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

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

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

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

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

Footprints of rules from the model.

<p>The footprints are centred on intron-exon junctions (I/E) with examples of rules in the model from the (A) ‘Spliced out’ class (covers 358 exons, −6 pp accuracy gain over independent histone, referred to as S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029911#pone-0029911-t001" target="_blank">Table 1</a>), (B) ‘Spliced out’ (296, +5 pp, S3), (C) ‘Spliced out’ (260, +12 pp, S5) and (D) ‘Included’ (3738, −8 pp, I1). The number of reads normalized to sequence depth as left axis.</p

Pair-wise co-occurrence of histone modifications in the model.

<p>(<b>A</b>) Rules predicting ‘spliced out’. Any each histone modification that occurs in any rule is labelled on the outer ring, and co-occurrence with other histone modifications are illustrated by ribbons across the circle. The width of the ribbon correlates to the number of rules where the two modifications co-occur as attributes. Colouring of the ribbons indicates ranking of number of rules with the given pair over the whole model with the lower 75% coloured in grey. The colours of the inner ring correspond to the colour in the outer ring in the other end of a given ribbon. The figure labels are ‘I’: H2BK5me1, ‘II’: H3K36me3, ‘III’: H3K4me1, ‘IV’: H3K9me1, ‘V’: H3K9me2, ‘VI’: H3K9me3, ‘VII’: H3R2me1, ‘VIII’: H4K16ac, ‘IX’: H4K20me1, ‘X’: H4K91ac; S: succeeding the exon, P: preceding the exon, E: on the exon; 0: is absent, 1: is present (e.g. “A P0” is interpreted as “H2BK5me1 preceding the exon is absent”). (<b>B</b>) Same as (A) but for the rules predicting ‘included’ exons. Images was generated using Circos <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029911#pone.0029911-Krzywinski1" target="_blank">[29]</a>.</p

Schematic representation of the constructed data set.

<p>For each histone modification, e.g. H3K36me3, we deem it as present or absent in the region centred over the exon and the regions preceding and succeeding depending on if the histone modification pileup in such region is higher than a defined cut-off. This cut-off is specific for each histone modification since they were sequenced at difference depths.</p