Okay, so let's use an example.

This just sort of illustrate how any kind of screening can be tricky, but

particularly geometric screening can be tricky.

So, assume that there is a disease in it.

Only about .01% of the population have that disease.

And so we have a test that's 99% sensitive.

That is, if you have the disease with 99% of the time,

it will say you have the disease.

And it's 99% specific.

So, that means that if you don't have the disease, then 99% of the time,

when you don't have the disease, it'll say that you don't have the disease.

So, that seems like a pretty good test.

So, the question is, what's the probability

of a person having the disease given the test result is positive?

In other words, what's the positive predictive value of this test?

So we're going to consider two cases, a general population where the rate of this

disease is 0.1% and then a higher at-risk sub-population.

So in the general population this is what it might boil down to.

So remember, it's a very accurate test.

So if you have the disease, 99% of the time it'll tell you that you have it.

And then if you don't have the disease,

99% of the time it'll tell you that you don't have it.

But these numbers are a little bit sort of unbalanced because almost no one has

the disease.

It's a highly rare disease, so if you actually go calculate the sensitivity and

the specificity, they're both very high, just like we expected.

But the positive predictive value is only 9%.

Why is that?

It's because your testing a huge number of people that don't have the disease, so

even though you only get a tiny fraction of those to be false,

there's a large number of them because you tested so many.

So, it turns out that the positive predictive value or

that the probability you actually have disease, if we tell you you have

the disease, is only 9%, which might not be that great for lot's of reasons.

One we might give you all sorts of treatments you don't necessarily

want to get.

For two, you might be nervous or scared because we told you you have the disease,

even though it's actually kind of unlikely that you have the disease.

Even though the test is, in this case what a lot of people consider to be a really,

really sensitive and specific test.

Now except for sort of rare disorders and some very specific variations, it's

very rare that you would get these numbers to be this high in a genomics experiment.

Typically the sensitivity and

the specificity are relatively low compared to what we're showing here.