Biomedical subjects

Alexandre Bureau

Publications and source records attributed to Alexandre Bureau.

6 recordsLinked to original sources

PubMed2005

Homozygous deletion of the very low density lipoprotein receptor gene causes autosomal recessive cerebellar hypoplasia with cerebral gyral simplification.

An autosomal recessive syndrome of nonprogressive cerebellar ataxia and mental retardation is associated with inferior cerebellar hypoplasia and mild cerebral gyral simplification in the Hutterite population. An identity-by-descent mapping approach using eight patients from three interrelated Hutterite families localized the gene for this syndrome to chromosome region 9p24. Haplotype analysis identified familial and ancestral recombination events and refined the minimal region to a 2-Mb interval between markers D9S129 and D9S1871. A 199-kb homozygous deletion encompassing the entire very low density lipoprotein receptor (VLDLR) gene was present in all affected individuals. VLDLR is part of the reelin signaling pathway, which guides neuroblast migration in the cerebral cortex and cerebellum. To our knowledge, this syndrome represents the first human lipoprotein receptor malformation syndrome and the second human disease associated with a reelin pathway defect.

Base Sequence↗

PubMed2005

Evidence for a novel glaucoma locus at chromosome 3p21-22.

Primary open-angle glaucoma (POAG) is one of the leading causes of blindness in the world. It is a clinically variable group of diseases with the majority of cases presenting as the late onset adult type. Several chromosomal loci have been implicated in disease aetiology, but causal mutations have only been identified in a small proportion of glaucoma. We have previously described a large six-generation Tasmanian family with POAG exhibiting genetic heterogeneity. In this family, approximately one third of affected individuals presented with a glutamine-368-STOP (Q368STOP) mutation in the myocilin gene. We now use a Markov Chain Monte Carlo (MCMC) method to identify a second disease region in this family on the short arm of chromosome 3. This disease locus was initially mapped to the marker D3S1298 and a subsequent minimum disease region of 9 cM between markers D3S1298 and D3S1289 was identified through additional mapping. The region did not overlap with any previously described locus for POAG. Using a multiplicative relative risk model, we identified a positive association between this region and the Q368STOP mutation of myocilin on chromosome 1 in affected individuals. These findings provide evidence of a new autosomal dominant glaucoma locus on the short arm of chromosome 3.

Chromosome Mapping↗

PubMed2005

Identifying SNPs predictive of phenotype using random forests.

There has been a great interest and a few successes in the identification of complex disease susceptibility genes in recent years. Association studies, where a large number of single-nucleotide polymorphisms (SNPs) are typed in a sample of cases and controls to determine which genes are associated with a specific disease, provide a powerful approach for complex disease gene mapping. Genes of interest in those studies may contain large numbers of SNPs that classical statistical methods cannot handle simultaneously without requiring prohibitively large sample sizes. By contrast, high-dimensional nonparametric methods thrive on large numbers of predictors. This work explores the application of one such method, random forests, to the problem of identifying SNPs predictive of the phenotype in the case-control study design. A random forest is a collection of classification trees grown on bootstrap samples of observations, using a random subset of predictors to define the best split at each node. The observations left out of the bootstrap samples are used to estimate prediction error. The importance of a predictor is quantified by the increase in misclassification occurring when the values of the predictor are randomly permuted. We extend the concept of importance to pairs of predictors, to capture joint effects, and we explore the behavior of importance measures over a range of two-locus disease models in the presence of a varying number of SNPs unassociated with the phenotype. We illustrate the application of random forests with a data set of asthma cases and unaffected controls genotyped at 42 SNPs in ADAM33, a previously identified asthma susceptibility gene. SNPs and SNP pairs highly associated with asthma tend to have the highest importance index value, but predictive importance and association do not always coincide.

Case-Control Studies↗

PubMed2004

Late onset Tay-Sachs disease in mice with targeted disruption of the Hexa gene: behavioral changes and pathology of the central nervous system.

Tay-Sachs disease is an autosomal recessive neurodegenerative disease resulting from a block in the hydrolysis of GM2 ganglioside, an intermediate in ganglioside catabolism. The mouse model of Tay-Sachs disease (Hexa -/-) has been described as behaviorally indistinguishable from wild type until at least 1 year of age due to a sialidase-mediated bypass of the metabolic defect that reduces the rate of GM2 ganglioside accumulation. In this study, we have followed our mouse model to over 2 years of age and have documented a significant disease phenotype that is reminiscent of the late onset, chronic form of human Tay-Sachs disease. Onset occurs at 11-12 months of age and progresses slowly, in parallel with increasing storage of GM2 ganglioside. The disease is characterized by hind limb spasticity, weight loss, tremors, abnormal posture with lordosis, possible visual impairment, and, late in the disease, muscle weakness, clasping of the limbs, and myoclonic twitches of the head. Immunodetection of GM2 ganglioside showed that storage varies widely in different regions, but is most intense in pyriform cortex, hippocampus (CA3 field, subiculum), amygdala, hypothalamus (paraventricular supraoptic, ventromedial and arcuate nuclei, and mammilary body), and the somatosensory cortex (layer V) in 1- to 2-year-old mutant mice. We suggest that the Tay-Sachs mouse model is a phenotypically valid model of disease and may provide for a reliable indicator of the impact of therapeutic strategies, in particular geared to the late onset, chronic form of human Tay-Sachs disease.

Age Factors↗

PubMed2003

Mapping complex traits using Random Forests.

Random Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case of a quantitative outcome, the tree predictor takes on a numerical value. We applied Random Forest to the first replicate of the Genetic Analysis Workshop 13 simulated data set, with the sibling pairs as our units of analysis and identity by descent (IBD) at selected loci as our explanatory variables. With the knowledge of the true model, we performed two sets of analyses on three phenotypes: HDL, triglycerides, and glucose. The goal was to approach the mapping of complex traits from a multivariate perspective. The first set of analyses mimics a candidate gene approach with a high proportion of true genes among the predictors while the second set represents a genome scan analysis using microsatellite markers. Random Forest was able to identify a few of the major genes influencing the phenotypes, such as baseline HDL and triglycerides, but failed to identify the major genes regulating baseline glucose levels.

Chromosome Mapping↗

PubMed2003

Applications of continuous time hidden Markov models to the study of misclassified disease outcomes.

Disease progression in prospective clinical and epidemiological studies is often conceptualized in terms of transitions between disease states. Analysis of data from such studies can be complicated by a number of factors, including the presence of individuals in various prevalent disease states and with unknown prior disease history, interval censored observations of state transitions and misclassified measurements of disease states. We present an approach where the disease states are modelled as the hidden states of a continuous time hidden Markov model using the imperfect measurements of the disease state as observations. Covariate effects on transitions between disease states are incorporated using a generalized regression framework. Parameter estimation and inference are based on maximum likelihood methods and rely on an EM algorithm. In addition, techniques for model assessment are proposed. Applications to two binary disease outcomes are presented: the oral lesion hairy leukoplakia in a cohort of HIV infected men and cervical human papillomavirus (HPV) infection in a cohort of young women. Estimated transition rates and misclassification probabilities for the hairy leukoplakia data agree well with clinical observations on the persistence and diagnosis of this lesion, lending credibility to the interpretation of hidden states as representing the actual disease states. By contrast, interpretation of the results for the HPV data are more problematic, illustrating that successful application of the hidden Markov model may be highly dependent on the degree to which the assumptions of the model are satisfied.

Adolescent↗