Clustering and Principal-Components Approach for Mapping Gene Expressions
Author Information
Author(s): Wang Yuanjia, Fang Yixin, Wang Shuang
Primary Institution: Columbia University
Hypothesis
Can clustering and principal-components analysis improve the mapping of multiple gene expressions in genetic studies?
Conclusion
The proposed methods using family structure information provide stronger linkage evidence for gene expression traits than traditional methods.
Supporting Evidence
- Clustering analysis identified three groups compared to six from standard methods.
- Principal components of heritability explained up to 77% of the total heritability in clusters.
- Linkage evidence was stronger for components derived from the proposed clustering method.
Takeaway
This study shows a new way to look at gene data that helps scientists find important genetic information more easily.
Methodology
Clustering analysis and principal-component analysis were applied to gene expression data, incorporating family structure information.
Limitations
The penalized PCH cannot determine which traits to include for collinear traits.
Statistical Information
P-Value
1.22 × 10-12
Statistical Significance
p<0.05
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