Additional file 1: of Identification of new biomarkers for Acute Respiratory Distress Syndrome by expression-based genome-wide association study

Table S1. Detailed representation of data obtained from GEO. ARDS gene expression submissions were retrieved from GEO using two terms “Acute lung injury” and “Lung injury”, which resulted in 23 and 25 data sets, respectively. These 48 entries were filtered down to 31 entries according to conditions described in Methods. The reason for filtering out an experiment is provided. (XLSX 16 kb

Additional file 2: of Identification of new biomarkers for Acute Respiratory Distress Syndrome by expression-based genome-wide association study

Table S2. Chi-square values of cross-referenced genes. Given that different microarray platforms have multiple probes for a single gene, the cross-referencing of such platforms generates numerous combinations of expression values for a given gene (32160 in this study). Chi-square value was calculated for each of these combinations and combination with the best chi-square value for a given gene was retained, which resulted in 3152 unique gene entries. These genes were linked to human genome and plotted against their location (Fig. 1). (XLS 185 kb

Pathway analysis.

<p>Top canonical pathways as predicted from the 65genes containing the76 SNPs that were identified using χ²tests. Pathway predictions were done using the Core Analysis function of Ingenuity Pathway Analysis. *, P-Value of <0.05 indicates a non-random association between the genes and pathway; **, Ratio of the number of genes in the dataset involved in the pathway to the total number of genes in the pathway.</p><p>Pathway analysis.</p

Manhattan plot of ARDS patients and 1000 genomes project controls.

<p>A number of strong associations with susceptibility to ARDS were observed using a χ² test. (<b>A</b>) A Manhattan plot of the whole exome sequence all ARDS cases vs. European Ancestry and ASW 1000 Genomes Project controls created using SVS v8.2.0. This is a graphic representation of the chromosome location (x axis) vs. the –log10 (χ² p-value) of the allele frequencies. SNPs whose chi-square tests yield a smaller p-value fall higher on the log scale are more significant <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111953#pone.0111953-Robinson1" target="_blank">[42]</a>. (<b>B</b>) The same Manhattan plot with a zoomed Y-axis.</p

Participant demographics and comorbidities for the ARDS cases.

<p>*8 samples, 4 exome sequenced and 4 TaqMan genotyped ARDS patients did not have these phenotypes available. An additional 2 exome sequenced ARDS patients did not have ventilator-free day’s data. In addition to the 8 patients missing severity and mortality phenotype data, 2 patients were excluded from the regression because their phenotypes were thought to be missing until after the regressions were completed.</p><p>Participant demographics and comorbidities for the ARDS cases.</p

Pipeline of the exome-seq data analysis workflow.

<p>After processing the data using the GATK pipeline, this filtering workflow was derived to identify SNPs which were associated with measures of susceptibility across the racial and etiology groups of cases. SNPs were filtered based on strength of association, coding effect, and functional prediction prior to testing for association with other ARDS phenotypes. *, The sample contains African American and Caucasian patients, so the EUR and ASW healthy controls from 1000 Genomes were used for comparison; **, In the 1000 Genomes Project exome sequence, the same 714,074 SNPs are present for all 440 EUR and ASW; §, HWE = Hardy Weinberg Equilibrium, p>0.0001; +, African American with pneumonia, African American with sepsis, Caucasian with pneumonia, Caucasian with sepsis; + +, χ² test of ARDS vs. respective 1000 Genomes Project control groups; ‡, SNPs with P-value <0.01 in the overall comparison, Caucasian ARDS comparison, and African American comparison with 1000 Genomes were filtered further by p<0.01 in the sepsis comparison and pneumonia comparison; ‡ ‡, All ARDS cases, all pneumonia cases, all sepsis cases, all African American cases, all Caucasian cases.</p

Quantile-quantile plots of Caucasian ARDS and EUR 1000 genomes.

<p>In the example of our Caucasian cases and EUR controls, we observe that correction for principal components improves the fit of our data with the expected distribution. (<b>A</b>) QQ plot of expected χ²values versus the actual χ²values for the genotypic trend test of case-control status. The data are filtered on HWE, LD, and SNP call rate but not PCA corrected. (<b>B</b>) QQ plot of expected χ²values versus the actual χ²values for the genotypic trend test of case-control status. The data have been filtered and corrected for 6 PCs. (<b>C</b>) QQ plot of expected χ²values versus the actual χ²values for the genotypic trend test of case-control status. The data have been filtered and corrected for 6 PCs and undergone sample outlier removal.</p

Overall association summary.

<p>NS, Not significant; * significantly associated in genotyped Caucasian, pneumonia, and Caucasian pneumonia subgroups; **, for 117 genotyped samples only, not in total 213; 1, Odds ratio for alternate allele (allelic test); 2, additive genotypic model.</p><p>Overall association summary.</p

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p