An (anonymous) reviewer of our forthcoming EJHG paper on IgE and STAT3 pointed me towards a JNCI paper that has a nice supplement – an excel sheet to calculate the probability that a positive report is false. It basically relies on (i) the magnitude of the p-value (ii) statistical power and (iii) fraction of tested hypothesis. While we certainly know (i) and (ii), (iii) is always hard to know with many datasets including hundreds of traits that allow indefinite numbers of subgroups. Are you really interested in a new paper about “An African-specific functional polymorphism in KCNMB1 shows sex-specific association with asthma severity” that encompasses 1 of virtually 100 ethnic groups; 1 or virtually 25000 genes; 1 of 2 sexes; 1 or virtually 50 asthma related traits, yea, yea.
Just reveived an email from the creators of the Genetic Association Database (GAD)
… Your published genetic association study has been included in the recent update of the NIH based, Genetic Association Database (GAD), the database of human genetic association studies. Continue reading Genome Browser now with GADview track
I am slow in commenting on a paper that has already been published earlier this year – Joe Terwillingers vivid refutation of the fundamental theorem of the hapmap proponents that
if a marker is in tight LD with a polymorphism that directly impacts disease risk, as measured by the metric r^2, then one would be able to detect an association between the marker and disease with sample size that was increased by a factor of 1/r^2 over that needed to detect the effect of the functional variant directly
I cannot comment on the statistical proof but fear from my recent experience with Crohn and asthma tags that he may be right with his assumption: Even marker in high LD with the functional variant may not show any association at all. These may be bad news for all those currently running large screening programs with hapmap based variants believing that P(A|BC)=P(A|Bc)=P(A|B), yea, yea.
Tag SNPs also do not work with CNVs