1 Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX.
2 Medstar Research Institute, Washington, DC.
3 Aberdeen Area Tribal Chairmens Health Board, Rapid City, SD.
4 Missouri Breaks Industries Research, Inc., Timber Lake, SD.
5 Center for American Indian Health Research, School of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK.
6 Epidemiology and Biometry Program, National Heart, Lung, and Blood Institute, Bethesda, MD.
7 Cornell University Medical College, New York, NY.
Received for publication November 12, 2001; accepted for publication September 16, 2002.
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ABSTRACT |
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cardiovascular diseases; environment; genetic predisposition to disease; Indians, North American; risk factors
Abbreviations: Abbreviation: CVD, cardiovascular disease.
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INTRODUCTION |
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Although CVD mortality in the United States has been steadily declining in recent decades, the American Indians of the Strong Heart Study display increasing mortality rates and incidence rates (4, 5). Indeed, CVD mortality rates among American Indians aged 4574 years were as high or higher than corresponding state rates in Arizona, Oklahoma, and North and South Dakota between 1984 and 1988 (5). Furthermore, Welty et al. (6) have reported increases in levels of many CVD risk factors across time.
The Strong Heart Family Study was initiated to investigate genetic effects on CVD risk factors. Risk factors for CVD have been identified both within and between populations, including diabetes, elevated blood pressure, adverse lipid profile, smoking, a family history of CVD, male gender, obesity, insulin resistance, a sedentary lifestyle, and a diet high in saturated fat and cholesterol (7, 8). Although many possible candidate genes for CVD have been identified, for only a few of these genes is their relation to CVD understood, and their contributions to variation in CVD risk factors tend to be small. Little is known about genes that contribute to population variability in precursors of CVD.
Because metabolic and epidemiologic studies have identified many quantitative CVD risk factors, there is great potential to identify the effects of specific genes on the development of CVD. The American Indian populations of the Strong Heart Study have high prevalences of diabetes, obesity, insulin resistance, and several other CVD risk factors, and therefore alleles that contribute to disease risk are most likely overrepresented. The identification of genes that increase disease susceptibility in American Indians will provide an opportunity to understand variability in response to environmental risk factors. This report describes the Strong Heart Family Study and summarizes the genetic and environmental contributions to CVD risk factors in American Indians from the first phase of the Strong Heart Family Study, as a first step in our search for CVD risk factor genes.
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MATERIALS AND METHODS |
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The Strong Heart Study has included three clinical examinations and mortality and morbidity surveillance of the original cohort, resident tribal members aged 4574 years. In 19981999, a pilot family study, the Strong Heart Family Study, was added in which 812 extended families (more than 300 family members at least 18 years of age) were recruited and examined at each center. An extension of the pilot family study is currently in progress and will involve recruitment of approximately 90 additional families, 30 from each center, comprising 2,700 family members. The Strong Heart Study protocol was approved by the Indian Health Service Institutional Review Board, by the institutional review boards of the participating institutions, and by the 13 participating tribes.
Strong Heart Family Study
The study populations
The Strong Heart Family Study population includes 13 communities in three geographic areas in Arizona, Oklahoma, and North and South Dakota. Details of the three field centers, published previously (9), are briefly described here.
Although the Dakota Center has examined participants from three Sioux Indian tribes (figure 1), the majority of Dakota Strong Heart Family Study participants were recruited from one tribe, the Cheyenne River Sioux tribe of the Cheyenne River Reservation, South Dakota. Overall, the mean and median levels of percentage of Indian heritage were 63 percent and 75 percent, respectively, with 121 participants reporting 100 percent American Indian heritage.
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The Arizona Center is located in Phoenix. This center has enrolled primarily Pima Indians, but there are also representatives of the closely related Maricopa and Tohono Oodham Indian tribes (9). The participants live in three communities near Phoenix: the Gila River and Salt River reservations (Pima/Maricopa) and the Ak-Chin Reservation (Tohono Ooodham/Pima) (3). The Gila River community is the largest of the three (figure 1). The Ak-Chin Indian community, located on the border of the Gila River Indian community (figure 1), has the smallest population, numbering in total 500 (8). The majority of the 345 family participants from the Arizona Center are members of two tribes, the Gila River (n = 194) and Salt River (n = 135) Pima/Maricopa. The majority of Arizona Center participants (319 of 345) reported 100 percent American Indian heritage, with mean and median levels of 98 percent and 100 percent, respectively.
Family recruitment
In preparation for this large-scale family study, we expanded upon data collected for the original Strong Heart Study cohort, using family history forms completed by each participant in phase I. These forms included the names and years of birth and death of each participants parents, full and half siblings, and offspring. They were computerized and linked into pedigrees using a name-matching algorithm (11) and other demographic information. From the 575 sibships identified in this manner (Dakota Center (n = 202), Oklahoma Center (n = 210), and Arizona Center (n = 163)), preliminary family trees were generated and sent to the field centers for potential recruitment.
To ensure that families were sufficiently large, we required that each have a "core sibship" with at least five living members, of whom three were original Strong Heart Study cohort members. The cohort members were required to have at least 12 living offspring who were at least 18 years of age (see prototype family tree in figure 2). The core sibship formed the basis for recruitment of extended families, which included members of the core sibship and their parents (if alive), spouses, offspring, spouses of offspring, and grandchildren who were 18 years of age or more.
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Family relationships were confirmed in interviews with the Strong Heart cohort members identified in the initial family trees. The family trees were continuously updated using Pedigree/Draw (12), with notations as to which family members should be the focus of the recruitment effort. To the extent possible, the validity of stated family relationships was verified during participant interviews. Every attempt was made to recruit both parents of every descendant of the core sibships, including any spouses who are non-Indian.
Phenotypic, demographic, and lifestyle data
The long-term goal of the Strong Heart Family Study is to map and identify genes that contribute to the risk of cardiovascular disease in American Indians. Genes that influence CVD risk factors are likely to interact with each other and with environmental factors. This study is using a strategy specifically designed to deal with this complexity by selecting phenotypes that are readily measurable in large numbers of individuals, that show reasonable repeatability over time, and that show promise of providing useful data for genetic analysis.
The Strong Heart Family Study examination consisted of a personal interview, physical examination, laboratory tests, and carotid ultrasonography. Several categories of phenotypes were assessed, such as body mass index and composition, lipoproteins, blood pressure, glycemic status, and clotting measures. Standard protocols, used for the collection of all data, are described in detail in previous publications (3, 9).
Briefly, fasting blood samples were obtained during the physical examination for the measurement of lipids, lipoproteins, apolipoproteins, insulin, glucose, plasma creatinine, plasma fibrinogen, and plasminogen activator inhibitor 1. All variables were assayed at MedStar Research Institute, Washington, DC, and the University of Vermont using standard laboratory methods as previously described (3, 9). Body fat mass was measured using an RJL bioelectric impedance meter (RJL Systems, Detroit, Michigan). The percentage of body fat was estimated by the RJL formula based on total body water (13). Blood pressure was measured three times, and the average of the last two measurements was used for analysis. Diabetes status was determined using World Health Organization criteria (14). Carotid ultrasonography was performed using previously described methods (15). Intimal-medial thickness and minimum and maximum diameters of the common carotid arteries were measured from digitized M-mode images. The presence of focal plaque was assessed using B-mode scanning with Doppler quantification of significant stenosis. Vascular stiffness of the common carotid artery was estimated using methods that incorporate arterial diameters and the central arterial pressure waveform back-calculated from radial artery tonometry by means of validated transfer functions (16).
Information was also collected on demographic characteristics, lifestyle variables, medical history, and reproductive history. During the personal interview, information on the following demographic characteristics was obtained by questionnaire: income, residence, marital status, number of household members, tribal enrollment, degree of Indian heritage, education, and other cultural factors. Participants were asked lifestyle questions, with a focus on smoking, alcohol intake, physical activity, and diet (24-hour recall). For questions on alcohol intake, current and ever drinking were defined as having had at least one alcoholic beverage in the last year and/or lifetime, respectively. Smoking was defined as having had at least 100 cigarettes. A medical history was taken, including the Rose questionnaire for angina pectoris and intermittent claudication. A reproductive history was taken including questions concerning parity, gravidity, menopausal status, and estrogen use.
Phenotypic data were transmitted by the Oklahoma Coordinating Center to the Southwest Foundation, where they were transferred to our pedigree data management system, PEDSYS (17).
Analytic techniques
To identify and evaluate the genetic and environmental contributions to CVD risk factors, we used a variance component approach implemented in SOLAR (18). The quantitative phenotype for an individual (y) is modeled as (19, 20)
where µ is the mean of the trait in males, ßj is the regression coefficient for the covariate j, vij is the value of covariate j in individual i, and gi and ei represent the deviations from µ for the individual i that are attributable to additive genetic effects and unmeasured environmental effects, respectively. gi and ei are assumed to be uncorrelated with one another and normally distributed with mean 0 and variances and
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In the simplest model, the covariance between a set of relative pairs () is a function of the additive genetic variance and the random environmental variance. To permit the analysis of arbitrary pedigree structures, each of the variances is multiplied by a structuring matrix. The structuring matrix for the additive genetic variance is two times the matrix of kinship coefficients (F), while the structuring matrix for the environmental variance is an identity matrix (I) (diagonal elements are ones and the rest of the elements are zeroes), which permits unique environments for each individual (18). Thus,
Once the expected mean and the covariance matrix for each pedigree are defined, the likelihood of a pedigree is evaluated using the multivariate normal density function and summed over all pedigrees. The likelihood of the phenotypes of the family members is assumed to follow a multivariate normal distribution, but the method is robust to violations of this assumption (18). The p values for the heritability calculations are obtained by likelihood ratio tests, where the likelihood of a model in which heritability is estimated is compared with the likelihood of the model in which the heritability is constrained to zero. Twice the difference in the natural logarithmic likelihoods is asymptotically distributed as a 1/2:1/2 mixture of a chi-squared variable with 1 df and a point mass at zero (21).
Data
Five classes of CVD risk factors were examined including obesity, lipoprotein, blood pressure, diabetes related, and clotting phenotypes. The specific CVD risk factors and their abbreviations are reported in table 1. The variables used for the estimation of covariate effects include basic demographic (age, sex, age x sex interaction, age2, age2 x sex interaction, self-reported American Indian heritage, and diabetes status), lifestyle (current and/or ever-smoking status and current and/or ever consumer of alcoholic beverages), and reproductive history (current and/or ever estrogen use) measures.
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The analysis of each phenotype was restricted to those individuals for whom all covariate data were complete. In addition, lipoprotein phenotypes were not analyzed for individuals currently taking lipid-lowering medications (n = 13), and blood pressure measures were not analyzed for 143 individuals currently taking antihypertensive medications. Insulin and glucose values were not analyzed for individuals currently taking antidiabetic medications (n = 153). For all phenotypes, any individual value greater than 4 standard deviations from the mean was removed from analysis.
During preliminary analyses, the effects of tribal membership and household, independent of additive genetic effects, were estimated (data not shown). Because no tribal membership or household effects were identified, tribal membership and household effects were removed from further analyses. However, linear center effects were identified (multiple tribes at each center), and the center was included as a covariate in all further analyses.
The initial analysis screened for the following classes of covariates: sex, age, age x sex interaction, diabetes status, percentage of Indian heritage, center, education, estrogen use, alcohol consumption, and smoking status. Any covariates whose effects were significant at the p 0.10 level in the initial analysis were retained in subsequent analyses, even if the significance levels decreased after inclusion of other covariates. After the initial covariate screening, maximum likelihood methods were used to estimate the effects of covariates and additive effects of genes.
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RESULTS |
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Table 7 displays the significant covariates for each CVD risk factor and the total proportion of variance accounted for by the significant covariates. Significant center effects were identified for 14 of 20 phenotypes examined. However, it is important to note that there is likely to be a confounding of this covariate with two others, tribal membership and percentage of Indian heritage. Once sample sizes are increased, analyses will be done separately for each center.
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Heritability of CVD risk factors
Table 8 presents the heritability estimates, measured as the proportion of residual phenotypic variance due to the additive effect of genes, after the effects of covariates have been accounted for, for selected CVD risk factors. The proportion of residual phenotypic variance due to the additive effects of genes ranges from 23 percent (systolic blood pressure and natural log-transformed fibrinogen) to 54 percent (waist/hip ratio). All of the obesity phenotypes have heritabilities greater than 40 percent. The heritabilities of all lipid phenotypes are 34 percent or greater. For example, 8 percent of the phenotypic variance in high density lipoprotein cholesterol is attributable to covariate effects, and 50 percent of the remaining phenotypic variance is due to the additive effects of genes. Both blood pressure phenotypes display significant components of genetic variation (p 0.0001). In addition, the heritability of the two diabetes phenotypes is 29 percent or greater, and the heritability of the two clotting phenotypes is 23 percent or greater. Because substantial heritability estimates for obesity measures were found, we additionally adjusted CVD risk factors for body mass index (table 8). Not unexpectedly, adjustment for body mass index slightly reduced the genetic signal for many of the CVD risk factors examined.
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DISCUSSION |
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The extended family structures within our sample enabled the estimation of common environmental effects, independent of additive genetic effects. Household and tribal membership effects are unmeasured nongenetic factors that are shared more closely by members of the same household or tribe than by individuals in different households or tribes and may represent unmeasured dietary or lifestyle factors (25). In this study, shared household environment and shared tribal membership had no significant effect on CVD risk factors (data not shown). Thus, neither measure of common environment (household and tribal membership) improved the fit of the variance component models. Given these findings, it may be that our measure of shared environment, while appealing in its simplicity, is imprecise. In future analyses, more sophisticated models of shared environment can be evaluated, such as shared sibship environment and common parent-offspring environment (26).
Variation attributable to measured covariates was substantial for many of the traits (ranging from 1 percent to 50 percent) (table 7). Unfortunately, covariate data were incomplete for three possibly important covariates: nutrition, physical activity, and amount of smoking per day or year. Nonetheless, several of the probable concomitants of CVD risk factors were identified in this population (i.e., age, sex, center, the use of reproductive hormones, current and/or ever smoking status, and current and/or ever alcohol consumption).
Heritabilities were estimated after accounting for covariate effects (table 8). The estimation of the genetic component of variance was limited to that attributable to additive effects and may result from actions of more than one gene. If other nonadditive sources of genetic variation exist, such as dominance or epistasis, then these observed heritabilities will represent lower bounds. Therefore, these estimates were conservative. In any case, the heritability estimates reported in table 8 demonstrate that genetic effects explain a substantial proportion of the variability for many CVD risk factors and related phenotypes and that these heritabilities are large enough to warrant further research. Moreover, the magnitude of these heritabilities was demonstrated by the minimal reduction of the genetic signal when additionally adjusted for body mass index.
Direct comparison of the heritability estimates from this study with those obtained from other studies is problematic. Different study designs, ascertainment schemes, methods of parameter estimation, and population-specific environmental contributions to the phenotypic variance can affect heritability estimates, resulting in different heritabilities even when the genetic variance estimates in the different populations are similar (25). Additionally, similar heritability estimates for a phenotype in different populations do not constitute evidence for the same genes in the expression of a trait, nor do dissimilar heritability estimates constitute evidence for the exclusion of the same genes in the expression of a trait (27). Despite these caveats, it is worth noting that the heritability estimates from this study are in the range of commonly reported heritability estimates from other populations (25, 2830). It is not surprising that for some of the phenotypes (e.g., lipoprotein a and glucose) the proportion of variance that is explained by genetic variation is reduced, since American Indian populations may be more genetically homogeneous than are larger population groups. Nonetheless, our results are comparable with those reported among the Pima Indians; for example, Sakul et al. (31) reported similar heritabilities for obesity measures (e.g., body mass index = 49 percent), and Hanson et al. (32) reported a similar heritability for fasting insulin (36 percent).
We are now genotyping each family member for nearly 400 anonymous markers distributed throughout the genome. A variance component linkage analysis of full pedigree data will be applied to determine the chromosomal locations of genes that influence disease risk factors (e.g., quantitative trait loci). The identification and localization of quantitative trait loci in American Indians will enable the examination of several specific questions. For example, are there quantitative trait loci that have large effects in explaining plasma cholesterol levels in American Indians? Can these genes be mapped to specific chromosomal regions? Such questions are essential for the decomposition of the risk of CVD and/or diabetes in the general population.
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ACKNOWLEDGMENTS |
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The authors would first like to thank the American Indian participants in the Strong Heart Family Study. Without their participation, this project would not have been possible. In addition, the cooperation of the Indian Health Service hospitals and the directors of the Strong Heart Study clinics, Betty Jarvis, Marcia OLeary, and Dr. Tauqeer Ali, and the many collaborators and staff of the Strong Heart Study have made this project possible. The authors would also like to thank Drs. John Blangero, Tony Comuzzie, and Jeff Williams for assistance with analytic approaches; Michael Crawford, David Frayer, Jeff Gilger, and Jim Mielke for their helpful suggestions on portions of these analyses; and Lisa Martin for her editorial assistance.
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REFERENCES |
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NOTES |
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REFERENCES |
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