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I want to go back a second to the
end of last time because in the

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closing moments there, we, or
at least I, got a little bit

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lost, and where
the plusses and

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minuses were at
a certain table.

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And, I want to go back and make
sure we've got that straight.

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We were talking about a situation
where we were trying to use genetics,

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and the phenotypes that might
be observed in mutants to try to

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understand the biochemical pathway
because we're beginning to try to

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unite the geneticist's point of
view who looks only at mutants,

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and the biochemist's point of view
who looks at pathways and proteins.

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And, I had hypothesized that there
was some biochemists who had thought

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up a possible pathway for the
synthesis of arginine that involved

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some precursor,
alpha, beta, gamma,

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where alpha is turned into beta;
beta is turned into gamma; and gamma

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is used to turn into
arginine. And, hypothetically,

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there would be some enzymes:
enzyme A that converts alpha,

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enzyme B that converts beta,
and enzyme C that converts gamma.

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And, we were just thinking about,
what would the phenotypes look like

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of different arginine auxotrophs
that had blocks at different stages

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in the pathway. If I had
an arginine auxotroph that

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had a block here because let's say
a mutation in a gene affecting this

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enzyme, or at a block here
at a mutation affecting, say,

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the gene that encodes enzyme C,
how would I be able to tell very

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simply that they were in
different genes? Last time,

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we found that we could tell they
were in different genes by doing a

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cross between a mutant
that had the first mutation,

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and a mutant that had the second
mutation, and looking at the double

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heterozygote, right? And,
if in the double heterozygote

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you had a wild type or a normal
phenotype, then they had to be in

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different genes,
OK? Remember that?

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That was called a test
of complementation.

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That was how we were able to sort
out which mutations were in the same

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gene, and which mutations
were in different genes.

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Now we can go a step further.
When we've established that they're

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in different genes, we
can try to begin to think,

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how do these genes relate
to a biochemical pathway?

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I wanted to begin to introduce,
because it'll be relevant for today,

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this notion: so, suppose I had a
mutation that affected enzyme A so

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that this enzymatic step
couldn't be carried out.

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Such a mutant, when I
just try to grow it on

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minimal medium won't be able to grow.
If I give it the substrate alpha,

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it doesn't do it any good because
it hasn't got the enzyme to convert

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alpha. So, given alpha, it
won't grow. But if I give it

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beta, what will happen? It
can grow because I've bypassed

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the defect. What about if
I give it gamma? Arginine?

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Now, if instead the mutation were
affecting enzymatic step here,

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then if I give it
on minimal or medium

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but it can grow on gamma.
What about this last line?

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If I have a mutation and
the last enzymatic step,

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minimal medium can't grow with
alpha, can't grow with beta,

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can't even grow with gamma.
But, it can grow with arginine

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because I've bypassed that step.
So, I get a different phenotype,

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the inability to
grow even on gamma,

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but I can grow on arginine. Now,
here, if I put together those

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mutants and make a double mutant,
a double homozygote, let's say,

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that's defective in both A and B,
which will it look like? Will it be

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able to grow on minimal medium?
Will it be able to grow on alpha?

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Will it be able
to grow on beta?

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Will it be able to grow on gamma
and arginine? What about if I have a

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double mutant in B and C,
minus, minus, minus, minus,

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plus? So this looks the same as
that. This looks the same as that.

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And so, by looking at
different mutant combinations,

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I can see that the phenotype of B
here is what occurs in the double

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mutant. So, this phenotype is
epistatic to this phenotype.

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Epistatic means stands upon,
OK? So, phenotypes, just like

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phenotypes can be recessive or
dominant, you can also speak about

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them being epistatic. And
epistatic means when you have

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both of two mutations
together at the epistatic

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then one of them is epistatic
to the other, perhaps.

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It will, in fact, be
the one that is present.

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So, this is not so easy to do
in many cases because if I take

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different kinds of mutation
affecting wing development,

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and I put them together in the same
fly, I may just get a very messed up

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wing, and it's very hard to
tell that the double mutant has a

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phenotype that looks like
either of the two single mutants.

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But sometimes, if they fall very
nicely in a pathway where this

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affects the first step,
this affects the second step

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this affects the third step,
this affects the fourth step,

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then the double mutant
will look like one of those,

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OK? And, that way you can somehow
order things in a biochemical

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pathway. Now, notice,
this is all indirect,

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right? This is what geneticists did
in the middle of the 20th century to

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try to figure out how to connect
up mutants to biochemistry.

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Actually, that's not true.
It's what geneticists still do

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today because you might think
that Well, we don't need to do this

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anymore, but in fact geneticists
constantly are looking at mutants

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and making connections trying to
say, what does this double combination

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look like? What does that
double combination look like,

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and how does that tell us
about the developmental pathway,

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which cell signals which cell?
This turns out to be one of the

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most powerful ways to figure out
what mutations do by saying the

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combination of two mutations
looks like the same as one of them,

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allowing you to order the
mutations in a pathway.

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And, there's no general way to
grind up a cell and order things in a

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pathway. Genetics is a very
powerful tool for doing that.

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Now, there are some ways to
grind up cells and order things,

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but you need both of these
techniques to believe stuff.

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Anyway, I wanted to go over that,
because it is an important concept,

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the concept of epistasis, the
concept of relating mutations to

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steps and pathways, but what
I mostly want to do today

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is go on now to talk about genetics
not in organisms like yeast or fruit

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flies or even peas,
but genetics in humans.

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So, what's different about genetics
in humans than genetics in yeast?

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You can't choose who mates
with whom. Well, you can.

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I mean, in the days of arranged
marriages maybe you couldn't,

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but you can choose who mates
with whom, but only for yourself,

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right? What you can't do is
arrange other crosses in the human

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population as an experimentalist.
Now, your own choice of mating,

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unfortunately or fortunately perhaps
produces too few progeny to be

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statistically significant. As
a parent of three, I think about

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what it would take to raise a
statistically significant number of

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offspring to draw any conclusions,
and I don't think I could do that.

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So, you're absolutely right. We
can't arrange the matings that

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we want in the human population.
So, that's the big difference.

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So, can we do genetics anyway?
How do we do genetics even though

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we can't arrange the matings
the way we'd like to? Sorry?

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Well, family trees. We have to
take the matings as we find them in

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the human population. You
can talk to somebody who might

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have an interesting phenotype,
I don't know, attached earlobes,

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or very early heart disease,
or some unusual color of eyes,

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and begin to collect a
family history on that person.

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It's a little bit of a dodgy thing
because you might just be relying on

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that person's recollection. So,
if you were really industrious

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about this, you'd go check out each
of their family members and test for

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yourself whether they have the
phenotype. People who do serious

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human genetic studies often go and
do that. They have to go confirm,

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either by getting hospital records
or interviewing the other members of

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the family, etc. So, this
is not as easy as plating

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out lots of yeasts
on a Petri plate.

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And then you get pedigrees. And
the pedigrees look like this.

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Here's a pedigree. Tell
me what you make of it.

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Now, symbols: squares are males,
circles are females by convention,

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a colored in symbol means
the phenotype that we're

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interested in studying at the
moment. So, in any given problem,

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somebody will tell you, well,
we're studying some interesting

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phenotype. You often have
an index case or a proband,

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meaning the person who
comes to clinical attention,

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and then you chase back in the
pedigree and try to reconstruct.

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So, suppose I saw a
pedigree like this.

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What conclusions could I draw?
Sorry? Recessive, sex link trait;

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why sex link trait? So,
let's see if we can get your

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model up here. You think
that this represents

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sex-linked inheritance. So,
what would the genotype be of

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this male here? Mutant:
I'll use M to denote a

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mutant carried on the X chromosome,
and a Y on the opposite chromosome.

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What's the genotype
of the female here?

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So, it's plus over plus where I'll
use plus to denote the gene carried

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on the normal X chromosome.
OK, and then what do you think

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happened over here?
So, mutant over plus,

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you mate to this male who is plus
over plus. Why is that male plus

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over plus? Oh,
right, good point.

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It's not plus over plus. It's
plus over Y. Why is that male

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plus over Y as opposed
to mutant over Y?

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He'd have the mutant phenotype.
So, he doesn't have the mutant

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phenotype so he can infer he's plus
over Y. OK, and then what happens

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here? Mutant over Y; this is plus
over Y. How did this person get

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plus over Y? They just the
plus for mom, and the daughters,

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Y from dad, and a plus from mom.
That's cool. Now, what about the

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daughters there?
They're plus over plus,

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or M over plus? Is one,
one, and one the other? Well,

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in textbooks it's always plus
over plus and M over plus,

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but in real life? We don't know,
right? So, this could be plus over

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plus, or M over plus,
we don't know, OK? Now,

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what about on this side
of the pedigree here?

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What's the genotype
here? Plus over Y, OK.

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Why not mutant over Y?
Because if they got the mutant,

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it would have to come from the, OK,
so here, plus over plus, and then

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here, everybody is normal because
there's no mutant allele segregated.

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Yes? Yeah, couldn't there
just be recessive? I mean,

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it's a nice story
about the sex link

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but couldn't it be recessive?
So, walk me through it being

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recessive. M over plus,
plus over plus. Wait, wait,

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wait, hang on. Could this be M over
plus, and that person be affected?

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It's got to be M over M,
right so mutants over mutants

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but that's possible. Yeah,
OK. So, what would this

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person be? Plus over plus,
let's say, come over here. Now,

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what would this person be? M plus.
It has to be M plus because, OK,

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and what about this person
here? M plus, now what about the

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offspring? So, one
of them is M over M,

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plus over plus, and two M pluses.
Does it always work out like that?

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[LAUGHTER] No, it doesn't
always work out like that at all.

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So, I'm just going to write
plus over plus here just to say,

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tough, right? In real life, it
doesn't always come out like that.

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What about over here? It would
have to be plus over plus.

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Why not? It doesn't because it
could be M over plus and have no

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effect at offspring by chance,
right? But, you were going to say

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it's plus over plus because in the
textbooks it's always plus over plus

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in pictures like this, right?
And then, it all turns out

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to be pluses and mutants, and
pluses and mutants, and all that,

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right? Well, which
picture's right?

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00:16:02,000 --> 00:16:08,000
Sorry? You don't know. So,
that's not good. There's supposed

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00:16:08,000 --> 00:16:14,000
to be answers to these things.
Could either be true? Which is

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00:16:14,000 --> 00:16:19,000
more likely? The one on the left?
Why? More statistically probable,

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00:16:19,000 --> 00:16:25,000
how come? Because it is. It
may not quite suffice as a fully

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00:16:25,000 --> 00:16:31,000
complete scientific
answer though.

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00:16:31,000 --> 00:16:48,000
Yes? Yep. Well, but I have
somebody who is affected

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00:16:48,000 --> 00:17:05,000
here. So, given that I've gotten
affected person in the family --

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00:17:05,000 --> 00:17:08,000
yeah, so it is actually, you're
right, statistically somewhat

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00:17:08,000 --> 00:17:12,000
less likely that you would have two
independent M's entering the same

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00:17:12,000 --> 00:17:16,000
pedigree particularly
if M is relatively rare.

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00:17:16,000 --> 00:17:20,000
If M is quite common, however,
suppose M were something

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00:17:20,000 --> 00:17:24,000
was a 20% frequency in the
population, then it actually might

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00:17:24,000 --> 00:17:28,000
be quite reasonable that this could
happen. So, what would you really

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00:17:28,000 --> 00:17:33,000
want to do to test this? Sorry?
Well, if you found any females here

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00:17:33,000 --> 00:17:39,000
maybe you'd be able to conclude that
it was autosomal recessive because

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00:17:39,000 --> 00:17:46,000
females never show a
sex-linked trait. Is that true?

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00:17:46,000 --> 00:17:53,000
No, that's not true. Why not?
You're right. So, you just have to

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00:17:53,000 --> 00:18:00,000
be homozygous for it on
the X. So, having a single

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00:18:00,000 --> 00:18:09,000
female won't, I mean, she's
not going to take that as

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00:18:09,000 --> 00:18:18,000
evidence. Get an affected female
and demonstrate that all of her male

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00:18:18,000 --> 00:18:28,000
offspring show the trait.
Cross her with, wait, wait.

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00:18:28,000 --> 00:18:31,000
This is a human pedigree guys
[LAUGHTER]. Whew! There are issues

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00:18:31,000 --> 00:18:35,000
involved here, right? You
could introduce her to a

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00:18:35,000 --> 00:18:39,000
normal guy, [LAUGHTER] but whether
you can cross her to a normal guy is

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00:18:39,000 --> 00:18:43,000
not actually allowed. So,
you see, these are exactly the

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00:18:43,000 --> 00:18:46,000
issues in making sense
out of pedigrees like this.

217
00:18:46,000 --> 00:18:50,000
So, what you have to do is you
have to collect a lot of data,

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00:18:50,000 --> 00:18:54,000
and the kinds of characteristics
that you look for in a pedigree,

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00:18:54,000 --> 00:18:58,000
but they are statistical
characteristics, and

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00:18:58,000 --> 00:19:02,000
notwithstanding -- So, this
could be colorblindness or

221
00:19:02,000 --> 00:19:06,000
something, but notwithstanding
the pictures in the textbook of

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00:19:06,000 --> 00:19:10,000
colorblindness and all that, you
really do have to take a look at

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00:19:10,000 --> 00:19:14,000
a number of properties.
What are some properties?

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00:19:14,000 --> 00:19:19,000
One you've already referred to
which is there's a predominance in

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00:19:19,000 --> 00:19:23,000
males if it's X-linked. Why
is there a predominance in

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00:19:23,000 --> 00:19:27,000
males? Well, there's a
predominance in males because if I

227
00:19:27,000 --> 00:19:32,000
have an X over Y and I've
got a mutation paired on

228
00:19:32,000 --> 00:19:36,000
this X chromosome, males
only have to get it on one.

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00:19:36,000 --> 00:19:40,000
Females have to get it on both, and
therefore it's statistically more

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00:19:40,000 --> 00:19:44,000
likely that males will get it.
So, for example, the frequency of

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00:19:44,000 --> 00:19:48,000
colorblindness amongst males
is what? Yeah, it's 8-10%,

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00:19:48,000 --> 00:19:52,000
something like that. I
think it's about 8% or so.

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00:19:52,000 --> 00:19:56,000
And, amongst females,
well, if it's 8% to get one,

234
00:19:56,000 --> 00:20:00,000
what's the chance
you're going to get two?

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00:20:00,000 --> 00:20:08,000
It's 8% times 8% is a
little less than 1% right?

236
00:20:08,000 --> 00:20:17,000
It's 0.64%, OK, in females.
So, we'll just go 8%

237
00:20:17,000 --> 00:20:25,000
squared. So in males, 8% in
females, less than one percent.

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00:20:25,000 --> 00:20:33,000
So, there is a
predominance in males

239
00:20:33,000 --> 00:20:39,000
of these sex-linked traits. Other
things: affected males do not

240
00:20:39,000 --> 00:20:46,000
transmit the trait to the kids,
in particular do not transmit it to

241
00:20:46,000 --> 00:20:53,000
their sons, right, because
they are always sending the

242
00:20:53,000 --> 00:21:00,000
Y chromosomes to their
songs. Carrier females

243
00:21:00,000 --> 00:21:10,000
transmit to half of their sons,
and affected females transmit to all

244
00:21:10,000 --> 00:21:20,000
of their sons. And, the
trait appears to skip

245
00:21:20,000 --> 00:21:30,000
generations, although I
don't like this terminology.

246
00:21:30,000 --> 00:21:35,000
It skips generations. These
are the kinds of properties

247
00:21:35,000 --> 00:21:40,000
that you have.
So, hemophilia,

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00:21:40,000 --> 00:21:45,000
a good example of this, if I
have a child with hemophilia,

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00:21:45,000 --> 00:21:50,000
male with hemophilia, would you
be surprised if his uncle had

250
00:21:50,000 --> 00:21:55,000
hemophilia? Which uncle would
it be, maternal or paternal?

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00:21:55,000 --> 00:22:00,000
The maternal uncle would
have hemophilia most likely.

252
00:22:00,000 --> 00:22:04,000
It's always possible it could be
paternal. This is the problem with

253
00:22:04,000 --> 00:22:08,000
human genetics is you've got to
get enough families so the pattern

254
00:22:08,000 --> 00:22:12,000
becomes overwhelmingly clear,
OK, because otherwise, as you can

255
00:22:12,000 --> 00:22:16,000
see with small numbers, it's
tough to be absolutely certain.

256
00:22:16,000 --> 00:22:20,000
So, these are properties
of X linked traits.

257
00:22:20,000 --> 00:22:24,000
How about baldness? Is
baldness, that's a sex-linked

258
00:22:24,000 --> 00:22:28,000
trait? How come? You don't
see a lot of bald females.

259
00:22:28,000 --> 00:22:32,000
Does that prove it's sex linked?
Sorry? Guys are stressed more.

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00:22:32,000 --> 00:22:37,000
[LAUGHTER] Is there evidence that
it has anything to do with stress?

261
00:22:37,000 --> 00:22:41,000
Actually, it has to do with
excess testosterone it turns out,

262
00:22:41,000 --> 00:22:46,000
that high levels of testosterone
are correlated with male pattern

263
00:22:46,000 --> 00:22:51,000
baldness, but does the fact that
males become bald indicate that this

264
00:22:51,000 --> 00:22:56,000
is a sex linked trait? No.
Just because it's predominant

265
00:22:56,000 --> 00:23:01,000
in male, we have to check
these other properties.

266
00:23:01,000 --> 00:23:05,000
Is it the case that bald
fathers tend to have bald sons?

267
00:23:05,000 --> 00:23:09,000
Any evidence on this point?
Common-sensical evidence from

268
00:23:09,000 --> 00:23:14,000
observation? It's pretty clear.
It's very clearly not a sex-linked

269
00:23:14,000 --> 00:23:18,000
trait. It's a sex-limited trait,
because in order to show this you

270
00:23:18,000 --> 00:23:23,000
need to be male because the high
levels of testosterone are not found

271
00:23:23,000 --> 00:23:27,000
in females even if they have the
genotype that might predispose them

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00:23:27,000 --> 00:23:33,000
to become bald if they were male.
So, it actually is not a sex-linked

273
00:23:33,000 --> 00:23:40,000
trait at all, and it's very clear
that male pattern baldness does run

274
00:23:40,000 --> 00:23:48,000
in families more vertically. So,
you've got to be careful about

275
00:23:48,000 --> 00:23:55,000
the difference between
sex linked and sex limited,

276
00:23:55,000 --> 00:24:02,000
and sex linked you can really pick
out from transmission and families.

277
00:24:02,000 --> 00:24:10,000
OK, here's another
one. New pedigree.

278
00:24:10,000 --> 00:24:43,000
She married twice here.
OK, what do we got?

279
00:24:43,000 --> 00:24:53,000
Yep? She married again. She
married twice. She didn't have

280
00:24:53,000 --> 00:25:01,000
any offspring the second
time. But that happens,

281
00:25:01,000 --> 00:25:06,000
and you have to be able
to draw it in the pedigree.

282
00:25:06,000 --> 00:25:12,000
She's entitled, all right.
OK, so she got married again,

283
00:25:12,000 --> 00:25:17,000
no offspring from this marriage.
That's her legal symbol. You guys

284
00:25:17,000 --> 00:25:22,000
think that's funny.
It's real, you know?

285
00:25:22,000 --> 00:25:28,000
OK, that doesn't mean she's married
to two people at the same time.

286
00:25:28,000 --> 00:25:33,000
This is not a temporal picture.
So, what do we got here? Yep?

287
00:25:33,000 --> 00:25:38,000
Sorry, of this person? Well,
I'm drawing them as an empty

288
00:25:38,000 --> 00:25:44,000
symbol here, indicating that we
do not think they have the trait.

289
00:25:44,000 --> 00:25:50,000
They're not carriers. How do
you propose to find that out?

290
00:25:50,000 --> 00:25:56,000
Look at the children. Well,
the children are affected. They

291
00:25:56,000 --> 00:26:02,000
could be carriers. The
data are what they are.

292
00:26:02,000 --> 00:26:09,000
You've got to interpret it.
Does this person have to be a

293
00:26:09,000 --> 00:26:16,000
carrier? What kind of
trait do you think this is?

294
00:26:16,000 --> 00:26:23,000
Dominant? Does this look like
autosomal dominant to you?

295
00:26:23,000 --> 00:26:30,000
Yep? Oh, not all the
kids have the trait

296
00:26:30,000 --> 00:26:34,000
in the first generation,
and if this was dominant,

297
00:26:34,000 --> 00:26:38,000
they'd all have it? What's a
possible genotype for this person?

298
00:26:38,000 --> 00:26:42,000
Mutant over plus. And, these
kids could be mutant over plus.

299
00:26:42,000 --> 00:26:46,000
This could be plus over plus,
and this could be plus over plus,

300
00:26:46,000 --> 00:26:50,000
mutant over plus, plus
over plus, mutant over plus,

301
00:26:50,000 --> 00:26:54,000
and plus over plus would be
one possibility. On average,

302
00:26:54,000 --> 00:26:58,000
what fraction of the kids
should get the trait? About half

303
00:26:58,000 --> 00:27:06,000
the kids, right? So, let's
see what characteristics

304
00:27:06,000 --> 00:27:18,000
we have here. We see the
trait in every generation.

305
00:27:18,000 --> 00:27:30,000
On average, half the
kids get the trait.

306
00:27:30,000 --> 00:27:42,000
Half of the offspring of an
affected individual are affected.

307
00:27:42,000 --> 00:27:54,000
What else? Males and females?
Roughly equal in males and females?

308
00:27:54,000 --> 00:28:02,000
Sorry? One,
two, three,

309
00:28:02,000 --> 00:28:08,000
four, five to two.
So, it's a 5:2 ratio?

310
00:28:08,000 --> 00:28:13,000
Oh, in the offspring it's a 2:1
ratio. So, this is like Mendel.

311
00:28:13,000 --> 00:28:19,000
You see this number and you say,
OK, 2:1. Isn't that trying to tell

312
00:28:19,000 --> 00:28:24,000
me something? Not with six
offspring. That's the problem is

313
00:28:24,000 --> 00:28:30,000
with six offspring, 2:1 might
be trying to tell you 1:1.

314
00:28:30,000 --> 00:28:34,000
And it is. If I had a dominantly
inherited trait where there's a

315
00:28:34,000 --> 00:28:39,000
50/50 chance of each offspring
getting the disease and it was

316
00:28:39,000 --> 00:28:44,000
autosomal, not sex linked,
there would be very good odds of

317
00:28:44,000 --> 00:28:48,000
getting two males and one female
because it happens: flip coins and

318
00:28:48,000 --> 00:28:53,000
it happens. So, you have
to take that into account,

319
00:28:53,000 --> 00:28:58,000
and here you see what else we have.
Roughly equal numbers of males and

320
00:28:58,000 --> 00:29:03,000
females, they transmit equally,
and unaffecteds never transmit.

321
00:29:03,000 --> 00:29:07,000
This would be the classic
autosomal dominant trait.

322
00:29:07,000 --> 00:29:11,000
Right, here this mutant
would go mutant over plus,

323
00:29:11,000 --> 00:29:15,000
mutant over plus, plus over plus,
mutant over plus, plus over plus,

324
00:29:15,000 --> 00:29:19,000
plus over plus, and you'd
see here that three out of

325
00:29:19,000 --> 00:29:23,000
the five here, and one,
two, three out of the six

326
00:29:23,000 --> 00:29:27,000
there: that's a little more
than half but it's small numbers

327
00:29:27,000 --> 00:29:33,000
here, right? This is a
classic autosomal dominant

328
00:29:33,000 --> 00:29:39,000
as in the textbooks. Yes?
Turns out not to make too

329
00:29:39,000 --> 00:29:46,000
much of a difference. It
turns out that there's lots of

330
00:29:46,000 --> 00:29:53,000
genome that's on either. And
so, it is true that males are

331
00:29:53,000 --> 00:30:00,000
more susceptible to
certain genetic diseases.

332
00:30:00,000 --> 00:30:04,000
So, it'll be some excess,
but it won't matter for this.

333
00:30:04,000 --> 00:30:09,000
Now, in real life it doesn't
always work so beautifully.

334
00:30:09,000 --> 00:30:13,000
We'll take an example: colon cancer.
There are particular autosomal

335
00:30:13,000 --> 00:30:18,000
dominant mutations here that
cause a high risk of colon cancer.

336
00:30:18,000 --> 00:30:23,000
People who have mutations
in a certain gene, MLH-1,

337
00:30:23,000 --> 00:30:27,000
have about a 70% risk of getting
colon cancer in their life.

338
00:30:27,000 --> 00:30:33,000
But notice, it's not 100%. You
might have incomplete penetrance.

339
00:30:33,000 --> 00:30:41,000
Incompletely penetrance means not
everybody who gets the genotype gets

340
00:30:41,000 --> 00:30:48,000
the phenotype. Not all
people with the M over plus

341
00:30:48,000 --> 00:30:56,000
genotype show the phenotype.
Once you do that, it messes up our

342
00:30:56,000 --> 00:31:03,000
picture colossally,
because, tell me,

343
00:31:03,000 --> 00:31:09,000
how do we know that this person over
here is not actually M over plus.

344
00:31:09,000 --> 00:31:15,000
Maybe they're cryptic. They
haven't shown the phenotype.

345
00:31:15,000 --> 00:31:21,000
And maybe, it'll appear in the
next generation. That'll screw up

346
00:31:21,000 --> 00:31:27,000
everything. It screws up our rule
about not transmitting through

347
00:31:27,000 --> 00:31:32,000
unaffected, it screws up the
rule about not being shown in

348
00:31:32,000 --> 00:31:36,000
every generation, and it
will even screw up our 50/50

349
00:31:36,000 --> 00:31:41,000
ratio because if half the
offspring get M over plus,

350
00:31:41,000 --> 00:31:46,000
but only 70% of that half show
the phenotype, then only 35% of the

351
00:31:46,000 --> 00:31:50,000
offspring will show the phenotype.
Unfortunately, this is real life.

352
00:31:50,000 --> 00:31:55,000
When human geneticists really
look at traits, many mutations,

353
00:31:55,000 --> 00:32:00,000
most except the most severe
are incompletely penetrant.

354
00:32:00,000 --> 00:32:04,000
And so you have to really begin to
gather a lot of data to demonstrate

355
00:32:04,000 --> 00:32:08,000
that you're dealing with an
autosomal dominant trait that's

356
00:32:08,000 --> 00:32:12,000
incompletely penetrant. And
then there are other issues.

357
00:32:12,000 --> 00:32:16,000
There's a gene on chromosome
number 17 called BRCA-1,

358
00:32:16,000 --> 00:32:20,000
mutations in which predisposed to a
very high risk of breast cancer but

359
00:32:20,000 --> 00:32:24,000
only in women. Males carry
the mutation and do not

360
00:32:24,000 --> 00:32:28,000
have breast cancer. There
are other mutations that do

361
00:32:28,000 --> 00:32:32,000
cause breast cancer in males.
Males have breast tissue,

362
00:32:32,000 --> 00:32:36,000
and can have breast cancer, but
the one on chromosome 17 does

363
00:32:36,000 --> 00:32:40,000
not. And so, there you would only
see this transmitted through females.

364
00:32:40,000 --> 00:32:45,000
It would skip into males
without showing a phenotype,

365
00:32:45,000 --> 00:32:49,000
etc. So, in real life,
life's a bit more complicated.

366
00:32:49,000 --> 00:32:53,000
All right, so autosomal dominance.
Now, let's take one more pedigree.

367
00:32:53,000 --> 00:32:58,000
Sorry? Sex limited,
but not sex linked.

368
00:32:58,000 --> 00:33:03,000
So, on chromosome 17, which
is a bona fide autosome,

369
00:33:03,000 --> 00:33:08,000
but it's sex limited in that
phenotype can only show itself in an

370
00:33:08,000 --> 00:33:13,000
individual who happens to be female.
Yes? Sorry? How come autosomal

371
00:33:13,000 --> 00:33:19,000
recessive? So, if that
left guy up there is

372
00:33:19,000 --> 00:33:24,000
actually a heterozygote,
and up there that individual,

373
00:33:24,000 --> 00:33:30,000
so if we had a homozygote,
homozygote, heterozygote,

374
00:33:30,000 --> 00:33:33,000
homozygote, ooh, you can
interpret that pedigree if

375
00:33:33,000 --> 00:33:37,000
you want to as an autosomal
recessive, provided that M is pretty

376
00:33:37,000 --> 00:33:40,000
frequent in the population.
That's right. Human geneticists,

377
00:33:40,000 --> 00:33:44,000
in fact, to really prove that
they've got the right model,

378
00:33:44,000 --> 00:33:48,000
collect a lot of pedigrees
and run a computer model.

379
00:33:48,000 --> 00:33:51,000
The computer model first
tries out autosomal recessive,

380
00:33:51,000 --> 00:33:55,000
tries out autosomal dominant,
tries out dominant with incomplete

381
00:33:55,000 --> 00:33:59,000
penetrance, and for every possible
model figures out the statistical

382
00:33:59,000 --> 00:34:03,000
probability that you would
see such data under that model.

383
00:34:03,000 --> 00:34:05,000
And when the data become
overwhelming and you say,

384
00:34:05,000 --> 00:34:08,000
yeah, with one pedigree, any
pedigree I draw on the board,

385
00:34:08,000 --> 00:34:11,000
it could actually fit almost any for
the models. It doesn't say this in

386
00:34:11,000 --> 00:34:14,000
the textbooks, but
it's true. I get enough

387
00:34:14,000 --> 00:34:17,000
pedigrees, and eventually I say the
odds are 105 times more likely that

388
00:34:17,000 --> 00:34:20,000
this collection of pedigrees would
arise from autosomal dominance,

389
00:34:20,000 --> 00:34:22,000
inheritance with incomplete
penetrance of about 80%.

390
00:34:22,000 --> 00:34:25,000
Then, from autosomal recessive
inheritance, then I get to write a

391
00:34:25,000 --> 00:34:28,000
paper about it. That's
really what human

392
00:34:28,000 --> 00:34:32,000
geneticists do is they
have to collect enough,

393
00:34:32,000 --> 00:34:36,000
now, any other organism,
you'd just set up a cross,

394
00:34:36,000 --> 00:34:41,000
but you can't. And, as long
as we have nontrivial models,

395
00:34:41,000 --> 00:34:46,000
we really have to collect a lot of
data. Let's take the next pedigree,

396
00:34:46,000 --> 00:34:50,000
great, that you're thinking
like a human geneticist.

397
00:34:50,000 --> 00:34:55,000
It's very good. Here's the
next pedigree. Actually,

398
00:34:55,000 --> 00:35:00,000
I'm going to reverse
it. There we go.

399
00:35:00,000 --> 00:35:03,000
What's that? Who knows? You
can't tell. Good, I've got you

400
00:35:03,000 --> 00:35:07,000
up to training to the point where,
but in textbooks, this would be

401
00:35:07,000 --> 00:35:11,000
autosomal recessive.
Or it could be anything.

402
00:35:11,000 --> 00:35:15,000
You know that, right? But the
textbooks would show you this

403
00:35:15,000 --> 00:35:18,000
picture as an autosomal recessive.
But of course, what else could it

404
00:35:18,000 --> 00:35:22,000
be? It could be an autosomal
dominant with incomplete penetrance.

405
00:35:22,000 --> 00:35:26,000
It could be sex linked. It
could be a lot of things.

406
00:35:26,000 --> 00:35:30,000
It could also be, I haven't
told you the phenotype.

407
00:35:30,000 --> 00:35:34,000
What if the phenotype here
was getting hit by a truck?

408
00:35:34,000 --> 00:35:38,000
[LAUGHTER] Would you
tend to observe this? Yep,

409
00:35:38,000 --> 00:35:42,000
so getting hit by a truck, for
example, if someone gets hit by

410
00:35:42,000 --> 00:35:46,000
a truck, it's unlikely either
their parents were hit by a truck,

411
00:35:46,000 --> 00:35:50,000
or going back several generations
that their grandparents were hit by

412
00:35:50,000 --> 00:35:54,000
a truck. So, how do you tell
being hit by a truck from,

413
00:35:54,000 --> 00:35:58,000
I mean, that is to say, how
do you know that something's

414
00:35:58,000 --> 00:36:01,000
genetic at all? When it's
relatively rare and it

415
00:36:01,000 --> 00:36:04,000
pops up in a pedigree, how
do you know it's genetic?

416
00:36:04,000 --> 00:36:07,000
Because of the DNA. But, I mean,
it takes a lot of work to find the

417
00:36:07,000 --> 00:36:10,000
gene and all that as we'll come to
the course. You might want a little

418
00:36:10,000 --> 00:36:13,000
bit of assurance before you go write
the grant to the NIH and say I'm

419
00:36:13,000 --> 00:36:16,000
going to find the gene for this
because you write it and say I'm

420
00:36:16,000 --> 00:36:19,000
going to find the gene
for getting hit by a truck,

421
00:36:19,000 --> 00:36:22,000
and they're going to write back and
say show me that it's worth spending

422
00:36:22,000 --> 00:36:25,000
money to find that gene.
Show me that it's true. So,

423
00:36:25,000 --> 00:36:28,000
what kind of things would we look
for? If we wanted to show something

424
00:36:28,000 --> 00:36:32,000
was autosomal recessive in a
population, what would we do?

425
00:36:32,000 --> 00:36:40,000
More data. So, we
collect a lot of families,

426
00:36:40,000 --> 00:36:48,000
and what would we see? As we
collected more and more families,

427
00:36:48,000 --> 00:36:56,000
we begin to see what things?
Sometimes we might see families like

428
00:36:56,000 --> 00:37:04,000
this, or we might see
families like this. [LAUGHTER]

429
00:37:04,000 --> 00:37:08,000
If both parents were mutants,
all the children would be mutant,

430
00:37:08,000 --> 00:37:13,000
right? We'd color them in mutant.
Is that true? Well, first off, it

431
00:37:13,000 --> 00:37:18,000
depends. Some of the things we
want to study are extremely severe

432
00:37:18,000 --> 00:37:23,000
medical genetical phenotypes,
and they're not going to live to

433
00:37:23,000 --> 00:37:28,000
have children. So, that's
an issue that you have

434
00:37:28,000 --> 00:37:33,000
to deal with. But, it
is true that if it was

435
00:37:33,000 --> 00:37:37,000
autosomal recessive, a mating
between two homozygotes for

436
00:37:37,000 --> 00:37:42,000
that gene would transmit.
[LAUGHTER] What if they were all in

437
00:37:42,000 --> 00:37:46,000
the same car? Which is
a very important part,

438
00:37:46,000 --> 00:37:51,000
because we joke about the car,
but diet, things like that, are

439
00:37:51,000 --> 00:37:55,000
familial correlated environmental
factors. There are environmental

440
00:37:55,000 --> 00:38:00,000
factors that correlate
within a family.

441
00:38:00,000 --> 00:38:04,000
And so, it's not trivial
to make this point. So,

442
00:38:04,000 --> 00:38:09,000
all right, we'll be able to
demonstrate what's the real proof of

443
00:38:09,000 --> 00:38:14,000
Mendelian inheritance here?
Because they could all be in the

444
00:38:14,000 --> 00:38:19,000
same car, or they all eat the same
kind of food or something like that,

445
00:38:19,000 --> 00:38:24,000
which predisposes them a certain way.
So, we're going to want some better

446
00:38:24,000 --> 00:38:29,000
proofs of these things.
How about Mendelian ratios?

447
00:38:29,000 --> 00:38:34,000
Mendelian ratios anyone? No,
because it could be incomplete

448
00:38:34,000 --> 00:38:38,000
autosomal dominance. I
don't want to mess you up.

449
00:38:38,000 --> 00:38:42,000
On the exams, you guys can think
cleanly about simple things.

450
00:38:42,000 --> 00:38:46,000
But, this could be dominant
with incomplete penetrance,

451
00:38:46,000 --> 00:38:50,000
though the TA's are going to hate
me because I'm telling you that,

452
00:38:50,000 --> 00:38:54,000
anyway, what about Mendelian ratios?
How about something that's a pretty

453
00:38:54,000 --> 00:38:58,000
good prediction? What
fraction of the offspring will

454
00:38:58,000 --> 00:39:02,000
be affected? We get
a lot of families,

455
00:39:02,000 --> 00:39:07,000
line them all up. What
fraction of the offspring?

456
00:39:07,000 --> 00:39:12,000
A quarter. Now, that's a
hard and fast prediction.

457
00:39:12,000 --> 00:39:17,000
One quarter of the offspring are
effective. When I have a mating

458
00:39:17,000 --> 00:39:22,000
between two homozygotes,
so what am I going to do?

459
00:39:22,000 --> 00:39:28,000
I'm going to go out. I'm going
to collect a lot of families.

460
00:39:28,000 --> 00:39:31,000
Maybe I'll collect 100 families
because it'll be a particular

461
00:39:31,000 --> 00:39:35,000
disease, diastrophic dysplasia
or something like that,

462
00:39:35,000 --> 00:39:39,000
xeroderma pygmentosa,
ataxia teleangiectasia,

463
00:39:39,000 --> 00:39:43,000
and I will go to the disease
foundation, and I will get all the

464
00:39:43,000 --> 00:39:46,000
pedigrees for all the families,
and I'll see how many times it was

465
00:39:46,000 --> 00:39:50,000
one affected, two affected, three
affected, etc. And on average,

466
00:39:50,000 --> 00:39:54,000
the proportion affecteds will be
a quarter, except it's not true.

467
00:39:54,000 --> 00:39:58,000
If I actually do that, I find that
the ratio of affecteds is typically

468
00:39:58,000 --> 00:40:03,000
more like a third.
It isn't a quarter.

469
00:40:03,000 --> 00:40:09,000
Now, this should disturb you
greatly because you know full well

470
00:40:09,000 --> 00:40:16,000
that M over plus by M over plus
should give you a quarter affecteds.

471
00:40:16,000 --> 00:40:23,000
But when you actually look
at human families, it's not.

472
00:40:23,000 --> 00:40:30,000
Why? In other words, when
we count up all the matings

473
00:40:30,000 --> 00:40:33,000
between heterozygotes, we'll
collect all the matings that

474
00:40:33,000 --> 00:40:37,000
produce one affected child.
We'll collect all the matings that

475
00:40:37,000 --> 00:40:41,000
produce two affected children.
We'll collect all the matings that

476
00:40:41,000 --> 00:40:45,000
produce three affected children.
But, we will fail to collect those

477
00:40:45,000 --> 00:40:48,000
matings between homozygotes that
produce zero affected children.

478
00:40:48,000 --> 00:40:52,000
And so, we will systematically
overestimate the proportion.

479
00:40:52,000 --> 00:40:56,000
Of course, what we really have to
do is go out and get all of those

480
00:40:56,000 --> 00:41:00,000
couples who were both carriers,
but because they had a small number

481
00:41:00,000 --> 00:41:04,000
of children didn't happen
to have an affected child.

482
00:41:04,000 --> 00:41:08,000
That's not very easy to do
especially when you don't know the

483
00:41:08,000 --> 00:41:12,000
gene in advance. So, when
human geneticists try to

484
00:41:12,000 --> 00:41:16,000
go out and measure the
one-quarter Mendelian ratio,

485
00:41:16,000 --> 00:41:21,000
you can't. But what you
can do is the following,

486
00:41:21,000 --> 00:41:25,000
conditional on the first trial
being affected, now what will be the

487
00:41:25,000 --> 00:41:30,000
proportion of subsequent
children who are affected?

488
00:41:30,000 --> 00:41:33,000
A quarter. If I make it conditional,
conditioning on having a first child

489
00:41:33,000 --> 00:41:37,000
who's affected, number
one child who's affected,

490
00:41:37,000 --> 00:41:40,000
then I know I've got a
mating between heterozygotes.

491
00:41:40,000 --> 00:41:44,000
Subsequent offspring now
do not have that bias.

492
00:41:44,000 --> 00:41:47,000
And so, as a matter of fact, you
think this pretty cool thought,

493
00:41:47,000 --> 00:41:51,000
right? You've got a condition on
one. It turns out there's a very

494
00:41:51,000 --> 00:41:54,000
famous paper about cystic fibrosis
where somebody forgot this point and

495
00:41:54,000 --> 00:41:58,000
made a huge big deal in the
literature about the fact that a

496
00:41:58,000 --> 00:42:02,000
third of the kids on average
had cystic fibrosis in these

497
00:42:02,000 --> 00:42:06,000
families, and proposed all sorts
of models about how cystic fibrosis

498
00:42:06,000 --> 00:42:11,000
might be advantageous and would lead
to fertility increases and all that.

499
00:42:11,000 --> 00:42:16,000
In fact, it was just a failure to
correct for this little statistical

500
00:42:16,000 --> 00:42:20,000
bias. OK, this is what human
geneticists do is they've got to

501
00:42:20,000 --> 00:42:25,000
deal with the popular, now,
there's one other trick that

502
00:42:25,000 --> 00:42:30,000
you can use to know that
something is autosomal recessive.

503
00:42:30,000 --> 00:42:37,000
That trick is this.
To site this trick,

504
00:42:37,000 --> 00:42:45,000
I have to go back to a person
called Archibald Garrett.

505
00:42:45,000 --> 00:42:53,000
Archibald Garrett was a
physician in London around 1900.

506
00:42:53,000 --> 00:43:01,000
Garrett studied children
with the trait alkoptonuria.

507
00:43:01,000 --> 00:43:06,000
Alkoptonuria was what alkopton
means black. Uria means urine.

508
00:43:06,000 --> 00:43:11,000
They had black urine. This was
evident because their urine turned

509
00:43:11,000 --> 00:43:16,000
black on treatment with alkaline.
How would you treat urine with

510
00:43:16,000 --> 00:43:22,000
alkaline. How would people know
this? Sorry? Outhouses with lime,

511
00:43:22,000 --> 00:43:27,000
yeah, and who's going to look at
the children's urine, or something

512
00:43:27,000 --> 00:43:32,000
like that? But you're
on the right track.

513
00:43:32,000 --> 00:43:38,000
How about diapers? You
wash diapers, cloth diapers,

514
00:43:38,000 --> 00:43:43,000
in alkaline. They turn black. This
was evident from black diapers.

515
00:43:43,000 --> 00:43:49,000
The kids' urine would turn black.
So, he observed this, and you know

516
00:43:49,000 --> 00:43:54,000
what Garrett noticed is when he
studied, children alkoptonuria,

517
00:43:54,000 --> 00:44:00,000
he found that a very
large fraction of affected

518
00:44:00,000 --> 00:44:10,000
offspring were in fact produced
from matings of first cousins.

519
00:44:10,000 --> 00:44:20,000
Consanguineous matings: now you
laugh, but in fact consanguinity has

520
00:44:20,000 --> 00:44:30,000
been something that has been
favored in many societies,

521
00:44:30,000 --> 00:44:35,000
and in Britain, particularly
amongst the upper class

522
00:44:35,000 --> 00:44:40,000
in Britain in 1900, marriage
or first cousins was quite

523
00:44:40,000 --> 00:44:45,000
common, but not as common as he
observed. He found that eight out

524
00:44:45,000 --> 00:44:50,000
of 17 alkoptonuria patients were the
products of first cousin marriages.

525
00:44:50,000 --> 00:44:55,000
That's way off the charts
because it's nearly a half,

526
00:44:55,000 --> 00:45:00,000
when in fact the typical rate in
Britain might have been about 5%.

527
00:45:00,000 --> 00:45:03,000
So, on the basis of that in the
early 1900's, Garrett was able to

528
00:45:03,000 --> 00:45:07,000
show only a few years after the
rediscovery of Mendel's work that

529
00:45:07,000 --> 00:45:11,000
this property of recessive traits,
enrichment in the offspring of

530
00:45:11,000 --> 00:45:15,000
consanguineous marriages,
was a clear demonstration of

531
00:45:15,000 --> 00:45:18,000
Mendelian inheritance.
Not only did he do that,

532
00:45:18,000 --> 00:45:22,000
but Garrett knew because of
the work of some biochemists,

533
00:45:22,000 --> 00:45:26,000
and this is way cool, that
the problem with the urine was

534
00:45:26,000 --> 00:45:30,000
that these patients put out in
their urine a lot of what's called

535
00:45:30,000 --> 00:45:42,000
homogentisic acid, HGA,
which basically is a phenolic

536
00:45:42,000 --> 00:45:54,000
ring. What Garrett did was he,
and that stuff turns black on

537
00:45:54,000 --> 00:46:02,000
exposure to air. What might
produce from the things

538
00:46:02,000 --> 00:46:07,000
you've learned already
some kind of ring like that?

539
00:46:07,000 --> 00:46:12,000
What building blocks do you know
have rings like that of things

540
00:46:12,000 --> 00:46:17,000
you've studied already?
Phenylalanine, tyrosine both have

541
00:46:17,000 --> 00:46:22,000
rings. Suppose somebody had
problems breaking down homogentisic

542
00:46:22,000 --> 00:46:27,000
acid. Suppose there was some
pathway where proteins were

543
00:46:27,000 --> 00:46:32,000
broken down into amino
acids including phenylalanine

544
00:46:32,000 --> 00:46:37,000
and tyrosine. And, they
were broken down into

545
00:46:37,000 --> 00:46:42,000
homogentisic acid. And
they were broken down into I

546
00:46:42,000 --> 00:46:47,000
don't know what. And,
suppose like we had up there,

547
00:46:47,000 --> 00:46:52,000
patients had a mutation in that
enzyme. What would happen if I fed

548
00:46:52,000 --> 00:46:57,000
patients a lot of protein? In
their urine, you would recover

549
00:46:57,000 --> 00:47:01,000
lots of homogentisic acid. Suppose
I fed them a lot of tyrosine.

550
00:47:01,000 --> 00:47:05,000
I'd get a lot of homogentisic acid
because the body couldn't break it

551
00:47:05,000 --> 00:47:09,000
down. Suppose I fed them
a lot of phenylalanine.

552
00:47:09,000 --> 00:47:13,000
They would excrete a
lot of homogentisic acid.

553
00:47:13,000 --> 00:47:16,000
Suppose I fed them homogentisic
acid. I would get quantitative

554
00:47:16,000 --> 00:47:20,000
amounts of homogentisic acid.
Garrett did this. These are the

555
00:47:20,000 --> 00:47:24,000
days before institutional
review boards, you know,

556
00:47:24,000 --> 00:47:28,000
informed consent. It turns out it's
harmless feeding them proteins and

557
00:47:28,000 --> 00:47:33,000
things like that.
But in fact, Garrett,

558
00:47:33,000 --> 00:47:39,000
in 1911, worked out that this trait
had to be recessive because of its

559
00:47:39,000 --> 00:47:46,000
population genetics, and
inferred a biochemical pathway

560
00:47:46,000 --> 00:47:53,000
by feeding different things along
the way and was able to connect a

561
00:47:53,000 --> 00:48:00,000
mutation in a gene to a problem
with a specific biochemical pathway.

562
00:48:00,000 --> 00:48:05,000
Sorry, 1908: this was his
Croonian Lecture in 1908.

563
00:48:05,000 --> 00:48:10,000
Eight years after the rediscovery
of Mendel, he's able to connect

564
00:48:10,000 --> 00:48:15,000
genetic defect, showing
it's genetic by transmission,

565
00:48:15,000 --> 00:48:20,000
to biochemical defect showing that
he has a pathway that he can feed

566
00:48:20,000 --> 00:48:25,000
things into. And, it all
blocks up at the inability to

567
00:48:25,000 --> 00:48:30,000
metabolize homogentisic acid. He
has connected gene to enzyme by

568
00:48:30,000 --> 00:48:35,000
1908. What do you
think the reaction to

569
00:48:35,000 --> 00:48:39,000
this was? Polite bewilderment,
and it sunk like a stone. Nobody

570
00:48:39,000 --> 00:48:43,000
was prepared to hear this. This
is very much like Mendel in my

571
00:48:43,000 --> 00:48:47,000
opinion. Now, he was a
distinguished professor.

572
00:48:47,000 --> 00:48:52,000
It was the Croonian Lecture. He
got lots of accolades and all that,

573
00:48:52,000 --> 00:48:56,000
and people said, what a
lovely lecture that was,

574
00:48:56,000 --> 00:49:00,000
and proceeded to completely forget
this connection between genes and

575
00:49:00,000 --> 00:49:05,000
enzymes, genes and proteins. It
was not until 40 years later or

576
00:49:05,000 --> 00:49:09,000
so that Beadle and Tatum,
working with a fungus, actually

577
00:49:09,000 --> 00:49:13,000
rosper not yeast, demonstrated
that all these mutants

578
00:49:13,000 --> 00:49:17,000
interfered with the ability to
digest or to make particular amino

579
00:49:17,000 --> 00:49:21,000
acids, and wrote this up as the one
gene, one enzyme hypothesis of how

580
00:49:21,000 --> 00:49:25,000
genes encode enzymes, and
won the Nobel Prize for this

581
00:49:25,000 --> 00:49:30,000
work, but in fact in
their Nobel address,

582
00:49:30,000 --> 00:49:34,000
Beetle and Tatum noted, actually,
you know, Garrett kind of

583
00:49:34,000 --> 00:49:39,000
knew all this. But,
people weren't ready,

584
00:49:39,000 --> 00:49:43,000
yet, to digest it. Genetics
had just come along,

585
00:49:43,000 --> 00:49:48,000
Biochemistry had just really been
invented in the last ten years,

586
00:49:48,000 --> 00:49:52,000
and the idea of uniting genetics
and biochemistry was just something

587
00:49:52,000 --> 00:49:57,000
people weren't prepared
for yet. More next time.