Biology subjects

Moffitt, T.

Publications and source records attributed to Moffitt, T..

4 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2020

Assessing the co-variability of DNA methylation across peripheral cells and tissues: implications for the interpretation of findings in epigenetic epidemiology

Summary/AbstractO_ST_ABSBackgroundC_ST_ABSMost epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated variation is specific to an individual cellular population. MethodsWe collected three peripheral tissues (whole blood, buccal and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4+ T cells, CD8+ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. ResultsWe identified significant differences in both the level and variability of DNAm between different tissues and cell types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, however, the proportion of variance explained was greater than that explained by either buccal or nasal tissues. Instead we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight. ConclusionsWe identified major differences in DNAm between blood cell types and peripheral tissues, with each sample type being characterized by a unique DNAm signature across multiple loci. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and provide important insights for the interpretation of EWAS performed in whole blood. Key MessagesO_LIWe identified major differences in DNA methylation between blood cell types and peripheral tissues, with each sample type being characterized by a unique DNA methylation signature across multiple loci. C_LIO_LIEstimates of DNAmAge and tobacco smoking from DNA methylation data can be highly variable across different sample types collected from the same individual at the same time. C_LIO_LIWhile individual blood cell types did predict more of the variation in whole blood compared to buccal epithelial and nasal epithelial cells, the percentage of variance explained was still small. C_LIO_LIInstead our data indicate that at the majority of sites, variation in multiple blood cell types additively combines to drive variation in DNA methylation in whole blood. C_LIO_LIThere are subset of sites where variable DNA methylation detected in whole blood can be attributed to variation in a single blood cell type. C_LI

genomics↗

bioRxiv2018

A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined N=8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.

neuroscience↗

bioRxiv2017

Effect modification of FADS2 polymorphisms on the association between breastfeeding and intelligence: results from a collaborative meta-analysis

BackgroundAccumulating evidence suggests that breastfeeding benefits the childrens intelligence. Long-chain polyunsaturated fatty acids (LC-PUFAs) present in breast milk may explain part of this association. Under a nutritional adequacy hypothesis, an interaction between breastfeeding and genetic variants associated with endogenous LC-PUFAs synthesis might be expected. However, the literature on this topic is controversial.\n\nMethods and FindingsWe investigated this GenexEnvironment interaction in a de novo meta-analysis involving >12,000 individuals in the primary analysis, and >45,000 individuals in a secondary analysis using relaxed inclusion criteria. Our primary analysis used ever breastfeeding, FADS2 polymorphisms rs174575 and rs1535 coded assuming a recessive effect of the G allele, and intelligence quotient (IQ) in Z scores. Using random effects meta-analysis, ever breastfeeding was associated with 0.17 (95% CI: 0.03; 0.32) higher Z scores in IQ, or about 2.1 points. There was no strong evidence of interaction, with pooled covariate-adjusted interaction coefficients (i.e., difference between genetic groups of the difference in IQZ scores comparing ever with never breastfed individuals) of 0.12 (95% CI: -0.19; 0.43) and 0.06 (95% CI: -0.16; 0.27) for the rs174575 and rs1535 variants, respectively. Secondary analyses corroborated these results. In studies with >5.85 and <5.85 months of breastfeeding duration, pooled estimates for the rs174575 variant were 0.50 (95% CI: -0.06; 1.06) and 0.14 (95% CI: -0.10; 0.38), respectively, and 0.27 (95% CI: -0.28; 0.82) and -0.01 (95% CI: -0.19; 0.16) for the rs1535 variant. However, between-group comparisons were underpowered.\n\nConclusionsOur findings do not support an interaction between ever breastfeeding and FADS2 polymorphisms. However, our subgroup analysis raises the possibility that breastfeeding supplies LC-PUFAs requirements for cognitive development (if such threshold exists) if it lasts for some (currently unknown) time. Future studies in large individual-level datasets would allow properly powered subgroup analyses and would improve our understanding on the role of breastfeeding duration in the breastfeedingxFADS2 interaction.

epidemiology↗