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Marcadores moleculares do tipo SNP (Single Nucleotide Polimorphism), com alta densidade dentro do DNA humano, têm sido largamente utilizados como amostradores do genoma. Este trabalho faz uso de uma modelagem estocástica de campos Markovianos de alcance variável para encontrar blocos independentes de SNPs, estruturando assim o genoma em regiões ilhadas de dependência. Foram utilizados dados públicos de SNPs de diferentes populações mundiais (projeto HapMap), além de uma amostra da população brasileira. Os blocos de SNPs configuram regiões de dependência ou janelas de influência as quais foram usadas para caracterizar as diferentes populações de acordo com sua ancestralidade e os resultados obtidos mostraram que as janelas da população brasileira têm, em média, tamanho maior, evidenciando a sua história recente de miscigenação, comparativamente às demais populações mundiais. É também proposta a construção de janelas de consenso maximais em determinadas regiões genômicas, tendo como base as janelas de influência de cada população do estudo. Identificando as janelas de consenso mais heterogêneas, a Análise de Correspondência e a teoria Biplots foram aplicadas na construção de um classificador capaz de determinar o percentual relativo de ancestralidade de um indivíduo, o qual, submetido à validação, obteve alta eficiência no acerto da população originária.

QTL mapping is an important tool for identifying regions in chromosomes which are relevant to explain a response of interest. It is a special case of the regression model where an unknown number of missing (non-observable) covariates is involved leading to a complex variable selection procedure. Although several methods have been proposed to identify QTLs and to estimate parameters in the associated model, minimum attention has been devoted to the estimated model adequacy. In this paper, we present an overview of a few methods for residual and diagnostic analysis in the context of Bayesian regression modeling and adapt them to work with QTL mapping. The motivation of this study is to identify QTLs associated with the blood pressure of F2 rats and check the fitted model adequacy.

In low-endemic areas for malaria transmission, asymptomatic individuals play an important role as reservoirs for malarial infection. Understanding the dynamics of asymptomatic malaria is crucial for its efficient control in these regions. Genetic host factors such as Toll-like receptor (TLR) polymorphisms may play a role in the maintenance or elimination of infection. In this study, the effect of TLR polymorphisms on the susceptibility to malaria was investigated among individuals living in the Atlantic Forest of Sao Paulo, Southern Brazil. A hundred and ninety-five Brazilian individuals were enrolled and actively followed up for malaria for three years. Twenty-four polymorphisms in five toll-like receptor (TLR) genes were genotyped by RFLP, direct sequencing or fragment analysis. The genotypes were analyzed for the risk of malaria. Ongoing Plasmodium vivax or P. malariae infection, was identified by the positive results in PCR tests and previous P. vivax malaria, was assumed when antiplasmodial antibodies against PvMSP119 were detected by ELISA. An evaluation of genomic ancestry was conducted using biallelic ancestry informative markers and the results were used as correction in the statistical analysis. Nine SNPs and one microsatellite were found polymorphic and three variant alleles in TLR genes were associated to malaria susceptibility. The regression coefficient estimated for SNP TLR9.-1237.T/C indicated that the presence of at least one allele C increased, on average, 2.3 times the malaria odds, compared to individuals with no allele C in this SNP. However, for individuals with the same sex, age and household, the presence of at least one allele C in SNP TLR9.-1486.T/C reduced, on average, 1.9 times the malaria odds, compared to individuals with no allele C. Moreover, this allele C plus an S allele in TLR6.P249S in individuals with same sex, age and ancestry, reduced, on average, 4.4 times the malaria odds. Our findings indicate a significant association of TLR9.-1237.T/C gene polymorphism with malarial infection and contribute to a better knowledge of the role of TLRs in malaria susceptibility in an epidemiological setting different from other settings.

Objective To assess the functional status of post-ICU patients using the Barthel Index (BI) and the Katz Index (KI) and to assess which is more suitable for this population. Design Retrospective longitudinal study. Setting Public tertiary hospital in Sao Paulo (Brazil). Participants Patients aged ≥18 years old, admitted to ICU, who were treated with mechanical ventilation (MV) ≥ 24 h and were discharged to ward. Exclusion criteria Inability to answer the BI and the KI; limiting neurological or orthopaedic conditions; ICU stay ≥90 days. Patients transferred to or from other hospitals or who died in the wards were not analysed. Intervention BI and KI were scored pre-ICU and post-ICU and the variation was calculated. Main outcome measures BI and KI scores were compared using analysis based on item response theory (IRT), using degree of difficulty and discriminating items as parameters. Results Median age was 52 years old, median APACHE II score was 15. Median ICU stay was 11 days and median MV duration was 4 days. BI variation was 44% and KI variation was 55%. In IRT analysis, BI considered a larger number of items with different levels of difficulty. Conclusion Both the BI and the KI revealed significant deterioration of functional status after ICU discharge. The IRT analysis suggested that the Barthel Index might be a better scale than the Katz Index for the assessment of functional status of patients discharged from ICU, since it presented better discrimination of the ability to carry out the tasks.

Many challenging problems in biomedical research rely on understanding how variables are associated with each other and influenced by genetic and environmental factors. Probabilistic graphical models (PGMs) are widely acknowledged as a very natural and formal language to describe relationships among variables and have been extensively used for studying complex diseases and traits. In this work, we propose methods that leverage observational Gaussian family data for learning a decomposition of undirected and directed acyclic PGMs according to the influence of genetic and environmental factors. Many structure learning algorithms are strongly based on a conditional independence test. For independent measurements of normally distributed variables, conditional independence can be tested through standard tests for zero partial correlation. In family data, the assumption of independent measurements does not hold since related individuals are correlated due to mainly genetic factors. Based on univariate polygenic linear mixed models, we propose tests that account for the familial dependence structure and allow us to assess the significance of the partial correlation due to genetic (between-family) factors and due to other factors, denoted here as environmental (within-family) factors, separately. Then, we extend standard structure learning algorithms, including the IC/PC and the really fast causal inference (RFCI) algorithms, to Gaussian family data. The algorithms learn the most likely PGM and its decomposition into two components, one explained by genetic factors and the other by environmental factors. The proposed methods are evaluated by simulation studies and applied to the Genetic Analysis Workshop 13 simulated dataset, which captures significant features of the Framingham Heart Study.

Faced with the lack of reliability and reproducibility in omics studies, more careful and robust methods are needed to overcome the existing challenges in the multi-omics analysis. In conventional omics data analysis, signal intensity values (denoted by M and values) are estimated neglecting pixel-level uncertainties, which may reflect noise and systematic artifacts. For example, intensity values from two-color microarray data are estimated by taking the mean or median of the pixel intensities within the spot and then subjected to a within-slide normalization by LOWESS. Thus, focusing on estimation and normalization of gene expression profiles, we propose a spot quantification method that takes into account pixel-level variability. Also, to preserve relevant variation that may be removed in LOWESS normalization with poorly chosen parameters, we propose a parameter selection method that is parsimonious and considers intrinsic characteristics of microarray data, such as heteroskedasticity. The usefulness of the proposed methods is illustrated by an application to real intestinal metaplasia data. Compared with the conventional approaches, the analysis is more robust and conservative, identifying fewer but more reliable differentially expressed genes. Also, the variability preservation allowed the identification of new differentially expressed genes. Using the proposed approach, we have identified differentially expressed genes involved in pathways in cancer and confirmed some molecular markers already reported in the literature.

Introduction: The engagement in sports or habitual physical activity (PA) has shown an extensive protective role against multiple diseases such as cancer, obesity, and many others. Additionally, PA has also a significant impact on life quality, since it aids with managing stress, preserving cognitive function and memory, and preventing fractures in the elderly. Objective: Considering there has been multiple evidence showing that genetic variation underpins variation of PA-related traits, we aimed to estimate the heritability (h2) of these phenotypes in a sample from the Brazilian population and assess whether males and females differ in relation to those estimates. Methods: 2,027 participants from a highly admixed population from Baependi, MG, Brazil, had information regarding their PA and sedentary behavior (SB) phenotypes collected through a questionnaire (IPAQ-SF). After data cleaning and transformation procedures, we obtained four variables to be evaluated: total PA (TPA MET), walking time, (WK MET), moderate-vigorous PA (MVPA MET), and SB. A model selection procedure was performed using a single-step covariate inclusion approach. We tested for BMI, waist, hip and neck circumferences, smoking, and depression separately, and performed correlation tests among covariates. Linear mixed models, selection procedure, and the variance components approach to estimate h2 were implemented using SOLAR-Eclipse 8.3.1. Results: We obtained estimates of 0.221, 0.109, 0.226, and 0 for TPA MET, WK MET, MVPA MET, and SB, respectively. We found evidence for gene-sex interactions, with males having higher sex-specific heritabilities than females for TPA MET and MVPA MET. In addition, we found higher estimates of the genetic variance component in males than females for most phenotypes. Discussion/Conclusion: The heritability estimates presented in this work show a moderate heritable set of genetic factors affecting PA in a sample from the Brazilian population. The evaluation of the genetic variance component suggests segregating genetic factors in male individuals are more heterogeneous, which can explain why men globally tend to need to practice more intense PA than women to achieve similar health benefits. Hence, these findings have significant implications for the understanding of the genetic architecture of PA and might aid to promote health in the future.

Studies of complex human diseases and traits associated with candidate genes are potentially vulnerable to bias (confounding) due to population stratification and inbreeding, especially in admixed population. In GWAS, the principal components (PCs) method provides a global ancestry value per subject, allowing corrections for population stratification. However, these coefficients are typically estimated assuming unrelated individuals, and if family structure is present and ignored, such substructures may induce artifactual PCs. Extensions of the PCs method have been proposed by Konishi and Rao [Biometrika 1992;79:631-641], taking into account only siblings' relatedness, and by Oualkacha et al. [Stat Appl Genet Mol Biol 2012, DOI: 10.2202/1544-6115.1711], taking into account large pedigrees and high-dimensional phenotype data. In this work, we extend these methods to estimate the global individual ancestry coefficients from PCs derived from different variance component matrix estimators using SNPs from two simulated data sets and two real data sets: the GENOA sibship data consisting of European and African-American subjects and the Baependi Heart Study consisting of 80 extended Brazilian families, both with genotyping data from the Affymetrix 6.0 chip. Our results show that the family structure plays an important role in the estimation of the global individual ancestry value for extended pedigrees but not for sibships.

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.

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.

A major challenge in biomedical research is to identify causal relationships among genotypes, phenotypes, and clinical outcomes from high-dimensional measurements. Causal networks have been widely used in systems genetics for modeling gene regulatory systems and for identifying causes and risk factors of diseases. In this chapter, we describe fundamental concepts and algorithms for constructing causal networks from observational data. In biological context, causal inferences can be drawn from the natural experimental setting provided by Mendelian randomization, a term that refers to the random assignment of genotypes at meiosis. We show that genetic variants may serve as instrumental variables, improving estimation accuracy of the causal effects. In addition, identifiability issues that commonly arise when learning network structures may be overcome by using prior information on genotype–phenotype relations. We discuss four recent algorithms for genotype–phenotype network structure learning, namely (1) QTL-directed dependency graph, (2) QTL+Phenotype supervised orientation, (3) QTL-driven phenotype network, and (4) sparsity-aware maximum likelihood (SML).

This study evaluated the influence of the dental substrates obtained after the use of different caries removal techniques on bonding of a self-etching system. Forty, extracted, carious, human molars were ground to expose flat surfaces containing caries-infected dentine surrounded by sound dentine. The caries lesions of the specimens were removed or not (control--G1) either by round steel burs and water-cooled, low speed, handpiece (G2), or by irradiation with an erbium, chromium:yttrium scandium gallium garnet (Er,Cr:YSGG) laser (2W, 20 Hz, 35.38 J/cm(2), fiber G4 handpiece with 0.2826 mm(2), non-contact mode at a 2 mm distance, 70% air/20% water--G3) or using a chemo-mechanical method (Carisolv--G4). Caries-infected, caries-affected and sound dentines were submitted to a bonding system followed by construction of a resin-based composite crown. Hour-glass shaped samples were obtained and submitted to a micro-tensile bond test. The bond strength data were compared by analysis of variance (ANOVA), complemented by Tukey's test (Por= 0.05). The samples of sound dentine presented higher bond strengths than did samples of caries-affected dentine, except for the groups treated with the Er,Cr:YSGG laser. The highest bond strengths were observed with the sound dentine treated with burs and Carisolv. The bond strengths to caries-affected dentine were similar in all groups. Additionally, bonding to caries-affected dentine of the Er,Cr:YSGG laser and Carisolv groups was similar to bonding to caries-infected dentine. Thus, caries-affected dentine is not an adequate substrate for adhesion. Moreover, amongst the caries removal methods tested, the Er,Cr:YSGG laser irradiation was the poorest in providing a substrate for bonding with the tested self-etching system.

Department of Biochemistry, Paulista School of Medicine, Federal Universityof Sao Paulo - Sao Paulo/SP, Brazil.Clinical and Toxicological Analysis Department, Sao Paulo University ofPharmaceutical Sciences - Sao Paulo/SP, Brazil.Institute of Mathematics and Statistics, Sao Paulo University - Sao Paulo/SP, Brazil.Heart Institute (InCor), Sao Paulo University Medical School, Sao Paulo/SP, Brazil.Email: t27026t@yahoo.com.br

Summary The incidence of mineralization of the stylohyoid ligament complex, according to radiographic findings, was analysed in a group of patients with temporomandibular disorders (TMD), and compared with that of a control group of asymptomatic individuals (AI). Both groups were similar considering the prevalence of the female gender as well as the distribution of cases according to age. ‘Elongated’ was the most frequent radiographic appearance in both groups; ‘partially mineralized’ was the most frequent radiographic pattern in the TMD group; and the lower ramus was the most frequent location of mineralization in both groups. When comparing between groups and according to each class, there was only one significant difference in radiographic appearance, ‘pseudoarticulated’ being more frequent in the TMD group. We can conclude that the occurrence and characteristics of mineralization of stylohyoid ligament complex were similar in TMD patients and AIs.

Iron depletion was suggested to be protective against the development of ischemic heart disease. Population studies have led to conflicting results, and such an association has not been addressed in patients with heart failure due to cardiomyopathy. We studied the distribution of hemochromatosis-related mutations in 319 patients with heart failure due to cardiomyopathy of different etiologies. The genotypic distribution showed a significantly higher prevalence of heterozygotes for the C282Y mutation in patients with ischemic cardiomyopathy than in patients with cardiomyopathy of nonischemic etiologies (p = 0.0036). The frequency of the D63 mutation was not significantly different between ischemic versus nonischemic groups. In multiple logistic regression models adjusted for age, sex, ethnicity, and different degrees of disease progression, there was a strong and significant association of the C282Y mutation with ischemic cardiomyopathy compared with the nonischemic group (odds ratio 6.64, 95% confidence interval 1.71 to 25.73, after adjustment). In our sample, genetic variation in the HFE gene was associated with ischemic cardiomyopathy. Such association merits further study regarding its value as a prognostic marker in patients with ischemic heart disease.

We consider the selection of covariables for the covariance adjustment of parameter estimators in growth curve models. The procedure consists of obtaining the best subset of linear combinations of higher-order polynomials for minimizing the variance of the adjusted estimator of a particular linear combination of the lower-order polynomial coefficients. The coefficients of the required linear combinations are expressed in terms of the eigenvectors of appropriate matrices and the gain in precision due to covariance adjustment is measured by the magnitude of the corresponding eigenvalues. The results are illustrated numerically using data analyzed previously in the literature.

This study aimed to evaluate the effect of an agrochemical mixture, Pyraclostrobin + Metiram (Cabrio Top)® (P + M), and Bacillus subtilis on polyamine levels and their potential relationship with plant growth stages. These experiments were conducted in an area of historical disease occurrence. Thirty-two hundred tomato plants (cv. “Saladinha Plus”) were used. The trays were divided into two groups; one group received a pre-treatment of P + M (3 g l −1) and Boscalid ® (0.3 g l −1), and no products were applied to the other half of the tray. After transplanting, the following spray treatments were used: P + M (2.0 g l −1), P + M (4.0 g l −1), B. subtilis (4.0 ml l −1) and Methamidophos® (1.0 ml l −1) for 20, 35, 50 and 65 days after (DAT). To analyze the free polyamine concentrations (putrescine, spermidine and spermine), the plant material was collected at 30, 45, 60, and 75 DAT. The fruit harvest and the assessment of the total production and average fruit weight were performed at 80 DAT by selecting 20 plants from each treatment group. Higher concentrations of putrescine and spermidine were found in the plants exposed to P + M at 4.0 g l −1, indicating that polyamine concentrations may be correlated with the plant’s resistance to stress. The optimal production and weight of the tomatoes were obtained following the application of P + M, demonstrating that this agrochemical mixture has great potential to protect tomato crop yields when applied during periods of high disease incidence.

Effective analytical tools are highly desirable for data analysis and for making the biological link between genotypic and phenotypic measures. In family data it is important to reconcile the methods that explain the phenotypic variability through fixed genetic effects and ones that estimate variance components using classical heritability methods. Thus, in this paper, we propose a method based on added-variable plot for polygenic linear mixed models applied to genome wide association studies in family-based designs. Our goal is to be able to discriminate genetic predictor variables in effects due to random polygenic and residual components. We also propose an index to detect influential families for each predictor variable identified with genetic effect. We assess the performance of our proposed method using our own family simulated data and the Genetic Analysis Workshop 17 family simulated data.

The most common beef cattle raised in Brazil is the Nelore breed (Bos indicus). Information obtained by ultrasonography on follicular growth in Bos taurus cattle has been accumulating rapidly. However, there are few publications to date on follicular development in Bos indicus breeds. The follicular dynamics in Nelore heifers and cows during natural or prostaglandin (PG)-induced estrous cycle were studied. From the detection of estrus onward, all animals were examined daily by ultrasonography for one (n = 35) or two (n = 10) consecutive estrous cycles. The follicular dynamic in Nelore cattle was characterized by the predominance of 2 follicular waves in the cows (83.3%, n = 18, P < 0.05) and 3 waves in the heifers (64.7%, n = 16, P < 0.05). Most of the cattle observed over 2 consecutive estrous cycles presented the same pattern of follicular waves in the first and second cycle, and only 30% showed variation in the number of waves from one cycle to the other. Most of the follicular parameters analyzed were not affected by PG treatment or age but were altered by follicular waves. Consequently, data on cows and heifers were combined according to the number of follicular waves. The ovulatory follicle was larger than the other dominant follicles (P < 0.05), and the ovulatory wave was shorter than the preceding waves (P < 0.05). The interovulatory interval was longer in animals showing 3 waves than those exhibiting 2 waves (P < 0.05). Maximum diameter of the dominant follicle (around 11 mm) and of the corpus luteum (CL, approximately 17 mm) were smaller than those reported for European breeds. In conclusion, the results demonstrate that although the dominant follicle and corpus luteum are smaller than in European breeds, the follicular dynamics in Nelore cattle were similar to those observed in European breeds and were characterized by 2 or 3 follicular waves for cows and heifers, respectively, during the natural or prostaglandin-induced estrous cycle.

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 (ĥ2=0.53) than without co variates (ĥ2=0.21) indicating that the covariates absorbed some of the error variance.

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.

Background The purpose of this study was to estimate the genetic influences on the initiation of cigarette smoking, the persistence, quantity and age-at-onset of regular cigarette use in Brazilian families. Methods The data set consisted of 1,694 individuals enrolled in the Baependi Heart Study. The heritability and the heterogeneity in genetic and environmental variance components by gender were estimated from variance components approaches, using the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package. The mixed-effects Cox model was used for the genetic analysis of the age-at onset of regular cigarette use. Results The heritability estimates were high (> 50%) for smoking initiation and were intermediate, ranging from 23.4 to 31.9%, for smoking persistence and quantity. Significant evidence for heterogeneity in variance components by gender was observed for smoking initiation and age-at-onset of regular cigarette use. Genetic factors play an important role in the interindividual variation of these phenotypes in females, while in males there is a predominant environmental component, which could be explained by greater social influences in the initiation of tobacco use. Conclusions Significant heritabilities were observed in smoking phenotypes for both males and females from the Brazilian population. These data add to the literature and are concordant with the notion of significant biological determination in smoking behavior. Samples from the Baependi Heart Study may be valuable for the mapping of genetic loci that modulate this complex biological trait.

Background It is commonly recognized that physical activity has familial aggregation; however, the genetic influences on physical activity phenotypes are not well characterized. This study aimed to (1) estimate the heritability of physical activity traits in Brazilian families; and (2) investigate whether genetic and environmental variance components contribute differently to the expression of these phenotypes in males and females. Methods The sample that constitutes the Baependi Heart Study is comprised of 1,693 individuals in 95 Brazilian families. The phenotypes were self-reported in a questionnaire based on the WHO-MONICA instrument. Variance component approaches, implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package, were applied to estimate the heritability and to evaluate the heterogeneity of variance components by gender on the studied phenotypes. Results The heritability estimates were intermediate (35%) for weekly physical activity among non-sedentary subjects (weekly PA_NS), and low (9-14%) for sedentarism, weekly physical activity (weekly PA), and level of daily physical activity (daily PA). Significant evidence for heterogeneity in variance components by gender was observed for the sedentarism and weekly PA phenotypes. No significant gender differences in genetic or environmental variance components were observed for the weekly PA_NS trait. The daily PA phenotype was predominantly influenced by environmental factors, with larger effects in males than in females. Conclusions Heritability estimates for physical activity phenotypes in this sample of the Brazilian population were significant in both males and females, and varied from low to intermediate magnitude. Significant evidence for heterogeneity in variance components by gender was observed. These data add to the knowledge of the physical activity traits in the Brazilian study population, and are concordant with the notion of significant biological determination in active behavior.

Grandes avanços estão ocorrendo na área de Genética e Genômica. Inicialmente cada gene era analisado, separadamente, com o intuito de se verificar, por exemplo, associações com desenvolvimento de doenças. Recentemente, surge a técnica de microarrays que permite que milhares de genes sejam avaliados, simultaneamente. Esta técnica apresenta diversas vantagens nas aplicações em muitas áreas do conhecimento como, por exemplo, a área médica, porém uma série de dificuldades são encontradas nestes experimentos devido às diversas fontes de variações sistemáticas que podem interferir nas mensurações obtidas, acarretando em resultados falso-positivos. Devido a estas variações e a outros problemas encontrados nestes experimentos, como o problema de múltiplos testes, pois milhares de genes são avaliados num mesmo momento, muitos são os esforços em se encontrar uma melhor abordagem de análise estatística para a identificação de genes, diferencialmente, expressos (DE). Baseado nestes aspectos, no presente estudo serão apresentadas e comparadas possíveis técnicas de análise estatísticas úteis na identificação de genes DE. Como aplicação e motivação deste trabalho, algumas técnicas de análise são aplicadas a um conjunto de dados reais com ratos diabéticos.

Background In family studies, it is important to evaluate the impact of genes and environmental factors on traits of interest. In particular, the relative influences of both genes and the environment may vary in different strata of the population of interest, such as young and old individuals, or males and females. Methods In this paper, extensions of the variance components model are used to evaluate heterogeneity in the genetic and environmental variance components due to the effects of sex and age (the cutoff between young and old was 43 yrs). The data analyzed were from 81 Brazilian families (1,675 individuals) of the Baependi Family Heart Study. Results The models allowing for heterogeneity of variance components by sex suggest that genetic and environmental variances are not different in males and females for diastolic blood pressure, LDL-cholesterol, and HDL-cholesterol, independent of the covariates included in the models. However, for systolic blood pressure, fasting glucose and triglycerides, the evidence for heterogeneity was dependent on the covariates in the model. For instance, in the presence of sex and age covariates, heterogeneity in the genetic variance component was suggested for fasting glucose. But, for systolic blood pressure, there was no evidence of heterogeneity in any of the two variance components. Except for the LDL-cholesterol, models allowing for heterogeneity by age provide evidence of heterogeneity in genetic variance for triglycerides and systolic and diastolic blood pressure. There was evidence of heterogeneity in environmental variance in fasting glucose and HDL-cholesterol. Conclusions Our results suggest that heterogeneity in trait variances should not be ignored in the design and analyses of gene-finding studies involving these traits, as it may generate additional information about gene effects, and allow the investigation of more sophisticated models such as the model including sex-specific oligogenic variance components.

Background/Aims: Statistical analysis of age-at-onset involving family data is particularly complicated because there is a correlation pattern that needs to be modeled and also because there are measurements that are censored. In this paper, our main purpose was to evaluate the effect of genetic and shared family environmental factors on age-at-onset of three cardiovascular risk factors: hypertension, diabetes and high cholesterol. Methods: The mixed-effects Cox model proposed by Pankratz et al. [2005] was used to analyze the data from 81 families, involving 1,675 individuals from the village of Baependi, in the state of Minas Gerais, Brazil. Results: The analyses performed showed that the polygenic effect plays a greater role than the shared family environmental effect in explaining the variability of the age-at-onset of hypertension, diabetes and high cholesterol. The model which simultaneously evaluated both effects indicated that there are individuals which may have risk of hypertension due to polygenic effects 130% higher than the overall average risk for the entire sample. For diabetes and high cholesterol the risks of some individuals were 115 and 45%, respectively, higher than the overall average risk for the entire population. Conclusions: Results showed evidence of significant polygenic effects indicating that age-at-onset is a useful trait for gene mapping of the common complex diseases analyzed. In addition, we found that the polygenic random component might absorb the effects of some covariates usually considered in the risk evaluation, such as gender, age and BMI.

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

Apresentamos neste trabalho um procedimento sistemático de seleção ótima das potenciais variáveis concomitantes envolvidas no ajuste por covariância introduzido por Rao (1965) do modelo de curvas de crescimento polinomial proposto por Potthof and Roy (1964). São obtidas combinações lineares ótimas destas variáveis que minimizam o traço, o determinante e a norma euclidiana da matriz de covariância do estimador ajustado de contrastes entre os coeficientes polinomias. Soluções para o problema de otimização são encontradas em termos de autovetores de matrizes e restrições quanto ao número máximo de combinações lineares que devem ser consideradas no ajuste são também obtidas. Além disso, procedimentos numéricos de otimização são abordados através do método de Lagrange e de uma técnica de projeção estereográfica para incorporar restrições convenientes na função objetivo, seguidos do uso do método de Newton-Raphson. Aplicações considerando dados descritos na literatura estatística indicam que o procedimento proposto é eficiente para a classe de problema estudada not available

Background The heritability of cardiovascular risk factors is expected to differ between populations because of the different distribution of environmental risk factors, as well as the genetic make-up of different human populations. Methods The purpose of this analysis was to evaluate genetic and environmental influences on cardiovascular risk factor traits, using a variance component approach, by estimating the heritability of these traits in a sample of 1,666 individuals in 81 families ascertained randomly from a highly admixed population of a city in a rural area in Brazil. Results Before adjustment for sex, age, age2, and age × sex interaction, polygenic heritability of systolic (SBP) and diastolic (DBP) blood pressure were 15.0% and 16.4%, waist circumference 26.1%, triglycerides 25.7%, fasting glucose 32.8%, HDL-c 31.2%, total cholesterol 28.6%, LDL-c 26.3%, BMI 39.1%. Adjustment for covariates increased polygenic heritability estimates for all traits mainly systolic and diastolic blood pressure (25.9 and 26.2%, respectively), waist circumference (40.1%), and BMI (51.0%). Conclusion Heritability estimates for cardiovascular traits in the Brazilian population are high and not significantly different from other studied worldwide populations. Mapping efforts to identify genetic loci associated with variability of these traits are warranted.

Background The genetic mechanisms underlying interindividual blood pressure variation reflect the complex interplay of both genetic and environmental variables. The current standard statistical methods for detecting genes involved in the regulation mechanisms of complex traits are based on univariate analysis. Few studies have focused on the search for and understanding of quantitative trait loci responsible for gene × environmental interactions or multiple trait analysis. Composite interval mapping has been extended to multiple traits and may be an interesting approach to such a problem. Methods We used multiple-trait analysis for quantitative trait locus mapping of loci having different effects on systolic blood pressure with NaCl exposure. Animals studied were 188 rats, the progenies of an F2 rat intercross between the hypertensive and normotensive strain, genotyped in 179 polymorphic markers across the rat genome. To accommodate the correlational structure from measurements taken in the same animals, we applied univariate and multivariate strategies for analyzing the data. Results We detected a new quantitative train locus on a region close to marker R589 in chromosome 5 of the rat genome, not previously identified through serial analysis of individual traits. In addition, we were able to justify analytically the parametric restrictions in terms of regression coefficients responsible for the gain in precision with the adopted analytical approach. Conclusion Future work should focus on fine mapping and the identification of the causative variant responsible for this quantitative trait locus signal. The multivariable strategy might be valuable in the study of genetic determinants of interindividual variation of antihypertensive drug effectiveness.

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.

O mapeamento de genes que controlam características quantitativas é de grande interesse em pesquisa genômica. Neste trabalho, foram descritos métodos de mapeamento genético de traços quantitativos, através de modelos de componentes de variância em populações humanas. Com base em conceitos genéticos expostos, foram apresentados alguns modelos de componentes de variância, envolvendo diversos componentes de ordem genética e ambiental. Tais modelos requerem informações específicas sobre os efeitos genéticos, que são não observáveis. Estima-se, então, as probabilidades de identidade por descendência entre os indivíduos relacionados, por meio de informação de marcadores moleculares. Alguns métodos de estimação dessas probabilidades foram tratados no presente trabalho, destacando-se o método proposto por Blangero et al. (1998), que utiliza a informação de múltiplos marcadores via regressão, para estimar localizações e efeitos de locos de traços quantitativos em conjuntos de famílias de complexidade e tamanho arbitrários. A formalização estatística de vários modelos e conceitos genéticos foi tratada em detalhes. Em seguida, métodos de estimação dos parâmetros, testes de hipóteses e algumas das propriedades dos modelos de componentes de variância foram discutidos. Finalmente, analisou-se dados simulados, utilizando-se o método de Blangero et al. (1998), por meio do aplicativo SOLAR. A aplicação foi útil para ilustrar o problema teórico e apontou para alguns resultados interessantes, como a detecção de mais de um gene principal, possíveis efeitos de epistasia, pleiotropia e interação entre gene e sexo not available

O Lago das Garças (23°39\'S, 46°37\'W) está situado dentro da área do Parque Estadual das Fontes do Ipiranga, cidade de São Paulo (SP). Com o objetivo de identificar as principais fontes de aporte de nutrientes (nitrogênio e fósforo) e a quantificação das cargas do entorno do corpo d\'água, o lago foi dividido em dois compartimentos. Foram realizadas amostragens mensais de janeiro de 1997 a junho de 1998, em dez estações de coleta. Foram feitas medidas de temperatura, pH, condutividade elétrica e concentrações de fósforo total, nitrogênio total, amônio, clorofila a, material em suspensão total, oxigênio dissolvido, em cinco profundidades no lago. Nos afluentes foram determinadas as concentrações de nitrogênio total e fósforo total. Os perfis obtidos na coluna d\'água em conjunto com os dados climatológicos e morfométricos do lago, indicaram que o aquecimento térmico pela radiação solar e a profundidade do sistema foram as principais funções de força nos processos de estratificação térmica e química. No período de estudo, as cargas de entrada no reservatório, foram de 6.519 Kg de fósforo e 37.156 Kg de nitrogênio, sendo que os afluentes representaram 99% e 34% da carga total de fósforo e nitrogênio, respectivamente. A eficiência de retenção do Lago das Garças foi de 61% para o fósforo total e 85% para o nitrogênio total. Em relação a trofia o lago foi classificado como eutrófico em todo o período de estudo. Através de equação matemática baseada na carga anual de fósforo, tempo de residência e profundidade média, foi possível estimar as concentrações de fósforo no reservatório, além de fazer simulações na redução das cargas. Considerando que a carga média de fósforo total foi de 4.891 mg/m2 ano, a simulação matemática indicou que reduções superiores a 90% da carga (valores máximos de 300 mg/m2 ano) alterariam o nível de trofia no sistema para oligotrófico/mesotrófico. The Lago das Garças (23°39\'S, 46°37\'W) is located in Parque Estadual das Fontes do Ipiranga, São Paufo city (SP). In order to identify the main sources of nutrients (nitrogen and phosphorus) and to quantify of the nutrient loads of its tributaries, the lake was divided in two compartments. Water sample were collected monthly from January 1997 to June 1998, in ten sampling station. Measurements of temperature, pH and electric conductivity and concentrations of totaf phosphorus, total nitrogen, ammonium, chlorophyll a,suspended solids, dissolved oxigen, were done in five depth of in the lake. ln each tributarys it was established only one sampling station where it was measured the concentration of total phosphorus and nitrogen. The profiles obtained in the water column, associated with the climatological data and morphological features of the lake, showed that the thermaI heating by the solar irradiation and depth of the system were the main force functions in the processes of thermal and chemical stratification. In the period study, the entrance loads into the Iake were of 6,519 Kg of pnosphorus and 37,156 Kg of nitrogen. The tributaries represented 99% and 34% of the total load of phosphorus and nitrogen, respectively. The retention efficiency of the Lago das Garças was 61% for total phosphorus and 85% for total nitrogen. In relation to the trophic status, this lake was cfassified as eutrophic all over the studied time. Using a mathematical equation based on the annual load of phosphorus, residence time and medium depth, it was possible to evaluate the phosphorus concentration in the lake and also to verify the effect of the redution of the loads. Considering that average load of total phosphorus was of 4.891 mg/m2.year, the mathematical model indicated there more than 90% of the load (maximum 300 mg/m2.year) should be reduced, to alter the trophic status in the system for oligotrophic/mesotrophic.

Nas pesquisas para o desenvolvimento de novos medicamentos tem sido crescente o interesse por metodologias estatísticas que analisem as respostas aos fármacos levando-se em conta a informação genética dos indivíduos presentes no estudo. Espera-se que com essas metodologias possam ser desenvolvidos novos medicamentos que possibilitem um melhor tratamento aos pacientes, seja por seus compostos serem mais adequados à genética individual, seja pela prescrição de um regime de dose e dosagem mais adequado às características genéticas de um indivíduo. O presente trabalho explora várias metodologias que incorporam a informação genética no estudo dos fármacos e desafios decorrentes: modelagem da farmacocinética e farmacodinâmica de um composto, análise de dados de famíias, correção para estrutura de populações e estudos com modelos animais de populações. Na análise de dados de famílias influentes na resposta a fármacos, resultados são extraídos da teoria do gráfico da variável adicionada aplicada a modelos de componentes de variância. As análises que envolvem correções para estrutura de populações consideram resultados da decomposição espectral de matrizes de dados genéticos e o gráfico Biplot. Algumas das metodologias supracitadas são aplicadas a dados reais fornecidos pelo Laboratório de Cardiologia e Genética Molecular do InCor/USP. Dentre eles, dados sobre a press~so arterial de diversas famílias da cidade de Baependi, no interior de Minas Gerais, dados de marcadores do tipo SNP de uma amostra de indivíduos não relacionados da região sudeste do Brasil e de 11 populações disponíveis no projeto HapMap, e medidas da pressão arterial de ratos provenientes de uma população 'F IND 2' nas condições basal, pós dieta de sal e pós tratamento com medicamento anti-hipertensivo. As análises foram realizadas usando, principalmente, os recursos computacionais do aplicativo SAS. not available

As doenças mais comuns e os problemas de saúde como a síndrome metabólica, doenças cardíacas ou câncer, por exemplo, resultam de uma complicada relação entre múltiplos fatores genéticos e ambientais. Estudos recentes enfatizam os aspectos multivariados da Genética, uma vez que considera-se que muitas doenças são correlacionadas e que possuem numerosos deterrminantes fisiológicos, genéticos e ambientais. A Modelagem de Equações Estruturais (MEE) é uma técnica multivariada relativamente nova, que usa as variâncias entre as variáveis como parâmetros de interesse. A idéia desses modelos é que as relações entre as variáveis determinem o padrão esperado de correlação. Uma metodologia, baseada em algumas etapas, combinando MEE e técnicas de mapeamento de genes para analisar múltiplos locos genéticos e múltiplos traços é proposta em Li et al., permitindo carcaterizar a arquitetura de sistemas genéticos de alta dimensão. Neste trabalho, descervemos e aplicamos a metodologia de Li et el. na análise de um conjunto de dados reais com o objetivo de modelagem do contxto global em que variáveis fenotípicas e genotípicas estão inseridas em sistemas pressóricos multifatoriais, como as pressões sistólica e diastólica, expostas ao sal e a medicamentos. Baseando-se nas etapas sugeridas, detectamos alguns QTLs por meio da análise de diferentes gráficos de perfis da estatística da razão de verossimilhanças, assim como também construímos os diagramas de caminho que definem o MEE. Esta técnica caracteriza-se pela flexibilidade em permitir incluir efeitos de interação entre locos genéticos e múltiplos traços com efeitos diretos e indiretos not available

O mapeamento genético p,ode ser definido como uma série de procedimentos experimentais e estatísticos que buscam detectar genes associados à etiologia e regulação de traços quantitativos, além de estimar os efeitos genéticos e as localizações genômicas correspondentes. Neste trabalho, apresentamos diferentes formulações de modelos de regressão úteis na identificação de QTL's (quantitative trait loci) considerando delineamentos experimentais que envolvem cruzamentos controlados de animais ou plantas. Com base em informações obtidas por meio dos mapas de marcadores moleculares, pode-se ajustar desde modelos mais simples, como é o caso daqueles utilizados na busca por evidências da presença de um único QTL, até modelos mais complexos, como os que permitem a busca por múltiplos QTL's e seus possíveis efeitos de interação (epistasia), e aqueles modelos com efeito de pleiotropia para os casos em que um mesmo conjunto de genes está associado a mais de um fenótipo. A aplicação destes modelos é considerada por meio da análise de um conjunto de dados genotípicos de ratos provenientes de um delineamento F2, coletados no Laboratório de Genética e Cardiologia Molecular do Instituto do Coração de São Paulo (InCor), com o objetivo de identificar genes relacionados a doenças cardiovasculares. not available

Grandes avanços estão ocorrrendo na área de genética e genômica.Inicialmente, cada gene era analisado separadamente com o intuito de verificar, por exemplo, associações com desenvolvimento de doenças. Recentemente, surge a técnica de microarrays, que permite que milhares de genes sejam avaliados simultaneamente. Esta técnica apresenta diversas vantagens nas aplicações em muitas áreas do conhecimento como, por exemplo, a área médica, porém uma série de dificuldades são encontradas nestes experimentosdevido às diversas fontes de variações sistemáticas que podem interferir nas mensurações obtidas, acarretando em resultados falso-positivos. Devido a estas variações e a outros problemas encontrados nestes experimentos, como o problema de múltiplos testes, pois milhares de genes são avaliados num mesmo momento, muitos são os esforços em se encontrar uma melhor abordagem de análise estatística para a identificação de genes diferencialmente expressos (DE). Baseado nestes aspectos, neste estudo são apresentados detalhes dos experimentos com microarrays, suas limitações e, frente a isso, possíveis técnicas de análise estatísticas são consideradas como alternativas não paramétricas de normalização de dados e modelos estatísticos de efeitos fixos e modelos mistos na identificação de genes DE. Como aplicação e motivação deste trabalho, algumas técnicas de análise são aplicadas a um conjunto de dados reais com ratos diabéticos. not available