Network-based Large-scale Identification oF disTal eQTL (NetLIFT)

Novel distal eQTL analysis demonstrates effect of population genetic architecture on detecting and interpreting associations

Matthew Weiser, Sayan Mukherjee, Terrence S. Furey

Mapping expression quantitative trait loci (eQTL) has identified genetic variants associated with transcription rates, and has provided insight for genotype-phenotype associations obtained from genome-wide association studies (GWAS). Traditional eQTL mapping methods present significant challenges for multiple testing burden, resulting in a limited ability to detect eQTL that reside distal to the affected gene. To overcome this, we developed a novel eQTL testing approach, NetLIFT, which performs eQTL testing based on the pairwise conditional dependencies between genes’ expression levels. When applied to existing data from yeast segregants, NetLIFT replicated most previously-identified distal eQTL, and identified 46% more genes with distal effects compared to local effects. In liver data from mouse lines derived through the Collaborative Cross project, NetLIFT detected 5,744 genes with local eQTL while 3,322 genes had distal eQTL. This analysis revealed founder of origin effects for a subset of local eQTL that may contribute to previously described phenotypic differences in metabolic traits. In human lymphoblastoid cell lines, NetLIFT was able to detect 1,274 transcripts with distal eQTL that had not been reported in previous studies, while 2,483 transcripts with local eQTL were identified. In all species, we found no enrichment for transcription factors facilitating eQTL associations; instead, we find that most trans-acting factors were annotated for metabolic function, suggesting that genetic variation may indirectly regulate multi-gene pathways by targeting key components of feedback processes within regulatory networks. Furthermore, the unique genetic history of each population appears to influence the detection of genes with local and distal eQTL.

Source Code: See README.txt for information on how to install and run. A small example simulated data set can be used to test the software.

Results: Local and distal eQTLs predicted by NetLIFT on yeast segregants, mice from the Collaborative Cross, and human lymphoblast cell lines.

Simulation Data: A total of ten gene expression data sets were simulated, each with 500 genes and 250 samples. SNPs were then simulated for each gene. For complete details, see paper.

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