The overall experimental design in shown in . We obtained publicly-available eQTL data generated from 3 different tissue types in healthy Caucasians: peripheral blood monocytes (8
), liver, and adipose tissue (10
). The first data set specifically involved monocytes isolated from the blood of 1490 healthy individuals recruited in the Gutenberg Heart Study. The eQTLs were mapped using analysis of variance (ANOVA), with a p-value cutoff of 5.78E-12, corresponding to a family-wise error rate of 0.05. This study reported 37,403 associations, comprising 29,912 SNPs and 2,745 expression traits. The liver and adipose datasets involved samples obtained from 1008 morbidly obese individuals at the time of gastric bypass surgery. Association was determined using the Kruskal-Wallis test, and results were reported at a 10% false discovery rate (FDR) based on permutations of the SNP genotypes and gene expression levels, for a total of 24,513 eSNPs associated with 15,241 transcripts or 9931 distinct genes.
To compare against these, we used the results of the genome wide association studies reported by the Wellcome Trust Case Control Consortium for 7 different diseases: bipolar disorder (BD), coronary artery disease (CAD), Crohn’s disease (CD), hypertension (HTN), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D).
We will refer to the p-value of eQTL association for an eSNP as peqtl
, and the p-value of disease association for a given SNP as pgwas
. For each of the three tissue types for which we had eQTL relationships, we studied the relationship of minimum eSNP p-value (peqtl
) to odds ratio against any of the seven diseases. For each disease studied by the WTCCC, we selected eSNPs in each tissue that were in very strong linkage disequilibrium (LD, R2
= 1) with any SNP having uncorrected pgwas
< 0.01 for that disease. Because testing multiple SNPs in LD with each other could result in spurious correlation, we selected one SNP with the minimum peqtl
from each LD block and performed Kendall rank correlation analysis of -log(peqtl
) and the absolute value of log(disease odds ratio) using the cor.test
function in R (11
). The resulting p-values of 21 statistical tests were provided as input to the qvalue
package in R to calculate corresponding q-values (12