Number of modes in the distribution of DNA methylation for each site.

<p>Number of modes in the distribution of DNA methylation for each site.</p

Summary statistics for the CGIs depending on the correlation between DNA methylation level and chronological age.

<p>Observations are ordered by the correlation coefficients and combined into 100 bins. The features of the CGIs within each bin is summarized as, A) Mean length of the CGIs, B) Mean percentage of CpGs in the islands, and C) Mean of observed to expected ration of CpGs in the islands.</p

Increase in DNA methylation level with age of one CpG site (cg16867657) in the promoter of the ELOVL2 gene and corresponding regression line.

<p>Increase in DNA methylation level with age of one CpG site (cg16867657) in the promoter of the ELOVL2 gene and corresponding regression line.</p

Location of CpG site depending on correlation between DNA methylation level and chronological age.

<p>Observations are ordered by the correlation coefficients and combined into 100 bins. The illustrations show the fraction of markers within each bin with a location in relation to, A) CGIs, island shores and islands shelves, B) Known promoter and enhancer regions, and C) Gene and transcription starting site.</p

Principal components for the DNA methylation levels among autosomal markers and corresponding correlation with age and variance.

*<p>Total variance attributed by age is calculated as Rhô2 * the proportion of variance explained by the PCs.</p

Distribution of A) Ages in the study cohort, B) DNA methylation levels for autosomal markers in males and females, C) DNA methylation level for autosomal markers in the youngest (age <18, N = 51) and oldest (age>71, N = 52) individuals of the study, and D) DNA methylation levels for X chromosomal markers in males and females.

<p>Distribution of A) Ages in the study cohort, B) DNA methylation levels for autosomal markers in males and females, C) DNA methylation level for autosomal markers in the youngest (age <18, N = 51) and oldest (age>71, N = 52) individuals of the study, and D) DNA methylation levels for X chromosomal markers in males and females.</p

Effect of genetic and environmental factors on protein biomarkers for common non-communicable disease and use of personally normalized plasma protein profiles (PNPPP)

<div><p></p><p><i>Objective</i>: To study the impact of genetic and lifestyle factors on protein biomarkers and develop <i>personally normalized plasma protein profiles</i> (PNPPP) controlling for non-disease-related variance.</p><p><i>Materials and methods</i>: Proximity extension assays were used to measure 145 proteins in 632 controls and 344 cases with non-communicable diseases.</p><p><i>Results</i>: Genetic and lifestyle factors explained 20–88% of the variation in healthy controls. Adjusting for these factors reduced the number of candidate biomarkers by 63%.</p><p><i>Conclusion</i>: PNPPP efficiently controls for non-disease-related variance, allowing both for efficient discovery of novel biomarkers and for covariate-independent linear cut-offs suitable for clinical use.</p></div

Ingenuity Pathway Analysis of gestational age-associated transcripts.

<p>Ingenuity Pathway Analysis of gestational age-associated transcripts.</p

Gestational age-associated transcripts (Bonferroni corrected <i>P</i><0.05, nominal <i>P</i><1.52^*10⁻⁶).

<p>Gestational age-associated transcripts (Bonferroni corrected <i>P</i><0.05, nominal <i>P</i><1.52^*10⁻⁶).</p

Preeclampsia-associated transcripts (Bonferroni corrected <i>P</i><0.05, nominal <i>P</i><1.52*10⁻⁶).

<p>Preeclampsia-associated transcripts (Bonferroni corrected <i>P</i><0.05, nominal <i>P</i><1.52*10⁻⁶).</p