Life after GWAS comments
Allen Roses, director of the Deane Drug Discovery Institute at Duke University, noted that GWAS has “largely disappointed its most enthusiastic proponents Forget about genes V weiterlesen
The experts in the field will immediately notice what I am suggesting here – an improved GWA plot that does not take into account p values alone but also effect sizes. I was experimenting some time with smile plots but finally ended with this bubble plot. Bubble size for 0.5<OR>2 is set to a minimum while all other ORs get increasing bubbles (BTW use for OR<1 a 1/OR transformation beforehand). Chromosomal colors are from a self defined palette using the colorRampPalette function in R which makes it look like pointillism art. The real question: Did the previous GWA p value screening miss some important effects? For example the important dot at x=4 and y=4?
So far in epidemiology case – control studies are defined by an approach where
… the past histories of patients (the cases) suffering from the condition of interest are compared to the past histories of persons (the controls) who do not have the condition of interest, but who otherwise resemble the cases in such particulars as age and sex ….
I usually explain controls as non-cases in the same overall environment When controls are no controls weiterlesen