Genetic Analysis of Age-at-Onset for Cardiovascular Risk Factors in a Brazilian Family Study

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. Copyright (C) 2008 S. Karger AG, Basel

Power of the joint segregation analysis method for testing mixed major-gene and polygene inheritance models of quantitative traits

Understanding the genetic architecture of quantitative traits can greatly assist the design of strategies for their manipulation in plant-breeding programs. For a number of traits, genetic variation can be the result of segregation of a few major genes and many polygenes (minor genes). The joint segregation analysis (JSA) is a maximum-likelihood approach for fitting segregation models through the simultaneous use of phenotypic information from multiple generations. Our objective in this paper was to use computer simulation to quantify the power of the JSA method for testing the mixed-inheritance model for quantitative traits when it was applied to the six basic generations: both parents (P-1 and P-2), F-1, F-2, and both backcross generations (B-1 and B-2) derived from crossing the F-1 to each parent. A total of 1968 genetic model-experiment scenarios were considered in the simulation study to quantify the power of the method. Factors that interacted to influence the power of the JSA method to correctly detect genetic models were: (1) whether there were one or two major genes in combination with polygenes, (2) the heritability of the major genes and polygenes, (3) the level of dispersion of the major genes and polygenes between the two parents, and (4) the number of individuals examined in each generation (population size). The greatest levels of power were observed for the genetic models defined with simple inheritance; e.g., the power was greater than 90% for the one major gene model, regardless of the population size and major-gene heritability. Lower levels of power were observed for the genetic models with complex inheritance (major genes and polygenes), low heritability, small population sizes and a large dispersion of favourable genes among the two parents; e.g., the power was less than 5% for the two major-gene model with a heritability value of 0.3 and population sizes of 100 individuals. The JSA methodology was then applied to a previously studied sorghum data-set to investigate the genetic control of the putative drought resistance-trait osmotic adjustment in three crosses. The previous study concluded that there were two major genes segregating for osmotic adjustment in the three crosses. Application of the JSA method resulted in a change in the proposed genetic model. The presence of the two major genes was confirmed with the addition of an unspecified number of polygenes.

Modeling sequencing errors by combining Hidden Markov models.

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Association Study of Common Genetic Variants and HIV-1 Acquisition in 6,300 Infected Cases and 7,200 Controls.

Multiple genome-wide association studies (GWAS) have been performed in HIV-1 infected individuals, identifying common genetic influences on viral control and disease course. Similarly, common genetic correlates of acquisition of HIV-1 after exposure have been interrogated using GWAS, although in generally small samples. Under the auspices of the International Collaboration for the Genomics of HIV, we have combined the genome-wide single nucleotide polymorphism (SNP) data collected by 25 cohorts, studies, or institutions on HIV-1 infected individuals and compared them to carefully matched population-level data sets (a list of all collaborators appears in Note S1 in Text S1). After imputation using the 1,000 Genomes Project reference panel, we tested approximately 8 million common DNA variants (SNPs and indels) for association with HIV-1 acquisition in 6,334 infected patients and 7,247 population samples of European ancestry. Initial association testing identified the SNP rs4418214, the C allele of which is known to tag the HLA-B*57:01 and B*27:05 alleles, as genome-wide significant (p = 3.6×10(-11)). However, restricting analysis to individuals with a known date of seroconversion suggested that this association was due to the frailty bias in studies of lethal diseases. Further analyses including testing recessive genetic models, testing for bulk effects of non-genome-wide significant variants, stratifying by sexual or parenteral transmission risk and testing previously reported associations showed no evidence for genetic influence on HIV-1 acquisition (with the exception of CCR5Δ32 homozygosity). Thus, these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size.

Genetic polymorphisms of the main transcription factors for adiponectin gene promoter in regulation of adiponectin levels: association analysis in three European cohorts.

Adiponectin serum concentrations are an important biomarker in cardiovascular epidemiology with heritability etimates of 30-70%. However, known genetic variants in the adiponectin gene locus (ADIPOQ) account for only 2%-8% of its variance. As transcription factors are thought to play an under-acknowledged role in carrying functional variants, we hypothesized that genetic polymorphisms in genes coding for the main transcription factors for the ADIPOQ promoter influence adiponectin levels. Single nucleotide polymorphisms (SNPs) at these genes were selected based on the haplotype block structure and previously published evidence to be associated with adiponectin levels. We performed association analyses of the 24 selected SNPs at forkhead box O1 (FOXO1), sterol-regulatory-element-binding transcription factor 1 (SREBF1), sirtuin 1 (SIRT1), peroxisome-proliferator-activated receptor gamma (PPARG) and transcription factor activating enhancer binding protein 2 beta (TFAP2B) gene loci with adiponectin levels in three different European cohorts: SAPHIR (n = 1742), KORA F3 (n = 1636) and CoLaus (n = 5355). In each study population, the association of SNPs with adiponectin levels on log-scale was tested using linear regression adjusted for age, sex and body mass index, applying both an additive and a recessive genetic model. A pooled effect size was obtained by meta-analysis assuming a fixed effects model. We applied a significance threshold of 0.0033 accounting for the multiple testing situation. A significant association was only found for variants within SREBF1 applying an additive genetic model (smallest p-value for rs1889018 on log(adiponectin) = 0.002, β on original scale = -0.217 µg/ml), explaining ∼0.4% of variation of adiponectin levels. Recessive genetic models or haplotype analyses of the FOXO1, SREBF1, SIRT1, TFAPB2B genes or sex-stratified analyses did not reveal additional information on the regulation of adiponectin levels. The role of genetic variations at the SREBF1 gene in regulating adiponectin needs further investigation by functional studies.

Cell therapy in dilated cardiomyopathy: from animal models to clinical trials

Dilated cardiomyopathy can be the end-stage form and common denominator of several cardiac disorders of known cause, such as hypertensive, ischemic, diabetic and Chagasic diseases. However, some individuals have clinical findings, such as an increase in ventricular chamber size and impaired contractility (classical manifestations of dilated cardiomyopathy) even in the absence of a diagnosed primary disease. In these patients, dilated cardiomyopathy is classified as idiopathic since its etiology is obscure. Nevertheless, regardless of all of the advances in medical, pharmacological and surgical procedures, the fate of patients with dilated cardiomyopathy (of idiopathic or of any other known cause) is linked to arrhythmic episodes, severe congestive heart failure and an increased risk of sudden cardiac death. In this review, we will summarize present data on the use of cell therapies in animal models of dilated cardiomyopathies and will discuss the few clinical trials that have been published so far involving patients affected by this disease. The animal models discussed here include those in which the cardiomyopathy is produced by genetic manipulation and those in which disease is induced by chemical or infectious agents. The specific model used clearly creates restrictions to translation of the proposed cell therapy to clinical practice, insofar as most of the clinical trials performed to date with cell therapy have used autologous cells. Thus, translation of genetic models of dilated cardiomyopathy may have to wait until the use of allogeneic cells becomes more widespread in clinical trials of cell therapies for cardiac diseases.

Recent developments in genetic data analysis: what can they tell us about human demographic history?

Over the last decade, a number of new methods of population genetic analysis based on likelihood have been introduced. This review describes and explains the general statistical techniques that have recently been used, and discusses the underlying population genetic models. Experimental papers that use these methods to infer human demographic and phylogeographic history are reviewed. It appears that the use of likelihood has hitherto had little impact in the field of human population genetics, which is still primarily driven by more traditional approaches. However, with the current uncertainty about the effects of natural selection, population structure and ascertainment of single-nucleotide polymorphism markers, it is suggested that likelihood-based methods may have a greater impact in the future.

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems.

Past, present and future mathematical models for buildings (ii)

This article is the second part of a review of the historical evolution of mathematical models applied in the development of building technology. The first part described the current state of the art and contrasted various models with regard to the applications to conventional buildings and intelligent buildings. It concluded that mathematical techniques adopted in neural networks, expert systems, fuzzy logic and genetic models, that can be used to address model uncertainty, are well suited for modelling intelligent buildings. Despite the progress, the possible future development of intelligent buildings based on the current trends implies some potential limitations of these models. This paper attempts to uncover the fundamental limitations inherent in these models and provides some insights into future modelling directions, with special focus on the techniques of semiotics and chaos. Finally, by demonstrating an example of an intelligent building system with the mathematical models that have been developed for such a system, this review addresses the influences of mathematical models as a potential aid in developing intelligent buildings and perhaps even more advanced buildings for the future.

Estimation models of variance components for farrowing interval in swine

Este trabalho teve como objetivo principal avaliar a importância da inclusão dos efeitos genético materno, comum de leitegada e de ambiente permanente no modelo de estimação de componentes de variância para a característica intervalo de parto em fêmeas suínas. Foram utilizados dados que consistiam de 1.013 observações de fêmeas Dalland (C-40), registradas em dois rebanhos. As estimativas dos componentes de variância foram realizadas pelo método da máxima verossimilhança restrita livre de derivadas. Foram testados oito modelos, que continham os efeitos fixos (grupos de contemporâneo e covariáveis) e os efeitos genético aditivo direto e residual, mas variavam quanto à inclusão dos efeitos aleatórios genético materno, ambiental comum de leitegada e ambiental permanente. O teste da razão de verossimilhança (LR) indicou a não necessidade da inclusão desses efeitos no modelo. No entanto observou-se que o efeito ambiental permanente causou mudança nas estimativas de herdabilidade, que variaram de 0,00 a 0,03. Conclui-se que os valores de herdabilidade obtidos indicam que esta característica não apresentaria ganho genético como resposta à seleção. O efeito ambiental comum de leitegada e o genético materno não apresentaram influência sobre esta característica. Já o ambiental permanente, mesmo sem ter sido significativo o seu efeito pelo LR, deve ser considerado nos modelos genéticos para essa característica, pois sua presença causou mudança nas estimativas da variância genética aditiva.

Epidemiology and genetics of endemic goiter. II. Genetic aspects

Complex genetic models and segregation analysis were applied to family data obtained in a hyperendemic goiter area in Brazil. The single locus and Falconer's models did not fit the data. Edward's model showed convergency, but statistical concordance has not been obtained. Although the genetic load model explains statistically the family data, it would be hard to imagine that endemic goiter could be explained by a model where synergism among genetic and environmental factors is not assumed.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and Bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. © 2013 American Dairy Science Association.

Genetic isolation and morphological divergence of Black Sea bottlenose dolphins

The Black Sea is a semi-enclosed body of water that differs from the adjacent Mediterranean Sea in terms of its biodiversity, oceanographical and ecological characteristics. There is growing international concern about pollution in the Black Sea and other anthropogenic threats to its fauna. The bottlenose dolphin (Tursiops truncatus) is one of three species of cetaceans living in the Azov-Black Sea basin. Despite considerable research on bottlenose dolphins elsewhere, the extent of human impacts on the Black Sea populations is unknown. Previous attempts to award special conservation status to Black Sea cetaceans have failed specifically because policy makers have viewed their ecological and evolutionary uniqueness as equivocal. This study assessed divergence between Black Sea, Mediterranean Sea and Atlantic Ocean bottlenose dolphins for 26 cranial measurements (n = 75 adult bottlenose dolphin skulls) and mitochondrial DNA (n = 99 individuals). Black Sea bottlenose dolphins are smaller than those in the Mediterranean, and possess a uniquely shaped skull. As in a previous study, we found the Black Sea population to be genetically distinct, with relatively low levels of mtDNA diversity. Population genetic models suggest that Black Sea bottlenose dolphins have so little gene flow with the Mediterranean due to historical isolation that they should be managed separately.

Population genetic and dispersal modeling data for Bathymodiolus mussels from the Mid-Atlantic Ridge

The zip folder comprises a text file and a gzipped tar archive. 1) The text file contains individual genotype data for 90 SNPs, 9 microsatellites and the mitochondrial ND4 gene that were determined in deep-sea hydrothermal vent mussels from the Mid-Atlantic Ridge (genus Bathymodiolus). Mussel specimens are grouped according to the population (pop)/location from which they have been sampled (first column). The remaining columns contain the respective allele/haplotype codes for the different genetic loci (names in the header line). The data file is in CONVERT format and can be directly transformed into different input files for population genetic statistics. 2) The tar archive contains NetCDF files with larval dispersal probabilities for simulated annual larval releases between 1998 and 2007. For each simulated vent location (Menez Gwen, Lucky Strike, Rainbow, Vent 1-10) two NetCDF files are given, one for an assumed pelagic larval duration of 1 year and the other one for an assumed pelagic larval duration of 6 months (6m).

A genetic two-factor model of the covariation among a subset of Multidimensional Aptitude Battery and Wechsler Adult Intelligence Scale - Revised subtests

The phenotypic and genetic factor structure of performance on five Multidimensional Aptitude Battery (MAB) subtests and one Wechsler Adult Intelligence Scale-Revised (WAIS-R) subtest was explored in 390 adolescent twin pairs (184 monozygotic [MZ]; 206 dizygotic (DZ)). The temporal stability of these measures was derived from a subsample of 49 twin pairs, with test-retest correlations ranging from .67 to .85. A phenotypic factor model, in which performance and verbal factors were correlated, provided a good fit to the data. Genetic modeling was based on the phenotypic factor structure, but also took into account the additive genetic (A), common environmental (C), and unique environmental (E) parameters derived from a fully saturated ACE model. The best fitting model was characterized by a genetic correlated two-factor structure with specific effects, a general common environmental factor, and overlapping unique environmental effects. Results are compared to multivariate genetic models reported in children and adults, with the most notable difference being the growing importance of common genes influencing diverse abilities in adolescence. (C) 2003 Elsevier Inc. All rights reserved.