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Genome-Wide Association Analysis of Metabolic Syndrome Quantitative Traits in the GENNID Multiethnic Family Study

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Wan et al. Diabetol Metab Syndr (2021) 13:59 https://doi.org/10.1186/s13098-021-00670-3 Diabetology & Metabolic Syndrome RESEARCH Open Access Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study Jia Y. Wan1, Deborah L. Goodman1, Emileigh L. Willems2, Alexis R. Freedland1, Trina M. Norden‑Krichmar1, Stephanie A. Santorico2,3,4,5 and Karen L. Edwards1*American Diabetes GENNID Study Group Abstract 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 multieth‑ nic 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 circumfer‑ ence, 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‑specifc genetic associations and heterogeneity across ethnicities. Most signifcant 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 identifed linkage region on chromosome 2, two signifcant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure efects. 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. Keywords: Metabolic syndrome, Genetic epidemiology, Family studies, Quantitative trait loci, Linkage Background Treatment Panel (NCEP ATP) III criteria [3], typically Metabolic syndrome (MetS) is a common, complex used in the United States for clinical diagnosis, defnes condition characterized by hyperlipidemia, hyperten- MetS as the presence of at least three of fve risk fac- sion, hyperglycemia, and excess abdominal fat [1–3]. tors: elevated systolic and/or diastolic blood pressure Te National Cholesterol Education Program’s Adult (SBP, DBP), elevated triglycerides (TG), decreased high density lipoprotein (HDL)-cholesterol, elevated fasting glucose, and abdominal obesity [1, 3]. Due to the cluster- *Correspondence: [email protected] 1 Department of Epidemiology and Biostatistics, Program in Public Health, ing of these characteristics [4, 5], individuals with MetS University of California, 635 E. Peltason Dr, Mail Code: 7550, Irvine, CA are at risk for cardiovascular and metabolic diseases 92697, USA such as stroke and diabetes [6–10]. Moreover, in several Full list of author information is available at the end of the article © The Author(s) 2021. 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 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:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wan et al. Diabetol Metab Syndr (2021) 13:59 Page 2 of 15 US-based studies of families [11–15], MetS quantitative on chromosome 3 among MA families [33]. Tese can- and multivariate factor traits are highly heritable with didate linkage regions are large (between 150 and 540 about half of the variation between subjects explained by Mbp), with multiple traits mapping to these regions and genetics in families of European descent [14, 15] and par- evidence for heterogeneity across ethnic groups [33]. A ticularly for obesity and lipid-related traits in families of more in-depth evaluation of these regions to determine if African Americans [12, 14], Mexican Americans [13] and linkage is due to pleiotropy or co-incident linkage/asso- Japanese Americans [11]. Family-based studies have been ciation, along with a broader focus on understanding if a primary approach for identifying genetic infuences on diferent trait clustering contributes to heterogeneity is a range of disease and still ofer many advantages [16, 17] needed. We used the GENetics of NonInsulin-dependent including being robust to confounding due to underlying Diabetes mellitus (GENNID) resource [37], a multieth- population structure and phenotype model misspecif- nic study of families with type 2 diabetes (T2D), and a cations, using pedigree structures and information on GWAS approach to identify quantitative trait nucleotides related individuals to detect genotyping errors [16], and (QTNs) with possible pleiotropic or coincident efects having more power to detect rare variants [16, 17]. and to examine evidence for heterogeneity in genetic Candidate gene [18–23] and genome-wide associa- association fndings for MetS traits across ethnic groups. tion studies (GWAS) [18, 24–27] have already gener- ated a number of candidate genes and variants possibly Methods associated with MetS. However, the number of variants Study subjects is still growing [8], particularly in the Asian population GENNID is an American Diabetes Association (ADA) [28]. Nonetheless, many questions still remain about the resource of genetic, questionnaire, and laboratory data underlying genetic architecture of MetS. For example, from multiplex, ethnically diverse AA, EA, JA and MA are the genetic infuences the same regardless of which families with T2D, diagnosed using the National Diabe- NCEP traits cluster within an individual? Accumulat- tes Data Group criteria [38]. In this cross-sectional study ing evidence suggests that the specifc combination of from 1993 to 1997, T2D families were ascertained in two traits may matter and could explain the large number of phases across multiple centers in the United States [37]. variants associated with MetS [29, 30]. Several obesity- Phase 1 focused primarily on larger, multi-generational related loci have been shown to be associated with dif- data collection of families with at least two T2D afected ferent MetS traits [8, 31]. For example, obesity, high TG, siblings in addition to at least three frst-degree relatives. high fasting insulin, and low HDL are associated with Phase 2 ascertained sibling pairs and nuclear families MIP1, MC4R, and PRKD1, yet when these same traits are with at least two T2D afected siblings, and if at most combined with hypertension, they are associated with one parent was ascertained, then data was collected on FTO and TMEM18 [8]. at least two additional siblings. AA, EA, and MA families Results from our previous studies suggest diferences were collected in both phases while JA families were only in the clustering due to the underlying genetics of MetS collected in Phase 1 [37, 39]. Tis study used all available traits by ethnicity [32–34]. For example, while a sig- data except for the Phase 2 EA data (N = 371 subjects) nifcant genetic correlation between weight and waist is which were not yet genotyped. Self-identifed race, fam- present in African American (AA), European American ily and medical histories, anthropometric and lab meas- (EA), Japanese American (JA) and Mexican American urements were obtained from participants. Specifcally, (MA) families [32, 34], the genetic correlation between we focused on eight MetS-related, quantitative traits high systolic blood pressure (SBP) and diastolic blood (i.e., HDL, TG, SBP, DBP, fasting insulin, fasting glucose, pressure (DBP) is seen only in AA, EA, and MA fami- weight, and waist circumference) defned from anthropo- lies [34]. Te signifcant genetic correlation of lipids (TG morphic and lab measurements. Pedigree relationships, and HDL) has been shown to be characteristic among age, sex, and diabetes status were obtained from the data EA and JA families [32, 34]. Tese diferences in cluster- collection and questionnaires. ing patterns may be driven by diferent sets of underlying genetic infuences and could explain the large number of Genotying and imputation genetic variants and genes associated with MetS. Previously, using microsatellite markers, linkage analy- Previously, family-based genetic linkage analyses nomi- ses identifed candidate regions for multivariate MetS nated chromosomal regions with putative causal vari- traits as described in Edwards et al. [32]. For this study, ants for individual and multivariate MetS traits. Results the Northwest
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