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It's always great to follow Ben.

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Because I think that,

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I, it's always great

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to hear
kind of some of the ethical

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arguments.

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You know, one of my first,

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disclaimers here is, of course,
like,

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like most like Ben and most
people

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I think are probably talking me
on the federal government

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employee.

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These are my own words,
and my own opinions.

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But, you know, I think the other
thing

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I would want to share with you
guys is

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I am trained as a genetic
counselor,

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but most of my research

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is in this
field of kind of implementation

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science,

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where I'm really very focused
on kind of practical,

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pragmatic outcomes.

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And kind of the,
you know, where the rubber meets

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the road

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when we start to think about
these ideas

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and how we operationalize them

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for patients.

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And in our work, in this,
you know, kind of messy

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environment where we're doing
this

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clinical research, with people.

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So, I think you should just know
that,

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again,
I have this really pragmatic

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approach.

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And I am situated

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as a clinician, and I also
don't, really,

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hold with this view that genetic
data,

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in almost all cases, is really
different

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from other kinds of health data.

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And so that's that idea of
genetic

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exceptionalism.

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That been mentioned.

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I, I just don't think that
that's something that

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that we need to spend a ton of
time

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thinking about, and I don't, I
don't

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I don't really hold with that
view

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too much.

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So in terms of an outline here,
I

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there's two kind of major
buckets

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of things that I would want to
talk about,

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which is, how research
participants

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think about or feel

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about getting genomic data
as a result of research

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participation.

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And then, of course,
what are the outcomes,

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what happens to people
once they learn this kind of

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information?

54
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And, you know, I think what I'll
do

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is refine this a little bit and,

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and focus
in on how people just generally

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think

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about and react to options
for learning genetic data.

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And the reason why I think it's
important

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to think beyond research
participants,

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when we think about how people
view

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genetic data, is that really

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any person has the potential
to join a research study right.

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And we know that there are many

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research studies
that are actively recruiting

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people

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to from the general population
to participate in genomic

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research.

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So we need to think just about,
you know,

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I think the views of the general
public

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are important to think about
here.

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And then, as Ben mentioned, most
of what

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I'm going to be talking about
is,

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are you know, what we understand
about,

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what happens to people once
they learn about actionable

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genomic data?

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And so, you

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know, when we think about
attitudes

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towards the return of results,

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there have been many that much
ink

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has been spilled on this topic.

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And I'll kind of circle back to
that

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in a minute.

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But, you know, as genomic,

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interrogation techniques
became more ubiquitous and,

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much less expensive,
there's been just a lot of work

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done on trying to solicit the
thoughts,

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opinions, motivations,

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attitudes, intentions
and preferences of of people,

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of different groups of people
towards

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genomic data.

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And so, you know, there's work
that's been done with, asking

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research

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participants, you know, what
kind of data

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they would like, the general
public.

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And then, of course,
you know, specific groups,

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like parents of, pediatric,

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sick children, basically,

99
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patients
who are presenting with,

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particular,

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you know, disease where
genomic interrogation might be

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helpful.

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And then again, some,
some studies of even more fine

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grained,

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you know, participant
attributes.

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And when we look
at this body of literature,

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there is an overwhelming
preference

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for return of results.

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And so, again, I think some of
this is

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just further is substantiating

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this argument that, you know,
if you ask people what they

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want,

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many people, the first thing
that's going to come to mind is

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not

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I don't actually want to know
this information.

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So, you know, across all of
these studies,

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in, you know, up to 100%

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of people
who participate in this kind of

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work,

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say that they want everything
that you could tell me.

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In general,
there is, with increasing

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actionability,

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there is an increasing
preference

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for return.

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So more people,
if you give them an array of

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choices,

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you know, would you like to know
about,

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things like your ancestry or the
baldness

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example?

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That was a really good one
or a non treatable disease.

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People have less interested in
learning

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some of that stuff, and more
interest

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in learning about actual actual
results

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that, you know, may make a
difference

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in their health.

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But, you know, this is not
the only boundary that people

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will cite.

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So many people will say, I want
you

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to tell me, even if it's normal.

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So I even want negative
findings.

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And then people also will desire
their raw

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sequence data, for research
studies,

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even if they don't have,

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you know, the, the expertise,

145
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to interpret
those data, they, they want to,

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they want to have, you know,
everything

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that you could possibly tell me

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is, is what many people say.

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People are also very interested
and motivated

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to learn about research results
that researchers think are

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interesting.

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So they'll say things like,
well,

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you know,

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if you think it's interesting,
like

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you should definitely share it
with me,

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even if it might be interesting
for some very esoteric reason

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that has to do with,
you know, some biochemical

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function,

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that a normal person
might not really care about.

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People still really want
that kind of information.

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And a very, very small
minority of people will say that

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they want

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none of their genetic data back
or that they only want primary

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data back.

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And what I mean by primary data
are,

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genetic results that relate
specifically

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to the reason why the sequencing
was done in the first place.

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So I joined a research study
because my child has autism, I

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only want

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results related to autism.

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That's it. Don't tell me
anything else.

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And when you ask people
why they want this information

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the most,

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the most salient reason
or the primary reason,

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is that people find this
to be very empowering.

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You know, this idea again,
that knowledge is power.

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That there's something
that they could do with the

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information,

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related to that.

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Many people will talk about how
well,

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there's a potential for me to
prevent

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disease in myself or in my
family members,

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which is again, related to this
idea

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that, you know,
perhaps this could be beneficial

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to me,

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beneficial
to my child, beneficial to my

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relatives.

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But a lot of people are also
just curious,

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which again,
this isn't rocket science,

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right?

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We have 23 and me and all of
these

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different direct to consumer

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genetic testing companies that
are selling

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the same kind of information.

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People feel like they can know
themselves

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better in some ways

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by having access to, you know,
their DNA.

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And again, I think that relates
to this other idea that, you

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know,

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people find this to be a way
that they can, kind of explore

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attributes

202
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about themselves
that maybe a clinician wouldn't

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put, as you know, the most high
priority item, like ancestry.

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But that is
has a lot of personal utility to

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a person.

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So these are all reasons why
people say

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that they want this information

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and they say that they want
all of this information or they

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want

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their genetic results, even as
they cite

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some of these concerns, like
privacy,

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you know, who's

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going to maintain my genomic
data

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and how might it be used in the
future?

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Is there a burden to what I
might,

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you know, what's what's

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the burden associated with the
knowledge

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that you're going to impart upon
me?

219
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Is it going to result in some
sort of poor

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outcome for myself

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if people are able to kind of
way

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and think about these risks,

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but they certainly do not
override

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this preference for returns.

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So again, I think people can
manipulate

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these concepts in their minds,

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and yet still come down on the
side

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of, you know, this is something
that I really, really want.

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And again, I think this really
speaks

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to this idea that people believe

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that their genomic data are
their data,

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they want to control it.

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They want to know who is looking
at it.

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When you talk to research
participants,

235
00:09:01,453 --> 00:09:02,842
they'll say things like, well,

236
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no one cares as much about my
sequence

237
00:09:06,085 --> 00:09:07,280
data as I do.

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00:09:07,280 --> 00:09:09,219
You know, you might be looking
at it

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00:09:09,219 --> 00:09:10,350
for this one reason.

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00:09:11,251 --> 00:09:15,922
But, I'm if you give it to me,
then I can revisit it

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00:09:15,922 --> 00:09:18,051
and think about it over time and
talk with

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00:09:18,051 --> 00:09:19,926
talk with my doctors moving
forward.

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00:09:21,361 --> 00:09:22,929
And so, you know,

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I'm uncomfortable with this idea
of someone else determining,

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00:09:26,533 --> 00:09:29,869
you know,
what's in my best interest, in

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00:09:29,869 --> 00:09:30,703
terms of,

247
00:09:33,273 --> 00:09:35,809
making decisions for me
about what kind of stuff

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00:09:35,809 --> 00:09:39,212
I should learn
from from, research, sequencing.

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And then I think, again,
the converse of this.

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Right.

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Not to know, as Ben mentioned,
is this right to know it's my

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data.

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I should have autonomy
and control over it.

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00:09:49,923 --> 00:09:52,260
And that people view
deliberation

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00:09:52,260 --> 00:09:53,393
and restriction

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00:09:53,393 --> 00:09:56,364
about the types of data
that are, that are given to

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people.

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Is viewed as paternalistic,

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00:10:00,400 --> 00:10:01,167
generally by

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00:10:01,167 --> 00:10:03,692
people
who are who are asked to think

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00:10:03,692 --> 00:10:04,170
about,

262
00:10:05,271 --> 00:10:08,115
what kind of, genetic data
they might want to get from

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00:10:08,115 --> 00:10:08,641
research.

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00:10:09,476 --> 00:10:12,347
So when you ask people,
you know, okay, we've

265
00:10:12,347 --> 00:10:13,113
established

266
00:10:13,113 --> 00:10:15,789
that you want to know this
information,

267
00:10:15,789 --> 00:10:17,917
what are your preferences for,

268
00:10:18,818 --> 00:10:21,213
for returning,
you know, for learning this

269
00:10:21,213 --> 00:10:21,955
information.

270
00:10:22,522 --> 00:10:26,293
And so people will say after
broad consent, so they want to

271
00:10:26,293 --> 00:10:27,060
be informed

272
00:10:27,060 --> 00:10:30,063
about what kind of genomic data
are going to be generated.

273
00:10:30,330 --> 00:10:32,160
And they also really want to
understand

274
00:10:32,160 --> 00:10:33,333
kind of what's the plan,

275
00:10:34,601 --> 00:10:37,704
to to get this back to me,
are you going to call me?

276
00:10:37,704 --> 00:10:40,139
Are you going to send me a
letter?

277
00:10:40,139 --> 00:10:42,442
And I think many people have,
have,

278
00:10:42,442 --> 00:10:44,627
have argued and we have a lot of
good data

279
00:10:44,627 --> 00:10:45,512
to suggest that,

280
00:10:46,045 --> 00:10:49,007
when you stick to a plan
to return results, it is a great

281
00:10:49,007 --> 00:10:49,215
way

282
00:10:49,215 --> 00:10:52,552
to foster, ongoing participant
engagement.

283
00:10:53,353 --> 00:10:55,817
And, you know,
we know that having participants

284
00:10:55,817 --> 00:10:56,289
actively

285
00:10:56,289 --> 00:10:58,300
engaged in research is, you
know,

286
00:10:58,300 --> 00:11:00,860
just a huge part of the whole
enterprise.

287
00:11:01,761 --> 00:11:03,448
And then there's some, you know,
data

288
00:11:03,448 --> 00:11:04,998
to suggest that, you know,
people

289
00:11:04,998 --> 00:11:09,649
prefer things like face to face
genetic counseling for return of

290
00:11:09,649 --> 00:11:10,303
results.

291
00:11:10,303 --> 00:11:12,767
But I think there's a lot of
room

292
00:11:12,767 --> 00:11:15,008
for, innovation here as well.

293
00:11:15,708 --> 00:11:18,758
And I'm going to come back
to the idea of consent a little

294
00:11:18,758 --> 00:11:19,178
bit as,

295
00:11:19,445 --> 00:11:20,547
a little bit later.

296
00:11:21,748 --> 00:11:24,751
I'm going to again cite Ben's
work here,

297
00:11:24,751 --> 00:11:29,665
because we can look at people
who refuse to, to get, genetic

298
00:11:29,665 --> 00:11:30,156
data.

299
00:11:30,156 --> 00:11:33,159
And so again, I'll just
emphasize that,

300
00:11:33,960 --> 00:11:36,429
these people are in the
minority.

301
00:11:36,429 --> 00:11:39,037
And I think another really
important thing

302
00:11:39,037 --> 00:11:41,334
to to be aware of when we're
looking

303
00:11:41,334 --> 00:11:43,733
at this literature is that a lot
of it

304
00:11:43,733 --> 00:11:45,438
is hypothetical or analog.

305
00:11:45,672 --> 00:11:47,540
So you would ask a group of
research

306
00:11:47,540 --> 00:11:48,942
participants, you know, if

307
00:11:48,942 --> 00:11:51,147
we were to implement genomic
sequencing

308
00:11:51,147 --> 00:11:53,580
in our study, what choices would
you make?

309
00:11:53,913 --> 00:11:56,151
Or if you were to be offered
these choices

310
00:11:56,151 --> 00:11:57,483
which sound best to you?

311
00:11:57,917 --> 00:12:01,521
There's a the body of literature
that's focused on people

312
00:12:01,521 --> 00:12:03,672
who are actually making these
decisions

313
00:12:03,672 --> 00:12:05,491
is much smaller compared to the,

314
00:12:05,491 --> 00:12:07,872
you know,
group of people who are, the

315
00:12:07,872 --> 00:12:08,561
literature

316
00:12:08,561 --> 00:12:10,468
that's on people who are
actually making

317
00:12:10,468 --> 00:12:11,564
some of these choices.

318
00:12:12,065 --> 00:12:15,602
And as been pointed out,
this refusal may not be durable.

319
00:12:15,602 --> 00:12:16,269
And so,

320
00:12:17,503 --> 00:12:19,906
again, there's, there's,

321
00:12:19,906 --> 00:12:22,609
some data to suggest that people

322
00:12:22,609 --> 00:12:25,761
can change their minds on this
over time

323
00:12:25,761 --> 00:12:28,047
and by kind of happenstance,

324
00:12:28,047 --> 00:12:31,951
I have talked to some of the
these people, these refusers

325
00:12:32,251 --> 00:12:35,421
and returned secondary findings
to them.

326
00:12:35,421 --> 00:12:36,823
And they are fine.

327
00:12:36,823 --> 00:12:39,759
It's anecdotal data, but they
were fine.

328
00:12:39,759 --> 00:12:41,361
And I think one thing that's

329
00:12:41,361 --> 00:12:44,030
that's an interesting point
that I will mention here,

330
00:12:44,030 --> 00:12:46,026
because you might be asking
yourself,

331
00:12:46,026 --> 00:12:47,267
well, if they refused,

332
00:12:47,700 --> 00:12:50,403
how did you come to talk to
them?

333
00:12:50,403 --> 00:12:52,739
I think this is another just
really

334
00:12:52,739 --> 00:12:56,342
practical argument
against things like checkboxes.

335
00:12:56,843 --> 00:13:01,047
Because if you are,
there's a lot of possibilities

336
00:13:01,047 --> 00:13:04,633
for, data
integrity issues to come into

337
00:13:04,633 --> 00:13:05,184
play.

338
00:13:05,184 --> 00:13:09,622
So if someone checks the box
no on a paper consent form,

339
00:13:09,856 --> 00:13:12,134
then that choice needs to be
carried

340
00:13:12,134 --> 00:13:13,526
forward with fidelity

341
00:13:13,526 --> 00:13:16,860
into whatever
your kind of research database

342
00:13:16,860 --> 00:13:17,163
is.

343
00:13:17,430 --> 00:13:20,233
And so I just think it
introduces the,

344
00:13:20,233 --> 00:13:23,903
a lot of potential
for kind of bidirectional error,

345
00:13:24,904 --> 00:13:27,062
where you do find yourself in a
position

346
00:13:27,062 --> 00:13:27,440
where,

347
00:13:27,440 --> 00:13:30,008
you know, maybe
we weren't really supposed to be

348
00:13:30,008 --> 00:13:30,543
analyzing

349
00:13:30,543 --> 00:13:33,479
the sequence data of this person
because they check the box for.

350
00:13:33,479 --> 00:13:36,880
No, but we did analyze the data,
and we're exactly in the

351
00:13:36,880 --> 00:13:37,417
position

352
00:13:37,417 --> 00:13:40,069
of the second case, that, Ben
mentioned,

353
00:13:40,069 --> 00:13:42,722
where we know something about
somebody.

354
00:13:42,722 --> 00:13:46,526
They said, no, how do we go back
and, you know, talk to them

355
00:13:46,526 --> 00:13:48,528
because now we know this
information

356
00:13:48,528 --> 00:13:49,529
and I think it's,

357
00:13:49,862 --> 00:13:53,310
it's a different situation
when you don't actually, you

358
00:13:53,310 --> 00:13:54,000
know, seek

359
00:13:54,233 --> 00:13:56,175
those, you know, those kind of
data

360
00:13:56,175 --> 00:13:58,504
from the sequence data that you
generate.

361
00:13:58,504 --> 00:14:00,695
You don't seek to look for
secondary

362
00:14:00,695 --> 00:14:01,607
findings. But,

363
00:14:02,642 --> 00:14:04,637
again, that's a bit of a
tangent,

364
00:14:04,637 --> 00:14:06,813
but I think that, I thinking
about,

365
00:14:07,613 --> 00:14:10,990
how we categorize people's
choices,

366
00:14:10,990 --> 00:14:12,051
when we do

367
00:14:12,051 --> 00:14:14,148
things like that is just
another,

368
00:14:14,148 --> 00:14:16,689
another opportunity to introduce
error.

369
00:14:19,225 --> 00:14:21,260
So, you know, I think when

370
00:14:21,260 --> 00:14:24,464
we think about,
you know, this whole topic,

371
00:14:25,698 --> 00:14:27,700
we are still in a situation

372
00:14:27,700 --> 00:14:30,936
where we know much more about
people's

373
00:14:30,936 --> 00:14:33,406
attitudes about genomic data

374
00:14:33,773 --> 00:14:36,609
and whether or not research,
you know, how research

375
00:14:36,609 --> 00:14:37,610
participants feel

376
00:14:37,610 --> 00:14:39,935
about getting these kinds of
data

377
00:14:39,935 --> 00:14:40,780
compared to

378
00:14:41,080 --> 00:14:43,883
what the outcomes are.

379
00:14:43,883 --> 00:14:45,912
And so I'm going to kind of
transition

380
00:14:45,912 --> 00:14:48,154
a little bit into talking about
outcomes,

381
00:14:48,154 --> 00:14:51,157
because I think we need to shift
this ratio.

382
00:14:51,390 --> 00:14:54,227
Again a lot of ink has been
spilled over.

383
00:14:54,227 --> 00:14:57,563
What ought we to do
and why aren't we to do it.

384
00:14:57,563 --> 00:15:00,285
And less has been on this area
of, well,

385
00:15:00,285 --> 00:15:02,802
what happens when we make
decisions,

386
00:15:03,102 --> 00:15:05,646
you know, what are the outcomes
associated with some of those

387
00:15:05,646 --> 00:15:06,105
decisions?

388
00:15:06,672 --> 00:15:10,810
And I think this is a point to
revisit.

389
00:15:11,310 --> 00:15:13,950
One of,
you know, revisit consent in one

390
00:15:13,950 --> 00:15:14,280
way,

391
00:15:15,348 --> 00:15:16,965
which is, you know, over the
years,

392
00:15:16,965 --> 00:15:18,351
I've talked to I've consented

393
00:15:18,351 --> 00:15:20,439
many people for genomic
sequencing

394
00:15:20,439 --> 00:15:22,588
with return of secondary
findings.

395
00:15:22,855 --> 00:15:25,472
And I always like it when people
decline

396
00:15:25,472 --> 00:15:27,827
participation and research
studies.

397
00:15:27,827 --> 00:15:30,830
You know, you really shouldn't
ideally,

398
00:15:31,597 --> 00:15:34,667
have 100% of your prospective

399
00:15:34,667 --> 00:15:37,670
participants signing that
consent form.

400
00:15:37,870 --> 00:15:39,005
Right.

401
00:15:39,005 --> 00:15:40,827
It's great if most everybody
does,

402
00:15:40,827 --> 00:15:42,542
but I think it's a really great

403
00:15:43,442 --> 00:15:45,510
I don't know, I like it
when people decline because it

404
00:15:45,510 --> 00:15:45,778
to me,

405
00:15:45,778 --> 00:15:47,696
it's kind of an an internal
check

406
00:15:47,696 --> 00:15:49,148
that I'm doing it right,

407
00:15:49,348 --> 00:15:51,174
because not everyone who I
talked to

408
00:15:51,174 --> 00:15:53,152
could possibly want to be in my
study,

409
00:15:53,452 --> 00:15:55,188
even of the selected population

410
00:15:55,188 --> 00:15:57,964
that comes through
to have that conversation with

411
00:15:57,964 --> 00:15:58,191
me.

412
00:15:58,491 --> 00:16:00,950
And so, you know, I think the
cases in

413
00:16:00,950 --> 00:16:02,762
which I've talked to people

414
00:16:02,762 --> 00:16:05,541
in, they are concerned
about receiving these, these

415
00:16:05,541 --> 00:16:06,032
findings

416
00:16:06,032 --> 00:16:07,652
kind of, again, go along with
what

417
00:16:07,652 --> 00:16:09,035
some of the things that Ben,

418
00:16:09,836 --> 00:16:13,306
was saying about these
like specific instances.

419
00:16:14,106 --> 00:16:15,628
You know, I've had a couple of
people

420
00:16:15,628 --> 00:16:16,409
who I've talked to

421
00:16:16,409 --> 00:16:19,609
who've been in the military
and so have been really

422
00:16:19,609 --> 00:16:20,613
concerned about

423
00:16:20,613 --> 00:16:22,882
where their genomic data are
going

424
00:16:22,882 --> 00:16:25,685
and how that will affect their
potential,

425
00:16:26,786 --> 00:16:29,435
fitness,
and concerns about a medical

426
00:16:29,435 --> 00:16:30,223
discharge.

427
00:16:30,223 --> 00:16:33,759
That's a really specific
and important case.

428
00:16:34,227 --> 00:16:36,762
That is, you know, important
to talk about.

429
00:16:36,762 --> 00:16:39,704
In another case,
I've, worked with some families

430
00:16:39,704 --> 00:16:40,132
where,

431
00:16:41,200 --> 00:16:45,805
there are some programs
where there's carrier screening

432
00:16:45,805 --> 00:16:48,288
for genetic conditions that are
really,

433
00:16:48,288 --> 00:16:50,643
more common in a certain
population.

434
00:16:51,277 --> 00:16:53,611
And so there are kind of
community

435
00:16:53,611 --> 00:16:54,847
genetic screening

436
00:16:54,847 --> 00:16:57,324
initiatives that are, again,
community

437
00:16:57,324 --> 00:16:59,018
led, community organized.

438
00:16:59,318 --> 00:17:02,762
And one of the features
of some of those programs is

439
00:17:02,762 --> 00:17:03,623
that, people

440
00:17:03,623 --> 00:17:06,025
who participate in the program
learn,

441
00:17:06,025 --> 00:17:08,427
don't learn their individual
results

442
00:17:08,427 --> 00:17:10,861
because the purpose of the
program

443
00:17:10,861 --> 00:17:12,365
is to help make sure

444
00:17:12,365 --> 00:17:14,742
that a potential partner doesn't
carry

445
00:17:14,742 --> 00:17:15,368
the same,

446
00:17:16,636 --> 00:17:19,518
pathogenic variant
for a specific condition that

447
00:17:19,518 --> 00:17:20,239
they might.

448
00:17:20,239 --> 00:17:23,597
So it reduces the probability
that they would have an affected

449
00:17:23,597 --> 00:17:23,976
child.

450
00:17:25,077 --> 00:17:26,412
The partnership is

451
00:17:26,412 --> 00:17:29,548
analyzed and the potential for
the that,

452
00:17:29,548 --> 00:17:32,541
aspect of partnership
to have an increased risk to

453
00:17:32,541 --> 00:17:33,319
have a child

454
00:17:33,319 --> 00:17:35,737
with one of these conditions, is
kind

455
00:17:35,737 --> 00:17:38,090
of, looked at on a community
level,

456
00:17:38,090 --> 00:17:39,964
but individual results are not
returned

457
00:17:39,964 --> 00:17:41,694
because that would be
stigmatizing.

458
00:17:41,994 --> 00:17:46,265
Again, this is such a specific
situation

459
00:17:46,265 --> 00:17:49,038
that it's great
that people have again, brought

460
00:17:49,038 --> 00:17:49,568
this up.

461
00:17:49,568 --> 00:17:51,953
And I think that's something
that absolutely should be

462
00:17:51,953 --> 00:17:52,571
accommodated.

463
00:17:52,939 --> 00:17:54,790
And then, you know, in other
instances,

464
00:17:54,790 --> 00:17:56,309
I've had people talk about how,

465
00:17:56,309 --> 00:17:58,311
you know, they just feel
uncomfortable

466
00:17:58,311 --> 00:17:59,312
with genetic data,

467
00:17:59,512 --> 00:18:01,503
because they've thought about,
you know,

468
00:18:01,503 --> 00:18:02,748
what does that mean for,

469
00:18:04,250 --> 00:18:06,102
you know, choices that people
might make

470
00:18:06,102 --> 00:18:07,954
in like, a reproductive setting,
right?

471
00:18:08,220 --> 00:18:10,636
Again,
I think these are all really

472
00:18:10,636 --> 00:18:11,257
specific

473
00:18:11,257 --> 00:18:13,559
examples that are worth
exploring.

474
00:18:13,559 --> 00:18:15,731
But, hopefully you can kind of
tell

475
00:18:15,731 --> 00:18:17,096
from what I'm saying,

476
00:18:17,096 --> 00:18:19,424
what Ben was saying, that these
are super,

477
00:18:19,424 --> 00:18:20,700
super, super specific.

478
00:18:20,700 --> 00:18:22,874
And again, I think we really
need to think

479
00:18:22,874 --> 00:18:23,703
about this idea

480
00:18:23,703 --> 00:18:25,745
that we have a huge amount of
data

481
00:18:25,745 --> 00:18:26,706
to suggest that

482
00:18:27,006 --> 00:18:29,221
by and large, people want us to
tell

483
00:18:29,221 --> 00:18:30,943
them these kinds of things.

484
00:18:32,311 --> 00:18:33,412
So I just want to take a

485
00:18:33,412 --> 00:18:35,882
step back
before we start talking about

486
00:18:35,882 --> 00:18:36,515
outcomes.

487
00:18:36,515 --> 00:18:39,518
And again, some practical
considerations.

488
00:18:40,286 --> 00:18:42,655
In returning these kinds of
findings,

489
00:18:42,655 --> 00:18:45,358
to just take a moment to define
them.

490
00:18:45,358 --> 00:18:46,993
Of course I need to.

491
00:18:46,993 --> 00:18:50,796
I have an xkcd comic also, just
like Ben.

492
00:18:51,397 --> 00:18:53,784
And I think, you know what's
interesting

493
00:18:53,784 --> 00:18:55,635
about this and the comic that,

494
00:18:56,435 --> 00:18:59,572
that, Ben, put up also is

495
00:18:59,572 --> 00:19:02,360
that, you know,
I think that when you talk to a

496
00:19:02,360 --> 00:19:02,775
person

497
00:19:02,775 --> 00:19:06,243
in the general public about,
like, genetic data, you know, I

498
00:19:06,243 --> 00:19:06,879
think that

499
00:19:07,213 --> 00:19:11,417
what they are hoping for or what
they think might happen in the

500
00:19:11,417 --> 00:19:11,951
future,

501
00:19:12,785 --> 00:19:15,354
is that, you know, at some point
we're going to go to the doctor

502
00:19:15,354 --> 00:19:19,152
or the doctor is going to do
like a fingerstick put a drop of

503
00:19:19,152 --> 00:19:19,525
blood

504
00:19:19,525 --> 00:19:22,828
in some like machine in the back
and then come out with some

505
00:19:22,828 --> 00:19:27,818
like wonderful results report,
then kind of prescribe to you

506
00:19:27,818 --> 00:19:28,234
ways

507
00:19:28,234 --> 00:19:32,132
to change your life or change
your health care to make you

508
00:19:32,132 --> 00:19:32,872
healthier.

509
00:19:32,872 --> 00:19:33,339
Right?

510
00:19:33,339 --> 00:19:35,207
So they're going to talk to you
about how you're

511
00:19:35,207 --> 00:19:37,977
more likely to have diabetes
or you shouldn't have gluten.

512
00:19:37,977 --> 00:19:39,492
I think this is what people
think about

513
00:19:39,492 --> 00:19:40,579
when they think about like,

514
00:19:40,579 --> 00:19:43,961
what is the utility of genetic
information

515
00:19:43,961 --> 00:19:45,651
in a health setting?

516
00:19:46,719 --> 00:19:47,620
And so,

517
00:19:47,620 --> 00:19:50,162
you know, I think that's a
context

518
00:19:50,162 --> 00:19:51,657
that I think about.

519
00:19:52,591 --> 00:19:56,151
It's the in talking with people
about exactly what it is that we

520
00:19:56,151 --> 00:19:56,429
find

521
00:19:56,762 --> 00:19:59,331
because we're still at the point
where,

522
00:19:59,331 --> 00:20:02,588
the kinds of findings
that we would propose to return

523
00:20:02,588 --> 00:20:03,202
to people

524
00:20:03,202 --> 00:20:06,172
through an opportunistic
screening program

525
00:20:06,172 --> 00:20:07,940
are much more delimited.

526
00:20:07,940 --> 00:20:08,340
Right.

527
00:20:08,340 --> 00:20:11,177
And they're they take a lot more
effort

528
00:20:11,177 --> 00:20:11,977
to produce

529
00:20:12,211 --> 00:20:14,749
than just, again, putting a drop
of blood

530
00:20:14,749 --> 00:20:16,482
in the machine in the back.

531
00:20:16,482 --> 00:20:17,750
Right.

532
00:20:17,750 --> 00:20:20,237
And so, you know, I think it's
important

533
00:20:20,237 --> 00:20:21,854
to discuss this idea that

534
00:20:21,854 --> 00:20:25,357
when we are returning research
results,

535
00:20:25,357 --> 00:20:28,494
research genetic results to
people,

536
00:20:28,494 --> 00:20:30,563
this is this is opportunistic
screening.

537
00:20:30,563 --> 00:20:32,832
It's not population screening.
Right.

538
00:20:32,832 --> 00:20:35,109
But I think it's really
important

539
00:20:35,109 --> 00:20:37,870
to examine, you know, what we
can learn

540
00:20:37,870 --> 00:20:39,748
from this opportunistic
screening,

541
00:20:39,748 --> 00:20:41,073
because I do think that

542
00:20:41,407 --> 00:20:43,625
the goal that we're all trying
to get to

543
00:20:43,625 --> 00:20:45,678
has been mentioned is to
incorporate

544
00:20:46,011 --> 00:20:48,331
genomic data
into our health care more

545
00:20:48,331 --> 00:20:48,881
broadly.

546
00:20:48,881 --> 00:20:51,884
But there are a lot of things
that we can learn from,

547
00:20:52,151 --> 00:20:55,751
this moment in time
when we are giving this

548
00:20:55,751 --> 00:20:56,755
information

549
00:20:56,755 --> 00:21:01,065
back to people,
who are in a certain place and

550
00:21:01,065 --> 00:21:01,627
time.

551
00:21:01,627 --> 00:21:02,394
Right.

552
00:21:02,394 --> 00:21:04,920
If you have the
if you're joining a research

553
00:21:04,920 --> 00:21:05,264
study

554
00:21:05,264 --> 00:21:07,402
where genomic sequencing is part
of it,

555
00:21:07,402 --> 00:21:09,101
you might have the opportunity

556
00:21:09,101 --> 00:21:12,364
to have your, you know,
some of these findings returned

557
00:21:12,364 --> 00:21:12,838
to you.

558
00:21:13,472 --> 00:21:16,450
As opposed to, you know, what,
if we were to implement this on

559
00:21:16,450 --> 00:21:16,642
the

560
00:21:16,642 --> 00:21:17,743
population level?

561
00:21:18,778 --> 00:21:20,618
And again, I think it's also
important

562
00:21:20,618 --> 00:21:21,780
to think about the fact

563
00:21:21,780 --> 00:21:25,160
that what we're really doing
here, in most instances, is

564
00:21:25,160 --> 00:21:25,885
repurposing

565
00:21:25,885 --> 00:21:28,200
this list of variants that was
published,

566
00:21:28,200 --> 00:21:29,555
not the variants genes,

567
00:21:29,555 --> 00:21:31,604
but specific variants that have
specific

568
00:21:31,604 --> 00:21:33,192
attributes and specific genes,

569
00:21:33,826 --> 00:21:36,247
by the American College
of Medical Genetics and

570
00:21:36,247 --> 00:21:36,762
Genomics.

571
00:21:36,762 --> 00:21:39,899
And this list was developed
because

572
00:21:41,433 --> 00:21:43,836
is developed for clinical
laboratories.

573
00:21:43,836 --> 00:21:44,170
Right.

574
00:21:44,170 --> 00:21:46,677
So many, many more people are
having,

575
00:21:46,677 --> 00:21:48,574
clinical genome sequencing.

576
00:21:49,508 --> 00:21:52,511
And the this list

577
00:21:52,511 --> 00:21:54,765
and this policy was developed
for,

578
00:21:54,765 --> 00:21:57,616
you know, how should clinical
laboratories

579
00:21:57,950 --> 00:22:01,854
essentially handle the problem
from Ben's case, right.

580
00:22:02,087 --> 00:22:04,300
They were asked to analyze
genomic data

581
00:22:04,300 --> 00:22:05,491
to try to understand

582
00:22:05,491 --> 00:22:08,857
why someone might have,
you know, autism or a seizure

583
00:22:08,857 --> 00:22:09,428
disorder

584
00:22:09,428 --> 00:22:12,922
or something like that, but they
they are in a position to know

585
00:22:12,922 --> 00:22:13,532
much more.

586
00:22:13,799 --> 00:22:15,301
How should they handle that?

587
00:22:15,301 --> 00:22:17,169
And the consensus was that,
well,

588
00:22:17,169 --> 00:22:18,734
they should look for these
things

589
00:22:18,734 --> 00:22:20,773
and they should return them to
people when

590
00:22:21,273 --> 00:22:24,343
when people express a preference
to learn this information.

591
00:22:25,110 --> 00:22:27,185
And so I think, you know, the
key

592
00:22:27,185 --> 00:22:27,813
attribute

593
00:22:27,813 --> 00:22:29,371
of all of these secondary
findings

594
00:22:29,371 --> 00:22:30,516
that we're talking about

595
00:22:30,516 --> 00:22:32,851
is that they're actionable,
has been mentioned.

596
00:22:32,851 --> 00:22:35,521
So if you were to know about
this,

597
00:22:35,521 --> 00:22:37,996
there was something
there's something you could do

598
00:22:37,996 --> 00:22:38,490
about it.

599
00:22:38,490 --> 00:22:39,091
And I think

600
00:22:39,091 --> 00:22:40,934
another thing that's important
to know

601
00:22:40,934 --> 00:22:42,728
is that, you know, most of the
find,

602
00:22:42,728 --> 00:22:44,945
most of the disorders on the
list

603
00:22:44,945 --> 00:22:47,766
are, have autosomal dominant
inheritance.

604
00:22:48,067 --> 00:22:51,503
And what that means is,
is that if you, as the patient,

605
00:22:51,770 --> 00:22:53,886
are found to have one of these
variants,

606
00:22:53,886 --> 00:22:56,108
all of your first degree
relatives are at

607
00:22:56,108 --> 00:22:59,311
50% risk to also share that
variant

608
00:22:59,311 --> 00:23:02,281
with you and presumably that
risk status.

609
00:23:02,281 --> 00:23:04,352
And that's risk status for a
treatable

610
00:23:04,352 --> 00:23:06,151
or preventable disease for which

611
00:23:06,151 --> 00:23:08,179
potentially life saving
treatment

612
00:23:08,179 --> 00:23:10,022
or surveillance is available.

613
00:23:11,323 --> 00:23:14,193
But again, I think this is an,

614
00:23:14,193 --> 00:23:15,848
an area
where we need to really think

615
00:23:15,848 --> 00:23:16,161
about,

616
00:23:16,161 --> 00:23:19,798
you know, is this really
secondary when,

617
00:23:20,633 --> 00:23:25,412
we are in, when the Acmg list
and the Acmg guidelines were

618
00:23:25,412 --> 00:23:26,071
written

619
00:23:26,071 --> 00:23:28,140
for clinical laboratories, where
there is

620
00:23:28,140 --> 00:23:31,176
someone who's ordering the test
for a clinical reason,

621
00:23:31,176 --> 00:23:33,933
and there's an interpreting
laboratory

622
00:23:33,933 --> 00:23:36,181
whose main expertise is trying

623
00:23:36,181 --> 00:23:39,218
to understand
genetic etiologies of specific

624
00:23:39,218 --> 00:23:42,221
clinical entities in research.

625
00:23:42,988 --> 00:23:45,624
You know, the ordering clinician
is maybe

626
00:23:45,624 --> 00:23:49,050
not going to be a geneticist,
and they may not really be

627
00:23:49,050 --> 00:23:49,662
ordering,

628
00:23:51,030 --> 00:23:54,033
you know, a genome interrogation
test,

629
00:23:54,733 --> 00:23:56,122
for the same reasons that
someone

630
00:23:56,122 --> 00:23:57,469
in the clinical world might be.

631
00:23:57,469 --> 00:23:59,395
So these are just a couple of
caveats to,

632
00:23:59,395 --> 00:24:01,273
to kind of think about and kick
around.

633
00:24:02,975 --> 00:24:06,111
But, you know,
if things go according to plan,

634
00:24:06,111 --> 00:24:10,249
ideally what we would see is a
is a secondary findings paradigm

635
00:24:10,249 --> 00:24:12,938
where as this finding is
disclosed

636
00:24:12,938 --> 00:24:14,520
and recommendations

637
00:24:14,520 --> 00:24:19,325
are made to the recipient,
and there are two major

638
00:24:19,325 --> 00:24:21,269
recommendations that ought to be
made

639
00:24:21,269 --> 00:24:23,162
to people who are in this
position.

640
00:24:23,162 --> 00:24:27,232
So the first thing is that,
the person should undergo

641
00:24:27,232 --> 00:24:29,955
some disease specific
evaluations

642
00:24:29,955 --> 00:24:31,770
because now they know

643
00:24:31,770 --> 00:24:33,774
that they have a genetic risk
factor,

644
00:24:33,774 --> 00:24:35,074
and so they should meet

645
00:24:35,074 --> 00:24:38,043
with an expert to,

646
00:24:38,410 --> 00:24:42,081
you know, begin surveillance or,
you know, see if they already

647
00:24:42,081 --> 00:24:45,518
have signs of this disease,
because they have this risk

648
00:24:45,518 --> 00:24:46,018
factor.

649
00:24:46,352 --> 00:24:49,021
And of course, the the hope
there would be

650
00:24:49,021 --> 00:24:51,765
that if they are found to have
features

651
00:24:51,765 --> 00:24:52,891
they, you know,

652
00:24:52,891 --> 00:24:56,173
could engage in earlier
diagnosis or prevention or

653
00:24:56,173 --> 00:24:56,895
treatment,

654
00:24:57,629 --> 00:25:00,456
but if they don't,
then they can participate in the

655
00:25:00,456 --> 00:25:00,899
kind of

656
00:25:00,899 --> 00:25:04,643
ongoing surveillance
that would allow intervention at

657
00:25:04,643 --> 00:25:05,137
a time

658
00:25:05,137 --> 00:25:07,685
when it is
most likely to have the best

659
00:25:07,685 --> 00:25:08,273
outcome.

660
00:25:09,041 --> 00:25:11,343
But then this gets to
this second recommendation.

661
00:25:11,343 --> 00:25:13,112
What I mentioned about these
genes

662
00:25:13,112 --> 00:25:15,047
that are on this list
is that their dominant.

663
00:25:15,047 --> 00:25:19,218
So again, if any person is found
to have one of these findings,

664
00:25:19,685 --> 00:25:23,922
an important secondary benefit
might be to family members.

665
00:25:23,922 --> 00:25:26,032
And so the second general
recommendation

666
00:25:26,032 --> 00:25:27,192
that people are given

667
00:25:27,426 --> 00:25:30,496
is to tell people in your family
about this variant.

668
00:25:30,729 --> 00:25:34,163
So not only should you share
this information with your

669
00:25:34,163 --> 00:25:34,600
family

670
00:25:34,600 --> 00:25:37,403
so that they can get tested,
we call that cascade

671
00:25:37,403 --> 00:25:39,471
testing is when you're tested
for it.

672
00:25:39,471 --> 00:25:42,656
We have genetic testing to see
if you have a known familial

673
00:25:42,656 --> 00:25:43,142
variant.

674
00:25:44,109 --> 00:25:44,510
And if you're

675
00:25:44,510 --> 00:25:46,552
positive, then you can kind of
enter

676
00:25:46,552 --> 00:25:47,913
this virtuous cycle of,

677
00:25:48,347 --> 00:25:49,845
you know, increasing your
surveillance

678
00:25:49,845 --> 00:25:51,383
that could potentially save your
life.

679
00:25:51,717 --> 00:25:53,819
But also,

680
00:25:53,819 --> 00:25:55,921
oftentimes, you know, these are
findings

681
00:25:55,921 --> 00:25:59,158
that are that people are not
necessarily are expecting.

682
00:25:59,158 --> 00:26:03,240
And you can better interpret
the clinical meaning of the

683
00:26:03,240 --> 00:26:03,896
findings

684
00:26:03,896 --> 00:26:07,232
if you understand
more about the family as well.

685
00:26:08,167 --> 00:26:10,711
You know,
so I think, it's important to

686
00:26:10,711 --> 00:26:11,103
think

687
00:26:11,103 --> 00:26:13,654
that, you know,
this is kind of how we hope

688
00:26:13,654 --> 00:26:14,306
things go.

689
00:26:14,840 --> 00:26:17,376
And, what we did a couple of
years ago

690
00:26:17,376 --> 00:26:20,112
is turn to the literature to see
kind of

691
00:26:20,112 --> 00:26:23,880
what is the evidence base for
any of the things that I just

692
00:26:23,880 --> 00:26:24,583
mentioned.

693
00:26:26,051 --> 00:26:27,853
And so we did a systematic
review

694
00:26:27,853 --> 00:26:32,439
where we looked in the,
the literature, where the unit

695
00:26:32,439 --> 00:26:33,459
of analysis

696
00:26:33,459 --> 00:26:36,521
was individual recipients of
secondary

697
00:26:36,521 --> 00:26:38,697
findings on the Acmg list.

698
00:26:39,598 --> 00:26:43,969
And so we were only able
to find about 700 reports of,

699
00:26:45,037 --> 00:26:47,549
these kinds
of findings being returned to

700
00:26:47,549 --> 00:26:48,040
people.

701
00:26:48,040 --> 00:26:49,743
And that was a bit surprising to
me,

702
00:26:49,743 --> 00:26:50,642
because, you know,

703
00:26:50,642 --> 00:26:55,113
the first iteration of this list
was published in 2013.

704
00:26:55,481 --> 00:26:59,551
And so almost ten years after,
the list was published,

705
00:26:59,551 --> 00:27:02,538
we still didn't
find a ton of reports about

706
00:27:02,538 --> 00:27:02,955
this.

707
00:27:03,322 --> 00:27:06,525
But we looked at,
what is known in the literature

708
00:27:06,525 --> 00:27:08,465
about how these findings are
returned

709
00:27:08,465 --> 00:27:10,562
and what outcomes happen after
receipt.

710
00:27:11,363 --> 00:27:15,716
And I think the, one important
finding that stood out to us

711
00:27:15,716 --> 00:27:16,602
immediately

712
00:27:16,802 --> 00:27:21,839
was that 97% of these reports
were participants in research

713
00:27:21,839 --> 00:27:22,608
studies.

714
00:27:22,608 --> 00:27:25,711
So really a tiny fraction are
people

715
00:27:25,711 --> 00:27:28,447
who are getting these kinds of
results

716
00:27:28,447 --> 00:27:29,815
in a clinical way,

717
00:27:29,815 --> 00:27:32,243
meaning the way the list was
intended

718
00:27:32,243 --> 00:27:33,752
to be operationalized.

719
00:27:34,152 --> 00:27:36,448
That's, has interesting
implications

720
00:27:36,448 --> 00:27:37,723
when we think about

721
00:27:37,723 --> 00:27:41,426
maybe like population screening,
and kind of clinical efforts.

722
00:27:41,426 --> 00:27:43,595
But it's good for us here
because most of these people

723
00:27:43,595 --> 00:27:45,330
are research participants.
And that's what we're talking

724
00:27:45,330 --> 00:27:48,333
about, is returning results
to research participants.

725
00:27:48,333 --> 00:27:50,913
So I'll just reiterate
some things that Ben already

726
00:27:50,913 --> 00:27:51,470
mentioned.

727
00:27:51,470 --> 00:27:53,049
You know, again,
there's been a lot of

728
00:27:53,049 --> 00:27:53,839
hand-waving about,

729
00:27:54,973 --> 00:27:56,208
what do people do?

730
00:27:56,208 --> 00:27:58,306
What are adverse psychological
outcomes

731
00:27:58,306 --> 00:28:00,512
when people learn this
information? And,

732
00:28:01,980 --> 00:28:05,502
we found an array of,
you know, in studies that

733
00:28:05,502 --> 00:28:06,251
evaluated

734
00:28:06,485 --> 00:28:08,649
how people approach these,
results

735
00:28:08,649 --> 00:28:10,622
from an emotional perspective.

736
00:28:10,622 --> 00:28:13,076
People will say things like,
they were surprised. They were

737
00:28:13,076 --> 00:28:13,492
relieved.

738
00:28:13,492 --> 00:28:15,288
Some people will say they're
sad,

739
00:28:15,288 --> 00:28:16,595
but hardly anybody says

740
00:28:16,862 --> 00:28:19,680
that they regret their choice
to learn this kind of

741
00:28:19,680 --> 00:28:20,399
information.

742
00:28:20,399 --> 00:28:22,404
So I think this is again,
dovetails

743
00:28:22,404 --> 00:28:23,435
with this, right?

744
00:28:23,435 --> 00:28:27,007
Not to know,
when given this option, very,

745
00:28:27,007 --> 00:28:27,773
very few

746
00:28:27,773 --> 00:28:31,443
people will say that they regret
learning this information.

747
00:28:32,144 --> 00:28:35,147
When we follow people over time,

748
00:28:35,514 --> 00:28:38,708
levels of depression
and anxiety from before to

749
00:28:38,708 --> 00:28:39,184
before

750
00:28:39,184 --> 00:28:41,402
results disclosure to after
results,

751
00:28:41,402 --> 00:28:43,188
disclosure remain unchanged.

752
00:28:43,188 --> 00:28:45,713
Although again,
there's not a ton of data on

753
00:28:45,713 --> 00:28:46,058
this.

754
00:28:46,058 --> 00:28:49,161
And some people even forget
that they've been that

755
00:28:49,161 --> 00:28:52,231
these that these data
have been shared with them.

756
00:28:52,231 --> 00:28:54,199
And I've experienced that
personally.

757
00:28:56,034 --> 00:28:58,848
High numbers
of people report sharing their

758
00:28:58,848 --> 00:28:59,371
results

759
00:28:59,371 --> 00:29:02,441
with their family,
especially to close relatives.

760
00:29:03,241 --> 00:29:05,544
And we also saw that,

761
00:29:05,544 --> 00:29:08,113
disclosure to primary care
doctors

762
00:29:08,113 --> 00:29:11,249
and then specialists, was
common,

763
00:29:11,249 --> 00:29:14,119
but certainly not universal.

764
00:29:14,119 --> 00:29:16,755
And I think this is an important
point,

765
00:29:16,755 --> 00:29:19,257
when we think about again
that that paradigm.

766
00:29:19,257 --> 00:29:21,693
And I'll come back to that in a
minute.

767
00:29:21,693 --> 00:29:24,396
We looked for,

768
00:29:24,396 --> 00:29:27,132
precision medicine surveillance
outcomes.

769
00:29:27,132 --> 00:29:29,932
And what I mean by that is, you
know,

770
00:29:29,932 --> 00:29:32,504
most of us will undergo
screening

771
00:29:32,504 --> 00:29:35,945
tests based on attributes
that have nothing to do with our

772
00:29:35,945 --> 00:29:36,241
DNA.

773
00:29:36,241 --> 00:29:36,842
Right.

774
00:29:36,842 --> 00:29:39,511
You're a woman over 40.
You have mammograms, right?

775
00:29:39,511 --> 00:29:40,679
You're a man over 50.

776
00:29:40,679 --> 00:29:43,215
You get your PSA tested right?

777
00:29:43,215 --> 00:29:46,151
A a precision medicine
approach to surveillance

778
00:29:46,151 --> 00:29:47,877
is, well, you're a woman over
40,

779
00:29:47,877 --> 00:29:49,655
and you have a variant and a
gene

780
00:29:49,655 --> 00:29:52,005
that increases your risk
for hereditary breast and

781
00:29:52,005 --> 00:29:52,758
ovarian cancer.

782
00:29:52,758 --> 00:29:54,496
So we should surveil you
differently

783
00:29:54,496 --> 00:29:56,428
from someone in the general
population.

784
00:29:57,295 --> 00:30:00,932
And so, adherence
across the range of studies

785
00:30:00,932 --> 00:30:05,771
where adherence was examined,
was generally pretty good.

786
00:30:06,171 --> 00:30:09,708
And people did refer people
to, you know, specialists.

787
00:30:09,708 --> 00:30:12,110
Many people were followed
by specialists up there

788
00:30:12,110 --> 00:30:14,146
after they received their
results.

789
00:30:14,146 --> 00:30:17,783
And in studies
where cost was can was examined,

790
00:30:18,650 --> 00:30:21,486
the costs were lower than
expected.

791
00:30:21,486 --> 00:30:23,311
And again,
if you look across this body of

792
00:30:23,311 --> 00:30:23,789
literature

793
00:30:23,789 --> 00:30:26,115
and I'm going to put a few cases
in, in a minute, you know, there

794
00:30:26,115 --> 00:30:26,258
are

795
00:30:26,258 --> 00:30:29,735
you can find reports of life
saving treatment after people

796
00:30:29,735 --> 00:30:30,095
learn

797
00:30:30,095 --> 00:30:31,196
this kind of information.

798
00:30:31,196 --> 00:30:33,098
And so this could include,

799
00:30:34,132 --> 00:30:34,399
you know,

800
00:30:34,399 --> 00:30:37,200
not taking medicines that avoid,
that,

801
00:30:37,200 --> 00:30:39,337
that increase your interval.

802
00:30:39,337 --> 00:30:40,781
It has to do with your heart
rate

803
00:30:40,781 --> 00:30:41,306
and rhythm.

804
00:30:41,306 --> 00:30:44,009
There's a genetic predisposition
for that,

805
00:30:44,009 --> 00:30:45,716
targeted therapy
for familial

806
00:30:45,716 --> 00:30:47,012
hypercholesterolemia,

807
00:30:47,379 --> 00:30:49,773
early identification of
cancerous lesions

808
00:30:49,773 --> 00:30:50,415
by imaging

809
00:30:50,415 --> 00:30:54,580
and then prophylactic surgeries,
which we know, reduce cancer,

810
00:30:54,580 --> 00:30:55,454
cancer risk.

811
00:30:55,954 --> 00:30:58,968
So if we overlay our findings on
to,

812
00:30:58,968 --> 00:31:00,726
this ideal paradigm,

813
00:31:00,726 --> 00:31:03,862
the blue circles here, are

814
00:31:03,862 --> 00:31:06,614
proportionate to the number of
cases

815
00:31:06,614 --> 00:31:08,066
where there's data

816
00:31:08,066 --> 00:31:10,590
to support a different,
you know, different parts of

817
00:31:10,590 --> 00:31:11,269
this process.

818
00:31:11,269 --> 00:31:12,835
And so I think what you'll
notice

819
00:31:12,835 --> 00:31:13,405
immediately

820
00:31:13,405 --> 00:31:17,876
is that, the proportion of data
that are in the literature

821
00:31:17,876 --> 00:31:21,447
about how we do this
and what we should tell people

822
00:31:21,447 --> 00:31:22,147
is quite,

823
00:31:22,781 --> 00:31:25,837
is very large
compared to, again, outcome

824
00:31:25,837 --> 00:31:26,284
data.

825
00:31:26,718 --> 00:31:28,896
And when we think about outcome
data

826
00:31:28,896 --> 00:31:31,256
specifically, there still is not
a ton

827
00:31:31,256 --> 00:31:35,135
of data in the literature about
what are these specific health

828
00:31:35,135 --> 00:31:35,761
outcomes.

829
00:31:35,761 --> 00:31:38,930
You know, are we really moving
the needle on people's health?

830
00:31:39,664 --> 00:31:42,544
And another way to think about
this is what is the clinical

831
00:31:42,544 --> 00:31:42,934
utility

832
00:31:42,934 --> 00:31:44,585
of sharing this kind of
information

833
00:31:44,585 --> 00:31:45,670
with people. So again,

834
00:31:45,670 --> 00:31:47,190
there are reports in the
literature

835
00:31:47,190 --> 00:31:48,840
I'm going to give some case
examples.

836
00:31:48,840 --> 00:31:51,555
But this is really an
opportunity

837
00:31:51,555 --> 00:31:54,846
where we can improve the
knowledge base

838
00:31:54,846 --> 00:31:57,926
about what happens to people
after they learn this kind of

839
00:31:57,926 --> 00:31:58,617
information.

840
00:31:59,084 --> 00:32:00,696
And so I'm going to wrap up by
talking

841
00:32:00,696 --> 00:32:02,521
about a couple of different
things that we

842
00:32:02,521 --> 00:32:04,022
do here at NIH.

843
00:32:04,990 --> 00:32:06,091
While also

844
00:32:06,091 --> 00:32:09,028
acknowledging that there are
lots of other groups who are

845
00:32:09,028 --> 00:32:09,594
working on

846
00:32:09,594 --> 00:32:12,239
developing these best practices
for return

847
00:32:12,239 --> 00:32:14,633
results and outcomes research
emerge.

848
00:32:14,633 --> 00:32:17,035
And all of us and the Geisinger
Mike code

849
00:32:17,035 --> 00:32:19,437
initiative are things that come
to mind.

850
00:32:20,071 --> 00:32:22,393
And again,
we're learning more and more

851
00:32:22,393 --> 00:32:23,108
data about,

852
00:32:23,108 --> 00:32:24,951
adherence and whether or not
people

853
00:32:24,951 --> 00:32:26,478
are sharing this information

854
00:32:26,478 --> 00:32:28,013
and what are the most efficient
ways

855
00:32:28,013 --> 00:32:30,524
to share information
across across health care

856
00:32:30,524 --> 00:32:31,016
systems?

857
00:32:31,583 --> 00:32:33,620
Might be I'm going to talk a
little bit

858
00:32:33,620 --> 00:32:35,187
about two different programs.

859
00:32:35,187 --> 00:32:36,988
One and of course
it's the federal government.

860
00:32:36,988 --> 00:32:38,790
So we have acronyms for both of
them.

861
00:32:38,790 --> 00:32:41,827
GSP is the genomic Services
research program.

862
00:32:42,160 --> 00:32:44,607
And this is our secondary
genomics

863
00:32:44,607 --> 00:32:45,831
finding service.

864
00:32:45,831 --> 00:32:47,833
So I'm going to start with GSP.

865
00:32:47,833 --> 00:32:49,952
And this is a study that we
that's

866
00:32:49,952 --> 00:32:51,136
currently underway

867
00:32:51,136 --> 00:32:53,319
specifically for people who've
received

868
00:32:53,319 --> 00:32:54,439
secondary findings.

869
00:32:54,940 --> 00:32:57,520
So we are, you know, actively
recruiting people for this

870
00:32:57,520 --> 00:32:57,843
study.

871
00:32:57,843 --> 00:33:00,812
We have about three,
300 people enrolled so far.

872
00:33:01,246 --> 00:33:03,555
And so we are broadly recruiting
people,

873
00:33:03,555 --> 00:33:04,883
across many different,

874
00:33:06,551 --> 00:33:06,952
recruitment

875
00:33:06,952 --> 00:33:08,950
mechanisms to ascertain
secondary

876
00:33:08,950 --> 00:33:10,222
findings recipients.

877
00:33:10,622 --> 00:33:13,859
And we're evaluating adherence
to medical recommendations

878
00:33:13,859 --> 00:33:15,994
and then communications
outcomes.

879
00:33:15,994 --> 00:33:18,563
But we also do some

880
00:33:18,563 --> 00:33:21,499
we do quite a bit of genotyping
of people in the family.

881
00:33:21,499 --> 00:33:24,572
So we'll offer
cascade testing, to relatives

882
00:33:24,572 --> 00:33:25,270
who might

883
00:33:25,270 --> 00:33:26,872
be potentially informative.

884
00:33:26,872 --> 00:33:28,533
And that's
because we want to really

885
00:33:28,533 --> 00:33:29,040
understand

886
00:33:29,040 --> 00:33:33,296
the prevalence of the phenotype
that's associated, with the

887
00:33:33,296 --> 00:33:33,945
variant.

888
00:33:34,246 --> 00:33:37,241
Another way of thinking about
that is your genes say you

889
00:33:37,241 --> 00:33:37,883
should have

890
00:33:38,183 --> 00:33:39,985
fill in the blank condition.

891
00:33:39,985 --> 00:33:42,254
What do you actually have it.
Right.

892
00:33:42,254 --> 00:33:44,706
And I think this is important
from kind of

893
00:33:44,706 --> 00:33:46,691
a population genetics
perspective

894
00:33:46,691 --> 00:33:50,679
because although the the risks
that we quote to people when we

895
00:33:50,679 --> 00:33:51,129
return

896
00:33:51,129 --> 00:33:54,829
these kinds of findings to them
come from a selected population,

897
00:33:54,829 --> 00:33:55,233
right?

898
00:33:55,233 --> 00:33:57,758
So we know that the risk to
develop

899
00:33:57,758 --> 00:34:00,572
ovarian cancer, if you have a
BRCA two

900
00:34:00,572 --> 00:34:03,340
variant is,
you know, 45% across the

901
00:34:03,340 --> 00:34:04,109
lifetime,

902
00:34:04,542 --> 00:34:07,112
that data point comes from,

903
00:34:08,179 --> 00:34:09,114
years and years of

904
00:34:09,114 --> 00:34:11,555
study of people who have
hereditary breast

905
00:34:11,555 --> 00:34:12,717
and ovarian cancer.

906
00:34:13,184 --> 00:34:15,687
And so, you know, there's a
there's a selection bias here.

907
00:34:15,687 --> 00:34:18,857
And so it's really important
to understand, you know, what

908
00:34:18,857 --> 00:34:22,404
might be the actual
what's the actual prevalence of

909
00:34:22,404 --> 00:34:22,961
disease

910
00:34:23,161 --> 00:34:24,465
in people
who get this kind of

911
00:34:24,465 --> 00:34:25,030
information.

912
00:34:25,030 --> 00:34:27,999
So I have a couple cases
to share with you.

913
00:34:27,999 --> 00:34:30,135
Lots of people
that we've seen here at the NIH.

914
00:34:30,135 --> 00:34:31,102
This is a pedigree.

915
00:34:31,102 --> 00:34:32,813
I don't know how many people
maybe

916
00:34:32,813 --> 00:34:34,372
have seen a diagram like this,

917
00:34:34,372 --> 00:34:37,542
but squares are,
men and circles are women.

918
00:34:37,542 --> 00:34:38,944
And our probe end here.

919
00:34:38,944 --> 00:34:41,230
That's the word that we use in
genetics

920
00:34:41,230 --> 00:34:43,281
for the first person who comes
to,

921
00:34:43,782 --> 00:34:47,852
clinical attention is a 43 year
old woman who learned about

922
00:34:47,852 --> 00:34:51,949
a pathogenic variant
in this gene called able B and

923
00:34:51,949 --> 00:34:52,190
B.

924
00:34:52,190 --> 00:34:54,070
This variant in Apob is
associated

925
00:34:54,070 --> 00:34:56,061
with familial
hypercholesterolemia.

926
00:34:56,061 --> 00:34:59,731
Amia which is a genetic form
of high cholesterol.

927
00:35:00,598 --> 00:35:02,385
And she found out about this
because

928
00:35:02,385 --> 00:35:04,469
she had ancestry testing that
she bought.

929
00:35:05,303 --> 00:35:07,505
Many of these companies
who do ancestry testing

930
00:35:07,505 --> 00:35:10,508
will put their offerings
on sale around Thanksgiving.

931
00:35:11,076 --> 00:35:12,243
And that's what happened to her.

932
00:35:12,243 --> 00:35:15,547
So you can choose to,
you can check the box for do

933
00:35:15,547 --> 00:35:17,992
I want to receive health data in
addition

934
00:35:17,992 --> 00:35:20,318
to knowing how Scottish or Irish
I am?

935
00:35:20,986 --> 00:35:22,721
And she was like, oh, it's on
sale. Sure.

936
00:35:22,721 --> 00:35:24,912
I'll go ahead and get that.
And that's when she learned

937
00:35:24,912 --> 00:35:25,390
about this,

938
00:35:26,658 --> 00:35:28,311
result that really increases the
risk

939
00:35:28,311 --> 00:35:29,294
for high cholesterol.

940
00:35:29,294 --> 00:35:31,129
And I know it's really hard to
read here,

941
00:35:31,129 --> 00:35:33,497
and we'll see if the cursor
cooperates,

942
00:35:33,497 --> 00:35:34,833
but you can see, that

943
00:35:34,833 --> 00:35:37,969
you know, I actually don't think
she needed a genetic test to

944
00:35:37,969 --> 00:35:40,021
to tell me this because she has
a brother

945
00:35:40,021 --> 00:35:41,973
who had high cholesterol at age
seven.

946
00:35:42,440 --> 00:35:45,543
Many paternal relatives
who've had heart attacks

947
00:35:45,543 --> 00:35:49,514
and bypass surgery in their 40s
and 50s.

948
00:35:49,948 --> 00:35:53,151
And then, four of her six
children,

949
00:35:53,852 --> 00:35:56,621
were also reported
to have high cholesterol.

950
00:35:56,621 --> 00:36:00,856
So this pattern screams
familial hypercholesterolemia to

951
00:36:00,856 --> 00:36:01,159
me.

952
00:36:01,826 --> 00:36:05,030
But when she enrolled in GSP,
she,

953
00:36:06,631 --> 00:36:10,802
had not really,
this had not been ascertained

954
00:36:10,802 --> 00:36:13,737
by any of the clinicians
that she or her children were

955
00:36:13,737 --> 00:36:14,172
seeing.

956
00:36:14,773 --> 00:36:17,139
So we brought her here to the
NIH,

957
00:36:17,139 --> 00:36:19,644
and I think this is a great
example

958
00:36:19,644 --> 00:36:23,181
of how sharing this information
with people.

959
00:36:23,515 --> 00:36:25,304
And again, we did a lot for this
person

960
00:36:25,304 --> 00:36:26,451
because on our protocol,

961
00:36:26,451 --> 00:36:28,196
we have the ability to bring
them

962
00:36:28,196 --> 00:36:29,888
to the NIH for for phenotyping.

963
00:36:29,888 --> 00:36:32,023
But a home clinician
could have done this too.

964
00:36:32,991 --> 00:36:35,532
Genomic ascertainment
can overcome barriers to

965
00:36:35,532 --> 00:36:36,361
clinical care.

966
00:36:36,361 --> 00:36:39,364
And so this is, a CT scan of her
heart.

967
00:36:39,931 --> 00:36:42,901
And she had the coronary
arteries of,

968
00:36:42,901 --> 00:36:45,537
woman in her upper 70s.

969
00:36:45,537 --> 00:36:48,390
So she had some evidence
of coronary artery calcification

970
00:36:48,390 --> 00:36:48,840
already.

971
00:36:49,541 --> 00:36:52,108
And we started two of her four
children

972
00:36:52,108 --> 00:36:54,412
on treatment for high
cholesterol.

973
00:36:54,412 --> 00:36:57,505
And hopefully that will lower
their risks

974
00:36:57,505 --> 00:36:59,617
of coronary artery disease.

975
00:37:00,385 --> 00:37:02,770
So, again, I think this is just
a concrete

976
00:37:02,770 --> 00:37:03,621
example of how

977
00:37:03,621 --> 00:37:06,191
this can move the needle for
people.

978
00:37:06,191 --> 00:37:09,104
Because it took
even though I would argue that

979
00:37:09,104 --> 00:37:09,294
it

980
00:37:09,294 --> 00:37:12,230
you didn't need a genetic, test
result

981
00:37:12,230 --> 00:37:14,010
to recognize this clinical
entity

982
00:37:14,010 --> 00:37:15,467
in this particular family.

983
00:37:15,900 --> 00:37:17,563
It took having those genomic
data,

984
00:37:17,563 --> 00:37:19,471
genetic data to make that
recognition.

985
00:37:20,472 --> 00:37:22,774
Here's another patient who we've
enrolled.

986
00:37:22,774 --> 00:37:24,808
This is a 36 year old, when she
when

987
00:37:24,808 --> 00:37:27,011
this woman we met her when she
was 40.

988
00:37:27,011 --> 00:37:31,193
But when she was 36, she joined
kind of a biobank study that was

989
00:37:31,193 --> 00:37:31,716
run out

990
00:37:31,716 --> 00:37:35,487
of, historically black
college and university and HBCU.

991
00:37:36,454 --> 00:37:37,972
And she was motivated by
curiosity,

992
00:37:37,972 --> 00:37:39,057
but she also didn't know

993
00:37:39,057 --> 00:37:40,593
very much
about her dad's side of the

994
00:37:40,593 --> 00:37:40,925
family.

995
00:37:40,925 --> 00:37:42,127
And she thought it was really
important

996
00:37:42,127 --> 00:37:44,563
for minorities
to be represented in genetic

997
00:37:44,563 --> 00:37:45,130
research.

998
00:37:45,430 --> 00:37:48,798
And through that, participation
in that study, she learned about

999
00:37:48,798 --> 00:37:49,167
a BRCA

1000
00:37:49,167 --> 00:37:50,735
two pathogenic variant.

1001
00:37:50,735 --> 00:37:53,104
This is a variant
that really increases your risks

1002
00:37:53,104 --> 00:37:54,906
to develop breast and ovarian
cancer.

1003
00:37:54,906 --> 00:37:56,641
Among other types of cancer.

1004
00:37:56,641 --> 00:37:59,344
She met with a genetic counselor
who counseled screening

1005
00:37:59,344 --> 00:38:01,497
enhanced screening through
mammography

1006
00:38:01,497 --> 00:38:02,347
in breast MRI.

1007
00:38:02,981 --> 00:38:06,050
And also her her primary care
provider,

1008
00:38:06,050 --> 00:38:07,915
recognized this
and urged her to have

1009
00:38:07,915 --> 00:38:08,520
mammograms.

1010
00:38:08,520 --> 00:38:10,922
And she actually was diagnosed
with breast cancer

1011
00:38:10,922 --> 00:38:13,174
at age
39 on her very, very first

1012
00:38:13,174 --> 00:38:13,925
mammogram.

1013
00:38:14,759 --> 00:38:18,296
But having this finding, allowed
her

1014
00:38:18,296 --> 00:38:20,685
to really motivated her to find
out much

1015
00:38:20,685 --> 00:38:23,134
more about her dad's side of the
family.

1016
00:38:23,134 --> 00:38:25,803
And so through this,
she was able to reconnect

1017
00:38:25,803 --> 00:38:28,122
with folks on her dad's side of
the family

1018
00:38:28,122 --> 00:38:30,275
and learned about this really
striking

1019
00:38:30,275 --> 00:38:32,933
history
of breast cancer and pancreatic

1020
00:38:32,933 --> 00:38:33,478
cancer,

1021
00:38:33,478 --> 00:38:36,781
which is also associated
with this variant, on her,

1022
00:38:37,048 --> 00:38:39,150
paternal side of the family.

1023
00:38:39,150 --> 00:38:43,621
So again, another example of,
you know, some outcomes

1024
00:38:43,621 --> 00:38:46,237
that can that can come
from sharing this information

1025
00:38:46,237 --> 00:38:46,891
with people.

1026
00:38:47,492 --> 00:38:50,619
Both of these families have been
on different episodes of the

1027
00:38:50,619 --> 00:38:51,029
podcast

1028
00:38:51,029 --> 00:38:51,996
DNA today.

1029
00:38:51,996 --> 00:38:54,165
So if you're interested,
you can find them there.

1030
00:38:55,667 --> 00:38:56,367
And I'm

1031
00:38:56,367 --> 00:38:59,021
going
to wrap up just by talking about

1032
00:38:59,021 --> 00:38:59,370
GFS,

1033
00:38:59,604 --> 00:39:01,908
which is again, the clinical
service

1034
00:39:01,908 --> 00:39:03,508
that Ben alluded to that

1035
00:39:03,508 --> 00:39:07,725
we run to help people return
these findings to, research

1036
00:39:07,725 --> 00:39:08,780
participants.

1037
00:39:08,780 --> 00:39:12,636
And, in 2010,
what was a very was the first

1038
00:39:12,636 --> 00:39:13,084
time

1039
00:39:13,084 --> 00:39:16,430
our group started using Acxiom
in genome sequencing to try to

1040
00:39:16,430 --> 00:39:17,088
understand,

1041
00:39:17,622 --> 00:39:21,292
the causes of some really rare
pediatric onset conditions.

1042
00:39:21,292 --> 00:39:23,028
And I really remember at the
time

1043
00:39:23,028 --> 00:39:24,395
writing the consent form,

1044
00:39:25,196 --> 00:39:28,233
for that study,

1045
00:39:28,233 --> 00:39:31,202
and really thinking about,

1046
00:39:31,202 --> 00:39:33,017
that my,
my position has obviously really

1047
00:39:33,017 --> 00:39:33,371
changed

1048
00:39:33,371 --> 00:39:35,382
because I thought, well, we
can't possibly

1049
00:39:35,382 --> 00:39:36,674
tell these people anything

1050
00:39:36,975 --> 00:39:39,259
other than the cause for their
kids

1051
00:39:39,259 --> 00:39:39,978
condition.

1052
00:39:40,645 --> 00:39:43,147
Because that just seems so,

1053
00:39:43,147 --> 00:39:45,617
seems like so much to to burden
them with.

1054
00:39:45,617 --> 00:39:46,751
Right.

1055
00:39:46,751 --> 00:39:49,287
But again, my thinking
has really, really changed here.

1056
00:39:50,221 --> 00:39:50,822
And I think

1057
00:39:50,822 --> 00:39:53,825
what I'll say is that, you know,
through things like HDFs,

1058
00:39:53,825 --> 00:39:56,327
what we're really doing here
is operationalizing

1059
00:39:56,327 --> 00:39:59,072
this institutional duty here at
the NIH

1060
00:39:59,072 --> 00:40:01,466
to return these kinds of
results.

1061
00:40:01,466 --> 00:40:02,533
And so,

1062
00:40:02,533 --> 00:40:04,047
this is a clinical service
that's

1063
00:40:04,047 --> 00:40:05,837
available to intramural
investigators,

1064
00:40:06,104 --> 00:40:08,395
where we screen sequence data
that's been

1065
00:40:08,395 --> 00:40:10,575
generated through a research
protocol.

1066
00:40:10,575 --> 00:40:12,585
So we don't sequence the people
they can't

1067
00:40:12,585 --> 00:40:14,212
they send us their sequence
data,

1068
00:40:14,579 --> 00:40:16,648
and we look for these acmg
findings.

1069
00:40:16,648 --> 00:40:19,817
And then we help research teams
to clinically validate

1070
00:40:19,817 --> 00:40:22,920
and return these these findings
to people.

1071
00:40:22,920 --> 00:40:26,498
And so I do this so I provide
genetic counseling for these

1072
00:40:26,498 --> 00:40:26,991
people.

1073
00:40:26,991 --> 00:40:27,859
And then we, you know,

1074
00:40:27,859 --> 00:40:29,527
get a family and personal
history

1075
00:40:29,527 --> 00:40:31,195
and then, make a local referral.

1076
00:40:32,163 --> 00:40:32,830
And I think,

1077
00:40:33,831 --> 00:40:35,867
again, the,

1078
00:40:35,867 --> 00:40:40,238
the, the major question
I get when people are applying

1079
00:40:40,238 --> 00:40:42,536
to use the service is, well,
again,

1080
00:40:42,536 --> 00:40:44,309
because the recommendation

1081
00:40:44,309 --> 00:40:47,302
is to do this prospectively,
how should I write my consent

1082
00:40:47,302 --> 00:40:47,612
form?

1083
00:40:47,612 --> 00:40:50,315
And, and can you give our team
some training on consent.

1084
00:40:50,315 --> 00:40:53,885
And I just consenting people to
this is just really not hard.

1085
00:40:53,885 --> 00:40:55,581
I get where you're coming from
because

1086
00:40:55,581 --> 00:40:57,188
I used to think it was really
hard.

1087
00:40:57,488 --> 00:40:59,915
But again,
thinking about the build up to

1088
00:40:59,915 --> 00:41:00,625
this point,

1089
00:41:00,625 --> 00:41:02,860
most people really want this
information.

1090
00:41:02,860 --> 00:41:06,631
Most people think
that genomic data are important,

1091
00:41:06,931 --> 00:41:08,973
and I don't know that we
necessarily need

1092
00:41:08,973 --> 00:41:10,268
to think about consenting

1093
00:41:10,268 --> 00:41:12,221
for receipt of genetic test
results

1094
00:41:12,221 --> 00:41:13,338
that much different

1095
00:41:13,338 --> 00:41:15,106
than any other kind of health
data.

1096
00:41:15,106 --> 00:41:17,042
So I'm just just think to
yourself,

1097
00:41:17,042 --> 00:41:19,310
if you have ever, you know, had
a doctor

1098
00:41:19,310 --> 00:41:21,403
order like a lipid panel or
hepatic panel,

1099
00:41:21,403 --> 00:41:23,047
have they ever spent a long time

1100
00:41:23,047 --> 00:41:26,027
talking with you about like,
so here's what we're going to

1101
00:41:26,027 --> 00:41:26,284
find

1102
00:41:26,284 --> 00:41:29,052
and we might find this also,
I think it's just kind of

1103
00:41:29,052 --> 00:41:29,821
implied right.

1104
00:41:30,922 --> 00:41:33,533
And so, you know, to wrap
up, what I'll say here is that,

1105
00:41:33,533 --> 00:41:33,991
you know,

1106
00:41:33,991 --> 00:41:38,214
this has been, there have been
some implementation challenges

1107
00:41:38,214 --> 00:41:38,629
here.

1108
00:41:39,897 --> 00:41:41,424
And I think these are important
things

1109
00:41:41,424 --> 00:41:42,066
to think about.

1110
00:41:42,066 --> 00:41:44,076
So, you know, there are some
individual

1111
00:41:44,076 --> 00:41:45,570
attributes about participate

1112
00:41:45,803 --> 00:41:48,895
that can make this process
of returning researchers

1113
00:41:48,895 --> 00:41:49,440
research

1114
00:41:49,440 --> 00:41:53,463
genetic results challenging,
things like their age or their

1115
00:41:53,463 --> 00:41:54,145
wellness.

1116
00:41:54,145 --> 00:41:57,648
If these people come from a kind
of a frail population and been,

1117
00:41:58,649 --> 00:42:02,520
Scott mentioned this earlier,
and then finding local referrals

1118
00:42:02,520 --> 00:42:05,733
for research participants
even in the US is not easy,

1119
00:42:05,733 --> 00:42:06,157
right?

1120
00:42:06,157 --> 00:42:08,349
Like there is not a
cardiovascular

1121
00:42:08,349 --> 00:42:10,928
genetic test in, you know,
within a 200

1122
00:42:10,928 --> 00:42:12,915
mile radius of people living in
places

1123
00:42:12,915 --> 00:42:14,432
like Idaho and North Dakota.

1124
00:42:15,099 --> 00:42:17,554
But I think the major concern
here

1125
00:42:17,554 --> 00:42:19,504
is recon targeting people.

1126
00:42:19,504 --> 00:42:22,208
And so, you know,
I think the biggest lesson

1127
00:42:22,208 --> 00:42:23,007
learned here

1128
00:42:23,007 --> 00:42:25,750
might be to just the way
you can make this move go

1129
00:42:25,750 --> 00:42:26,244
smoothly

1130
00:42:26,444 --> 00:42:29,080
is to maintain contact
with your research participants

1131
00:42:29,080 --> 00:42:32,083
so that you're able
to get to them, later in time.

1132
00:42:32,650 --> 00:42:34,963
So just some very brief
anecdotal data

1133
00:42:34,963 --> 00:42:37,155
about kind of what we found in
CFS.

1134
00:42:38,523 --> 00:42:40,986
The clinical
genetic testing I think is, is

1135
00:42:40,986 --> 00:42:41,559
uncommon.

1136
00:42:41,559 --> 00:42:45,062
So another benefit of doing
this whole thing,

1137
00:42:45,663 --> 00:42:47,761
is that many people, like the
family

1138
00:42:47,761 --> 00:42:49,801
with familial
hypercholesterolemia

1139
00:42:49,801 --> 00:42:52,951
that I presented
earlier have signs of what's

1140
00:42:52,951 --> 00:42:53,371
going

1141
00:42:53,371 --> 00:42:56,474
on, and they are not
getting diagnostic testing.

1142
00:42:56,474 --> 00:42:58,222
So if we look across, you know,
the,

1143
00:42:58,222 --> 00:43:00,311
the people who we've done this
for in CFS,

1144
00:43:00,645 --> 00:43:03,328
about 25% report a clinical
phenotype

1145
00:43:03,328 --> 00:43:06,083
or diagnosis, but no genetic
testing.

1146
00:43:06,417 --> 00:43:07,618
And that's not great, right?

1147
00:43:07,618 --> 00:43:09,484
Because if they've got a
clinical

1148
00:43:09,484 --> 00:43:11,689
diagnosis, maybe, you know, they
think

1149
00:43:11,689 --> 00:43:13,077
there's it's important to kind
of

1150
00:43:13,077 --> 00:43:14,759
have the molecular background
for that.

1151
00:43:15,326 --> 00:43:18,229
And about 50% of people
who we talked to report

1152
00:43:18,229 --> 00:43:20,040
a consistent personal or family
history

1153
00:43:20,040 --> 00:43:21,666
that's consistent with the
variant

1154
00:43:21,899 --> 00:43:25,002
without a clinical diagnosis
or genetic testing.

1155
00:43:25,002 --> 00:43:27,572
So, in conclusion, I think, I
hope

1156
00:43:27,572 --> 00:43:29,803
I've convinced you that most
people want

1157
00:43:29,803 --> 00:43:31,309
this information and that,

1158
00:43:32,310 --> 00:43:32,910
there's the

1159
00:43:32,910 --> 00:43:35,238
potential for some really good
outcomes

1160
00:43:35,238 --> 00:43:36,013
that result,

1161
00:43:36,013 --> 00:43:39,016
when we do this,
but we need to study this more.

1162
00:43:40,051 --> 00:43:42,086
And again,
I think we do have the potential

1163
00:43:42,086 --> 00:43:43,955
to improve outcomes for
participants.

1164
00:43:43,955 --> 00:43:46,756
But the biggest, again, lesson
learned for me is that this

1165
00:43:46,756 --> 00:43:47,191
requires

1166
00:43:47,191 --> 00:43:50,871
ongoing investment
on the part of institutions,

1167
00:43:50,871 --> 00:43:51,262
who,

1168
00:43:51,262 --> 00:43:52,846
you know, are thinking about
this

1169
00:43:52,846 --> 00:43:54,765
institutional duty to do this,
but also

1170
00:43:55,032 --> 00:43:58,569
on, you know, participants,
we do put a burden on them

1171
00:43:58,569 --> 00:44:01,906
to follow up with the,
you know, recommendations that

1172
00:44:01,906 --> 00:44:02,473
we make.

1173
00:44:03,007 --> 00:44:05,498
But also, you know, individual
researchers

1174
00:44:05,498 --> 00:44:06,210
to maintain

1175
00:44:06,210 --> 00:44:09,213
contact with their participants
to facilitate this.

1176
00:44:09,213 --> 00:44:11,087
But again, I think this has the
potential

1177
00:44:11,087 --> 00:44:12,550
to close gaps in clinical care.

1178
00:44:12,550 --> 00:44:16,387
And I think we're highlighting
some real systemic challenges,

1179
00:44:16,921 --> 00:44:18,322
and alleviating some of those.

1180
00:44:18,322 --> 00:44:20,136
So a lot of people have
contributed to

1181
00:44:20,136 --> 00:44:22,093
some of the work that I
presented today,

1182
00:44:22,093 --> 00:44:23,194
and I'm very grateful to them.

1183
00:44:23,194 --> 00:44:25,496
And you can find all my
references here.