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

Family environment patterns in families with bipolar children

Background: We studied the characteristics of family functioning in bipolar children and healthy comparison children. We hypothesized that the family environment of bipolar children would show greater levels of dysfunction as measured by the Family Environment Scale (FES). Methods: We compared the family functioning of 36 families that included a child with DSM-IV bipolar disorder versus 29 comparison families that included only healthy children. All subjects and their parents were assessed with the K-SADS-PL interview. The parents completed the FES to assess their current family functioning. Multivariate analysis of variance was used to compare the family environment of families with and without offspring with bipolar disorder. Results: Parents of bipolar children reported lower levels of family cohesion (p<0.001), expressiveness (p=0.005), active-recreational orientation (p<0.001), intellectual-cultural orientation (p=0.04) and higher levels of conflict (p<0.001) compared to parents with no bipolar children. Secondary analyses within the bipolar group revealed lower levels of organization (p=0.03 1) and cohesion (p=0.014) in families where a parent had a history of mood disorders compared to families where parents had no history of mood disorders. Length of illness in the affected child was inversely associated with family cohesion (r=-0.47, p=0.004). Limitations: Due to the case-control design of the study, we cannot comment on the development of these family problems or attribute their cause specifically to child bipolar disorder. Conclusion: Families with bipolar children show dysfunctional patterns related to interpersonal interactions and personal growth. A distressed family environment should be addressed when treating children with bipolar disorder. (C) 2007 Elsevier B.V. All rights reserved.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

A single nucleotide deletion at the C1 inhibitor gene as the cause of hereditary angioedema: insights from a Brazilian family

Background: Hereditary angioedema is an autosomal dominant disease characterized by episodes of subcutaneous and submucosal edema. It is caused by deficiency of the C1 inhibitor protein, leading to elevated levels of bradykinin. More than 200 mutations in C1 inhibitor gene have been reported. The aim of this study was to analyze clinical features of a large family with an index case of hereditary angioedema and to determine the disease-causing mutation in this family. Methods: Family pedigree was constructed with 275 individuals distributed in five generations. One hundred and sixty-five subjects were interviewed and investigated for mutation at the C1 inhibitor gene. Subjects reporting a history of recurrent episodes of angioedema and/or abdominal pain attacks underwent evaluation for hereditary angioedema. Results: We have identified a novel mutation at the C1 inhibitor gene, c.351delC, which is a single-nucleotide deletion of a cytosine on exon 3, resulting in frameshift with premature stop codon. Sequencing analysis of the hypothetical truncated C1 inhibitor protein allowed us to conclude that, if transcription occurs, this protein has no biological activity. Twenty-eight members of the family fulfilled diagnostic criteria for hereditary angioedema and all of them presented the c.351delC mutation. Variation in clinical presentation and severity of disease was observed among these patients. One hundred and thirty-seven subjects without hereditary angioedema did not have the c.351delC mutation. Conclusion: The present study provides definitive evidence to link a novel genetic mutation to the development of hereditary angioedema in patients from a Brazilian family.

Evaluation of computational methods for the reconstruction of HLA haplotypes

Human leukocyte antigen (HLA) haplotypes are frequently evaluated for population history inferences and association studies. However, the available typing techniques for the main HLA loci usually do not allow the determination of the allele phase and the constitution of a haplotype, which may be obtained by a very time-consuming and expensive family-based segregation study. Without the family-based study, computational inference by probabilistic models is necessary to obtain haplotypes. Several authors have used the expectation-maximization (EM) algorithm to determine HLA haplotypes, but high levels of erroneous inferences are expected because of the genetic distance among the main HLA loci and the presence of several recombination hotspots. In order to evaluate the efficiency of computational inference methods, 763 unrelated individuals stratified into three different datasets had their haplotypes manually defined in a family-based study of HLA-A, -B, -DRB1 and -DQB1 segregation, and these haplotypes were compared with the data obtained by the following three methods: the Expectation-Maximization (EM) and Excoffier-Laval-Balding (ELB) algorithms using the arlequin 3.11 software, and the PHASE method. When comparing the methods, we observed that all algorithms showed a poor performance for haplotype reconstruction with distant loci, estimating incorrect haplotypes for 38%-57% of the samples considering all algorithms and datasets. We suggest that computational haplotype inferences involving low-resolution HLA-A, HLA-B, HLA-DRB1 and HLA-DQB1 haplotypes should be considered with caution.