When testing multiple hypotheses, what does it mean when there are not enough extremes? Suppose you are testing a large number of hypotheses, say a million. Unlike the usual situation where you have a lot of very small p-values, in this case all of your p-values are greater than 5%. 
What does that imply, and what's the best way to handle something like this?
 A: As the sample size ($n$) grows, hypothesis tests become significant. However, as the number of independent hypothesis tests ($k$) grows, individual tests still behave the same. 
The problem of multiple testing is that the overall chance of a false positive becomes deceivingly large. Among multiple tests, the chance of at least one false positive is larger than the significance level ($\alpha$). Hence why you should apply multiple testing correction. 
Suppose the effects/differences you are testing for are simply not present in the population, or they are so infinitesimally small that you cannot even detect them with your current hypothesis tests. This is essentially what you assume when applying a Bonferroni correction: There are no true effects, so every test has only the ability to produce a false positive. There are now $k$ potential false positives and a chance of $1 - (1 - \alpha)^k$ of at least one false positive.

So what does it mean when you don't observe extremely small $p$-values? Under the null-hypothesis, the $p$-value is uniformly distributed so even if there are no true effects you would expect the number of values closer to $0$ to increase with the number of tests, since you would essentially be drawing $k$ numbers from $\mathsf{Unif}(0,1)$. 
If you are running a very large number of tests and don't conclude any nominally significant differences (uncorrected), then perhaps your test is not powerful enough, or your tests are not actually independent. However, if you conclude approximately $\alpha\cdot100\%$ nominally significant $p$-values, then nothing strange is going on. (In your example, you would expect about $50,000$ $p$-values below $0.05$.)

Lastly, as for the conclusion: It might be more interesting to report a set of confidence intervals / credibility ranges, so you can say something about the effect sizes. Alternatively, if your sample size is indeed large and you want to demonstrate that there are no effects, then you should be running tests of equivalence instead.

To elaborate on what Glen_b aluded to in the comments:
If your tests are not actually independent, then neither are your $p$-values. In other words, $p$-values only follow a uniform distribution if you (1) repeatedly draw samples from the same population and test the same hypothesis, or (2) perform independent tests for different effects. A simple, albeit somewhat contrived example would be if you were to perform the same test multiple times. In this case, every $p$-value is identical and may well be above the significance threshold. 
