rs11751605 LPA. 6 T C. 0.0000 0.0035 -6.0398 -5.8994 -4.6420. 14 rs9365233 MAP3K4. 6 T G. 0.0089 0.7291 3.2676 2.1319 0.9800. 15 rs7962595 C12orf27.
SUPPLEMENTARY DATA 1. Methods Descriptive analysis of pleiotropy using conditional Q-Q plots Quantile-Quantile plots (Q-Q plots) are a descriptive tool for visualizing the difference between an observed distribution and a theoretical distribution. In the analysis of GWAS, quantiles of the observed (nominal) p-values, denoted by ‘p’, are plotted on the y-axis, with the quantiles of the theoretical null distribution (i.e. the uniform distribution estimated by the empirical cumulative distribution function (cdf), q = Np / N,), here denoted by ‘q’, on the x-axis. We thus denoted the y-axis as nominal -log10 pvalue p, and the x-axis as empirical -log10 p-value q. Commonly, the -log10 of transform is used to emphasize tail areas. If there is no deviation from the null distribution and thus no genetic association present, a Q-Q plot falls on the line x=y. Leftward deflections of the observed distribution from the null line reflect increased tail probabilities in the distribution of the test statistics, and consequently an overabundance of low p-values compared to that expected by chance, often termed ‘enrichment’. Conditional Q-Q plots were used to depict whether distributions differ as a function of pre-specified categories1, 2. Here, we presented Q-Q plots conditioned on summary statistics of a second phenotype potentially sharing a genetic component with the phenotype of interest. Specifically, we constructed the categories based on p-values of SNPs for the second, conditioning phenotype. Categories were defined using the following p-values cut-offs: –log10(p) ≥ 1, –log10(p) ≥2, –log10(p) ≥3 corresponding to p ≤ 1, p ≤ 0.1, p ≤ 0.01, p ≤ 0.001, respectively. We investigated whether the distribution of p-values for the phenotype of interest is different in these pre-specified categories and if certain categories show higher levels of enrichment. Q-Q plots for p-values in PCA from the iCOGS study3 conditioned by the CVD risk factors LDL, HDL, TG, and T2D are presented in Figure 1. The remaining Q-Q plots were presented Supplementary Figure 1. We replicated the general enrichment pattern of PCA|LDL, and PCA|TG in the smaller UK 2008 GWAS4 (Supplementary Figure 3). The definition of the cut-offs that defined the
categories were arbitrary, and did not reflect any prior knowledge of the data. For completeness, we also showed a stricter definition of categories (–log10(p) ≥ 1, –log10(p) ≥2, –log10(p) ≥3, and additionally – log10(p) ≥ 4, –log10(p) ≥5) in Supplementary Figure 4 for PCA|LDL and PCA|TG . We used the Anderson-Darling K-sample test5 to assign significance for the enrichment with respect to a secondary trait. The Anderson-Darling test checks if two or more samples are drawn from the same distribution. Low p-values indicate that the two samples are drawn from different distributions. In the context of the conditional Q-Q plots the Anderson-Darling test was used to test if p-values for the first trait of interest can be stratified by a secondary trait so that the strata have characteristics as if they were drawn from different distributions. We tested the SNPs in the three strata from the conditional Q-Q plot ( –log10(p) ≥ 1, –log10(p) ≥2, –log10(p) ≥3) against the depleted category plot (–log10(p) < 1) . This ensured that we were comparing disjoint sets of SNPs. For each trait of the 7 CVD traits, we reported all p-values for all three strata (compared against the depleted category), and corrected for multiple testing using the Bonferroni adjustment for 21 tests (Supplementary Table 2). Then, we moved forward with those 5 traits that had at least one significant stratum. The implementation of the Anderson-Darling test was based on a bootstrapping scheme that ensures that all SNPs used for the computation are independent. In particular, in each bootstrapping step we draw a randomly pruned, and thus independent set of SNPs (r22)
0.000
0.001
0.051
1.000
0.674
1.000
1.000
strata1 (-log10 p-value >1)
1.000
0.004
0.000
0.019
1.000
0.543
1.000
p-values (after Bonferroni correction for multiple testing of 21 tests) for the Anderson-Darling test that we used to assign significance of enrichment. More specifically, we were interested in the three different strata from the Q-Q plots. We used the Anderson-Darling test to check if the distribution of PCA p-values given one of these specific strata in the secondary phenotype (e.g. – log10 p-value in LDL >3, –log10 p-value in LDL >2, or –log10 p-value in LDL >1) is different from the distribution of the PCA p-values in the strata depleted in the secondary phenotype (– log10 p-value in LDL < 1). Each of the 3 strata (strata3 (-log10 p-value >3), strata2 (-log10 pvalue >2), strata1 (-log10 p-value >1)) as presented in the rows was tested against the depleted category, so that we test disjoint sets of SNPs against each other. The 7 different traits are represented in the columns. We highlighted the significant strata (after multiple testing correction) in bold, and also the 5 traits with at least one significant strata in bold. For more details on the Anderson-Darling test we refer to Methods section.
Suppl. Table 3. Effect sizes of the 18 pleiotropic loci (conjunction FDR
