The recent success of genome-wide association studies (GWAS) is now followed by 
the challenge to determine how the reported susceptibility variants mediate 
complex traits and diseases. Expression quantitative trait loci (eQTLs) have 
been implicated in disease associations through overlaps between eQTLs and GWAS 
signals. However, the abundance of eQTLs and the strong correlation structure 
(LD) in the genome make it likely that some of these overlaps are coincidental 
and not driven by the same functional variants. In the present study, we propose 
an empirical methodology, which we call Regulatory Trait Concordance (RTC) that 
accounts for local LD structure and integrates eQTLs and GWAS results in order 
to reveal the subset of association signals that are due to cis eQTLs. We 
simulate genomic regions of various LD patterns with both a single or two causal 
variants and show that our score outperforms SNP correlation metrics, be they 
statistical (r(2)) or historical (D'). Following the observation of a 
significant abundance of regulatory signals among currently published GWAS loci, 
we apply our method with the goal to prioritize relevant genes for each of the 
respective complex traits. We detect several potential disease-causing 
regulatory effects, with a strong enrichment for immunity-related conditions, 
consistent with the nature of the cell line tested (LCLs). Furthermore, we 
present an extension of the method in trans, where interrogating the whole 
genome for downstream effects of the disease variant can be informative 
regarding its unknown primary biological effect. We conclude that integrating 
cellular phenotype associations with organismal complex traits will facilitate 
the biological interpretation of the genetic effects on these traits.