
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72240
10.1038/s41598-024-72240-2
Article
The d3GHR carrier epigenome in Druze clan longevity
Falah Ghadeer 1
Kurolap Alina 2
Paperna Tamar 3
Ekhilevitch Nina 3
Moustafa Nivin 3
Damouny-Naoum Nadine 3
Amir Yam 2
Sharvit Lital 1
Moghrabi Rihan 3
Hassoun Gamal 4
Fares Fuad 1
Baris Feldman Hagit 25
Atzmon Gil gatzmon@univ.haifa.ac.il

16
1 https://ror.org/02f009v59 grid.18098.38 0000 0004 1937 0562 Department of Human Biology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
2 https://ror.org/04nd58p63 grid.413449.f 0000 0001 0518 6922 The Genetics Institute and Genomics Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
3 https://ror.org/01fm87m50 grid.413731.3 0000 0000 9950 8111 The Genetics Institute, Rambam Health Care Campus, Haifa, Israel
4 https://ror.org/01fm87m50 grid.413731.3 0000 0000 9950 8111 Institute of Allergy, Clinical Immunology & AIDS, Rambam Health Care Campus, Haifa, Israel
5 https://ror.org/04mhzgx49 grid.12136.37 0000 0004 1937 0546 School of Medicine, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
6 https://ror.org/05cf8a891 grid.251993.5 0000 0001 2179 1997 Departments of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY USA
13 9 2024
13 9 2024
2024
14 214192 6 2024
5 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The Druze are a distinct group known for their close community, traditions, and consanguineous marriages, dating back to the eleventh century. This practice has led to unique genetic variations, impacting both pathology and gene-associated phenotypes. Some Druze clans, particularly those with exceptional long-lived family heads (ELLI), attracted attention. Given that the bulk of these ELLI were men, the d3GHR polymorphism was the first obvious possibility. Among the 73 clan members, 8.2% carried the d3GHR isoform, with nearly 11% being males. There was a significant age-related increase (p = 0.04) in this isoform among males, leading to examination of potential environmental mediators affecting gene regulation among these carriers during life (namely epigenetic). We focused on DNA methylation due to its crucial role in gene regulation, development, and disease progression. We analyzed DNA samples from 14 clan members with different GHR genotypes, finding a significant (p < 0.05) negative correlation between DNA methylation levels and age. Employing a biological age clock, we observed a significant + 4.229 years favoring the d3GHR group over the WT and heterozygous groups. In conclusion, this study highlights the advantage of d3GHR carriers among this unique Druze clan and underscores the importance of genotype-environment interaction in epigenetic regulation and its impact on health.

Keywords

DNA methylation
Epigenetics
Methylation array
GHR
D3GHR
Subject terms

Computational biology and bioinformatics
Genetics
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

The field of genetics focuses on heritable alterations in gene function or activity that result from direct modifications to the DNA sequence. These changes include translocations, insertions, deletions, and point mutations. Conversely, the field of epigenetics investigates heritable alterations to the function or activity of genes that transpire in the absence of modifications to the fundamental DNA sequence1,2. Despite having the same genetic content in any given organism, different cell types express different genes at different times. More broadly, in multicellular organisms, diverse gene expression is mediated in a range of cells and tissues by several factors, including epigenetic mechanisms such as DNA methylation, histone modifications, chromatin remodeling, and non-coding RNA-mediated regulation3.

One of the most studied and mechanistically understood epigenetic modification is the DNA methylation, which is crucial to the development of mammals4, and is conserved in the majority of plants, animals, and fungi5. DNA methylation is regulated by DNA methyltransferases, methyl-CpG binding proteins, and other chromatin-remodeling agents6. It is described as the addition of a methyl (CH3) group into DNA, and it mostly happens on CpG sites or cytosine that precede guanine nucleotides in mammalian cells7. The impact of DNA methylation can vary based on their location in the genome. Methylation at promoter regions typically suppresses gene expression, while enhancing it when found within the gene sequence8. DNA methylation changes chromatin structure and other epigenetic markers, like histone modifications and transcription factors, thus controlling gene expression8,9. An increasing prevalence of human diseases is linked to improper establishment and/or maintenance of this epigenetic information, emphasizing the significance of DNA methylation10,11. Moreover, there is growing interest in creating pharmacological interventions to correct epigenetic abnormalities12.

The importance of comprehensively studying the human DNA methylation in relation to human health and disease has been acknowledged with the advancement of technologies that enable the measurement of DNA methylation at both the individual and population levels across significant regions of the human genome. High-throughput technologies such as the Illumina Infinium HumanMethylation450 BeadChip13,14, the MethylationEPIC BeadChip7, allow for the assessment of methylation at hundreds of thousands of CpG sites across the genome. Additionally, next-generation sequencing (NGS) techniques, including whole-genome bisulfite sequencing (WGBS)15 and reduced representation bisulfite sequencing (RRBS)16, provide detailed and comprehensive methylation profiles. In this study we used the newly developed “Infinium HumanMethylationEPIC version 2.0”17, an array including over 920,000 CpG probes.

In the present study, we investigate whether different isoforms of a growth hormone receptor (GHR) gene can exhibit distinct DNA methylation patterns and contribute to epigenetic variation. The GHR gene can appear in two isoforms: the full-length wild-type (WT) GHR (flGHR), and a shorter gene with a deletion of exon 3 (d3GHR); this common polymorphic deletion variant was described in 2000, and is associated with increased receptor activity as a result of improved signal transduction18. The distribution of GHR isoforms includes, on average, 50% of the population with flGHR (fl/fl), 30–40% that are heterozygous for the exon 3 deletion (fl/d3), and 10–20% that are homozygous for the deletion (d3/d3)19–22. According to our previous study23, exon 3 deletion is associated with increased height, lower serum IGF-1 levels, and enhanced GH sensitivity. Moreover, the d3/d3 genotype extends life expectancy in males by about 10 years, based on multivariate regression analysis23. Building upon these fascinating discoveries, we carried out an in-depth analysis of DNA methylation in order to gain a better understanding of this isoform’s impact on aging, with the anticipation of uncovering differences in aging trends and biological age estimates due to genetic differences. Furthermore, according to published studies, this deletion was associated with more gradual age-related weight and height gains24, greater spontaneous growth25,26, and a better response to human growth hormone treatments in a variety of growth disorders, compared to individuals with the full-length genotype19,27,28.

This project involves individuals from different Druze clans residing in various distinct villages in the northern region of Israel. The Druze religion has its roots in the eleventh century, and they are known for being a closed and endogamous population with a high level of consanguinity, as the common practice is first cousins marriages29. Isolated genetic populations offer a valuable resource for research focused on the genetic predisposition behind diseases and their characteristics.

In the current study, we studied the DNA methylation profiles of individuals with different d3GHR isoforms. Given that DNA methylation alterations are essential for regulating gene expression and body functions30, we hypothesized that individuals with the homozygous deletion will show differentially methylated positions (DMPs) and regions (DMRs) compared with the heterozygous and WT groups, and will have a beneficial DNA methylation pattern which promotes positive gene expression regulation, cellular differentiation, and overall genomic stability, and exhibit younger biological age. Our findings indicate that varying levels of methylation are found among individuals based on their genotype, and that this is associated with exceptional biological and chronological age. To conclude, this study contributes to the growing field of epigenetics and provide insight into how genetics and epigenetic mechanisms impact biological diversity and health outcomes in individuals carrying at least one d3 allele of GHR.

Results

Subject characteristics and genotyping

We analyzed the genotypes of 73 individuals from six families living in four Druze villages/townships, notably, these families were unrelated to one another, ensuring diverse genetic backgrounds for our investigation. The findings indicated that 43 individuals (46.57%) possessed the WT GHR genotype (fl/fl), 33 individuals (45.2%) were carriers (heterozygous) of the exon 3 deletion (d3/fl), and 6 individuals (8.21%) had two copies (homozygous) of the exon 3 deletion (d3/d3). The maximum age observed in the WT group was 104 years, with the youngest participant being 23 years old; in the heterozygous group, ages ranged from 21 to 100 years; and in the homozygous group, participants ranged in age from 43 to 109 years. These age ranges provided a diverse representation of age groups within each genotype, enabling a comprehensive analysis of age-related differences in DNA methylation patterns. The d3GHR genotype is more common in older individuals than in younger individuals, and its prevalence increases with age (P < 0.05), with males showing an increase from 9 (ages 41–65) to 10% (ages 66–85), and a substantial rise to 20% in the 90 + age group. Similarly, females exhibit an increase in d3GHR frequency from 7% (ages 41–65) to 16.6% (ages 90 +), indicating pronounced age-related variations in genotype distribution among both genders (Fig. 1).Fig. 1 Prevalence of d3GHR homozygotes among (a) males (n = 40), and (b) females (n = 33). *P < 0.05.

A group of 14 individuals was chosen for additional examination of DNA methylation patterns. This group included individuals with WT (n = 4), heterozygous (n = 6), and homozygous (n = 4) genotypes (Supplementary Table 1), belonging to six distinct families (Fig. 2). The 14 individuals chosen for DNA methylation analysis were selected based on factors, such as sample availability, family relationships, genetic diversity, and representation of target genotypes to thoroughly study epigenetic variations associated with the investigated genotypes. The familial lineage of these individuals encompassed information on ages and genotypes for all collected samples. This enabled us to analyze and contrast DNA methylation patterns among various genotypic backgrounds.Fig. 2 Representation of the familial relationships, ages, and genotypes of the individuals included in our study among six separate clans (a–f). Black arrows indicate the individuals who were included in the DNA methylation analysis, a diagonal line indicates individuals who have died (Abbreviations: YO—years old).

DNA methylation

DNA methylation data from all 14 individuals (marked in Fig. 2 and in supplementary Table 1) underwent quantile normalization. Quantile normalization normalizes the methylation beta values (β_meth) across samples, therefore all samples get similar β_meth values (Fig. 3A). Mean β_meth values were calculated for each sample based on beta values of CpG sites that passed QC filtering, serving as an indicator of overall DNA methylation levels (Fig. 3).Fig. 3 Average methylation level comparison across groups. Methylation levels were analyzed using the Infinium MethylationEPIC Array and converted to beta values using preprocessQuantile to eliminate background read signal with Minfi R package. (a) Average β_meth values per group, WT = 0.5778, Heterozygous = 0.5788, and Homozygous = 0.5781, no significant difference between groups were identified. (b) Linear regression analysis of average β_meth values across age (p < 0.05).

Differential methylation analyses- DMP and DMR

We analyzed the three separate groups by looking at their DNA methylation patterns in order to identify potential links to health conditions. Examination of methylation data across the genome showed significant variations in DNA methylation patterns between the groups, especially in CpG sites linked to biological functions. To examine the epigenetic factors linked to exon 3 deletion, we compared methylation levels in individuals with homozygous flGHR, heterozygotes, and homozygotes for the exon 3 deletion. We identified five unique methylated position DMPs which were notably significant (Table 1) (P-values have been adjusted using the Benjamini–Hochberg procedure, and are labeled as ‘adj.p-value (BH)). In this analysis, we only included male subjects and combined heterozygous and WT individuals (since there is only one male with the WT genotype) to compare with d3 homozygous individuals. The unique patterns seen indicate potential epigenetic signatures specific to each group. In the d3GHR group, three DMPs exhibited hypermethylation: cg09306641 on chr18, cg09508496 on chr19, and cg23837191 on chr18. Furthermore, two DMPs showed hypomethylation compared to the WT/Heterozygous group: cg26168992 on chr5 and ch.14.569218R on chr14 (Padj < 0.05) (Fig. 4). Table 1 Top 5 significant DMPs that were identified.

CpG	CHR	POS	logFC	p- value	adj.p-value (BH)	Gene	Island + / − 	Islands_Name	Associated Non-Coding RNA (ncRNA)	
cg26168992_BC21	chr5	469,723	− 2.92425601	3.7E−08	0.011525	–	Shelf	chr5:472,485-475,204	ncRNA	
cg09863266_BC21	chr14	50,640,736	 − 1.43023842	1.1E−07	0.021446	SAV1	OpenSea		None	
cg23837191_TC21	chr18	79,575,239	2.57147249	6.0E−10	0.000549	–	Shore	chr18:79,576,081-79,576,282; chr18:79,579,076-79,579,348	None	
cg09306641_BC21	chr18	79,575,412	1.75286852	1.3E−08	0.006097	–	Shore	chr18:79,576,081-79,576,282; chr18:79,579,076-79,579,348	None	
cg09508496_TC11	chr19	38,565,444	2.41303251	8.47E−08	0.019269	RYR1	Island	chr19:38,561,164-38,561,618; chr19:38,564,859-38,565,739	None	
DMP differentially methylated position, CHR chromosome, POS position, p-value- unadjustes p value, adj.p-value (BH)- adjusted p value (Benjamini–Hochberg (BH) procedure).

Fig. 4 5 Differentially Methylated Positions (DMPs) that were identified. Displaying the DMP value for each group with hyper- or hypo-methylation. The x-axis indicates the GHR isoform genotype and the y-axis represents the methylation beta value of each CpG site. The WT/heterozygous and homozygous groups are depicted by red and blue boxes, respectively. *p < 0.05, **p < 0.005.

Next, we proceeded with the identification of differentially methylated regions (DMRs) and found two significant DMRs (Table 2), each composed of two and three CpG sites, respectively (FDR < 0.05) (Table 3 and Fig. 5). Two CpGs are located in proximity to each other on chromosome 18 (cg23837191 and cg09306641), while another three are located in close proximity on chromosome 19 (cg02452418, cg02452966 and cg09508496). The DMR distributions and their significance to the chromosomes are shown in Table 2. The homozygous d3/d3 group had higher methylation levels than the WT and heterozygous groups in all CpG sites (Fig. 5). Our findings revealed two DMRs located on chromosome 18 and demonstrated a hypermethylation pattern. These DMRs are adjacent to the Nuclear Factor of Activated T Cells 1 (NFATC1) gene (chr18: 79,395,930-79,529,323), which is involved in the activation of cytokine gene expression in T-cells, particularly in driving IL-2 or IL-4 gene transcription. It also regulates gene activation in developing cardiomyocytes31. Additionally, another DMR locates next to the RYR1 gene (chr19: 38,433,69-38,587,564), which plays a crucial role in muscle function and regulation of calcium signaling32. This DMR is located on chromosome 19 at genomic coordinates 38,565,444-38,565,576, and exhibits hypermethylation in our analysis compared to WT/Heterozygous samples. Table 2 Top 2 significant DMRs identified between the GHR isoforms.

DMR	CHR	Start	End	HMFDR	Gene	
DMR_1	chr18	79,575,239	79,575,412	0.001008808	NFATC1	
DMR_2	chr19	38,565,444	38,565,576	0.044666242	RYR1	
DMR- differentially methylated regions, HMFDR- harmonic mean of the individual component FDRs, CHR- chromosome.

Table 3 DMPs identified in the two significant DMRs.

CpG	CHR	logFC	adj.P.Val	POS	Islands_Name	Relation to_Island	UCSC_RefGene Name	
cg23837191_TC21	chr18	2.571472487	0.000549	79,575,239	chr18:79,576,081-79,576,282;

chr18:79,579,076-79,579,348

	Shore	NFATC1	
cg09306641_BC21	1.752868515	0.006097	79,575,412	chr18:79,576,081-79,576,282;

chr18:79,579,076-79,579,348

	Shore	NFATC1	
cg09508496_TC11	chr19	2.413032511	0.019269	38,565,444	chr19:38,561,164-38,561,618;

chr19:38,564,859-38,565,739

	Island	RYR1	
cg02452418_BC21	0.835299195	0.775747	38,565,486	chr19:38,561,164-38,561,618;

chr19:38,564,859-38,565,739

	Island	RYR1	
cg02452966_TC11	2.399322682	0.071525	38,565,576	chr19:38,561,164-38,561,618;

chr19:38,564,859-38,565,739

	Island	RYR1	
DMP differentially methylated position, CHR chromosome, POS position.

Fig. 5 DNA methylation levels of 2 differentially methylated regions (DMRs). Five genomic positions were identified. The x-axis indicating the GHR isoform genotypes and the y-axis represents the adjusted methylation beta value of each CpG site. The WT/heterozygous and homozygous groups are depicted by red and blue boxes, respectively. *p < 0.05, **p < 0.005.

These findings highlight distinct epigenetic signatures associated with each group, providing insights into the underlying molecular mechanisms that may contribute to their unique physiological and pathological characteristics.

To visualize the locations of the DMRs identified in our study, we constructed a genetic map of the relevant chromosomes. This map highlights the precise positions of each DMR, providing a clear overview of the methylation changes across the chromosomal landscape (Fig. 6). In specificity, the genetic map (Fig. 6b) shows the exact location of DMR2 in relation to the RYR1 gene.Fig. 6 The plot illustrates the genetic map of the specified chromosomes, highlighting the positions of the identified Differentially Methylated Regions (DMRs) identified by the DMRcate analysis, the positions of nearby genes (including DMR2 in relation to the RYR1 gene), and the methylation levels of individual samples displayed as a heatmap. DMRs are indicated by violet bars and marked with black arrows, while the RYR1 gene is represented by orange bars. Genomic coordinates are based on data obtained from the UCSC Genome Browser.

DNAm age clocks

Epigenetic clocks allow comparison of an individual’s chronological age to their biological age, as calculated based on DNAm clock CpGs. Estimators of age based on DNA methylation, include the pan-tissue epigenetic clock by Horvath 2013, which is based on the values of 353 distinct CpG sites to create a multivariate age predictor33, and an estimator that was created based on 71 CpGs in leukocytes by Hannum 201334. Following Horvath’s first DNA methylation clock ‘HorvathAge’, additional second-generation methylation clocks have been created, e.g. GrimAge and PhenoAge, to focus on CpG sites that may play a more significant role in aging. All age clocks aim to estimate an individual’s biological age based on molecular markers and offer insights into an individual’s biological aging processes and potential health outcomes. This study uncovered an interesting finding about the connection between biological age and chronological age among the study participants. Our findings consistently showed that the predicted biological age was lower than the real chronological age for the majority of participants studied, uncovering intriguing patterns within the genotypic groups that were analyzed.

According to our results, the mFitAge biological age clock revealed significant differences between chronological age and biological age among all individuals assessed (P < 0.05). Notably, as individuals progressed in chronological age, the disparity from biological age increased, indicating a widening gap between actual age and the biological aging processes. This trend was consistent across all biological age clocks utilized in our analysis, emphasizing the robustness of the observed phenomenon.

The “DNAmFitAge” calculator indicated varied biological ages in all genotypic groups (Supplementary Table 2). More precisely, the WT and heterozygous group had a mean biological age of 93.968 years, while the homozygous group had a mean biological age of 89.104 years. The delta age, which represents the difference between chronological and biological age, revealed a notable trend favoring the homozygous d3GHR group. The results revealed a positive delta age of + 4.229 years for the d3GHR group, indicating a possible deceleration or more favorable aging phenotype in which biological age was lower than chronological age. In contrast, the heterozygous and WT groups had negative delta ages of -0.593 years. The observed delta age differences were statistically significant (P < 0.05), indicating distinct aging trajectories associated with different genotypic backgrounds (Fig. 7). The observed increase in delta age among the d3GHR group highlights the potential influence of genetic variations, such as the exon 3 deletion in the growth hormone receptor (GHR) gene, on aging mechanisms and estimations of biological age. This finding underscores the importance of considering genetic differences in assessing biological age and emphasize the importance of considering both chronological age and biological age metrics in aging-related research and health outcomes.Fig. 7 Comparison of the delta age (chronological age and biological age) among various GHR genotypes of individuals aged 65 and above. The x-axis represents the different genotypes (WT and heterozygous vs. homozygous d3), the y-axis represents the delta age values, calculated as the discrepancy between a person’s chronological age and biological age (p < 0.05).

To conclude, the discovery of a consistently younger biological age brings up fascinating inquiries about the various factors that could be causing this occurrence, such as genetics, phenotypes, environment, and lifestyle choices.

Discussion

Epigenetics is becoming increasingly common among researchers in various fields, including cancer, molecular medicine, behavior, development, physiology, morphology, and cell biology35–38. This research field plays a pivotal role in regulating cellular function, and encompasses a wide range of research areas focused on understanding how gene expression and phenotypic characteristics are regulated and influenced by factors beyond the DNA sequence itself39. DNA methylation, the most abundant epigenetic regulator, is crucial for controlling genes and differentiating cells, and has become a field of interest in complex disease processes due to its ability to either direct or mirror transcriptional activity40,41. Variability in DNA methylation levels in healthy individuals is also suggested to impact susceptibility to common diseases and response to therapies42,43. Furthermore, variations in DNA methylation have also been observed among different ethnic groups44,45.

In a previous study, we found that men who are homozygous for the d3GHR isoform live 10 years longer on average23. Additionally, according to published studies, the d3GHR has a positive impact on growth disorders, including increased spontaneous growth19,25,46,47. Building on these fascinating results, we carried out a thorough DNA methylation analysis to learn more about this isoform’s involvement in the aging process. Our research sought to pinpoint the d3GHR isoform’s differentially methylated positions (DMPs) and regions (DMRs), in order to shed light on the epigenetic processes that underlie its lifespan-extending benefits. Furthermore, we compared the biological age of homozygotes for the d3GHR isoform to those with the full-length gene, hoping to uncover potential differences in aging trajectories and biological age estimates caused by these genotypic variations in a family-based structure. This study is a step toward understanding the complex interplay between genetic variations, epigenetic modifications, and aging-related phenotypes, which have implications for longevity-related mechanisms and age-related diseases.

Here we report on methylation profile analyses comparing between individuals homozygous for the full length GHR (fl/fl) and heterozygotes, and individuals homozygous for the exon 3 deletion isoform (d3/d3). The participants in our study are from the Druze community. The Druze community in Israel is a distinctive religious and ethnic minority found mainly in the northern part of the country. Due to their high rate of consanguinity, it is more common to observe recessive traits, which allows the family to maintain specific isoforms or traits across generations. Consequently, in this study, if the oldest individual (proband) in such a lineage possesses the d3GHR isoform, this isoform is likely to be observed in subsequent generations, including children and grandchildren, in both homozygous and heterozygous forms. This inheritance pattern highlights the impact of familial genetic relationships on the preservation of particular genetic traits and ensuring the preservation and transmission of such recessive traits within the family over successive generations.

Among 73 Druze clan members in this research, 8.2% had the d3GHR isoform, with almost 11% being males. A noticeable age-related rise (p = 0.04) in this isoform was observed among males. In our recently published research on Ashkenazi Jews, a noticeable variation in allele frequency for the d3GHR polymorphism was observed in Ashkenazi males. While the male control group had only 4% homozygote deletions, male centenarians and their offspring carried 12 and 11%, respectively23.

Our analysis revealed a significant decrease in DNA methylation levels with advancing age across the study cohort (Fig. 3B). This age-related decrease in methylation was observed at specific CpG sites, as well as in broader genomic regions, indicating a widespread epigenetic remodeling process associated with aging. Our results align with and are backed up by many studies showing a decline in DNA methylation in different populations and tissues as individuals grow older48–50. Moreover, after reaching adulthood, numerous studies have observed an average reduction in blood DNA methylation as individuals grow older34,51,52. This reduction can result in losing control over transcription and can lead to or contribute to negative effects of aging53.

In the present study, despite the constraints of a small sample size, we found five significant DMPs with unique methylation patterns. These DMPs exhibited reliable variations in DNA methylation levels, underscoring their possible biological significance. These discoveries set the foundation for future research on the functional significance of these DMPs and their possible involvement in disease development or biological functions. Furthermore, our analysis revealed the presence of two significant DMRs characterized by distinct methylation patterns. Notably, one DMR consisted of two CpG sites located on chromosome 18, while the other encompassed three CpG sites located on chromosome 19, each showing significant differences in DNA methylation levels in the d3 homozygous samples compared to control samples (WT and heterozygotes).

We identified two DMRs, which are constituted by five DMPs (Table 3), those DMPs are located adjacent to NFATC1 and RYR1 genes. Notably, these positions showed hypermethylation patterns among the d3 homozygous group. This finding suggests a regulatory association between DNA methylation status and the genetic loci that surround NFATC1 and RYR1. The observed hypermethylation near these genes in d3 homozygous individuals highlights potential epigenetic mechanisms that may influence gene expression and cellular processes related to NFATC1 and RYR1, necessitating additional research into their functional implications and role in biological pathways.

Regarding the DMPs that were identified in Table 1, although their functions are unknown, the notable differences in methylation at these sites indicate their possible significance in epigenetic control and cellular functions. Continued research efforts to understand the importance of these non-coding or unannotated regions could reveal new knowledge about the intricacies of the epigenome and its role in different conditions.

As recognition of the important impact of epigenetic changes on the aging process increases, DNA methylation patterns are now used to determine biological age, known as the epigenetic clock. Levels of DNAm have been used to create precise composite biomarkers for measuring biological age33,34,54,55. Studies of epigenetic age acceleration in age-related disorders have been conducted using various epigenetic clocks. Multiple studies indicated that DNA methylation age acceleration is linked to the occurrence, future development, and death rates of various cancer types56–58. Individuals may have a biological age that is younger than their actual chronological age, since age-associated methylation changes can affect the expression of genes33, and distinct DNA methylation profiles may contribute to extended lifespan, these profiles often include the maintenance of youthful methylation patterns in genes related to immune response, inflammation, and metabolic regulation59. In our study, the DNAmFitAge clock has yielded compelling results regarding the delta age, representing the difference between chronological age and biological age, which consistently favored the homozygous group. This finding aligns perfectly with our initial hypothesis, suggesting that genetic variations, specifically the homozygous d3GHR state, may contribute to a more favorable aging phenotype characterized by a lower biological age compared to chronological age. This underscores the potential impact of genetic factors, such as the presence of specific alleles or genetic variants, on aging processes and biological age estimates. The observed positive trend in delta age provides further support for the hypothesis that the exon 3 deletion of the GHR gene, represented by the homozygous group in our study, may exert beneficial effects on aging trajectories.

In conclusion, the inclusion of a family structure sample size enhances the reliability and generalizability of our findings, allowing for more comprehensive insights into DNA methylation patterns and their associations with various biological processes and phenotypes. Our results highlight the significance of studying different genotypes in such structure, as they may lead to various genetic effects, particularly in influencing DNA methylation patterns. Furthermore, by considering genotype-specific analyses, we can gain deeper insights into the interplay between genetic variations and epigenetic mechanisms, providing a more nuanced understanding of how genetic factors contribute to complex biological processes and disease susceptibilities. These considerations are crucial for advancing precision medicine approaches and developing targeted interventions aimed at modulating epigenetic regulation for improved health outcomes.

Materials and methods

Sample collection and preparation

We recruited long-lived Druze probands (age > 90 years), as well as available spouses, children and grandchildren. In accordance with the principles outlined in the Helsinki committee approval, all procedures involving human participants were approved by “Rambam Health Care Campus, Haifa, Israel, 0038-14-RMB”. Written informed consent was obtained from all participants prior to their inclusion in the study. Family history information, including ages, morbidity and mortality, was collected in detail from each family, and all participants were evaluated by medical professionals. We collected 9 mL of EDTA whole blood for genomic DNA (gDNA) extraction, using the FlexiGene DNA Kit (Qiagen)DNA concentration was quantified using the DS-11 spectrophotometer (DeNovix).

Multiplex PCR and samples genotyping

Individual genotypes were assessed for the d3GHR polymorphism. A pair of primers were used to amplify the extracted DNA via PCR. The G1 and G2 primers, which encompass exon 3, result in a PCR fragment of 532 base pairs for the deletion (d3) allele, while the G1 and G3 primers yield a PCR fragment of 935 base pairs for the full-length WT (fl) allele (G3 is an antisense primer located inside exon 3) (GenBank accession number AF155912) (Supplementary Table 3). The PCR reaction mixture contained 0.5 µl of forward primer and 0.5 µl of reverse primer, 10 µl Taq X2 Master Mix, 1 µl template DNA, and 8 µl nuclease-free water. Utilizing a Bio-Rad device, the following parameters were used for the PCR process: 94 °C for 5 min, 35 cycles of 94 °C for 30 s, 60 °C for 30 s, 72 °C for 90 s, and a final extension at 72 °C for 7 min. A 1.5% ethidium bromide stained agarose gel stained was used to separate the PCR products by size (Supplementary Fig. 1). To successfully confirm the deletion, the 953 and 532 bands were cut and purified using a GeneJET Gel Extraction Kit (Thermo Fisher Scientific, Cat. No. K0692), followed by Sanger sequencing by Macrogene sequencing services.

EPIC-methylation assay and bioinformatics analysis

Extracted gDNA was sent to Diagenode for epigenome DNA methylation analysis using the Infinium MethylationEPIC Array Service v2.0. This array is an enhanced version of EPIC v1.0, the new cutting-edge DNA methylation analysis technique by Illumina that utilizes bisulfite conversion. It permits the quantitative measurement of the overall methylation status of more than 930,000 methylation sites across the human genome with a resolution of single nucleotide. It provides an extensive range of information on CpG islands, enhancer regions, open chromatin sites, and other key areas of the methylome (Diagenode Cat# G0209006). Briefly, data analysis was performed within the R v4.2.2 statistical environment, raw intensity (.idat) files were imported into R using the minfi package60, followed by the calculation of M and methylation Beta values. Methylation beta (β_meth) values refer to the proportion of methylation at a specific CpG site, ranging from 0 (no methylation) to 1 (complete methylation). A detection p-value was computed to assess signal quality at specific sites. Stratified quantile normalization of beta and M values was performed using the preprocessQuantile function in the minfi package. Density plots were generated to assess the distribution of beta values before and after quantile normalization (Supplementary Fig. 2). Only male participants were considered for the differential methylation examination (n = 9), and the comparison between “homozygous versus heterozygous/wild-type” was employed to increase statistical strength in all models examining differential methylation (DMP and DMR). A flow chart diagram depicting the analysis of the “Infinium MethylationEPIC Array” is presented in Supplementary Fig. 3.

Differentially methylated probes (DMPs)

DMPs associated with a genotype were identified using M values and a linear model from the limma R package (v.3.54.2)61. The effect of the ‘genotype’ coefficient was extracted to identify significant DMPs, with all DMPs having an adjusted p-value under 0.05 considered significant, lmFit function from limma R package was used. Adjustment was performed with Benjamini–Hochberg procedure. The Benjamini–Hochberg adjustment is a statistical method that controls for false discovery rate (FDR).

Differentially methylated regions (DMRs)

For the identification of DMRs spanning multiple CpG sites, the DMRcate R package (v.2.12.0) was employed62. All DMRs that met the criterion of a false discovery rate (FDR) below 0.05 were deemed statistically significant.

Probes were annotated using the IlluminaHumanMethylationEPICv2anno.20a1.hg38 package [https://github.com/jokergoo/IlluminaHumanMethylationEPICv2anno.20a1.hg38], distinguishing between Type I and Type II probes and annotating them to CpG Islands, open sea regions, shores and shelves.

Creation of genetic map

To illustrate the locations of the Differentially Methylated Regions (DMRs) in their genomic context, we used the “DMR.plot” function. This function allows for the creation of detailed genetic maps that display the positions of DMRs relative to the chromosome and to nearby genes. The resulting plots provide a clear visual representation of how these DMRs are distributed along the genome and their proximity to key genetic features.

Epigenetic clock and DNA methylation (DNAm) age calculation

We utilized epigenome-wide methylation information to calculate the epigenetic clocks using the DNA Methylation Age Calculator (https://dnamage.genetics.ucla.edu). The data used for the calculation were adjusted using the DNA methylation Age Calculator.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72240-2.

Acknowledgements

The study was partially supported by the Gassner Fund.

Author contributions

G.F., A.K., T.P., N.E., N.M., N.D.N., Y.A., L.S., R.M., G.H, F.F., H.B.F., G.A. planned and managed the project as well as reviewed the paper. G.F., G.A. performed the data analysis.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Jablonka E Lamb MJ The inheritance of acquired epigenetic variations Int. J. Epidemiol. 2015 44 395 401
Jablonka, E. & Lamb, M. J. The inheritance of acquired epigenetic variations. Int. J. Epidemiol. 44, 395–401 (2015).
2. Richards EJ Inherited epigenetic variation - Revisiting soft inheritance Nat. Rev. Genet. 2006 7 395 401 10.1038/nrg1834 16534512
Richards, E. J. Inherited epigenetic variation - Revisiting soft inheritance. Nat. Rev. Genet. 7, 395–401 (2006).16534512 10.1038/nrg1834
3. Handy DE Castro R Loscalzo J Epigenetic modifications: Basic mechanisms and role in cardiovascular disease Circulation 2011 123 2145 2156 10.1161/CIRCULATIONAHA.110.956839 21576679
Handy, D. E., Castro, R. & Loscalzo, J. Epigenetic modifications: Basic mechanisms and role in cardiovascular disease. Circulation 123, 2145–2156 (2011).21576679 10.1161/CIRCULATIONAHA.110.956839
4. Smith ZD Meissner A DNA methylation: Roles in mammalian development Nat. Rev. Genet. 2013 14 204 220 10.1038/nrg3354 23400093
Smith, Z. D. & Meissner, A. DNA methylation: Roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).23400093 10.1038/nrg3354
5. Feng S Jacobsen SE Reik W Epigenetic reprogramming in plant and animal development Science 2010 330 622 627 10.1126/science.1190614 21030646
Feng, S., Jacobsen, S. E. & Reik, W. Epigenetic reprogramming in plant and animal development. Science 330, 622–627 (2010).21030646 10.1126/science.1190614
6. Robertson KD DNA methylation and human disease Nat. Rev. Genet. 2005 6 597 610 10.1038/nrg1655 16136652
Robertson, K. D. DNA methylation and human disease. Nat. Rev. Genet. 6, 597–610 (2005).16136652 10.1038/nrg1655
7. Pidsley R Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling Genome Biol. 2016 10.1186/s13059-016-1066-1 27717381
Pidsley, R. et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol.10.1186/s13059-016-1066-1 (2016).27717381 10.1186/s13059-016-1066-1
8. Moore LD Le T Fan G DNA methylation and its basic function Neuropsychopharmacology 2013 38 23 38 10.1038/npp.2012.112 22781841
Moore, L. D., Le, T. & Fan, G. DNA methylation and its basic function. Neuropsychopharmacology 38, 23–38 (2013).22781841 10.1038/npp.2012.112
9. Oudelaar AM Higgs DR The relationship between genome structure and function Nat. Rev. Genet. 2021 22 154 168 10.1038/s41576-020-00303-x 33235358
Oudelaar, A. M. & Higgs, D. R. The relationship between genome structure and function. Nat. Rev. Genet. 22, 154–168 (2021).33235358 10.1038/s41576-020-00303-x
10. Simmons, D. Epigenetic Influences and Disease. Nat. Educ. 1, (2008).
11. Jin Z Liu Y DNA methylation in human diseases Genes Dis. 2018 5 1 8 10.1016/j.gendis.2018.01.002 30258928
Jin, Z. & Liu, Y. DNA methylation in human diseases. Genes Dis. 5, 1–8 (2018).30258928 10.1016/j.gendis.2018.01.002
12. Brown R Strathdee G Epigenomics and epigenetic therapy of cancer Trends Mol. Med. 2002 8 S43 S48 10.1016/S1471-4914(02)02314-6 11927287
Brown, R. & Strathdee, G. Epigenomics and epigenetic therapy of cancer. Trends Mol. Med. 8, S43–S48 (2002).11927287 10.1016/S1471-4914(02)02314-6
13. Bibikova M High density DNA methylation array with single CpG site resolution Genomics 2011 98 288 295 10.1016/j.ygeno.2011.07.007 21839163
Bibikova, M. et al. High density DNA methylation array with single CpG site resolution. Genomics 98, 288–295 (2011).21839163 10.1016/j.ygeno.2011.07.007
14. Dedeurwaerder S Evaluation of the infinium methylation 450K technology Epigenomics 2011 3 771 784 10.2217/epi.11.105 22126295
Dedeurwaerder, S. et al. Evaluation of the infinium methylation 450K technology. Epigenomics 3, 771–784 (2011).22126295 10.2217/epi.11.105
15. Barros-Silva D Marques CJ Henrique R Jerónimo C Profiling DNA methylation based on next-generation sequencing approaches: New insights and clinical applications Genes 2018 9 429 10.3390/genes9090429 30142958
Barros-Silva, D., Marques, C. J., Henrique, R. & Jerónimo, C. Profiling DNA methylation based on next-generation sequencing approaches: New insights and clinical applications. Genes 9, 429 (2018).30142958 10.3390/genes9090429
16. Meissner A Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis Nucleic Acids Res. 2005 33 5868 5877 10.1093/nar/gki901 16224102
Meissner, A. et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868–5877 (2005).16224102 10.1093/nar/gki901
17. Noguera-Castells A García-Prieto CA Álvarez-Errico D Esteller M Validation of the new EPIC DNA methylation microarray (900K EPIC v2) for high-throughput profiling of the human DNA methylome Epigenetics 2023 18 2185742 10.1080/15592294.2023.2185742 36871255
Noguera-Castells, A., García-Prieto, C. A., Álvarez-Errico, D. & Esteller, M. Validation of the new EPIC DNA methylation microarray (900K EPIC v2) for high-throughput profiling of the human DNA methylome. Epigenetics 18, 2185742 (2023).36871255 10.1080/15592294.2023.2185742
18. Filopanti M The exon 3-deleted growth hormone receptor: Molecular and functional characterization and impact on GH/IGF-I axis in physiological and pathological conditions J. Endocrinol. Investig. 2011 10.1007/BF03346731
Filopanti, M. et al. The exon 3-deleted growth hormone receptor: Molecular and functional characterization and impact on GH/IGF-I axis in physiological and pathological conditions. J. Endocrinol. Investig.10.1007/BF03346731 (2011).10.1007/BF03346731
19. Dos Santos C A common polymorphism of the growth hormone receptor is associated with increased responsiveness to growth hormone Nat. Genet. 2004 10.1038/ng1379 15208626
Dos Santos, C. et al. A common polymorphism of the growth hormone receptor is associated with increased responsiveness to growth hormone. Nat. Genet.10.1038/ng1379 (2004).15208626 10.1038/ng1379
20. Pantel J Species-specific alternative splice mimicry at the growth hormone receptor locus revealed by the lineage of retroelements during primate evolution J. Biol. Chem. 2000 10.1074/jbc.m001615200 10764769
Pantel, J. et al. Species-specific alternative splice mimicry at the growth hormone receptor locus revealed by the lineage of retroelements during primate evolution. J. Biol. Chem.10.1074/jbc.m001615200 (2000).10764769 10.1074/jbc.m001615200
21. Audí L Exon 3-deleted/full-length growth hormone receptor polymorphism genotype frequencies in Spanish short small-for-gestational-age (SGA) children and adolescents (n = 247) and in an adult control population (n = 289) show increased fl/fl in short SGA J. Clin. Endocrinol. Metab. 2006 91 5038 5043 10.1210/jc.2006-0828 17003087
Audí, L. et al. Exon 3-deleted/full-length growth hormone receptor polymorphism genotype frequencies in Spanish short small-for-gestational-age (SGA) children and adolescents (n = 247) and in an adult control population (n = 289) show increased fl/fl in short SGA. J. Clin. Endocrinol. Metab. 91, 5038–5043 (2006).17003087 10.1210/jc.2006-0828
22. Montefusco L D3-Growth hormone receptor polymorphism in acromegaly: Effects on metabolic phenotype Clin. Endocrinol. (Oxf). 2010 10.1111/j.1365-2265.2009.03703.x 20447065
Montefusco, L. et al. D3-Growth hormone receptor polymorphism in acromegaly: Effects on metabolic phenotype. Clin. Endocrinol. (Oxf).10.1111/j.1365-2265.2009.03703.x (2010).20447065 10.1111/j.1365-2265.2009.03703.x
23. Ben-Avraham D The GH receptor exon 3 deletion is a marker of male-specific exceptional longevity associated with increased GH sensitivity and taller stature Sci. Adv. 2017 10.1126/sciadv.1602025 28630896
Ben-Avraham, D. et al. The GH receptor exon 3 deletion is a marker of male-specific exceptional longevity associated with increased GH sensitivity and taller stature. Sci. Adv.10.1126/sciadv.1602025 (2017).28630896 10.1126/sciadv.1602025
24. Butler MG Hossain W Hassan M Manzardo AM Growth hormone receptor (GHR) gene polymorphism and scoliosis in Prader-Willi syndrome Growth Horm. IGF Res. 2018 39 29 33 10.1016/j.ghir.2017.12.001 29273483
Butler, M. G., Hossain, W., Hassan, M. & Manzardo, A. M. Growth hormone receptor (GHR) gene polymorphism and scoliosis in Prader-Willi syndrome. Growth Horm. IGF Res. 39, 29–33 (2018).29273483 10.1016/j.ghir.2017.12.001
25. Wegmann MG The exon3-deleted growth hormone receptor gene polymorphism (d3-GHR) is associated with insulin and spontaneous growth in short SGA children (NESGAS) Growth Horm. IGF Res. 2017 35 45 51 10.1016/j.ghir.2017.07.001 28719834
Wegmann, M. G. et al. The exon3-deleted growth hormone receptor gene polymorphism (d3-GHR) is associated with insulin and spontaneous growth in short SGA children (NESGAS). Growth Horm. IGF Res. 35, 45–51 (2017).28719834 10.1016/j.ghir.2017.07.001
26. Garrido NP Growth hormone receptor gene polymorphism. Spontaneous catch up growth in small for gestational age patients Medicina (B Aires) 2021 81 1669 9106
Garrido, N. P. et al. Growth hormone receptor gene polymorphism. Spontaneous catch up growth in small for gestational age patients. Medicina (B Aires) 81, 1669–9106 (2021).
27. Bernabeu I The exon 3-deleted growth hormone receptor is associated with better response to pegvisomant therapy in acromegaly J. Clin. Endocrinol. Metab. 2010 95 222 229 10.1210/jc.2009-1630 19850678
Bernabeu, I. et al. The exon 3-deleted growth hormone receptor is associated with better response to pegvisomant therapy in acromegaly. J. Clin. Endocrinol. Metab. 95, 222–229 (2010).19850678 10.1210/jc.2009-1630
28. Dörr HG Different relationships between the first 2 years on growth hormone treatment and the d3-growth hormone receptor polymorphism in short small-for-gestational-age (SGA) children Clin. Endocrinol. 2011 75 656 660 10.1111/j.1365-2265.2011.04104.x
Dörr, H. G. et al. Different relationships between the first 2 years on growth hormone treatment and the d3-growth hormone receptor polymorphism in short small-for-gestational-age (SGA) children. Clin. Endocrinol. 75, 656–660 (2011).10.1111/j.1365-2265.2011.04104.x
29. Firro KM The Druze faith: Origin, development and interpretation Arabica 2011 58 76 99 10.1163/157005811X550309
Firro, K. M. The Druze faith: Origin, development and interpretation. Arabica 58, 76–99 (2011).10.1163/157005811X550309
30. Jones PA Functions of DNA methylation: Islands, start sites, gene bodies and beyond Nat. Rev. Genet. 2012 13 484 492 10.1038/nrg3230 22641018
Jones, P. A. Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484–492 (2012).22641018 10.1038/nrg3230
31. Hogan PG Chen L Nardone J Rao A Transcriptional regulation by calcium, calcineurin, and NFAT Genes Dev. 2003 17 2205 2232 10.1101/gad.1102703 12975316
Hogan, P. G., Chen, L., Nardone, J. & Rao, A. Transcriptional regulation by calcium, calcineurin, and NFAT. Genes Dev. 17, 2205–2232 (2003).12975316 10.1101/gad.1102703
32. Phillips MS The structural organization of the human skeletal muscle ryanodine receptor (RYR1) gene Genomics 1996 34 24 41 10.1006/geno.1996.0238 8661021
Phillips, M. S. et al. The structural organization of the human skeletal muscle ryanodine receptor (RYR1) gene. Genomics 34, 24–41 (1996).8661021 10.1006/geno.1996.0238
33. Horvath S DNA methylation age of human tissues and cell types Genome Biol. 2013 10.1186/gb-2013-14-10-r115 24138928
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol.10.1186/gb-2013-14-10-r115 (2013).24138928 10.1186/gb-2013-14-10-r115
34. Hannum G Genome-wide methylation profiles reveal quantitative views of human aging rates Mol. Cell 2013 10.1016/j.molcel.2012.10.016 23177740
Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell10.1016/j.molcel.2012.10.016 (2013).23177740 10.1016/j.molcel.2012.10.016
35. Artemov AV Genome-wide DNA methylation profiling reveals epigenetic adaptation of stickleback to marine and freshwater conditions Mol. Biol. Evol. 2017 34 2203 2213 10.1093/molbev/msx156 28873953
Artemov, A. V. et al. Genome-wide DNA methylation profiling reveals epigenetic adaptation of stickleback to marine and freshwater conditions. Mol. Biol. Evol. 34, 2203–2213 (2017).28873953 10.1093/molbev/msx156
36. Rubenstein DR Sex-specific fitness effects of unpredictable early life conditions are associated with DNA methylation in the avian glucocorticoid receptor Mol. Ecol. 2016 25 1714 1728 10.1111/mec.13483 26588348
Rubenstein, D. R. et al. Sex-specific fitness effects of unpredictable early life conditions are associated with DNA methylation in the avian glucocorticoid receptor. Mol. Ecol. 25, 1714–1728 (2016).26588348 10.1111/mec.13483
37. Heckwolf MJ Two different epigenetic information channels in wild three-spined sticklebacks are involved in salinity adaptation Sci. Adv. 2020 6 eaaz1138 10.1126/sciadv.aaz1138 32219167
Heckwolf, M. J. et al. Two different epigenetic information channels in wild three-spined sticklebacks are involved in salinity adaptation. Sci. Adv. 6, eaaz1138 (2020).32219167 10.1126/sciadv.aaz1138
38. Husby A Wild epigenetics: Insights from epigenetic studies on natural populations Proc. R. Soc. B: Biol. Sci. 2022 289 20211633 10.1098/rspb.2021.1633
Husby, A. Wild epigenetics: Insights from epigenetic studies on natural populations. Proc. R. Soc. B: Biol. Sci. 289, 20211633 (2022).10.1098/rspb.2021.1633
39. Egger G Liang G Aparicio A Jones PA Epigenetics in human disease and prospects for epigenetic therapy Nature 2004 429 457 463 10.1038/nature02625 15164071
Egger, G., Liang, G., Aparicio, A. & Jones, P. A. Epigenetics in human disease and prospects for epigenetic therapy. Nature 429, 457–463 (2004).15164071 10.1038/nature02625
40. Zilberman D Gehring M Tran RK Ballinger T Henikoff S Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription Nat. Genet. 2007 39 1 61 69 10.1038/ng1929 17128275
Zilberman, D., Gehring, M., Tran, R. K., Ballinger, T. & Henikoff, S. Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nat. Genet. 39(1), 61–69 (2007).17128275 10.1038/ng1929
41. Lappalainen T Greally JM Associating cellular epigenetic models with human phenotypes Nat. Rev. Genet. 2017 18 441 451 10.1038/nrg.2017.32 28555657
Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017).28555657 10.1038/nrg.2017.32
42. Heyn H DNA methylation contributes to natural human variation Genome Res. 2013 23 1363 1372 10.1101/gr.154187.112 23908385
Heyn, H. et al. DNA methylation contributes to natural human variation. Genome Res. 23, 1363–1372 (2013).23908385 10.1101/gr.154187.112
43. Chatterjee A Genome-wide DNA methylation map of human neutrophils reveals widespread inter-individual epigenetic variation Sci. Rep. 2015 5 17328 10.1038/srep17328 26612583
Chatterjee, A. et al. Genome-wide DNA methylation map of human neutrophils reveals widespread inter-individual epigenetic variation. Sci. Rep. 5, 17328 (2015).26612583 10.1038/srep17328
44. Zhang FF Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood Epigenetics 2011 6 623 629 10.4161/epi.6.5.15335 21739720
Zhang, F. F. et al. Significant differences in global genomic DNA methylation by gender and race/ethnicity in peripheral blood. Epigenetics 6, 623–629 (2011).21739720 10.4161/epi.6.5.15335
45. Coit P Ethnicity-specific epigenetic variation in naïve CD4+ T cells and the susceptibility to autoimmunity Epigenet. Chromatin 2015 8 1 13 10.1186/s13072-015-0037-1
Coit, P. et al. Ethnicity-specific epigenetic variation in naïve CD4+ T cells and the susceptibility to autoimmunity. Epigenet. Chromatin 8, 1–13 (2015).10.1186/s13072-015-0037-1
46. Renehan AG Growth hormone receptor polymorphism and growth hormone therapy response in children: A bayesian meta-analysis Am. J. Epidemiol. 2012 175 867 877 10.1093/aje/kwr408 22494952
Renehan, A. G. et al. Growth hormone receptor polymorphism and growth hormone therapy response in children: A bayesian meta-analysis. Am. J. Epidemiol. 175, 867–877 (2012).22494952 10.1093/aje/kwr408
47. Wassenaar MJE Impact of the exon 3-deleted Growth Hormone (GH) receptor polymorphism on baseline height and the growth response to recombinant human GH therapy in GH-Deficient (GHD) and non-GHD children with short stature: A systematic review and meta-analysis J. Clin. Endocrinol. Metab. 2009 94 3721 3730 10.1210/jc.2009-0425 19584188
Wassenaar, M. J. E. et al. Impact of the exon 3-deleted Growth Hormone (GH) receptor polymorphism on baseline height and the growth response to recombinant human GH therapy in GH-Deficient (GHD) and non-GHD children with short stature: A systematic review and meta-analysis. J. Clin. Endocrinol. Metab. 94, 3721–3730 (2009).19584188 10.1210/jc.2009-0425
48. Xiao FH Kong QP Perry B He YH Progress on the role of DNA methylation in aging and longevity Brief. Funct. Genom. 2016 15 elw009 10.1093/bfgp/elw009
Xiao, F. H., Kong, Q. P., Perry, B. & He, Y. H. Progress on the role of DNA methylation in aging and longevity. Brief. Funct. Genom. 15, elw009 (2016).10.1093/bfgp/elw009
49. Grönniger E Aging and chronic sun exposure cause distinct epigenetic changes in human skin PLoS Genet. 2010 6 e1000971 10.1371/journal.pgen.1000971 20523906
Grönniger, E. et al. Aging and chronic sun exposure cause distinct epigenetic changes in human skin. PLoS Genet. 6, e1000971 (2010).20523906 10.1371/journal.pgen.1000971
50. Ong ML Holbrook JD Novel region discovery method for Infinium 450K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways Aging Cell 2014 13 142 155 10.1111/acel.12159 24112369
Ong, M. L. & Holbrook, J. D. Novel region discovery method for Infinium 450K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways. Aging Cell 13, 142–155 (2014).24112369 10.1111/acel.12159
51. Bjornsson HT Daniele Fallin M Feinberg AP An integrated epigenetic and genetic approach to common human disease Trends Genet. 2004 20 350 358 10.1016/j.tig.2004.06.009 15262407
Bjornsson, H. T., Daniele Fallin, M. & Feinberg, A. P. An integrated epigenetic and genetic approach to common human disease. Trends Genet. 20, 350–358 (2004).15262407 10.1016/j.tig.2004.06.009
52. Horvath S Aging effects on DNA methylation modules in human brain and blood tissue Genome Biol. 2012 13 1 18 10.1186/gb-2012-13-10-r97
Horvath, S. et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 13, 1–18 (2012).10.1186/gb-2012-13-10-r97
53. Unnikrishnan A Revisiting the genomic hypomethylation hypothesis of aging Ann. N. Y. Acad. Sci. 2018 1418 69 79 10.1111/nyas.13533 29363785
Unnikrishnan, A. et al. Revisiting the genomic hypomethylation hypothesis of aging. Ann. N. Y. Acad. Sci. 1418, 69–79 (2018).29363785 10.1111/nyas.13533
54. Lin Q DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy Aging 2016 8 394 10.18632/aging.100908 26928272
Lin, Q. et al. DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy. Aging 8, 394 (2016).26928272 10.18632/aging.100908
55. McGreevy KM DNAmFitAge: biological age indicator incorporating physical fitness Aging 2023 15 3904 10.18632/aging.204538 36812475
McGreevy, K. M. et al. DNAmFitAge: biological age indicator incorporating physical fitness. Aging 15, 3904 (2023).36812475 10.18632/aging.204538
56. Levine ME DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative Aging 2015 7 690 10.18632/aging.100809 26411804
Levine, M. E. et al. DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative. Aging 7, 690 (2015).26411804 10.18632/aging.100809
57. Zheng Y Blood epigenetic age may predict cancer incidence and mortality EBioMedicine 2016 5 68 73 10.1016/j.ebiom.2016.02.008 27077113
Zheng, Y. et al. Blood epigenetic age may predict cancer incidence and mortality. EBioMedicine 5, 68–73 (2016).27077113 10.1016/j.ebiom.2016.02.008
58. Ambatipudi S DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility Eur. J. Cancer 2017 75 299 307 10.1016/j.ejca.2017.01.014 28259012
Ambatipudi, S. et al. DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility. Eur. J. Cancer 75, 299–307 (2017).28259012 10.1016/j.ejca.2017.01.014
59. Salameh Y Bejaoui Y El Hajj N DNA methylation biomarkers in aging and age-related diseases Front. Genet. 2020 11 480672 10.3389/fgene.2020.00171
Salameh, Y., Bejaoui, Y. & El Hajj, N. DNA methylation biomarkers in aging and age-related diseases. Front. Genet. 11, 480672 (2020).10.3389/fgene.2020.00171
60. Aryee MJ Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays Bioinformatics 2014 30 1363 1369 10.1093/bioinformatics/btu049 24478339
Aryee, M. J. et al. Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369 (2014).24478339 10.1093/bioinformatics/btu049
61. Ritchie ME Limma powers differential expression analyses for RNA-sequencing and microarray studies Nucleic Acids Res. 2015 43 e47 e47 10.1093/nar/gkv007 25605792
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).25605792 10.1093/nar/gkv007
62. Peters TJ De novo identification of differentially methylated regions in the human genome Epigenet. Chromatin 2015 8 1 16 10.1186/1756-8935-8-6
Peters, T. J. et al. De novo identification of differentially methylated regions in the human genome. Epigenet. Chromatin 8, 1–16 (2015).10.1186/1756-8935-8-6
