1 / 2

Genotype, g

Y is binary (0/1), prob Y=1 depends on genotype, g prob = odds / (1+odds) odds = prob / (1-prob). Prob(g). Genotype, g. Odds(Y|g). Prob(Y|g). OR=3. (1-p) 2. AA. 1/4. 1/5. 2p(1-p). Aa. 3/4. 3/7. p 2. aa. OR=20. 5. 5/6. y=rbinom(n, 1, prob.y).

zuzela
Download Presentation

Genotype, g

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Y is binary (0/1), prob Y=1 depends on genotype, g • prob = odds / (1+odds) • odds = prob / (1-prob) Prob(g) Genotype, g Odds(Y|g) Prob(Y|g) OR=3 (1-p)2 AA 1/4 1/5 2p(1-p) Aa 3/4 3/7 p2 aa OR=20 5 5/6 y=rbinom(n, 1, prob.y) • Knowing p (and n) lets us generate g • Odds(Y|AA), the two log-ORs, and g tells us how to generate data g=rbinom(n, 2, p)

  2. Basic outline • Write function to generate data • Write function(s) to fit add/dom/recc models, and return p-values • Use both LOTS of times, and see how often p<0.05; this proportion is the power • Finally, repeat for different p, Odds(Y|AA) and log ORs. Record the power each time

More Related