A data-driven approach to preprocessing Illumina 450K methylation array data

Pidsley, Ruth; Wong, Chloe; Volta, Manuela; Lunnon, Katie; Mill, Jonathan; Schalkwyk, Leonard C.

doi:10.1186/1471-2164-14-293

Cited by 904 publications

(951 citation statements)

References 21 publications

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“…Subsequently, we used a background correction method ‘noob’ [28] from the methylumi package and a normalization method ‘dasen’ [29] from the watermelon package. Next, we corrected normalized data for cellular compositions, which were estimated by the Houseman method [20] using a blood cell reference panel [30].…”

Section: Methodsmentioning

confidence: 99%

Epigenetic influences on aging: a longitudinal genome-wide methylation study in old Swedish twins

et al. 2018

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Age-related changes in DNA methylation were observed in cross-sectional studies, but longitudinal evidence is still limited. Here, we aimed to characterize longitudinal age-related methylation patterns using 1011 blood samples collected from 385 Swedish twins (age at entry: mean 69 and standard deviation 9.7, 73 monozygotic and 96 dizygotic pairs) up to five times (mean 2.6) over 20 years (mean 8.7). We identified 1316 age-associated methylation sites (P<1.3×10−7) using a longitudinal epigenome-wide association study design. We measured how estimated cellular compositions changed with age and how much they confounded the age effect. We validated the results in two independent longitudinal cohorts, where 118 CpGs were replicated in Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS, 390 samples) (P<3.9×10−5), 594 in Lothian Birth Cohort (LBC, 3018 samples) (P<5.1×10−5) and 63 in both. Functional annotation of age-associated CpGs showed enrichment in CCCTC-binding factor (CTCF) and other transcription factor binding sites. We further investigated genetic influences on methylation and found no interaction between age and genetic effects in the 1316 age-associated CpGs. Moreover, in the same CpGs, methylation differences within twin pairs increased with 6.4% over 10 years, where monozygotic twins had smaller intra-pair differences than dizygotic twins. In conclusion, we show that age-related methylation changes persist in a longitudinal perspective, and are fairly stable across cohorts. The changes are under genetic influence, although this effect is independent of age. Moreover, methylation variability increase over time, especially in age-associated CpGs, indicating the increase of environmental contributions on DNA methylation with age.

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Section: Methodsmentioning

confidence: 99%

Epigenetic influences on aging: a longitudinal genome-wide methylation study in old Swedish twins

et al. 2018

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“…The raw methylation data were normalized using ‘noob’ background correction [14] in combination with ‘dasen’ normalization [15]. Normalized data were then adjusted for cellular composition using the Houseman method [16] and the Combat method [17] was used to correct for batch effects.…”

Section: Methodsmentioning

confidence: 99%

Implementing a method for studying longitudinal DNA methylation variability in association with age

2018

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Interindividual variability of DNA methylation is a mechanism of the epigenetic drift in aging. Studies on cross-sectional data have discovered a change in methylation variability in association with age. However, thus far, no method explored DNA methylation variability in longitudinal data, which was the aim of this study. First, we performed a simulation study to explore methods for estimating methylation variability in longitudinal data. Second, an epigenome-wide association study (EWAS) on 1011 longitudinal samples (385 individuals followed up to 18 years) was performed to identify age-varying methylation sites using these methods. Following Breusch–Pagan test of heteroscedasticity, we showed that a linear regression model, where the residuals were used in a mixed effect model with a random intercept, properly estimated the change of interindividual variability over time. Our EWAS identified 570 CpG sites where methylation variability was significantly associated with age (P < 1.3 × 10−7). Gene regions of identified loci were enriched in nervous system development functions. In conclusion, we provide a method for analyzing methylation variability in longitudinal data and further identified age-varying methylation loci in a longitudinal analysis using these methods.

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“…39 The final DNAm value for each site was computed as the M-value, the log ratio of the methylated to the unmethylated intensity. 40 CpG sites were assigned to genes according to Illumina annotation, which included sites in the promoter region, 5 0 untranslated region, gene body, and 3 0 untranslated region.…”

Section: Dnammentioning

confidence: 99%

Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: The multi-ethnic study of atherosclerosis

et al. 2015

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y Co-first authors. Keywords: DNA methylation, gene expression, inflammation, socioeconomic status, stress reactivityEpigenetic changes, such as DNA methylation, have been hypothesized to provide a link between the social environment and disease development. The purpose of this study was to examine associations between life course measures of socioeconomic status (SES) and DNA methylation (DNAm) in 18 genes related to stress reactivity and inflammation using a multi-level modeling approach that treats DNAm measurements as repeat measures within an individual. DNAm and gene expression were assessed in purified monocytes for a random subsample of 1,264 nonHispanic white, African-American, and Hispanic participants aged 55-94 from the Multi-Ethnic Study of Atherosclerosis (MESA). After correction for multiple testing, we found that low childhood SES was associated with DNAm in 3 stressrelated genes (AVP, FKBP5, OXTR) and 2 inflammation-related genes (CCL1, CD1D), low adult SES was associated with DNAm in one stress-related gene (AVP) and 5 inflammation-related genes (CD1D, F8, KLRG1, NLRP12, TLR3), and social mobility was associated with DNAm in 3 stress-related genes (AVP, FKBP5, OXTR) and 7 inflammation-related genes (CCL1, CD1D, F8, KLRG1, NLRP12, PYDC1, TLR3). In general, low SES was associated with increased DNAm. Expression data was available for 7 genes that showed a significant relationship between SES and DNAm. In 5 of these 7 genes (CD1D, F8, FKBP5, KLRG1, NLRP12), DNAm was associated with gene expression for at least one transcript, providing evidence of the potential functional consequences of alterations in DNAm related to SES. The results of this study reflect the biological complexity of epigenetic data and underscore the need for multi-disciplinary approaches to study how DNAm may contribute to the social patterning of disease.

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A data-driven approach to preprocessing Illumina 450K methylation array data

Cited by 904 publications

References 21 publications

Epigenetic influences on aging: a longitudinal genome-wide methylation study in old Swedish twins

Epigenetic influences on aging: a longitudinal genome-wide methylation study in old Swedish twins

Implementing a method for studying longitudinal DNA methylation variability in association with age

Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: The multi-ethnic study of atherosclerosis

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