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Hyperuricemia is associated with the metabolic syndrome, gout, renal and cardiovascular disease (CVD). American Indians have high rates of CVD and 25% of individuals in the strong heart family study (SHFS) have high serum uric acid levels. The aim of this study was to investigate the genetic determinants of serum uric acid variation in American Indian participants of the SHFS. A variance component decomposition approach (implemented in SOLAR) was used to conduct univariate genetic analyses in each of three study centers and the combined sample. Serum uric acid was adjusted for age, sex, age x sex, BMI, estimated glomerular filtration rate, alcohol intake, diabetic status and medications. Overall mean +/- SD serum uric acid for all individuals was 5.14 +/- 1.5 mg/dl. Serum uric acid was found to be significantly heritable (0.46 +/- 0.03 in all centers, and 0.39 +/- 0.07, 0.51 +/- 0.05, 0.44 +/- 0.06 in Arizona, Dakotas and Oklahoma, respectively). Multipoint linkage analysis showed significant evidence of linkage for serum uric acid on chromosome 11 in the Dakotas center [logarithm of odds score (LOD) = 3.02] and in the combined sample (LOD = 3.56) and on chromosome 1 (LOD = 3.51) in the combined sample. A strong positional candidate gene in the chromosome 11 region is solute carrier family22, member 12 (SLC22A12) that encodes a major uric acid transporter URAT1. These results show a significant genetic influence and a possible role for one or more genes on chromosomes 1 and 11 on the variation in serum uric acid in American Indian populations.

American Indians have a higher prevalence of albuminuria than the general population, likely resulting from a combination of environmental and genetic risk factors. To localize gene regions influencing variation in urinary albumin-to-creatinine ratio, we performed a linkage analysis and explored gene-by-diabetes, -hypertension, and -obesity interactions in a large cohort of American Indian families. We recruited >3600 individuals from 13 American Indian tribes from three centers (Arizona, North and South Dakota, and Oklahoma). We performed multipoint variance component linkage analysis in each center as well as in the entire cohort after controlling for center effects. We used two modeling strategies: Model 1 incorporated age, gender, and interaction terms; model 2 also controlled for diabetes, BP, body mass index, HDL, LDL, triglycerides, and smoking status. We evaluated interactions with diabetes, hypertension, and obesity using additive, interaction-specific linkage and stratified analyses. Loci suggestive for linkage to urinary albumin-to-creatinine ratio included 1q, 6p, 9q, 18q, and 20p. Gene-by-diabetes interaction was present with a quantitative trait locus specific to the diabetic stratum in the Dakotas isolated on 18q21.2 to 21.3 using model 1 (logarithm of odds = 3.3). Gene-by-hypertension interaction was present with quantitative trait loci specific to the hypertensive stratum in the Dakotas on 7q21.11 using model 1 (logarithm of odds = 3.4) and 10q25.1 using model 2 (logarithm of odds = 3.3). These loci replicate findings from multiple other genome scans of kidney disease phenotypes with distinct populations and are worthy of further study.

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.

To localize quantitative trait loci for insulin metabolism and obesity, genome scans/linkage analyses were performed on >900 members of 32 extended families participating in phase 3 of the Strong Heart Study, an investigation of the genetic and environmental determinants of cardiovascular disease in American-Indian populations from Arizona, Oklahoma, and North and South Dakota. Linkage analyses of fasting insulin and two obesity-related phenotypes, BMI and percent fat mass, were performed independently in each of the three populations. For log fasting insulin, we found a genome-wide maximum, robust logarithm of odds (LOD) score of 3.42 at 51 cM on chromosome 2p in the Dakotas. Bivariate linkage analyses of log fasting insulin with both BMI and fat mass indicate a situation of incomplete pleiotropy, as well as several significant bivariate LOD scores in the Dakotas.