Autosomal-Dominant Locus for Restless Legs Syndrome in French-Canadians on Chromosome 16p12.1
Center of Excellence in Neuromics, CHUM Research Center-Notre Dame Hospital, Quebec, Canada. Movement Disorders
(Impact Factor: 5.68).
01/2009; 24(1):40-50. DOI: 10.1002/mds.22263
We describe an autosomal-dominant locus for Restless Legs Syndrome (RLS) in a French-Canadian (FC) pedigree. Genome-wide microsatellite scan and linkage analysis were used in this study. The locus maps to chromosome 16p12.1 and spans 1.18 Mega bases. The maximum multipoint LOD scores are of 3.5 over the total of 10 markers. Evidence for the same locus was also found in a smaller FC pedigree sime095. The analysis of the sequence of 8 annotated genes within the region did not reveal any pathogenic mutations. Copy number variation and karyotype analyses did not reveal any chromosomal abnormality in the region. Further analyses of the region are necessary to find the genetic cause of RLS in this family.
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- "Linkage studies have identified several genomic loci to be associated with RLS (RLS1 – RLS5 on chromosomes 12q, 14q, 9p, 2q, and 20p, respectively) in single families, but no gene mutation has yet been identified.34–38 Further evidence of a locus on chromosome 9p, which is probably distinct from RLS3,39 and of a locus on chromosome 16p40 was detected by linkage, and suggestive evidence exists for linkage to chromosome 19p.41 Given the subjective nature of the RLS phenotype, until specific variants are identified within the loci described above, care should be taken in interpreting their significance. "
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ABSTRACT: Restless legs syndrome (RLS) is a sensorimotor disorder, characterized by a circadian variation of symptoms involving an urge to move the limbs (usually the legs) as well as paresthesias. There is a primary (familial) and a secondary (acquired) form, which affects a wide variety of individuals, such as pregnant women, patients with end-stage renal disease, iron deficiency, rheumatic disease, and persons taking medications. The symptoms reflect a circadian fluctuation of dopamine in the substantia nigra. RLS patients have lower dopamine and iron levels in the substantia nigra and respond to both dopaminergic therapy and iron administration. Iron, as a cofactor of dopamine production and a regulator of the expression of dopamine type 2-receptor, has an important role in the RLS etiology. In the management of the disease, the first step is to investigate possible secondary causes and their treatment. Dopaminergic agents are considered as the first-line therapy for moderate to severe RLS. If dopaminergic drugs are contraindicated or not efficacious, or if symptoms are resistant and unremitting, gabapentin or other antiepileptic agents, benzodiazepines, or opioids can be used for RLS therapy. Undiagnosed, wrongly diagnosed, and untreated RLS is associated with a significant impairment of the quality of life.
Nature and Science of Sleep 09/2010; 2:199-212. DOI:10.2147/NSS.S6946
Available from: umontreal.ca
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ABSTRACT: Le Syndrome d’Impatiences Musculaires de l’Éveil (SIME) est une maladie neurologique caractérisée par un besoin urgent de bouger les jambes. C’est également l’une des causes les plus fréquentes d’insomnie. C’est une maladie très répandue, avec une prévalence de presque 15 % dans la population générale. Les maladies multifactorielles comme le SIME sont souvent le résultat de l’évolution d’une composante génétique et d’une composante environnementale. Dans le cadre du SIME, les études d’association génomique ont permis l’identification de 4 variants à effet modéré ou faible. Cependant, ces quatre variants n’expliquent qu’une faible partie de la composante génétique de la maladie, ce qui confirme que plusieurs nouveaux variants sont encore à identifier. Le rôle des déséquilibres génomiques (Copy Number Variations ou CNVs) dans le mécanisme génétique du SIME est à ce jour inconnu. Cependant, les CNVs se sont récemment positionnés comme une source d’intérêt majeur de variation génétique potentiellement responsable des phénotypes. En collaboration avec une équipe de Munich, nous avons réalisé deux études CNVs à échelle génomique (biopuces à SNP et hybridation génomique comparée (CGH)) sur des patients SIME d’ascendance germanique. À l’aide d’une étude cas-contrôle, nous avons pu identifier des régions avec une occurrence de CNVs différentes pour les patients SIME, comparés à différents groupes contrôles. L’une de ces régions est particulièrement intéressante, car elle est concordante à la fois avec des précédentes études familiales ainsi qu’avec les récentes études d’associations génomiques. Restless Legs syndrome (RLS) is a neurological disorder characterized by the urge to move one’s limbs. It is also one of the most frequent causes of insomnia. The prevalence of RLS is estimated to be around 15% in the general population. Complexes disorders like RLS are often the result of the evolution of genetic and environmental components. For RLS, recent Genome Wide Association Study (GWAS) have identified four variants with mild to moderate effects. However, those four variants explain only a small part of the disease heritability and thus, we expect that many new variants are still to be found. The impact of Copy-Number Variation (CNV) in the genetic mechanism of RLS is still unknown. However, many studies have recently position the CNVs as a significant source of genetic variation potentially responsible of phenotypes. In collaboration with a team from Munich, we conducted two genome-wide CNVs studies (Genome Wide SNP chips and Comparative Genomic Hybridization (CGH)) on RLS patients from Germany. Using cases-controls studies, we identified regions with a different occurrence of CNVs for RLS patients, compared to different groups of controls. One of these regions is particularly interesting, as it has already been identified by both linkage and association studies.
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ABSTRACT: In this paper, we are concerned with the problem of how a physical
robot can get an appropriate internal representation to its task and
environment. Learning from experience is effective for the problem, but
it is very time-consuming to learn a representation from the beginning
in a real environment. On the other hand, the representation learned
only in a simulated environment has the risk of not serving the purpose
in a real environment because of the uncertainty in sensors, actuators,
and the environment. In, order to have the best of both worlds, it is
effective to transplant the learned state representation of a virtual
agent to a physical robot. For this purpose., we improved our developed
incremental learning architecture for use in the real environment and
developed a new architecture, called STNS-R. In this architecture,
inappropriate negative instances caused by uncertainties are found on
the basis of the distribution of instances and removed in order to
correct the distorted shapes of the states. The effectiveness of STNS-R
is shown in the experimental results
Systems, Man, and Cybernetics, 2001 IEEE International Conference on; 02/2001
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