A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines.

TitleA cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines.
Publication TypeJournal Article
Year of Publication2013
AuthorsLiang L, Morar N, Dixon AL, Lathrop MG, Abecasis GR, Moffatt MF, Cookson WOC
JournalGenome Res
Volume23
Issue4
Pagination716-26
Date Published2013 Apr
ISSN1549-5469
KeywordsChromosome Mapping, Computational Biology, Databases, Nucleic Acid, Gene Expression, Gene Expression Profiling, Genome-Wide Association Study, Genomics, Genotype, Humans, Internet, Lymphocytes, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Siblings
Abstract

Gene expression levels can be an important link DNA between variation and phenotypic manifestations. Our previous map of global gene expression, based on ~400K single nucleotide polymorphisms (SNPs) and 50K transcripts in 400 sib pairs from the MRCA family panel, has been widely used to interpret the results of genome-wide association studies (GWASs). Here, we more than double the size of our initial data set with expression data on 550 additional individuals from the MRCE family panel using the Illumina whole-genome expression array. We have used new statistical methods for dimension reduction to account for nongenetic effects in estimates of expression levels, and we have also included SNPs imputed from the 1000 Genomes Project. Our methods reduced false-discovery rates and increased the number of expression quantitative trait loci (eQTLs) mapped either locally or at a distance (i.e., in cis or trans) from 1534 in the MRCA data set to 4452 (with <5% FDR). Imputation of 1000 Genomes SNPs further increased the number of eQTLs to 7302. Using the same methods and imputed SNPs in the newly acquired MRCE data set, we identified eQTLs for 9000 genes. The combined results identify strong local and distant effects for transcripts from 14,177 genes. Our eQTL database based on these results is freely available to help define the function of disease-associated variants.

DOI10.1101/gr.142521.112
Alternate JournalGenome Res.
PubMed ID23345460