Supplementary Figure 1: overlap between fetal and colon cancer hypomethylated blocks. Black:
proportion of fetal blocks at a given “area statistic” FWER cutoff that overlap the list of colon
cancer blocks, red: proportion of overlapping blocks that are directionally consistent (lower
DNAm levels in fetal and lower levels in cancer) among those blocks that overlap, blue line:
cutoff corresponding to FWER ≤ 0.05 and overlap proportions reported in main text.
Supplementary Figure 2: principal component analysis (PCA) of DNA methylation data,
principal component 1 versus 2. Surrogate variables were estimated and regressed out of the
methylation estimates prior to PCA and subsequent visualization. Samples are colored by age.
Supplementary Figure 3: estimated neuronal composition versus age. Neuronal composition
was estimated for each bulk tissue sample using flow sorted dorsolateral prefrontal cortex data
(DLPFC) available from Guintivano et al (2013) as reference data and applying a statistical
algorithm for cell composition deconvolution (Houseman et al, 2012; Montaño et al, 2013). The
left-most panel shows the fetal samples, with x-axis values representing gestational age in weeks
– other panels show age in years. The red line shows the linear spline (described in the Methods
section) fit to the data.
Supplemental Table 1: location of fetal DNA methylation “blocks”. Genomic coordinates are
relative to genomic build hg19; “value” indicates the average difference in DNAm levels across
the block – negative values mean fetal DNAm levels were lower than post-natal levels; “area” is
the sum of the values within the block and the ranking metric; “L” and “clusterL” are the
number of Illumina 450k probe groups and probes within the block, respectively; four measures
of significance are provided – p.value and fwer (family-wise error rate) are based on the block
“value” and p.valueArea and fwerArea are based on the block “area”, and were generated across
1000 permutations; “nearestCancerBlockDist” and “nearestCancerBlockSign” are the distance in
base pairs to the nearest published colon cancer block, and its directionality (-1 means lower
DNAm levels in cancer) from Hansen et al (2011)
Supplemental Table 2: location and summary statistics of developmental DMRs. Genomic
coordinates are relative to genomic build hg19; “value” indicates the average F-statistic for the
region (see Methods section) ; “area” is the sum of the F-statistics values within the region and
the ranking metric; “L” and “clusterL” are the number of Illumina 450k probes in the region and
the number of probes in the probe group, respectively; three measures of significance are
provided – fwer (family-wise error rate), pooled p-value and corresponding q-value (see
Methods section and Jaffe et al (2012)) are based “area” statistics, and were generated across
500 permutations; the next 12 columns depict gene annotation information and can be
generated using bumphunter::matchGenes() on this table. The next 6 columns depict the
Pearson correlation and corresponding p-value for all, fetal, and post-natal samples,
respectively, for the more correlated gene expression probe matching the gene symbol in the
“name” column. Lastly, two additional gene identifiers are provided (Entrez Gene ID and
RefSeq ID) based on the gene symbol. Regions with fwer ≤ 0.05 are considered genome-wide
significant and discussed in the main text.
References
Guintivano J, Aryee MJ, Kaminsky ZA (2013) A cell epigenotype specific model for the
correction of brain cellular heterogeneity bias and its application to age, brain region and major
depression. Epigenetics : official journal of the DNA Methylation Society 8: 290-302
Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, Wen B, Wu H, Liu
Y, Diep D, Briem E, Zhang K, Irizarry RA, Feinberg AP (2011) Increased methylation variation in
epigenetic domains across cancer types. Nature genetics 43: 768-775
Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke
JK, Kelsey KT (2012) DNA methylation arrays as surrogate measures of cell mixture
distribution. BMC bioinformatics 13: 86
Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, Irizarry RA (2012) Bump
hunting to identify differentially methylated regions in epigenetic epidemiology studies.
International journal of epidemiology 41: 200-209
Montaño CM, Irizarry RA, Kaufmann WE, Talbot K, Gur RE, Feinberg AP, Taub MA (2013)
Measuring cell-type specific differential methylation in human brain tissue. Genome biology 14:
R94