Additional file 1: Table S1. of Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies

Simulated predominant interaction effects (example data for N = 3000). Table S2. Simulated marginal genetic effects (example data for N = 3000). Comparison of GRS approaches and lasso regression. Figure S1. Power and sign-misspecifications comparison. Figure S2. Type I error comparison. (DOCX 405 kb

Mining the Secretome of C2C12 Muscle Cells: Data Dependent Experimental Approach To Analyze Protein Secretion Using Label-Free Quantification and Peptide Based Analysis

Secretome analysis faces several challenges including detection of low abundant proteins and the discrimination of bona fide secreted proteins from false-positive identifications stemming from cell leakage or serum. Here, we developed a two-step secretomics approach and applied it to the analysis of secreted proteins of C2C12 skeletal muscle cells since the skeletal muscle has been identified as an important endocrine organ secreting myokines as signaling molecules. First, we compared culture supernatants with corresponding cell lysates by mass spectrometry-based proteomics and label-free quantification. We identified 672 protein groups as candidate secreted proteins due to their higher abundance in the secretome. On the basis of Brefeldin A mediated blocking of classical secretory processes, we estimated a sensitivity of >80% for the detection of classical secreted proteins for our experimental approach. In the second step, the peptide level information was integrated with UniProt based protein information employing the newly developed bioinformatics tool “Lysate and Secretome Peptide Feature Plotter” (LSPFP) to detect proteolytic protein processing events that might occur during secretion. Concerning the proof of concept, we identified truncations of the cytoplasmic part of the protein Plexin-B2. Our workflow provides an efficient combination of experimental workflow and data analysis to identify putative secreted and proteolytic processed proteins

Population attributable risks and odds ratios due to genetic factors.

<p>Population attributable risks (PARs) and odds ratios (ORs) were calculated from the data of the present study and summarized from previously published studies for different genetic factors. Numbers in brackets refer to the publications in which the PARs and ORs were published.</p>a<p>Adjusted for age, gender, smoking habits, all measured SNPs and study site; crude PAR/OR: 16%/1.39; adjusted for age and gender: 16%/1.37; adjusted for all measured SNPs: 15%/1.36.</p>b<p>Adjusted for age, gender, smoking habits, all measured SNPs and study site; crude PAR/OR: 5%/1.09; adjusted for age and gender: 3%/1.05; adjusted for all measured SNPs: 5%/1.10.</p>c<p>Data from Moore et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051880#pone.0051880-Moore1" target="_blank">[48]</a> and Garcia-Closas et al.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051880#pone.0051880-GarcaClosas2" target="_blank">[47]</a> result in PARs of 2–18%.</p>d<p>Combined PAR, individual SNP OR and PAR adjusted for age, gender, smoking habits and all measured SNPs.</p>e<p>Range of individual SNP OR adjusted for age, gender, smoking habits, all measured SNPs and study site depending on the mode of inheritance.</p

Top ten three-way interactions found in the analysis of the ever smokers.

<p>The top ten of the 1,760 possible three-way interactions comprised of the six SNPs and <i>GSTM1</i>, as well as their odds ratios (OR) with 95% confidence intervals (CI) are listed in order of their p-values, where the p-values were adjusted for multiple comparisons by the Bonferroni correction.</p

Optimal odds ratios for combinations of one to seven polymorphisms.

<p>For the computation of the optimal odds ratios (OR), all possible combinations of one to seven of the polymorphisms rs1014971, rs9642880, rs710521, rs8102137, rs11892031, rs1495741 and <i>GSTM1</i> were considered. (A) Profile plots for the odds ratios in the total group (black line) and the subgroups of ever smokers (red line), current smokers (green), former smokers (blue) and non-smokers (cyan). The lines were included for clarity of information and not to suggest a continuous development. Dashed lines indicate when number of cases and/or number of controls fall below 100. In these situations, the corresponding odds ratios should be interpreted with caution. (B)–(F): For the optimal combinations shown in (A), box plots of odds ratios computed in 500 bootstrap samples from (B) the total group, (C) the ever smokers, (D) the current smokers, (E) the former smokers and (F) the non-smokers. In twelve of the bootstrap samples (all but one in the analyses of the seven-way interactions in the total and the smoker group), the odds ratios were larger than 15. For a better presentation, these odds ratios are not displayed in the corresponding box plots. The crosses mark the odds ratios of the optimal combinations in the original analysis. The corresponding plots of the test statistics are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051880#pone.0051880.s001" target="_blank">Figure S1</a>.</p

Alignment depth for gene COL1A1.

<p>Align depth in genetic region COL1A1 after cutting out intronic regions and regions with low alignment depth. Group-wise mean alignment depth values have been smoothed using loess regression.</p

Age, gender and UV-exposition related effects on gene expression in <i>in vivo</i> aged short term cultivated human dermal fibroblasts

<div><p>Ageing, the progressive functional decline of virtually all tissues, affects numerous living organisms. Main phenotypic alterations of human skin during the ageing process include reduced skin thickness and elasticity which are related to extracellular matrix proteins. Dermal fibroblasts, the main source of extracellular fibrillar proteins, exhibit complex alterations during <i>in vivo</i> ageing and any of these are likely to be accompanied or caused by changes in gene expression. We investigated gene expression of short term cultivated <i>in vivo</i> aged human dermal fibroblasts using RNA-seq. Therefore, fibroblast samples derived from unaffected skin were obtained from 30 human donors. The donors were grouped by gender and age (Young: 19 to 25 years, Middle: 36 to 45 years, Old: 60 to 66 years). Two samples were taken from each donor, one from a sun-exposed and one from a sun-unexposed site. In our data, no consistently changed gene expression associated with donor age can be asserted. Instead, highly correlated expression of a small number of genes associated with transforming growth factor beta signalling was observed. Also, known gene expression alterations of <i>in vivo</i> aged dermal fibroblasts seem to be non-detectable in cultured fibroblasts.</p></div

Align depth estimates for gene ID1.

<p>The figure displays alignment depth in absolute numbers. Three lines estimate mean alignment depth for each age group (y = Young, m = Middle, o = Old).</p

CPM values for five genes.

<p>CPM (counts per million) values derived from <i>summarizeOverlaps</i> for Genes ATOH8, ID3, ID1, SMAD7 and FAM83G for all 54 samples.</p

Additional file 1 of Chronic air pollution-induced subclinical airway inflammation and polygenic susceptibility

Additional file 1. Air pollution assignment within the European Study of Cohorts for Air Pollution Effects. Details of air pollution measurements. Genotyping, quality control and imputation. Details of genotyping, quality control and imputation