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Big data: possibilities and threats for complex disease genetics
Zoltan Kutalik
Friday, Feb. 1, 2019 · 12:04 p.m. · 26m 50s
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thanks very much a bunch of your turtles were patient so yes that that would be a lot of
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conflicts probably what i'm gonna tell you know uh and it's not my to confuse you but
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yeah uh what it just like this sort of talk about how big data existing genetics and uh
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how this can help wasn't how this can maybe help in the future to probably cure diseases
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um at the structure be very simple first i will tell you how
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these genetic data used to discover low sign your genome your
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d. n. a. sequence that are associated diseases mostly able talk about common diseases but that will show a couple of examples
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uh and i will tell you then how these associations what we learn from them what learnt about
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individual genes in the action and in the mechanism individual genes
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and the rolling diseases but what we can also learn
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over the entire architecture of a genetic architecture because it
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as high as in the title was complex diseases
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how complex but the reason is because there are have probably dozens of different reasons that lead to these diseases and these are these
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different courses that we want to identify you know to to protect them numbered on the stand them able to treat them eventually
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so the architecture would be very important for this and valuable also mentioned
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some pitfalls because it's very important not to misinterpret these results
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and it's a very hot topic in the current ah literature whether these genetic tests that we can now provide
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should they be using clinics to identify individuals that high risk uh especially very early on
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and if i have time i also want to show how these genetic data can help us to
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yeah distinguish between courses consequences and confounding can't really find true causes and
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establish the extent of the calls a on a certain diseases
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so it might be very cryptic to you uh i hope you get more later so this is that you know all three people
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every position in the genome we'll have three billion base pairs three point two billion
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roughly one in every thousand base pairs is changing across people in the population
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so the rest of the genome these part which is the same for everyone is
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pretty boring what you're interested is a part of the genome which is different
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every location inherited an l. deal from a mother and a father so we carry two of
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these ideas soul most typically we can have difficulty geno types at each position so somebody
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can be a a disposition a. g. or g. g. three different uh possible job types
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and we want to relate these geno types whether you carry more areas of re does it lead to certain disease
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or certain traits so what we dealing with various or simply is we have a large amount of genetic data so these are typically
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bands or at least millions of markers in the genome data
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variable across population uh across people very frequently and
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actually nowadays you measured i mean hundreds of thousands of people because what we learn from if you just
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have have a few thousand this would be not enough to understand really what's going on behind
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and i will tell you the reason for it and but luckily these things
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exist unfortunately switzerland because we're looking at least ten years behind all
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the european countries mostly bored northern european countries hope will catch up one
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day but if you look around at u. k. denmark sweden finland
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uh estonia all these countries are a gathering now by banks of of this kind of sizes
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uh and the key here is not just a genetic data if you're enough money you can do this is not a problem
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the problem is here can you get their high quality clinical environment of
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data for these people to be able to really clearly understand diseases
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so what we do one seventy genetic data in some outcome i would
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just take for starters body mass index it has been already mentioned
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as a as important respect or for um for arthritis a body mass index a country does not commonly what the model it
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or the genetic markers in the genome that can predict whether you will become obese or not in your life
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and at what age so we take the body mass index of individuals and we hope that we do more number
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values carry that certain position your genome may also increase your body mass index this is the real example
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this is this is his data so small they don't but that's what we have here about six thousand people
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where you have full dimensional typing and this is one particular variant in the gene called f. t. o.
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and you can classify the individuals in this call working these populations was population how
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many a year you somebody carries at this very location in the genome
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and you can measure their body mass index and squint enough you see a slight slope there and that's what it means is that
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every additional a. l. you will increase your weight by a kilogram
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or average over your life so this is not very
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maybe the for large for you but this is what's happening at this is the idea that the
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largest effect on body mass index and this is typically why is a complex disease because
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uh such abuse will not have large fact there would be many many many hundreds of values
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each of them it really tiny fact we can do this association so this
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is just linear regression you want estimate the slope your how many
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uh how much that trait changes with each additional copies of of uh it
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partly the any sequence you can do it not just one marker
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but you can do it for tens of millions of markers in your genome and that's what you plot here this is called manhattan plot
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what age dot is one of these ten million markers in the genome
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on the x. axis official one which chromosome it lies and where it lies within from was on
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the y. axis shows essential the strength of the association like ha strongly easy changing to trade
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and f. deal this variant here is read the top one so they can you can really see this is the
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the commentary to the largest effect on but i didn't see next okay so that's the started that's
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what uh has been done ten years ago how much can we move forward from these
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we can run decisions for many traits in increasing sample size so this is a study which
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we published a now was two years ago on human height it's a very simple trait
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uh here each dot again is a genetic marker and on the x. axis we showed
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the frequency in a population and y. axes we show their effect on height
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and you see that essentially very frequent markers they cannot have large effect on height because they would've been selected against
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uh and they're free press would be pushed down and these are the ones here these are the reverence
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the present in one thousand people so probably not one person in this room
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yeah but they change a high by two centimetres some of them pushing you big dollar someone make it shorter
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so now we have the technology to go down a to even wanting thousand people and to make a
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collection of all genetic markers so that's what very standing that if you take all together these markers
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they still probably explain about thirty percent of height very guilty it's tiny fraction
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height is eighty percent palpable be explained thirty percent incident eighty percent
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and something where we're much more successful is is straight that are closer to the uh to the molecular
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to to the d. n. a. itself for example if you look at your i guess it levels
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uh which is very important market to high levels of the dog out uh if we run
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associations can that's how the genome looks like in terms of contribution to your guesses levels
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and the strongest association is in this club nineteen as a c. to a nine
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this is very common variant but still large effect the single variant explains especially the
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mean spends about six percent in variations of these you because it levels
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so uh we have many many of these association peaks bought these are
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common variance what we can do is we can go deep inside
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in sequence these jeans and try to find ray variance maybe the
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reverence would be more important than indeed what turns out that
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the in two of the most important genes that have been that that it showed up in his association scans
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and they have reverence inside them it much much larger effect on you requested levels than the common variance
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so we see that rare and common variance together contribute to traits we still don't have enough samples actually too
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to explain everything probably would need at least hundred million people collected
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and full genome sequence to be able to understand everything
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but what do we do with these for example in this study uh also arthritis it's very impressive sample
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size thirty thousand patients have been collected build you know that so here that is not the problem
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but it is not the probably have enough data but the problem is
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the small effects that each individual a genetic markers a tiny contribution
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here the studies are already good enough that we can very accurately estimate
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that this is sixteen percent heart trouble so it means that
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we will only ever able to explain the genetic sixteen percent operate will develop the disease and will not
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uh that such a large study will just still it has discovered about a few dozen maybe
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twenty last sign nine which we're doing this and it was published last year so
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so far just by looking at your associations we haven't learned necessary that much
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but what is interesting is that these can be using level explain later
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how we can estimate effects for example body mass index how how much
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the causal effect of body mass index on also right right this
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anybody choices want lower per square meter weight gain as roughly about three
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kilograms for an average person it increases by ten percent your odds
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to develop the disease and this is really causal if in theory you could lose weight sort that's how much uh you would
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a decrease your chances develop the disease what is very interesting the studies that if we look at these be zoom in
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on those men templates and we look at the g. only context of the genes line you buy these associations signals
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there are always many many genes but the signals are not always falling into
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jeans so we don't necessarily understand straightaway genes are implicated in these diseases
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so here we uh i just pulled up a few uh regions in the genome where we have several jean
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candidates and and then what you can ask for example what in the study they they look at
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which genes among these have different expression levels in patients versus controls so that they can tell us more
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that these are potentially very interesting but markers which might be actually cool though preceding the disease
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something which is just happening court currently that this is not very informative because it will not necessary tell you
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uh you will not allow you to predict longterm had and also it will
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be probably useless for treatment because it's just a side effect of
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of the of the disease or consequence of the disease itself it's very important to distinguish between
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the courses of these in the consequences or just something which is a correlated the disease
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so here many of the genes that have been uh uh that are in these last i discover
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they seem to vary significantly different expression levels iterations and controls
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what can be done put forward is that we can then look at the jeans some of these genes that are emerging for this also are trying to study
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and then see which jobs are targeting these jeans and hope that
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maybe this can guy does which treatment could be useful
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and also if i know that my patient has mutations are probably more prisons in these genes as opera disposing
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maybe at such a drug would be more efficient use for that patient i mean this
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is not working currently large scale we need to probably understand a lot more
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but there are several very positive examples where it's known and where these discoveries of
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the genes the lead to a discovery of the all the proper medication
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so as you can see many of them are in phase to phase three trials it's always the the big risks to uh
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to develop such drugs because eventually will not know it will work or not but we have
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some good indications at least for for the g. is who could be worthwhile being targeted
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how can we learn a bit more not just about individual genes maybe from
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individual genes we can move to drugs if you're good enough understanding of
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the mechanism of action of the drugs which you often lack the often don't know the target of the drug or we just know few targets
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that can be tons of of started effects which really to side effect these
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we also have to be taken into account but these genetic studies
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if i know now that that these drugs actually targeting another gene which is not the most relevant i can
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look up what could be the impact of suppressing the expression of level of these gene on certain diseases
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so the joystick architecture does a bit complicated slide so maybe it's just that if you don't even look at to just listen to me
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so what we can learn with the study is is you can estimate overheard ability of the
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straight so know how much a genome itself the d. n. a. sequence contributes to it
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so that's basically looking at this is on heightened b. m.
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i. model traits the allies about uh seems that from
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the data we have if you just had more samples we could almost estimate thirty per
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se explain thirty percent of the hurt ability for height is approaching fifty five percent
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so it's all getting more promising the just the more data so we could build more better predictors
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you can already did pretty good predictors for height and b. m. i. and many other diseases
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uh but still we need probably more data protection is one thing and understanding by which is another
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what we can also estimate is uh probably skip this part but you can also estimate the production is it is
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what it says what what it means here is that what fraction of
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the genome is probably a involved in the modulation of the disease
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as you can see for uh for height it's roughly five percent of the genome
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so it means that every twenty is gene in the genome is somehow
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taking apart an active part in determining couple you would become
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why phobia my it's much much like almost ten percent of your genome is probably playing a
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role in determining your height so sixteen apologetic there will not be the gene for anything
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yeah if you have something very rare disease one concrete a a patient may have just a
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single mutation in a single gene but as soon as we move the more common diseases
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yeah it can be much more complicated and the same thing for even for red is is it can because by many many variants
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so we can understand how many genes contribute how much can
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they commit to explain we can also estimate the
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correlation between how frequent these markers are and how large their effect is
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essentially there's a strong negative correlation between them it means they asked strongly on the negative selection
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and uh and often these actually are in the clinics that most preferred application would be just let's
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predict the disease so we have all the gen typifies which is very easy to measure
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and this is what happens for a couple of common traits for example this is types baby does it
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against the study from last year taking together several hundreds of markers in the genome trying to predict
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basically you can group individuals here degrading to forty different things
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based on the genetic risk so these groupies here
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the highest jenna degrees group is the lowest your degrees group and then you can go to external study
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and ask what's the tied to the recent type two diabetes prevalence indigo could highs genetic risk
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and you can see it for more than the login decrease craven at least triple compared to the population average so that's
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that can be very informative because we're bored with our genome so adverse we could tell this information to the people we
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don't need to wait much longer and we don't need any clinical flop so at least for the extreme groups
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the same for coronary artery disease if you look at the top percentile
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in terms of genetic risk so basin justine d. n. a. sequence
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we can tell uh and it again it is fivefold higher risk compared population average so these effects
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identifying only a subset only one hundred one hundred people but we can give them valuable information probably
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uh at that they're more sensitive transfer like education level or in these cases the she g. c. s. e.s course
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so all here again this is pollution agree score for educational attainment which is also sounds
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crazy hard genetics tell you how long you will will you stay in education
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actually they do and and the genetics already now explain about ten percent of these
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but you have to be very careful how to interpret these results you can still group the people like it top ten percentile based on your genome
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and indeed do we achieve better in this test but does it mean that these people should have different education these
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people should have a higher level education and you should person as a our education system and probably absolutely not
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these are told that to be put forward in this review which i completely disagree with and the reason for
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this is that many of these genetic risk what you're predicting the disease or not necessary meaningful biologically
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this is a map of the u. k. and the colour is according
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to the risk the knowledge and apologetic score for educational attainment
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so the higher red is darker red is the higher your education will be about that the
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genetically predicted education level and if you see look at the map these are all cluster
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and they're clustered around cities and typically so that these ones are here which are uh
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uh would be to a black boundary these are the ones that are coal mines
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so obviously if you build a predictor uh that there is a constipation certification going also the people who are from
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an awful themselves they have slight ideal frequency differences they carry
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more or less often by chance just by migration
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a certain areas and if we can predict if your from here
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and you can productively genetics very accurately whether you're from here or from their from there you can be this predictors
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and you can tell you from eugene where you are give bob is already ten years ago
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so do we predict anything more than actually just where you are from and whether
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you have moved away from a coal mine already you design the city
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actually probably half of these genetic predictor is just predicting where you're from
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so this is just predicting your social comic state is this not
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because of biology because these genes would change a brain structure these are sort of peripheral jean very for probably more prisons which have
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nothing to do actually to the mechanism the disease so it's a huge danger to use this for anything to it just helps prejudice
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uh but the steelers these are good predictors in the u. k. once you move to another country or you want to compare the
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same you you you want to do the same in another continent this will not work it will work much much worse
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so this is a very very danger currently to to try to apply these college
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uh next course uh and predict a into on other continents another nation's
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the same for height heightened situational sounds great and you know that you northern europeans up all the southern europeans there are
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tens of that probably at least ten thousand values that have also if a frequency
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difference much more frequent in northern in the south so these ideas will
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be good to pretty tight but they just predicting where you're from again in
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europe and that's been shown if you compare latitude with apologetic score
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actually part of disposing score that has been dry for height is predicting again where you're from north south
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uh not just neural bottle wasn't work so these are good
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predictors we can build stronger stronger predictors the opportunities always
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the hard to be given to trade which is on average for most of traits would be around twenty percent
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so this is just one part of the picture but it's very easy to measure and you can get
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it if eventually we'll have many samples but the interpretation of these will be still a different issue
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i would like to use the final few minutes on we give it away from genetics could genetics is
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just one small piece of the puzzle of course are interesting transcript or makes your interest matador makes
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what is always gonna help us production and explain uh we respond to therapy or not
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and also there are the cause of environmental factors but it's more to do physical activity and so on
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uh actually genetics can even help with these ones and this is something called mandy random
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is asian studies so what we do is for me making randomised control trial
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we want to predict if you change something if you changed expression with jean what impact it will have on the disease
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the way we do it is that you know the gene expression is her table on average it's
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again about twenty five percent each gene has about twenty five percent heartedly so there are
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nanny known genetic markers that are changing expression little digits so not what i can do i want to for example
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estimate by there you will uh uh develop a or start right is
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what i can do is i have a genetic marker that i know that it's changed expression of
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the of the g. i. can now split everybody in this auditorium according to what you
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carry this marker or you don't carry them also carry the market will have increased expression about
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from birth we know this from very large studies that have been done gene expression levels
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uh what i will look at these people i will not not together expression that was because i
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know it for much larger studies that of course this group will have higher expression level
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uh what i want to know is whether what fraction of those people developed particular
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disease like a start right is or or or obesity or or diabetes
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and if the prevalence difference will tell me the causal effect
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of the gene expression level on that uh outcome
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and it's very important because then i would like to design a drug which is targeting these gene and it will have
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the opposite effect and it will surprise uh the impression that the expression of the gene so to prevent the disease
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of course this is an idealistic scenario because uh each drug with not just target one gene
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and it would be not only one gene expression would because of for the disease
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but we we build a different statistical models to to try to model it and
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and to find the optimal trucks at least to prioritise uh under position
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so just to a show an example for remote that arthritis we have
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these associations signals but we can map now the actual the kosovo
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a link between the genes in the region and we can identify genes that are actually even common
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because this involvement that arthritis which makes a lot of sense because these shared with allergy
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but we can prior those jeans and we can now hopefully design treatments for those
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and the very last slide is about we can do it for less than it was fun study to do we can try to predict how long you can leave
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again genetics probably spent ten percent of it what isn't more interesting is what other factors are
00:21:28
in there and you from living longer not surprising if you're developing diseases you look shorter
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but uh things of the genetics we can use the genetic markers estimate effect of smoking of course smoking is the
00:21:38
biggest killer that will shorten your lifespan the most what extent july spend the most is going to university
00:21:43
and again it's probably not because you you you spend time at university you believe you wasted your life uh at least five years
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actually you gained is back because each year you spend at university extend your lifespan by your
00:21:54
so the years again back but it can also use it for body mass
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index that each kilogram you gain it shortens july by two months
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so the genetic studies can now really put a number on these calls
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estimates and we'll not just get here correlates of something but
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uh but we'll get really the courses and uh obviously the correlations always much larger but the cause
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effect somewhat smaller uh so the studies can be really useful to these out these these causality
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so that's all i wanted to say and i would like to thank my group uh
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and a very nice collaborators the studies always requiring several countries in several research groups
00:22:29
to put together all what we have is data and and work together toward the same goal so it's it's a very
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uh it's great pleasure to participate in these big
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corporations thanks so much
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for attention ha
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put questions if any
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the person who's running the we have moved to ask a question no i'm kidding 'cause it's okay
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if any question so actually should not be big that i should
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be huge data because there is so much diversity that's yeah
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so we need the last thing to be really a hacker rate what
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where you can display a double on just changing things on the
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two small sample size and then you change the world wheeze new
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interface didn't to really make captain in the proper way right
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yeah so what are the big is always relative it's going be time so currently now we have this data bytes
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of data which we need to delve into what's important is since we have millions of variables you need
00:23:31
hundreds of thousands of samples to do anything accurate and there's just like good example previously shown was if
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you have a small sample size and you did the risk of over fitting is very large
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and you will have too much confidence in in fitting data which is similar to yours
00:23:44
but if comes from another m. r. i. machine you with this option pigeons methods
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of statistical methods work as well it's the question in i guess the curve on the
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responsible for example we were you were talking but be my c. well heavy
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loss of one you need to be in mind would you say you like that a lot but i guess that
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one point you losing too much you will lose life again so slow queries directly you curve say yes
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and then it's not like a linear or interpretation so interpretation of these done also
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have to be very careful the way that just couldn't reactions about the problem
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that would just rebounds back and so yeah so we we have the another the power to
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i uh actually do the local estimation so you know that from only to be
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on my twenty to twenty five a no no the the causal effect and moving down
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was indeed it for the the curve like this and it has been just reasonable
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yeah you made the point starts genetic associations can be misleading and that maybe some
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of the genes that are associated with an outcome or maybe not involved
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at all but happy excludes the nature nurture not your facts so that the
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gene those play role but only to the interaction with the environmental factors
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yeah the that's a good point uh i wish i had that slide euro well it's a supplementary so
00:24:55
these genetic effects for example for education that is gonna also shown is one third of these effects are not directly going to you
00:25:01
and changing something for your brain structure or or or or functionality of your brain
00:25:05
but it's actually you cheryl is that your parents and it's your parental care
00:25:10
which which also is is on genetics so it's how your parents are raised you to
00:25:15
to enforce basically to encourage you to go to school so one third of the fact is pointing directly
00:25:20
this is very difficult to find without having parent of a parental genotype data and all sequence data
00:25:26
uh and gene environment interactions also very important points which we are my group is actively researching into
00:25:31
uh the the difficulty with that is is the scale issue if you look at the uh mine the if you have a lot genetic
00:25:38
exposure so many many genetic risk lives doing we exposed to be hobbies
00:25:43
and you have also many environmental you have an object environment
00:25:46
the combination of the two will not have just a linear effect adding one and two it would have simple interface with an extra burden for these people
00:25:53
but if you change debian mind we look at low b. m. i. just transform the trade
00:25:58
most of these stations do these interactions disappear it's always scale
00:26:01
dependence what's very important is that is being my important
00:26:05
goes early or or is the low b. m. i. is something which is is better predicting disease
00:26:10
so without that context it's difficult to tell whether is that you know moment interaction important on
00:26:18
thank you very much so all ha
00:26:24
hi no before moving to the lunchtime uh we have
00:26:29
uh and not be tied talk where will the
00:26:32
inlet to uh introduced to so that's a senator from virginia you talk about a ah health
00:26:38
and uh i hope that would use the opener you apply for the
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