Carreras-Gallo et al. Epigenetics Communications
https://doi.org/10.1186/s43682-024-00028-6(2024) 4:5

Epigenetics Communications

Open Access

RESEARCH

Creation and validation of the first infinium
DNA methylation array for the humanimprintome
Natalia Carreras-Gallo1, Varun B. Dwaraka1, Dereje D. Jima2,3, David A. Skaar2,4,5, Tavis L. Mendez1,
Antonio Planchart2,4,5, Wanding Zhou6,7, Randy L. Jirtle2,4,5, Ryan Smith1* and Cathrine Hoyo2,4,5,8*Abstract
Background Differentially methylated imprint control regions (ICRs) regulate the monoallelic expression of
imprinted genes. Their epigenetic dysregulation by environmental exposures throughout life results in the formationof common chronic diseases. Unfortunately, existing Infinium methylation arrays lack the ability to profile theseregions adequately. Whole genome bisulfite sequencing (WGBS) is the unique method able to profile the ICRs.
However, it is very expensive and it requires not only a high coverage, but it is also computationally intensive to assess
these regions.
Findings To address this deficiency, we developed a custom methylation array containing 22,819 probes. Amongthem, 10,438 are CG probes targeting unique CpG sites, with 9,757 probes successfully mapping to 1,088 out of
the 1,488 candidate ICRs recently described. To assess the performance of the array, we created matched samples
processed with the Human Imprintome array and WGBS, which is the current standard method for assessing themethylation of the Human Imprintome. We compared the methylation levels from the shared CpG sites, and obtained
a mean R2 = 0.569. We also created matched samples processed with the Human Imprintome array and the Infinium
Methylation EPIC v2 array, and obtained a mean R2 = 0.796. Furthermore, replication experiments demonstrated highreliability (ICC: 0.799–0.945).
Conclusions Our custom array will be useful for replicable and accurate assessment, mechanistic insight, andtargeted investigation of ICRs. This tool should accelerate the discovery of ICRs associated with a wide range of
diseases and exposures, and advance our understanding of genomic imprinting and its relevance in development
and disease formation throughout the life course.
Keywords Genomic imprinting, Imprintome, Imprint Control Region (ICR), Custom methylation array, DNA
methylation*Correspondence:
Ryan Smith
ryan@trudiagnostic.com
Cathrine Hoyo
choyo@ncsu.edu
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Carreras-Gallo et al. Epigenetics Communications(2024) 4:5

Introduction
Interdisciplinary evidence accumulated over the past
decades supports that environmental stressors — where
the environment is broadly defined to include the built
environment, contaminants, and psychosocial andsocioeconomic stressors — are estimated to contribute 70–90% of the burden of common chronic diseases
in humans [1–3]. Nevertheless, empirical human dataremains limited as disease formation often occurs many
years after environmental exposure.
Epigenetics is a means by which genes respond to awide range of environmental stressors to stably change
gene expression, replication, and repair that can cause
long-term health effects [4]. Mechanistically, this isachieved by altering the organization and function ofchromatin with the use of modifications to DNA and histones, as well as non-coding RNA interference [5]. The
most studied epigenetic mechanism in humans is DNA
methylation, owing to the stability of the DNA molecule.
Indeed, data generated in the last decade, much of it frommultiple iterations of Illumina Methylation Arrays, demonstrate the mediating role of CpG methylation in theeffects of a wide range of environmental exposures andchronic diseases [6–8]. Sometimes the interpretation of
the findings is ambiguous because the DNA methylation
modifications identified in the accessible tissues, such as
blood, are different from the marks in the diseased tissue[9, 10]. Moreover, many epigenetic marks must respondto environmental exposure throughout the life course,
making cause-and-effect difficult to infer in related disease outcomes.
Known exceptions to this variability are CpG methylation marks that are stochastically established before
tissue specification that controls metastable epiallele
expression [11], and those that reside in the imprint control regions (ICRs) that regulate the monoallelic expression of imprinted genes [12–14]. CpG methylation in
ICRs is established before gastrulation and is mitotically
heritable, such that these epigenetic marks are normallysimilar across tissues and cell types over the life course.
ICRs are defined by parent-of-origin specific methylation marks that are important gene dosage regulators
and are similar across individuals. The stability of thesemethylation marks with age also makes them long-term‘records’ of early exposures that are difficult to obtainthrough questionnaires or other exposure assessment
assays [14]. Moreover, multiple lines of evidence support that changes in the methylation patterns in ICRs
are implicated in many disorders such as cancer [15, 16],
neurological disorders [17], specific syndromes such as
Prader–Willi syndrome [18] and Angelman syndrome[19], as well as chronic diseases that result from exposure
to environmental contaminants [20, 21].

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While these features make ICRs attractive targets forunraveling the mechanisms underlying many chronic
diseases with developmental origins and in developing
potential therapeutic strategies, until a year ago, only 24
ICRs were characterized [13, 22]. In a recent study, Jimaet al. identified 1,488 candidate differentially methylated
ICRs in humans by performing whole-genome bisulfite
sequencing (WGBS) [12]. The replicable measurementof CpG methylation in these regions in a large numberof samples is only possible using a high throughput DNA
methylation sequencing method.
While the cost of WGBS continues to decline relative to the depth of sequence, the cost to sequence, andcomplexity of whole genome sequencing analysis areprohibitive for analyzing just these regions. The existing targeted method of pyrosequencing can only analyze
single sequences, and has a limited length of high-quality
sequence obtained (often < 100 bp). Targeted amplification and pooled sequencing of small numbers of ICRs(< 20) is a feasible approach, but is only effective once asmall target set is established, and not effective for generalized screening. Hybridization capture methods toenrich for target sequences from genomic libraries, suchas targeted methyl-seq assays from Twist Bioscience, Inc.
(San Francisco, CA) are capable of targeted sequencing
on the scale required; however, like the other sequencingbased methods, require substantial technical expertise,
and a variety of specialized instrumentation.
Microarrays provide a means of simultaneous methylation quantitation on the scale required, using less complex sample processing methods, shorter times for datacollection than Next Generation Sequencing (NGS), andare more automatable. While microarrays are not able tomeasure every ICR CpG site, they are capable of interrogating a subset of CpG sites within nearly all candidate
ICRs.
The most common DNA methylation microarraysin academic and commercial research are the Illumina
Infinium HumanMethylation450, the Infinium Illumina EPIC850k BeadChip, and the Infinium Methylation EPICv2 BeadChip (Illumina, Inc., San Diego, CA).
The reference human genome contains 22,157 CpG sites
mapped to the 1,488 ICRs. However, only 6.8% of them
are represented on the EPICv2 array. Thus, the current
arrays are not able to comprehensively profile ICRs, limiting our ability to use these arrays to investigate the roleof imprinting in disease development.
Herein, we describe the development of a custom
methylation array specifically designed to target andmeasure the DNA methylation status of the candidate
ICRs involved in imprinted gene expression. This technology provides a targeted and comprehensive approachthat allows accurate assessment, mechanistic insights,

Carreras-Gallo et al. Epigenetics Communications(2024) 4:5

and the identification of aberrations within ICRs associated with various diseases.

Methods
Sample cohort information
Alzheimer’s disease brain tissues

DNA derived from autopsy brain specimens was
obtained from the Joseph and Kathleen Bryan Brain Bankof the Duke University/University of North Carolina at
Chapel Hill Alzheimer’s Disease Research Center (Duke/
UNC ADRC). These brain tissues were selected according to their neuropathologic diagnosis of Alzheimer’s
disease. Eight brain samples from AD autopsies (4 nonHispanic Blacks – NHBs – and 4 non-Hispanic Whites– NHWs) and eight brain samples from control autopsies(4 NHBs and 4 NHWs) were processed using both the
Human Imprintome array and WGBS with 10-15X coverage [23].
Alzheimer’s disease whole blood

A pilot case control study of 50 cases and 50 controls
was also conducted at the Duke Memory Clinic. Peripheral whole blood was collected by the lancet and capillary method into lysis buffer and DNA extracted. In
total, DNA samples from 17 individuals were randomly
selected. Among them, 10 were Alzheimer’s disease cases
and 7 were controls. All the samples were processed
twice using the Human Imprintome array to assess theperformance of the array. Moreover, three more controls
were randomly selected to process them with the Human
Imprintome array and the EPICv2 array.
Newborn epigenetics study (NEST) cohort umbilical cord

NEST is an ongoing prospective birth cohort study with
2,681 pregnant women recruited in two waves between
2005 and 2011; enrollment of participants is described indetail elsewhere [24, 25]. Briefly, pregnant women wererecruited from prenatal clinics serving Duke University
Hospital and Durham Regional Hospital obstetrics facilities in Durham, NC. Eligible participants were: (1) pregnant, (2) at least 18 years of age, (3) English-speaking,and (4) intending to deliver at one of two obstetric facilities. Women with HIV or intending to give up custody of
their offspring were excluded. At delivery, umbilical cord
blood was obtained. For the current study, we used 8
umbilical cord blood samples, and processed them twicewith the Human Imprintome Array to assess the reliability of the array.
Preprocessing of the human imprintome and EPIC arraydata

For the preparation of samples, 200 ng of DNA werebisulfite converted using the EZ DNA Methylation kit (Zymo Research, Irvine, CA) according to the

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manufacturer’s instructions. Bisulfite-converted DNA
samples were randomly assigned to a chip well on the
Infinium Human Methylation EPIC v2 BeadChip (Illumina, Inc., San Diego, CA) or in the Human Imprintome
array BeadChip (Illumina, Inc., San Diego, CA), amplified, hybridized onto the array, stained, washed, andimaged with the Illumina iScan SQ instrument (Illumina,
Inc., San Diego, CA) to obtain raw image intensities. For
the EPIC processing, the data were processed at TruDiagnostic, Inc. (Lexington, KY) using a custom EPIC v2
array that include all the probes from the regular EPICv2
array [26] and 6,930 additional probes spiked in.
To preprocess the DNA methylation values, we utilized
the sesame package [27] due to its compatibility with custom arrays. Specifically, we employed the readIDATpair
followed by the getBetas functions, which require the
IDAT file locations and the custom manifest to generate
a beta value matrix. To visualize the distribution of beta
values we employed the densityPlot function from theminfi package [28].
Processing of the WGBS data

For the Alzheimer’s disease autopsy tissues (n = 16),
libraries were prepared using EpiGnome™ Methyl-Seq
reagents (Illumina, Inc, San Diego, CA), index-tagged
for multiplexing, and sequenced on an Illumina NextSeq platform (Illumina, Inc, San Diego, CA). Reads wereassigned back to individuals by indexing, and aligned insilico to a bisulfite-converted reference genome (versionHg38), eliminating reads without unique alignments (dueto either repetitive genomic sequence or loss of specificity from bisulfite conversion of cytosines) and duplicate
reads (indicative of clonal amplification of original random DNA fragments). From these reads, methylation
fractions and read counts were calculated for all CpG
sites in the genome. There was > 97% bisulfite conversion
in all samples, with sequence coverage between 10X-15X
and no sequence duplication bias. To compare against
array data, the percent of methylated reads over the total
number of reads was used to estimate overall methylation
values. We removed the CpG sites that had fewer than 10
reads per probe from the WGBS data to ensure a correct
estimation of the methylation level. These values werethen compared to unnormalized beta values extracted
from arrays.

Results
Design of the human imprintome array

For the development of the custom Human Imprintome
array, we first identified the CpG sites from the reference human genome that were mapped to the 1,488 ICRs
described by Jima et al. using WGBS [12]. To this end,
we extracted 29.4 million CpG locations from the humangenome (hg38), and intersected with ICR locations using

Carreras-Gallo et al. Epigenetics Communications(2024) 4:5

Table 1 Description of the probes contained in the humanimprintome array
Probe TypeCG - Uniquemappers
CG - Backgroundnormalization
CG - Multimapping
Bisulfite conversion I
Bisulfite conversion II
ExtensionHybridization

Negative
Non-polymorphic
Norm_A
Norm_C
Norm_G
Norm_T
Restorationrs
Specificity I
Specificity II
Staining

Target removal# Probes Description
10,438
Probes mapping to unique locations.
9,757 probes of them are mapping
to ICRs.
10,364
Probes with “cgBK” prefix includedin the manifest to enable additional
background normalization capabilities
1,313
Probes mapping to multiple locations
10
Infinium I probes to monitor efficiency of bisulfite conversion
4
Infinium II probes to monitor efficiency of bisulfite conversion
4
Control probes to test the efficacyof A, T, C, and G nucleotides from ahairpin probe3
Control probes to test the overall
performance of the entire assay usingsynthetic targets instead of amplified
DNA
411
Control probes that should not
hybridize to the DNA template9
Non-polymorphic control probes
27Internal normalization control probesthat incorporate a base A
58
Internal normalization control probesthat incorporate a base C
27
Internal normalization control probesthat incorporate a base G
58
Internal normalization control probesthat incorporate a base T
1
Control probes to assess the efficiency of DNA restoration in the
Infinium HD FFPE protocola
69
Single Nucleotide Polymorphism
control probes
12
Control probes to monitor allele-specific extension for Infinium I probes
3
Control probes to monitor allele-specific extension for Infinium II probes
6
Control probes to examine the efficiency of the staining step in both
the red and green channels
2
Control probes to test the efficiencyof the stripping step after the extension reactionaRestoration probes are only informative when following the Infinium
Formalin-Fixed Paraffin-Embedded (FFPE) workflow

the intersect function from bedtools. The total number of CpG sites mapped to ICRs was 22,157 (Table S1).
Illumina, Inc. (San Diego, CA) used this list in order todesign an array with the maximum number of probes targeting those CpG sites.
Remarkably, we used threshold scores designed andvalidated by Illumina, Inc. (San Diego, CA) to only selecthigh-quality probes. Mainly, we used a general scorebased on probe sequence, including GC content andPage 4 of 10

Table 2 Representation of the human imprintome in the humanimprintome array, EPICv1 array, and EPICv2 array compared to thecurrent standard method, whole genome bisulfite sequencing(WGBS)
WGBS

Probes mapped to
ICRs
ICRs represented (%)
Probes/ICR (mean)
Probes/ICR (range)

22,157(100%)
1,488(100%)
15.1
4–242

Human
Imprintome
Array
9,757 (44.0%)
1,088 (73.1%)
9.0
1–171

EPICv1array

EPICv2array

307 (1.4%)1497(6.8%)
156 (10.5%) 548(36.8%)
2.0
2.7
1–29
1–52

annealing temperature. The minimum probe score was
set to 0.3 for the converted strand and 0.2 for the opposite strand.
As a result, the Human Imprintome array manifest(Table S3) comprises a total of 22,819 probes, categorized
into 704 control probes and 22,115 CG probes (Table 1).
Out of the CG probes, 10,438 are mapped to unique
CpG sites, with 9,757 successfully aligned with one of
the 1,488 identified ICRs (Table S4). The remaining CG
probes served various purposes, including multimapping, background normalization, or mapping to distinct
locations that did not intersect with any ICR. The manifest includes 10,364 cgBackground (“cgBK”) probes withmissing chromosomes that were included in the manifest to enable any additional background-normalization
capabilities, such as quantile normalization or Noob.
The determination of this probe count stemmed froman evaluation conducted by Illumina, Inc. (San Diego,CA), assessing the impact of integrating varying proportions of Infinium I and Infinium II probes for normalization purposes. Consequently, 10,364 cgBK probes havebeen retained in the manifest of all custom arrays. Thisset includes 9,163 Infinium II assays, 485 Green extending Infinium I probes, and 716 Red extending Infinium I
probes.
Previous studies have identified the necessity to
exclude low-specificity probes that can bind to multiple
sequences within the genome, as well as probes that contain genetic variants in their underlying sequence [29,30]. In the Human Imprintome array, we identified 1,313
multimapper probes. The number of mapping genomic
positions per probe in the GRCh38 Build Genome ranged
from 2 to 100 (Fig. S1; Table S2). However, since these
1,313 multimapper probes were representative of a highnumber of ICRs, we decided to keep these probes on thearray although the chromosome and the position in the
manifest were set to 0 to avoid confusion.
Out of the 1,488 ICRs, a subset of 1,088 ICRs
(73.1%) had successful probe alignments, as outlinedin Table 2. The distribution of probes per ICR in the
Human Imprintome array revealed a mean value of 9,

Carreras-Gallo et al. Epigenetics Communications(2024) 4:5

encompassing a range from a minimum of 1 probe to a
maximum of 171 probes. Notably, a significant proportion (n = 672) of ICRs exhibited successful mappings withmore than 5 probes.
Comparison with other arrays

To compare the Human Imprintome array with otherarrays, we calculated the ICR representation in eachsequencing method. As a reference, we used the WGBS
method, since it is able to target all 22,157 CpG sites thatwere identified to be representative of the 1,488 ICRs[12]. Figure 1 demonstrates that the Imprintome arrayhas a much larger representation of ICRs compared
to EPICv1 and EPICv2 arrays. As shown in Table 2, theEPICv1 array only examines 307 probes out of the 22,157(1.4%), and has a representation of 156 ICRs. Moreover,
the average number of probes per ICR is 2. The last version of the EPIC array, version 2, can sequence a higher
number of Human Imprintome probes compared to version 1. In this case, 6.8% of the probes are analyzed andthey represent 548 ICRs with an average of 2.7 probes per
ICR. In contrast, the Human Imprintome array evaluates
the methylation level of 9,757 probes that are mapped tounique locations (44.0%), and represent 1,088 ICRs withan average of 9 probes per ICR. Remarkably, the first 345
ICRs, which are mapped to chromosomes 1 to 5, and thePage 5 of 10

ICRs from 1221 to 1300, which are mapped to chromosome 21, show a low representation in all the arrays. Thisis due to the highest frequency of multi-mapping probes
on those regions.
To check whether the beta values obtained using the
Human Imprintome array are comparable with the beta
values using the EPICv2 array, we examined three whole
blood samples from the Alzheimer’s cohort that were
processed using both the EPICv2 array and the Human
Imprintome array. The EPICv2 and the Human Imprintome array contain some CpG sites that are targeted using
multiple probes. This is implemented because someareas may need more than one probe to generate accurate analysis due to the CG enrichment or the presenceof single nucleotide polymorphisms (SNPs) in the probe
sequence. Thus, multiple probes can target mutations on
the same site with each probe for a different alternative
allele. In the EPICv2, 4174 CpG sites are targeted twice
and 1016 are targeted three times for different alternative
alleles [26]. In the Human Imprintome Array, 1,182 CpG
sites were targeted twice. To ensure comparability acrossarrays, we collapsed multiple probes targeting the same
CpG site by calculating their mean.
Utilizing this approach, we identified 1,703 probes
sites that overlapped between the EPICv2 and Human
Imprintome arrays. We obtained Pearson correlation

Fig. 1 Coverage across Imprint Control Regions (ICRs) for each sequencing method. Whole genome bisulfite sequencing (WGBS) is the reference and it
contains the 1,488 ICRs described [12], as well as the 22,157 probes mapped to these regions. The other sequencing methods have a lower number of
ICRs and probes representing those ICRs

Carreras-Gallo et al. Epigenetics Communications(2024) 4:5

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Fig. 2 Correlation plots between Human Imprintome and the Infinium Methylation EPIC arrays. We selected the 1,703 probes overlapped between botharrays to plot the Pearson correlation. (A) Sample AD-125, which belongs to a female Non-Hispanic White (NHW) sample. (B) Sample AD-157, which
belongs to a male Non-Hispanic White sample. (C) Sample AD-173, which belongs to a male Non-Hispanic Black (NHB) sample. Each color represents a
different probe type; “cg” probes are colored in red; control probes (“ctl”) are colored in green; and single nucleotide polymorphism control probes (“rs”)
are colored in blue

Fig. 3 Comparison of the beta distribution between all the probes in the Human Imprintome array (A), the probes mapped to ICRs from the Human
Imprintome array (B), and all the probes from the Infinium Methylation EPIC v2 array (C) in the same samples

coefficients of 0.788, 0.811, and 0.789, respectively, as
shown in Fig. 2. As expected, there is a cluster of control
probes around 0 and around 1 in both methods.
Moreover, using the same samples, we compared
the beta value density plots using the probes from eacharray. The beta values from all the probes in the Human
Imprintome array exhibit three peaks at 0, 0.5, and 1(Fig. 3A). However, when we remove all the probes thatare not mapped to any ICR, we only observe a peak at 0.5,
which is indicative of monoallelic methylation (Fig. 3B).

Carreras-Gallo et al. Epigenetics Communications(2024) 4:5

This pattern arises from the presence of approximately
100% methylation on one parental allele and 0% methylation on the other. The density plot of the probes analyzed
in the EPICv2 array displays two peaks at 0 and 1, reflecting biallelic methylation, where both probes were either
methylated or unmethylated (Fig. 3C). This shows thatthe Human Imprintome array is correctly assessing themethylation levels of the Human Imprintome probes.
Finally, we compared the DNA methylation levels along
all the probes mapped to the ICR_548, since it was one of
the ICRs with higher coverage in the Human Imprintome
array compared to the EPICv2. Figure 4 shows that the
Human Imprintome array is useful for a better comprehension of the DNA methylation levels along ICR_548.
Additionally, we compared the Human Imprintome
array to the current standard method for assessing themethylation of the Human Imprintome (i.e., WGBS). We
used 16 brain samples from Alzheimer’s cases and controls. These samples were classified into 4 controls and4 cases of NHBs and 4 controls and 4 cases of NHWs.
To correctly assess the correlations between the Human
Imprintome array and WGBS, we combined the 4 samples from each group by averaging the DNA methylationlevels in the Human Imprintome array, and by combining

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the reads in WGBS. This increased the coverage andobtained a higher number of shared CpG sites between
both methodologies. We collapsed the probes that weretargeting the same CpG site in the Human Imprintome
array.
We removed the CpG sites that had fewer than 10 reads
per probe from the WGBS data because the probability of
estimating an incorrect methylation level is higher when
the number of reads per probe is low. We then calculated
the percentage of methylated reads compared to the totalof reads mapped to each probe to compare this value
to the beta values from the Human Imprintome array.
Finally, we calculated the correlations using the CpG sites
shared in each group. The highest number of CpG sites
shared was for NHB cases with 7746 and the lowest was
4513 for NHW controls. The correlations ranged from
0.532 for NHW cases to 0.657 for NHB controls with amean of 0.569 (Fig. 5). We also did a filtration at 1, 3, 5,
and 20 reads per probe, but the best results with a sufficient number of probes to compare were obtained with
10 (Table S5).

Fig. 4 DNA methylation levels along all the probes from the ICR_548 in the Human Imprintome (A & B) and EPICv2 arrays (C & D) in the same samples. (A)Violin plot showing the DNA methylation levels along 171 probes mapped to ICR_548 in the Human Imprintome array. (B) DNA methylation levels along
the genomic positions within the ICR_548 for the Human Imprintome array. (C) Violin plot showing the DNA methylation levels along 9 probes mapped
to ICR_548 in the EPICv2 array. (D) DNA methylation levels along the genomic positions within the ICR_548 for the EPICv2 array

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Fig. 5 Correlation between the Human Imprintome array and WGBS. (A) Correlation plot for the combination of four Non-Hispanic White (NHW) Alzheimer’s Disease cases. (B) Correlation plot for the combination of four Non-Hispanic Black (NHB) Alzheimer’s Disease cases. (C) Correlation plot for the
combination of four NHW controls. (D) Correlation plot for the combination of four NHB controls

Performance in replicates

To test the performance of the array, we analyzed replicates in the laboratory. We used 8 samples from theumbilical cord blood (NEST cohort [23, 24]) and 17
samples from whole blood (Alzheimer’s cohort). Using all
the probes, we calculated the intraclass correlation (ICC)
between the first and the second replicate for each sample. The ICC values ranged from 0.799 to 0.945, having amean of 0.868 (Table S6; Figs. S2-S3).

Discussion
The Illumina BeadArray technology has undergone substantial redevelopment over the years, and the total
number of CpG sites that can be simultaneously analyzed has increased substantially from ~ 25,000 in 2008
(HumanMethylation27KBeadChip) [31], to ~ 485,000 in2011 (HumanMethylation450K BeadChip) [32], to over
~ 850,000 CpG sites in 2016 (MethylationEPIC BeadChipv1.0) [33], and finally to over ~ 935,000 CpG sites in 2022
when MethylationEPIC BeadChip v2.0 was released inJune of 2023 [26].
In this study, we have successfully developed and characterized a Human Imprintome custom array, which
offers an innovative approach for investigating DNA

methylation patterns specifically associated with ICRs
and parental allele-specific methylation. Using replicate
samples, we have demonstrated that this custom array
exhibits high reliability when capturing DNA methylation information. Furthermore, the analysis of beta values
obtained from the shared CpG sites between the Human
Imprintome array, EPIC v2 array, and WGBS has revealed
a high degree of correlation, reinforcing the robustness
and reliability of the Human Imprintome custom array.
The correlation between the Human Imprintome arrayand the WGBS was lower than the correlation with the
EPICv2. It is likely caused by the low coverage of the
WGBS data and also by the different methodologies forassessing DNA methylation (array type vs. sequencing).
Imprinted genes play essential roles in embryonicdevelopment and growth regulation. Understanding theirimprinting patterns and functions is vital for comprehending normal development and potentially identifying
the causes of developmental disorders. Imprinting disorders, such as Prader-Willi [34, 35], Silver-Russell [36],Angelman [34, 35], and Kagami-Ogata syndromes [37]result from abnormalities in imprinted genes. Investigating in more detail the ICRs can provide insights into
the molecular mechanisms underlying these disorders,Carreras-Gallo et al. Epigenetics Communications(2024) 4:5

leading to better diagnostics, treatments, and potential
prevention strategies. Moreover, understanding better
how imprinted genes influence growth regulation could
lead to novel treatments for growth disorders or cancers
that involve the dysregulation of imprinting.
The Human Imprintome array represents a powerfultool that provides researchers with a focused platform
for high-resolution analysis of DNA methylation dynamics in imprint control regions. Custom arrays like the
Human Imprintome array can be a cost-effective solution compared to using commercially available arraysthat contain probes unrelated to the research focus. By
focusing on specific regions of interest, researchers canalso optimize their resources, and obtain more relevant
data. Notably, existing commercial arrays have representation for a limited number of ICRs. The EPICv1 and
EPICv2 arrays have 10.5% and 36.8% of the ICRs represented, respectively, with an average of 2 probes per ICR.
In comparison, the Human Imprintome array contains
73.1% of the ICRs with an average of 9 probes per ICR.
Infinium arrays are extensively used and there are manybioinformatic pipelines to process them. Although not
all of them work with custom arrays, sesame is an existing package that seamlessly integrates with the Human
Imprintome array, and ensures accurate data processing[27]. Moreover, the utilization of arrays provides precise
estimates of DNA methylation at specific sites, enabling
the design of epigenetic biomarkers specific to the
Human Imprintome array.
To enhance the utility of the Human Imprintome array,
we have provided a comprehensive annotation of theprobes contained in the array, as well as detailed information regarding the ICRs and the probes mapped tothem. This annotation allows researchers to gain a deeper
understanding of the regions and probes interrogatedby the array, facilitating more targeted and insightful
analyses.
Although the Human Imprintome array exhibits
remarkable performance, we acknowledge its limitations, including the absence of probes representing 400
ICRs and the relatively low coverage in some represented
ICRs, especially those mapped to chromosomes 1 to 5,
and chromosome 21. However, we are actively addressing these limitations, and working towards the development of a new version of the array that includes probes
mapped to the missing ICRs.
In conclusion, the Human Imprintome custom arrayhas the potential to significantly contribute to the identification of CpG sites and ICRs with altered methylation levels. This, in turn, may provide valuable insights
into the emergence and evolution of diseases associated with aberrant DNA methylation patterns involved
in imprinted gene regulation. The Human Imprintome
array, with its focused design, extensive annotation, andPage 9 of 10

promising performance, holds great promise as a powerful tool for unraveling the complex relationship between
DNA methylation, imprinting, and disease pathogenesis.

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s43682-024-00028-6.
Supplementary Material 1
Supplementary Material 2
Acknowledgements
We thank the Newborn Epigenetics Study (NEST) families for their
participation. We also thank Dr. Andrew Lui of the Duke Memory Clinic forenrolling Alzheimer’s disease cases and controls.
Author contributions
RS, CH, RLJ, and DAS conceived and designed the study. NCG, VBD, and DDJ
performed the bioinformatic analyses. DAS and TLM analyzed the samples atthe lab. AP and WZ performed validation of the analyses to ensure accuracy of
the results. All authors drafted and edited the manuscript.
Funding
This work was supported by NIH: R01ES093351, R01HD098857, R01MD011746,
and R21HD093351. DDJ is supported with The CHHE grant P30ES025128.
Data availability
Data will be available from the authors upon request. It will also be updated
and available at the website https://humanicr.org/. This website will also have
NCBI accession numbers when available.

Declarations
Ethics approval and consent to participate
The study protocol from the NEST and Alzheimer’s Disease study wereapproved by the Duke University Institutional Review Board. Autopsy
specimens were exempted from IRB review by Duke and NC State University
IRBs.
Competing interests
NC, VBD, RS, and TLM are employees of TruDiagnostic. The other authors
declare no conflict of interest.
Author details
1
TruDiagnostic. Inc, Lexington, KY, USA
2
Center for Human Health and the Environment, North Carolina State
University, Raleigh, NC, USA
3
Bioinformatics Research Center, North Carolina State University, Raleigh,NC, USA
4
Toxicology Program, North Carolina State University, Raleigh, NC, USA
5
Department of Biological Sciences, North Carolina State University,
Raleigh, NC, USA
6
Center for Computational and Genomic Medicine, Children’s Hospital ofPhiladelphia, Philadelphia, PA, USA
7
Department of Pathology and Laboratory Medicine, University ofPennsylvania, Philadelphia, PA, USA
8
Center for Human Health and the Environment, Department of
Biological Sciences, North Carolina State University, Raleigh, NC, USA
Received: 18 January 2024 / Accepted: 28 June 2024

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