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“Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as
the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known Vorinostat and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, GW4064 solubility dmso 27% of the tested probes show a significant genetic association in cis, and we validate that the additional
eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.”
“Although sucrose availability is crucial for commitment to plant cell division during G1 phase, it has remained uncertain how protein levels of core cell cycle genes are regulated. We found that Arabidopsis RETINOBLASTOMA-RELATED
PROTEIN1 (AtRBR1) and three E2F proteins were degraded under limited sucrose conditions, while protein abundance increased in response to treatment with the proteasome EPZ5676 purchase inhibitor MG132. We conclude that Arabidopsis key cell cycle proteins are degraded in a proteasome-dependent manner during sucrose starvation in Arabidopsis suspension MM2d cells. (C) 2011 Elsevier Masson SAS. All rights reserved.”
“Background: This study was conducted to establish the minimal detectable change (MDC) and clinically important differences (CIDs) of the physical category of the Stroke-Specific Quality of Life Scale in patients with stroke.
Methods: MDC and CIDs scores were calculated from the data of 74 participants enrolled in randomized controlled trials investigating the effects of two rehabilitation programs in patients with stroke.