Difference between revisions of "Power Calculations: Quantitative Traits"
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== A Simple Genetic Association Study == | == A Simple Genetic Association Study == | ||
| − | In this example, we will use [http://www.r-project.org/ R] to carry a simple power calculation for a genetic association study. We will assume that you are interested in a quantitative trait and that you have phenotyped and genptyped | + | In this example, we will use [http://www.r-project.org/ R] to carry a simple power calculation for a genetic association study. We will assume that you are interested in a quantitative trait and that you have phenotyped and genptyped ''N'' randomly sampled individuals. Further, we will assume that you are using signficance level <math>alpha</math> for your analyses (typically, <math>alpha = 0.05 / M</math> where ''M'' the number of independent markers examined) and ''H2'' is the variance explained by additive effects at the marker of interest. |
In this setting, a simple power calculation might look like: | In this setting, a simple power calculation might look like: | ||
Revision as of 10:50, 17 June 2010
Calculating power for simple study designs is one of the most common tasks faced by a Biostatistician. However, for most of them, it barely makes the list of the top 1000 things they might enjoy doing. So, make your neighborhood biostatistician marginally happier by calculating your own power tables before you next meet with them (yes, even if you can calculate power, you will probably still need professional advice on study design!).
A Simple Genetic Association Study
In this example, we will use R to carry a simple power calculation for a genetic association study. We will assume that you are interested in a quantitative trait and that you have phenotyped and genptyped N randomly sampled individuals. Further, we will assume that you are using signficance level for your analyses (typically, where M the number of independent markers examined) and H2 is the variance explained by additive effects at the marker of interest.
In this setting, a simple power calculation might look like:
N = 1000 alpha = 0.05 H2 = 0.01
threshold = qchisq(alpha, df = 1, lower.tail = FALSE) power = pchisq(threshold, df = 1, lower.tail = FALSE, ncp = N * H2)
