Method for Analyzing Correlated Traits in Genome Scans
Author Information
Author(s): Feng Tao, Zhang Shuanglin, Sha Qiuying
Primary Institution: Michigan Technological University
Hypothesis
Can clustering correlated traits improve the detection of genetic regulators in genome scans?
Conclusion
The proposed method shows that clustering correlated traits can yield more linkage signals than analyzing traits individually.
Supporting Evidence
- Clustering traits improved the detection of genetic regulators.
- The first principal component explained more than 53% of the variation in each cluster.
- 18 clusters were identified with an average correlation coefficient larger than 0.5.
- 6 out of 18 PC traits had at least one regulator.
- Considering clusters yielded more linkage signals than individual traits.
Takeaway
This study found a way to group similar traits together to better understand their genetic influences, which helps in finding more signals that indicate genetic regulators.
Methodology
The study used a clustering method to group correlated traits and performed linkage scans on the first principal component of each cluster.
Limitations
The choice of parameters for clustering may affect the results.
Participant Demographics
Members of 14 CEPH Utah families.
Statistical Information
P-Value
1.67 × 10-5
Statistical Significance
p<0.05
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