Controlling False Positives in eQTL Mapping
Author Information
Author(s): Peng Jie, Wang Pei, Tang Hua
Primary Institution: University of California, Davis
Hypothesis
Can we improve the detection of trans-hubs in eQTL studies by accounting for correlations among gene expressions?
Conclusion
Accounting for correlations among gene expressions significantly reduces the number of detected trans-hubs, suggesting that previous findings should be interpreted with caution.
Supporting Evidence
- After accounting for correlations, previously detected trans-hubs were no longer significant.
- Two candidate trans-hubs were identified but lost significance when expression correlations were considered.
- The study highlights the importance of controlling for false positives in genetic studies.
Takeaway
The study shows that when looking for gene regulation hotspots, we need to be careful because many of the initial findings might just be mistakes caused by how genes are related to each other.
Methodology
The study used a robust score statistic method and elastic net regression to analyze eQTL data from three-generation pedigrees.
Potential Biases
Potential biases may arise from the complex correlation structure of gene expressions.
Limitations
The findings may not generalize beyond the specific datasets used, and the method's effectiveness in other contexts is uncertain.
Participant Demographics
Data from 14 three-generation pedigrees.
Statistical Information
P-Value
<7 × 10-9
Statistical Significance
p<0.001
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