48th European Mathematical Genetics Meeting (EMGM) 2020
Pleiotropy denotes the association of a hereditary unit (SNP, locus or gene) to multiple features or phenotypes. Detection of true biological pleiotropic locus, instead of mediated or spurious ones, may inform on the etiology of complex diseases as well as on their categorization. While many methods have been proposed in literature for the detection of pleiotropic locus, their applicability is to a large extent defined by the type of data available and their relative performance is yet unclear.
... Here, we investigated different forms of meta-analysis based approaches, namely classical metaanalysis (MA), conditional false discovery rate (cFDR; Andreassen et al., 2013; Liley &Wallace, 2015), subset-based meta-analysis (ASSET; Bhattacharjee et al., 2012), and CPBayes (CPB; Majumdar et al.,2018). To this end, we performed a simulation study where we repeatedly simulated case-control data sets of varying sizes and with different underlying etiologies from a resampled population of 50,000 individuals of European ancestry based on the 1000 Genomes project. We considered various combinations of factors, including varying effect sizes, variant allele frequencies, numbers of disease SNPs, varying sample sizes and the degree of genetic overlap between the phenotypes, and assessed their impact on power and type I error of these methods. While work is ongoing, we will present preliminary results from our simulation study. A major challenge in human genetics is translating non-coding GWAS loci to mechanistic understanding about the disease causing processes. Local gene expression quantitative trait loci (cis-eQTLs) regularly implicate multiple putative target genes whose disease relevance and function is often poorly understood. In contrast, genetic variants that are associated with the expression of multiple target genes in trans, have the potential to directly identify the cellular processes affected by disease variants. However, trans-eQTLs are difficult to detect due to small sample sizes of current eQTL datasets and large number genes tested. Here, we have jointly analysed five eQTL dataset profiling gene expression in naive and stimulated B-cells, T-cells, monocytes, neutrophils and platelets from up to 710 individuals. To improve interpretability of trans-eQTLs and reduce multiple testing burden, we used five matrix factorisation techniques to infer gene co-expression modules from expression data. We find that trans-eQTLs regulating co-expression modules are highly cell type specific and are often detected by a single matrix factorisation approach. These include established trans-eQTLs, such as the platelet-specific ARH-GEF3 locus associated with mean platelet volume and monocyte-specific IFNB1 locus associated with activation of genes downstream of the type 1 interferon signalling pathway upon LPS stimulation. Co-expression modules under cell-type specific genetic control also exhibit higher variance in the cell types where the associations are detected, suggesting that our results are not simply an artefact of limited power. Thus the contexts in which trans-eQTL are active are likely to be missed when studying bulk tissues such as whole blood.