Welcome to the Centre for Personalised Medicine Podcast,
where we explore the promises and pitfalls of personalised medicine and ask questions about the ethical and societal challenges it creates.
I'm Rachel Horton and I'm here with Gabby Samuel. And in today's episode, we're looking at diversifying genomics,
a key aspect of ensuring that the benefits of personalised medicine can be accessed by everyone.
We're joined by Dr. Francois Hardcastle, research fellow at the Clinical Ethics Law and Society Group at Oxford.
Vaneck has just led a brilliant review aiming to identify key ethical, legal and social challenges in diversifying data and found that this.
Could you start by talking us through how you got interested in this area of diversifying genomics?
Yes. Thanks, Rachel. So I'm an associate technical researcher,
and I explore how technologies and societies shape each other and evolve together and how we can
intervene in this evolution to direct it towards a point where their benefits are equally distributed.
I was looking at the question of diversity from an AI angle because there was a
lot of discussion about how a lack of data or data that and that's inequalities.
When they are fed into machine learning algorithms, they might actually exacerbate existing issues.
And there is a similar problem in genomics, which is quite well known problem that there is a lack of diversity in genomic data repositories
and biobanks have data that are basically skewed towards individuals of European ancestry.
And so a lot of other ancestral groups are underrepresented in these repositories.
There have been ongoing efforts to try to redress this problem,
but there are ethical issues around these efforts that we should know before doing anything.
And the Clinical Ethics Law and Society Research Group that I'm part of is exploring similar issues as the field is shaping.
And so this was one this was of interest to our research group.
And when we saw a call for a review on the ethical issues of diversifying genomic data, we got on to it.
Could you kind of explain to us why diversity is so important in genomic datasets?
Sure. So we all have approximately 99.9% of our DNA sequence in common and exploring that 0.1% that varies between
those can advance our understanding of how genetic factors may contribute to disease or protection from disease.
And that's why a lot of times scientists study DNA differences between individuals and groups.
Those variants that are common in the population are usually unlikely to cause disease, and if they are rare, they may contribute to causing disease.
But this may also very much depend on various other factors, like social and environmental factors.
Another thing worth noting here is that there's probably more genetic variation within ancestral groups than between them.
So, for example, there are more DNA differences between individuals with North African and East
African ancestry than between individuals with African and European ancestry.
And so we only if we only study the data from individuals of European ancestry,
then we may not get enough insights about the genetic variations in other ancestral groups.
So it's sort of a matter of having a good enough reference.
And if you don't have a population well-represented enough to know that something's common,
you might then think it's rare and kind of make too many conclusions from that about whether it's causing disease.
Can I ask a question about the examples? Because I know that you've always got quite a few really nice examples up your
sleeve of where when you talk about these biases in either A.I. or genetics,
could you talk us through some of the examples where they could or have looked like some health disparities?
Yes. So I can tell you about a study by Harvard researchers that was done on hypertrophic cardiomyopathy.
But this study initially used data that overrepresented European ancestry individuals.
But the researchers found that genetic variants were initially misclassified as disease causing,
whereas they were in fact common in individuals with African ancestry.
And so they had to be reclassified as benign. And as Rachel was saying earlier, it's sort of when particular groups are underrepresented in the data,
it's much more difficult to classify their variants as read or comment,
and they end up either being misclassified or being labelled as variants of unknown significance.
And so this yeah, this example is just one of those examples that shows how easy it is to misclassify when we don't have enough data.
So the kind of consequence of it like on the ground is people getting.
Like actively the wrong diagnosis or their family being tested for the wrong thing.
But yeah, I mean, that's so interesting. And then so is most of this knowledge known?
I mean, y you, you conducted this review.
What were you aiming to find in the review when you went to look at the types of ethical and social issues that you were after?
Yeah, as you say, it's very well known problem.
And so it's been more than a decade now that scientists and clinicians are have been calling for more diversity in genomic data.
And there's been lots of efforts to try to redress this problem.
It's just that the scale of the problem is so big that it's taking so long to progress for that.
And so we were interested in understanding why this wasn't really happening.
And that's how we got into understanding that actually diversifying data is very challenging from a legal,
social and ethical perspective itself as well. And so the review really wanted to understand what these challenges are.
So yeah, so we just wanted to know what are the ethical issues around the attempts to divest large data.
You've got to tell us more. What what were the glitches that you came across in your view?
I get it. But before I get into anything, I just want to say that the sort of search that we do for our review work has some limitations.
One of the limitations was that most of the papers that we reviewed were from North America.
And also now that intention was that our search mainly focussed on underrepresentation that was based on gender, race and ethnicity.
So that leaves out other underserved groups such as children, elderly, psychiatric patients, prisoners and so on.
And this is kind of like this speaks to a problem about the attempt to diversify,
which is that these categories a lot of times don't actually map to ancestral categories.
So so that's that's one of the challenges.
So in terms of finding, we found that sometimes research practices can be exclusionary and this needs to change.
One example is approaches to recruitment or data collection that don't consider the cultural setting in which social persons are situated.
So, for example, for a group, grave concern might be really important, but a lot of research practices may only focus on individual consent.
The literature suggested that practices need to have more cultural humility,
which is often used to emphasise the importance of being reflexive and do active listening and taking responsibility for interactions.
And on the side of researchers and research institutions say that seems like a huge issue to think about.
And could you just tell us a little more about what else came up?
The second finding that I would like to mention is the literature really emphasised
the key role of co-production in identifying and avoiding potential problems.
So it's really important that social podcasters are seen as active researchers and knowledge producers.
And if we really don't have such mindset, then participant engagement is very easy for it to come tokenistic,
and that's going to turn on risk, exacerbate existing problems, or create new forms of inequalities.
We also held a workshop asked what values to review, which helped us to complement the findings with some expert recommendations.
And one of the things that came out is a sort of wider,
mature view and expert recommendations was that there are lots of structural
issues that we need to really keep in mind in efforts to diversify genomic data.
Please, can you tell us more about these structural issues? One of them is that a lot of times researchers might leave data as neutral,
but this ignores the fact that data and technologies cannot be separated from the social context in which they are created,
and they tend to reflect our biases and social inequalities.
If if that's not kept in mind, that it's been easy to kind of make conclusions based on shallow, sort of simplistic things that just come up later.
The second structural issue was that these aspects need to really be contextualised
within the historical trajectory of structural racism and legacies of colonialism.
And the sad one was that classification and categorisation, as I was saying earlier,
has political consequences and they really need to be closely interrogated.
Could I just ask you a little bit more about, you know, you were talking about data not being neutral.
It'd be great to hear more about that and what that means.
So for example, during the pandemic,
there was some research coming out and saying that there was some genetic susceptibility to COVID based on racial categories.
And that was to me, one of the examples of research is going with the mindset that the data as neutral,
the reason the cause of that's what was perceived as susceptibility to disease,
genetic susceptibility was perhaps more grounded in social inequalities.
So that it's almost like we need to be more scientific about these sort of DNA findings and interpretations.
I think it's also about what data are collecting.
So data is out there we choose to collect and what type of data is we choosing to collect and why and what does that say about our values?
It's all kind of embedded in, I suppose, the data. Absolutely.
And also the tools and methods that were used to measure things.
They were all created by people at the end of the day.
And those people that came from their own perspective or their own experiences into that invention and an application.
And so it's all it's all a matter of trying to contextualise all of these things that we use.
And it's not about rejecting them and saying that they shouldn't be used.
It's about positioning them in the wider picture to say that it's obviously comes from a particular angle.
I might not work well when we're using it in a different context.
It sounds like there are so many barriers and obstacles, I suppose for research and it wants to go in and try and do,
I don't know, try to diversify their data in a way that is in line with ethically best practice.
Did you come across I mean, especially if it's at the structure level, did you come across any researchers that actually, I don't to say this,
but almost got it right, like where you kind of read the papers and felt that that worked well or that that had the effect it was supposed to have.
Yes, we did find some really nice and best practices that were from other cultures that had been trying to co-produce genomic knowledge.
But in the context of the U.K., it may be that we need to really try and work out what works best for a sort of super diverse society like the UK.
So again, because best practices also talk about, you know, going to a specific community, I'm just trying to get them engaged in research.
But but how is it to start from the beginning in a very diverse society?
How can co-production naturally? It's something that we haven't really, really explored yet.
It sounds so complicated, right?
Because when you talk about going out to communities and diverse societies, I suppose that leads to the question of what?
What is the community and what kind of even demographics are you looking for within the community?
Because you said at the beginning, right,
that the trinity between communities is so broad and even you even looking for a community based on genetics or socio economical.
I suppose. Yeah, it's a really interesting question. Well, that's a really good question.
I guess that's what I was trying to say is that a lot of times the ethnic or racial categories
that we have which are socially constructed don't actually map to ancestral groups.
But what we know is that there's things like racism or structural racism,
or that structural inequalities for decades have had biological effects on people.
So, yes, I mean, it's a really good question.
How you would define diversity then, how you would define community, how some people, for example, define community based on geographical proximity,
and some others talk about shared characteristics such as racial or ethnic categories or shared lived experiences.
But yeah, it's, it's a really good question and it's something that it has to be determined in
discussion with everybody and with all those people that I'll be talking about.
And so the answer is, is in co-production, I guess.
I remember in your reports that you spoke a little bit about diverse workforce and the importance of going beyond diverse data.
And it reminds me of something I read the other day about and decolonising AI and the need.
So it's not just about the categories that need to be thought about that.
When we're thinking about the categories, it's who's actually conducting research and what knowledge is being produced.
And as limiting, if you could just talk a little bit more about that. Yeah, sure.
So I think the categories have their own significance and importance in this discussion.
But one of the things that we discussed in the report was that the push for diversity shouldn't just be about the data.
It should also be about the sort of knowledge that is being made and the sort of workflows
that are in place and also the disciplines that are getting engaged in the research.
At the moment, there is a problem of lack of diversity in genomic workflows as well.
And so of course these are all very much connected to each other.
And the more we have a diverse workforce, the more chances of having diverse data at the end and so on.
Yeah, I mean, there's a lot of obstacles about how to cultivate a culture in the work environment,
to sustain that diversity of explicit discipline and so on.
And so lack of diversity is one of those things that you can't really tackle from one particular angle and in silos,
and it has to really be thought through in terms of the bigger picture, thank you for anarchism.
It's the French because like I guess the problem of a under-represented dataset feels so kind of like, Oh, we need to make that dataset more diverse.
But I think this just beautifully illustrates how it's not as simple as just doing that.
Like there's so many questions to consider and so many things that get raised on this
path to like how we achieve genomics that is going to work better for everyone.
If you were picking one message for people to take away from this podcast, what would it be?
So it's really hard to convey all the complexity and challenges of this area in just one point.
But clearly there is a real problem that if we don't have representative datasets to inform genetic tests,
it worsens the outcomes for people who are represented in those datasets.
And this just one example of structural racism, having a system where the quality of testing you can access is so influenced by your ancestry.
But getting those datasets more representative needs to be a part of getting the whole enterprise of genomics more that that's not the goal in itself.
In fact, the key message from our review is that diverse datasets should have an end point in themselves.
Just collecting genomic data from people with a range of ancestries doesn't address the diversity problem.
And this is because even if we have diverse data, that doesn't mean we have considered diversity in the true meaning of the term.
To include diversity means thinking about diversity in terms of inclusion of underrepresented groups in all stages of the research process,
ensuring that harms and benefits are equally distributed,
and to co-create knowledge so that the knowledge that is created is the knowledge that the diverse populations are interested
in knowing and ensuring that the benefits of that knowledge are fed back to a community or to that diverse population.
Where could we go to find out more about your work? So the draft of this review is now online on a preprint server,
and we are at the moment in the process of writing some academic papers from the review that hopefully will come out come out in the next year.
I'm so excited to read these. It's such a fascinating field and thank you so much for making the time to talk to us today about it.
Now, thank you for inviting me. Thank you very much for listening to this episode at the Centre for Personalised Medicine Podcast.
If you'd like to find out more about personalised medicine and its promises and challenges,
please visit the Centre of Personalised Medicine website at CPM, well boxed or AC UK.