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Hum Mol Genet DOI:10.1093/hmg/ddu228

Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types.

Publication TypeJournal Article
Year of Publication2014
AuthorsLi, Q, Stram, A, Chen, C, Kar, S, Gayther, S, Pharoah, P, Haiman, C, Stranger, B, Kraft, P, Freedman, ML
JournalHum Mol Genet
Date Published2014 Oct 01
KeywordsAlleles, Breast Neoplasms, Chromosome Mapping, Colonic Neoplasms, Female, Gene Expression, Gene Expression Profiling, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Kidney Neoplasms, Lung Neoplasms, Male, MicroRNAs, Neoplasms, Prostatic Neoplasms, Quantitative Trait Loci, Risk, RNA, Messenger

The majority of trait-associated loci discovered through genome-wide association studies are located outside of known protein coding regions. Consequently, it is difficult to ascertain the mechanism underlying these variants and to pinpoint the causal alleles. Expression quantitative trait loci (eQTLs) provide an organizing principle to address both of these issues. eQTLs are genetic loci that correlate with RNA transcript levels. Large-scale data sets such as the Cancer Genome Atlas (TCGA) provide an ideal opportunity to systematically evaluate eQTLs as they have generated multiple data types on hundreds of samples. We evaluated the determinants of gene expression (germline variants and somatic copy number and methylation) and performed cis-eQTL analyses for mRNA expression and miRNA expression in five tumor types (breast, colon, kidney, lung and prostate). We next tested 149 known cancer risk loci for eQTL effects, and observed that 42 (28.2%) were significantly associated with at least one transcript. Lastly, we described a fine-mapping strategy for these 42 eQTL target-gene associations based on an integrated strategy that combines the eQTL level of significance and the regulatory potential as measured by DNaseI hypersensitivity. For each of the risk loci, our analyses suggested 1 to 81 candidate causal variants that may be prioritized for downstream functional analysis. In summary, our study provided a comprehensive landscape of the genetic determinants of gene expression in different tumor types and ranked the genes and loci for further functional assessment of known cancer risk loci.


Alternate JournalHum. Mol. Genet.
PubMed ID24907074
PubMed Central IDPMC4215106
Grant ListUL1 TR000430 / TR / NCATS NIH HHS / United States
U19 CA148065 / CA / NCI NIH HHS / United States
/ / Howard Hughes Medical Institute / United States
U19 CA148537 / CA / NCI NIH HHS / United States
10124 / / Cancer Research UK / United Kingdom