Clara Moreau “Impact of genetic heterogeneity & pleiotropy in psychiatry on brain functional connectivity”

Transcript

• 00:06So I will present our work on the
• 00:08impact of genetic heterogeneity
• 00:10and pleiotropy in psychiatry and
• 00:13brain functional connectivity.
• 00:15So a bit of genetics.
• 00:18And so psychiatric condition
• 00:19as you might know,
• 00:20I've been mainly studied through
• 00:22a top down approach starting
• 00:24from the behavioral diagnosis,
• 00:26moving down to brain on the phenotype,
• 00:28cellular component, trying to
• 00:30identify genetic risk factors etcetera.
• 00:33Such approaches are revealed a lot
• 00:35of heterogeneity at any any scales
• 00:37from the phenome to the genomes
• 00:39with a lot of comorbidities.
• 00:42At the clinical level,
• 00:43it's almost impossible to find a patient
• 00:45with autism without an additional.
• 00:48Diagnosis.
• 00:48And at the imaging level we were
• 00:51not really able to find clear
• 00:54reproducible biomarker and when
• 00:56we found something it was not
• 00:58really specific to that condition.
• 01:00So it could not,
• 01:01it was not really a biomarker.
• 01:03This result at the imaging level
• 01:05where mirror at the genetic level
• 01:08with these two phenomena called
• 01:10polygenic city and player trophy.
• 01:12So I'm going to dive into this.
• 01:15Things during my talk.
• 01:17So when I say polygenic city,
• 01:19I meant thousands of genes
• 01:22involved all over the genome.
• 01:24So for example in this recent
• 01:27review from labrum and colleague,
• 01:30they reported more than 1500 high
• 01:33confidence genes associated with
• 01:35neurodevelopmental disorders.
• 01:38And on top of that they identify six
• 01:41stars more than 6000 candidate ND genes.
• 01:44These genes as you can see, we're located.
• 01:46All over the genome.
• 01:47So it's like in the brain,
• 01:49you don't have the region of autism
• 01:51and you don't have the chromosome
• 01:53or the low thigh of autism.
• 01:55And to add complexity to that we have
• 01:58this second mechanism called pleiotropy.
• 02:00Pleiotropy is when the same genetic
• 02:02mutation confer risk for several
• 02:04condition at the same times.
• 02:06So if we take the example
• 02:09of autism and schizophrenia,
• 02:10so you can see that in autism you
• 02:13have rare and common variant involved,
• 02:15so CVS.
• 02:16And single nucleotide polymorphism
• 02:18it's also the case for schizophrenia.
• 02:21But as you can see in this Venn diagram,
• 02:23you have an overlapping part where
• 02:26genetic variant covers for both
• 02:28condition at the same time and
• 02:30some of them also confer risk
• 02:32for ADHD and other condition.
• 02:34OK,
• 02:34so these two concepts can be
• 02:36summarized by this sentence,
• 02:37one for all,
• 02:38all for one,
• 02:39where one for all stand for pleiotropy
• 02:42when one genetic risk factor confer risk
• 02:45for several condition and all for ones,
• 02:48then for polygenic city when
• 02:50several genetic risk factor confer
• 02:52risk for the same condition.
• 02:55So OK,
• 02:56so I'm going to start with player trophy.
• 02:59Play Toby has been mainly studied at the
• 03:02genetic level using genetic correlation,
• 03:04snip base so common variant based.
• 03:07So for example if you want to
• 03:09study the genetic link between
• 03:11schizophrenia and autism so
• 03:13you can use GWAS summaries,
• 03:15you can use that GWAS summaries
• 03:18from schizophrenia and statistics
• 03:21summaries for schizophrenia and
• 03:24for ASD you just correlate this.
• 03:27These tables and you obtained
• 03:29a degree of correlation between
• 03:31these two condition which is reported here.
• 03:34So for example between schizophrenia and ASD
• 03:37you have a correlation around these 0.330%.
• 03:40So the Psychiatric Genomics Consortium
• 03:43did that for this eight condition
• 03:46using the eight largest GWAS available
• 03:49into 2019 and they reported 8 three
• 03:52group of anti related disorder.
• 03:55So Alien said. Developmental disorder,
• 03:59mood and psychotic disorder and
• 04:02disorder with compulsive paviors.
• 04:04Similar results have been obtained
• 04:07at the transcriptomic level,
• 04:09so by this study published
• 04:11by Gundalian colleague.
• 04:13So in the X axis you have the
• 04:15previous result published at the
• 04:16genetic level and in the Y axis.
• 04:19That's a new result from this
• 04:21study published at the obtained
• 04:23at the gene expression level.
• 04:25And you can see that you have a good
• 04:28concordance between observation at
• 04:30the genetic and transcriptomic level.
• 04:33And here we wanted to know if such a
• 04:36pleiotropy mechanism could also be
• 04:39observed at the connectomics level.
• 04:42So for this purpose do we collect it?
• 04:44I mean we didn't collect,
• 04:45we used publicly available datasets.
• 04:49For more than 1000 subjects with
• 04:51psychiatric condition as schizophrenia,
• 04:53autism, bipolar disorder, ADHD,
• 04:55we also used local Montreal data data set
• 04:59and we used UK Biobank as general population.
• 05:02We preprocess the data using yak
• 05:04software developed by Pierre Bellec.
• 05:06We persuaded the brain into 12
• 05:09and 12 network and 1664 region.
• 05:12We perform sequence analysis and a Pearson
• 05:16correlation as well as permutation test.
• 05:19To it as the significance of the
• 05:21correlation and we obtained this list
• 05:23of value represented in this diagram.
• 05:26So for example,
• 05:28we have schizophrenia versus bipolar disorder
• 05:32that are correlated around like 450%.
• 05:3750% ASD versus schizophrenia,
• 05:39so we have a correlation around
• 05:4230% and neuroticism,
• 05:44neuroticism versus freedom diligence
• 05:46where anticorrelated at like minus 30.
• 05:49So we compare this value with this
• 05:52bunch of nature genetics publication
• 05:54that have been that have studied,
• 05:57we have studied genetic correlation.
• 06:00So, uh, here in the exact X axis,
• 06:03you have our result at the connectivity
• 06:05level and in the Y axis you have
• 06:07the result at the genetic level.
• 06:09So as you can see,
• 06:10we have a good concordance.
• 06:12I was quite surprised about that.
• 06:15We don't have any correlation with
• 06:17our control condition, which was IBD,
• 06:19inflammatory bowel disease.
• 06:21So if we go back to our example
• 06:23about ASD and schizophrenia,
• 06:25we have a correlation at the brain level
• 06:28around 0.3 and it was quite similar.
• 06:30To what was obtained at the genetic level.
• 06:33So yeah,
• 06:34it was quite impressive and then we
• 06:37compared also with what has been
• 06:39published at the transcriptomic level,
• 06:42but in that case we only have we
• 06:44only had six pairs of condition to
• 06:48compare but still it was quite good.
• 06:51So I guess you versus schizophrenia
• 06:53you can see like we are still at
• 06:5630 at the brain level and they
• 06:58were around like 40-5 at the.
• 07:01Transcriptomic gene expression level.
• 07:03So what we have seen in this first
• 07:06part is the stability of the degree of
• 07:08overlap of player trophy across the scales
• 07:11of observation which in a way validate
• 07:15our biological overlap across these
• 07:17clinical diagnosis and also highlight
• 07:19the need to move from our case control
• 07:22diagnostic first approach to more like
• 07:25dimensional approach such as our dog as
• 07:28third explain yesterday introduced more
• 07:31than me yesterday it also looks like.
• 07:33Functional connectivity seems to be a
• 07:35good on the phenotype to study psychiatry.
• 07:38And before moving to Polygen City,
• 07:42we also tested some variable at the
• 07:45anatomical level and we focused
• 07:48on early onset anorexia,
• 07:50which is a type of anorexia with
• 07:52an onset before puberty, puberty.
• 07:54So we characterize the impact of early,
• 07:58early onset anorexia on cortical sickness
• 08:01and subcortical volume and as you can see.
• 08:04So we use local data from Parisian hospital,
• 08:07so 100 subject,
• 08:08around 100 subject with early
• 08:11onset anorexia and 100 control.
• 08:131/4. And we reported a reduced.
• 08:20Thickness reduction in parietal area and
• 08:23occipital area as well as post central
• 08:26gyrus and the reduced volume of the dynamics.
• 08:29But so we compare this.
• 08:33Since brain map of alteration with
• 08:35with what has been reported by the
• 08:37Enigma Consortium for OCD, ASD, ADHD,
• 08:40OC, T, so we obtain this correlation,
• 08:45so 49% with OCD,
• 08:47minus 20 with ASD,
• 08:49minus 39 with ADHD and minus 25 with obesity.
• 08:54We then as you might expect compare that with
• 08:58genetic correlation previously published.
• 09:01So for example, OK.
• 09:03You're gonna try to move my face?
• 09:05Yeah,
• 09:06for example.
• 09:08You see,
• 09:08you can see in there babe in like
• 09:10the previous paper I presented.
• 09:14And struggling like a medium
• 09:17link between anorexia and OCD.
• 09:20So it was a correlation around 50 and we
• 09:22had like similar degree of correlation.
• 09:25They didn't have any correlation with ASD.
• 09:27We have a negative correlation.
• 09:29We had also negative correlation
• 09:31with ADHD as they did report and a
• 09:34negative correlation with obesity.
• 09:36So it really seems that there is some
• 09:40similarity between genetic results and
• 09:42anatomical and functional connectivity.
• 09:44Result. As.
• 09:50As a parent as a note,
• 09:52I was thinking that I was wondering
• 09:56why we found this parietal.
• 09:59Region with sickness reduction.
• 10:02So I was comparing,
• 10:05I was thinking about developmental process.
• 10:07So I was comparing our map of sickness
• 10:10alteration with what has been published
• 10:12by Richard Bethlehem and colleague
• 10:14in this nature paper last year.
• 10:16And I was really surprised to find
• 10:2073% of similarity between our result
• 10:23in anorexia and their that's not
• 10:26like a case controlled map that
• 10:28just the regional maturation.
• 10:30Timeline here but, and in our case,
• 10:34our patient are all the same age,
• 10:36like they're all not the same
• 10:38age but between 7 and 13,
• 10:39so it was quite surprising.
• 10:42So we're wondering if this person had
• 10:44genus and superior parietal area might
• 10:47be metabolically more costly region.
• 10:52Yeah. It's just like an open question.
• 10:55Now. I'm going to move to the
• 10:57second part about polygenic city.
• 11:00So as I told you, we have like one
• 11:02more than 1000 genetic risk factor
• 11:04conferring risk prophetic condition.
• 11:07So we have two possible scenario,
• 11:09I mean simplistic scenario a scenario.
• 11:13So if you have 101,080 crisp factor you
• 11:17could end up with 1000 form of autism.
• 11:21The alternative scenario is you might
• 11:23have some convergence from the genome
• 11:26to the phenome to at the end obtain a
• 11:30limited number of ASD subtype let's say.
• 11:35So we wanted to test that.
• 11:38So first.
• 11:41Just an explanation about how to classify
• 11:46really easily or genetic variant.
• 11:49You can simply classify them
• 11:51based on their frequency.
• 11:52So for example a single nucleotide
• 11:55polymorphism are a common variant
• 11:58and they are detected by GWAS.
• 12:00We use them to compute polygenic risk score,
• 12:04polygenic score, they have a low,
• 12:06they have a high frequency
• 12:08and a low effect size, white,
• 12:10red variant are detected.
• 12:11Through all the exam sequencing or
• 12:14or CGH area and they have a higher
• 12:17effect size on the lower frequency.
• 12:20So here we in particular focused
• 12:21on CMV which is a type of rare
• 12:24variants that are present in 10
• 12:26to 15% of children seen in the in
• 12:29the neurodevelopmental clinics.
• 12:30So because the frequency before
• 12:33were for general population.
• 12:35CNN's are deletion or duplication
• 12:37of the DNA segment that typically
• 12:40encompassed several genes.
• 12:41So for example,
• 12:42if we take the 16 P 1.2 CNV,
• 12:45which is one of the highest
• 12:47risk factor for autism,
• 12:48you can see that you you have
• 12:51the deletion of 2019 on the
• 12:53short arm of the chromosome 16.
• 12:56And so as I said,
• 12:57the deletion is associated with
• 13:00autism but also with obesity
• 13:02macrocephaly over connectivity.
• 13:05While the duplication at the same
• 13:07locus is associated with schizophrenia,
• 13:10anorexia.
• 13:12Micro microcephaly and underconnectivity.
• 13:16So it's quite unflagging.
• 13:18We have several examples like
• 13:21that in the across the genomes.
• 13:23So we collected the envies data
• 13:26from a different hospital as much as
• 13:30possible and so from Montreal hospital,
• 13:34Lozanne Hospital Safari Consortium,
• 13:37UCLA, Cardiff University and UK Biobank.
• 13:41We also study common variants using
• 13:44polygenic score which is simply the sum.
• 13:47Of an independent effect of each snips.
• 13:51So we computed PRS polygenic
• 13:53score for autism, schizophrenia,
• 13:54bipolar cross disorder,
• 13:56IQ and control players.
• 13:59So we first characterize their
• 14:02impact on connectivity and so here
• 14:05we simply so they are represented in
• 14:07this brain maps and here we simply
• 14:10represented the their effect and ranked
• 14:12their effect size and connectivity.
• 14:14And 1st what you can see is that polygenic
• 14:17score has a really tiny effect on
• 14:21connectivity which makes sense if you
• 14:24think that's based on common variant
• 14:26and that's a sum of of independent.
• 14:29Effect of each snip that might
• 14:31have different effects,
• 14:32so you can just cancel your signal.
• 14:34So Pierce and Pierce seems not
• 14:37really ready to be used for now.
• 14:41That's our observation.
• 14:43So we were looking for converging pattern.
• 14:46Of connectivity across our genetic risk.
• 14:51And as you can see here,
• 14:52I represented the 12 functional
• 14:54networks that we studied.
• 14:56And as you can see first it looks
• 14:59like it's we don't have the
• 15:01perfect convergence pattern at all.
• 15:03All our networks are altered
• 15:06by some genetic risk factors.
• 15:08So there is not no clear converging pattern,
• 15:12but we don't have a lot of genetic
• 15:14risk factor as well.
• 15:16But still it looks like we have a
• 15:18recurrent over connectivity pattern
• 15:19of basal ganglia. That I miss.
• 15:23Network and the Motor Network as well
• 15:25as the recurrent underconnectivity
• 15:27pattern of the lambic and auditory
• 15:30posterior insular network networks.
• 15:32So we correlated our genetic risk factor.
• 15:37We'll connect wall brain connectivity
• 15:39pattern and perform a PCA to try to
• 15:43understand which connection where
• 15:45responsible of our correlation
• 15:47across our genetic risk.
• 15:49And we found that the first PC was
• 15:51explaining 25% of the variance.
• 15:54And was mainly driven by connection
• 15:56between between the thalamus and basal
• 15:59ganglia here and the somatomotor networks.
• 16:03And if you look at the tamic.
• 16:07Connectivity profile across our
• 16:08genetic risk factors and traits.
• 16:11You can see recurrent over connectivity
• 16:14pattern with like Motorola auditory
• 16:17and visual area.
• 16:18We also found the even if the effect
• 16:22size for peers is really small,
• 16:25we didn't find it for G factor which
• 16:28was like just a control condition.
• 16:31So to to explain,
• 16:35to introduce our theoretical
• 16:37framework to summarize this result.
• 16:40So if we come back to our thousands
• 16:42of genetic risk factors.
• 16:43So on the bottom part of my slide,
• 16:49you can try to.
• 16:53Group them based on their role
• 16:55at the cellular level.
• 16:57So for example,
• 16:58we already know that a bunch of
• 17:00genes involved in autism such as
• 17:03Cheng free or FMRP highly involved
• 17:06at the synaptic level.
• 17:07So you can for example just like
• 17:10rank your genes based on what they
• 17:12do on the at the synapse or if
• 17:15they are involved in like neuronal
• 17:18migration or like pruning or etcetera.
• 17:21So that is.
• 17:22The hypothesis is that this limited
• 17:25number of cellular mechanism could
• 17:28altered a limited number of brain profile.
• 17:32That could be connectivity,
• 17:33but that might be anatomy,
• 17:34that might be eggs, that might be I don't.
• 17:38I don't know what.
• 17:39And so in our case,
• 17:41we found this converging pattern
• 17:43of the talamo motor connectivity
• 17:46profile. And so the hypothesis
• 17:48is that this pattern could alter
• 17:52some dimension that are impaired
• 17:55across different diagnosis.
• 17:56So that's not specific to autism,
• 17:59it's not specific to schizophrenias,
• 18:00not specific to CD's that could
• 18:03just like altered sensory motor
• 18:05function that are known to be
• 18:08altered across different diagnosis.
• 18:10So that's really going into this
• 18:13demand dimensional approach
• 18:14such as the one proposed by.
• 18:16Our dog.
• 18:19That's definitely really reductionist,
• 18:21but that's just a way to try to simplify
• 18:25what we are trying to do. Um, so.
• 18:32Just to finish we so we are a bit stuck
• 18:36here because we are going to struggle
• 18:40continuing this approach because we
• 18:44cannot connect this genetic variant are
• 18:47really rare if we use common variant,
• 18:50they are quite atherogenesis for now so.
• 18:55It will be quite complicated.
• 18:57So geneticists for now try to
• 19:00delineate general mechanism of
• 19:02genetic risk factor of on cognition.
• 19:06So they are testing several genetic
• 19:10score to try to predict the effect
• 19:13of any deletion on cognition
• 19:16and on IQ and autism risk.
• 19:19And they reported that the probability of
• 19:21being loss of function and relevant was
• 19:23the best predictor of the effect of our.
• 19:26Genetic risk on IQ and autism risk so
• 19:30they developed this this model and.
• 19:34Ohh, we have now clinical application.
• 19:36So if you go on this website,
• 19:39so CNV prediction,
• 19:41if you enter your genetic coordinate
• 19:44and you for example you have the
• 19:48deletion of the 16 P 11.2 so you would
• 19:51like 16 the coordinate and deletion
• 19:54and you get the estimated score of
• 19:57the impact of your mutation on IQ.
• 19:59So we wanted to to apply that
• 20:03on connectivity.
• 20:04So we and it was great because it
• 20:06allows us to go beyond this case control
• 20:09like one CNV other time analysis.
• 20:12So like that we can include any
• 20:16CNV carriers in our data set not
• 20:19only the recurrent one.
• 20:22So we simply annotated genes encompassed
• 20:25into each CNV using this genetic
• 20:27score and we could just like we just
• 20:31looked at the linear effect of this.
• 20:34Critics score on connectivity.
• 20:37By doing that, we've we found that.
• 20:41This genetic score was.
• 20:45Was impacting mostly the thalamus.
• 20:48As you can see,
• 20:50you have 30% of the connectivity
• 20:52profile of the of the connection of the
• 20:55dynamics that were impacted by this.
• 20:57Genetic score,
• 20:58constraint score and if you look at
• 21:00the dynamic again connectivity profile,
• 21:02it was again over connected with
• 21:05motor and auditory area etcetera.
• 21:07It was not only the thalamus as you can
• 21:10see it's like a complicated profile,
• 21:12but it was,
• 21:13it's just like a way to summarize
• 21:17the information.
• 21:18So to conclude,
• 21:19we have seen that pleiotropy and
• 21:22polygenic city do impact our ability to
• 21:25detect brain connectivity alteration.
• 21:27In their names like treat manifestation.
• 21:29So it might be complicated to
• 21:32identify clear biomarker of our
• 21:34current clinical diagnosis.
• 21:36We still found a clinical contribution
• 21:39of the thalamus and the somatomotor.
• 21:42But OK,
• 21:43that could be associated with
• 21:46with sensory impairment observed
• 21:49across different diagnosis?
• 21:52So this mutation also do impact
• 21:54the rest of our body,
• 21:55it's not only the brain.
• 21:57So we might need to include data
• 21:59from other organs in future study,
• 22:02but we need to pursue this study to
• 22:05have access to more like imaging genetic
• 22:08database such as exams like the represented.
• 22:11I think it's the one of the only
• 22:14database with imaging genetic
• 22:16data in psychiatric conditions.
• 22:18So we have UK biobank etcetera,
• 22:20but HCP.
• 22:21And it's general population,
• 22:23so we really need like a.
• 22:26A data set enriched in this
• 22:31rare genetic variance.
• 22:33And we also need to use like
• 22:37cognitive scores that are
• 22:39not specific to a diagnosis.
• 22:41So for the only score that
• 22:43we have in common across our
• 22:45different diagnosis is IQ so far.
• 22:47So it's quite complicated to to.
• 22:50And IQ depend off on age.
• 22:53So it's quite complicated to compare
• 22:55our diagnosis based on our our
• 22:57continuous dimension because we don't
• 22:59have that much to compare for example,
• 23:02you have specific score.
• 23:04For each diagnosis as you might know,
• 23:06so to characterize social impairment,
• 23:09language impairment like attention
• 23:11process etcetera, but it's not,
• 23:14it's specific to the to ADHD
• 23:16or to ASD or to schizophrenia.
• 23:18So it's complicated to compare this.
• 23:24Diagnose you?
• 23:25So I want to thank my previous.
• 23:30Lab so Sebastian Jackman,
• 23:32diabetic Toma Burgeon at Paris,
• 23:35and Richard Alarm and my new
• 23:37lab at USC with Paul Thompson.
• 23:46Meet up sometime. Very nice.
• 23:52Theoretical model.
• 23:58I wonder. So often this is the model
• 24:00of unfortunately right and often when
• 24:03we're thinking about opportunity,
• 24:05you want to like the patients into stuff,
• 24:08right, so that we can.
• 24:11The way I'm interpreting this and
• 24:13that you would essentially get if
• 24:15you did that in a in a robust way,
• 24:18you would essentially get a
• 24:20different answer at each other.
• 24:21And so that makes it complicated because
• 24:25what level kind of maps on to treatment?
• 24:27You know, how do we validate?
• 24:29If we often we we post from one level
• 24:31and then we use another level to kind
• 24:34of try to validate differences and
• 24:36this seems to me more likely to be.
• 24:41Maybe, but I wonder what it means
• 24:43for the search for something.
• 24:48Yeah, definitely I I was also thinking
• 24:52instead of going like bottom up,
• 24:55going top down like people
• 24:57are currently going top down,
• 24:59I think we should do like complementary,
• 25:01we should do it like we should do it both
• 25:04way because both way will be informative.
• 25:08And we could like do sub types and try to see
• 25:11if they are enriched in genetic mutation.
• 25:14But for that we need this imaging data,
• 25:16this data set with imaging
• 25:19genetics in psychiatry.
• 25:20But yeah, I totally agree with that.
• 25:23Which is great reductionist,
• 25:26but it's a way of summarizing. Sure.
• 25:31One question regarding.
• 25:34All of these analysis obviously
• 25:36based on people being patients,
• 25:39so being actually diagnosed seeking
• 25:41help and also that's one to put in.
• 25:47More related to resilience.
• 25:50In particular, whether you actually
• 25:53become over the negotiation.
• 25:55Plus having for pology,
• 25:58but not by the well enough bottled up so
• 26:01that you don't end up being a patient.
• 26:04Um, so. That would be what
• 26:08you would see the resilient.
• 26:12He makes you impatient.
• 26:14Somebody who just says it. A yeah.
• 26:26OK.
• 26:30So I'm trying to think.
• 26:36Yeah, it could be the case, but it will be a.
• 26:43I mean resilience is really,
• 26:45definitely really interesting
• 26:46to study right now. For example,
• 26:48we are trying to study in like general
• 26:50population like in UK Biobank.
• 26:52We have a study right now trying to
• 26:55investigate carriers of our genetic risk
• 26:58factor who did not get any diagnosis.
• 27:01So why is it the case?
• 27:02Is it's like the rest of the genome,
• 27:03is it the environment like?
• 27:07And I think it's like really
• 27:09a hot topic right now,
• 27:11but I don't really know if it's the
• 27:16player Tropic then I it really looks
• 27:19like the majority of the games are
• 27:21played topic only some of them have
• 27:25high penetrance and the specificity.
• 27:27Most of them have like are
• 27:29expressed at different places and
• 27:31conference for several diagnosis.
• 27:33So quite so it's this we will
• 27:36definitely not be able to do any.
• 27:39Gene editing or anything in psychiatry,
• 27:41because it's not specific.
• 27:46Thank you very much.