Genomic control coefficient lambda λ_GC obtained for the different tests of association performed on the simulated scenario of stratification in regions R11 and R12.

a<p>Cochran-Mantel-Haenszel test accounting for the 2 different regions.</p>b<p>Test not corrected for population stratification.</p>c<p>Test corrected for population stratification using different number of PCs computed on different pruned MAF sets (i.e.; common.2 means that the PCs were computed on the common varint sets and 2 the test is adjusted on 2 such PCs).</p>d<p>Test performed using the mixed model implemented in EMMAX with the relatedness matrix computed either on the common, low frequency or rare variant sets.</p

PCA for the different pruned MAF sets.

<p>The mean of PC1 and PC2 scores (top row) and of PC2 and PC3 scores (bottom row) in each region are plotted considering the common variants, the low frequency variants and the rare variants.</p

Correlation (R² values) between the first two PCs (PC1.x and PC2.x) obtained on the different subsets of variants (x = “common”, “lowfreq” or “rare”).

<p>The last three columns Top10.x give the cumulative R² values over the top 10 PCs to show how each PC.x in line is captured by the combined top 10 PCs of the different subsets.</p

Flowchart showing the different QC steps and the number of SNPs in the different MAF categories.

<p>Flowchart showing the different QC steps and the number of SNPs in the different MAF categories.</p

Chemical Exposure Highlighted without Any <i>A Priori</i> Information in an Epidemiological Study by Metabolomic FT-ICR-MS Fingerprinting at High Throughput and High Resolution

Epidemiological studies aim to assess associations between diseases and risk factors. Such investigations involve a large sample size and require powerful analytical methods to measure the effects of risk factors, resulting in a long analysis time. In this study, chemical exposure markers were detected as the main variables strongly affecting two components coming from a principal component analysis (PCA) exploration of the metabolomic data generated from urinary samples collected on a cohort of about 500 individuals using direct introduction coupled with a Fourier-transform ion cyclotron resonance instrument. The assignment of their chemical identity was first achieved based on their isotopic fine structures detected at very high resolution (Rp > 900,000). Their identification as dimethylbiguanide and sotalol was obtained at level 1, thanks to the available authentic chemical standards, tandem mass spectrometry (MS/MS) experiments, and collision cross section measurements. Epidemiological data confirmed that the subjects discriminated by PCA had declared to be prescribed these drugs for either type II diabetes or cardiac arrhythmia. Concentrations of these drugs in urine samples of interest were also estimated by rapid quantification using an external standard calibration method, direct introduction, and MS/MS experiments. Regression analyses showed a good correlation between the estimated drug concentrations and the scores of individuals distributed on these specific PCs. The detection of these chemical exposure markers proved the potential of the proposed high-throughput approach without any prior drug exposure knowledge as a powerful emerging tool for rapid and large-scale phenotyping of subjects enrolled in epidemiological studies to rapidly characterize the chemical exposome and adherence to medical prescriptions