title: Novel distal eQTL analysis demonstrates effect of population genetic architecture on detecting and interpreting associations.
abstract: Mapping expression quantitative trait loci (eQTL) has identified genetic variants associated with transcription rates and has provided insight into genotype-phenotype associations obtained from genome-wide association studies (GWAS). Traditional eQTL mapping methods present significant challenges for the 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, " NET: work-based, L: arge-scale I: dentification o F: dis T: al eQTL" (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 5744 genes with local eQTL while 3322 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 1274 transcripts with distal eQTL that had not been reported in previous studies, while 2483 transcripts with local eQTL were identified. In all species, we found no enrichment for transcription factors facilitating eQTL associations; instead, we found that most trans-acting factors were annotated for metabolic function, suggesting that genetic variation may indirectly regulate multigene 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.
authors.affiliation: Curriculum in Bioinformatics and Computational Biology, Departments of Genetics and Biology, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599.
authors.forename: Matthew
authors.initials: M
authors.lastname: Weiser
authors.affiliation: Departments of Statistical Science, Computer Science, and Mathematics, Duke University, Durham, North Carolina 27708.
authors.forename: Sayan
authors.initials: S
authors.lastname: Mukherjee
authors.affiliation: Departments of Genetics and Biology, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599 tsfurey@email.unc.edu.
authors.forename: Terrence S
authors.initials: TS
authors.lastname: Furey
mesh_terms: D000818:Animals; D002460:Cell Line; D020090:Chromosome Segregation / Q000235:genetics; D003198:Computer Simulation; D003433:Crosses, Genetic; D005260:Female; D005786:Gene Expression Regulation; D063990:Gene Ontology; D005828:Genetics, Population; D055106:Genome-Wide Association Study; D006801:Humans; D007178:Inbreeding; D008297:Male; D051379:Mice; D058977:Molecular Sequence Annotation; D040641:Quantitative Trait Loci / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics
keywords: eQTL; gene expression; gene networks; genetical genomics