Contingency table for American subpopulations, rows correspond to detected communities, columns to actual subpopulations.

<p>PUR—Puerto Rican, CLM—Colombian, MXL–Mexican</p><p>Contingency table for American subpopulations, rows correspond to detected communities, columns to actual subpopulations.</p

Average proportions of significant SNPs in the simulation study.

<p>The values in the table represent the proportions of SNPs (averaged over 10 replications) found to be significant. The significance level was set to 0.0001. The results are present for 4 scenarios, which are described in the section: "Evaluation via simulated association studies ".</p><p>* For these methods in the scenario with 3 underlying discrete subpopulations we took 2 principal components and 2 ancestry estimates, as recommended by the authors.</p><p>Average proportions of significant SNPs in the simulation study.</p

3 American subpopulations.

<p>The polygons around the nodes represent the detected communities. The node colors represent the actual labels.</p

3 African subpopulations.

<p>The polygons around the nodes represent the detected communities. The node colors represent the actual labels.</p

5 European subpopulations.

<p>The polygons around the nodes represent the detected communities. The node colors represent the actual labels.</p

Description of datasets, used in the analysis.

<p>Description of datasets, used in the analysis.</p

A consistent pattern of slide effects in Illumina DNA methylation BeadChip array data

Background: Recent studies have identified thousands of associations between DNA methylation CpGs and complex diseases/traits, emphasizing the critical role of epigenetics in understanding disease aetiology and identifying biomarkers. However, association analyses based on methylation array data are susceptible to batch/slide effects, which can lead to inflated false positive rates or reduced statistical power Results: We use multiple DNA methylation datasets based on the popular Illumina Infinium MethylationEPIC BeadChip array to describe consistent patterns and the joint distribution of slide effects across CpGs, confirming and extending previous results. The susceptible CpGs overlap with the Illumina Infinium HumanMethylation450 BeadChip array content. Conclusions: Our findings reveal systematic patterns in slide effects. The observations provide further insights into the characteristics of these effects and can improve existing adjustment approaches.</p

Genome-wide gene-by-smoking interaction study of Chronic Obstructive Pulmonary Disease

Risk for Chronic Obstructive Pulmonary Disease (COPD) is determined by both cigarette smoking and genetic susceptibility, but little is known about gene-by-smoking interactions. We performed a genome-wide association analysis of 179,689 controls and 21,077 COPD cases from UK Biobank subjects of European ancestry, considering genetic main effects and gene-by-smoking interaction effects simultaneously (2-degree-of-freedom (2df) test) as well as interaction effects alone (1-degree-of-freedom (1df) interaction test). We sought to replicate significant results in the COPDGene study and SpiroMeta Consortium. We considered two smoking variables: (1) ever/never and (2) current/non-current. In the 1df interaction test, we identified one genome-wide significant locus on 15q25.1 (CHRNB4) and identified PI*Z allele (rs28929474) SERPINA1 and 3q26.2 (MECOM) in an analysis of previously reported COPD loci. In the 2df test, most of the significant signals were also significant for genetic marginal effects, aside from 16q22.1 (SMPD3) and 19q13.2 (EGLN2). The significant effects at 15q25.1 and 19q13.2 loci, both previously described in prior genome-wide association studies of COPD or smoking, but not 16q22.1 or 3q26.2, were replicated in the COPDGene and SpiroMeta. In our study, we identified interaction effects at previously reported COPD loci, however, we failed to identify novel susceptibility loci

Power curves for RITSS1, RITSS2, GESAT, and GAMsv over increasing signal density <i>p</i>_<i>XE</i>.

Significance level α = 0.005, μXE = 0.05, and results based on 1,000 replicates. Power was simulated for pXE = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5.</p

Power curves for RITSS1, RITSS2, GESAT, and GAMsv over increasing signal density <i>p</i>_<i>XE</i>.

Significance level α = 0.005, μXE = 0.1, and results based on 1,000 replicates. Power was simulated for pXE = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5.</p