MOESM3 of “Gap hunting” to characterize clustered probe signals in Illumina methylation array data

Additional file 3: Table S1. Distribution of group counts for gap signals in SEED. Breakdown of number of groups or clusters in the 11,007 gap signals found in SEED samples

MOESM4 of “Gap hunting” to characterize clustered probe signals in Illumina methylation array data

Additional file 4: Table S2. Breakdown of all C/G and SBE site measured polymorphism scenarios. We isolated specifics scenarios in which the following conditions were met: a probe contained a measured SNP that mapped to the C, G, or SBE sites of a probe, and it also did not contain any other form of mapping SNP. This table contains a list of all SNP C, G and SBE site scenarios herein and their corresponding Figure #. Also included is the number of probes analyzed for each scenario, along with the count and proportion of those probes that were classified as gap signals. Most probes in SEED that overlapped with measured SNPs were not classified as gap signals (though ~80% of gap signals did overlap with SNPs, see Additional file 7)

MOESM11 of “Gap hunting” to characterize clustered probe signals in Illumina methylation array data

Additional file 11: Figure S34. Filtering on variably methylated probes at various cutoffs in the context of gap signals. We calculated the proportion of gap and non-gap signals at various percentile thresholds of standard deviation cutoff (1–99%) to define a variably methylated probe. Researchers who filter on variable methylation prior to association analysis should be cautioned to be increasingly aware of gap signals (and subsequently their implications on DNAm related to disease described herein) as the cutoff to define a variably methylated probe increases

MOESM9 of “Gap hunting” to characterize clustered probe signals in Illumina methylation array data

Additional file 9: Table S3. Group distributions of 3 different classifications of gap signals. We compared the group distribution for the three groups—mapping measured SNP, mapping annotated SNP, and no mapping SNP—of gap signals. The two groups with mapping SNPs had a very similar relative proportion of groups, while the group with no mapping SNPs was comparatively enriched for distributions with 2 clusters or groups. This result lends additional rationale to a different mechanism besides SNPs as leading the gap signal behavior

Additional file 5 of Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Figures S3. Quadrant plots depicting concordance in effect sizes between suggestively associated (p < 1 × 10− 4) CpG sites in peripheral blood and three brain regions. A) Prefrontal cortex B) Temporal Cortex C) Cerebellum. Points in red indicate those sites with p < 1 × 10− 5 in peripheral blood. (PNG 21 kb

Additional file 3 of Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Table S2. Full summary statistics and meta-analysis results for all 445,608 CpG sites that were present in both the cleaned SEED and SSC datasets. (CSV 40707 kb

Additional file 4 of Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Table S3. Concordance between suggestively associated (p < 1 × 10− 4) CpG sites in peripheral blood and their corresponding effect sizes in three brain regions. (CSV 3 kb

Additional file 2 of Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Table S1. Demographic characteristics for samples in the SSC (S2) dataset. (XLSX 11 kb

Additional file 1 of Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Figures S1-S2. Depiction of surrogate variable selection process for SEED (S1) and SSC (S2). Panel A: Heatmap indicating degree of association with known potential technical variables or confounders with estimated surrogate variables. Panel B: Inflation factor (lambda) calculated for progressively including surrogate variables in association models. The number of surrogate variables to include in the ultimate association testing model was to determine to be that which properly controlled the inflation factor and adequately captured known technical variables or confounders. See “Methods” for additional explanation. (PDF 19 kb