Comparison of methylation capture sequencing and Infinium MethylationEPIC array in peripheral blood mononuclear cells

Abstract

Background

Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium MethylationEPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price.

Methods

Epigenome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (> 1000 ng), medium (300–1000 ng), and low (150 ng–300 ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array.

Results

After quality control, an average of 3,708,550 CpG sites per sample were detected by MC-seq with DNA quantity > 1000 ng. Reproducibility of DNA methylation in MC-seq-detected CpG sites was high among samples with high, medium, and low DNA inputs (r > 0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98–0.99). However, methylation for a small proportion of CpGs (N = 235) differed significantly between the two platforms, with differences in beta values of greater than 0.5.

Conclusions

Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.

Introduction

The rapid increase in the number of epigenome-wide association studies (EWAS) have successfully identified differentially methylated CpG sites that are associated with environmental exposures and diseases [1–6]. Such DNA methylation marks have been used as biomarkers for diagnosing, subtyping, and monitoring disease progression [7–11]. The most popular and affordable methods to profile epigenome-wide DNA methylation are array-based platforms, primarily the Illumina Human Methylation 450 K (450 K) and Infinium MethylationEPIC (EPIC) BeadChips (Illumina Inc, San Diego, CA). These arrays utilize Illumina’s beadchip technology that does not require polymerase chain reaction (PCR), but is subject to dye intensity biases between the two platforms [12]. These arrays have limited coverage of the methylome and can only detect up to 870,000 CpGs across the epigenome, leaving a large proportion of CpG sites unmeasured. Moreover, the EPIC array offers improved but still suboptimal coverage of regulatory elements [13]. Whole-genome bisulfite sequencing (WGBS) is able to capture more than 28 million CpGs, but the feasibility remains low for the population-based EWAS due to high cost and large genomic DNA input requirements to compensate for degradation during DNA bisulfite treatment. Alternatively, Methylation Capture Sequencing (MC-seq) is able to detect DNA methylation at single-nucleotide resolution utilizing a targeted next-generation sequencing approach [14]. It permits profiling of significantly more CpG sites than the EPIC array, requires less genomic DNA input than WGBS, and less expensive than WGBS, but can be susceptible to bias due to the presence of PCR duplicates. Feature-to-cost comparisons among different platforms can help understand the utilities of each platform and provide guidance for investigators in choosing a methylation profiling platform.

A few studies have compared the CpG coverage, reproducibility, and performance of array-based and MC-seq platforms [15–17]. Teh et al. compared MC-seq and the 450 K array in seven DNA samples extracted from saliva [15]. A recent study compared the EPIC array and TruSeq targeted bisulfite sequencing in four cord blood DNA samples [17]. However, no comparisons of MC-seq and array-based methylome profiling of peripheral blood mononuclear cells (PBMCs) has been reported. Here, we profiled the DNA methylome in PBMCs using the Agilent SureSelect Methyl-Seq platform and compared the results to the EPIC array in DNA samples extracted from PBMCs.

Methods

Methylation capture sequencing (MC-seq)

DNA samples description

DNA was extracted from de-identified PBMCs collected from four individuals. Genomic DNA quality was determined by estimating the A260/A280 and A260/A230 ratios by spectrophotometry and concentration by fluorometry. DNA integrity and fragment size were confirmed using a microfluidic chip run on an Agilent Bioanalyzer. To assess the reproducibility of MC-seq by DNA quantity, DNA samples from each participant were profiled in triplicate times with high (> 1000 ng), medium (300–1000 ng), and low (150–300 ng) DNA input. In total, 12 DNA samples were measured by MC-seq. Bisulfate conversion was conducted for each DNA sample as described below.

Methyl-seq target enrichment library prep

Indexed paired-end whole-genome sequencing libraries were prepared using the SureSelect XT Methyl-Seq kit (Agilent, part#G9651B). Genomic DNA was sheared to a fragment length of 150–200 bp using focused acoustic energy delivered by the Covaris E220 system (Covaris, part#500003). Fragmented sample size distribution was determined using the Caliper LabChip GX system (PerkinElmer, Part#122000). Fragmented DNA ends were repaired with T4 DNA Polymerase and Polynucleotide Kinase and “A” base was added using Klenow fragment in a single reaction followed by AMPure XP bead-based purification (Beckman Coulter, part#A63882). The methylated adapters were ligated using T4 DNA ligase followed by AMPure XP bead purification. Quality and quantity of adapter-ligated DNA were assessed using the Caliper LabChip GX system. Samples yielding > 350 ng were enriched for targeted methylation sites by using the custom SureSelect Methyl-Seq Capture Library. Hybridization was performed at 65 °C for 16 h using a C1000 Thermal Cycler (BIO-RAD, part# 1851197). Once the enrichment was completed, the samples were mixed with streptavidin-coated beads (Thermo Fisher Scientific, part#65602) and washed with a series of buffers to remove non-specific bound DNA fragments. DNA fragments were eluted from beads with 0.1 M NaOH. Unmethylated C residues of enriched DNA were modified by bisulfite conversion using the EZ DNA Methylation-Gold Kit (Zymo Research, part#D5005). The SureSelect enriched, bisulfite-converted libraries were PCR amplified using custom-made indexed primers (IDT, Coralville, Iowa). Dual-indexed libraries were quantified by quantitative polymerase chain reaction (qPCR) using the Library Quantification Kit (KAPA Biosystems, Part#KK4854) and inserts size distribution was assessed using the Caliper LabChip GX system. Samples with a yield of ≥ 2 ng/μl were proceeded to sequencing.

Flow cell preparation and sequencing

Sample concentrations were normalized to 10 nM and loaded onto an Illumina NovaSeq flow cell at a concentration that yields 40 million passing filter clusters per sample. Samples were sequenced using 100 bp paired-end sequencing on an Illumina HiSeq NovaSeq according to Illumina standard protocol. The 10 bp dual index was read during additional sequencing reads that automatically follows the completion of the first read. Data generated during sequencing runs were simultaneously transferred to the Yale Center for Genome Analysis high-performance computing cluster. A positive control (prepared bacteriophage Phi X library) provided by Illumina was spiked into every lane at a concentration of 0.3% to monitor sequencing quality in real time.

Preprocessing and quality control

Signal intensities were converted to individual base calls during a run using the system’s Real Time Analysis (RTA) software. Sample de-multiplexing and alignment to the human genome was performed using Illumina’s CASAVA 1.8.2 software suite. The sample error rate was required to be less than 1% and the distribution of reads per sample in a lane was required to be within reasonable tolerance.

Quality control (QC) on MC-seq was conducted following standard procedure as previously described [18]. Quality of sequence data was examined by using FastQC (ver. 0.11.8). Adapter sequences and fragments at 5′ and 3′ (phred score < 20) with poor quality were removed by Trim_galore (ver. 0.6.3_dev). We used Bismark pipelines (ver. v0.22.1_dev) to align the reads to the bisulfite human genome (hg19) with default parameters [19]. Quality-trimmed paired-end reads were transformed into a bisulfite converted forward strand version (C → T conversion) or into a bisulfite-treated reverse strand (G → A conversion of the forward strand). Duplicated reads were removed from the Bismark mapping output by deduplicate_bismark and CpG, CHG, and CHH (where H = A, T, or C) were extracted by bismark_methylation_extractor.

All CpG sites were grouped by sequencing coverage, also known as read depth. The groups with coverage of 1× to 100× were used to test the relationship between coverage and number of CpG sites. Only the CpG sites with coverage > 10× depth were used for final comparisons to ensure MC-seq data quality. Genes were annotated using Homer annotatePeaks.pl, including intergenic, 5′UTR, promoter, exon, intron, 3′UTR, transcription start site (TTS), and non-coding categories. CpG island, shore, shelf, and open sea annotation were defined by locally developed bash and R scripts based on genomic coordinates (hg19) of CpG islands from the UCSC genome browser. CpG shores was defined as up to 2 kb from CpG islands and CpG shelf was defined as up to 2 kb from a CpG shore.

Assessment of reproducibility

We assessed CpG- and participant-based reproducibility for MC-seq among 12 samples with DNA quantity of high, medium, and low input in two ways. First, CpG-based reproducibility was assessed by calculating Pearson correlations using the CpG sites in common of the samples from the same participant with different input DNA quantities. Scatterplots were rendered showing 10,000 randomly selected common CpG sites comparing samples with high and medium, high and low, and medium and low DNA inputs. Second, participant-based reproducibility was assessed by comparing methylation profiles among pairs of participants using the samples with high DNA inputs, by calculating Pearson correlations of common CpG sites.

EPIC array data preprocessing

The Infinium MethylationEPIC array (Illumina, San Diego, CA, USA) was used to measure PBMC DNA methylation profiles from the same four participants. These four samples with DNA input of 1000 ng were preprocessed using standard procedures as previously described [20]. Briefly, the predicted sex based on methylome was consistent with self-reported sex for all samples. All samples had a call rate greater than 0.15. A total of 19,090 CpG sites on X chromosomes and 537 CpG sites on Y chromosomes were filtered. A total of 846,464 CpG sites passed quality control.

Comparison of methylation at each CpG site between MC-seq and EPIC array

The overall distribution of gene annotation in relation to CpG island and genetic region between MC-seq and EPIC array data from the four participants was compared. Common CpG sites between MC-seq and EPIC array assays were defined according to genomic coordinates. Pearson correlation and the absolute beta-difference value (Δβ) were calculated among common CpG sites between MC-seq methylation percentage values and EPIC methylation beta values by using R (ver. 3.5.1). If median Δβ of the common CpG site between two platforms was > 0.1, it was defined as a discordant CpG pair; otherwise, the CpG site was defined as a concordant CpG pair. The density plot of Δβ and a Manhattan plot showing the distribution of Δβ across epigenome were illustrated. Scatterplots were rendered showing the correlation of β values from 10,000 randomly selected CpG sites measured by both MC-seq and EPIC array.

Results

MC-seq overview and reproducibility

In MC-seq, all sequences were efficiently mapped to the reference genome with greater than 89% mapping efficiency. Interestingly, the number of non-CpG sites was significantly greater than the number of CpG sites. Among all detected methylation sites by MC-seq, 11% were CpG sites, 65% were CHH sites, and 24% were CHG sites (Fig. 1a).

Fig. 1.

Methylation Capture Sequencing (MC-seq). a Distribution of methylation sequence context (CpG, CHH, CHG); b Coverage depth versus a number of detected CpG sites; c Detected CpG sites in low, medium, and high DNA inputs for four participants using MC-seq with minimum coverage ≥ 10×; d Scatterplots comparing 10,000 randomly selected common CpG sites among samples with high, medium, and low DNA input quantities and their Pearson correlations

Figure 1b shows the relationship of the number of detected CpG sites and depth of sequence coverage by MC-seq in one sample. The depth of read at which the majority of sites were sequenced was estimated to be approximately 10× coverage, observed as the inflection point of the distribution of Fig. 1b. An increase of depth only slightly increased the capture of CpG sites and the inflection point is on 10× coverage, consistent with previous literature [15, 17]. Thus, the number of CpG sites with coverage ≥ 10× from MC-seq was used in subsequent analyses.

After quality control filtering, MC-seq captured an average of 2,878,207 methylation CpG sites with coverage ≥ 10× among the 12 DNA samples, with an average of 3,708,550 CpG sites among samples with high DNA input (> 1000 ng), an average of 3,046,172 CpG sites among samples with medium DNA input (300–1000 ng), and an average of 1,879,898 CpG sites among samples with low DNA input (150–300 ng) (Fig. 1c and Table 1). Despite the fact that the detected number of CpG sites varied depending on DNA input quantity, CpG-based correlation among the common CpG sites between samples with high and medium, high and low DNA input quantities exceeded r > 0.95. Correlations of common CpG sites between medium and low DNA inputs were also high with r in 0.92–0.94 (Table 2). Figure 1d shows the scatterplot of 10,000 randomly selected common CpGs between samples with high and medium, high and low, and medium and low DNA input quantities. Pair-wise participant-based correlations were high as r > 0.98 among common CpG sites (Table 3). Overall, MC-seq exhibited good reproducibility. The methylation profile generating in high DNA input from each participant was used for subsequent analyses.

Table 1.

Detected CpG number by DNA amount in MC-seq with coverage ≥ 10×

DNA amount	Participant ID	CpG number	Average CpG number
Low	S1	1,774,940	1,879,898
	S2	1,831,086
	S3	2,154,732
	S4	1,758,834
Medium	S1	2,768,456	3,046,172
	S2	3,338,200
	S3	3,119,259
	S4	2,958,772
High	S1	3,406,879	3,708,550
	S2	3,642,776
	S3	3,722,552
	S4	4,061,994
Total average			2,878,207

Table 2.

Comparison of MC-seq between samples with high, medium, and low DNA input amount

Participant ID	DNA amount
	High	Medium	Common CpG	Pearson correlation
S1	3,406,879	2,768,456	2,747,844	0.984
S2	3,642,776	3,338,200	3,283,296	0.984
S3	3,722,552	3,119,259	3,101,938	0.977
S4	4,061,994	2,958,772	2,957,239	0.979

	DNA amount
	High	Low	Common CpG	Pearson correlation
S1	3,406,879	1,774,940	1,771,936	0.960
S2	3,642,776	1,831,086	1,829,919	0.966
S3	3,722,552	2,154,732	2,153,175	0.974
S4	4,061,994	1,758,834	1,758,622	0.963

	DNA amount
	Medium	Low	Common CpG	Pearson correlation
S1	2,768,456	1,774,940	1,745,241	0.942
S2	3,338,200	1,831,086	1,827,536	0.943
S3	3,119,259	2,154,732	2,135,980	0.939
S4	2,958,772	1,758,834	1,744,416	0.928

Table 3.

Overlap of detected CpG across samples with high DNA input amount by MC-seq

Participant ID 1	Participant ID 2	Common CpG	Pearson R
S1	S2	3,336,037	0.980
S1	S3	3,350,314	0.976
S1	S4	3,394,970	0.982
S2	S3	3,519,772	0.978
S2	S4	3,613,753	0.982
S3	S4	3,676,406	0.978

Distribution of methylome regions by MC-seq and EPIC

We compared genome-wide DNA methylation captured by MC-seq and by EPIC array in the four high DNA input samples. An average of 3,708,550 CpG sites were detected by MC-seq and 846,464 CpG sites by EPIC array. Overall, MC-seq detected 11.5 times more CpG sites in exons and 10.2 times more CpG sites in 5′ UTR region compared to the EPIC array, and 4.8 to 8.9 times more CpG site in other categories of genomic regions by MC-seq compared to EPIC array. However, the proportion of CpGs out of all CpGs successfully measured that map to gene regions in MC-seq as compared to the EPIC array did not significantly differ between these two platforms. For example, the proportion of CpG sites in transcription termination site (TTS) regions was similar between two platforms. MC-seq showed slightly greater proportions of CpG sites in 5′UTR and exon regions, while the EPIC array detected a greater proportion of CpG sites in promoter regions (Fig. 2a). In terms of CpG sites in relation to CpG islands including open seas, shelves, and shores, MC-seq detected 10.9 times more CpG sites located on CpG islands and 5.4–6.2 times more on other regions compared with the EPIC array. The proportion of CpG islands detected by MC-seq was greater than by the EPIC array (42% versus 29%), while the EPIC array detected a modestly higher percentage of CpG sites located in open seas than the MC-seq (39% versus 31%) (Fig. 2b).

Fig. 2.

Comparison of CpG proportion in epigenomic regions between MC-seq and EPIC. a Distribution of genomic regions (intergenic, promoter, 5′UTR, exon, intron, non-coding, 3′UTR, transcription termination site (TTS), and non-coding). b Distribution of CpG position relative to CpG islands (CpG island, shore, shelf, and open sea)

Comparison of Common CpG sites Measured by MC-seq and EPIC

A total of 472,540 CpG sites were measured by both platforms. Overall, the correlations of these shared CpG sites was high, ranging from r = 0.983 to 0.985 across the four samples (Fig. 3a). Figure 3b presents the distribution of the absolute difference of methylation β values between MC-seq and EPIC. A small proportion of CpG sites (1.4%) were discordant (i.e., Δβ > 0.1), while 98.6% of CpG sites were concordant (i.e., Δβ < 0.1). Figure 3a presents the concordant (blue) and discordant CpG sites (green) between MC-seq and EPIC for participant S1 (Fig. 3a). The 60,753 discordant CpG sites appeared to be randomly distributed across the epigenome (Additional file 1: Figure S1). Among the discordant CpG sites, we identified 239 CpG sites with highly discrepant methylation (i.e., Δβ > 0.5) (Table 4). Addition file 2: Table S1 presents top 100 discordant CpG sites with medium discrepant methylation (Dβ = 0.1 ~ 0.4)Additional file 3: Figure S2 shows that participants S2, S3, and S4 have similar distribution of concordant and discordant plots as participant S1.

Fig. 3.

Comparing methylation values among common CpG sites between MC-seq and EPIC. a Correlation of methylation values measured by MC-seq and EPIC array among common CpG sites in participant S1. Blue dots represent concordant CpGs with Δβ < 0.1 between the two platforms and green dots represent discordant quality with Δβ ≥ 0.1; b The distribution of median Δβ in common CpG sites between MC-seq and EPIC array. The red dotted line represents Δβ = 0.1 as a cutoff for concordant CpG site between two platforms. c The density plot of methylation values among common CpG sites profiled by MC-seq and EPIC array in participant S1

Table 4.

Discordant CpG sites between MC-seq and EPIC (difference>0.5)

Probe	Chr	Position	Gene	MC-seq median	EPIC median	Median Difference between MC-seq and EPIC	Refgene group	Relation to CpG island
cg09156519	9	103361572		0.009	0.960	0.95		S_Shore
cg18176117	9	96097296	C9orf129	0.000	0.932	0.93	Body	N_Shore
cg14268958	10	133453066		0.000	0.899	0.89		S_Shelf
cg10576280	10	124133822	PLEKHA1	0.072	0.948	0.88	TSS1500	N_Shore
cg01005486	3	13246006		0.047	0.886	0.84		Island
cg23433318	19	667542		0.007	0.866	0.84		Island
cg10766172	7	27498479		0.989	0.147	0.82
cg11812439	4	68928706	LOC550113;SYT14P1;TMPRSS11F	0.973	0.155	0.82	Body; Body; Body
cg23950473	5	154393265	KIF4B; KIF4B	0.992	0.174	0.82	1stExon; 5’UTR
cg00259849	8	4183880	CSMD1	0.000	0.821	0.82	Body
cg23981150	1	161111090		1.000	0.217	0.78		Island
cg09698465	12	133000178		0.906	0.080	0.78		Island
cg20450977	11	10529463	MTRNR2L8; MTRNR2L8	0.964	0.189	0.77	3’UTR; 1stExon
cg01053463	1	26186087	C1orf135	0.021	0.757	0.76	TSS1500	Island
cg12499827	2	202004893	CFLAR; CFLAR; CFLAR; CFLAR; CFLAR; CFLAR; CFLAR; CFLAR; CFLAR	0.971	0.215	0.76	TSS200; Body; Body; Body; Body; Body; Body; Body; Body
cg03133777	2	170361364	BBS5	1.000	0.244	0.75	3’UTR
cg19040702	17	22023833		0.969	0.230	0.75
cg21675871	11	69813397		0.217	0.960	0.74		Island
cg04240493	3	148414664	AGTR1; AGTR1; AGTR1; AGTR1	0.979	0.258	0.72	TSS1500; TSS1500; TSS1500; TSS1500	N_Shore
cg16889427	10	127584375	FANK1	0.036	0.759	0.71	TSS1500	Island
cg25916505	18	32820654	ZNF397; ZNF397	0.000	0.711	0.71	TSS1500; TSS1500	N_Shore
cg13525026	17	18061071	MYO15A	1.000	0.290	0.71	Body
cg07825433	4	1215099	CTBP1; CTBP1	0.000	0.716	0.71	Body; Body	N_Shelf
cg03846641	2	109746751	LOC100287216; SH3RF3	0.239	0.952	0.71	TSS200; Body	Island
cg19188207	2	10340837	C2orf48	1.000	0.290	0.71	Body
cg11495544	17	73402155	GRB2; GRB2	0.753	0.049	0.70	TSS1500; TSS1500	S_Shore
cg06931905	8	42036940	PLAT; PLAT	0.896	0.197	0.70	Body; Body
cg03348902	1	569603		0.869	0.168	0.70
cg27120934	6	129480619	LAMA2; LAMA2	0.979	0.297	0.69	Body; Body
cg07576219	1	55012408	ACOT11; ACOT11	0.927	0.250	0.69	TSS1500; TSS1500	S_Shelf
cg08400246	5	156570642	MED7; MED7	0.153	0.870	0.68	TSS1500; TSS1500	S_Shore
cg27626141	8	103876469	AZIN1; AZIN1	0.000	0.682	0.68	TSS200; TSS200	Island
cg26688472	2	203638928	ICA1L	0.984	0.303	0.68	3’UTR	Island
cg26101183	10	65930786		0.957	0.279	0.68		Island
cg27090007	13	28519388	ATP5EP2	0.985	0.321	0.67	Body
cg11896012	19	53696753	ZNF665	0.048	0.700	0.67	TSS200	S_Shore
cg02606018	12	10658281		0.979	0.323	0.66
cg00438164	4	100870480	H2AFZ; LOC256880	0.004	0.650	0.65	Body; TSS1500	Island
cg21164300	9	136098495		0.000	0.644	0.64		N_Shelf
cg15891076	10	65930618		0.971	0.328	0.64		Island
cg05948389	5	1641924		0.014	0.660	0.64		N_Shelf
cg10507965	10	102107251	SCD; SCD	0.011	0.642	0.64	5’UTR; 1stExon	Island
cg21662326	11	14521493	COPB1; COPB1; COPB1	0.643	0.012	0.63	TSS200; TSS200; TSS200
cg09646578	8	5019363		0.310	0.934	0.63
cg24717964	20	61477008	DPH3B; DPH3B; TCFL5	0.986	0.356	0.63	1stExon; 5’UTR; Body
cg07437919	8	142234483	SLC45A4	0.957	0.313	0.63	Body	N_Shore
cg01105403	2	240723304		0.050	0.890	0.63
cg20482143	7	64340804		0.982	0.346	0.63
cg11187452	22	49698612		0.017	0.653	0.63		Island
cg24504954	3	61237217	FHIT; FHIT	0.017	0.649	0.62	TSS200; TSS200	Island
cg27434351	11	14521491	COPB1; COPB1; COPB1	0.639	0.016	0.62	TSS200; TSS200; TSS200
cg15864074	2	120974042		0.976	0.354	0.62
cg00913521	12	89893799	WDR51B	0.977	0.339	0.62	Body
cg27534567	1	568536		0.834	0.262	0.62
cg24515136	17	49024834		0.949	0.328	0.62		S_Shelf
cg01417615	1	52456419	RAB3B	0.629	0.015	0.61	TSS200	Island
cg00236302	12	69004867	RAP1B; RAP1B	0.000	0.612	0.61	5’UTR; 5’UTR	Island
cg10747603	22	29197018	XBP1; XBP1	0.022	0.627	0.61	TSS1500; TSS1500	S_Shore
cg03594447	1	20359744		1.000	0.358	0.61
cg23045277	4	190587808		0.299	0.910	0.61
cg02218809	16	29973300	TMEM219; TMEM219	0.020	0.612	0.61	TSS200; TSS200	Island
cg05646491	10	135379754	SYCE1; SYCE1; SYCE1	0.988	0.382	0.60	TSS1500; 5’UTR; TSS1500	Island
cg07596174	20	55926107	RAE1; RAE1	0.014	0.613	0.60	TSS1500; TSS200	N_Shore
cg03543448	16	4384967	GLIS2	0.927	0.315	0.60	Body
cg25793197	5	31923469	PDZD2	0.976	0.379	0.60	Body
cg21392229	2	161223778	RBMS1; RBMS1	1.000	0.384	0.60	Body; Body
cg05607320	12	53342553	KRT18; KRT18	0.064	0.651	0.60	TSS200; TSS1500	N_Shore
cg13896861	9	94878241	SPTLC1; SPTLC1	0.117	0.711	0.60	TSS1500; TSS1500	S_Shore
cg03064900	4	190566141		0.323	0.921	0.60		N_Shore
cg16199859	3	75263685		0.861	0.276	0.60
cg15006843	1	205720633	NUCKS1	0.880	0.260	0.60	TSS1500	S_Shore
cg02498218	4	26361371	RBPJ; RBPJ; RBPJ; RBPJ	0.979	0.388	0.59	Body; Body; 5’UTR; Body	Island
cg07116712	15	96887959		0.091	0.681	0.59		Island
cg11643306	20	34204831	SPAG4	0.038	0.630	0.59	Body	S_Shore
cg08568561	7	42834498		0.981	0.392	0.59
cg06669598	6	127622363	ECHDC1; ECHDC1; ECHDC1; ECHDC1; ECHDC1	0.984	0.351	0.59	3’UTR; 3’UTR; Body; Body; Body
cg22805431	3	113955600	ZNF80	0.983	0.409	0.59	1stExon
cg24636332	17	4437925	SPNS2	0.301	0.939	0.59	Body	N_Shore
cg05924191	15	35279830	ZNF770	0.008	0.605	0.59	5’UTR	N_Shore
cg14402194	14	23398944	PRMT5; PRMT5; PRMT5; PRMT5; PRMT5; PRMT5; LOC101926933	0.028	0.592	0.58	TSS200; TSS200; TSS200; TSS200; TSS200; TSS200; Body	S_Shore
cg01737532	4	190862170	FRG1	0.000	0.584	0.58	1stExon	Island
cg25744017	15	52819324	MYO5A; MYO5A	0.957	0.379	0.58	Body;Body	N_Shore
cg03432151	15	89745000	ABHD2; ABHD2	0.948	0.360	0.58	3’UTR; 3’UTR
cg27196695	10	134571377	INPP5A	1.000	0.420	0.58	Body
cg27571351	10	17619364		0.986	0.407	0.58
cg02775804	2	120974080		0.977	0.401	0.58
cg16461530	10	134798264		0.664	0.106	0.58
cg12654770	10	52487693		0.962	0.385	0.58
cg16112880	1	201123745	TMEM9	0.003	0.579	0.58	TSS200	Island
cg20641423	8	125315065		0.911	0.338	0.57		S_Shore
cg23248615	10	2005709		0.905	0.313	0.57
cg25550279	7	53254983		0.965	0.381	0.57		Island
cg01070250	1	569687		0.843	0.271	0.57
cg06977575	4	139481990		0.953	0.376	0.57		Island
cg14511644	9	15055021		0.977	0.399	0.57
cg08947542	8	35383200	UNC5D	0.879	0.309	0.57	Body
cg10258063	2	217363243	RPL37A	0.043	0.613	0.57	TSS1500	N_Shore
cg24209723	18	12913133		0.973	0.399	0.57		S_Shore
cg02265379	5	87898506	LOC645323	0.971	0.384	0.57	Body	Island
cg18925601	7	158752715		0.006	0.574	0.57		Island
cg01406075	11	58731104		0.885	0.309	0.57		N_Shore
cg13545297	12	54404315	HOXC8	0.229	0.791	0.57	Body	S_Shore
cg09036531	10	96991505		0.968	0.402	0.57
cg25649283	9	140714075	EHMT1	0.382	0.950	0.57	Body	Island
cg06204030	17	7792051	CHD3; CHD3; CHD3	0.761	0.141	0.57	TSS200; TSS200; Body	S_Shelf
cg18627328	19	621561	POLRMT	0.980	0.411	0.56	Body	Island
cg13085681	8	48920761	UBE2V2	0.009	0.576	0.56	TSS1500	N_Shore
cg00999469	6	25107287	CMAHP	0.043	0.931	0.56	Body
cg20960039	9	130213605	LRSAM1; RPL12; LRSAM1; LRSAM1; LRSAM1; RPL12	0.020	0.582	0.56	TSS1500; 1stExon; TSS200; TSS200; TSS200; 5’UTR	Island
cg04400841	2	208988863	CRYGD	0.215	0.764	0.56	Body	Island
cg12476298	19	58426697	ZNF417	0.977	0.407	0.56	Body	N_Shore
cg23997402	19	14275669	LPHN1; LPHN1	0.972	0.442	0.56	Body; Body	S_Shore
cg16935370	5	154393281	KIF4B; KIF4B	0.981	0.414	0.56	1stExon; 5′UTR
cg04222159	1	204981786	NFASC; NFASC; NFASC; NFASC	0.630	0.073	0.56	Body; Body; Body; Body
cg06396237	8	120779442	TAF2	0.985	0.430	0.56	Body
cg06599543	6	165749446	PDE10A;PDE10A	0.857	0.332	0.56	Body;Body	S_Shore
cg11566832	10	88659593	BMPR1A	0.145	0.693	0.55	Body
cg20334010	15	41047916	RMDN3; RMDN3	0.020	0.570	0.55	TSS1500; TSS1500	S_Shore
cg18245781	5	3659697		0.283	0.852	0.55
cg03761810	2	10264850	RRM2; RRM2	0.018	0.567	0.55	Body; Body	S_Shore
cg02122372	3	149657597	RNF13; RNF13	1.000	0.439	0.55	Body; Body
cg06753227	18	9475508	RALBP1	0.000	0.553	0.55	TSS200	Island
cg14131834	13	45914250	LOC100190939; TPT1	0.040	0.594	0.55	TSS1500; Body	N_Shore
cg25583180	5	177614382	GMCL1L	1.000	0.451	0.55	Body	Island
cg09112623	6	33756905	LEMD2; LEMD2	0.568	0.019	0.55	5’UTR; 1stExon	Island
cg11759477	4	190861959	FRG1	0.000	0.548	0.55	TSS200	Island
cg12796755	14	51132292	SAV1	0.932	0.383	0.55	Body	N_Shelf
cg19693446	14	102144192		0.958	0.407	0.55
cg25187648	3	49395165	GPX1; GPX1; GPX1	0.018	0.568	0.55	Body; 3’UTR; 1stExon	Island
cg20391833	6	167116208	RPS6KA2	0.964	0.431	0.55	Body
cg09705232	6	97611802	MIR548H3; C6orf167	0.974	0.428	0.55	Body; Body
cg04643437	12	14518655	ATF7IP; ATF7IP	0.000	0.561	0.55	1stExon; 5’UTR	Island
cg17558062	13	45965415	LOC100190939	1.000	0.456	0.54	Body	Island
cg26825848	4	190566175		0.350	0.899	0.54		N_Shore
cg13943141	9	93205862		0.846	0.296	0.54
cg26951705	19	56612697	ZNF787	0.000	0.542	0.54	Body	Island
cg24654094	1	160340832	NHLH1	0.964	0.433	0.54	Body	Island
cg02996355	14	81879375		0.909	0.364	0.54
cg11914812	12	56904792		1.000	0.459	0.54
cg24895977	19	35861796		0.990	0.450	0.54
cg11637682	6	147124984	LOC729176; C6orf103	0.867	0.300	0.54	TSS200; Body
cg07089633	14	73396378	DCAF4; DCAF4; DCAF4; DCAF4; DCAF4	1.000	0.450	0.54	5’UTR; 5’UTR; 5’UTR; 5’UTR; 5’UTR	S_Shelf
cg20360416	4	7246127	SORCS2	0.018	0.659	0.54	Body
cg25627920	17	39992620	NT5C3B; NT5C3B; NT5C3B; KLHL10	0.016	0.552	0.54	TSS200; TSS200; TSS200; TSS1500	Island
cg24000259	5	55488291	ANKRD55	0.961	0.411	0.54	Body
cg09138437	11	64527189	PYGM; PYGM	0.993	0.445	0.54	1stExon; 1stExon
cg02673636	1	109647056		0.976	0.437	0.54		S_Shelf
cg18740872	5	39220260	FYB; FYB	1.000	0.466	0.53	TSS1500; TSS1500
cg14354292	7	63353606		0.960	0.426	0.53
cg17704839	19	9939038	UBL5; UBL5	0.014	0.542	0.53	Body; Body	S_Shore
cg05971373	7	157498604	PTPRN2; PTPRN2; PTPRN2	1.000	0.467	0.53	Body; Body; Body	S_Shelf
cg05291429	17	1494566	SLC43A2	0.402	0.969	0.53	Body	S_Shelf
cg08841342	3	156528470	PA2G4P4	0.976	0.444	0.53	Body
cg04096697	6	37012867		0.983	0.451	0.53		Island
cg26878995	1	168106731	GPR161	0.052	0.570	0.53	TSS1500	S_Shore
cg24031524	20	19804606		0.990	0.468	0.53
cg19311470	4	39460490	RPL9; RPL9; LIAS; LIAS	0.004	0.529	0.53	TSS1500; 5’UTR; TSS200; TSS200	Island
cg02181482	5	178942685		0.956	0.449	0.53
cg05346902	19	47910374	MEIS3; MEIS3	0.068	0.593	0.53	Body; Body	Island
cg16470772	10	8203304		0.971	0.445	0.53
cg10115022	1	27527942		0.974	0.464	0.53		Island
cg27231717	6	26319377		0.905	0.386	0.53
cg09451549	19	8386408	RPS28; NDUFA7; RPS28	0.000	0.527	0.53	5’UTR; TSS200; 1stExon	Island
cg07628841	2	27851430	GPN1; CCDC121; GPN1; GPN1; CCDC121; CCDC121; GPN1; GPN1	0.010	0.536	0.53	TSS200; 1stExon; TSS200; TSS1500; 5’UTR; 1stExon; TSS200; TSS1500
cg03816081	10	29577743	LYZL1	0.863	0.301	0.52	TSS1500
cg00762003	21	45393541	AGPAT3; AGPAT3	0.383	0.892	0.52	Body; Body	Island
cg19466922	7	130138026	MEST; MEST; MEST; MEST; MEST; MEST	1.000	0.476	0.52	Body; Body; Body; Body; Body; Body
cg07712165	17	80899280	TBCD	0.440	0.959	0.52	Body	Island
cg01199952	13	25591486		0.984	0.456	0.52		N_Shore
cg11374834	3	75263691		0.950	0.428	0.52
cg02974491	1	1162280	SDF4; SDF4	0.403	0.964	0.52	Body; Body	Island
cg10555853	1	33516627		0.929	0.407	0.52		Island
cg21216606	2	207275704		0.985	0.464	0.52
cg17711541	6	26124704	HIST1H2AC; HIST1H2BC	0.007	0.529	0.52	1stExon; TSS1500	Island
cg06412823	7	22541074	STEAP1B; STEAP1B	0.196	0.739	0.52	TSS1500; TSS1500	S_Shore
cg03054343	11	50238214		1.000	0.463	0.52		Island
cg05766605	1	19384827		0.423	0.938	0.52
cg07684215	10	132976057	TCERG1L	0.181	0.923	0.52	Body
cg27193858	6	41169120	TREML2	0.181	0.691	0.52	TSS200
cg00964321	16	15083956	PDXDC1	0.906	0.386	0.52	Body	Island
cg25394572	11	56457777	OR8U8	0.949	0.429	0.52	Body
cg10667969	3	149181941		0.967	0.458	0.52
cg18394854	5	8457818		0.212	0.732	0.52		Island
cg05741225	10	133917303	JAKMIP3	0.906	0.370	0.52	TSS1500
cg06026769	12	20704492	PDE3A	0.993	0.473	0.52	Body	N_Shore
cg09032630	6	27831956	HIST1H2AL	0.802	0.311	0.52	TSS1500	N_Shore
cg16626480	22	25575426	KIAA1671	0.950	0.422	0.52	Body	Island
cg24534731	17	36888147	CISD3	0.969	0.457	0.52	Body	S_Shore
cg16202259	14	104625420	KIF26A	0.058	0.962	0.52	Body	Island
cg25325592	8	1439535		0.408	0.923	0.52		N_Shore
cg00391025	3	100427239	TFG; TFG	1.000	0.477	0.52	TSS1500; TSS1500	N_Shore
cg25149037	17	39736213		0.820	0.454	0.52
cg19120749	11	1431650	BRSK2; BRSK2; BRSK2; BRSK2; BRSK2; BRSK2	0.468	0.983	0.52	TSS1500; TSS200; Body; Body; Body; Body	Island
cg24270624	10	95721318	PIPSL	0.947	0.421	0.52	Body
cg16346588	10	242978	ZMYND11; ZMYND11; ZMYND11	0.970	0.459	0.51	Body; Body; Body
cg02750322	15	83673816	C15orf40; C15orf40; C15orf40; C15orf40; C15orf40	0.966	0.451	0.51	Body; 3’UTR; Body; Body; Body
cg04363536	3	49466872	NICN1	0.000	0.515	0.51	TSS200	S_Shore
cg17883371	1	91359225		1.000	0.480	0.51		Island
cg25018832	1	564471	LOC101928626	0.602	0.088	0.51	TSS200
cg16838729	4	43901032		0.903	0.394	0.51
cg23222247	17	47302219	PHOSPHO1; PHOSPHO1	0.009	0.531	0.51	Body;Body	Island
cg19496566	19	48249018	GLTSCR2	0.009	0.535	0.51	1stExon	Island
cg03165426	7	30726958	CRHR2; CRHR2	0.429	0.942	0.51	Body; 5’UTR
cg19600494	2	106959525		0.968	0.455	0.51		Island
cg10854807	17	79479308	ACTG1	0.004	0.522	0.51	Body	Island
cg20699097	11	111957680	TIMM8B; TIMM8B; SDHD	0.009	0.524	0.51	TSS200; TSS200; 1stExon	Island
cg22819767	10	11866910	C10orf47	0.958	0.440	0.51	5’UTR	S_Shore
cg20254251	8	144557206	ZC3H3	0.993	0.450	0.51	Body
cg00590830	1	32385224	PTP4A2; PTP4A2; PTP4A2; PTP4A2; PTP4A2	0.971	0.437	0.51	1stExon; 1stExon; 5’UTR; 5’UTR; 5’UTR
cg03877767	2	11680057	GREB1; GREB1	0.172	0.683	0.51	5’UTR; TSS200
cg00487526	15	90818384		0.956	0.444	0.51		Island
cg17501384	2	217364031	RPL37A	0.017	0.521	0.51	Body	S_Shore
cg17646418	6	166911767	RPS6KA2; RPS6KA2	0.987	0.468	0.51	Body; Body
cg06757405	5	140789450	PCDHGA4; PCDHGA9; PCDHGA1; PCDHGB1; PCDHGB6; PCDHGB6; PCDHGB3; PCDHGA6; PCDHGA8; PCDHGA5; PCDHGB4; PCDHGA3; PCDHGA2; PCDHGB2; PCDHGA7; PCDHGB5	0.108	0.607	0.51	Body; Body; Body; Body; 1stExon; 1stExon; Body; Body; Body; Body; Body; Body; Body; Body; Body; Body	Island
cg13448596	8	2031599	MYOM2	0.381	0.887	0.51	Body
cg16711165	11	111957658	TIMM8B; TIMM8B; SDHD	0.017	0.522	0.51	TSS200; TSS200; 1stExon	Island
cg16786640	4	3485263	DOK7; DOK7	0.447	0.954	0.51	Body; Body	N_Shore
cg07638938	10	131348599	MGMT	0.986	0.492	0.51	Body
cg05407710	8	143329409	TSNARE1	0.960	0.451	0.51	Body	N_Shelf
cg07869994	3	174095190		0.988	0.482	0.51		Island
cg07455406	14	21077527		0.017	0.523	0.51		N_Shore
cg04576847	17	12623611	MYOCD; MYOCD; MYOCD; MYOCD	0.958	0.447	0.51	Body; 5’UTR; Body; 1stExon
cg15699853	18	57684747		0.976	0.467	0.51
cg11231240	8	82434638		1.000	0.482	0.51		Island
cg06157924	4	942005	TMEM175	0.451	0.957	0.51	Body	S_Shore
cg23679141	4	165118930	MARCH1; ANP32C	0.946	0.445	0.51	5’UTR; TSS200
cg03053358	17	1029917	ABR; ABR	0.446	0.964	0.51	Body; 5’UTR	S_Shore
cg13705894	9	138305338		0.978	0.495	0.51		S_Shore
cg18512780	17	76117734	TMC6; TMC6	0.033	0.529	0.50	Body; Body
cg06307940	16	46660818		0.986	0.487	0.50
cg15541008	5	95297508	ELL2; ELL2	0.000	0.516	0.50	1stExon; 5’UTR	S_Shore
cg19969624	13	95954210	ABCC4; ABCC4; ABCC4; ABCC4	0.011	0.507	0.50	TSS1500; TSS1500; TSS1500; TSS1500	Island
cg01758870	7	23719630	C7orf46; C7orf46; C7orf46	0.028	0.545	0.50	TSS200;TSS200;TSS200	Island
cg08323201	15	101835348	SNRPA1	0.000	0.503	0.50	1stExon	Island
cg21863998	11	19770288	NAV2; NAV2; NAV2	0.439	0.905	0.50	Body; Body; Body
cg11471802	8	47529015		0.348	0.882	0.50		Island
cg22281935	2	162934111		0.982	0.481	0.50		S_Shelf
cg06032540	15	43941563	CATSPER2; CATSPER2; CATSPER2	1.000	0.500	0.50	TSS1500; TSS1500; TSS1500	Island
cg18761878	1	568475		0.893	0.401	0.50

Density plots of methylation β showed bimodal distribution using both the MC-seq and the EPIC array platforms (Fig. 3c). Density of methylated CpG sites was slightly higher than the density of unmethylated CpG sites on both platforms. However, the two peaks in the EPIC array density plot were closer than the two peaks in the MC-seq density plot (Fig. 3c), indicating that MC-seq captures a higher dynamic range (i.e., more methylated and unmethylated) of CpG sites than the EPIC array. Additional file 4: Figure S3 shows that participants S2, S3, and S4 have similar density plots.

Discussion

We profiled the same PBMC samples using the MC-seq and EPIC array platforms and compared their performance. Our results show that the Agilent SureSelect Methyl-Seq targeted enrichment platform produced high-quality DNA methylation sequencing data at single base-pair resolution. MC-seq can reliably detect CpG sites with DNA input quantities as low as 300 ng. Overall, MC-seq detected 3–4 times more CpG sites than the EPIC array; however, the proportion of CpG sites mapped on functional genomic regions was similar between the two platforms. Methylation at a majority of CpG sites between the two platforms was highly correlated, while methylation at a low percentage of CpG sites differed significantly between the two platforms. Specifically, we found that methylation at 239 CpG sites differed significantly between the two platforms with absolute Δβ values greater than 0.5, which suggests that these CpG sites should be interpreted with caution in EWAS studies.

Our results show that MC-seq produces highly reliable CpG site methylation estimates across the genome. The observed CpG-based reproducibility is high, suggesting that technical variation on CpG calls is low. Inter-personal methylation variation is important for EWAS analysis. We found that our participant-based methylation on common CpG sites across four participants is also highly correlated, which further demonstrates the high reproducibility of this platform.

One disadvantage of sequencing-based approaches is the requirement for a larger quantity of input DNA than array-based approaches for methylation profiling. The recommended input DNA for Agilent SureSelect platform is 1ug, while input DNA quantity for EPIC array can be as low as 250 ng. Input DNA quantity is one important consideration influencing study design and methylation assay platform selection for population-based EWAS. Agilent has reported that DNA quantity can be as low as 250 ng for SureSelect sequencing [14]. To examine whether DNA quantity impacts the performance of MC-seq and to test whether low input DNA quantity also produces reliable CpG detection, we compared the capacity of CpG site detection across three different DNA input quantities. We found that medium DNA input quantity (i.e., 300 ng to 1000 ng) reliably detected CpG sites is comparable to the number of CpG sites captured by high DNA input quantity (i.e., greater than 1000 ng). Low DNA input quantity (i.e., less than 300 ng) detected the lowest number of CpG sites compared with high and medium DNA input quantity. For samples with low DNA input quantity, additional PCR cycles are needed to ensure post-capture library yield that results in extensive duplicate reads. In the four low DNA input samples, the duplicate rate exceeds 80%. Thus, removing duplicate reads is an important step in the QC process for MC-seq. We found that the number of CpG sites in low DNA input samples without duplicated reads still is significantly higher than the number of CpG sites detected by the EPIC array.

Consistent with previous reports, we found that methylation at the majority of CpG sites measured by both approaches (> 98%) is highly consistent between MC-seq and array-based methods. However, we identified 1.4% of CpG sites with discrepancies in CpG methylation that exceeds 10%. More importantly, 239 out of 60,753 discordant CpG sites had methylation differences exceeding 50%. These CpG sites are located on 159 gene regions (Table 4). Some of these genes have been previously reported to be associated with diseases. For example, SLC45A4 was reported to harbor an epigenetic marker for adiposity [21]. The methylation β differs on the CpG site of this gene by as much as 0.63 between the two platforms. We have also identified those CpG sites that showed less but still apparent discrepancy between the two assay platforms (i.e., absolute difference of beta values between 0.1 and 0.5). The top 100 CpG sites discrepant in a range of 0.1–0.4 between two platforms are presented in Table S2 to allow investigators to consider this potential source of bias in EWAS findings. The discrepancy might be due to bias in the performance of the beadchip assay at these positions, sequence context-dependent impacts on the performance of sequencing, batch effects, or a combination of these possibilities. This large discrepancy warrants further investigation and interpretation of findings at these CpG sites must be interpreted with caution.

One of the limitations of this study is the small number of participants used to estimate inter-sample variability. A previous study used a benchmark approach to evaluate performance of different platforms [17] and concluded that the EPIC array performed better than the MC-seq platform. However, the study did not remove duplicate reads as part of their data processing, which may have compromised the QC for MC-seq data processing as discussed above. Future studies, including benchmarking using a larger sample size, could further improve the analysis of platform performance. Of note, MC-seq detected high percentages of CHG and CHH sites across four methylome, which is consistent with previous reports [15]. The significances of those methylation sites warrant further investigation.

New approaches to measurement of DNA methylation continue to emerge that may warrant similar investigation in an ongoing effort to provide users with empiric comparisons to inform decisions about platform selection. One recent approach is enzymatic methyl-sequencing (EM-seq) (e.g., NEBNext EM-seq by New England Biolabs, Ipswich, MA) [22]. The input genomic DNA requirement is low 10–200 ng and EM-seq has comparable performance to WGBS [22], but its performance in relation to array- or capture sequencing-based approaches has not been reported. Should EM-seq gain popularity, it would be important to directly compare the performance of MC-seq and EM-seq to provide empiric evidence to users to inform platform selection.

Nevertheless, we have demonstrated that MC-seq is an efficient, reliable, and affordable platform that allows medium input quantity of DNA input (i.e., > 300 ng), which is equivalent to DNA input required for EPIC array. MC-seq has the advantage of capturing significantly more CpG sites than the EPIC array. Although methylation measurements between the two platforms are highly consistent, we have identified a small number of CpG sites that must be interpreted with caution if they are associated with a trait of interest because they showed significant discrepancies between the two platforms.

Conclusions

Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.

Abbreviations

EM-seq

Enzymatic methyl-seq

EPIC

Illumina Infinium MethylationEPIC Beadchip

EWAS

Epigenome-wide association study

MC-seq

Methylation capture sequencing

PBMC

Peripheral blood mononuclear cell

PCR

Polymerase chain reaction

QC

Quality control

RTA

Real-time analysis

TTS

Transcription termination site

WGBS

Whole-genome bisulfite sequencing

Authors’ contributions

CS contributed to data analysis and the first draft of manuscript. XZ contributed to data processing, quality control, analysis, and manuscript preparation. BA was involved in manuscript preparation and provided peripheral blood monocyte cells. KX contributed to study design, analytical strategies, and manuscript preparation. All the authors read and approved the final manuscript.

Data availability

All methylation data from MC-seq and EPIC platforms are deposited in GEO (GSE152922).

Ethics approval and consent to participate

The study was approved by the committee of the Human Research Subject Protection at Yale University and the Institutional Research Board Committee of the Connecticut Veteran Healthcare System. De-identifiable samples were from Women’s Interagency HIV Study cohort. All participants provided written consents.

Competing interests

The authors declare that they have no competing interests.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s13072-020-00372-6.