Whole Genome Approaches to Complex Kidney Disease
February 11-12, 2012 Conference Videos

Ethnicity and Community: Impact of Genetic Findings and Disclosing Results
Malia Fullerton, University of Washington

Video Transcript

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SARA HULL: So, now I’m going to introduce Stephanie Malia Fullerton who’s an Associate Professor of Bioethics in Humanities at the

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University of Washington School of Medicine. She holds adjunct positions in the departments of genome sciences and epidemiology and is a co-

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investigator of the University of Washington Center for Genomics and Healthcare Equality. Her work explores scientists’ understandings of

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human genetic variation and its relationship to disease risk, the use of racial and ethnic constructs and the conduct and interpretation of

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genetic research, and the responsible incorporation of genomic methodologies into broader programs of health disparities research.

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She’s also very involved in exploring participant perspectives on data sharing, research use, and result return in the context of genomics. And so,

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she’s going to add an additional nuance to this conversation, talking with us about ethnicity and community and its impact on these issues.

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STEPHANIE FULLERTON: Thank you very much, Sara, and thank you so much to the organizers for inviting me to participate, and thank you to all

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of you who are here in the audience. Maybe it’s just because on West Coast time but I’m normally sound asleep at this time on Sunday morning. So,

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I’m very pleased that you’re here and I hope that I will be able to keep you awake with some of my thoughts. I want to sort of preface this by saying,

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as Sara mentioned, I am involved in a range of research ethics-related research within and around the conduct of genomic research, and I’m

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used to expounding from the position of having actual data. I think in this domain where we’re thinking in particular about the implications of

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findings from exome and whole genome-level research for communities, we’re actually in a bit of a data void at the moment; we don’t have much

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good data. And so, most of what I’m going to be talking to you about today is speculation and basically a call for more research as you all move

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forward doing the very important science that you’re trying to do. So, just by way of a road map, I’m going to try to do three things in today’s

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presentation. I’ll keep an eye on the time here. First, I want to…oh, and I wanted to say at the outset, so we’ve had a genetic counselor talk to

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us about informed consent, we had a lawyer talk to us about return of results, and now you’re going to have a population geneticist talk to you

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about community, because that’s what my primary training is actually, in human population genetics, and that sort of colors my take on this

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topic and I wanted to sort of say that at the outset. I’m going to talk a little bit about the nature of the findings that are anticipated from whole

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exome and genome sequence analysis and its implications for recruitment and result return as it impacts communities. I’m going to talk a little bit

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more about another aspect of the kinds of anticipated results that might be generated, focusing in particular on the population

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distribution of those anticipated findings, and talk a little bit about the possible implications for study design and communication with communities, as

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well as the ultimate translation of the information that we’re going to be generating by these approaches. And then in the last few minutes of

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my presentation, begin to kind of very briefly address potential strategies that we might wish to employ in addressing these challenges that are

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posed by this new scale of genomic science. Okay. This was basically based on a review of science as I was aware of it prior to yesterday’s

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presentations. I tried very hard not to fiddle with these slides, but fortunately I think they’re commensurate with a lot of stuff we heard

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yesterday. So, what are we going to expect to find from whole exome and whole genome sequencing? As we’ve already heard, there’s

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going to be a deluge—a Niagara Fall, a fire hose, all those water analogies—of genetic information that’s going to be generated. As we also know,

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with the switch to sequencing, particularly in the exonic regions of the genome, the vast majority of the genetic variation that’s going to be

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identified is going to be rare. So, we’re moving out and away from this focus on common genetic variations that might be shared across

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populations and we need to know, looking at variations in any given population, are going to be typically in the fractions of percent, certainly less

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than half a percent, and many of the data we heard about yesterday, less than a tenth of a percent in a population relative frequency. And in

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addition and very relevantly and importantly to Ben’s presentation that we just heard, a significant fraction of the variation that is going to

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be identified, particularly in the exonic regions—not all of it but a significant amount of it—is going to be functionally relevant, with functional

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relevance variably and incompletely and imperfectly defined and understood. So, let’s just stop and think about those two aspects of the

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kinds of information that are likely to be generated from whole exome and whole genome sequencing. What are some of the implications?

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As we heard yesterday, in a turn to looking at rare variation and trying to understand the phenotypic effects of rare variation, we are

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going to need to increasingly look at very large numbers of research participants. You thought we needed large sample sizes in GWAS; it’s

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going to be even more acute to have large populations as we turn to this brave, new world of exome and whole genome sequencing. And

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there’s a really big problem from a research ethics standpoint and the really big problem here is that the populations that we have available to

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us and that we have studied for decades now in the genome sciences are very skewed towards populations of European ancestry. Work that was

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done by Anna Need and David Goldstein and then subsequently commented on by Carlos Bustamante and colleagues last summer

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describing the population distribution of the GWAS studies that have been conducted to date, 96% of GWAS studies have been conducted on

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populations of European descent, 4% on non-European samples. So even as we need to be increasing sample sizes, it is not going to be

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possible if we are interested in understanding diverse contributions to genetic risk to simply go to our freezers and start with what we have.

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There’s going to be an acute need for the recruitment of diverse populations from varied population backgrounds. What are some other

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implications? A very interesting one. I don’t really have any direct data that I can show you but, we know this. This is a feature from population

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genetics and it’s rather different from the situation with common genetic variants. As we increase the number of the people that we studied, our

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problem is going to get harder, not easier, because as sample size increases we’re going to identify more and more and more rare variation,

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okay? This is not the case with common variations: the more you look, the more you see what you already know is there and it easier it

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becomes to make sense of it. When we switch to looking at rare sequences, rare variations, there’s going to be a significant signal-to-noise problem

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and we’re going to have to figure out how to sort that out. And although, as we heard yesterday, there’s some very really neat-o cool statistical

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ways of making sense of an increasing statistical power surrounding, in particular, the analysis of an aggregate of rare variations, it’s not at all clear

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how we’re going to make effective clinical use of this information. We’re moving away from thinking about genetic risk variations as something that

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we might be able to kind of use from a broad public health context, moving much more into the clinical domain, and it’s not exactly clear how

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we’re going to make use of this information, so I just want to kind of flag those. We can about that more in questions-and-answers. So, those are

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some implications of the fact that we’re going to identify a lot of very rare variation. Another feature of the data, as I indicated, is that we’re

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going to identify a very large number, a very large number—hundreds and likely, thousands—of variations which our algorithms tells us are

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functionally relevant, and this is not in total, this is per person, okay? And that’s a very interesting and important scientific problem but it makes this

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business of thinking about the return of information to research participants that we know there’s a lot of interest in and excitement

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about currently in the genomics community, enormously complicated, because it’s been talked about and, as we heard in a very nice

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presentation by Dr. Solomon yesterday, variant effects are going to need to be understood and prioritized with regard to their clinical salience

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prior to communicating individual findings to research participants. And as Ben has already indicated, and I don’t want to spend a lot of time

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on this but actually, we don’t quite know how to do this yet. There’s a lot of disagreement in the bioethics community. Even in the clinical genetics

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community I have come to understand there are proposals on the table. This particular paper was alluded to in Dr. Solomon’s presentation yesterday

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by Berg and colleagues, a way of actually sort of categorizing or binning possible types of incidental findings that might be generated from

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whole exome and whole genome approaches, which basically relies on a joint adjudication of the clinical utility and validity of this information in

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conjunction with whether we have some prior knowledge or understanding of whether variants are deleterious or presumed deleterious, and

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depending on how information falls out in terms of those two bits of kind of decision making, this gives us an indication…let me see if I can figure

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out how to do this…never mind. This gives us an indication of which findings we might likely wish to return. This already is complicated and begins

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to start giving me a headache as someone who’s interested in this issue. What has been very sobering to realize is you put three medical

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geneticists in a room and they will not even agree with regard to the clinical utility of a lot of this information, and it gets very tricky. So, there’s

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going to be a lot of work to be done before we can actually meet, even potentially as understood currently, ethical obligations with regard to return,

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and I just wanted to make the point that functional relevance, particularly as we were talking about it yesterday where we were talking about sort of

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computer-based algorithms which will tell us about the likely effect on protein function, even those algorithms do not necessarily agree with

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each other. Functional relevance and an adjudication of functional relevance which might possibly be algorithmically tractable, is not the

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same as clinical utility. As I’ve already said, there’s a lot of disagreement in the medical-genetics community about clinical utility. We came

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face-to-face with this issue in the context of deliberative work as part of the Electronic Medical Records and Genomics Research Network that

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I’m involved in. We have a paper that is about to come out next week in Genetics in Medicine

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talking about our experience with deliberating on findings in the context of that research network, and these were incidental findings generated in

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the context of genome-wide association studies, not exome or genome scale investigations—sequencing investigation—where we determined

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after much discussion and debate and deliberation that there were four possible classes of findings that might rise to the level of return,

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and then after we finally came to that agreement and a consensus of the network, people took that back to their individual sites, looked very carefully

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at medical record data for the individuals who were affected by the particular genotypes, and where invariably at every single site and due to a

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confluence of factors including informed consent, including the understandings of the institutional review board, and other considerations,

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community interactions, no decision was made to return any finding. And in a classic statement—understatement—that I attribute to my many years

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of living in the United Kingdom, we wrote, “Although a criterion of ‘clinical actionability’ suggests a clear threshold for identifying results

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that should be considered for return, in practice, the identification of specific clinically actionable finding generated from the eMERGE studies was

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not straightforward.” This was a very difficult conversation. It took a very long time to figure this out for four incidental findings and we are about

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to enter in a brave, new world where we’re thinking about this now not for four, but for hundreds and possibly thousands of potentially

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functionally relevant variants, so lots of food for thought there. Okay. So, that is one class of finding, one way of thinking about the data. The

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data is generating many rare variants, the data is generating a fraction of which are going to be likely functionally relevant and may pose

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obligations, but exactly how to act on those obligations is going to be extraordinarily difficult to sort out. Where does ethnicity and community

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figure in here? Well, this brings me to the second way in which I want to think about the potential nature of the findings likely to be generated from

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the whole exome and whole genome data. This is just a snippet of the very considerable data that were reported out of the 1000 Genomes Project,

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a project to sort of sequence in considerable detail the whole genomes of a large number of samples collected from around the world and in

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data that was reported in 2010 and the emerging data from the Exome Sequencing Project which was talked about yesterday, but unfortunately,

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those data are not yet available publicly. We know that when we go in and look at populations of individuals sampled from different parts of the

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world and we look in particular in the exome regions of the genes where there’s lots of this rare variation, that what you identify that has

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previously been described tends to be more often shared between populations and what is rare and new and being seen for the first time is

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invariably population-specific, and this has very profound implications for how we’re going to actually communicate with communities about the

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conduct of this research. So, a little bit more on expected findings. Rare variation not only will require large sample sizes and have lots of

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functional relevance, but as I just indicated, rare variation will frequently be geographically restricted, will be population-specific, and in

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addition and this is something which was not talked about very much yesterday—it’s something that I don’t think has received nearly enough

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attention but I want to put it on the table for discussion—rare variants are going to be non-randomly distributed amongst individuals and

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families. Some individuals and families will have more of this rare novel variation than others. Why? Because of differences in background,

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polymorphic variation that have to do with human evolutionary history, and that are largely a function of genetic ancestry, which as we know

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as we saw in data presented yesterday by Suzanne Leal and others, is often correlated with self-described ethnicity. Okay, let’s think about the

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ethical implications of some of this stuff. Population-Specificity. Well, population-specificity, I think, is going to be an inevitable feature of much

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of the data being generated by these methodologies. We have to be very careful of how we talk about such results because

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population-specificity could frequently be misunderstood. It might lead us to overemphasize race- or community-specific genetic

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vulnerabilities in preference to other shared factors, and in an interesting way and a way that has not at all been addressed or explored, it

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could have some profound effects on the identity of people. For example, one could imagine the situation where an individual of one

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self-described ethnicity is told that they harbor a variant that has otherwise been described as specific to a different population, a different

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ethnicity, a different region of the world. Let us not underestimate the extent to which this is likely. There are some truly remarkable population

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genetic data out here. This is one of the most colorful and exemplary examples that I’m aware of, work by John Novembre and colleagues

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looking at patterns of genetic variation as assayed from sequencing in large numbers of people from Europe and where, basically, these

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are not…the colored circles have to do with sort of composite estimates of patterns of genetic variation and first and second principal

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components, but where there’s a very clear tendency for individuals whose heritage basically hails from particular places in Europe to have

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their genetic variation very closely resemble one another. So this propensity to have rare variants mapped very closely at specific geographic

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locations, particularly for individuals and families who have been in situ for several generations, is very high and we’re going to need to be thinking

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about this carefully as we begin figuring out how we’re going to communicate with participants about the kinds of findings we’re generating. In

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addition, population-specificity could, frankly, be not only misunderstood but potentially misused. It could distract us from the consideration of other

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relevant public health remedies in the context of the diseases and traits that we’re interested in studying and understanding, and it might open up

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the potential for stigmatization or discrimination in particular groups on the basis of the apparent population-specificity of findings that are

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indicated. There’s no evidence that this has happened to date, necessarily, but there interesting little suggestions of what possibly

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could occur. I just want to make reference to this paper which came out several years ago by Carlos Bustamante’s group, doing a resequencing

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study of a very small number of individuals, self-described Europeans or European Americans or African Americans and going in and making some

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inferences in reference to an out-species—chimpanzee—about the likely deleterious nature of genetic variation and coming to, at the time, the

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surprising and somewhat controversial conclusion that there was proportionately more deleterious genetic variation in European as

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opposed to African populations. Now, leaving aside the fact that they came to this conclusion on the basis of analysis of 15 Europeans and 15

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Africans, which may or may not be problematic, I think it’s very easy to imagine if the headline had been reversed—if they had found that there was

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deleterious variation in Africans than in Europeans—how we might think about the social salience of that, particularly in the context of

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really pronounced and profound health disparities which exist in the United States context. There’s just an example here of cancer disease burden

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by different racial and ethnic categories and sexes and incidence and death, and it would be very easy to jump to the likely erroneous

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conclusion that that deleterious variation is explaining a broader disease burden that many, many researchers believe have a much more

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complicated and multifactorial basis and a basis in social determinants of health. So obviously, there’s a need for great caution. That’s the

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population-specificity aspect of the data. There’s also this issue of the fact that the ways in which rare variations are going to be found in individuals

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that we sample are going to be non-randomly distributed and that indeed, population genetics tells us—and we already have data to support

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this observation—that individuals of sub-Saharan African ancestry are likely to have more rare and consequently more novel variation. What does

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this mean for us as investigators? Does this make individuals of sub-Saharan African ancestry interesting objects of investigation, or instead,

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difficult to study? I don’t know how to answer that question, but the difficult to study explanation has been one of the explanations provided for

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the fact that we have such an uneven distribution of samples currently in human genetics and genomics. For sure, there are going to be fewer

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definitive results to be communicated to research participants from such population genetic backgrounds in the near term, and this is already

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something that we have begun to encounter in the context of clinical genetic testing that is already clinically available. These are data from a

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particularly…they’re indicative data but it’s from a relatively small study of women who’ve developed breast cancer early in their lives,

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showing the genetic population mutational distribution of BRCA1 and BRCA2 variants and basically—I know this is very hard to see—but

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what I want to draw your attention to is the row describing variants of uncertain significance showing that approximately four times as many

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variants identified in the BRCA1 and BRCA 2 genes are found to be of uncertain clinical significance in individuals of African American

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ancestry, in part because they have simply been less well-studied to date. We can take what we’re experiencing in the clinical domain and

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bring that into the research context, add to the fact that we’re going to have a lot more rare and novel variation to being with and that we’re not

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going to be able to tell people about, and I think this poses some particularly profound ethical concerns. In addition, and this goes to the

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question that I had for Dr. Rich at the end of his presentation yesterday and in particular, his clear indication and I believe that the writing is on the

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wall, that the kinds of designs that are going to work best for the interrogation of rare variation turned up in exome and whole genome

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sequencing studies are family-based, pedigree-based investigations with the expected return to such study designs and the context of this kind

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of research. We’re increasingly going to be looking to the need to recruit large and extended pedigrees from all sorts of populations, and yet

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we don’t really know very much about how easy it is. I was very gratified to hear that there are a large number of pedigrees already available from

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underrepresented ethnic minority communities in the kidney disease domain; that’s wonderful. More will need to be collected and yet we don’t

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know, really, how to think about recruitment or even the communication of findings in a family-based context in minority communities. The most

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definitive report that was published a couple of years ago now on the use of family history in clinical research, making some recommendations

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for how to do this work well, unfortunately noted that there’s actually very little evidence to suggest the role that race or ethnicity, cultural

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background, religious belief or other characteristics might have on one’s willingness or ability to report on their family history, or

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presumably, to participate in research that would involve family-based ascertainment. These are just big, huge lacuna in our understanding and

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we’re going to have to be grappling with these issues, I think, as we move forward, of necessity in order to do the science that we want to do. So,

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just to quickly re-cap on my sort of summary of the data and where it leads us in terms of its ethical implications. The need to study and

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understand rare variation is going to, out of necessity, require the recruitment of new—not simply just using what we have to

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hand—recruiting new large cohorts. Understanding uncertain functional significance is going to require us to recruit and analyze diverse

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cohorts that are adequately powered. I didn’t really talk about that very much but it’s not just enough to sort of include minority representatives

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in our research, but we need to have enough of them in order to make robust inferences; so adequately powered to identify local population

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and possibly familial effects. Because of the population-specific nature of much of this variation we’re going to have to exercise extreme

00:24:06,466 --> 00:24:14,099
care in describing findings that are apparently restricted to particular social strata, and because of the non-random distribution of rare variation

00:24:14,100 --> 00:24:20,866
we’re going to have to acknowledge lots of complications inherent to the participation of certain communities. Even as we are inviting the

00:24:20,866 --> 00:24:29,799
participation, we’re going to have to make clear that we may not have the same kinds of information to return to them. What’s a researcher

00:24:29,800 --> 00:24:40,700
to do in the face of these somewhat uncomfortable facts about where the science is leading us? Well, obviously we need to be paying

00:24:40,700 --> 00:24:48,133
attention to the role of reciprocity in research. We’ve had some wonderful discussion already this morning about the role of informed consent

00:24:48,133 --> 00:24:57,733
and relationship-building and partnership-building with participants; I am all for that. As we do this work I think we have to acknowledge that the

00:24:57,733 --> 00:25:04,066
longer term public health benefits of much of what we’re doing in the near term are unclear and are going to be hard to explain to research

00:25:04,066 --> 00:25:16,099
participants. I cannot overestimate enough that altruistic research participation, which is what we all depend upon, is a luxury. It is easy for

00:25:16,100 --> 00:25:22,766
certain groups of people and it is not for others and we need to begin to acknowledge that, and rather than talking about populations that are hard

00:25:22,766 --> 00:25:29,766
to recruit, we need to be going out of our ways in order to collect the kinds of people and information that are going to make clear that

00:25:29,766 --> 00:25:37,232
public health benefits are going to be equitably distributed, while we recognize that this distant promise of public health benefit may not suffice

00:25:37,233 --> 00:25:47,399
as a rationale for participation for many communities. Near-term benefits are also likely to vary. The fact that a lot of the individual research

00:25:47,400 --> 00:25:52,766
findings generated from research are going to be indeterminant—and they’re going to be differentially indeterminant in particular

00:25:52,766 --> 00:25:59,732
communities and families—is important and is going to need to be acknowledged. We’re going to have to start having some honest

00:25:59,733 --> 00:26:08,133
conversations about the reason why we don’t know as much about certain groups is because we haven’t studied you to date and we need to

00:26:08,133 --> 00:26:15,766
be figuring out other ways to demonstrate reciprocity and to reward compensation that might be something other than returning individual

00:26:15,766 --> 00:26:25,166
findings. And then, recognizing that all of this conversation, all of this careful work, all of this engagement with populations and communities is

00:26:25,166 --> 00:26:33,166
basically the research community asking participants for our trust, and that if we’re going to ask participants for trust, we need to be

00:26:33,166 --> 00:26:46,899
demonstrating trustworthiness, and that is quite difficult and complicated, but I think it’s doable. I am very privileged to be a part of a study out of

00:26:46,900 --> 00:26:52,266
Morehouse University call MH-GRID, Minority Health-Genomic Research Infrastructure Development project, led by Gary Gibbons, which

00:26:52,266 --> 00:26:58,832
is going to be looking at African Americans, doing exome sequencing in African Americans in the context of hypertension risk and where we’re

00:26:58,833 --> 00:27:05,199
going to be doing some ethical research, talking to the participants as part of that study about their attitudes towards and preferences with regard to

00:27:05,200 --> 00:27:13,366
participation in exome sequencing research. The data that we have to hand, other data about just general African American attitudes towards

00:27:13,366 --> 00:27:20,799
participation in genetic research is that African Americans are as likely as whites to express their willingness to participate, and yet, also are

00:27:20,800 --> 00:27:29,333
more likely to report feeling that genetic research is going to possibly result in higher insurance; to not benefit their communities, to promote racism,

00:27:29,333 --> 00:27:38,266
and to use minorities as guinea pigs. These perceptions are real; they must be adequately addressed and dealt with in the context of our

00:27:38,266 --> 00:27:45,666
research engagements. At the same, we need to recognize that in many cases the greatest barriers to the participation of the communities

00:27:45,666 --> 00:27:53,899
that we want to involve may not be due to an inherent mistrust of research, but rather to a lack of access to healthcare and research

00:27:53,900 --> 00:27:59,833
opportunities, and so we need to be going out of our way to make ourselves available. And we also need to be thinking about research

00:27:59,833 --> 00:28:09,566
participants as true stakeholders in the research process. Based on some work that we did out of a group health cooperative not with ethnic

00:28:09,566 --> 00:28:18,132
minority communities—with white, middle-class, well-off, well-insured participants—we’ve come to some conclusions about ways in which we

00:28:18,133 --> 00:28:24,833
might or steps we might take in the context of genomic research to enhance respectful engagement. A lot of this has to do with things

00:28:24,833 --> 00:28:32,933
that Julie talked about in her presentation this morning of maintaining ongoing communication with participants; thinking of informed consent as

00:28:32,933 --> 00:28:41,133
a process; making sure that we stay in regular contact; we tell participants what we’re doing, why we’re doing it, and the ways in which we

00:28:41,133 --> 00:28:47,933
hope it will beneficial to themselves and, ultimately, to their broader communities. We need to have ways to provide access to the

00:28:47,933 --> 00:28:54,299
information about how samples are being used. We need transparent, accountable oversight processes, which is why some of the information

00:28:54,300 --> 00:29:01,466
that Laura Rodriguez talked about last night is so incredibly important. Even as we’re asking people to sign on to have their data be placed in federally

00:29:01,466 --> 00:29:09,499
controlled, semi-public access repositories, we need to be explaining to them why that is necessary and how their data are going to be

00:29:09,500 --> 00:29:18,766
protected and not misused, and we need to continually provide opportunities for research participants of all stripes to provide direct input on

00:29:18,766 --> 00:29:25,299
the stewardship of their data. And I know that’s something that we’re not really used to in the genomic research community, but we need to be

00:29:25,300 --> 00:29:34,000
moving in that direction. And then in those cases where such ongoing engagement or re-contact for particular research uses is not feasible, we

00:29:34,000 --> 00:29:40,733
need to be developing other effective methods to communicate with the public about responsible realistic study procedures, including thinking

00:29:40,733 --> 00:29:50,566
comprehensibly about ways to educate the public about the need to conduct research of this kind prior to any research ask in the line of the

00:29:50,566 --> 00:30:03,832
question that we had earlier today. So, not a lot of hard data, I know, mostly speculation, but what I hope you will go home with are three conclusions

00:30:03,833 --> 00:30:09,666
that I have come to as I’ve begun to think more deeply about the implications of this research for different ethnic groups and communities,

00:30:09,666 --> 00:30:16,699
particularly in the United States context. Whole genome approaches to complex kidney disease is going to require the participation of large and

00:30:16,700 --> 00:30:23,966
diverse cohorts. We are not simply going to be able to go our freezers; new recruitment is going to be needed and new recruitment and

00:30:23,966 --> 00:30:30,966
engagement strategies are going to be required. “Making sense” of the information that we are going to generate in the context of our research

00:30:30,966 --> 00:30:38,766
is not going to be straightforward and we’re going to have to exercise great care in describing those findings and returning results to individuals

00:30:38,766 --> 00:30:47,499
and to their families. It’s posing a whole new set of issues, and yet ironically in the way that was pointed out in the question-and-answer at the

00:30:47,500 --> 00:30:53,500
end of the day yesterday, in some ways returning us to sets of ethical concerns that we grappled with a long time ago when we were

00:30:53,500 --> 00:31:01,833
first working in the realm of linkage analysis. And in my opinion and in the opinion of my colleagues at the Center for Genomics and Healthcare

00:31:01,833 --> 00:31:10,466
Equality, ongoing respectful engagement which is attentive to and attends to community-specific concerns is ultimately the way in which we’re

00:31:10,466 --> 00:31:19,499
going to enhance research participation and reduce the perception of harm and actual harms involved in the conduct of such research. So,

00:31:19,500 --> 00:31:26,000
thank you very much for your attention…just some of the various organizations that I’ve been involved in. I’ve also had a consulting role in the

00:31:26,000 --> 00:31:46,600
Exome Sequencing Project as well as MH-GRID as I mentioned. So thank you very much. I wowed you all. Yeah?

00:31:46,600 --> 00:31:56,366
ROBERT KLETA: Robert Kleta, University College London. If I may, I just want to make a comment about whole genome approaches, complex

00:31:56,366 --> 00:32:05,566
kidney disease and large, large cohorts because my impression here is that you and others yesterday left the community in the room a little bit

00:32:05,566 --> 00:32:15,332
under the impression that we can’t move forward for rare diseases and the point I’m trying to make is, I think there’s a little bit of disconnect with rare

00:32:15,333 --> 00:32:27,199
variant and rare disease. So what I’m trying to say is, if you do linkage studies, then 4 to 10 samples—even recessive or dominant

00:32:27,200 --> 00:32:33,400
fashion—can net significant findings and you have the locus and you find the gene and you know what’s going and that can even be true for

00:32:33,400 --> 00:32:42,200
complex kidney diseases. If you go to genome-wide association studies—and I think that’s what now many people in the room think about—then I

00:32:42,200 --> 00:32:52,300
take the liberty also of saying actually you just need a well-defined cohort and it can be as complex as it be, but 100 samples are enough to

00:32:52,300 --> 00:33:01,133
find the locus and the genes of interest. Even two genes can be involved and can be very complex. So what I’m afraid is happening from the

00:33:01,133 --> 00:33:09,999
event of genome-wide association studies and common diseases is it’s in everybody’s mind, oh, you need huge, huge, huge cohorts, and I think it

00:33:10,000 --> 00:33:19,133
would just be sad if this paradigm is now continued for complex kidney diseases which may just be complex because of the nature but

00:33:19,133 --> 00:33:26,799
not because of the approach that should be taken. So again, to try to make the point, I think nothing of what has been said yesterday and

00:33:26,800 --> 00:33:37,566
today is wrong, but there’s a disconnect in understanding using these tools in terms of what do we do and what’s right in terms of the biology

00:33:37,566 --> 00:33:50,166
or the theoretics behind it? Again, in my opinion, you need well-defined cohorts but then actually you need small cohorts to move forward. You do

00:33:50,166 --> 00:34:00,199
not need 1,000 or 2,000 or 10,000 samples. If you study common disease, where you, indeed have lots of noise and many rare variants playing to

00:34:00,200 --> 00:34:08,100
different pathways, then sure. That was the disaster of the past years of genome-wide association studies, but I think this should now be

00:34:08,100 --> 00:34:18,066
over and we should understand how we could use the tools. So again, no offense intended. If I would know anything—and I take pride, actually,

00:34:18,066 --> 00:34:25,232
being trained here at the National Human Genome Research Institute in genetics—I would now have the message, go home, okay, we have 100 or

00:34:25,233 --> 00:34:35,766
200 samples of a disorder I’d like to study. I understand that’s my cohort but I’m saying, look, the first science paper, genome-wide association

00:34:35,766 --> 00:34:44,732
study 2005 by Kline and colleagues, 86 samples, I will be presenting tomorrow in the Neptune study our approach to membranous nephropathy,

00:34:44,733 --> 00:34:55,633
people in the room probably agree that’s a complex disease. 75 samples we find the first locus, 150 we find both loci. Sorry, I just thought I

00:34:55,633 --> 00:35:00,699
want to put this here in front of everybody. Thank you.

00:35:00,700 --> 00:35:06,533
STEPHANIE FULLERTON: Yes, thank you, and I would actually really invite those of you who are more sort of up on the methodology to sort of

00:35:06,533 --> 00:35:19,433
comment on that point. I will say, though, where I am coming from as a bioethicist who interacts with communities around making kind of the case

00:35:19,433 --> 00:35:26,666
for research participation and as someone who is enormously concerned that as we generate genomic knowledge, we do not generate

00:35:26,666 --> 00:35:34,699
knowledge which will preferentially benefit certain populations and not others. The fact that from a methodological standpoint we can adopt a

00:35:34,700 --> 00:35:43,000
study design which only requires a small number of samples and which will get us definitive information on a small number of genes, does not

00:35:43,000 --> 00:35:49,600
begin to address the population burden, the pronounced disparities in kidney disease outcomes that are present in this country. And so

00:35:49,600 --> 00:35:58,933
even while such designs as you talk about might be tractable from the point of view of identifying particular rare etiological contributions is not

00:35:58,933 --> 00:36:05,999
going to make a dent in that public health problem, and that is the problem that the communities that I interact with care about, right? And so, we have

00:36:06,000 --> 00:36:15,166
to start figuring out how to do both at the same time and I think this is a complicated issue.

00:36:15,166 --> 00:36:22,966
MALE: That was a lovely talk and I want to bring you back to, I think, what was the title of the talk about ethnicities and communities and populations

00:36:22,966 --> 00:36:33,766
and I worked with Jeffrey Kopp on a beautiful study where we identified an important locus for renal disease in HIV infection. When we designed

00:36:33,766 --> 00:36:42,099
that study we were concerned about the ethnicities and we asked that all patients—because it was proposed to be African American

00:36:42,100 --> 00:36:51,300
patients—have four—I think that’s as many grandparents as you’re entitled to—four African American grandparents. We’ve also been

00:36:51,300 --> 00:37:03,066
involved in the FIND in the study that when we started it was particularly looking at different ethnicities because we thought that the

00:37:03,066 --> 00:37:10,299
expression of renal disease might be very different, both in its genetic susceptibilities and bases and in its clinical expression in different

00:37:10,300 --> 00:37:23,833
populations. Now I’m wondering, as people from the 50s and 40s are dying out and we have a new sort of multi-ethnic character in populations

00:37:23,833 --> 00:37:32,766
in the United States, what’s going to happen to our principal component analyses? What’s going to happen to our idea about using identified—self-

00:37:32,766 --> 00:37:41,699
identified—ethnic backgrounds when we don’t know what those mean anymore or people are fluid in their identification of ethnicities? So, that

00:37:41,700 --> 00:37:45,400
should be a fun question. STEPHANIE FULLERTON: Thank you. I mean,

00:37:45,400 --> 00:37:50,000
that’s a great question and we had a preliminary answer and I know you weren’t able to be with us for a lot of the day yesterday. There was a

00:37:50,000 --> 00:37:58,433
preliminary answer from our analytical people, and Suzanne Leal in particular, showing the ways in which we use the aggregate genetic

00:37:58,433 --> 00:38:06,033
information to say things about population genetic background, and increasingly in the genomics community, we use this information in preference

00:38:06,033 --> 00:38:16,699
to self-reported ethnicity. And yes, that’s absolutely relevant and important from the point of view of controlling for background population,

00:38:16,700 --> 00:38:28,133
allele frequency differences, it might be confounding and association analysis. It becomes more problematic, and this gets to topics that I

00:38:28,133 --> 00:38:38,633
talked about a couple of years when I last spoke at an NIDDK meeting, this whole issue of it is not bags of genes that walk into doctors’ offices, but

00:38:38,633 --> 00:38:47,466
it’s people who are ascribed to particular racial and ethnic identities. And so even as we can use the genetic information and people’s backgrounds

00:38:47,466 --> 00:38:57,066
to kind of put them in the right bin for our genetic analyses, there’s still very, very hard questions about then how that translates back out to the

00:38:57,066 --> 00:39:06,366
people of mixed ethnicity who are making up an increasingly large part of the population, and we had this sort of interesting, from my point of view,

00:39:06,366 --> 00:39:16,666
sort of hopeful response that as invariably in this work there is a return to a more family-based ascertainment in investigation, that those

00:39:16,666 --> 00:39:25,066
particular problems of population background confounding go away, because we can actually look very precisely at patterns of variation in the

00:39:25,066 --> 00:39:35,432
family and that there’s less of a concern about at least controlling for that background population in genetic variation. I don’t exactly know how to

00:39:35,433 --> 00:39:44,166
evaluate those claims. I still think they’re very interesting. I still think we’re going to have problems with recruitment and with making sense

00:39:44,166 --> 00:39:54,966
of the functional significance of these findings in a body that has a collection of genes and that walks through the world with a particular

00:39:54,966 --> 00:40:00,399
understanding of its place in the world, and that’s where it gets very complicated.

00:40:00,400 --> 00:40:06,133
MALE: [inaudible question from audience] STEPHANIE FULLERTON: Sure. Yes, exactly, and

00:40:06,133 --> 00:40:19,233
it’s very complicated and I think this is…as you know, my preference is to kind of keep self-reported ethnicity as a variable—as a covariate—

00:40:19,233 --> 00:40:26,399
in our analyses even while we’re controlling for genetic population background. But still, then how to use this information in a public health context

00:40:26,400 --> 00:40:32,333
becomes very, very complicated. Yes? GERJAN NAVIS: Gerjan Navis, Groningen. I

00:40:32,333 --> 00:40:42,833
recognize and appreciate the complexity of this self-reported ethnicity but there are relevant biological variables that go along with ethnicity

00:40:42,833 --> 00:40:52,899
and that’s lifestyle, and of course, it might be more easy to grasp the lifestyle differences between people of varying ethnicity, whatever

00:40:52,900 --> 00:41:09,700
that may be, and that might also give us tools to intervene with, let’s say, risk behavior, and that also relates to the remark that was being made

00:41:09,700 --> 00:41:15,366
about use of small, very well-characterized populations because, of course, it’s a lot of work to bring lifestyle and to document it properly and

00:41:15,366 --> 00:41:27,232
to document it in a reliable fashion. But it might be a way around this very complicated issue of “what is ethnicity?” At least it’s something you

00:41:27,233 --> 00:41:31,966
can measure and that’s actionable. STEPHANIE FULLERTON: Yes, absolutely, and I

00:41:31,966 --> 00:41:46,166
wholeheartedly agree. I think we need to be measuring very well, very comprehensively all sorts of factors that might be contributing to

00:41:46,166 --> 00:41:56,932
disease incidence, and lifestyle factors are a very important consideration. There is also the very interesting issues of…the interesting issue

00:41:56,933 --> 00:42:02,499
is: can we basically take ethnicity and sort of disaggregate it into its component pieces, its genetic pieces, and its lifestyle pieces? And I

00:42:02,500 --> 00:42:11,100
know there’s a lot of interest in doing that. I and others are interested in that but would like to retain the social identifiers as well because we

00:42:11,100 --> 00:42:18,733
also have this problem, which may be less of a problem in the Netherlands, I’m not sure, but it’s certainly a problem in the United States of racism.

00:42:18,733 --> 00:42:27,733
And so, even as we account for and take advantage of information on lifestyle factors and on genes, there still might be value in holding in

00:42:27,733 --> 00:42:38,233
play in our analyses the ways in the people walk through the world so that we understand better the ways in which social interactions intersect

00:42:38,233 --> 00:42:45,866
with lifestyle and other behaviors, as well as genetic influences. We need to have it all.

00:42:45,866 --> 00:42:51,632
GERJAN NAVIS: I think we agree and unfortunately yes, in the Netherlands racism is a problem also and a big concern, yes.

00:42:51,633 --> 00:42:55,199
STEPHANIE FULLERTON: Yes, thank you. SARA HULL: Thank you. That was a very

00:42:55,200 --> 00:43:05,166
interesting presentation. It was great and some important points are being made about the impact of self-reported ethnicity and race on science

00:43:05,166 --> 00:43:13,899
and interpretation and I think you also hinted at the inverse point that some of this research is going to have an impact on how people self-

00:43:13,900 --> 00:43:22,333
identify. I just wanted to mention a very interesting and complicated ethics consult that we had recently that I guess I would characterize

00:43:22,333 --> 00:43:34,733
as misattributed ethnicity and a finding where a SNP that had once been classified as being found only in a certain ethnic or racial group was

00:43:34,733 --> 00:43:43,833
reclassified later on, and whether that kind of result should be communicated back to an individual who is receiving results. It launched a

00:43:43,833 --> 00:43:52,099
very interesting conversation about self-perceptions and whether it’s racist to ask such questions in consults, but I think that’s just a

00:43:52,100 --> 00:44:02,500
glimpse at the feedback and how this is going to go in both directions and the importance of being aware of the impact of emerging research of the

00:44:02,500 --> 00:44:09,966
fact that, when you have initial results, that evolving results are going to change over time as you generate more and more information—your

00:44:09,966 --> 00:44:18,666
very first point—and what that’s going to mean for how we communicate with people will have really important identity implications.

00:44:18,666 --> 00:44:27,499

Date Last Updated: 9/18/2012

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