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

Sunday Plenary Discussion and Working Lunch
Reports From Cohorts, ELSI, and Nephrology Practice Groups

Video Transcript

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JEFFREY KOPP: I think we’ll begin our reports now. We have three sessions to hear from. I imagine the three presenting individuals or teams

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will want to spend something like five minutes, maybe, going over points and I guess I’ll let them decide if they want to have discussion point-

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by-point or wait until the end of their presentation and open it up. And so I hope those who couldn’t attend that particular session may have additional

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comments and maybe those who are in the session may want to elaborate. So, I think we’ll start with cohorts first: Martin and Linda.

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MARTIN POLLACK: So, we had the task of discussing cohorts, what we should be collecting in terms of samples, patients, information, and we

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solved all of these problems in 45 minutes. We talked about a bunch of different things in terms of collection size, in terms of what we should be

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collecting in terms of biomaterials, in terms of simple straightforward things like 24-hour urine collections. It was pointed out that this is

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important not just for the usual measurement of estimated GFR but there’s a lot of information regarding nutritional status, nutritional history of

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participants that can be obtained which might be useful for gene-environment kinds of studies. There was a fair amount of discussion about sort

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of technical difficulties that perhaps are somewhat inherent in kidney studies having to do with kidney tissue and correlating biological

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changes in kidney tissues and kidney cells with genetic data. It came up that NIDDK has a protocol on standardized urine collection for research

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purposes which is available on the Web. There was some discussion about measuring kidney function, eGFR, urine albumin-to-creatine ratios,

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and perhaps other biomarkers that might represent certain underlying phenotypes that are hard to assess better, the longitudinal phenotypes

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that normally take a long time to define such as GFR trajectory. There was some discussion about proteomics: whether and how to include

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urine proteomics and metabolomics in large-scale studies.

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LINDA KAO: And linking existing studies to other databases for long-term follow up and other complication outcomes. And then in terms of more

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novel phenotypes, there was discussion about non-invasive imaging. We heard from Wendy Hoy about assessing glomerular number; that there is

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great heterogeneity in that. And so, again I think there needs to be more exploration and more uniform data collection; I think that it’s one

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common topic that was discussed in great deal about uniform data collection. Even if people weren’t in large consortium studies, even if

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people were just doing small studies, it would be good for everybody to use similar protocols so that results can be more comparable.

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MARTIN POLLACK: Also there was some discussion of tissue resources for follow up on genetic studies, GWAS-based studies, and

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discussion of the NDRI resource for kidney tissue and the quality of RNA and the problems with that, the GTEX resource for tissue from cadavers

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and associated genotyping. Information was discussed but it was mentioned by some that there are quality issues in terms of the tissue of

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kidney, the issue of kidney tissue, the problems inherent in studying kidneys and getting kidney tissue came up. We didn’t solve that problem. The

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Neptune resource was mentioned as an ongoing prospective collection of well-phenotyped tissue for a variety of possible ancillary studies was

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mentioned, and questions were raised about the availability of archived samples for the general community, what’s out there, and can information

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about the various databanks that are available to the community be collated somehow for use in the community? We also talked about issues

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having to do with diversity in collecting information from other ethnic groups. Using genetics, it’s in some ways easier to classify

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people than self-identification in terms of at least geographic history, but the problem is getting participants of diverse ethnicities to participate in

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our studies as being the biggest challenge in terms of ethnic diversity.

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LINDA KAO: And even though there are some studies that already exist, I think many of them could be underutilized. I know personally from

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me, I learned about a couple of other fairly large cohorts of African American populations that are out there which I didn’t know about. And then,

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even if people do study genetics in other non-European ethnic groups, there’s a great deal of difficulty in confirming their findings and getting

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that additional replication sample. MARTIN POLLACK: And then finally, Wendy Hoy

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talked about…what? LINDA KAO: I have no bullet points about that.

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MARTIN POLLACK: Yeah. She talked about some of the resources in Australia that she’s been developing and interestingly, at least I thought it

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was very interesting, that it seemed that the genetic risk, at least for kidney disease in Aborigines in Australia seemed to be different

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from the genetic risk factors that have been observed in other ethnically-defined populations, suggesting there’s a lot more to learn from

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studies of kidney disease and its relationship to ethnicity.

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JEFFREY KOPP: You’re not off the hook quite yet. So, one thought occurred to me, and I was in the session but didn’t have the chance to talk about it,

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would be environmental factors to collect. So things like smoking, maybe non-steroidals if that’s important, illicit drug use, maybe toxins, heavy

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metals, maybe other environmental toxins. This gets into the idea that I know you guys touched at least briefly idea of proteomics and metabolomics

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and knowing how to store samples for these various assays and having the resources to bank them in plasma and serum and urine is not trifling,

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but something to think about. LINDA KAO: Yeah, you might have been out of

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the room. The Genome Institute had started a project called PhenX—P-H-E-N-X—and the goal there was to provide standardized protocols for

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the collection of environmental data in the context of gene-by-environment studies. So there were, I think, 20 different domains and some of the data

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are questionnaire-based data, others…I know I participated in a diabetes group. There we provided common protocols for collection of blood

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specimen, which A1C assay do you use? So, that might be a good place to start. It’s sort of the most…most of the factors considered in that

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program are sort of the simplest, maybe not the most high-tech factors that most studies can accomplish, but that’s a great point, I think.

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JEFFREY KOPP: Thank you. MALE: Thank you. A very nice discussion about

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what went on. Do you think it would be worthwhile for the survivors—everybody who’s really staying for the most interesting part of the

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conference—to talk about the longitudinal samples that have data that are important for kidney disease, like Framingham, CRIC, MESA,

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ARIC? It would be very useful to talk about them so people knew exactly where one could go to. I may have missed some in my little microphone

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delineation. MARTIN POLLACK: I don’t know if anyone

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wanted to mention particular studies, but I agree that longitudinal follow-up, what happens to the kidney over time is really the phenotype we care

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about most and, you know, it’s difficult. I mean, it’s not just important for genetic studies but I think it’s important for all sorts of clinical studies. It’s

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important in terms of development of therapeutics and in terms of relationships and studies with the pharmaceutical industry because what we really

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care about is not if a GFR is a little bit low, but if the GFR is low and getting worse over time and we want to be able to assess longitudinal

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outcomes without having…it would be nice to have a single point in time surrogate for what happens over 10 years so that we can really

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study the endpoints that we care most about in a more compact time frame.

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MALE: One of the nice things are because a product of this conference is the breakout group recommendations get put on our website and get

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widely distributed, hopefully, is to encourage studies that are supported by other funding agencies to get some simple, cheap

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measurements that would be very useful for the public health regarding kidney disease, such as the urine protein-to-creatinine ratio, the urine

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albumin-to-creatinine ratio, creatinine, and we should encourage that from our brother and sister institutions and other investigators.

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MALE: You mentioned in the first part of your presentation 24-hour urine collections and I’m just wondering if you guys discussed best practices

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for that. I’ve had a couple of cord studies and the thing that I hear back most from my coordinators what limits participation and returning for future

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visits is a lot of participants do not like doing 24-hour urine collections. You can do almost anything to them, but if you ask them to do a

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24-hour urine collection then it’s suddenly…I mean, that really crosses a line. So, I’m just wondering if people have suggestions on how

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to… LINDA KAO: We heard about the Netherlands.

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GERJAN NAVIS: Actually, I’m the one who came up with this suggestion. I’m very well-aware of the skepticism about 24-hour urine. If you are part

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of a, let’s say, clinical trial or let’s say a large-scale screening, yes, it’s difficult and cumbersome. But if you have renal patients that

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come back to your outpatient clinic again and again and again and you tell them that this is an investment in the future because it gives you

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non-biased information on the lifestyle that’s impossible to get otherwise because food labeling is very poor, and when you give them

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feedback so that it helps them to comply with your dietary restrictions, in our hands it works. So, you have to integrate it in your clinical

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practice and you use it for feedback to your patients to show them that it helps improve their lifestyle. So then it’s a tool for let’s say supporting

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something that’s very difficult, changing your lifestyle. So, the 24-hour urine is your friend. So, that’s what I tell my patients and then it works.

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ROBERT KLETA: What we have found very useful is if they get a small fee, actually, if they complete a 24-hour urine collection to significantly

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increase the compliance. MARTIN POLLACK: It occurs to me it might be

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even useful to develop sort of a Wiki for best practices for various phenotyping. One issue that occurred to me in the course of a study I’m

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involved with in terms of kidney stone-related phenotypes is time of 24-hour collection within season of the year and its potential effect on

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sunlight, length of day, and vitamin D metabolism; something that hadn’t occurred to me at all until deep into the study. So if there was a mechanism

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for ongoing discussion and refinement of best practices that was some kind of, you know, Wiki-like web-based forum, it might be useful to the

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community. MALE: I just wanted to follow up on what Paul

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said about longitudinal. I think all those studies are great to think about but we also need to think about using the new resources with electronic

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records and being able to look across systems now, you know, hospital-based systems to follow patients. I mean, if we can re-identify samples,

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those are rich resources for longitudinal follow-up, both retrospectively and potentially prospectively that I think we’re just beginning to

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understand. I really think that’s where the future’s going because it’s so expensive to do longitudinal studies in the way we’re used to it, but now

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hospital systems are collecting this information and, granted, there’s problems with people talking to each other but even in Cleveland where all of

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the hospitals hate each other, I’m able to look in the Cleveland Clinic and they can look back for clinical reasons now, and we can look into Kaiser

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and back, and we can get 70% of the region’s information. So, that’s a possibility to get that information you want, too.

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MARTIN POLLACK: I don’t think we’re going to be able to have one method of collecting all this data on all cohorts. I mean, I’m a big fan that individual

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investigators and groups should design the studies that they want to study and I think that’s been extremely fruitful in genetic studies of the

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kidney to date, but to me, one of most important things would be to unify what the various tests and assays mean, even whether labs at two

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different institutions mean the same thing by a parathyroid hormone level or a creatinine level is not even that clear. So to me, that’s unifying

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descriptors of phenotypes is among the biggest issues that would help.

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SARA HULL: So, I was in the breakout group that focused in on the ethical, legal, and social implications and the optimal way to conduct

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whole genome research. It was a very small—we’ll call intimate---group and we joked that was because our speakers were just so

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tremendously clear, that there was nothing left to say. We actually solved all of our problems before we even got into the room, and of course

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that’s never the case when it comes to ethics. So, each of the speakers that we heard from yesterday and today in this theme presented

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what I think are a very nice list of points to consider and action items to keep in mind for researchers. In our session we delved a little

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more deeply into the idea of these action items and points and sort of generate specific researcher obligations that carry certain

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challenges and burdens and opportunities, and so we tried to explore some of those in a little more detail. I threw this together and hopefully it

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will make some coherent sense. Around the issue of data sharing, which we heard about in detail yesterday but was really a cross-cutting theme

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that came up again and again, of course there’s recognition of the need for collaboration and sharing and this starts with the physical act of

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depositing data in repositories like dbGaP and we actually talked through some of the requirements and nuances associated with doing so. There

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was some discussion yesterday and again in the session about special population considerations, so developing a plan that’s acceptable for

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different communities. It’s going to look very different what the requirements under a Syrian regime are going to be as compared to a

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historically black college where a cohort’s being generated or recruited from, and so there’s those considerations to pay attention to. We also tuned

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in to the fact that consent and the adequacy of consent for doing data deposition is a big part of this and one of the themes that hadn’t come out

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explicitly during the presentations but that we paid attention to is the concept of re-consent and for existing cohorts who have already given their

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consent to participate in some kind of research, whether it’s research related to kidney disease or genetic research in some form of a description of

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genetic. Is that going to be sufficient to allow the sharing of whole exome and whole genome data and how do we make decisions about when re-

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consent is going to be required and also engaging with IRBs on these questions, too, and their transition? Investigators can actually play a role in

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helping to educate IRBs about those realities as much as the other way around. And then an opportunity, I think, to consider—for the research

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community to consider—is really tuning-in to and paying attention to evolving policies. We heard that very clear policies exist in the context of

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GWAS SNP data and this is a moving target and evolving issue for broader genomic data sharing types, at least within the NIH and NIH-funded

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research, what the mandates will go along with that. There’s going to be Federal Register notices, there’s going to be an opportunity for public

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discourse, and it would be extremely useful for the research community to share its experiences and perspectives on some of the success

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stories, as well as challenges, around GWAS and some of the emerging—and I mean “emerge” not as the consortium, but in the original sense of

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the word—some of the experience around sharing exomic data as is happening in at least some institutes and with some cohorts. Okay, so

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management of results. There’s this also emerging sense that there may be some obligation to really pay attention to and figure out

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what to do with results that are inevitably going to come up that aren’t necessarily directly related to research questions around kidney disease that

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investigators might be asking but are definitely related to the technology being used. We delved into a little bit about the sort of personal decision

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that any given investigator is going to have to make about whether to do this as it comes up if they happen to see if or whether there’s a more

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active obligation to engage in the kind of interrogation that Ben Solomon and others have talked about. I thought a really nice point was

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made about the need for additional support and resources and we talked about all of the entities that are trying to develop lists of actionable

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variants and the fact that it would be burdensome for any one investigator to have to come up with this list independently, based on the particular

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population and disease and research questions that they’re focused on and that a more central effort to do this would be really valuable and help

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relieve some of the burden on investigators, and that the ability to both identify and to be able to disclose these results require both funding and

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additional infrastructure, which are institutional considerations and funding agency considerations. But then there was sort of a

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cautionary—I wouldn’t call it push-back—but a balancing to drive home the point that lists might be helpful but they’re not sufficient; that having a

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list of actionable variants is a little bit of an oversimplification. It’s not just a checklist that you can go back to any given person and say “if you

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have this, this, and this.” The “this” is much deeper. It involves making decisions about which variants within a region and what it means based

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on family history; what it means in terms of the personal, the family history as well as the individual’s medical history; and how they’ll

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receive that information, and preserving the opportunity for choices when that’s appropriate. One additional issue that was flagged is coming

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up with ways to share. So when there are these lists and investigators do make choices about what is actionable and make a case-by-case

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decision about what to return or what not to return, communicating this back to the broader community would be another area for sharing

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and for more openness, and right now the way the human…HGM…the medical…the mutation database—I can’t remember the acronym—that

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conveys the current literature on the status of various mutations, to be able to annotate that database in some way and let people know what

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parts of the literature have been on a case-by-case basis sort of overruled in individual clinical decision-making. That conversation is something

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that has to occur in a more robust way and presents another obligation but also an opportunity for investigators. Third was this area

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of paying attention to the role of race and ethnicity and engaging with communities. So, we talked this morning about an obligation is for

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investigators who recognize the need for diversity and the collection of data in the recruitment of participants, and there’s sort of

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this theme hanging in the air—the point that was made yesterday—that we have exome chips that are only suitable for use in European populations

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just by virtue of how the data were curated and pulled together. And so to help us move beyond that kind of technology, I think there’s a

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consensus that we have to pay attention to this, but how exactly we do this and what this means in terms of figuring out how to engage with

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communities was something we talked about in our discussion—in our breakout session—and really the need to look to others, partnering with

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people who have been successful at doing this is a step that requires investigators to kind of step out of maybe the view of the world and the

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particular slice of any given population that they’re used to dealing with, and this might even involve stepping outside of the world of genomics

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and looking at other kinds of research and other ways that we engage with diverse communities. But this is a huge area that I think some more

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robust conversation and attention to will benefit. And then finally there was this theme of informed consent and we talked a little bit about the idea of

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moving beyond the, “What do I do? Just tell me what to do and I’ll do it. Give me the consent form template and I’ll cut-and-paste it, “ to the need for

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broader conversations, and I keep on saying this idea of broadening the conversation, but here with respect to informed consent we identified a

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few key stakeholders. Communication with subjects: when you have a potential subject sitting before you thinking about the decision

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about whether or not to enroll in research and making all of the issues that Julie Sapp talked about transparent, that’s one challenge,

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remembering that some of these participants are also viewing the world from a consumer perspective. Some of them are coming to the

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table with a great command of information that’s available on the internet, they might have even through 23andMe and have some of their

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sequence data in-hand when they show up and how that changes the conversation. Conversation with communities: define both

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those diverse populations as well as advocacy groups and thinking about new ways to engage with populations and to help bring everybody on

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board with what’s going on in the research arena. Conversations with the public in a broader way as well, and this dovetailed, really, with the

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idea of conversations with the media, helping the public develop better media literacy, and to interpret any given finding that makes variancy

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much more important than maybe they are in reality, and helping scientists figure out how to better engage with the media so that the media

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represent our findings with some more accuracy. This is all part of a bigger picture that will help with the informed consent process and general

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understanding and transparency and trust in the research enterprise. I hope that fairly represents and organizes the themes that we covered in that

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session. Do we want to open it up for people to follow up on these points? Add to things that I missed? Oh, I’m sorry. One more point. Jeffrey

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made the very important point that a lot of the value of this conference is the conversations that occur in the hallway and one important

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conversation I had in the hallway raised a point that didn’t come out explicitly anywhere but I just took the liberty to stick up—it’s really a cross-

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cutting theme—that some of the discussion about data sharing across geographic and political boundaries runs up against an issue where

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certain communities might be hesitant to share their own resources with the broader community, at least without a conversation about what’s in it

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for them and how to protect their own interests in this information and people have started to talk about the idea of sovereignty and national

00:28:15,333 --> 00:28:28,666
concerns, but that’s another theme to be tuned-in to and to pay attention to as we talk about the goals of data sharing.

00:28:28,666 --> 00:28:36,832
JEFFREY KOPP: Thank you for that nice summary. So we’ve heard about various resources. Papers have been mentioned and so

00:28:36,833 --> 00:28:43,766
forth. There are local resources at each of our institutions, but if somebody wants to continue this conversation, can I suggest that you or

00:28:43,766 --> 00:28:51,332
others might be willing to have phone conversations or point to people who would be willing to do that?

00:28:51,333 --> 00:28:55,366
SARA HULL: Absolutely. JEFFREY KOPP: Because you have, particularly

00:28:55,366 --> 00:29:01,799
at the NIH Clinical Center, I think there’s a depth of expertise in coping with, for example, whole exome informed consent issues. Some

00:29:01,800 --> 00:29:09,400
institutions are already there and others may be just starting, so I think that would be a wonderful resource to have available.

00:29:09,400 --> 00:29:18,466
SARA HULL: I think all of our contact information is in the packets that you received when you checked in, but absolutely, and I will more likely

00:29:18,466 --> 00:29:26,332
than not be able to help refer you to the expert who knows. But we do have a good amount of experience; we’ve been looking at the whole

00:29:26,333 --> 00:29:38,533
exome and genome studies. The first hint that this was coming for our IRB at NHGRI was in 2006, so the IRB has some experience and the benefit

00:29:38,533 --> 00:29:48,299
of people like Julie Sapp who have been—I’m pointing to where she was sitting before, she’s not here—who had been teaching us about the

00:29:48,300 --> 00:29:57,866
best ways to do this, so I think we do have the advantage of some early experience. But I think, as was pointed out, we’re not necessarily the

00:29:57,866 --> 00:30:06,799
most generalizable of places where research is occurring, and so I think I appreciate all the comments that I’ve heard here about the specific

00:30:06,800 --> 00:30:15,600
challenges, funding and otherwise, at each individual site and it’s context-specific. So, I just throw out that as a limitation to my knowledge

00:30:15,600 --> 00:30:22,066
base, but absolutely please follow up. We might have one more comment.

00:30:22,066 --> 00:30:35,166
FEMALE: In addition to standardized protocol of how to collect phenotype, is it possible for us to have some consent form that’s being evaluated

00:30:35,166 --> 00:30:45,499
by experts in bioethics and genetics, test writing one or two studies already so we know what language to use when we, too, want to work or

00:30:45,500 --> 00:30:49,666
when we address the issue of incidental findings and re-contacting.

00:30:49,666 --> 00:30:56,599
SARA HULL: Yeah, so I think that’s a really interesting idea, the idea of developing some kind of resource for sharing consent forms and I

00:30:56,600 --> 00:31:05,666
found myself very curious to compare. We heard about, I think, an Australian cohort study and maybe one from the Netherlands where there

00:31:05,666 --> 00:31:16,299
were differences in language and the experience and success with respect to getting consent. I often think that when people say “no” to a

00:31:16,300 --> 00:31:23,966
consent form, I still consider that a success story because maybe the consent form really laid out some of the nuances and let people make a good

00:31:23,966 --> 00:31:34,099
decision, but maybe it’s concerning as well if we’re scaring them. But I think sharing consent forms is a great idea and there is a couple

00:31:34,100 --> 00:31:42,533
of…maybe I’ll make sure to communicate these resources that were there. There is a website that NHGRI has developed that has some model

00:31:42,533 --> 00:31:49,866
consent form language around genomics and research for certain kinds of studies and that’s evolving, but maybe this group wants to make a

00:31:49,866 --> 00:31:59,566
decision, too, to share its own consent forms that they’ve been using and develop a sense of best practices. I would endorse that as long as people

00:31:59,566 --> 00:32:08,099
promise to resist the temptation simply to cut-and-paste and to actually think about how it pertains to their specific cohorts and how it plugs into the

00:32:08,100 --> 00:32:11,333
broader conversation. What? MALE: Maybe a PDF.

00:32:11,333 --> 00:32:20,033
SARA HULL: Right, we’ll share our PDFs. MALE: [inaudible comment]

00:32:20,033 --> 00:32:47,466
SARA HULL: Scanned PDFs you can’t cut-and-paste; it’s only the ones that you generate on the computer. Okay. That sounds like a great idea.

00:32:47,466 --> 00:33:04,366
JOHN SEDOR: Our group was charged to talk about where we are in the kidney community and incorporating genetic data into practice. We really

00:33:04,366 --> 00:33:14,499
focused on: is there anything clinically actionable now rather than what could potentially happen down the road? So, I think there was a

00:33:14,500 --> 00:33:22,900
consensus that we’re certainly not ready for community-based, I should say, screening yet, prediction or pharmacogenomics. There’s been a

00:33:22,900 --> 00:33:33,666
lot of nice studies across a number of diseases, but recently and including and applying the CDKGen data sets, it suggests that right now

00:33:33,666 --> 00:33:41,066
genetic information that we have isn’t going to be useful for those sorts of things and we really focused on the possibility of using genetic tests

00:33:41,066 --> 00:33:49,866
as a biomarker or when we brought up the risk stratification, almost similar to using the LDL and we focused on really probably the only potentially

00:33:49,866 --> 00:34:02,566
clinically actionable finding we have is the c22 APOL1/MYH9 variants, and wanted to make the point that studies of the clinical utility of these

00:34:02,566 --> 00:34:11,599
findings should be proceeding in parallel with people interested in the underlying biology of the findings and what it means in terms of disease

00:34:11,600 --> 00:34:23,366
mechanism. We really tried to focus on potential studies that the community could be thinking about now and we came up with three different

00:34:23,366 --> 00:34:34,166
areas that related to the APOL1 finding. One would be HIVAN. Would this would potentially be something we could use to initiate antiretroviral

00:34:34,166 --> 00:34:45,599
therapy earlier than would otherwise be dictated by CD4 or viral load findings? That may go out the window if the HIV clinical community decides that

00:34:45,600 --> 00:34:53,333
everybody’s going to need to be treated early now with the findings about preventing progression, but it’s still a possibility. We

00:34:53,333 --> 00:35:05,966
discussed transplantation. Barry had to leave, but of course his provocative paper that genotyping the donors had a predicted outcome in the

00:35:05,966 --> 00:35:14,199
recipients and necessarily needs to be confirmed and I think this is potentially a major issue in the African American donor community. If they’re

00:35:14,200 --> 00:35:23,300
really at increased risk, would it be worthwhile knowing their genotype? It’s certainly something that would amenable to a good study. Then we

00:35:23,300 --> 00:35:31,600
discussed in some detail potentially the use…and I should put a non-diabetic chronic kidney disease in the African American community. Can you use

00:35:31,600 --> 00:35:43,933
APOL1 allele information? Erwin proposed an interesting idea to feed back to clinicians through electronic records to see how that would affect

00:35:43,933 --> 00:35:54,499
implementation of standard goals that we have in terms of both process. Are you measuring albumins, urinary albumins? Are you measuring

00:35:54,500 --> 00:36:06,133
blood pressures, as well as looking at outcomes? Obviously, all these things have a lot of ELSI issues. I thought that, without having to state the

00:36:06,133 --> 00:36:15,599
obvious, I think there would be a lot of interest and people interested in those issues that would be important and this clearly is a gene that’s

00:36:15,600 --> 00:36:27,633
linked to the African American community and there’s all kinds of issues in terms of racism and the way the public can interpret these findings in

00:36:27,633 --> 00:36:36,999
negative ways, and I think those are ripe for study also. Jeffrey brought up the issue, as we were moving on to a different talk, that is

00:36:37,000 --> 00:36:44,833
potentially developing a curated database of kidney disease genetic information: where you can get the test done, what does it mean, is any

00:36:44,833 --> 00:36:56,133
of that clinically actionable? The potissin mutation came up as potentially, should everybody be genotyped now before you initiate therapy and

00:36:56,133 --> 00:37:07,933
decide if it was going to be appropriate or not in those patients? There’s obviously a lot of opportunity here for community engagement

00:37:07,933 --> 00:37:23,599
research, also. If we’re going to focus in on APOL1, it’s an ideal opportunity of getting feedback from the community about: are they

00:37:23,600 --> 00:37:32,133
interested in this information? How will it be used? What’s the best way for us to proceed? And then Rob, at the end, raised there’s a lot of

00:37:32,133 --> 00:37:39,499
other opportunities but we didn’t get into that. I know it would be interesting to know the genetic architecture of the number of other issues in

00:37:39,500 --> 00:37:47,800
kidney disease research but that was a little outside of what we were focusing on. For example, what are the genes that regulate rate of

00:37:47,800 --> 00:37:58,333
progression? Can you use genetic information to help people that are at high risk for stones? Another good one would be, AKI recovery or

00:37:58,333 --> 00:38:07,966
not? If we learn more about the genetic architecture, would there be a way to use that information in clinical practice? Hopefully, I

00:38:07,966 --> 00:38:15,999
summarized. It was a good discussion. I think a lot of people have been thinking interesting thoughts on this and I think it’s exciting that we

00:38:16,000 --> 00:38:24,933
have potentially a clinically actionable finding that we can start thinking of ways to test in research settings; how this might help us in our practice

00:38:24,933 --> 00:38:39,699
now and not be waiting for understanding the biology whenever that may come. So, I’ll stop there.

00:38:39,700 --> 00:38:49,633
MALE: Thank you, John. I came late to your discussion, I was in the cohorts discussion first and I just want to point out -that a very large,

00:38:49,633 --> 00:39:03,799
very simple clinical study that is being funded by several of the institutes at NIH—the SPRINT study, which is NHLBI, NIDDK, the Aging Institute and

00:39:03,800 --> 00:39:13,566
some other institute support as well—there is an ancillary study looking at genetics both in patients with chronic kidney disease and non-chronic

00:39:13,566 --> 00:39:26,199
kidney disease controls where the study is a study of various medications used with different blood pressure goals. It’s very clinical and it’ll be

00:39:26,200 --> 00:39:38,300
very interesting to see if any clinical utility comes out regarding the APOL1 variants and MYH9 variants.

00:39:38,300 --> 00:39:44,233
GERJAN NAVIS: I would like to come back to what you said about kidney transplants. I don’t think it’s that provocative. I think the kidney

00:39:44,233 --> 00:39:52,466
transplant setting is a unique setting, let’s say, to do genetics and actually we have some data—not in African Americans because we don’t have

00:39:52,466 --> 00:40:06,732
too much of them—but in our [---] large transplant cohort where we replicate findings from GWAS as risk locus for CKD for graft loss and we’ve

00:40:06,733 --> 00:40:13,999
also found differences in a new frequency between donors and recipients, so this is independent replication in a different setting

00:40:14,000 --> 00:40:25,233
within a single cohort. But of course, all the better journals ask for independent replication. So if we have people in the audience here with transplant

00:40:25,233 --> 00:40:36,766
cohorts who would be interested in, let’s say, to replicate our findings or to come up with findings of their own that we could replicate, I think it’s a

00:40:36,766 --> 00:40:44,632
very powerful tool. Also, it’s a dissection tool because if you’re not sure about the biology of risk allele and it follows the kidney, it’s an

00:40:44,633 --> 00:40:52,466
inter-renal effect. I realize that’s too simple, but… JOHN SEDOR: Yeah. Thank you. Interesting

00:40:52,466 --> 00:41:08,466
comments, I agree. Okay. JEFFREY KOPP: Okay. Thanks to all you hardy

00:41:08,466 --> 00:41:17,566
participants who stayed to the end. Again, I’d like to thank the committee that organized this, the speakers that have traveled quite a distance in

00:41:17,566 --> 00:41:27,399
some cases, not so far in other cases, and all of you. I think it’s been a very good meeting. It is being video-archived, as you know, and I think

00:41:27,400 --> 00:41:34,833
we’ll try to follow up on some of the suggestions to maybe take some of these workout group summaries and condense them and put them

00:41:34,833 --> 00:41:40,833
together in some kind of a format. So, thank you all and have a safe trip back home.

00:41:40,833 --> 00:41:45,999
FEMALE: [---] was the leadership of this conference and he [---].

00:41:46,000 --> 00:41:48,166
JEFFREY KOPP: Oh, thank you.===

Date Last Updated: 9/18/2012

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