bioRxiv Science⌕ Search

Biology subjects

Giosan, I.

Publications and source records attributed to Giosan, I..

2 recordsLinked to original sources

Creating DNAm Algorithms Using the Illumina Methylation Screening Array (MSA)

Most established DNA methylation (DNAm) biomarkers were developed on legacy Illumina EPIC arrays. The Infinium Methylation Screening Array (MSA) offers a lower-cost, higher-throughput alternative with reduced probe content, but EPIC-trained algorithms cannot be assumed to transfer directly. Here we present a reproducibility-based framework for developing and transferring DNAm algorithms on the MSA. Using paired biological replicates profiled on EPICv1 and MSA (1,764 EPICv1-MSA sample pairs, plus within-array MSA replicates on the same and different beadchips), we quantified probe-level agreement using mean absolute error (MAE) and intraclass correlation coefficients (ICC). Of 140,150 CpG sites shared between EPICv1 and MSA, 40,786 (29.1%) met both stability criteria (MAE < 0.05 and ICC(2,k) > 0.6). This stable feature space supported two modelling streams. First, we trained 134 epigenetic biomarker proxies (EBPs) natively on MSA, with and without kernel principal component analysis (kPCA) for sample-level harmonisation. All 134 reached same-beadchip ICC(2,1) >= 0.80 (median 0.97) and 96.3% reached different-beadchip ICC(2,1) >= 0.60 (median 0.81), with a median Spearman correlation of 0.48 against observed values. Among the 72 kPCA-selected models with a comparable stable-probe baseline, 70 (97%) showed higher cross-beadchip ICC (median improvement +0.18). Second, we transferred three established clocks using model-specific strategies: OMICmAge and SystemsAge were retrained to estimate their EPICv1-derived values (held-out test-set rho = 0.944 and 0.912-0.949), whereas DunedinPACE required stable-probe normalisation and robust linear calibration, which raised cross-array ICC(2,1) from 0.784-0.810 to 0.891-0.925 and reduced MAE from 0.085-0.089 to 0.041-0.050 across three sample sets. Reduced probe content does not preclude reproducible DNAm biomarker measurement, and transfer strategy must be matched to model architecture.

bioinformatics↗

Fasting Status and Epigenetic Clock Stability: Implications for Aging Research

BackgroundEpigenetic clocks are DNA methylation-based biomarkers increasingly used in aging research and clinical trials. A recent assessment of 18 clocks across multiple short-term perturbations concluded that most demonstrate only moderate biological reliability, raising concerns about their translational utility. However, epigenetic clocks differ substantially in their construction and in the biological signals they capture, and their sensitivity to physiological perturbation may not be a flaw but a consequence of the construction. To understand this more clearly, we undertook a focused investigation of biological reliability for a single, well-characterised perturbation, an overnight fast followed by acute refeeding, examining how and why clock estimates may shift with physiological state. MethodsWe evaluated 24 epigenetic clocks spanning five construction categories - first and second generation classical clocks (eg. Horvath, Hannum, PhenoAge), the PC versions of the classical clocks, SystemsAge organ-system clocks, mortality-trained clocks (GrimAge, PCGrimAge, OMICmAge), pace of aging clocks (DunedinPACE) and the IntrinClock, across three datasets: a within-person paired fasting design (n = 15 pairs), a cross-sectional cohort of fasted vs non-fasted (n = 2,895), and EPICv2custom technical replicates (n = 96 samples from 4 individuals). For each clock, we quantified the acute fasting effect with and without immune cell adjustment, decomposed between-person and within-person variance at successive adjustment levels (Raw, EAA, IAA), and benchmarked biological variability against the technical measurement floor. ResultsFasting followed by acute refeeding was associated with group-level shifts of 0.5-3 years in immune-sensitive clocks, while within-person reliability remained high (Raw clock ICC median ~0.96). These observations are compatible because fasting effects are small relative to the age-driven between-person variance that dominates the ICC denominator. The magnitude of the observed shift varied by clock. PC transformations showed larger effects than their classical counterparts in the paired cohort (PC Hannum -2.03 vs. Hannum -1.37 years; PC PhenoAge > PhenoAge; PC Horvath > Horvath), SystemsAge showed the largest effects (1.15-2.9 years younger when fasted), and mortality-trained clocks (GrimAge V1/V2, OMICmAge) and DunedinPACE showed no detectable acute effect (all FDR p > 0.10). Immune cell adjustment attenuated or eliminated the fasting effects in sensitive clocks (PC Hannum 88% attenuation; SystemsAge Blood 99.7%); no clock retained a significant fasting effect after FDR-corrected immune adjustment in either cohort. Within the cross-sectional cohort, a clocks immune content, which is the fraction of its age-independent variance explained by immune cell composition, was correlated with the degree to which immune adjustment attenuated its fasting effect (r = 0.68, p = 0.003). IntrinClock, designed to exclude immune-variable CpGs, showed no fasting effect in either cohort (immune R2 = 3.2%), serving as a negative control. Technical replicates confirmed near-perfect measurement reproducibility (median Raw ICC > 0.97), establishing that variance in fasting pairs reflects biology, not noise. Immune-adjusted ICCs behaved differently across clocks in ways consistent with their composition: for clocks where fasting generated within-person variance, immune adjustment removed it and ICC increased (SystemsAge EAA 0.768 to IAA 0.913); for clocks unaffected by fasting, immune adjustment removed between-person structure and ICC fell substantially (OMICmAge 0.922 to 0.160), reflecting the estimation cost of fitting many immune cell predictors to stable residuals. Cross-sectional replication (n = 2,895) confirmed immune cell redistribution at scale. Mortality clocks reached significance cross-sectionally despite resistance to acute fasting. ConclusionsEpigenetic clock responses to an overnight fast followed by acute refeeding varied systematically by training category in our data. PC-based clocks, which concentrate correlated CpG variance including that associated with immune cell composition, showed the largest shifts; mortality-trained clocks showed no detectable acute effect. A framework that summarises a clock by its ICC alone, without identifying the biological source of its within-person variation, can misread structured, perturbation-driven biology as measurement noise. ICC is not a fixed property of a clock, it is shaped by the study design, the population heterogeneity, the perturbation, and the adjustment applied. We recommend that clock reliability be assessed on a perturbation-specific, clock-by-clock basis, with variance decomposition at each adjustment level and explicit benchmarking against technical replicates.

bioinformatics↗