elin grundberg JULY 2018 JK - PRESENTATION- 11 MINS [MIXED RESPONDENTS] [Other comments:]

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Eugene Morrison
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1 elin grundberg JULY 2018 JK - PRESENTATION- 11 MINS [MIXED RESPONDENTS] [Other comments:] So, we're going to be talking about epigenomic research, so, Elin, are you around? Come over. You can introduce yourself Elin, so, please, go ahead. Thank you. Yes, I am Elin Grundberg, and previously of MacEwan University in Canada, now relocated to the US, so, I will talk to you with both hats on, MacEwan, and the new institution, the Children's Mercy Kansas City. First, thank you Biobank for this invitation. I'm very excited to be here. I see myself talking on behalf of many of you here that I've spoken to earlier today, and on s, that are excited to see the possibilities of expanding the molecular profiling in UK Biobank, including epigenetic profiling, and I will show you some of our insights in doing this enlarged cohort, and, hopefully, we'll come up with a strong proposal and convince funders that this is the next layer of information that we need in UK Biobank. So, during the day today, I think we got an amazing overview about UK Biobank as a research, with deep phenotypes, but also, on the genetic side, genotyping spanning out from [unclear word 0:01:12.6] and now with Regeneron and [unclear word 0:01:14.8], with exome sequencing, as well as the whole genome sequencing, and similarly on the environmental lifestyle factor, how this is very well covered with the deep catalogue of information. So, what I will try to convince you is that we can use epigenetics to connect environmental with genetic factors and learn phenotypic risk and disease biology, and I will talk about how we can use epigenetic changes, or epigenetic variation, to interpret or translate genetic variation into regulatory causal pathways, and that epigenetic variation might be superior to the previous transcriptomic variation that was popularly use in complex disease studies, but also show you that environmental lifestyle factors actually can impact our epigenome, and cause epigenetic modifications, and that, in a similar way, can impact and identify causal or regulatory pathways for complex disease, and just touch upon the fact that, compared to genetic factors, the environmental factors encourage some epigenetic changes. We can get a consequence of that, based on the disease. So, as probably many of you know, this is what we do when we do an epigenome-wide association study, very much similar to a genome-wide association study. We pick our study populations of interest, either a case control, or a population base, and we do epigenetic profiling, either dependent on funding, or interest targeted by array, or we do whole genome or at least [unclear word 0:02:55.3] the sequencing base, and then we do large-scale association studies to find regions of the epigenome, in terms of marks that have an association to our disease of interest. I think many of you would agree with me that the most commonly used epigenetic trait in large-scale population-based studies is DNA methylation, and I will list three reasons why I think that's the trait to use. Obviously, we're dealing with very robust and reproduceable assets. Most of them are based on bisulphite conversion of our DNA, and we do have automated solutions in place, meaning that we can do this now in large scale, and we can both have microarray-based method, as well as NGS, that do this in a very accurate manner.

2 2 Obviously, DNA methylation benefits from being a quantitative trait. We're mostly dealing with bulk tissue, so we're dealing with a distribution of zero to 100 per cent of our trait, which makes it easier than other reprogenetic trace to do statistical analysis, but, obviously, as we're dealing with a single base pair, our solutions vary, and we do have up to 30 million of these sites that we can measure genome-wide, and also, compared to transcriptomic [unclear word 0:04:18.2], epigenetic, or DNA methylation is a stable trait, and we can use this, actually, as a predictor of regulatory element. So, what have we learned in doing epigenome variation, or DNA methylation in large scales, based on [unclear word 0:04:33.6]? We started, and I have a long-standing collaboration with The Twins UK, so we did this a couple of years ago now, where we were interested to see, if we do this genome-wide, using whole genome bisulphite sequencing, how is the DNA methylation variation dependent across individuals, across tissue, and across regions of the genome, and it was really striking to see that DNA methylation variation is very region-dependent. We do see various static patterns of DNA methylation in certain areas, such as the promotors, whereas the variability is really enriched in more distal regulatory elements as the enhancers. So, we were not keen on using the larger scale, the targeted array at that time, the 450k array, we saw that it's really a biosource attainment with a large proportion of those CPGs being in promotors. Similarly, we noticed that where we have an association with a complex, or a disease trait, these are also in a similar way as we've seen for genetic variants, enriched in these enhancer elements. Obviously, at that time, and still, the whole genome bisulphite sequencing is costly, but, also, that we're dealing with a large proportion that actually might not be very useful, as it's not variable across individuals. We wanted to see how we can improve the profiling of DNA methylation by still restricting to certain areas, so we initiated a collaboration with Roche NimbleGen to follow the same procedures as a whole genome bisulphite sequencing, so, a shotgun library, but instead of sequencing that full library, we target and enrich, similar as in an exome sequencing experiment, we enrich for regulatory elements which we were interested to do, and by doing so, we can do multiplexing, we can cut down the cost, and we can increase our coverage of the regions that we were interested. So, we're designing these panels based on our tissue of interest. Obviously, blood is a tissue that we do have most samples available from, so we design panels based on regulatory regions, specifically targeted for blood, so making sure that we're covering all regulatory elements in all, or at least the data that's available from different purified blood cells, and also, we include other types of regions that we think is useful. For instance, the CPGs that have been added on other types of methods. The benefit from this, we did genome-type at the same time so we make sure that we include also genetic variants, rarer variants that we can then genome-type at the same time. The bottom figures is basically showing what this looks like. Basically, the red bar showing the regions that we cover, and then the So, basically, this is what we cover, and this is when we're sequencing, and you can compare that with a whole genome. Obviously, it's not fully covered, but compared with 450k, we do cover densely the regions that we're interested in. By having full, or dense coverage of regulatory elements, we can also start to see the pattern of ethnogenetic association within an

3 3 element, and we have started to detect very interesting features. This is showing a statistical significant association with a trait, and those that are enhanced, there seems to be preferably mapping at the midpoint of the enhancers, and if we then limit to promotors, the significant disease epigenetic variants have a preferable association towards the gene regions. So, if we then mirror that, and see what do we have available on arrays, we do see that there's an underrepresentation of that region, specifically on promotors, which is the majority of the regions on this array, so we were kind of still interested to start, or continue to do this dense coverage of the regulatory elements, so if we do that in disease studies, how do they look? We used cigarette smoking as one of the proof of principle, environmental factors, that epigenome or environmental factor actually changed our epigenome quite dramatically, but also learn what is the advantage of doing dense regulatory sequencing over scatters, so we obviously confirmed this is the most famous chromosome five regions for smoking, the HRR. That is the most reproduceable, or replicable finding in epigenome-wide association studies, but this is the enhancer that has been strongly associated with the activity, and expression of this gene, but we do also see two additional regulatory elements that shows signals and been affected by smoking. But, maybe most interesting is that we would look in these specific regions that we know has strongest effect, based on smoking, where the blue line is actually seeing if we sequenced that element in higher depth, we do fine-map the association, and have a CPG with a much better predictable scores when we're trying to predict an individual's smoking status. Then the green one, which is the CPG that has been on the array based, but also we were interested to see if we have this preferentially mapping of epigenetic variants within regulatory element. Is that useful, does it tell us something, so we can zoom in and look at these associations in the midpoints, as well as in the downstream of the promotor, and actually learn how the transcription regulation is linked to this. We find, in this case, it's metabolic disease associations, and we see strong enrichment of transcription factors that are binding in these specific regions, and we can follow up and do additional thing. We can obviously do our replication, but we can link it to gene expression, and then do similar thing. Instead of doing the regulatory element and transcription factor, per se, we can see the downstream genes and do pathway analysis, and actually see that similar pathways are coming up as our transcription factor binding analysis. Then we zoom in on one of these genes that has been strongly linked to this metabolic trait. We know it's the vascular and endothelial growth factor. There is a genome-wide association study using the UK Biobank data here, strongly showing an association as downstream with the gene. We identify a regulatory element, rather far upstream of the gene. It's a tissue-specific enhancer, robustly associated with a metabolic trait that's relatively linked to the GWAS trait, and, obviously, with the genetic information in these cohorts, we could integrate that and see that the association of epigenetic variation in this region is non-genetic, so that's basically, potentially indicating that we have an additive effect of a locus that is relevant to this disease. Obviously, as I mentioned, the key feature of the epigenetic or molecular profiling is to understand genetic susceptibility, and, obviously, we can do this too with DNA methylation, and, as I said, even better

4 4 than using gene expression. I take this example which Nicole Serrant, as we heard earlier today, published together with Tommy [?Preston 0:12:25.3] at our institution a few years ago, using the blueprint data, basically collecting a large number of individuals with various different blood cells, and different layers of molecular trait, and trying to see what is the co-localisation of a disease, where it shows molecular, and, actually, that the phytogene expression only accounts for a minority of the disease link, where the majority is actually epigenetic variation, and specifically DNA methylation as a strong molecular trait, explaining the link between a genetic variance and the disease outcome. Again, we can zoom in and take an example to try to link it to data from the UK Biobank. This is an interesting signal from the Blood Pressure Consortium, using UK Biobank data, and similar, here is a tissue-specific only occurring in blood tissue, a strong regulatory element with robust epigenetic variation of DNA methylation within the GDF7 gene, and, compared to the previous example I took if we integrate genetic information with this DNA methylation, we see a perfectly nicely co-localisation where further integration with gene expression data actually can indicate that we're dealing with this gene that seems to be genetically modulated through epigenetic variation at this locus. Finally, as we're dealing with epigenetic tissue specificity, and talking about blood tissue, a tissue heterogeneity is a key thing to consider. It matters, it's important, and we need to account for that. I, personally, was less worried about this than the previous year, basically, since we know we can actually correct for this relatively well. Many of you have seen the vast amount of available packages, basically, reference-free, referencebased, and mainly our supporting array-based data. What we actually do, we can actually do supervised prediction of cell counts in our next generation sequencing-based data, by basically identifying CPGs that are very specific to a blood cell type, and we can use publicly available data for up to [unclear word 0:14:49.5] purified cell type, really by using stringent criteria, and identify hundreds of CPGs that are only active or hypermethylating in a cell type, and using those then to fetch the average methylation in whole blood data, we do see that they vey well correlate with our lab tests, meaning that we actually can use this in statistical analysis, in cohorts where blood counts are not available, and, actually adjusting for this in a much more accurate manner than if we have the CBC test alone. So, I'll summarise, and try to highlight why I think epigenetics is an important and useful trait in large resources, such as the UK Biobank. We do know that we can discover regulatory pathway that underlies complex trait and diseases. These can both be genetically-driven, but with epigenetic, we can actually pick up environmental, or lifestyle-driven effects. We believe that the higher resolution profiling regulatory element has a great advantage, as it has this opportunity to add additional insight into regulatory pathways. DNA methylation really is a perfect molecular trait to annotate the punitive functional consequences of genetic variant, and, again, if we're doing very dense coverage of DNA methylation, we'll be able to cover the majority of these regulatory disturbances that non-coding genetic variants cause. Finally, the opportunity to add epigenetic profiling, really and have the potential to allow us to build this combined risk scores, maybe, including epigenetic variance as well, and for having prediction of later life

5 5 outcomes, and, obviously, we're really dependent on large cohorts and [unclear word 0:16:46.3] type studies in order to do that, and obviously UK Biobank is the perfect tool for that, so I will end there. Just highlight everyone who has been involved in our project, but specifically the UK Biobank team, with Rory Collins, and Paul Matthews, and both Jimmy Bell and Gemma that's here, that's been very supportive in discussing this, and hopefully we'll come up with a strong proposal and convince funders that epigenetic profiling is the next trait to be asset. Thank you. [Applause] [END OF TRANSCRIPT]

Aris Baras JULY 2018 CRI - PRESENTATION - 17 MINS [MALE RESPONDENT] [Other comments:]

Aris Baras JULY 2018 CRI - PRESENTATION - 17 MINS [MALE RESPONDENT] [Other comments:] Thanks to Sekar for taking us through a good example about having this population scale of genome typing data available