Cis Regulations of Gene Expression
Author Information
Author(s): Shi Jianxin, Siegmund David O, Levinson Douglas F
Primary Institution: Stanford University
Hypothesis
Can statistical corrections improve the detection of gene expression linkages?
Conclusion
The study found that correcting for skewness and correlations among tests significantly reduced the number of false positives in gene expression linkage analysis.
Supporting Evidence
- Using a robust score statistic, the study detected linkage for the expression levels of 22 genes.
- 19 of the detected linkages were found to be cis-regulated.
- The study corrected for skewness and correlations, leading to a more accurate estimation of false positives.
Takeaway
The researchers looked at how genes are regulated and found that many linkages are close to the genes themselves, which is important for understanding how genes work.
Methodology
The study re-analyzed existing data using robust score statistics and corrections for skewness and correlations among tests.
Potential Biases
There is a risk of underestimating false positives due to uncorrected dependencies among tests.
Limitations
The analysis may not account for all potential biases in the data.
Participant Demographics
The study involved 14 pedigrees from the Centre d'Etude du Polymorphisme Humain.
Statistical Information
P-Value
0.016
Statistical Significance
p<0.05
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