1Quest Statistics Genomics Module #3 cheet sheet.without code. 1. Load the example SNP data with the following code: 1 2 3 4 5 6 7 library (snpStats) library (broom) data( for .exercise) use <- seq( 1 , ncol(snps .10 ), 10 ) sub .10 <- snps .10 [,use] snpdata = sub .10 @.Data status = subject.support$cc Fit a linear model and a logistic regression model to the data for the 3rd SNP. What are the coefficients for the SNP variable? How are they interpreted? (Hint: Don't forget to recode the 0 values to NA for the SNP data) 1 / 1 point Linear Model = -0.16 Logistic Model = -0.04 Both models are fit using a dominance model. So in the linear model case, the coefficient is the decrease in probability associated with one copy of the minor allele. In the logistic regression case, it is the decrease in the log odds ratio associated with one copy of the minor allele. Linear Model = 0.54 Logistic Model = 0.18 Both models are fit on the additive scale. So in both cases the coefficie