125 research outputs found
Loss of Cardioprotective Effects at the ADAMTS7 Locus as a Result of Gene-Smoking Interactions
BACKGROUND: Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk. METHODS: We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a P value of <1.0x10(-3) (Bonferroni correction for 50 tests). RESULTS: We identified novel gene-smoking interaction for a variant upstream of the ADAMTS7 gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (P= 1.3x10(-16)) in comparison with 5% in ever-smokers (P= 2.5x10(-4)), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction P value= 8.7x10(-5)). The protective T allele at rs7178051 was also associated with reduced ADAMTS7 expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of ADAMTS7. CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced ADAMTS7 expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular ADAMTS7 expression may contribute to the loss of CHD protection in smokers.Peer reviewe
Genomewide association analysis of coronary artery disease
Background - Modern genotyping platforms permit a systematic search for inherited components of complex diseases. We performed a joint analysis of two genomewide association studies of coronary artery disease.
Methods - We first identified chromosomal loci that were strongly associated with coronary artery disease in the Wellcome Trust Case Control Consortium (WTCCC) study (which involved 1926 case subjects with coronary artery disease and 2938 controls) and looked for replication in the German MI [Myocardial Infarction] Family Study (which involved 875 case subjects with myocardial infarction and 1644 controls). Data on other single-nucleotide polymorphisms (SNPs) that were significantly associated with coronary artery disease in either study (P<0.001) were then combined to identify additional loci with a high probability of true association. Genotyping in both studies was performed with the use of the GeneChip Human Mapping 500K Array Set (Affymetrix).
Results - Of thousands of chromosomal loci studied, the same locus had the strongest association with coronary artery disease in both the WTCCC and the German studies: chromosome 9p21.3 (SNP, rs1333049) (P=1.80x10–14 and P=3.40x10–6, respectively). Overall, the WTCCC study revealed nine loci that were strongly associated with coronary artery disease (P80%) of a true association: chromosomes 1p13.3 (rs599839), 1q41 (rs17465637), 10q11.21 (rs501120), and 15q22.33 (rs17228212).
Conclusions - We identified several genetic loci that, individually and in aggregate, substantially affect the risk of development of coronary artery disease
Using Rough logic for predicting price movements on financial markets
Financial markets and especially their movements are very difficult to predict. For this reason, it cannot be clearly concluded what market will do. We cannot use basic logical operators such as if A happens, then comes B. Since we cannot use simple decision rules and we work in high uncertainty we cannot easily build classical mathematical model because of uncertainty of each and every result. However to analyze this type of data we can used Rough logic which is design to work with uncertainty. The aim of this thesis is use of Rough logic to create a mathematical model, which will be able to some extent to understand and eventually predict individual market movements. Market uncertainty Purpose of the article: Using Rough logic for predicting price movements. Scientific aim: Rough Set. Conclusions: Methodology for using Rough set in financial markets
L'Auto-vélo : automobilisme, cyclisme, athlétisme, yachting, aérostation, escrime, hippisme / dir. Henri Desgranges
28 juillet 19321932/07/28 (A33,N11548)
Comparison of methylation level estimates for the bisulfite sequencing, HumanMethylation 450K and MeDIP-seq data.
<p>Data are shown for the 28 islands (associated with 36 genes) containing CpG sites that overlapped with those interrogated by HumanMethylation 450K array for sample GM01240. Evolutionary strata information is shown to the right of the ideogram of the human X chromosome <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050233#pone.0050233-Ross1" target="_blank">[66]</a>: the blue line represents the S3 stratum; the purple line represents the S2 stratum and the red line the S1 stratum. Both names are given for genes sharing a CpG island separated by “/”. Methylation level estimates for each of the techniques are shown to the right of the gene names in light green (low), green (medium), and dark green (high). Examples of four genes are shown in more detail on the right of the figure. The gene names are highlighted in colour at the top of each panel and in a corresponding colour on the gene list. Data for the bisulfite sequencing (BS-s), HumanMethylation 450K (450K) and MeDIP-seq (MD-s) are shown at the top, center and bottom of each panel, respectively. The genes shown give examples where the three techniques agree in methylation level: low level methylation in the gene ZFX, medium level methylation in the PRPS2 gene, and a high level of methylation in the ACRC gene. Data are also given for the HCFC1/TMEM187 genes, for which different methods show inconsistency in the classified methylation levels. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050233#pone.0050233.s004" target="_blank">Figure S4</a> for data for sample GM01247.</p
Coverage by MeDIP-seq and the HumanMethylation 450K BeadChip of different genomic features.
<p>The different features are described along the bottom axis. 100% coverage is defined as covering all of the elements of a particular type in the human genome. Coverage for MeDIP-seq data (MD-s) (averaged for GM01240 and GM01247) is shown as blue bars and for the HumanMethylation 450K (450K) as red bars. Average percentages covered for each technique for each group of features are given above the bar chart. For MeDIP-seq the region or feature was defined as being covered if any part of the region or feature was covered by or overlapped any part of one or more sequencing reads. The coverage for the MeDIP-seq was consistent between the two samples (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050233#pone.0050233.s005" target="_blank">Table S1</a>), illustrating a high degree of reproducibility for the technique. The coverage shown for the HumanMethylation 450K is reported as the number of features where at least one probe present on the array mapped within the features under consideration i.e. is based on the array design.</p
Concordance of the HumanMethylation 450K (450K) and MeDIP-seq (MD-s) data with bisulfite sequencing (BS-s) data.
<p>The top part of the table gives the concordance of the average beta-values for the 326 probes on the X chromosome from the HumanMethylation 450K (450K) and the methylation score calculated by the MEDIPS software for the MeDIP-seq data (MD-s) to the methylation levels for the bisulfite data (BS-s) from MethTools. The second half of the table contains the concordance for a similar analysis for the HumanMethylation 450K and MeDIP-seq data for all autosomal chromosomes.</p
Marginal Significance (−log<sub>10</sub> p-value as Determined by <i>t-</i>Test) of the Wavelet Coefficients from Four Annotations as Predictors of the Coefficients of the Decomposition of Human-Chimpanzee Divergence
<div><p>Red boxes highlight significant positive linear relationships, and blue boxes, negative. The intensity of the colour is proportional to the degree of significance.</p><p>(A) Smoothed coefficients.</p><p>(B) Detail coefficients.</p></div
Results of the MZ intra-pair difference association analysis.
<p>This table shows the results for the MZ discordance analysis. Results are listed for the ten most highly correlated probes with corresponding gene, Spearman rank correlation coefficient (rho) and significance of correlation (p-value).</p><p>Results of the MZ intra-pair difference association analysis.</p
Effect of DNA methylation on gene expression in skin and effect of gene expression on PC1. A
<p>. DNA methylation residuals showed a weak negative correlation (r = −0.02) with expression residuals of TCF25 in skin samples. Both quantile normalised DNA methylation betas and quantile normalised gene expression values were adjusted for experimental batch effects (chip and position on the chip for methylation betas and experimental batch and RNA concentration for gene expression profiles) previous to analysis. The regression line (blue line) depicts the linear association between DNA methylation residuals and gene expression residuals. <b>B</b>. DNA methylation residuals at probe cg18877514 were weakly negatively correlated (r = −0.06) with <i>POLE</i> expression residuals in skin tissue. Both quantile normalised DNA methylation betas and quantile normalised gene expression values were adjusted for experimental batch effects (chip and position on the chip for methylation betas and experimental batch and RNA concentration for gene expression profiles) prior to analysis. The regression line (blue line) depicts the linear association between DNA methylation residuals and gene expression residuals. <b>C</b>. <i>TCF25</i> expression residuals in skin showed a weak positive correlation (r = 0.12) with PC1. Quantile normalised gene expression values were adjusted for experimental batch effects and RNA concentration. The regression line (blue line) depicts the linear association between gene expression residuals and PC1 values. <b>D</b>. <i>POLE</i> expression residuals in skin showed a weak positive correlation (r = 0.16) with PC1. Quantile normalised gene expression values were adjusted for experimental batch effects and RNA concentration. The regression line (blue line) depicts the linear association between gene expression residuals and PC1 values.</p
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