Evidence for distinct genetic effects on obesity and lipid-related CVD risk factors in diabetic compared to nondiabetic American Indians: the Strong Heart Family Study

A framework for enhancing ethical genomic research with Indigenous communities

Integration of genomic technology into healthcare settings establishes new capabilities to predict disease susceptibility and optimize treatment regimes. Yet, Indigenous peoples remain starkly underrepresented in genetic and clinical health research and are unlikely to benefit from such efforts. To foster collaboration with Indigenous communities, we propose six principles for ethical engagement in genomic research: understand existing regulations, foster collaboration, build cultural competency, improve research transparency, support capacity building, and disseminate research findings. Inclusion of underrepresented communities in genomic research has the potential to expand our understanding of genomic influences on health and improve clinical approaches for all populations.

Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects

BACKGROUND

Shifts in body composition, such as accumulation of body fat, can be a symptom of many chronic human diseases; hence, efforts have been made to investigate the genetic mechanisms that underlie body composition. For example, a few quantitative trait loci (QTL) have been discovered using genome-wide association studies, which will eventually lead to the discovery of causal mutations that are associated with tissue traits. Although some body composition QTL have been identified in mice, limited research has been focused on the imprinting and interaction effects that are involved in these traits. Previously, we found that Myostatin genotype, reciprocal cross, and sex interacted with numerous chromosomal regions to affect growth traits.

RESULTS

Here, we report on the identification of muscle, adipose, and morphometric phenotypic QTL (pQTL), translation and transcription QTL (tQTL) and expression QTL (eQTL) by applying a QTL model with additive, dominance, imprinting, and interaction effects. Using an F2 population of 1000 mice derived from the Myostatin-null C57BL/6 and M16i mouse lines, six imprinted pQTL were discovered on chromosomes 6, 9, 10, 11, and 18. We also identified two IGF1 and two Atp2a2 eQTL, which could be important trans-regulatory elements. pQTL, tQTL and eQTL that interacted with Myostatin, reciprocal cross, and sex were detected as well. Combining with the additive and dominance effect, these variants accounted for a large amount of phenotypic variation in this study.

CONCLUSIONS

Our study indicates that both imprinting and interaction effects are important components of the genetic model of body composition traits. Furthermore, the integration of eQTL and traditional QTL mapping may help to explain more phenotypic variation than either alone, thereby uncovering more molecular details of how tissue traits are regulated.

Genetic and environmental relationships between Framingham Risk Score and adiposity measures in Koreans: the Healthy Twin study

BACKGROUND AND AIMS

We examined heritability and bivariate analyses for the Framingham Risk Score (FRS) and adiposity measures among Koreans.

METHODS AND RESULTS

We analysed the data from 2496 participants (962 men, 1534 women, age 30-74 years), including 1320 non-twin family members, 468 monozygotic (MZ) and 120 dizygotic (DZ) twin pairs, collected from the Healthy Twin study of Korea. Adiposity measurements comprised BMI, waist circumference (WC), waist-to-hip ratio and waist-to-height ratio (WHTR). Analyses were conducted using the Sequential Oligogenic Linkage Analysis Routines (SOLAR) package software. The co-twin control analyses shows that estimates of within-pair regression coefficients in the relationship between adiposity traits and FRS were attenuated for MZ twin pairs, relative to DZ twin pairs (0.11-0.26 vs. 0.60-0.71). The heritability estimate for FRS was 0.37, and the estimates for adiposity traits ranged from 0.45 to 0.63 (P < 0.001). Bivariate analysis revealed genetic correlations between FRS, and all of the adiposity traits ranged from 0.16 (for WHTR, P > 0.05) to 0.46 (for WC, P < 0.001). The common environmental correlations between FRS and each of the adiposity traits ranged from 0.43 to 0.66 (P < 0.001).

CONCLUSIONS

FRS and each of the obesity traits shared common genetic and environmental relationships. These findings support a pleiotropic action between genes associated with adiposity traits and FRS and a need of further investigations for identifying specific common environmental factors.

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

Linkage analysis of diabetes status among hypertensive families: the Hypertension Genetic Epidemiology Network study

Type 2 diabetes susceptibility is determined by multiple genetic and environmental factors. Genome-wide linkage scans have localized common regions, possibly harboring susceptibility genes on chromosomes 1, 2, 12, and 20. Variability in linkage findings underscores the probable genetic heterogeneity of type 2 diabetes. Thus, we conducted a genome scan of diabetes status using maximum likelihood methods that model affection status by a liability threshold model. Hypertensive sibships and their offspring and/or parents in the Hypertension Genetic Epidemiology Network study were recruited from five field centers. The diabetes phenotype was derived using the World Health Organization criteria and adjusted for race/study center, age, age2, sex, and with and without percent body fat. In total, 567 diabetic participants were identified in 437 families. Variance component linkage analysis was performed among 1,545 Caucasians and 1,608 African Americans using race-specific marker allele frequencies. We detected a quantitative trait loci (QTLs) influencing diabetes variance (logarithm of odds = 3.4) on chromosome 22, which overlaps a positive type 2 diabetes finding among Canadian Oji-Cree Indians. We also observed suggestive evidence for linkage on chromosomes 1, 2, 5, 8, 14, 17, and 19. The identification and replication of type 2 diabetes QTLs will bring us closer to the detection of functional genes that influence diabetes susceptibility.

Evidence for joint action of genes on diabetes status and CVD risk factors in American Indians: the strong heart family study

OBJECTIVES

Previous research among American Indians of the strong heart family study (SHFS) has demonstrated significant heritabilities for CVD risk factors and implicated diabetes as an important predictor of several of the phenotypes. Moreover, we recently demonstrated that genetic effects on CVD risk factors differed in diabetic and nondiabetic individuals. In this paper, we investigated whether a significant genetic influence on diabetes status could be identified, and whether there is evidence for joint action of genes on diabetes status and related CVD risk factors.

METHODS AND RESULTS

Approximately 950 men and women, age 18 or older, in 32 extended families, were examined between 1997 and 1999. We estimated the effects of genes and environmental covariates on diabetes status using a threshold model and a maximum likelihood variance component approach. Diabetes status exhibited a residual heritability of 22% (h2=0.22). We also estimated the genetic and environmental correlations between diabetes susceptibility and eight risk factors for CVD. All eight CVD risk factors displayed significant genetic correlations with diabetes status (BMI (rhoG=0.55), fibrinogen (rhoG=0.40), HDL-C (rhoG=-0.37), ln triglycerides (rhoG=0.65), FAT (rhoG=0.38 ), PAI-1 (rhoG=0.67), SBP (rhoG=0.57), and WHR (rhoG=0.58)). Three of eight traits (HDL-C (rhoE=-0.32), ln triglycerides (rhoE=0.33), and fibrinogen (rhoE=0.20)) displayed significant environmental correlations with diabetes status.

CONCLUSIONS

These findings suggest that in the context of a high prevalence of diabetes, still unidentified diabetes genes may play an important role in influencing variation in CVD risk factors.