Sano Genetics 070
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[00:00:00] DR PATRICK SHORT: Welcome everyone to the genetics podcast. I'm really excited to be here today with Dr. Cecilia Lindgren who's the professor of genomic endocrinology and metabolism at the Nuffield department of population health at the university of Oxford. Cecilia is also the director of the Oxford big data Institute, which we're going to today and co-chair of the international common disease Alliance. And I can also just say on a personal note that I met Cecilia many years ago, I was a PhD student and she's not only a scientist with the number of tremendous research accomplishments, but also one of the most collaborative and supportive scientists that I know.

So on that note to say, I'm really excited to have you on the podcast and thank you so much for taking the time. 
Cecilia
[00:00:39] Cecilia LINGREN: So excited to be here. Thank you for asking. 

[00:00:42] DR PATRICK SHORT: So I listened to an interview while I was preparing for this, that you did with Gil McVean at Oxford. And in the interview, he actually asked you about your biggest Eureka moment that you ever had and I love the question and I also really loved your answer. So I thought maybe it would be a good question to start off with here and set the scene for the discussion. So what is the biggest Eureka moment you've had and maybe it's changed since then, but, but maybe not. I'd love to hear about it. 

[00:01:02] Cecilia LINGREN: I love the question and I'm really excited about the answer as well and it has remained the same actually. When I set out and did my PhD, the field of genetics was really plagued with inconsistencies. It was hard to do genetics. Then we didn't have anything of everything at the fingertips on a computer. So when I came to Oxford in 2006, I started to work on the first genome-wide association study.

And we spent the better over a year, just setting up pipelines and QCing, uh, theater, and that sort of culminated to a late December evening that I will forever remember where I sat with deaf parents and nicotine sewn in a sweat Delisa room on the welcome trust center for human genetics in Oxford and ate pizza and analyze data.

And just the feeling of running that analysis and seeing that Manhattan plot. For those of you who don't know what that is, it's a plot that basically maps out your results in a very beautiful way and just seeing screamingly significant, robust results coming in. And having Mark McCarthy and under hectically and Elysee Guinea on the phone from South Africa and team failing and Mike Lee on the phone from Exeter, just waiting for the results was such an emotional and exciting experience because I think for me, that was the first time that I was involved in something where, A, I felt that I'd really worked funding myself as it were crafted a lot of it myself. And that was that robust and that significant and that compelling and that it really felt like we were making a break through.

And I think that's a scientist. That's all you can ask for. 

[00:02:35] DR PATRICK SHORT: We did that project come about in the first place. because you alluded to the fact that it was a wild west of candidate gene studies and things that were impossible to replicate. Where did the idea come from? And was it difficult at that time to convince the various funding bodies that actually we needed to do this way bigger and way more systematically?

How did that actually come? 

[00:02:54] Cecilia LINGREN: Well, I was junior at the time, so I shouldn't take credit for the Genesis of the project. I'll, I'll give credit where credit is due. So I think, you know, there were a slew of discoveries that led up to the insight that you could actually genotype a much smaller number of markers and get good coverage across the genome.

So the first sort of piece of that puzzle would be the human genome sequence that was launched 2000. That led to the snip consortium or the genetic variance consortium. If you want to say an easier that found that we had a common variants, every 300 base pairs or so in the human genome. And then that led to a breakthrough finding by Mark Daley and colleagues showing that these common variants have a relationship with each other so you don't have to type and need every marker to extract information and that in parallel with some wonderful work from Jonathan martini and colleagues, sort of figuring out how you can actually impute or infer data that you haven't genotype based on these relationships, then sprung to both AFI metrics, then alumina and other companies sort of putting forward chips that would allow you to genotype, maybe half a million markers and extract most of these that have common genetic variation, out of individuals., which then in turn allowed you to do this in a cheaper way. It wasn't possible to do that before because of scale and economy. So now suddenly you can take thousands of cases and thousands of controls and genotype them and infer the full set of genetic background and do association analysis where you basically in every single way, compare 80 frequencies between two different groups for each of the variants. And see if there are variants that have significant deviations or sort of differences between each other. So in a way, relatively straightforward, but that sort of led a group of PIs in the UK and in the U S in parallel to go to their funding buddies still in the UK, we have the welcome trust case control consortium, where Lon Cardon, David Clayton, John Todd, Peter Donnelley, Mark McCarthy, Hugh Watkins, I missing names here because I don't know, but there were seven deceases and a pool of control samples that came together and did this. And I was active in that. Take two diabetes branch of basis. One of the lead analysts together with him, son, Mike, we don't think freely and Alicia Guinea.

And it was just so exciting to be able to analyze this data. Yes, I really love it. 

[00:05:21] DR PATRICK SHORT: And what did you learn from that first association study specifically? What were the big findings? 

[00:05:27] Cecilia LINGREN: Well, so the first finding was that we're wildly underpowered. So we thought, you know, we had previous, ample numbers of cases, but those weren't enough for most diseases at all to release it, to dissect that out.

So that was a key insight. And that has actually led to a piece of work that has been a red thread for me throughout my entire career to work in large group setting and consortium setting. Because it sort of showed that we had to work with their American colleagues and do meta-analyses where you combine your data sets to increase power in ever increasing numbers and iterations.

So, um, that was the first thing that we didn't have enough power and that we needed much larger sample sizes in the order of hundreds of thousands to millions of samples to really map out the genetic information. But then also we glean the first robust associations that I mentioned. We did find a couple of nuggets for each of the traits and diseases, and also lent us a lot of tools that we sort of brought forward with us, that we had sharpened over time.

Like the amputation, we figured out their robust p-value correction. And the first set of biological insights, then after a couple of rounds of meta-analyses, this started to just sort of produce insights that have provided them another springboard for how to make sense of all these genetic variants.

[00:06:49] DR PATRICK SHORT: Yeah. And maybe it'd be great to fast forward to today. And you could compare the scale that you were operating then verus, if we take obesity as an example, the scale you're operating today. And I'd also love to hear how the questions have changed back then, it was can we find robust associations? And now you're into multiple layers of the onion deeper in terms of really dissecting the biology.

So maybe you could compare and contrast that first study with, with the kind of studies you run today, 

[00:07:14] Cecilia LINGREN: Yes so currently in Giants we're operating on the level of 3 million samples versus 3000 cases. Well, uh, for obesity and we're collaborating between these three and me and commercial partners, which is something that we wouldn't have done there.

One of the things that excites me is that we are in a much more collaborative environments, both with the biotech and pharma at startups and academia. So that's one thing that has changed a lot I feel, because we bring different skillsets to the table and there is a wider appreciation of that. I feel.

So the second thing beyond skill that is really started to happen, the layer we're acting the onion right now, I would say is what are we doing with these variants to really make sense of them at scale? So when we found the first variants and I think FTO is such a beautiful example. So when we found the first variance, which is an electronic variant.

On Twitter. It was said that we call the gene STO and that we had said with STL was the causal variant. We never said that. So if you read the paper, it actually says it's in the first. So we knew pretty early on that it was likely to be a regulatory variant. Right. And there's been courageous work by and others showing it's a long range QTL that is the most likely affect then it was shown to have an affect on thermogenesis in adipocyte like cells. It has also been suggested to have a function of iron homeostasis in a region in the brain and the most recent conclusion for STO you know, 14 years or so after discovery is that it's probably both operating in brain and in adipose tissue and then a genetic variant can function and operate in many different cell systems and cells types as well in many different processes, which just shows us the complexity of it all. You just have to look at another human to realize that it has to be quite complex, right. But that's an exciting insight I feel.

And then we should take a moment and just realize that we have hundreds of thousands of robust associations. Thousands of trades and disorders. So we think keen scientists that almost all human traits and conditions have a common genetic component. And that insight leads then to also reflecting on the fact that environment has an effect on most of these.

So that into place, or is it something that we haven't started to scratch on yet? And that's a power game again. So roughly between the thumb and the another finger, that's a Swedish thing. You would say that you probably need four times as large sample sizes to discover a gene by environment interactions as you do for genetic discoveries.

So that gives you a sense of scale that you would have to do to dissect that out properly. So that's another insight. And then I think importantly, the treasure trove of variants we have today have really started show us which cell types and tissue types we should operate in with beautiful work. And develop methods to look at tissue enrichments out to various prescription datasets and so forth.

So it has opened a lot of insights on the complexity expected. Cell types and tissues that are likely to be most important. The first insights to which processes and mechanisms are likely to be perpetuated or is sort affected by the genetic variation. And then also, again, this scale, with, which we need to operate in so NIH and other people are messaging currently that about two thirds of biological data is irreproducible in another lab and that's a shocking number. So for me, that's something that I spent three years now in ICBA or the international common disease Alliance to think about why is that? What can we do about it? And how do we really go from these hundreds of thousands of variations?

It took us 14 years to reach as somewhat of a conclusion for one variance in FTO. If we have hundreds and thousands, just scale that up and think about the magnitude. So we need to again, think about how to get the functional. Part of the scientific team working together with the genetic team and thinking about operating this at scale with a robust statistical backbone and with the sort of methodological development that are just popping up everywhere, which is super exciting.

So that's where I'm at in my onion. Did that make sense? 

[00:11:37] DR PATRICK SHORT: Yes. Yeah, completely. And there's a few areas that would be great to dig into, especially on that last point of. 15 years to understand that a single location what's going on here actually. And probably the story is still being written because I think what you said about the gene being active in the brain and in adipose tissues, it was only a year or two ago, right.

That, that was pretty firmly ironed out. So that story is still being written. What ideas do you have about how we go from doing that more quickly or, or at a greater scale? So as you mentioned as a hundred thousand plus locations where we'd really like to understand in great detail what's going on, but we don't have that much postdocs and PhD students that can dedicate 10 years of their life to focus on that one location yet, do we. 

[00:12:20] Cecilia LINGREN: The best use of their time. I'm super excited. So I think in the, in the field of sort of functional genomics, where there is a big appetite in Oxford and in the wider UK and our friends at Sanger, I think as well as Exeter and, and, you know, uh, our friends has got gender also thinking about this. So I think when we think about functional genomics, there are a couple of toolkits that have come out that are super exciting I feel. The first one I will mention is probably the CRISPR screening, you know, that you can systematically knock out all the genes in a genome, if you have a nice agentic cell line and you can do various readouts. So for me, that's super exciting. I think we need to scale up in terms of cell lines. I'm really excited about the work in IPC that then Gaffney and Helena Kilpin and others have done and thinking about ways of using that genetic common information and doing cellular readouts, and then tying that back into disease

I think it's really cool. So together with Milena Claus, who is a wing mate and a good friend of mine. She's awesome. She's such a sparkly scientist. We set up cellular Gus's in 700 adequacy. That have this sort of common genetic versus natural genetic background. And we're currently doing readouts for outpost trait related phenotypes on the cellular level.

And then together we've Ben Neal thinking about how we develop this statistical framework, so we can actually tie it back to the original genetic discovery on the phenotype. So that excites me. And I know that there are many of other groups, Paul Anderson is working on similar stuff. But, so, so I think that's an area that is really sort of budding in parallel.

You know, Matt hurls and his colleagues are setting up this consortium where the goal is to sort of in a systematic way, interrogate every protein, coding variance in the genome. And that's also ICD nascent activity. And I think that's a really cool sort of way of thinking about it, and again, them massive parallel reporter assets that can sort of screen thousands of variants at once in a single cell sort of system will allow us to in a systematic way, interrogate at scale again, the functional levels. So the key thing for me is then to come back to this. So I think when I started as a PhD student, we were told that we had to have positive and negative controls for every experiments and that every plate, when we did genetics had to have a certain set of samples that were carried over.

I mean, if you think about the SEF controls, how many times have they been sequenced in genotype? And that wasn't perceived as waste of money, but in biology, if you even read them the top two journals, that's not required in experiment. So that means that the gold standard baseline for most experiments isn't mapped out, then that's something we in ICD I've been thinking deeply about.

And I talk a lot about this lack of positive and negative standards. So if you and I started to collaborate on a functional genomics project together, how would we know if my product is really replicated in your lab or not. 

[00:15:27] DR PATRICK SHORT: And why is that? Why has that changed so much over the last decade or two? Is it pressure to increase the velocity of new findings or is it the way, the way the technology has changed that it's made things more hypothesis free? And I'm curious of why that is and because it's clearly a big issue. 

[00:15:43] Cecilia LINGREN: I don't think it's new. I think it's been discovered and talked about, I think we have spent more time trying to replicate each other's finding, the industry is becoming more and more communicative and more and more interactive. And the first thing that many of my colleagues in industry do when they read a cool paper is try to replicate.

So that they can motivate by, they want to do the next steps. And if they then can't do that, it's not sort of instilling a lot of confidence in the first, not just say who's ranked in that scenario, but still, if you can't reproduce it, there is an inconsistency that needs to be ironed out. Right. 

[00:16:16] DR PATRICK SHORT: Muddies the waters, doesn't it. And then you, you end up arguing over whether whether one group did it right or wrong rather than building on top of it. 

[00:16:22] Cecilia LINGREN: Yeah. And I do think that the reason for it is that there hasn't been the entire sort of genetic field has come together and work together. And in biology, people have had a tendency to work in smaller groups. In more sort of isolated situations and it's not to, you know, throw away everything that we know about biology you have today and say that people don't know how to do it. I'm just saying that we, as a genetic field, we're at a similar stage where we had to come together to extrapolate the best practice standard if we wanted to scale up.

So that's what I'm saying. Not that everything today has been poorly done. 

[00:17:03] DR PATRICK SHORT: Well, it's always on a spectrum. Isn't and I think it brings back to the point you made at the very beginning of our conversation that, um, there was a little bit of a wild, wild west period pre large-scale genome-wide case control type projects didn't mean everything was wrong.

It just meant there was a lot of around all the signals. So, so it sounds like we're maybe be another one of those moments now where we need to collectively determine how do we align on a common standard and frameworks. I wonder if you could talk a little bit about the ICTA, how it came about and the number of things that you all are working on, because it does sound like a really, I mean, just looking at the website, there's a, a list of the most incredible names in human genetics that are involved. I've, I've met some of them, others. I know that by reputation, but I'd love to hear how you all came together, such a great group. And I know you're interacting a lot with industry as well. As you mentioned, a big focus is around translating genetic findings into drug discovery and more personalized medicines so we can get onto that as well, a little bit later. 

[00:17:56] Cecilia LINGREN: In 2012 after my first postdoc here. I went back to the road to be a scholar in residence for three years. During that time, much of that genetic explosion in the field in various places across the world that sort of happened. And we started to realize how difficult it was going to be to map out each of these single variances as I said, so that led to a conversation between a lot of the PIs in the field. Globally about how do we tackle this at scale? Then I think about 30 of us decided that we should just meet up in New York and have a serious conversation over a couple of days about what we could really do. And if there was an appetite to actually come together and think about it, that turned out to be really positive.

So later about hundred people, either self nominated or people recommended other people to set up come along met in Oxford. And a couple of funders had heard about this and asked if they could come along. 

[00:18:53] DR PATRICK SHORT: That's always a good sign, 

[00:18:55] Cecilia LINGREN: but it's also interesting because it's sort of, I would come back to that. ICD is not a funding agency. We don't distribute sending money. We're a nimble scientific ecosystem that is there to enable scientists to do the very best state of the art scientist. So we don't take over. We don't run the parties for people, but we are a community that, you know, individuals, I run projects that are probably going to be aligned, but it's not like ICBA projects that we just want to enable everyone.

And one idea that the funders had was that they wanted to listen in very early, because they had realized that this functional genomics problem was a big one and needed to be tackled. So we agreed that we would sort of start just up. ICBA we had a meeting in Helsinki following where we discussed structure and names and sort of vision and mission.

I had been asked to coach here with Eric Lander, which is you asked what the Eureka moment was. That was probably the DUS finding in one of my proudest moments was actually when I was asked to coach here with Eric, because he was my part time PhD supervisor. Uh, so it felt very set of, I was very honored that he asked that. And it's also, I love working with people that I took that responsibility very seriously. So then when we started to get going this ICBA and we developed a process where we wrote a white paper it's freely available on the web. And we have an organizing committee that is 36 people strong. We spend six continents, we have close to 50, 50 gender ratio.

We're not as good on diversity as we would want to be. And that's something I can connect and later, but we have grown quite quickly. Uh, we have had a series of, uh, scientific symposia and town halls where we discussed the program. The entire OSI and also members of the community have come together to craft the white paper and the 23 recommendations that fall into eight broad categories if you want.

So we've sort of talked a lot about like the maps to mechanisms, to medicines, a challenge that stands upon us. We're basically, we're trying to find the bottlenecks to that are sort of inhibiting the acceleration in from genetic discovery into the mechanisms that these genetic variants are pretty debating and then how that insight can lend itself to therapeutic hypotheses.

And sort of the clinical translation, if you want. So we have identified bottlenecks a couple of them. So we have had our first workshops where the working groups in the various recommendations have come together and pitched amazing projects that we're currently in the processing synthesizing. Um, product proposals into even bigger projects than an individual researcher or a small group of researchers can sort of take on to make them truly global.

And I think that's where we are. So it's been a process over about three years. A lot of people have worked incredibly hard. We have 1,500 members, again, every six continents anyone can join. It's not an exclusive club. And if you want to join, you should just email and flag the level of enthusiasm and what you want to do.

[00:22:03] DR PATRICK SHORT: Amazing. I'd love to dig into that maps, to mechanisms, to medicine's concept. I really liked that it's catchy and it's clear, and it's exactly what we all want to do at the end of the day. And maybe you could talk through one or two of the biggest bottlenecks, but maybe if you could just explain that concept with his famous examples where that has worked, like PCSK nine and theres now many others.

There's also detractors who say this whole genetics business is not really delivering. And I'd love to just get your thoughts on that and, you know, dive into that MTM, Tam and a little bit more detail and where we're at today. 

[00:22:34] Cecilia LINGREN: So I'm going to start with talking about a few examples that, right. I think it is really worth, I mean, you mentioned PSK nine and that was first discovered in a family based study, actually a small family based study where they saw a variant segregate with dyslipidemia. I think it was then Helen Hobbs and her team found variants in the gene that were associated again with hyperlipidemia and heart disease. And then there has been a slew of drugs targeting the gene or the variants in the gene with various sort of therapeutic strategies that is now one of the most efficient drug against hyperlipidemia and cardiovascular disease.

So an obvious showcase of a situation where you have a genetics study in a family-based study that indicates a gene, and then you find common variation affecting more common phenotypes. So that's an allelic series. We would call that. And then that in turn gives you an indication of its therapeutic effect that sort of gives you an a priori hypothesis for the fact that it's likely to work.

You can't always predict that adverse side effects or outcomes, but it gives you an indication of higher success likelihood. So here, I think it's important to reflect that both AstraZeneca and GSK have put out papers where they retrospectively come back into their pipelines and they looked at targets that have worked or not worked in their pipelines.

And in both cases, they see a significant enrichment of success. If you had other monogenic or common variance in drug target, protein, coding genes, or in the and the strongest sort of bets you have, if you have what we call an allelic series, when you have an overlap of the two. So I think that's pretty compelling reasons.

Other stories that would be remissed to mention is the cystic fibrosis. Sort of targeted, um, drugs from vertex that, you know, I mean, it was a death sentence to have cystic fibrosis, uh, only a decade ago. And today, I mean, I don't know if you've seen this astonishing movie of the man who sort of receives the drug and could suddenly breathe freely.

Which brings me to tears. Um, we also have a couple of really nice examples from Regeneron that come out, not to discriminate between any companies here. So I think we're starting to really see success stories. I think also we should reflect on the time it takes from, you know, genetic discovery to drug on the market where I don't think the genetic discovery is the slow part.

I think it's the developing drugs that is slow. And that's a problem. I believe for industry that's not within my area of expertise, but I do think that the recent piece of work probably to be done, they're thinking about how could we speed it up in a safe, ethical, and legal way. And what would it take to do that?

That would be a really interesting problem, right? 

[00:25:34] DR PATRICK SHORT: Yeah, absolutely. I'd love to jump on to the diversity point that you made earlier in both on a team and kind of team science level. But maybe before we jump into that, I'm interested from a scientific perspective and in a, seen a lot of your work and others in the field has shifted from 10 years ago, large genome-wide association studies in primarily European ancestry populations. And now the studies are getting much more sophisticated to look at different genetic variants in different populations and starting to, you know, to do a much better job at making findings that are really apply to a much broader population.

Although we still have a lot of work to do. I wonder if you could talk a little bit about the work you all are doing there to make our scientific data sets more. 

[00:26:14] Cecilia LINGREN: So that ties in really nicely into the second part of the question that you asked before, like what were the bottlenecks that we had observed?

So one of the bottlenecks that we haven't discussed a lot in ICBA is that we can't really aim to do therapeutic solutions for people globally. If we don't have a full representation of ancestry, both in genetics studies, But also in functional studies. So if you look at G techs and code roadmap and so forth, even the human cell Atlas up till date, haven't been very diverse when it comes to the ancestries and their huge efforts in the field now to remedy that. It's not to pay attention to just having representation it's because it's the right thing scientifically to do. So if you want to harness that genetic information, we are almost identical genetically, but there is information in the Genome that will lend itself for fine mapping, for instance, because they talked about the relationship between variants before, even if the variants are more or less the same, there's going to be slight differences in how they relate to each other.

That will lend itself in an amazing way to inform us to both make the most out of our data and be ensure that what we find is of global application. Um, so I think it's something we're working on. So we really proud to have supported the stand up of the Latin genomes effort or our south American colleagues are working day and nights instead of make this happen and a south American genome effort. We're working with, uh, friends in India and different parts of Asia to support them and see how we can sort of help sort of progressing that. I think it's amazing to see the middle Eastern. There was just the first sequencing project from Qatar coming out on med archive X today, which is my reading for tonight.

They're amazing efforts. It was announced today about the sequencing efforts from WHO then are going to be supported in Africa. And we also have the book on trust, supported, history Africa efforts, so we think there is a recognition of the necessity and the scientific need to do this on a genetic level and we should propel that up to the functional genomics again. 

[00:28:26] DR PATRICK SHORT: How do you see the trade-off between the massive scale team science that you're doing with groups around the world? It almost reminds me of the, like the large Hadron Collider and the physics studies that have teams of a thousand people working for four years to make monumental discoveries and on the very end, other end of the spectrum.

There's your individual groups or individuals that are working on really small scale, but blue skies research. I know there's always the discussion about where funding is allocated and ultimately there's merit to both. Um, I'm wondering, I wonder what your thoughts are on that and how you balance between these, you know, monumental large scale efforts that require coordination versus a little bit more of a lean and maybe more competitive, environment.

[00:29:09] Cecilia LINGREN: So I think there is space for both. I think they don't preclude each other. I think that some of the smartest people, I know I'm just going to exempt the fight from the obesity field because that's my field, but I'm such an admirer of Steven O'Reilly and Sonesta rookies work. They've traditionally worked on brace specific hypotheses and, you know, produce some of the most elegant work that I know of in obesity and I'm in awe you know about what they are doing. Some people say that it's a bit un-clever to work in large scale collaborations and that, you know, the individual don't have to support doing anything, but having spent a lot of my time during a postdoc, being an analyst, for instance, if you read one of our DMS paper, it actually represents 36 year of post-op work.

And when I explained that to my current boss, he was just like, wow. I thought that was like an hours work to get that graph together.

There are different types of science that are going to deliver equally elegant and smart solutions. Both of them are necessary to really drive us to the best possible solution. And it's a little bit like you have to have people that are diving really deep on certain things to deliver clues to what needs to be done at scale.

And then you need smart people to figure out how to scale it up and also how to get people to work together to actually want to deliver it. So it's. Excuse me that's but I wouldn't say one is more necessary or better than the other. And I do think funders recognize this. They often sit on grant panels, post for the Wellcome trust and NIH.

And it's a recurring discussion that we shouldn't exclude the individual genius. This is of the push versus the large scale effort. I don't like the connotation that the individual scientists would be more clever, but I do understand, the necessity of some individuals working really hard for a long time on a problem.

And that that problem will then lend itself to scalability and an impact. 

[00:31:05] DR PATRICK SHORT: Yeah. Well, what I really like about your approach with ICTA is, as you said, it's not a top down funding. It's really a way to make sure that people who agree on the common set of problems and potential set of solutions are not kind of toiling completely separately and, and bring their heads up to, to find they've been doing the exact same thing in two separate locations for two years. Cause I think we see that all the time in the field where there's a group in the U S and a group somewhere in Europe and a group somewhere in Asia.

And they're all doing actually the exact same thing on a very similar scale. And if they had been in touch with one another earlier, they probably could have tripled the scale, done it together and accomplished a lot more in the same amount of time. 

[00:31:44] Cecilia LINGREN: And on that note as well, we talked about the reproducibility issue for me, one of the key things that I'm excited about in iCBA that we also come together under a core set of principles, and one of them is open transparent science, where we are going to share data, and we're going to share protocols. We're going to share code, and we're going to do that as fast as we can. So with that team plate, it will become imminently obvious. If our data isn't reproducible, anybody can troubleshoot our code.

I'd be super excited if somebody found errors, you know, not because I'm excited about the errors, but I'm excited about somebody finding them so we could correct them. And they think in that spirits team, science will be very powerful because there is always somebody double, triple checking. And with that openness, I think that's one way that we can sort of overcome the reproducibility issues but similarly to what we did in genetics before. 

[00:32:35] DR PATRICK SHORT: Absolutely as the penultimate question here, and then I've got one more that I'd like to close out on. I wonder if you could talk about one or two things that you and your colleagues are working on now that you're most excited about for the next two or three years, what are the problem or set of problems that gets you excited and out of bed in the morning?

[00:32:51] Cecilia LINGREN: So the first thing that I'm, I think, I mean, it's not totally officially yet, but that we're thinking a lot about, and then it sort of sprung out of the workshop we had this summer was that there was an enormous appetite from everyone involved to develop global analytical task force. That's something that is very close to my heart, partly because I worked in global task forces most of my career, but also because I think in this day and age, if we take equality, diversity and inclusion seriously, and that we also acknowledge the fact that we need a wider global spread in terms of data representation.

Which echoes the ethos of ICD perfectly. It also allows us to collaborate globally on a taskforce because it doesn't matter where you sit globally. As long as you have a computer as set of headphones and access to a server somewhere where you can run data in a legal, ethical, and instead of GDPR controlled way, you can do analysis.

And that is actually like a really big revolution that is going to happen that more and more people can get engaged. So again, if you think from our colleagues who are working in resource sparcer environments in low and middle income countries. If they can find a computer and a set of headphones and we can figure out the legal issues of them connecting to servers and analyze data, then suddenly they can do state of the art research on data that is produced somewhere else.

So that really excites me. So I think that's going to keep us busy for a while. The projects, I'm going to be more careful in talking about it because I don't want to claim other people's work is my own, but I think we have a sort of core set of products in a couple of flagship diseases and also thinking about foundational resources that are missing in the field that we feel needs to be stood up to tackle the type of questions that we're talking about, and we're going to launch them later this Autumn so I'm excited about that. 

[00:34:40] DR PATRICK SHORT: Absolutely. Could you talk a little bit more about the equality, diversity and inclusion angle? As I mentioned at the very start of the discussion, one of the things that I've always been massively impressed with you besides your scientific accomplishments with stand on their own is it's just the level of respect that you have in the community for your mentorship and team science.

So. What are the big problems you see there and what do we need to be doing differently now to make sure that we're building teams that are diverse and inclusive and can actually solve these problems and not just, you know, leaving part of the population out of this important work. 

[00:35:12] Cecilia LINGREN: So you who don't see me, but for Patrick who does, will see that I'm actually blushing.

That's very sweet. Thank you. I've always been super excited about mentorship. It's one of the things that excites me the most about being in science to help propel other people to succeed. It's something gorgeous about that. That just thrills me so much to see other people happy and do well. I won't say I'm the best supervisor and mentor for everyone, but I can say that I always do my very best. I'm very, very grateful that you said that. 

So for me, equality, diversity and inclusion is a lot about being a human. And instead of respecting other people for what they are insight, not what they look like or, or, you know, where they come from or what their heritage is are or what their various preferences or sort of environmental sort of structures in post them that are.

I think if you have that as a basic starting point, then my mum brought me up with that. So if you have that as a basic starting point, life gets easy. So I almost don't see EDI. It's something that I just think should be present for all of us, especially for us to work in genetics. We know that diversity is the key to success, right?

The key to survival. So for me, it comes very neatly. And part of being in science is also that it's a mind game. It's a mind puzzle. So it's not about who you are, if you're in a wheelchair or if you're born in certain parts of the world, it's what you bring to the table when you're thinking and the way you up the thing or your writing, or, and I think if you have, again, if you have that as a starting point suddenly becomes an equal partner at the table.

So one of the things that I was really thrilled about. And Rick and Mark who's now coaching with me and Martinelli and also Ben Neale who's intimately involved in this together with the entire organizing committee has been so committed to this from the start. And it's really been sort of an excitement about bringing this on, on a number of levels.

The first on the researcher level. Uh, you know, we shouldn't have everyone at the table because we're going to find solutions that we wouldn't, if the same five people are looking at that problem all the time, right. This second level sort of is the research. Like we discussed the population samples that we're studying, both in a genetic and functional level.

And the third level is probably the output of science, the dissemination of our work, and making sure that it's equally important and impactful in every part of the world. And again, we have an amazing ethics group. We have an amazing policy group and EDI group. I'm trying to sort of instill that and run that in the BDI now as well and again, people responded and people get excited and people step forward and want to partake. And when I see my kids, for instance, they, they don't even think about this because they just exist in a vacuum. So I think anyone who doesn't have that at hard had probably learned somewhere that it's not important.

And I want to instill in everyone to make it a natural important thing. And that feels like it's very close to my heart. And it's something that I'm so glad that I'm in a venue. Not that I can message it so that it becomes an everyday thing for everyone. 

[00:38:19] DR PATRICK SHORT: Absolutely and you're also a prolific Twitter user and almost always positive and optimistic and I feel like that's been a shining light for me the last 18 months or so during this pandemic is seeing your, um, unflappable optimism on Twitter. 

[00:38:32] Cecilia LINGREN: I think again, sort of, it is easier to be critical and angry and skeptical than to find and seek light and see good in others. And I think I take upon myself to be polite, positive, and persistence.

That's my twitter their hashtag PPPs. And that's actually from my grandmother who grew up in incredibly harsh times, she was one of the first women to had a PhD in a stem subject in the Austrian Hungarian, Rajesh at the time. And she wasn't even allowed into the classroom. You know, she had to stand outside of the classroom and take notes through a window and she endured two world wars and all kinds of things. And throughout this, she just said that you choose to be positive. 

[00:39:17] DR PATRICK SHORT: That's so great. That is something we can all take away. Wow. I've got goosebumps listening to that story. Well, listen, we're running out of time here. I just want to thank you so much for your time. I think that's a perfect note to end on. I really enjoyed the conversation as always. So thank you so much for taking the time. 

[00:39:31] Cecilia LINGREN: Thank you so much for having me. 

My pleasure. And thanks everyone for listening as always, please share with a friend. If you liked the episode, leave us a review on your favorite podcast player and we'll see you next time. .