Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks

Analysis of epigenetic clocks links yoga, sleep, education, reduced meat intake, coffee, and a SOCS2 gene variant to slower epigenetic aging

DNA methylation (DNAm) clocks hold promise for measuring biological age, useful for guiding clinical interventions and forensic identification. This study compared the commonly used DNAm clocks, using DNA methylation and SNP data generated from nearly 1000 human blood or buccal swab samples. We evaluated different preprocessing methods for age estimation, investigated the association of epigenetic age acceleration (EAA) with various lifestyle and sociodemographic factors, and undertook a series of novel genome-wide association analyses for different EAA measures to find associated genetic variants. Our results highlighted the Skin&Blood clock with ssNoob normalization as the most accurate predictor of chronological age. We provided novel evidence for an association between the practice of yoga and a reduction in the pace of aging (DunedinPACE). Increased sleep and physical activity were associated with lower mortality risk score (MRS) in our dataset. University degree, vegetable consumption, and coffee intake were associated with reduced levels of epigenetic aging, whereas smoking, higher BMI, meat consumption, and manual occupation correlated well with faster epigenetic aging, with FitAge, GrimAge, and DunedinPACE clocks showing the most robust associations. In addition, we found a novel association signal for SOCS2 rs73218878 (p = 2.87 × 10-8) and accelerated GrimAge. Our study emphasizes the importance of an optimized DNAm analysis workflow for accurate estimation of epigenetic age, which may influence downstream analyses. The results support the influence of genetic background on EAA. The associated SOCS2 is a member of the suppressor of cytokine signaling family known for its role in human longevity. The reported association between various risk factors and EAA has practical implications for the development of health programs to improve quality of life and reduce premature mortality associated with age-related diseases.

Age prediction using DNA methylation of Y-chromosomal CpGs in semen samples

In cases of sexual assault, the evidence often exists as a mixture of female and male body fluids, and in many cases, contains a higher proportion of female body fluids than males. In these cases, Y-STR, rather than autosomal STRs, can provide useful information. It becomes very difficult to identify the true suspect if there is no match among known suspects or if a match exists for two or more suspects, e.g. two suspects from the same paternal lineage. However, age prediction using the DNA methylation of Y-chromosomal CpGs can help narrow the search for unknown suspects and discriminate between older and younger suspects. Therefore, the DNA methylation profiles of semen samples from 56 healthy Korean males were generated using Illumina's Infinium MethylationEPIC BeadChip Array. Among the ten identified age-associated CpG markers located in the Y-chromosome, nine were used to construct age prediction models. The identified markers were further investigated in the MPS analysis of 147 semen samples, and the multiplex assay was validated with the reliability, reproducibility and sensitivity tests. Several age prediction models were constructed using the MPS data with the multiple linear regression, stepwise linear regression, ridge linear regression, lasso regression, elastic net linear regression and support vector machine analyses, and all showed MAEs of 5 to 7 years in the test set samples. Six single-source female samples were also subjected to MPS analysis but showed very low coverage that could not affect the analysis of the mixed samples. Therefore, the age prediction models of the present study are expected to provide useful investigative leads, especially in mixed male and female samples from sexual assault cases.