Identifying Genetic Pathways for Complex Traits Using Genome-Wide eQTL Associations
Author Information
Author(s): Min Josine L., Taylor Jennifer M., Richards J. Brent, Watts Tim, Pettersson Fredrik H., Broxholme John, Ahmadi Kourosh R., Surdulescu Gabriela L., Lowy Ernesto, Gieger Christian, Newton-Cheh Chris, Perola Markus, Soranzo Nicole, Surakka Ida, Lindgren Cecilia M., Ragoussis Jiannis, Morris Andrew P., Cardon Lon R., Spector Tim D., Zondervan Krina T.
Primary Institution: The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
Hypothesis
Can integrating gene expression data from lymphoblastoid cell lines (LCLs) and genotype data uncover associations with complex traits?
Conclusion
The study found that using LCLs for eQTL analysis did not yield robust associations with complex traits, suggesting that larger sample sizes and more relevant tissues are needed.
Supporting Evidence
- The study profiled 19,573 expression probes in lymphoblastoid cell lines from 299 twins.
- 939 expression probes correlated with at least one quantitative trait at a nominal significance value.
- 131 potential eQTLs were identified, but none significantly replicated in larger cohorts.
Takeaway
Researchers looked at how genes affect traits by studying twins' blood cells, but they didn't find strong links, showing they need more data and better samples.
Methodology
The study profiled gene expression in lymphoblastoid cell lines from twins and correlated it with various quantitative traits, followed by eQTL analysis across multiple datasets.
Potential Biases
Potential biases due to the artificial conditions of LCLs and the limited generalizability of findings to other tissues.
Limitations
The study was limited by small sample sizes and the use of LCLs, which may not accurately represent gene expression in relevant tissues.
Participant Demographics
299 female twins from the UK Adult Twin registry.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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