Dataset Information

Diabetes-specific genetic effects on obesity traits in American Indian populations: the Strong Heart Family Study.

ABSTRACT:

Background

Body fat mass distribution and deposition are determined by multiple environmental and genetic factors. Obesity is associated with insulin resistance, hyperinsulinemia, and type 2 diabetes. We previously identified evidence for genotype-by-diabetes interaction on obesity traits in Strong Heart Family Study (SHFS) participants. To localize these genetic effects, we conducted genome-wide linkage scans of obesity traits in individuals with and without type 2 diabetes, and in the combined sample while modeling interaction with diabetes using maximum likelihood methods (SOLAR 2.1.4).

Methods

SHFS recruited American Indians from Arizona, North and South Dakota, and Oklahoma. Anthropometric measures and diabetes status were obtained during a clinic visit. Marker allele frequencies were derived using maximum likelihood methods estimated from all individuals and multipoint identity by descent sharing was estimated using Loki. We used variance component linkage analysis to localize quantitative trait loci (QTLs) influencing obesity traits. We tested for evidence of additive and QTL-specific genotype-by-diabetes interactions using the regions identified in the diabetes-stratified analyses.

Results

Among 245 diabetic and 704 non-diabetic American Indian individuals, we detected significant additive gene-by-diabetes interaction for weight and BMI (P < 0.02). In analysis accounting for QTL-specific interaction (P < 0.001), we detected a QTL for weight on chromosome 1 at 242 cM (LOD = 3.7). This chromosome region harbors the adiponectin receptor 1 gene, which has been previously associated with obesity.

Conclusion

These results suggest distinct genetic effects on body mass in individuals with diabetes compared to those without diabetes, and a possible role for one or more genes on chromosome 1 in the pathogenesis of obesity.

SUBMITTER: Franceschini N

PROVIDER: S-EPMC2572048 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Publications

Diabetes-specific genetic effects on obesity traits in American Indian populations: the Strong Heart Family Study.

Franceschini Nora N Almasy Laura L MacCluer Jean W JW Göring Harald H H HH Cole Shelley A SA Diego Vincent P VP Laston Sandra S Howard Barbara V BV Lee Elisa T ET Best Lyle G LG Fabsitz Richard R RR North Kari E KE

BMC medical genetics 20081014

<h4>Background</h4>Body fat mass distribution and deposition are determined by multiple environmental and genetic factors. Obesity is associated with insulin resistance, hyperinsulinemia, and type 2 diabetes. We previously identified evidence for genotype-by-diabetes interaction on obesity traits in Strong Heart Family Study (SHFS) participants. To localize these genetic effects, we conducted genome-wide linkage scans of obesity traits in individuals with and without type 2 diabetes, and in the ...[more]

PMID: 18854016

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Project description:BackgroundMany American Indian (AI) communities are in areas affected by environmental contamination, such as toxic metals. However, studies assessing exposures in AI communities are limited. We measured blood metals in AI communities to assess historical exposure and identify participant characteristics associated with these levels in the Strong Heart Study (SHS) cohort.MethodArchived blood specimens collected from participants (n = 2014, all participants were 50 years of age and older) in Arizona, Oklahoma, and North and South Dakota during SHS Phase-III (1998-1999) were analyzed for cadmium, lead, manganese, mercury, and selenium using inductively coupled plasma triple quadrupole mass spectrometry. We conducted descriptive analyses for the entire cohort and stratified by selected subgroups, including selected demographics, health behaviors, income, waist circumference, and body mass index. Bivariate associations were conducted to examine associations between blood metal levels and selected socio-demographic and behavioral covariates. Finally, multivariate regression models were used to assess the best model fit that predicted blood metal levels.FindingsAll elements were detected in 100% of study participants, with the exception of mercury (detected in 73% of participants). The SHS population had higher levels of blood cadmium and manganese than the general U.S. population 50 years and older. The median blood mercury in the SHS cohort was at about 30% of the U.S. reference population, potentially due to low fish consumption. Participants in North Dakota and South Dakota had the highest blood cadmium, lead, manganese, and selenium, and the lowest total mercury levels, even after adjusting for covariates. In addition, each of the blood metals was associated with selected demographic, behavioral, income, and/or weight-related factors in multivariate models. These findings will help guide the tribes to develop education, outreach, and strategies to reduce harmful exposures and increase beneficial nutrient intake in these AI communities.

Project description:Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants.We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS).We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing.Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10-5).This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24. Citation: Balakrishnan P, Vaidya D, Franceschini N, Voruganti VS, Gribble MO, Haack K, Laston S, Umans JG, Francesconi KA, Goessler W, North KE, Lee E, Yracheta J, Best LG, MacCluer JW, Kent J Jr., Cole SA, Navas-Acien A. 2017. Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: the Strong Heart Family Study (SHFS). Environ Health Perspect 125:15-22;?http://dx.doi.org/10.1289/EHP251.

Project description:Background: Genetic research may inform underlying mechanisms for disparities in the burden of type 2 diabetes mellitus among American Indians. Our objective was to assess the association of genetic variants in cardiometabolic candidate genes with B cell dysfunction via HOMA-B, insulin resistance via HOMA-IR, and type 2 diabetes mellitus in the Strong Heart Family Study (SHFS). Methods and Results: We examined the association of variants, previously associated with cardiometabolic traits (?200,000 from Illumina Cardio MetaboChip), using mixed models of HOMA-B residuals corrected for HOMA-IR (cHOMA-B), log transformed HOMA-IR, and incident diabetes, adjusted for age, sex, population stratification, and familial relatedness. Center-specific estimates were combined using fixed effect meta-analyses. We used Bonferroni correction to account for multiple testing (P < 4.13 × 10-7). We also assessed the association between variants in candidate diabetes genes with these metabolic traits. We explored the top SNPs in an independent, replication sample from Southwestern Arizona. We identified significant associations with cHOMA-B for common variants at 26 loci of which 8 were novel (PRSS7, FCRL5, PEL1, LRP12, IGLL1, ARHGEF10, PARVA, FLJ16686). The most significant variant association with cHOMA-B was observed on chromosome 5 for an intergenic variant near PARP8 (rs2961831, P = 6.39 × 10-9). In the replication study, we found a signal at rs4607517 near GCK/YKT6 (P = 0.01). Variants near candidate diabetes genes (especially GCK and KCNQ1) were also nominally associated with HOMA-IR and cHOMA-B. Conclusion: We identified variants at novel loci and confirmed those at known candidate diabetes loci associations for cHOMA-B. This study also provided evidence for association of variants at KCNQ2, CTNAA2, and KCNQ1with cHOMA-B among American Indians. Further studies are needed to account for the high heritability of diabetes among the American Indian participants of the SHFS cohort.

Dataset Information

Diabetes-specific genetic effects on obesity traits in American Indian populations: the Strong Heart Family Study.

Background

Methods

Results

Conclusion

Publications

Diabetes-specific genetic effects on obesity traits in American Indian populations: the Strong Heart Family Study.

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