AU - Beck, Jeanne C.
AU - Norris, Jill
AU - Alan Permutt, M.
AU - Behn, Philip
AU - Raffel, Leslie
AU - Robbins, David C.
N1 - Funding Information:
This work was supported by the American Diabetes Association. We would like to thank the following: the ADA for providing access to the GENNID resource, Harwood Garland and Lewis A. Simon for their review and discussion of the manuscript, and Brian Fish for computing support. Genetic material collected by, and families characterized by, the American Diabetes Association GENNID Study Group, which includes Eric Boerwinkle, Ph.D., University of Texas Health Science Center; John Buse, MD, Ph.D., University of North Carolina; Ralph DeFronzo, MD, University of Texas Health Science Center; David Ehrmann, MD, University of Chicago; Steven C. Elbein, MD, University of Utah/University of Arkansas; Wilfred Fujimoto, MD, and Steven E. Kahn, MB, ChB, University of Washington; Craig L. Hanis, Ph.D., University of Texas Health Science Center; Richard A. Mulivor, Ph.D., and Jeanne C. Beck, Ph.D., Coriell Cell Repositories; Jill Norris, Ph.D., University of Colorado School of Medicine; M. Alan Permutt, MD, and Philip Behn, MD, Washington University School of Medicine; Leslie Raffel, MD, Cedars-Sinai Medical Center; and David C. Robbins, MD, Medlantic Research Institute, USA.
Funding Information:
This work was supported by the American Diabetes Association. We would like to thank the following: the ADA for providing access to the GENNID resource, Harwood Garland and Lewis A. Simon for their review and discussion of the manuscript, and Brian Fish for computing support. Genetic material collected by, and families characterized by, the American Diabetes Association GENNID Study Group, which includes Eric Boerwinkle, Ph.D., University of Texas Health Science Center; John Buse, MD, Ph.D., University of North Carolina; Ralph DeFronzo, MD, University of Texas Health Science Center; David Ehrmann, MD, University of Chicago; Steven C. Elbein, MD, University of Utah/University of Arkansas; Wilfred Fujimoto, MD, and Steven E. Kahn, MB, ChB, University of Washington; Craig L. Hanis, Ph.D., University of Texas Health Science Center; Richard A. Mulivor, Ph.D., and Jeanne C. Beck, Ph.D., Coriell Cell Repositories; Jill Norris, Ph.D., University of Colorado School of Medicine; M. Alan Permutt, MD, and Philip Behn, MD, Washington University School of Medicine; Leslie Raffel, MD, Cedars-Sinai Medical Center; and David C. Robbins, MD, Medlantic Research Institute, USA.
Funding Information:
The design of the study and collection, analysis, interpretation of data and the writing the manuscript was funded by NHLBI (1R01HL113189, Edwards KL, PI): Life After Linkage Consortium.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Family studies
KW - Genetic epidemiology
KW - Linkage
KW - Metabolic syndrome
KW - Quantitative trait loci
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U2 - 10.1186/s13098-021-00670-3
DO - 10.1186/s13098-021-00670-3
M3 - Article
C2 - 34074324
AN - SCOPUS:85108326987
SN - 1758-5996
VL - 13
JO - Diabetology and Metabolic Syndrome
JF - Diabetology and Metabolic Syndrome
IS - 1
M1 - 59
ER -