Fig. 2

Data analysis procedures of the current study. (A) The statistical analyses using two approaches. In Approach I (grey color in the left half), omics-wide association analyses were conducted within each omics to assess the association between each individual feature and telomere length. In Approach II (green color in the right half), multiple (pre-selected) omics were analyzed via a supervised method, multi-block sparse Partial Least Squares (sPLS), against four telomere length measures. (B) The literature-based omics feature pre-selection in genome-wide CpG methylation and gene expression. All green boxes represent procedures based on literature or databases, while the blue box stands for a data-driven filtering of gene transcripts where the transcript with the highest variance within the same gene was selected. Stage ①: significant SNPs from published genome-wide association studies (GWAS’s) of telomere length (TL) were used to extract DNA methylation quantitative trait loci (mQTL) and gene expression quantitative trait loci (eQTL) from publicly accessible databases, which were in turn used to select a first set of CpGs in the DNA methylation data and a first set of transcripts in the gene expression data. Stage ②: genes involved in telomere regulation and two cellular aging-related signaling pathways, mTOR and AMPK pathways, were used to select a second set of gene transcripts, and to extract gene expression quantitative trait methylation (eQTM) from a published study which were then used to select a second set of CpGs. Stage ③: an epigenome-wide association study (EWAS) of TL was used to select a third set of CpGs. All selected CpGs were further filtered considering the probe reliability in the Illumina 450 K array