Based on this ratio and assuming a null model of a constant rate of recombination across the genome, we estimate that we would observe >30 nonrecombinant SNPs in a row by chance with a probability of <0.001 (0.79).
The method begins by assuming a lower bound on the number of null effects.
First, we assumed a null hypothesis of neutrality and constant population size (Table 1).
This procedure begins by assuming the null hypothesis whereby the order of the observed conditional means is arbitrary (i.e. the observed mean in Condition A is just as likely to have occurred in Condition B).
Conversely, the second test modifies this by assuming in the null hypothesis that binding locations of the given TF falls inside gene regions of the given disease proportionally to how often the binding locations of this TF on average falls inside gene region sets across all diseases.
However, one can use many fewer permutations by instead assuming a parametric form of the null distribution and then fitting the few required parameters to the test statistics generated from the permutations (Lee et al., 2012b).
The choice of a null hypothesis assuming a uniform distribution of the strength is motivated by its simplicity and by the availability of analytical results like the one in Equation 1.
We compared the likelihood of the null model (assuming a single branching process for the tree) to the GMYC model (assuming significant changes in branching time).
We also calculated a null case by assuming individuals 'move' (i.e. are randomly assigned) to one of the four immobilized individuals independently and with equal probability.
