Genome-wide association study for four measures of epigenetic age acceleration and two epigenetic surrogate markers using DNA methylation data from Taiwan biobank

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Gene-Environment Interactions and Gene-Gene Interactions on Two Biological Age Measures: Evidence from Taiwan Biobank Participants

PhenoAge and BioAge are two commonly used biological age (BA) measures. The author here searched for gene-environment interactions (GxE) and gene-gene interactions (GxG) on PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 111,996 Taiwan Biobank (TWB) participants, including a discovery set of 86,536 TWB2 individuals and a replication set of 25,460 TWB1 individuals. Searching for variance quantitative trait loci (vQTLs) provides a convenient way to evaluate GxE and GxG. A total of 4 nearly independent (linkage disequilibrium measure r2 < 0.01) PhenoAgeAccel-vQTLs are identified from 5,303,039 autosomal TWB2 SNPs (p < 5E-8), whereas no vQTLs are found from BioAgeAccel. These 4 PhenoAgeAccel-vQTLs (rs35276921, rs141927875, rs10903013, and rs76038336) are further replicated by TWB1 (p < 5E-8). They are located in the OR51B5, FAM234A, and AXIN1 genes. All 4 PhenoAgeAccel-vQTLs are significantly associated with PhenoAgeAccel (p < 5E-8). A phylogenetic heat map of the GxE analyses showed that smoking exacerbated the PhenoAgeAccel-vQTLs' aging effects, while higher educational attainment attenuated the PhenoAgeAccel-vQTLs' aging effects. Body mass index, chronological age, alcohol consumption, and sex do not prominently modulate PhenoAgeAccel-vQTLs' aging effects. Based on these vQTL results, rs141927875-rs35276921 interaction (p = 4.7E-61) and rs76038336-rs10903013 interaction (p = 3.3E-116) on PhenoAgeAccel are detected.

Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes

Introduction: After the era of genome-wide association studies (GWAS), thousands of genetic variants have been identified to exhibit main effects on human phenotypes. The next critical issue would be to explore the interplay between genes, the so-called "gene-gene interactions" (GxG) or epistasis. An exhaustive search for all single-nucleotide polymorphism (SNP) pairs is not recommended because this will induce a harsh penalty of multiple testing. Limiting the search of epistasis on SNPs reported by previous GWAS may miss essential interactions between SNPs without significant marginal effects. Moreover, most methods are computationally intensive and can be challenging to implement genome-wide. Methods: I here searched for GxG through variance quantitative trait loci (vQTLs) of 29 continuous Taiwan Biobank (TWB) phenotypes. A discovery cohort of 86,536 and a replication cohort of 25,460 TWB individuals were analyzed, respectively. Results: A total of 18 nearly independent vQTLs with linkage disequilibrium measure r 2 < 0.01 were identified and replicated from nine phenotypes. 15 significant GxG were found with p-values <1.1E-5 (in the discovery cohort) and false discovery rates <2% (in the replication cohort). Among these 15 GxG, 11 were detected for blood traits including red blood cells, hemoglobin, and hematocrit; 2 for total bilirubin; 1 for fasting glucose; and 1 for total cholesterol (TCHO). All GxG were observed for gene pairs on the same chromosome, except for the APOA5 (chromosome 11)-TOMM40 (chromosome 19) interaction for TCHO. Discussion: This study provided a computationally feasible way to search for GxG genome-wide and applied this approach to 29 phenotypes.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Cardiovascular health and four epigenetic clocks

BACKGROUND

Cardiovascular health (CVH) was defined by the American Heart Association as an integrative idealness of seven clinical or lifestyle factors. Based on populations of European ancestry, recent studies have shown that ideal CVH is associated with a slower aging rate. The aging rate is measured by levels of epigenetic age acceleration (EAA), usually obtained from the residuals of regressing DNA methylation (DNAm) age on chronological age. However, little has been known about the association of CVH with biological aging in Asian populations.

METHODS AND RESULTS

We here analyzed blood DNAm data and clinical/lifestyle factors of 2474 Taiwan Biobank (TWB) participants, to investigate the association of CVH with EAA. CVH was assessed by seven components: smoking status, physical activity, dietary habits, body mass index, total cholesterol, fasting glucose, and blood pressure levels. Four measures of EAA were applied, among which two were based on the first-generation DNAm clocks (HannumEAA and IEAA) and two were based on the second-generation clocks (PhenoEAA and GrimEAA). After excluding 276 individuals with cardiovascular diseases, we regressed EAA on the CVH score (ranging from 0 to 7, integrating the abovementioned seven components) while adjusting for sex, drinking status, and educational attainment. Our results showed that a decrease in one point in the CVH score was associated with a 0.350-year PhenoEAA (p = 4.5E-4) and a 0.499-year GrimEAA (p = 4.2E-15). By contrast, HannumEAA and IEAA were not significantly associated with the CVH score. We have obtained consistent results within each generation of epigenetic clocks.

CONCLUSIONS

This is one of the first studies to comprehensively investigate the associations of CVH with four epigenetic clocks. Our TWB data showed that ideal CVH is associated with lower levels of EAA calculated according to the second-generation epigenetic clocks (PhenoEAA and GrimEAA). Having an ideal CVH status can lower EAA and reduce the risk of aging-related disorders.

Genetic loci and metabolic states associated with murine epigenetic aging

Changes in DNA methylation (DNAm) are linked to aging. Here, we profile highly conserved CpGs in 339 predominantly female mice belonging to the BXD family for which we have deep longevity and genomic data. We use a 'pan-mammalian' microarray that provides a common platform for assaying the methylome across mammalian clades. We computed epigenetic clocks and tested associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. We describe the multifactorial variance of methylation at these CpGs and show that high-fat diet augments the age-related changes. Entropy increases with age. The progression to disorder, particularly at CpGs that gain methylation over time, was predictive of genotype-dependent life expectancy. The longer-lived BXD strains had comparatively lower entropy at a given age. We identified two genetic loci that modulate epigenetic age acceleration (EAA): one on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, and the other on Chr19 that contains a cytochrome P450 cluster. Both loci harbor genes associated with EAA in humans, including STXBP4, NKX2-3, and CUTC. Transcriptome and proteome analyses revealed correlations with oxidation-reduction, metabolic, and immune response pathways. Our results highlight concordant loci for EAA in humans and mice, and demonstrate a tight coupling between the metabolic state and epigenetic aging.