Repeated-measures model for the investigation of temporal trends using longitudinal family studies: application to systolic blood pressure

Genetic and Environmental Influences on Blood Pressure and Serum Lipids Across Age-Groups

Aging plays a crucial role in the mechanisms of the impacts of genetic and environmental factors on blood pressure and serum lipids. However, to our knowledge, how the influence of genetic and environmental factors on the correlation between blood pressure and serum lipids changes with age remains to be determined. In this study, data from the Chinese National Twin Registry (CNTR) were used. Resting blood pressure, including systolic and diastolic blood pressure (SBP and DBP), and fasting serum lipids, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were measured in 2378 participants (1189 twin pairs). Univariate and bivariate structural equation models examined the genetic and environmental influences on blood pressure and serum lipids among three age groups. All phenotypes showed moderate to high heritability (0.37-0.59) and moderate unique environmental variance (0.30-0.44). The heritability of all phenotypes showed a decreasing trend with age. Among all phenotypes, SBP and DBP showed a significant monotonic decreasing trend. For phenotype-phenotype pairs, the phenotypic correlation (Rph) of each pair ranged from -0.04 to 0.23, and the additive genetic correlation (Ra) ranged from 0.00 to 0.36. For TC&SBP, TC&DBP, TG&SBP and TGs&DBP, both the Rph and Ra declined with age, and the Ra difference between the young group and the older adult group is statistically significant (p < .05). The unique environmental correlation (Re) of each pair did not follow any pattern with age and remained relatively stable with age. In summary, we observed that the heritability of blood pressure was affected by age. Moreover, blood pressure and serum lipids shared common genetic backgrounds, and age had an impact on the phenotypic correlation and genetic correlations.

Developing community-based health education strategies with family history: Assessing the association between community resident family history and interest in health education

BACKGROUND

Family history (FH) is an underutilized genetically informative tool that can influence disease prevention and treatment. It is unclear how FH fits into the development of community-based health education. This study examines the role that FH plays in perceived threat and health education related to mental and chronic physical conditions in the context of the health belief model.

METHODS

Data were collected from 1,048 adult participants aged 18-90 years. Approximately 76% of participants indicated African-American race/ethnicity and 35% had less than high school level education. Self-report data were collected on FH of four disorders: anxiety, depression, diabetes, and high blood pressure. Interest in receiving information regarding prevention as well as future testing efforts was assessed broadly. A series of logistic regressions examined the association between FH for each of the disorders and interest in receiving information on (1) prevention of diseases in general and (2) testing for diseases in general. These associations were also analyzed after accounting for the influence of perceived threat of conditions.

RESULTS

Interest in receiving general health education was significantly associated with FH of depression (OR = 2.72, 95% CI = 1.74-4.25), anxiety (OR = 2.26, 95% CI = 1.45-3.22), and high blood pressure (OR = 2.54, 95% CI = 1.05-6.12). After adjustment for perceived threat, the magnitude of these associations was reduced substantially. The associations between perceived threat and either interest in receiving information on disease testing or receiving general health education were strong and significant across all conditions (OR = 2.11-3.74).

DISCUSSION

These results provide evidence that perceived threat mediates the association between FH and engagement with health education. Currently available health education programs may benefit from considering the role of FH in an individual's motivation for participation in health education activities alongside other factors.

Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits

The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, because it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects.

Genetic and environmental influences on blood pressure and physical activity: a study of nuclear families from Muzambinho, Brazil

Blood pressure (BP) and physical activity (PA) levels are inversely associated. Since genetic factors account for the observed variation in each of these traits, it is possible that part of their association may be related to common genetic and/or environmental influences. Thus, this study was designed to estimate the genetic and environmental correlations of BP and PA phenotypes in nuclear families from Muzambinho, Brazil. Families including 236 offspring (6 to 24 years) and their 82 fathers and 122 mothers (24 to 65 years) were evaluated. BP was measured, and total PA (TPA) was assessed by an interview (commuting, occupational, leisure time, and school time PA). Quantitative genetic modeling was used to estimate maximal heritability (h²), and genetic and environmental correlations. Heritability was significant for all phenotypes (systolic BP: h² = 0.37 ± 0.10, P < 0.05; diastolic BP: h² = 0.39 ± 0.09, P < 0.05; TPA: h² = 0.24 ± 0.09, P < 0.05). Significant genetic (r g) and environmental (r e) correlations were detected between systolic and diastolic BP (r g = 0.67 ± 0.12 and r e = 0.48 ± 0.08, P < 0.05). Genetic correlations between BP and TPA were not significant, while a tendency to an environmental cross-trait correlation was found between diastolic BP and TPA (r e = -0.18 ± 0.09, P = 0.057). In conclusion, BP and PA are under genetic influences. Systolic and diastolic BP share common genes and environmental influences. Diastolic BP and TPA are probably under similar environmental influences.

Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data

Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main effects and gene-age interactions with longitudinal family data. The simulated Genetic Analysis Workshop 16 Problem 3 data sets were used to evaluate the method. Genome-wide association analyses were conducted based on cross-sectional data, i.e., each of the three single-visit data sets separately, and also on the longitudinal data, i.e., using data from all three visits simultaneously. Results from the analysis of coronary artery calcification phenotype showed that the longitudinal association tests were much more powerful than those based on single-visit data only. Gene-age interactions were evaluated under the same framework for detecting genetic effects that are modulated by age.

Genetic Effect on Blood Pressure Is Modulated by Age

Genome-wide linkage analysis was performed for systolic and diastolic blood pressures in the Hypertension Genetic Epidemiology Network. We investigated the role of gene–age interactions using a recently developed variance components method that incorporates age variation in genetic effects. Substantially improved linkage evidence, in terms of both the number of linkage peaks and their significance levels, was observed. Twenty-six linkage peaks were identified with maximum logarithm of odds scores ranging between 3.0 and 4.6, 15 of which were cross-validated by the literature. The chromosomal region 1p36 that showed the highest logarithm of odds score in our study was found to be supported by evidence from 3 studies. The new method also led to vastly improved validation across ethnic groups. Ten of the 15 supported linkage peaks were cross-validated between 2 different ethnic groups, and 2 peaks on chromosomal region 1q31 and 16p11 were validated in 3 ethnic groups. In conclusion, this investigation demonstrates that genetic effects on blood pressure vary by age. The improved genetic linkage results presented here should help to identify the specific genetic variants that explain the observed results.

Ignoring temporal trends in genetic effects substantially reduces power of quantitative trait linkage analysis

Linkage analysis has been one of the most widely used methods for identifying regions of the human genome which contain genes responsible for human diseases. Evidence suggests that the effects of some of the trait causing genes may vary with the age of an individual, giving rise to temporal trends in genetic effects. Linkage analysis routinely tends to ignore such gene-by-age interactions. While linkage analysis methods have been proposed for analysis of longitudinal family data for exploring temporal trends, there are no models to characterize such trends nor methods for analysis of cross-sectional family data. We extend variance component linkage analysis methodology by modeling the variance components due to the quantitative trait locus (QTL) and that due to the polygenic effect to be age dependent. With this model, we investigate the power of linkage analysis in the presence of temporal trends. We show that modeling true temporal trends in QTL effects can substantially increase the power of linkage analysis even when the average locus-specific heritabilities (when trends are ignored) are quite low, thereby demonstrating that, ignoring the gene-by-age interactions, when present, could jeopardize gene discovery.

Multivariate and multilocus variance components method, based on structural relationships to assess quantitative trait linkage via SEGPATH

A general-purpose modeling framework for performing path and segregation analysis jointly, called SEGPATH (Province and Rao [1995] Stat. Med. 7:185-198), has been extended to cover "model-free" robust, variance-components linkage analysis, based on identity-by-descent (IBD) sharing. These extended models can be used to analyze linkage to a single marker or to perform multipoint linkage analysis, with a single phenotype or multivariate vector of phenotypes, in pedigrees. Within a single, consistent approach, SEGPATH models can perform segregation analysis, path analysis, linkage analysis, or combinations thereof. SEGPATH models can incorporate environmental or other measured covariate fixed effects (including measured genotypes), genotype-specific covariate effects, population heterogeneity models, repeated-measures models, longitudinal models, autoregressive models, developmental models, gene-by-environment interaction models, etc., with or without linkage components. The data analyzed can have any missing value structure (assumed missing at random), with entire individuals missing, or missing on one or more measurements. Corrections for ascertainment can be made on a vector of phenotypes and/or other measures. Because of the flexibility of the class of models, the SEGPATH approach can also be used in nongenetic applications where there is a hierarchical structure, such as longitudinal, repeated-measures, time series, or nested models. A variety of specific models are provided, as well as some comparisons with other linkage analysis models. Particular applications demonstrate the importance of correctly accounting for the extraneous sources of familial resemblance, as can be done easily with these SEGPATH models, so as to give added power to detect linkage as well as to protect against spuriously inferring linkage.

Searching for the mountains of the moon: genome scans for atherosclerosis

Several research groups have begun mounting large, ambitious family studies to map genes for atherosclerosis, heart disease, and their major risk factors using whole genome linkage and/or disequilibrium scans. Some of the problems, pitfalls, and challenges of this exciting effort are examined and illustrated with lessons from an earlier mapping problem.

Familial studies on the genetics of cardiovascular diseases: the Stanislas cohort

In a given individual, the level of cardiovascular risk results from the combination of and interactions between genetic and environmental components. We choose to investigate segregation analysis of intermediate phenotypes in healthy nuclear families, belonging to the Stanislas cohort, a large familial cohort composed of 1006 families, which will be followed for 10 years. We developed a panel of 35 genetic markers including genes involved in lipid metabolism, regulation of blood pressure, thrombosis, platelet function, and endothelial cell adhesion. The allele frequencies of the studied polymorphisms were in agreement with those reported in other Caucasian populations. As an example of segregation analysis, we investigated carotid intima-media thickness (CIMT) variability in a subset sample of the Stanislas cohort. We found that about 30% of CIMT variability was attributable to genetic factors. Associations between CIMT and polymorphisms in apo CIII, cholesteryl ester transfer protein, methylene tetrahydrofolate reductase, and fibrinogen genes were observed and explained about 20% of CIMT variability in men. Furthermore, as another example of association studies, we investigated the relations between E-selectin polymorphisms and blood pressure interindividual variability and longitudinal changes in unrelated adults of this familial population. The E-selectin Phe554 allele was found associated with lower systolic blood pressure and diastolic blood pressure.

NHLBI family blood pressure program: methodology and recruitment in the HyperGEN network. Hypertension genetic epidemiology network

PURPOSE

Hypertension is a common precursor of serious disorders including stroke, myocardial infarction, congestive heart failure, and renal failure in whites and to a greater extent in African Americans. Large genetic-epidemiological studies of hypertension are needed to gain information that will improve future methods for diagnosis, treatment, and prevention of hypertension, a major contributor to cardiovascular morbidity and mortality.

METHODS

We report successful implementation of a new structure of research collaboration involving four NHLBI "Networks," coordinated under the Family Blood Pressure Program. The Hypertension Genetic Epidemiology Network (HyperGEN) involves scientists from six universities and the NHLBI who seek to identify and characterize genes promoting hypertension. Blood samples and clinical data were projected to be collected from a sample of 2244 hypertensive siblings diagnosed before age 60 from 960 sibships (half African-American) with two or more affected persons. Nonparametric sibship linkage analysis of over one million genotype determinations (20 candidate loci and 387 anonymous marker loci) was projected to have sufficient power for detecting genetic loci promoting hypertension. For loci showing evidence for linkage in this study and for loci reported linked or associated with hypertension by other groups, genotypes are compared in hypertensive cases versus population-based controls to identify or confirm genetic variants associated with hypertension. For some of these genetic variants associated with hypertension, detailed physiological and biochemical characterization of untreated adult offspring carriers versus non-carriers may help elucidate the pathophysiological mechanisms that promote hypertension.

RESULTS

The projected sample size of 2244 hypertensive participants was surpassed, as 2407 hypertensive individuals (1262 African-Americans and 1145 whites) from 917 sibships were examined. Detailed consent forms were designed to offer participants several options for DNA testing; 94% of participants gave permission for DNA testing now or in the future for any confidential medical research, with only 6% requesting restrictions for tests performed on their DNA. Since this is a family study, participants also are asked to list all first degree relatives (along with names, addresses, and phone numbers) and to indicate for each relative whether they were willing to allow study staff to make a contact. Seventy percent gave permission to contact some relatives; about 30% gave permission to contact all first degree relatives; and less than 1% asked that no relatives be contacted. Successes after the first four years of this study include: 1) productive collaboration of eight centers from six different locations; 2) early achievement of recruitment goals for study participants including African-Americans; 3) an encouraging rate of consent for DNA testing (including future testing) and relative contacting; 4) completed analyses of genetic linkage and association for several candidate gene markers and polymorphisms; 5) completed genotyping of random markers for over half of the full sample; and 6) early sharing of results among the four Family Blood Pressure Program networks for candidate and genome search analyses.

CONCLUSIONS

Experience after four years of this five-year program (1995-2000) suggests that the newly initiated NHLBI Network Program mechanism is fulfilling many of the expectations for which it was designed. It may serve as a paradigm for future genetic research that can benefit from large sample sizes, frequent sharing of ideas among laboratories, and prompt independent confirmation of early findings, which are required in the search for common genes with relatively small effects such as those that predispose to human hypertension.

Heritability of longitudinal changes in coronary-heart-disease risk factors in women twins

Numerous studies have demonstrated genetic influences on levels of coronary heart disease (CHD) risk factors, but there also may be genetic effects on the intraindividual variation in these risk factors over time. Changes in risk factors are likely to reflect genetic-environmental interactions and may have important implications for understanding CHD risk. The present study examines the heritability of changes in CHD risk factors, using data from the two examinations by the Kaiser Permanente Women Twins Study, performed a decade apart. The sample consisted of 348 pairs of women twins who participated in both examinations, including 203 MZ pairs and 145 DZ pairs. Average ages at the two examinations were 41 and 51 years, respectively. By means of three different statistical analytic approaches, moderate heritability estimates were demonstrated for changes in LDL cholesterol (h2 = .25-.36) and in HDL cholesterol (h2 = .23-.58), some of which were statistically significant. Although small to moderate heritability estimates were found for systolic blood pressure (.18-.37; P < .05 for some estimates), no genetic influence on changes in diastolic blood pressure was detected. Based on longitudinal twin data in women, this study demonstrates a genetic influence on changes in both lipoprotein risk factors and systolic blood pressure over a decade. In addition to environmental factors, which clearly are operating, the effect of various "variability genes" may be acting independently of the genetic influences on the absolute levels of these risk factors. Both mapping the gene(s) underlying intraindividual variations in these CHD risk factors and understanding their function(s) could lead to targeted intervention strategies to reduce CHD risk among genetically susceptible individuals.

General purpose model and a computer program for combined segregation and path analysis (SEGPATH): automatically creating computer programs from symbolic language model specifications

A general purpose model and a flexible computer program, called SEGPATH, have been developed to assist in the creation and implementation of a variety of genetic epidemiological models. SEGPATH is a computer program which can be used to generate programs to implement linear models for pedigree data, based upon a flexible, model-specification syntax. SEGPATH models can perform segregation analysis, path analysis, or combined segregation and path analysis using any user-specified path model and can be structured to analyze any number of multivariate phenotypes, environmental indices, and/or measured covariate fixed effects (including measured genotypes). Population heterogeneity models, repeated-measures models, longitudinal models, auto-regressive models, developmental models, and gene-by-environment interaction models can all be created under SEGPATH. Pedigree structures can be defined to be arbitrarily complex, and the data analyzed with programs generated by SEGPATH can have any missing value structure, with entire individuals missing, or missing on one or more measurements. Corrections for ascertainment can be done on a vector of phenotypes and/or other measures. Because the model specification syntax is general, SEGPATH can also be used in non-genetic applications where there is a hierarchical structure, such as longitudinal, repeated-measures, time series, or nested models. A variety of applications are demonstrated.

Pedigree analysis of blood pressure in subjects from rural Greece and relatives who migrated to Melbourne, Australia

Diastolic blood pressure readings taken in 1983-1984 on 1,474 Greek individuals (628 living on the island of Levkada, 846 relatives having migrated to Melbourne, Australia) from 204 two generational pedigrees were analysed. Blood pressure was regressed as a quadratic in age by sex and migrant status, and on temperature. Variance increased with age and was greater in migrant males. The covariance between relatives in different countries was significant. Variation was modeled by a multivariate normal model for pedigree analysis in terms of genetic effects, a common environment effect, and effects particular to an individual. The genetic component was 25.9 mm Hg2, independent of sex and migrant status. Importantly, the common environment component was not significant. The third component was greatest in migrant males. Spouse correlation was -0.09 (SE = 0.03). Exclusion of 86 individuals who reported currently receiving medication for elevated blood pressure stabilised the variance and decreased the genetic component. The data suggest that familial aggregation of diastolic blood pressure is due to genetic factors which produce the same variation in males and females, living on Levkada or in Melbourne. Nongenetic factors explain the greater variation in blood pressure of migrant males living in Melbourne.

Genetic and environmental effects on blood pressure in a Norwegian sample

Systolic (SBP) and diastolic (DBP) blood pressures were measured in a health screening of the adult population in Nord-Trøndelag, Norway. Correlations were computed for 23,936 pairs of spouses, 43,586 pairs of parent and offspring, 19,151 pairs of siblings, 1,251 pairs of grandparents-grandchildren, 1,146 pairs of biological uncles/aunts-nephews/nieces (avuncular), 801 non-biological avuncular pairs, 169 pairs of same-sex twins, and smaller groups of other types of relationships. Spouse correlations of 0.08 and 0.09 were approximately constant or slightly decreasing with marital duration. The correlation values for SBP and DBP were approximately 0.16 for parents-offspring, 0.19 to 0.23 for same-sex siblings with similar values for DZ twins, 0.19 and 0.16 for opposite-sex siblings, 0.52 and 0.43 for MZ twins, and close to zero for most of the second-order relationships. Genetic additive variance was estimated at 0.29 and genetic dominance variance at 0.18 with the best model for SBP. The corresponding estimates from the best models for DBP were 0.29 or lower and 0.22 or lower, the sum not exceeding 0.35. There was evidence of a moderate effect of environmental factors shared by same-sex siblings and twins (for DBP), but no cultural transmission, and whether or not adult relatives live together does not affect familial resemblance for BP. The data did not permit a very precise resolution of the relative magnitude of genetic dominance and sibling effects. The correlation structure did not show sex-specific genetic effects.