We found five times more signals in close proximity ( cis-) to transcripts than in our genome-wide analysis. Finally, we restricted our analysis to the markers and transcripts that were closely located. Furthermore, we demonstrated that filtering on genotype counts substantially reduced spurious detection. Specifically, we showed that removing non-expressed genes by filtering on expression variability effectively reduced the number of tests by nearly 50%.
Using the Genetic Analysis Workshop 15 (GAW15) Problem 1 data, we demonstrated the value of data filtering for reducing the number of tests and controlling the number of false positives.
This two-step method produces very similar results to the full mixed model method, with our method being significantly faster than the full model.
We applied a simple and efficient two-step method to analyze a family-based association study of gene expression quantitative trait loci (eQTL) in a mixed model framework.
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