# HW1_answers - 0.0127 9 0.0244 6 0.1733 0.0244 6 0.00846 9...

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Answers to exercises due Feb 17th: 1. 2. 1 1 0 0 2 2 1 2 1 2 1 2 1 2 0 0 1 1 3 4 3 3 2 2 1 3 2 1 2 2 1 3 1 3 1 2 1 4 0 0 1 1 2 2 2 2 2 2 1 5 2 1 2 2 1 3 1 3 1 2 1 6 0 0 1 1 2 4 2 3 2 2 1 7 2 1 2 1 2 4 2 3 2 2 1 8 0 0 1 1 1 3 1 3 1 2 1 9 2 1 2 2 1 4 1 3 1 2 1 10 0 0 1 1 2 3 2 3 2 2 1 11 4 3 1 2 2 3 2 3 1 2 1 12 6 5 1 2 1 4 1 3 1 2 1 13 8 7 2 2 1 2 1 2 1 2 1 14 10 9 1 2 1 3 1 3 1 2 The p-values for the three markers should be 0.1173, 0.2511, 0.008469. Since we have tested 3 markers and we have no information about their relationship, we should correct this analysis for three independent tests. So instead of requiring a p-value of 0.05 for significance, we should require 0.05/3 or 0.0167. Since the third marker gives a p-value smaller then this, we have a significant result, even when correcting for multiple testing. 3. The p-values for the 10 markers should be:

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Unformatted text preview: 0.0127 9 0.0244 6 0.1733 0.0244 6 0.00846 9 0.1733 1 0.7233 1 0.7516 If these were 10 independent tests, we would use a threshold of 0.05/10 or p=0.005 as significant. If any of our results were smaller then this, we could declare then significant. However, since we know these markers have allelic association between them, doing this kind of Bonferroni correction is overly conservative. It would be helpful to know how closely correlated the marker genotypes are to decide what kind of correction might be appropriate. As it stands, it is very difficult to determine if these results 3 4 1 2 1 2 1 3 4 4 1 3 1 4 1 3 2 2 1 2 3 4 are significant or not. With no correction, they would be. With the most conservative correction, they would not be. But the proper correction is something in between....
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HW1_answers - 0.0127 9 0.0244 6 0.1733 0.0244 6 0.00846 9...

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