Additionally, there are several other methods for detecting the association between genes and traits using the GWAS and eQTL data [58–60]. The COLOC method [58] is a very useful tool to detect co-localization of GWAS and eQTL signals at known GWAS risk loci with a Bayesian analysis approach; however it does not provide thresholds for the posterior probabilities to control for genome wide false positive rate. The PrediXican method [59] directly tests the molecular mechanism through which genetic variation affects a study trait; however it requires individual-level genotype and gene expression data in the training data set, and individual-level genotype and phenotype data in the target data set. Therefore, this approach may currently have limited power and feasibility because of the limited availability and small sample sizes of such required data sets at present. Gusev et al. [60] proposed a method to overcome this problem by performing a polygenic prediction analysis using summary-level statistic data. However, unlike the SMR method, neither PrediXcan nor the Gusev’s method distinguishes between pleiotropy and linkage.
