Mortality among Coast Guard Shipyard workers: A retrospective cohort study of specific exposures

<p>In a previous analysis of a cohort of shipyard workers, we found excess mortality from all causes, lung cancer, and mesothelioma for longer work durations and in specific occupations. Here, we expand the previous analyses by evaluating mortality associated with 5 chemical exposures: asbestos, solvents, lead, oils/greases, and wood dust. Data were gathered retrospectively for 4,702 workers employed at the Coast Guard Shipyard, Baltimore, MD (1950–1964). The cohort was traced through 2001 for vital status. Associations between mortality and these 5 exposures were calculated via standardized mortality ratios (SMRs). We found all 5 substances to be independently associated with mortality from mesothelioma, cancer of the respiratory system, and lung cancer. Findings from efforts to evaluate solvents, lead, oils/greases, and wood dust in isolation of asbestos suggested that the excesses from these other exposures may be due to residual confounding from asbestos exposure.</p

Phylogenetic Trees for <i>VEGF</i> Haplotypes and Association with Bladder Cancer Risk among 926 Cases and 900 Controls with DNA in the iPLEX Assay, Spanish Bladder Cancer Study

<div><p>See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0030029#pgen-0030029-g001" target="_blank">Figure 1</a> for block definitions. Of the 29 <i>VEGF</i> SNPs determined, two had low genotypic variation in our population; therefore, haplotype analyses were based on the remaining 27 SNPs. Polymorphic bases are in 5′ to 3′ order: Block 1(<b>rs833052</b> and rs866236); Block 2 (<b>rs1109324</b>, <b>rs1547651</b>, rs833060, rs699947, rs1005230, rs833061, rs1570360, rs2010963, <b>rs25648</b>, rs833067, rs3025042, rs833068, <b>rs3024994</b>, rs735286, rs3024998, rs3025000, and rs3025006); and Block 3 (rs3025030, rs3025033, rs3025035, and rs3025036). Bolded rs numbers are for individual SNPs significantly associated with bladder cancer risk.</p><p>Eleven cases and 13 controls with missing data on more than 15 of the 17 SNPs in Block 2 were excluded from haplotype analyses because their inclusion resulted in lack of convergence. Nucleotide changes significantly associated with risk in the individual genotype analyses are shown in boxes. The most common haplotye is the reference category. Haplotypes with the common variant for each individual SNP are CC for Block 1, GAGCCGTGCTGGCCCCC for Block 2, and GACC for Block 3.</p></div

Gene Map and LD Plot of <i>VEGF</i> Gene

<p>Color scheme is based on D′ and logarithm of the odds of linkage (LOD) score values: white D′ < 1 and LOD < 2, blue D′ = 1 and LOD < 2, shades of pink/red: D′ < 1 and LOD ≥ 2, and bright red D′ = 1 and LOD ≥ 2. Numbers in squares are D′ values (values of 1.0 are not shown). Block definition is based on the Gabriel et al. method [34]. Two (rs3024989 and rs367173) of the 29 SNPs determined are not shown because of low variation in this population. Red rectangles in the gene map represent exons.</p

Genetic risk prediction for type-2 diabetes.

<p>PRS models were built based on the summary statistics from a meta-analysis of DIAGRAM consortium and GERA data (17,802 cases and 105,109 controls in total) and validated in independent 1500 cases and 1500 controls in GERA. (A) Prediction R² (observational scale) for 1D PRS with or without winner’s curse correction. “NO”: no winner’s correction for association coefficients; “Lasso”: regression coefficients were modified by a lasso-type correction; “MLE”: association coefficients were modified by maximizing a likelihood function conditioning on selection. (B) Quantile-quantile plot for −<i>log</i>₁₀(<i>P</i>) for high priority (HP) SNPs vs. low priority (LP) SNPs. SNPs were pruned to have pairwise <i>r</i>² ≤ 0.1. Here, the HP SNPs were eSNPs/meSNPs in adipose tissue or SNPs related with the H3K4me3 mark in pancreatic islet cell line with data downloaded from the ROADMAP project. The HP SNPs were strongly enriched in the discovery data. (C) Prediction R² for 2D PRS with lasso-type winner’s curse correction. The SNP set was the same to (B). The best prediction (R² = 3.53%) was achieved when we included HP SNPs using criterion <i>P</i> ≤ 0.03 and LP SNPs with <i>P</i> ≤ 0.005. (D) The prediction R², the area under the curve (AUC) and the significances for testing whether an alternative PRS was better than the standard 1D.</p

Simulation results for comparing polygenic risk prediction methods and different high priority SNP sets.

<p>Quantitative traits were simulated conditioning on the genotypes of LD-pruned SNPs in lung cancer GWAS with 10,000 discovery samples and 1,924 validation samples. For each simulation, we used 5,000 causal SNPs and 9,940 high priority (HP) SNPs (either randomly selected or the SNPs related with conserved regions). Δ denotes the enrichment fold change of the HP SNP. In the x-axis, “1D” denotes 1D PRS without winner’s curse correction; “1D-LASSO(MLE)” denotes 1D PRS with lasso-type (MLE) correction; “2D-random” indicates 2D PRS with HP SNP sets randomly selected from the LD-pruned SNPs in the genome; “2D-CR” indicates 2D PRS using SNPs in conserved regions as HP SNPs.</p

GWAS data with summary level data.

<p>GWAS data with summary level data.</p

Comparison of polygenic risk prediction methods for 13 complex diseases.

<p>For all figures, the y-coordinate is the prediction R² in the observational scale. “1D” denotes 1D PRS; “2D, blood eSNPs” denotes 2D PRS using blood eSNPs as high-prior SNP set. In the x-axis, “NO” denotes PRS without winner’s curse correction; “LASSO” and “MLE” denote lasso-type and MLE winner’s curse correction, respectively. (A) Prediction R² values for six diseases in WTCCC data, estimated based on five-fold cross-validation. (B) Prediction R² values for three GWAS of cancers, estimated based on ten-fold cross-validation. (C) Prediction R² values for four complex diseases estimated based on independent validation samples.</p

Theoretic investigation of prediction performance and optimal thresholds for SNP selection in 2D PRS.

<p>The theoretic calculation assumes <i>M</i> = 53,163 independent SNP, of which 5,000 are causal for a binary trait, similar to simulation studies. The high-prior (HP) SNP set has 5,000 SNPs and the low-prior (LP) SNP set has 48,163 SNPs. <i>Δ</i> is the enrichment fold of HP SNPs in the causal SNP set. (A) The prediction AUC for 1D PRS and 2D PRS. (B) The optimal P-value thresholds for including HP and LP SNPs in 2D PRS. For both plots, x-coordinate is the discovery sample size, assuming equal number of cases and controls.</p