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

Details on 2,772 AA normal diploid subjects and 117 AA subjects with hemizygous deletions on chr 3p26.1 (77 of these 117 AA subjects carryied CNV components achieving genome-wide significance had larger hemizygous deletions (spanning an additional 1.6kb), while 40 of 117 AA subjects had shorter hemizygous deletions in this section).

<p>Details on 2,772 AA normal diploid subjects and 117 AA subjects with hemizygous deletions on chr 3p26.1 (77 of these 117 AA subjects carryied CNV components achieving genome-wide significance had larger hemizygous deletions (spanning an additional 1.6kb), while 40 of 117 AA subjects had shorter hemizygous deletions in this section).</p

Mean and 95% Confidence Interval (CI) plot of pack-years for two groups of AA subjects.

<p>a. Mean pack-years and 95%CI in 77 hemizygous deletion carriers vs. rest of the subjects(2812) in chr 3p26.1. The observed mean difference in pack-years was statistically significantly (p = 0.00629) between these two groups.</p

Genome-wide CNV association scan for pack-years in African American COPDGene subjects: Each point represents a CNV component estimated by PennCNV.

<p>The red dotted line at 3.5 represents the genome-wide significance levels needed to maintain a family-wise error rate (FWER) of 5%, obtained via 10,000 permutation tests. The black dotted line at 3.87 represents the genome-wide significance levels for Bonferroni correction. 22 vertical bands indicate the 22 autosomes examined.</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

Analysis configurations and number of genetic variants incorporated.

Analysis configurations and number of genetic variants incorporated.</p

Additional information regarding genetic variants tested in the UK Biobank analysis.

These tables contain all genetic variants as well as the m4/m3 information for the sex-interaction analysis in the UK Biobank. (XLSX)</p

Type 1 error: Quantile-quantile-plots for RITSS1, RITSS2, GESAT, and GAMsv for scenarios 1–5 with SELECT:no.

All results based on 10,000 replicates. P-values with p−10 were set to p = 10−10. SELECT:no refers to the scenario where all simulated genetic variants are included in the analysis.</p