Flowchart of the simulations.

<p>1) The allele-specific copy number states were simulated for controls given the expected frequencies. 2) The expected frequencies in the case population were calculated given the frequencies in the control population and the relative risks (RR) of both the allelic and the copy number effects (RR_allele and RR_CN respectively). 3) The allele-specific copy-number states were simulated for cases given the expected frequencies in cases. 4) We deducted the bi-allelic genotypes information from the allele-specific copy-number states, given the probabilities of each allele-specific copy-number state to be respectively called as AA, AB, BB or missing. Association tests were performed on both the allele-specific copy-number states that contain the complete information on allele and copy-number, and the bi-allelic genotypes that contain partial information on the allele. Classical criteria used in SNP analysis such as the Hardy-Weinberg equilibrium (HWE) departure in controls and the percentage (%) of missing data were computed on the bi-allelic genotypes.</p

Advantage of Using Allele-Specific Copy Numbers When Testing for Association in Regions with Common Copy Number Variants

<div><p>Copy number variants (CNV) can be called from SNP-arrays; however, few studies have attempted to combine both CNV and SNP calls to test for association with complex diseases. Even when SNPs are located within CNVs, two separate association analyses are necessary, to compare the distribution of bi-allelic genotypes in cases and controls (referred to as SNP-only strategy) and the number of copies of a region (referred to as CNV-only strategy). However, when disease susceptibility is actually associated with allele specific copy-number states, the two strategies may not yield comparable results, raising a series of questions about the optimal analytical approach. We performed simulations of the performance of association testing under different scenarios that varied genotype frequencies and inheritance models. We show that the SNP-only strategy lacks power under most scenarios when the SNP is located within a CNV; frequently it is excluded from analysis as it does not pass quality control metrics either because of an increased rate of missing calls or a departure from fitness for Hardy-Weinberg proportion. The CNV-only strategy also lacks power because the association testing depends on the allele which copy number varies. The combined strategy performs well in most of the scenarios. Hence, we advocate the use of this combined strategy when testing for association with SNPs located within CNVs.</p> </div

Power of the Joint strategy when errors are introduced in the allele-specific copy number states.

<p>This figure displays the power of the <i>Joint</i> strategy applied to allele-specific copy number states (ascn) without errors, of the <i>Joint</i> strategy applied to ascn states with errors due to a sensitivity (SE) of CNV detection reduced to 0.2 or 0.5 respectively and the power of the <i>Allele</i> (bi) strategy applied to the bi-allelic genotypes. The power was calculated using 10,000 replicates and considering 4 scenarios, all with a frequency of the B allele of 0.2, with relative risks of the number of copies and of the allele of 1.2 and a specificity of CNV detection of 0.99 for the ascn states with errors. Differences between the scenarios concern the type of CNV considered (deletions only (Del only) or both deletions and duplications (Del & Dup)) and the frequency of normal chromosomes carrying one copy of the CNV (f(norm) that was set to 0.33 or 0.80.</p

Power of each of the five investigated strategies when only deletions were present.

<p>The power was computed for a sample of 1,000 cases and 1,000 controls under the different scenarios of risk and frequencies. On the X axis, f(B) and f(norm) refer respectively to the frequency of the allele B and to the frequency of normal chromosome carrying one copy of the CNV.</p

Estimations of the odds ratios using the <i>Joint</i> model.

<p>For each of the 16 scenarios of association strengths, this figure displays the average of the odds ratios for the effect of the copy number (left panel), and of the allele (right panel), obtained by transforming the <i>Joint</i> model coefficients. Averages were computed over 360,000 replicates (over all the 36 scenarios of frequencies and 10,000 replicates for each scenario) considering a sample of 1,000 cases and 1,000 controls.</p

Power of each of the five investigated strategies when both deletions and duplications were present.

<p>The power was computed for a sample of 1,000 cases and 1,000 controls under the different scenarios of risk and frequencies. On the X axis, f(B) and f(norm) refer respectively to the frequency of the allele B and to the frequency of normal chromosome carrying one copy of the CNV.</p

Example of a correlation plot for <i>MMP7</i> detected by the Global model using ENET but not using LASSO.

<p>The bar color represents the levels of correlation from 0 (no correlation) to 1 (perfect correlation) between SNPs and CpGs that were selected for the <i>MMP7</i> models. Three nets of correlated variables are the ones responsible that the gene is only selected by ENET and not by LASSO.</p

Significant genes obtained by ENET&Permuted based maxT algorithm for the three models (SNP, CPG, and Global) in the original dataset (EPICURO Study) and the replication dataset (TCGA).

<p>Significant genes obtained by ENET&Permuted based maxT algorithm for the three models (SNP, CPG, and Global) in the original dataset (EPICURO Study) and the replication dataset (TCGA).</p

Deviance across the genome when applying LASSO and ENET to select SNPs, CpGs or both (Global model).

<p>The dots in the figure indicate the deviance of each gene located in the corresponding position in the genome. There are a total of 20,899 gene expression probes measured. Significant genes after applying the permutation-based MaxT method are tagged. The figures represent the deviance per gene expression probe using LASSO for the SNP model (A), the CpG model (B) and the Global model (C) and using ENET for the SNP model (D), the CpG model (E) and the Global model (F).</p

Scenario and workflow of the overall analysis implemented.

<p>The integrative framework proposed is based on three steps. Step 1 corresponds to the selection of SNPs and CpGs in 1MB window upstream and downstream from each probe in the gene expression array. Step 2 corresponds to the application of LASSO and ENET to each probe obtaining the deviance per probe. Step 3 corresponds to the permutation-based MaxT method application where gene expression levels within the individuals are permuted B = 100 times obtaining the deviance per probe.</p