Cohort profile: the Baependi Heart Study-a family-based, highly admixed cohort study in a rural Brazilian town.

PURPOSE: Cardiovascular disease (CVD) is a major challenge to global health. The same epidemiological transition scenario is replayed as countries develop, but with variations based on environment, culture and ethnic mixture. The Baependi Heart Study was set up in 2005 to develop a longitudinal family-based cohort study that reflects on some of the genetic and lifestyle-related peculiarities of the Brazilian populations, in order to evaluate genetic and environmental influences on CVD risk factor traits. PARTICIPANTS: Probands were recruited in Baependi, a small rural town in the state of Minas Gerais, Brazil, following by first-degree and then increasingly more distant relatives. The first follow-up wave took place in 2010, and the second in 2016. At baseline, the study evaluated 1691 individuals across 95 families. Cross-sectional data have been collected for 2239 participants. FINDINGS TO DATE: Environmental and lifestyle factors and measures relevant to cardiovascular health have been reported. Having expanded beyond cardiovascular health outcomes, the phenotype datasets now include genetics, biochemistry, anthropometry, mental health, sleep and circadian rhythms. Many of these have yielded heritability estimates, and a shared genetic background of anxiety and depression has recently been published. In spite of universal access to electricity, the population has been found to be strongly shifted towards morningness compared with metropolitan areas. FUTURE PLANS: A new follow-up, marking 10 years of the study, is ongoing in 2016, in which data are collected as in 2010 (with the exception of the neuropsychiatric protocol). In addition to this, a novel questionnaire package collecting information about intelligence, personality and spirituality is being planned. The data set on circadian rhythms and sleep will be amended through additional questionnaires, actimetry, home sleep EEG recording and dim light melatonin onset (DLMO) analysis. Finally, the anthropometric measures will be expanded by adding three-dimensional facial photography, voice recording and anatomical brain MRI.

Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics.

The goal of this paper is to present an implementation of stochastic search variable selection (SSVS) to multilevel model from item response theory (IRT). As experimental settings get more complex and models are required to integrate multiple (and sometimes massive) sources of information, a model that can jointly summarize and select the most relevant characteristics can provide better interpretation and a deeper insight into the problem. A multilevel IRT model recently proposed in the literature for modeling multifactorial diseases is extended to perform variable selection in the presence of thousands of covariates using SSVS. We derive conditional distributions required for such a task as well as an acceptance-rejection step that allows for the SSVS in high dimensional settings using a Markov Chain Monte Carlo algorithm. We validate the variable selection procedure through simulation studies, and illustrate its application on a study with genetic markers associated with the metabolic syndrome.

Using item response theory to model multiple phenotypes and their joint heritability in family data.

Many important complex diseases are composed of a series of phenotypes, which makes the disease diagnosis and its genetic dissection difficult. The standard procedures to determine heritability in such complex diseases are either applied for single phenotype analyses or to compare findings across phenotypes or multidimensional reduction procedures, such as principal components analysis using all phenotypes. However each method has its own problems and the challenges are even more complex for extended family data and categorical phenotypes. In this paper, we propose a methodology to determine a scale for complex outcomes involving multiple categorical phenotypes in extended pedigrees using item response theory (IRT) models that take all categorical phenotypes into account, allowing informative comparison among individuals. An advantage of the IRT framework is that a straightforward joint heritability parameter can be estimated for categorical phenotypes. Furthermore, our methodology allows many possible extensions such as the inclusion of covariates and multiple variance components. We use Markov Chain Monte Carlo algorithm for the parameter estimation and validate our method through simulated data. As an application we consider the metabolic syndrome as the multiple phenotype disease using data from the Baependi Heart Study consisting of 1,696 individuals in 95 families. We adjust IRT models without covariates and include age and age squared as covariates. The results showed that adjusting for covariates yields a higher joint heritability (h2=0.53) than without co variates (h2=0.21) indicating that the covariates absorbed some of the error variance.

Brazilian urban population genetic structure reveals a high degree of admixture.

Advances in genotyping technologies have contributed to a better understanding of human population genetic structure and improved the analysis of association studies. To analyze patterns of human genetic variation in Brazil, we used SNP data from 1129 individuals--138 from the urban population of Sao Paulo, Brazil, and 991 from 11 populations of the HapMap Project. Principal components analysis was performed on the SNPs common to these populations, to identify the composition and the number of SNPs needed to capture the genetic variation of them. Both admixture and local ancestry inference were performed in individuals of the Brazilian sample. Individuals from the Brazilian sample fell between Europeans, Mexicans, and Africans. Brazilians are suggested to have the highest internal genetic variation of sampled populations. Our results indicate, as expected, that the Brazilian sample analyzed descend from Amerindians, African, and/or European ancestors, but intermarriage between individuals of different ethnic origin had an important role in generating the broad genetic variation observed in the present-day population. The data support the notion that the Brazilian population, due to its high degree of admixture, can provide a valuable resource for strategies aiming at using admixture as a tool for mapping complex traits in humans.

Congenic strains provide evidence that four mapped loci in chromosomes 2, 4, and 16 influence hypertension in the SHR.

To dissect the genetic architecture controlling blood pressure (BP) regulation in the spontaneously hypertensive rat (SHR) we derived congenic rat strains for four previously mapped BP quantitative trait loci (QTLs) in chromosomes 2, 4, and 16. Target chromosomal regions from the Brown Norway rat (BN) averaging 13-29 cM were introgressed by marker-assisted breeding onto the SHR genome in 12 or 13 generations. Under normal salt intake, QTLs on chromosomes 2a, 2c, and 4 were associated with significant changes in systolic BP (13, 20, and 15 mmHg, respectively), whereas the QTL on chromosome 16 had no measurable effect. On high salt intake (1% NaCl in drinking water for 2 wk), the chromosome 16 QTL had a marked impact on SBP, as did the QTLs on chromosome 2a and 2c (18, 17, and 19 mmHg, respectively), but not the QTL on chromosome 4. Thus these four QTLs affected BP phenotypes differently: 1) in the presence of high salt intake (chromosome 16), 2) only associated with normal salt intake (chromosome 4), and 3) regardless of salt intake (chromosome 2c and 2a). Moreover, salt sensitivity was abrogated in congenics SHR.BN2a and SHR.BN16. Finally, we provide evidence for the influence of genetic background on the expression of the mapped QTLs individually or as a group. Collectively, these data reveal previously unsuspected nuances of the physiological roles of each of the four mapped BP QTLs in the SHR under basal and/or salt loading conditions unforeseen by the analysis of the F2 cross.

Strategy and model building in the fourth dimension: a null model for genotype x age interaction as a Gaussian stationary stochastic process.

BACKGROUND: Using univariate and multivariate variance components linkage analysis methods, we studied possible genotype x age interaction in cardiovascular phenotypes related to the aging process from the Framingham Heart Study. RESULTS: We found evidence for genotype x age interaction for fasting glucose and systolic blood pressure. CONCLUSIONS: There is polygenic genotype x age interaction for fasting glucose and systolic blood pressure and quantitative trait locus x age interaction for a linkage signal for systolic blood pressure phenotypes located on chromosome 17 at 67 cM.

Longitudinal familial analysis of blood pressure involving parametric (co)variance functions.

BACKGROUND: For analyzing longitudinal familial data we adopted a log-linear form to incorporate heterogeneity in genetic variance components over the time, and additionally a serial correlation term in the genetic effects at different levels of ages. Due to the availability of multiple measures on the same individual, we permitted environmental correlations that may change across time. RESULTS: Systolic blood pressure from family members from the first and second cohort was used in the current analysis. Measures of subjects receiving hypertension treatment were set as censored values and they were corrected. An initial check of the variance and covariance functions proposed for analyzing longitudinal familial data, using empirical semi-variogram plots, indicated that the observed trait dispersion pattern follows the assumptions adopted. CONCLUSION: The corrections for censored phenotypes based on ordinary linear models may be an appropriate simple model to correct the data, ensuring that the original variability in the data was retained. In addition, empirical semi-variogram plots are useful for diagnosis of the (co)variance model adopted.