Additional file 2: Figure S1. of Combinatorial identification of DNA methylation patterns over age in the human brain

Histogram of beta values after filtering for standard deviations across all samples. Figure S2. Histogram of the samples’ ages. Table S3. Top 5 rules classifying the ‘fetus’ class. Table S4. Top 5 rules classifying the ‘Age 0To4’ class. Table S5. Top 5 rules classifying the ‘Age 5To27’ class. Table S6. Top 5 rules classifying the ‘Age 28plus’ class. (DOCX 82 kb

Analysis methodology for factors related to childhood allergy in the epidemiologic studies BAMSE and PARSIFAL.

<p>Allergy phenotypes were modeled based on genetic and exposure data to identify (<b>A</b>) rules using gene and (<b>B</b>) gene and environment data. MCFS selected significant predictors of a phenotype, which was used to generate rules by ROSETTA. First model used 110 SNPs in BAMSE and PARSIFAL, while the second model included both genetic and exposure data in PARSIFAL, using BAMSE for validation when applicable.</p

Combinations of genetic variants and/or environmental factors in relation to allergy and asthma in PARSIFAL.

<p>Odds ratios are shown for the top-hits rules identified for (<b>A</b>) allergic eczema; affected^1-10 and unaffected^58-67 (<b>B</b>) asthma; affected^1-10 and unaffected^44-53 and (<b>C</b>) atopic sensitization; affected^1-10 and unaffected^37-46. The odds ratios were calculated for children that fulfill all conditions in the rule using all other children as reference.</p

Visualization of co-occurring factors in rules for allergic eczema, asthma and atopic sensitization in PARSIFAL.

<p>Rule networks for (<b>A</b>-<b>B</b>) allergic eczema, (<b>C</b>-<b>D</b>) asthma and (<b>E</b>-<b>F</b>) atopic sensitization; affected and unaffected, respectively. Conditions that occur in the rules are on the outer ring, and co-occurrences of conditions in the rules are illustrated by ribbons across the circle connecting the conditions. The ribbon color indicates high (red) to low (grey) scores. The width of the edges is proportional to the number of correctly classified children.</p

SNP combinations with relevance for current asthma and wheeze in BAMSE and PARSIFAL.

<div><p>The combination of specific genetic variants in (<b>A</b>) <i>ORMDL3-RORA</i> increases the risk for current asthma¹, and in (<b>B</b>) <i>ORMDL3-RORA-COL29A1</i> increase the risk for wheeze². The risk for current asthma and wheeze increased with the number of risk genotypes described by corresponding rule (<b>C</b>-<b>D</b>). ORs and 95% confidence interval are shown. The major allele count is indicated for each gene below i.e. describing 0, 1 or 2 copies of the major allele. The reference category includes children who do not fulfill the rule.</p> <p>¹ IF ORMDL3_rs2305480=2[GG] AND RORA_rs17270362=1[AG] THEN current asthma.</p> <p>² IF COL29A1_rs11917356=2[AA] AND ORMDL3_rs7216389=0[TT] AND RORA_rs17270362=1[AG] THEN wheeze.</p></div

Comparison of canFam2.0 and canFam3.1.

<p>Comparison of canFam2.0 and canFam3.1.</p

Location of lincRNAs and single-exon intergenic non-coding transcripts.

<p>(a) We show the mapping of lincRNAs, broken down by sample across chromosome 1. (b) Histogram of distance from intergenic transcripts to the next known transcribed element in both the 5′ (left) and 3′ (right) direction. The average distance is around 150,000 nucleotides, suggesting that these loci are not closely associated with known genes.</p

Transcribed loci per tissue and library preparation.

<p>N/A, due to poor alignment performance, this library was excluded from subsequent analyses.</p

Distance trees of expression profiles.

<p>We constructed neighbor-joining trees based on the correlation between expression values (FPKM>1.0) between samples, with 1 minus Spearman's rho defining the distance. Colors denote library construction methods (poly-A: blue, DSN: red). We divided transcribed loci into (a) protein coding genes with RNA-Seq support, either annotated by EnsEMBL in dog or EnsEMBL in the human orthologous regions. Replicates cluster together, so do the library constructions methods poly-A and DSN, as well as related tissues, such as heart and muscle; (b) antisense transcripts, that overlap at least one exon of a protein coding gene, as defined in (a). With the exception of testis, poly-A and DSN separate the samples, with both the poly-A and DSN sub-trees maintaining closer relationships between the related tissues heart and muscle; (c) spliced intergenic loci, excluding sequences that have coding potential. Similar to protein coding genes, the poly-A and DSN group by tissue first, with the exception of kidney DSN; and (d) intergenic and uncharacterized single-exon transcript loci. In this set, DSN and poly-A are, similar to antisense loci, the most dominant factor when grouping samples.</p