To correct for these multiple comparisons we apply the Benjamini-Hoch-berg procedure [17] to control for the false discovery rate a.

For example, if 100 comorbidities are identified with a false discovery rate a of a = 0.01, the eXpected number of false positives among these comorbidities is one.

We Will therefore be interested in the recall R(a) as a function of the false discovery rate a. R(a) is the probability that a diabetic comorbidity listed in Table 1 is also identified by our co-occurrence analysis at a given level of a.

Each diagnosis where the null hypothesis of statistical independence with either DM1 or DMZ can be rejected with a given value of the false discovery rate in at least one of the age groups is identified as a comorbidity.

A false discovery rate of a = 0.001 gives a list of 75 significant comorbidities and a recall of R(a = 0.001) = 0.59.

Indeed, our discovery rate of all US.

We therefore expect that haplotypes discovery rate should remain appreciable with continued sampling given that the current sample size is about 3.97% of the European American populations.

Surprisingly, the discovery rate for new alleles in the IMGT database appears to be increasing, in contrast with the conclusion that most alleles are known, raising the suspicion that the total number of existing alleles is much larger than current estimates.

With a much larger definition of alleles and a higher discovery rate , the fundamental power law relationship would be expected to change at some point in time as the data sources move towards SBT versus older oligo or serology based methods.