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Dragiev P.,University of Quebec at Montreal | Dragiev P.,Genome Quebec Innovation Center | Nadon R.,Genome Quebec Innovation Center | Nadon R.,McGill University | Makarenkov V.,University of Quebec at Montreal
Bioinformatics | Year: 2012

Motivation: Rapid advances in biomedical sciences and genetics have increased the pressure on drug development companies to promptly translate new knowledge into treatments for disease. Impelled by the demand and facilitated by technological progress, the number of compounds evaluated during the initial high-throughput screening (HTS) step of drug discovery process has steadily increased. As a highly automated large-scale process, HTS is prone to systematic error caused by various technological and environmental factors. A number of error correction methods have been designed to reduce the effect of systematic error in experimental HTS (Brideau et al., 2003; Carralot et al., 2012; Kevorkov and Makarenkov, 2005; Makarenkov et al., 2007; Malo et al., 2010). Despite their power to correct systematic error when it is present, the applicability of those methods in practice is limited by the fact that they can potentially introduce a bias when applied to unbiased data. We describe two new methods for eliminating systematic error from HTS data based on a prior knowledge of the error location. This information can be obtained using a specific version of the t-test or of the χ2 goodness-of-fit test as discussed in Dragiev et al. (2011). We will show that both new methods constitute an important improvement over the standard practice of not correcting for systematic error at all as well as over the B-score correction procedure (Brideau et al., 2003) which is widely used in the modern HTS. We will also suggest a more general data preprocessing framework where the new methods can be applied in combination with the Well Correction procedure (Makarenkov et al., 2007). Such a framework will allow for removing systematic biases affecting all plates of a given screen as well as those relative to some of its individual plates. © The Author 2012. Published by Oxford University Press. All rights reserved. Source

Bonnefond A.,French National Center for Scientific Research | Bonnefond A.,University of Lille Nord de France | Clement N.,French Institute of Health and Medical Research | Clement N.,University of Paris Descartes | And 44 more authors.
Nature Genetics | Year: 2012

Genome-wide association studies have revealed that common noncoding variants in MTNR1B (encoding melatonin receptor 1B, also known as MT 2) increase type 2 diabetes (T2D) risk. Although the strongest association signal was highly significant (P < 1 - 10 g 20), its contribution to T2D risk was modest (odds ratio (OR) of g1/41.10g1.15). We performed large-scale exon resequencing in 7,632 Europeans, including 2,186 individuals with T2D, and identified 40 nonsynonymous variants, including 36 very rare variants (minor allele frequency (MAF) <0.1%), associated with T2D (OR = 3.31, 95% confidence interval (CI) = 1.78g6.18; P = 1.64 - 10 g 4). A four-tiered functional investigation of all 40 mutants revealed that 14 were non-functional and rare (MAF < 1%), and 4 were very rare with complete loss of melatonin binding and signaling capabilities. Among the very rare variants, the partial- or total-loss-of-function variants but not the neutral ones contributed to T2D (OR = 5.67, CI = 2.17g14.82; P = 4.09 - 10 g4). Genotyping the four complete loss-of-function variants in 11,854 additional individuals revealed their association with T2D risk (8,153 individuals with T2D and 10,100 controls; OR = 3.88, CI = 1.49g10.07; P = 5.37 - 10 g 3). This study establishes a firm functional link between MTNR1B and T2D risk. © 2012 Nature America, Inc. All rights reserved. Source

Guay S.-P.,Universite de Sherbrooke | Voisin G.,Genome Quebec Innovation Center | Brisson D.,ECOGENE 21 and Lipid Clinic | Munger J.,Universite de Sherbrooke | And 4 more authors.
Epigenomics | Year: 2012

Aim: This study aims to assess whether epigenetic changes may account for high-density lipoprotein cholesterol (HDL-C) level variability in familial hypercholesterolemia (FH), a recognized human model to study cardiovascular disease risk modulators. Materials & methods: A genome-wide DNA methylation analysis (Infinium HumanMethylation27 BeadChip, Illumina) was performed on peripheral blood DNA samples obtained from men with FH with low (n = 10) or high (n = 11) HDL-C concentrations. The initial association with one of the top differentially methylated loci located in the promoter of the TNNT1 gene was replicated in a cohort of 276 FH subjects using pyrosequencing. Results: According to the Ingenuity Pathway Analysis software, the HDL-C differentially methylated loci identified were significantly associated with pathways related to lipid metabolism and cardiovascular disease. TNNT1 DNA methylation levels were positively correlated with mean HDL particle size, HDL-phospholipid, HDL-apolipoprotein AI, HDL-C and TNNT1 expression levels. Conclusion: These results suggest that epigenome-wide changes account for interindividual variations in HDL particle metabolism and that TNNT1 is a new candidate gene for dyslipidemia. © 2012 Future Medicine Ltd. Source

Pekas N.,McGill University | Pekas N.,Canadian National Institute For Nanotechnology | Zhang Q.,McGill University | Juncker D.,McGill University | Juncker D.,Genome Quebec Innovation Center
Journal of Micromechanics and Microengineering | Year: 2012

We describe a new class of electrostatic actuators with a compliant electrode made of liquid metal alloy contained by a thin elastomeric membrane. We illustrate the use of such actuators as on-chip microvalves for gas flow control. The microvalve comprises of one fixed electrode spanning the floor and sidewalls of the trapezoidal gas channel and one corresponding flexible electrode suspended across the channel. Details of fabrication and preliminary characterization of on/off and proportional valving are presented. © 2012 IOP Publishing Ltd. Source

Ruchat S.-M.,Universite de Sherbrooke | Ruchat S.-M.,ECOGENE 21 Laboratory and Lipid Clinic | Houde A.-A.,Universite de Sherbrooke | Houde A.-A.,ECOGENE 21 Laboratory and Lipid Clinic | And 13 more authors.
Epigenetics | Year: 2013

Offspring exposed to gestational diabetes mellitus (GDM) have an increased risk for chronic diseases, and one promising mechanism for fetal metabolic programming is epigenetics. Therefore, we postulated that GDM exposure impacts the offspring's methylome and used an epigenomic approach to explore this hypothesis. Placenta and cord blood samples were obtained from 44 newborns, including 30 exposed to GDM. Women were recruited at first trimester of pregnancy and followed until delivery. GDM was assessed after a 75-g oral glucose tolerance test at 24-28 weeks of pregnancy. DNA methylation was measured at > 485,000 CpG sites (Infinium HumanMethylation450 BeadChips). Ingenuity Pathway Analysis was conducted to identify metabolic pathways epigenetically affected by GDM. Our results showed that 3,271 and 3,758 genes in placenta and cord blood, respectively, were potentially differentially methylated between samples exposed or not to GDM (p-values down to 1 × 10-06; none reached the genome-wide significance levels), with more than 25% (n = 1,029) being common to both tissues. Mean DNA methylation differences between groups were 5.7 ± 3.2% and 3.4 ± 1.9% for placenta and cord blood, respectively. These genes were likely involved in the metabolic diseases pathway (up to 115 genes [11%], p-values for pathways = 1.9 × 10-13 < p < 4.0 × 10-03; including diabetes mellitus p = 4.3 × 10-11). Among the differentially methylated genes, 326 in placenta and 117 in cord blood were also associated with newborn weight. Our results therefore suggest that GDM has epigenetic effects on genes preferentially involved in the metabolic diseases pathway, with consequences on fetal growth and development, and provide supportive evidence that DNA methylation is involved in fetal metabolic programming. © 2013 Landes Bioscience. Source

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