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Here I really enjoyed the stimulating
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discussions Yeah so far and hopefully I
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can follow suit within equally
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interesting set of discussions and what
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I wanna do is talk to you about my
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experience invasion in doing target in
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clinical trials though was that
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leverage genetics and you know makes I
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had a lot of discussions with different
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regulatory sees in united states most
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notably the FTA a that's such trials
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I'll you some examples of trials than
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that actually made it way make their
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way through the FTA a bureaucracy yeah
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all of the focus on the micro by what I
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think again we get taste for the sort
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of study that I believe could be
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pursued with the micro by nutrition in
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particular here the topics I'd like to
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talk about first all give a brief
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overview that none of you were
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strangers to an individualised
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personalised Madison in nutrition focus
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on genetic diversity and identifying
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the genetic basis if you've infinitive
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pick expression this'll be crucial to
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later point something my talk summarise
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to the degree possible much of the
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discussion so far on the micro by was a
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marker of health and dietary change
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then hey might not having to do with
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the end of one studies their design
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implement he Shannon extensions and one
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study then talk importantly about a new
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directions and then once the that to do
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with aggregated and one studies and how
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these studies really don't test
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interventions so much as an algorithm
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for matching gentlemen profiles to
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specific interventions and this is a
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topic again that's on the minds of many
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people in the united states of their
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relevant regulatory agency. So here's
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the fee of my talk the unique genetic
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and biochemical individuality humans
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suggested optimal nutritional demands
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for health maintenance may have to be
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tailored to each person is unique
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biological exposure profile again no
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one in this room is a stranger to the
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sort of thing. So can question one that
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has come up in previous speakers talked
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is what is the role of the microbe I'm
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an optimising health via nutritional
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dietary manipulations how can one
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leverage the micro by what is the proof
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that it's actually meaningful. We could
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ask is being like a by a lot of of
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being the type for assessing relevant
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changes attributable to dietary
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manipulations could be treated as a
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surrogate endpoint for nutritional
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impact just requires a affordable
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questions to be addressed to answer
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that is what is a healthy my combine
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could be a therapeutic target that is
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we could manipulate species composition
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with and they got to list some of that
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market is therapeutic angle in and of
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itself that as we could use legal
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transplants to actually put in certain
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species into the gods of others and
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hope to listen to change. So first are
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personalised Madison some time ago
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nature biotechnology pass the question
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is personalise Madison finally arriving
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in fact textbooks written at hitting
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the the use personalised Madison than
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that practise for years later nature by
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biotechnology ask the question what
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happened to personalise minutes I think
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what happened to personalise Madison is
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there wasn't enough proof to show that
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actually worked. So the question is how
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does one has the personalised treatment
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actually works. The same could be said
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personalise nutrition. Now there's many
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different vehicles advocating for the
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user personalised interaction fact the
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recent paper describing the growth in
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publications alone describing neutral
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genetics the belief you can tailor and
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I someone's uni do you profile but
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again the question is how does one test
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the personalised I actually works. So
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before I get into the design of studies
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and then I wanna give some background
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for why genetics is so important in the
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construction relevant clinical trial.
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So as many speakers appointed up before
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the cost of you know sequencing of
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going down tremendously making it
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possible in this teenage to see once
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entire human genome. And fact work with
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them and craig better was the first
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person to see once an individual gina.
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And that that time it costs probably
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five hundred million dollars now I can
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see what's singled you know for about a
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thousand bucks how does one identify
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genetic variations actually influenced
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rates and diseases that one of anyone's
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intelligence but I wanna walk you
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through the basic methodology here.
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That's why can convey the results of
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such studies often some meaningful way
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on the later. So essentially how this
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works as one collects the number of
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people with the disease and without a
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disease and sequences. And then looks
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for variations things that separate the
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C once between the case control. And
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tries right then to fire variant that
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is more frequent among the people with
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the disease than without it certain
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statistical criteria are met you
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couldn't or that you've identified a
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genetic variance that is just also you
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did with the disease. And hopefully in
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a car so it doesn't always work that
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way that it's not quite as easy as that
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might seem typical there are variations
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that are real to real change phones the
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biologic the significance of which is a
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no however if what one can do. This
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what I assume that the variations there
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are variations on one we can use it
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once it's actually impact the
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functioning of certain genes then one
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could argue that that collection of
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where eric's seen in the cases.
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contributes to disease susceptibility
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So it's not the case that only comment
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variations are likely contributed
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disease it could be the collections of
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where variations each with the small
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affected but collectively of a large
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effect could influence a disease. And
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there's always to identify both common
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where variance there associated with
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disease in fact many diseases head unit
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variance identified they're and would
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be associated with their express. This
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is what is known as a Manhattan plot.
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"'cause" it reflects the Manhattan
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skyline. But ultimately what it
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consists of is on the Y axis the
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strength of the association between
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genetic variations whose positions are
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given along the X axis. So each little
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dot here corresponds to the location on
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the human genome of a genetic variance
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that is tested for association with
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particular disease yeah the string and
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the association is given in this
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instance by the minus log ten P value
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that's that a higher value indicates a
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stronger association no statistical
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thresholds have to be set. So that you
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don't just identified variance that for
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all intents and purposes are noisy and
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not reflective of to signal. So one has
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this past very rigorous statistical
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criteria in order to make the claim.
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that are very isn't fat associated And
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that has to do with the fact that
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you're testing thousand that tens of
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thousands if not millions of variance
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or association if you do a study genome
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why today in the slightest little data
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but it's the most up to date and to
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offer a number of genetic variations of
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identified who's locations are depicted
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on each one of these little cartoon
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representations of the genome. And
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these are different diseases that the
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variations are known to be associated
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with and they all need this very
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rigorous just make you so we can say
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that these variations so on. local
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association with a particular disease
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So for example variations on from
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someone into influence certain
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digestive system diseases variance on
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from us arms and say eleven and twelve
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influence bottom intervene types so
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some five hundred to a thousand
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different variations then identifying
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unequivocally associated with certain
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diseases yeah as a result having
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multiple variance identified that are
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associated with any disease. It's the
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case that some diseases are what are
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known as apologetic that is we have
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many many parents that can right most
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human Tina types are influenced by many
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genetic variance apologetic models
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implicate many many variant speech with
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weak effect but collectively have a
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large effect one can characterise these
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power eugenics affects by using
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appropriate statistical methods. So
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what one can ask is not are there
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particular individual very that
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influence a disease. But are their
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collections of areas that influence of
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disease suggesting again at that rate
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might be probably agenda about
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contribute to some of the literature
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describing how one can do that. But
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this does not mean that there are
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variants with large effects just that
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there may be a large number of variance
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with small affects the modify the
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influence of the gene with the large
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artifacts most diseases are
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heterogeneous as a result with some
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manifestations to do a single variant
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with a large effect. And other
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manifestations of that disease in due
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to a large number of Tina their lives
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the case this is the case because back
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local in genetic networks don't work
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the simple linear that as you probably
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know in fact most genetic networks and
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replete with feedback and redundancy
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mechanisms such that if there is one
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particular gene that is or maybe the
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system could make up for it but name
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not that there's a perturbation at a
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different site. So what is not to be
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going on with many diseases of the
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genetic level is there that work since
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is a very simple that we can probably
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not reflective of any diseases so the
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network you can see that there's one
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central gene that preacher is gonna
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upset the functioning of the entire
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network if we see this sort of
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situation arise perspective disease
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then it's probably monotonic simple
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perturbation here "'cause" as the
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collapse of the entire system leading
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to a disease you could also be the case
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that the networks are wired a little
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differently such that you need to
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mutations are variations in jeans but
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for the system as a whole collapses. So
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this would be consistent with what
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people refer to as apologetic diseases
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pathogenic diseases are such that you
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need multiple perturbations dipper down
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the system and lead disease. So anyone
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disease may have margin forms a legion
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like forms apologetic for yeah before
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going to the the the upshot of genetic
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heterogeneity user describe one talk a
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little bit about the genetic diversity
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of human populations in here I just
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want to rehearse the out of Africa
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about this you get I don't mean to
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offend anyone's intelligence they just
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like the walking through this. So get
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some appreciation for some of the
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statements and then make later. So
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what's going to happen sometime ago is
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there a number of individuals that were
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populating Africa in these coloured
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symbols here just represent different
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genetic variations there were present
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at the time I think migration out of
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Africa some subset of people living in
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Africa at the time decided to move to
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the middle east. So what's important to
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keep in mind is that it wasn't everyone
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in Africa who ultimately made the way
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to the middle east only some subset
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that suggest that only some subset of
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mutations are genetic variations were
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present in that at the time all we need
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a way into the middle east at some
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point a little later some people in the
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middle east decided to move to europe.
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And only a subset of the genetic
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variations that made their way out of
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Africa into the middle east only made
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where you're this has important car
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once is for the contemporary standing
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genetic variation in the population it
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suggests that in non african
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communities only the migrant the types
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of you represent could also suggested
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since there's more recent populations
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in Europe and the middle east and of
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course other parts of the world that
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resulted from this migration out of
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Africa there'd be less time for
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selection to wash away the deleterious
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effect the genetic variations that
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could cause disease essentially you
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might see certain forms disease causing
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various a higher frequency and then I
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in populations like maybe someday is
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yes this is and true also because there
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were smaller populations that founded
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these not african communities there'd
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be a likely greater you can see of home
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as a gossipping. And that has to do
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with S phenomena of random inbreeding
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when you're in a small finite
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population you have a small number of
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makes to choose from so the probability
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that you're gonna make with someone who
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is related to some level is high if you
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take huge population with a large
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number of minutes than the probability
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of that occurring smaller. So we
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actually did a study with complete you
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know max about five or six years ago
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where we sampled hold you notes from
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individuals throughout different parts
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of the world and just ask the question
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how diverse these you know more with
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respect to functional content that is
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very issues that might cause disease.
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And this was published again a little
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while ago first we have to make lame
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that the variations were likely to be
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functional that is cause disease. So
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how could one do that if we had
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millions of variations which in fact we
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did sit for music computational
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techniques and these are widely
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available this is just according to the
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gene and all the elements in and around
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the gene in but are are likely to cause
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between the dysfunction and hence be
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functional and could lead to disease.
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with computational techniques we can
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make claims about genetic variation You
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know all these parts in that you know
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so what we did was take all those times
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we had a disposal run these programs on
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it and just catalogue number
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deleterious or likely disease causing
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mutations or variations of the present
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almost you know and then contrast or
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frequency across the different
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population groups that we yeah so we
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get this maple bar chart showing what
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we found easier african populations we
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have for our populations european
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population ageing papa patient into
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that next populations in indian
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population and a mexican population
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first thing you can see is that there's
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a greater number of just genetic
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variations in total along yeah african
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individuals which is consistent with
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what we knew about this migratory
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pattern out Africa was the oldest
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population it was the most diverse the
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time. So we see a greater number of
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variations. Now we see that there's
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almost a six million variations as in
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the average african americans african.
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you know and that's because the way we
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quantified the presence of the variance
00:14:56
was not comparing them to other human
00:14:58
genome that actually she T know now why
00:15:01
we do that. That's okay contrasting
00:15:03
different global populations and would
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make sense to use as a reference a
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genome for any one of those
00:15:09
populations. So we ask how many
00:15:11
variations were present in each one of
00:15:13
these you know that we have the
00:15:14
radiated from the change you know so
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it's a fair comparison so we saw a six
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million or so variance present yeah and
00:15:21
you know and substantially less in the
00:15:25
manner you know consistent with this
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hypothesis. Now if we counted up the
00:15:29
number of their dollar here is
00:15:31
variations ever present in the genome
00:15:33
you can see that as a result of there
00:15:35
being a greater frequency of just
00:15:37
variations overall and there and you
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know there's a greater frequency
00:15:40
deleterious are likely disease causing
00:15:42
variations in the and you know however
00:15:46
if we ask how many homicide gets Gina
00:15:50
types there in the different
00:15:51
populations that we can see that
00:15:53
despite the fact there's a greater
00:15:55
number of overall variations in the
00:15:57
african populations there's actually
00:15:58
last as a gas. And again that's
00:16:01
consistent with what we know about the
00:16:03
migratory patterns as a result of there
00:16:05
being greater homozygous genotype so
00:16:07
the non african populations. There's a
00:16:09
greater frequency and damaging likely
00:16:12
disease causing variations that are
00:16:13
homozygous no not african populations
00:16:16
what to suggest is that on a global
00:16:18
scale humans are quite diverse just
00:16:22
think that each individual might have a
00:16:23
unique mix the disease and heart
00:16:25
disease causing very very when you
00:16:28
wanna individualised treatment for
00:16:30
someone's a disease. Yeah there armada
00:16:34
genetic disease. that a correct the
00:16:36
ball through nutritional therapy Some
00:16:38
of these diseases go by the way of
00:16:41
inborn errors of metabolism many you
00:16:44
probably familiar with these so for
00:16:45
example if K use a classic if someone
00:16:48
is born with the PKU mutation the way
00:16:50
to correct potential deleterious effect
00:16:53
that station is to treat the newborn
00:16:55
with Ida there's actually a large
00:16:58
number of efforts in different
00:17:00
communities one in the Amish mennonite
00:17:02
community these diseases or screen and
00:17:04
it was also on Sir you given to the
00:17:07
idea up to the people that the have
00:17:09
been shown to have these mutations
00:17:10
insist no then retail. So again this is
00:17:13
sort of an example about obvious you
00:17:15
originally example. Now the micro by
00:17:18
and sports that'd be nice people but an
00:17:19
interesting question is you want
00:17:21
actually measure the like a mile and
00:17:23
individuals with PK you would they see
00:17:25
any differences in the Michael by
00:17:27
knowing that there are certain it's a
00:17:29
pretty pronounced nutritional
00:17:30
deficiencies. I don't you sequencing
00:17:33
experiments just to let you know a
00:17:35
little bit about what the results are
00:17:37
are just beginning to that I think are
00:17:38
the most interesting after this
00:17:40
setting. So we see points to the genome
00:17:44
to individuals severe anorexia this is
00:17:47
a colleague okay in the back row
00:17:49
responded by a channel prices also in
00:17:51
the back of the room and what we found
00:17:53
is there was a very that was more
00:17:55
frequent among anorexic if the HX two
00:17:58
that is involved in fact that
00:17:59
metabolism in may be consistent with
00:18:02
some of the food cover versions the
00:18:04
anorexic have so sequencing can shed
00:18:07
light on a whole Lou of a very
00:18:09
interesting diet related factors. So
00:18:13
one study having to do with anorexia we
00:18:17
also see once among other individuals
00:18:19
to superset variance asking that one
00:18:22
and why did these individuals lives
00:18:24
alone. So we took their genome we
00:18:25
sequenced them we try I define unique
00:18:28
variations that makes explain why they
00:18:30
had some protective mechanisms why they
00:18:32
did not a disease that they have very
00:18:34
officially beginning care capacity
00:18:37
these sorts of questions what we found
00:18:40
were in this is a hard to chart to read
00:18:42
is that there were in fact some unique
00:18:44
variations present in bowls percent
00:18:46
variance that we sequence of the
00:18:48
biological signal can see these
00:18:49
variations is still a up for debate
00:18:52
what we're working on so sequencing
00:18:54
studies Spanish have a lifetime unique
00:18:56
features that individuals have that
00:18:58
might be contributed toward to their
00:19:01
health or disease that there's one of
00:19:04
the most important studies that like
00:19:05
our bring your attention I don't know
00:19:08
how many people or where to various
00:19:10
publications like tense back calling in
00:19:12
the UK where they look to genetic
00:19:14
influences and metabolite levels like
00:19:17
metabolite levels in humans. So what
00:19:20
they found was that there were many
00:19:23
spots on the human genome we're genetic
00:19:26
variations were present in something
00:19:28
the jewels it influenced the levels of
00:19:31
metabolites found in those individual
00:19:33
these are all locations and you know
00:19:37
harbouring variants that influence
00:19:39
different metabolite levels some were
00:19:42
associated with diseases other sorts of
00:19:44
process ease. They showed quite
00:19:48
strongly that these variants were in
00:19:51
fact influencing the levels and
00:19:54
metabolites a couple were very
00:19:57
interesting so for example there were
00:19:59
twelve variance there identified it
00:20:02
influence penetrate the fan production
00:20:04
in biology that we have speaker
00:20:06
yesterday talk about the use of trip
00:20:09
yeah yeah mediating certain diseases.
00:20:14
So the upshot of this paper is
00:20:15
inherited variance influence metabolite
00:20:17
levels many these metabolites or
00:20:20
disease related in fact they could
00:20:21
explain my individuals are susceptible
00:20:23
to disease. Because these variants
00:20:25
influence the level the metabolite that
00:20:27
metabolite is then essential for
00:20:30
certain activities a leading to disease
00:20:32
if there's a depletion say that that
00:20:34
have like a sunset these metabolites
00:20:37
might also be influenced by the micro
00:20:39
by in this is crucial. But also
00:20:42
contributes to greater human
00:20:43
individuality. So jump to a different
00:20:47
topic the microbe I'm as a marker of
00:20:49
health and dietary change I'm gonna
00:20:51
summarise some of the things from the
00:20:53
literature that other speakers have
00:20:54
talked first microbiology is terrible
00:20:59
this was shown by a doctor the boss
00:21:01
suggesting that models we got twins the
00:21:04
more similar like a buyer profile Liza
00:21:06
Goddard twins suggesting that ability
00:21:09
up to the micro by a profile yeah I've
00:21:11
been involved in research looking at
00:21:12
different Michael Myers here's one
00:21:15
looking at the world microbiology these
00:21:18
this eight here is just they Herod
00:21:20
ability it goes from zero to whatever
00:21:23
percent. So anything close to gonna
00:21:24
percentage yes there is a strong
00:21:26
genetic basis for the species a
00:21:30
proposition anymore or Michael by a
00:21:33
highly significant. So this is variance
00:21:37
as we discussed earlier a good
00:21:39
identified that influence the micro
00:21:41
buyer well so here to we have to take
00:21:45
to jean and he really like group had
00:21:47
been shown to influence or we
00:21:49
associated that with my combine
00:21:50
profiles a good question then is how
00:21:54
stable is microbe I mean this was
00:21:56
discussed earlier where in certain
00:21:58
disease states the micro by is less
00:22:01
stable. And in others it appears to be
00:22:03
more stable. But a good question is but
00:22:06
since the micro wireless table or is it
00:22:09
the case that the micro by so stay
00:22:11
stable that you can change and yeah
00:22:12
obvious answer that question is no
00:22:15
because the microbe I'm is in fact
00:22:16
associated with many many health
00:22:18
related factors including the diet as
00:22:21
has been discussed that this was a
00:22:23
study published while while ago first
00:22:25
look at the Michael by mystical back
00:22:28
there. But also in humans suggesting
00:22:30
the individuals with different food
00:22:32
intake at different like a mile
00:22:34
profiles a very interesting study that
00:22:37
again wasn't mentioned by the previous
00:22:39
speakers published published recently
00:22:42
looked at the got microbiology on a
00:22:44
daily basis for two individuals. So
00:22:47
these are the micro by a profile so to
00:22:49
individuals power over an entire year
00:22:51
what was interesting is there were
00:22:53
certain points at which the microbe I'm
00:22:55
change dramatically they could be
00:22:57
attributed to a particular "'cause"
00:22:58
this individual lived abroad and you
00:23:00
can see that the microphone and change
00:23:02
substantially the other individual
00:23:04
apollo had a diary real illness and
00:23:07
that course substantially change that
00:23:08
individuals Michael by one thing that
00:23:11
was interesting from the study is
00:23:12
because they had three ring sixty five
00:23:14
data points but the microbiology. And
00:23:17
they also kept track of dietary or food
00:23:19
intake. They could correlate certain
00:23:21
nutrients with my combine profiles and
00:23:25
found a number of significant
00:23:26
correlations suggesting that food
00:23:28
intake actually did influence them I
00:23:31
combine in single subjects another
00:23:35
interesting study had to do with the
00:23:37
diversity of the species that populate
00:23:39
again. But the stability of them motel
00:23:42
in the same individuals this suggests
00:23:46
that although there might be species to
00:23:48
develop some product to the formation
00:23:50
of the metabolite there could be other
00:23:51
species also contribute to the
00:23:53
formation of the metabolite such that
00:23:55
you could have a say metabolite level
00:23:57
despite the fact you have differences
00:24:00
in the species that a really I we also
00:24:03
for earlier about the influence of
00:24:07
endogenous processes that influence
00:24:10
metabolite levels. So it could be that
00:24:13
humans ability to synthesise an certain
00:24:15
cab light also influences metabolite
00:24:18
levels in suspicion in a few a papers
00:24:23
over the years use one by Gary squeeze
00:24:25
that former colleague of mine fields
00:24:26
strive to show that at least for if and
00:24:30
levels there were two sources there was
00:24:32
the source of the to that and being
00:24:36
synthesised in the Lever and also I'm
00:24:40
sure the fancy produced by the species
00:24:42
that populate the guy in rats and they
00:24:44
found a number of metabolites ones that
00:24:46
this is the case suggesting that again
00:24:49
there was an endogenous and exact
00:24:51
source for at least on account. like
00:24:53
creating variation that might be quite
00:24:56
broad minded individual basis and again
00:24:58
this is consistent with what we know
00:25:00
about genetic influences a metabolite
00:25:02
levels as I described earlier. So what
00:25:05
may influence metabolite levels are of
00:25:08
inherited genetic factors plus
00:25:10
acquiring by law So you can imagine the
00:25:13
following situation someone is a
00:25:15
genetic variation because of them to
00:25:17
have Lauren average for the levels they
00:25:20
don't have the appropriate I would
00:25:23
include trip if and they can't make up
00:25:25
for that this deficiency and hands that
00:25:27
could lead to disease. They could be
00:25:29
corrected by giving the person more
00:25:31
trip trips the latest installation of
00:25:35
the human microbiology project is gonna
00:25:37
collect longitudinal data to try to put
00:25:38
this together as many you know and this
00:25:41
is being pursued for a couple of
00:25:42
different diseases. So the longitudinal
00:25:44
data really tell us a lot about how
00:25:47
changes the micro by might be impacted
00:25:50
right dietary changes and vice versa so
00:25:54
that mind I wanna talk about how one
00:25:55
can design studies just sorta relate
00:25:57
dietary changes changes in the micro by
00:26:01
now focus on end of one studies in
00:26:03
describing the through a simple
00:26:05
example. So let's say that you wanna
00:26:08
treat someone for hypertension you
00:26:11
measure their what pressure you down
00:26:13
some sort of baseline you then provide
00:26:16
a banana pretences even yeah the drug
00:26:20
appeared to work a normal blood
00:26:22
pressure you might watch the law that
00:26:24
drug and give them another drug. And
00:26:26
see a lower blood pressure in this some
00:26:28
number of times. So the end of the day
00:26:30
you could say objectively whether or
00:26:32
not that drug actually more you could
00:26:35
imagine another individual who was
00:26:39
created in exactly the same way we had
00:26:40
a different profile such that a
00:26:43
different computer work leading it. So
00:26:46
the bottom line is in designing these
00:26:48
studies the idea is to try to find the
00:26:50
optimal treatment for that individual
00:26:52
in an objective as way possible yeah
00:26:56
when design these studies using all the
00:26:58
stitches technology there's been thrown
00:27:00
at large scale phase respect. So you
00:27:04
can leverage randomisation you could
00:27:07
use washout up here you could blah well
00:27:10
the patients and the subjects to what
00:27:12
drug around to be as objective as
00:27:13
possible you could use sequential or
00:27:15
their designs and you could also try to
00:27:18
assess multivariate being types that
00:27:20
was a number of issues associated with
00:27:22
these studies the most pronounced is
00:27:24
serial correlation between the
00:27:26
observations since you collectively
00:27:28
observations over time we have account
00:27:29
for the fact could imagine time one is
00:27:32
correlated with the measure time to
00:27:33
don't necessarily have to do that in a
00:27:35
lot population based whilst there could
00:27:38
be carryover that's sofa takes some
00:27:40
time for drug would be washed out of
00:27:42
the body you can switch to a person
00:27:45
over two different drummer facts of the
00:27:46
previous drug could be lingering and
00:27:49
"'cause" the phenotype to look is it to
00:27:51
make it look as though the second right
00:27:53
was in fact having some effect there's
00:27:56
a few other issues that there's a
00:27:58
number of extensions one I'm gonna
00:27:59
highlight later having to do with
00:28:00
aggregated and of one trials kind of
00:28:03
one trials have been around for a
00:28:04
while. And have in fact pursue for a
00:28:07
number of different diseases there are
00:28:10
issues in their design all this talk
00:28:11
about these now you could completely
00:28:14
randomised assignment such that the
00:28:16
order in which the drugs are given or
00:28:19
the interventions are given is right
00:28:21
reminds you to block random assignment
00:28:24
random assignment of the of a different
00:28:26
sort non random assignment you could
00:28:29
also ask question how many periods
00:28:31
would you need in which you actually
00:28:33
administer the different interventions.
00:28:35
So should you use five periods followed
00:28:37
by factories than other drug we're go
00:28:39
to to to to these can all be discussed
00:28:44
one thing that we've done is look to
00:28:46
see how efficient different designs are
00:28:48
with respect and one studies asking the
00:28:51
question what is the power these
00:28:53
studies if one decided to use different
00:28:55
design. So maybe we could try a only
00:28:58
two alterations of a particular drive
00:29:01
or maybe four and we could ask the
00:29:03
question again how powerful these
00:29:05
designs would be in what we could show
00:29:07
is that if you break out the studies in
00:29:10
certain ways. I could have greater
00:29:13
power. There's different applications
00:29:15
for of one studies again some of these
00:29:17
I've been brought to the attention of
00:29:18
the FDA centre up the being discussed
00:29:22
at a recent conference a couple years
00:29:24
ago one use that is being discussed
00:29:29
atlanta. I has to do with using them to
00:29:32
re report constructs many drugs is you
00:29:35
know Diane phase two because there
00:29:37
don't show the the appropriate human
00:29:39
biological relevance. So if you had
00:29:41
individuals we you call to a large
00:29:44
amount of we need to pick data you
00:29:45
could come to the conclusion that the
00:29:47
drugs actually having a by logical
00:29:48
effect. So these designs are actually
00:29:51
gaining traction with the number of
00:29:53
companies as well as we have here yeah
00:29:57
yeah to collect the appropriate data to
00:29:58
make the claim that your interventions
00:30:01
actually in some sort of the fact that
00:30:03
could be evaluating the micro by I'll
00:30:05
but can also be evaluating all sorts of
00:30:06
other phenotype for which modern
00:30:08
wireless devices could use. So for
00:30:10
example you could use a continues
00:30:13
glucose monitor to monitor levels
00:30:15
insulin in diabetics change different
00:30:18
treatments and see if in fact
00:30:19
treatments have a better or worse in
00:30:21
fact I can direct six you could use
00:30:24
weight gain scales different ways of
00:30:28
assessing a body mass not to see if in
00:30:31
fact interventions having any effect
00:30:33
whatsoever a BC activity levels are
00:30:37
obvious I think pursue hypertension
00:30:40
discontinuous blood pressure monitoring
00:30:42
could be used I believe that the real
00:30:45
value in these studies just collect as
00:30:47
much information as possible including
00:30:49
the mood and anxiety another levels
00:30:51
psychological profiles of individuals
00:30:53
undergoing these trials there's a lot
00:30:56
of motivation to pursue these trials
00:30:58
many people are actually tracking their
00:31:01
health status using such devices the
00:31:03
whole movement quantified self movement
00:31:06
other leverages these devices to make
00:31:08
claims about their health you actually
00:31:11
conducted through these trials of this
00:31:13
could you one having to do with the
00:31:15
benefits of use we randomised two
00:31:19
different treatment wanted directly and
00:31:21
one an ace inhibitor it looks as though
00:31:24
the ace inhibitor lower blood pressure
00:31:26
to a greater degree that than the
00:31:28
direct in this example however that
00:31:31
wasn't quite the case because on the
00:31:33
second administration oh the operation
00:31:37
here are the individual lost a large
00:31:39
amount of weight. So we couldn't
00:31:40
attribute the reduction in blood
00:31:42
pressure to the actual trial rather to
00:31:44
weight loss. We actually use these
00:31:47
studies in gene based trial not so long
00:31:50
ago I worked as a couple of former
00:31:52
postdoc said mine to sequence the
00:31:54
genome of the girl within thinking
00:31:56
about the condition get a condition
00:31:57
that the fighters you guys analysis. So
00:31:59
we sequence the genome to see if we
00:32:01
couldn't find a mutation could explain
00:32:03
or disease condition we did that and
00:32:06
found a couple of variance that looked
00:32:09
distill it might explain very unique we
00:32:11
need to pick features they have this
00:32:13
was a girl who'd been confined to a
00:32:14
wheelchair for about fifteen years and
00:32:16
had very sincere a neuromuscular a
00:32:18
defects. We ultimately tested the
00:32:21
functional consequences of this
00:32:23
variation using a bunch of different
00:32:25
construct and convinced ourselves that
00:32:27
this might be pathogenic variance
00:32:29
running or disease. It turned out that
00:32:31
there was a drug that actually targeted
00:32:33
the protein was harbouring visiting. So
00:32:35
what we did was actually measure
00:32:37
symptoms over a period of a year and
00:32:39
administer the draw and what we found
00:32:41
was that when we administer the drug
00:32:43
symptoms seems just aside. So true and
00:32:46
of one study we did use different drugs
00:32:49
in fact we found that in certain
00:32:51
settings wanna drugs given it actually
00:32:53
exacerbated the symptoms. So on the
00:32:56
basis of identifying a gene that
00:32:58
appeared happening for this little girl
00:33:00
symptoms we're gonna come up with the
00:33:01
drug and then tested out super actually
00:33:04
alleviated her symptoms now the
00:33:07
different variations on these sorts of
00:33:09
studies one can use a sequential
00:33:12
designs to see if in fact you can
00:33:14
identify the change in a shorter
00:33:16
possible but I'm not so you could
00:33:19
ultimately identified a time at which
00:33:22
the the intervention appeared to be
00:33:23
having an effect then what you could do
00:33:25
to convince yourself that effect was
00:33:27
attributable to the intervention you
00:33:28
take them a the intervention. And see
00:33:32
if the removal of the intervention
00:33:34
actually cost progression. And thereby
00:33:36
establish causality such that if you
00:33:38
got the money intervention probably
00:33:40
would still I have recreation of the
00:33:42
symptoms that you not the the symptoms
00:33:45
may never turn yeah there's also an of
00:33:48
one monitoring studies where one needs
00:33:50
to establish population versus personal
00:33:53
threshold I give an example of this is
00:33:55
actually from a project I'm involved in
00:33:57
that you can read about if you want a
00:33:59
tear project at work but there is this
00:34:01
if you measure the levels of a
00:34:04
particular by more this discreetly
00:34:05
species in the got like providing let's
00:34:07
say and you have an individual that's
00:34:09
right well and what's a high levels of
00:34:12
the species content or consistent to
00:34:13
disease. Then what might happen is you
00:34:16
could establish a population structure.
00:34:18
So that anyone who has levels that by a
00:34:21
marker greater than this partial are
00:34:23
likely to have disease to the point is
00:34:25
to keep someone away from that
00:34:26
threshold but you can't measure. This
00:34:30
file marker over time you see
00:34:32
fluctuations in the level that by more
00:34:34
so it's that if you press in this a
00:34:38
long enough time period you could
00:34:40
establish a personal threshold with
00:34:44
errors. And whatnot. And it in fact
00:34:48
this person started deviate going
00:34:50
forward from this kind of personal
00:34:51
average then you could argue that maybe
00:34:54
they're moving towards a disease state
00:34:56
you know we have the by mark a level
00:34:58
does not hear the population threshold.
00:35:00
So with longitudinal data you might in
00:35:03
fact be able to establish a personal
00:35:05
threshold. And thereby look really
00:35:09
signs of disease. Okay discuss this in
00:35:13
the literature in the context to
00:35:15
genetic susceptibility they won't go
00:35:17
through this but the second time anyone
00:35:19
jump into these aggregated end of one
00:35:21
studies I think this is where all the
00:35:22
action is what are activated and one
00:35:25
studies very simple an orientation you
00:35:27
just conduct anyone studies and a
00:35:29
number of different people and
00:35:30
establish who might be responding or
00:35:32
not responding with particular
00:35:33
intervention you just before that so if
00:35:36
you do enough of on the people
00:35:38
categorise responders another spotters.
00:35:40
And then look for patterns in those
00:35:43
individuals Gina types or Michael
00:35:45
combine the I correspond who is in is
00:35:48
in responder. So there's no reason why
00:35:50
you can't pursue sort of Meta analyses
00:35:52
on the results of these out of one
00:35:53
study yeah the biggest challenge in in
00:35:59
pursuing and of one studies is to in
00:36:01
fact makes sense to them in Edward and
00:36:03
there is a way to pursue this and has
00:36:05
to do with the creation algorithms for
00:36:07
matching interventions to patient
00:36:09
profiles. So typical clinical trials
00:36:12
test a specific intervention right.
00:36:15
They select individuals on the basis of
00:36:18
a wide variety factors maybe my combine
00:36:20
profile but maybe clinical profile and
00:36:23
the goal is to make claims about the
00:36:24
utility that intervention. So
00:36:26
alternative if there is a wide variety
00:36:28
of potential interventions like dietary
00:36:30
manipulations is to think of using
00:36:34
those different interventions one
00:36:35
appropriate in actual clinical trial
00:36:38
setting. So the probably see if there
00:36:40
are people who get an intervention that
00:36:41
matches their profile and ultimately do
00:36:44
better. So inappropriate trial in this
00:36:47
context would not that a particular
00:36:49
intervention. But an algorithm for
00:36:52
matching interventions to the patient
00:36:54
profile is actually done this in the
00:36:56
context of cancer or quickly go through
00:37:00
this. So is many you know their
00:37:02
particular drivers if administered the
00:37:04
patients with particular tumour
00:37:06
perturbations drawn seem to do better.
00:37:08
So if you have the Philadelphia promise
00:37:11
presently have leukaemia think leaned
00:37:13
back is a drug you should be given if
00:37:15
in fact you have EG power over
00:37:17
expression in your tumour in should be
00:37:18
given in media far inhibitor. So the
00:37:21
ways of matching drugs but a particular
00:37:23
tumour perturbations. However this is a
00:37:26
problem because most people's tumours
00:37:29
have multiple perturbations in the the
00:37:31
might be causing the tumours to grow.
00:37:33
So what you need to do is match
00:37:34
multiple drugs for certain combination
00:37:36
therapies to the tumour profiles in
00:37:38
order to make them more and the
00:37:41
combination of variations that anyone's
00:37:43
to remind have might be totally new
00:37:44
wants to make it a different
00:37:46
combination of drugs anyone cancer
00:37:48
page. This is actually a worn out of
00:37:52
the following example of a rather
00:37:54
infamous example this person had messed
00:37:57
like melanoma they could be attributed
00:37:59
to a particular genetic perturbation or
00:38:02
so it was thought there is a drug out
00:38:04
there that targeted that particular
00:38:05
perturbation and when administered to
00:38:07
the patient melted away the tumours.
00:38:09
However eight weeks later the choice in
00:38:12
back in the patient died because there
00:38:14
was clearly other perturbations present
00:38:16
in the tumour that accounted for when
00:38:18
they administer the drug. So this to
00:38:20
overcome one would need matching
00:38:23
multiple perturbations in the patient's
00:38:25
tumour with a wide variety of drugs.
00:38:28
And the different rules we're doing
00:38:30
this I'm not gonna go through them
00:38:31
simple rules match runs based on
00:38:33
pathways you can try to predict drug
00:38:36
responsiveness using indeed your mouse
00:38:38
models you can use something called
00:38:40
time to think that to up with room
00:38:42
where you can use integrated approaches
00:38:45
the basic strategy for this is you have
00:38:47
a patient and you don't know how to
00:38:48
treat them. So you draw or extract
00:38:51
material and you come up with some
00:38:53
profile the microphone patient's
00:38:55
pathology this could be a micro by it
00:38:57
could be sequencing the genome it could
00:38:59
be gene expression profile on the
00:39:01
tumour you have that signature and you
00:39:04
go to say in any case where the people
00:39:06
have been administered certain drugs or
00:39:09
interventions or combinations of
00:39:10
interventions in email the outcome. So
00:39:13
then you take your patience profile and
00:39:14
see it matches any in the database if
00:39:17
it does then you read off what
00:39:18
intervention it might make sense to
00:39:20
provide for that particular patient and
00:39:22
hopefully administer that intervention
00:39:25
to the patient interact through the
00:39:27
disease. We're actually given a pretty
00:39:30
sizable talk to change to look at this
00:39:33
in the context of cancer having to do
00:39:35
with a unique form of melanoma Colby
00:39:38
ref while I don't know so these are
00:39:40
just some of the team members but the
00:39:42
idea was to in fact test a method for
00:39:46
matching drugs to the chamber profiles.
00:39:48
So the important thing once we were
00:39:50
actually batting an algorithm for
00:39:52
matching the interventions that to the
00:39:55
unique a patient profiles one question
00:39:58
that came up that's being debated still
00:40:01
by the FBI officials has to do with the
00:40:04
ethics of actually conducting a study
00:40:07
where you've batting an algorithm like
00:40:08
this. What we could have done in this
00:40:11
context was just register patient and
00:40:14
then randomise them to either getting
00:40:16
the drugs that master tumour profile or
00:40:19
get the standard of care. So after the
00:40:21
randomisation they either get drug
00:40:24
match them or standard here. This was
00:40:26
thought to be an inefficient design
00:40:28
because if the creation had a profile
00:40:31
there wasn't obvious what combination
00:40:33
drug give the but they're randomised to
00:40:35
getting the drug that master to profile
00:40:38
than that would water down the signal
00:40:40
associated with the you know he died
00:40:44
there and lead to the possibility that
00:40:47
wouldn't see a difference between the
00:40:49
group they got that you a profile and
00:40:51
that you know what we guided drives
00:40:52
versus the standard treatment. So what
00:40:54
the FDA insisted on with the design
00:40:56
like file rewrites location we do the
00:40:59
genome profile yeah we can actually
00:41:02
identify a match. So a set of drugs
00:41:05
that here the to to be able to combat
00:41:09
the unique profile in the patience to
00:41:11
more than they were rolled in the
00:41:13
study. I really samplers that they had
00:41:17
a profile that might benefit from
00:41:18
certain combinations of drugs then they
00:41:20
were randomised together getting
00:41:23
originally guided therapy or standard
00:41:26
of care. And the reason that this was
00:41:28
complicated quickly as if in fact the
00:41:31
position knew that there was a match
00:41:32
there then they wouldn't wanna see
00:41:34
their patient randomise the standard of
00:41:36
care. So there's a lot of they still
00:41:39
growing debate about whether this is
00:41:41
the most appropriate trial design to
00:41:43
actually that algorithms for matching
00:41:45
interventions to patient profiles
00:41:47
rather than testing unique
00:41:49
intervention. So they're important
00:41:52
questions in these sorts of aggregated
00:41:54
designs what constitutes a tumour
00:41:57
profile intervention match is it just
00:41:59
just score whatever low ranking matches
00:42:01
chosen be down wait the patient in your
00:42:04
analysis what if the available
00:42:06
information interventions might be lit
00:42:08
a limited making the matches difficult
00:42:11
what about low frequency profiles
00:42:12
people that have very rare Michael by
00:42:15
profiles or tumour profiles what you do
00:42:17
with that in your try what asked a
00:42:19
should be used to establish the profile
00:42:23
you might need to consider combinations
00:42:25
of interventions that it never been
00:42:26
that show to have utility before it
00:42:30
would be crucial never crossover
00:42:31
mechanism if the therapy ultimately
00:42:34
settled on did not appear to work out
00:42:37
of the box. But of course the knowledge
00:42:39
base will increase over the course of
00:42:41
the trial. So you could ultimately
00:42:42
design trials to that these algorithms
00:42:45
for matching interventions to to
00:42:48
patient profiles in that way. However
00:42:52
there is one real motivation for these
00:42:53
done and that's one that's been
00:42:56
discussed a lot recently. I again most
00:42:59
trials has been intervention in the
00:43:00
population at large using C standard
00:43:03
faced retrial designs focus an average
00:43:06
of text and the population we heard
00:43:07
number of trials in the previous
00:43:10
speakers talks about so try their all
00:43:15
the comparator intervention or placebo
00:43:16
being used in a very regimented open on
00:43:19
real world setting the main idea is to
00:43:22
get the intervention determine its
00:43:23
utility going for or with future
00:43:25
patients right. So the subjects in the
00:43:29
trial are essentially sacrificing
00:43:30
themselves for the benefit of the
00:43:32
future patients so they're sort of
00:43:34
being treated as in it that's is true
00:43:35
of most things retrial especially when
00:43:37
it to see that was used however with
00:43:40
these and one trial the goal is to
00:43:42
focus on the individual's health. And
00:43:45
determine what might be optimal for
00:43:46
that particular patient. Now in the
00:43:48
process you gonna learn a lot of
00:43:51
interesting biology but what would be
00:43:52
correlated to work that patient at the
00:43:55
end of the day you'll come up with
00:43:56
something the page you can benefit
00:43:58
right then in there. And that is unique
00:44:00
in these sorts of spent okay so was not

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Conference program

Introduction of the Session 1 : The Gut Microbiome: Facts and Figures
Josef Penninger, Institute of Molecular Biotechnology, Vienna
23 Oct. 2014 · 9:07 a.m.
The role of commensal bacteria in the gut
Willem de Vos, Wageningen University, The Neterlands
23 Oct. 2014 · 9:31 a.m.
Q&A : The role of commensal bacteria in the gut
Willem de Vos, Wageningen University, The Neterlands
23 Oct. 2014 · 10:29 a.m.
Gut microbial richness impacts human health
Dusko Ehrlich, INRA, Jouy-en-Josas, France
23 Oct. 2014 · 11:07 a.m.
Q&A : Gut microbial richness impacts human health
Dusko Ehrlich, INRA, Jouy-en-Josas, France
23 Oct. 2014 · 11:44 a.m.
Cross-talk between the mucosal immune system and environmental factors
Hiroshi Kiyono, The University of Tokyo, Japan
23 Oct. 2014 · 11:56 a.m.
Q&A : Cross-talk between the mucosal immune system and environmental factors
Hiroshi Kiyono, The University of Tokyo, Japan
23 Oct. 2014 · 12:31 p.m.
Introduction of the Session 2 : Host - Microbiome Interaction
Susan Suter, University of Geneva, Switzerland
23 Oct. 2014 · 1:41 p.m.
Mechanisms of cross talk in the gut
Annick Mercenier, Nestlé Research Center, Lausanne, Switzerland
23 Oct. 2014 · 1:55 p.m.
Q&A : Mechanisms of cross talk in the gut
Annick Mercenier, Nestlé Research Center, Lausanne, Switzerland
23 Oct. 2014 · 2:34 p.m.
Relationship of diet to gut microbiota diversity, stability and health in older people
Paul O'Toole, University College Cork, Ireland
23 Oct. 2014 · 3:52 p.m.
Q&A : Relationship of diet to gut microbiota diversity, stability and health in older people
Paul O'Toole, University College Cork, Ireland
23 Oct. 2014 · 4:27 p.m.
Gut microbes and their role in malnutrition and obesity
Rob Knight, University of Colorado, Boulder, USA
24 Oct. 2014 · 9:16 a.m.
Q&A : Gut microbes and their role in malnutrition and obesity
Rob Knight, University of Colorado, Boulder, USA
24 Oct. 2014 · 10:01 a.m.
The gut metagenome - your other genome
Jun Wang, BGI, Shenzhen, China
24 Oct. 2014 · 10:19 a.m.
Q&A : The gut metagenome - your other genome
Jun Wang, BGI, Shenzhen, China
24 Oct. 2014 · 10:53 a.m.
Fecal transplant to mine for novel probiotics
Max Nieuwdorp, Amsterdam Medical Center, The Netherlands
24 Oct. 2014 · 11:04 a.m.
Q&A : Fecal transplant to mine for novel probiotics
Max Nieuwdorp, Amsterdam Medical Center, The Netherlands
24 Oct. 2014 · 11:25 a.m.
Introduction of the Session 4 : Nutritional Interventions
Keiko Abe, The University of Tokyo, Japan
24 Oct. 2014 · 12:46 p.m.
Interactions between gut microbiota, host genetics and diet
Liping Zhao, Jiao Tang University, Shanghai, China
24 Oct. 2014 · 12:56 p.m.
Pediatric intervention - what works and what doesn't work
Hania Szajewska, The Medical University of Warsaw, Poland
24 Oct. 2014 · 1:47 p.m.
Q&A : Pediatric intervention - what works and what doesn't work
Hania Szajewska, The Medical University of Warsaw, Poland
24 Oct. 2014 · 2:15 p.m.
Perspectives for nutrition and the gut microbiome
Nicholas Schork, J. Craig Venter Institute, La Jolla, USA
24 Oct. 2014 · 3:02 p.m.
Q&A : Perspectives for nutrition and the gut microbiome
Nicholas Schork, J. Craig Venter Institute, La Jolla, USA
24 Oct. 2014 · 3:46 p.m.

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