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

Group Discussion—Issues in Data Analysis: What are the Challenges?
Andrey Shaw, Washington University in St. Louis and Cheryl Winkler, National ancer Institute

Video Transcript

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ANDREY SHAW: I’ll start the ball rolling. So, we had some discussions about QC. From my perspective as a practitioner, how do we

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implement QC? What do we decide what good QC is, and how do we make that a part of the standard review of data that’s going to be either

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uploaded to the Web or published? SUZANNE LEAL: I think right now that there is no

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standard and it’s something that’s still evolving, and there’s different levels of QC, so what I spoke about was QC on the VCF file level, but

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really, to do proper QC, you have to really go back to the alignment and look at the alignments, too. So, I think this is something that’s still evolving

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and more thought has to be put into properly QCing data. I mean, there are clearly some things you should but I think there’s much more that we

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have to think about and develop. JOAN BAILEY-WILSON: And there’s some

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consortia that are starting to be put together sort of checklists, much like Geneva has done and published for GWAS QC where they’ve taken the

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things that lots of different groups have figured out are good practices and sort of made a standardized pipeline. I know some of the folks at

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the Broad are doing this for some of their studies. Gonçalo Abecasis’ consortia are doing that, this new Austism Consortium’s talking about making

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sure everybody is doing the same, but we’re not all…we’re not at the point where Geneva finally published that paper, what, a year ago, two

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years ago at most? We’re struggling. Everybody’s telling each other what they’re finding and how to do things but we are still at that development

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stage, and there are some things that people are all starting to agree on like read depth less than 10, it’s probably garbage, and, you know, there

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are some rules like that, but it is still not final. Jamie’s saying “yes.”

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XIHONG LIN: Yeah, I agree, and I think that also the QC also depends on the sequencing depth and if the sequencing depth is shallow, then you

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probably need to have more stringent criteria and if the sequencing is deep…so right now, for example, for the Utah Sequencing Center—I think

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similar at Broad—they do at least a 330k 30X or 100X. So in that situation, it seems like the quality is pretty good. And also, the other way to think

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about a QC, if you have GWAS data, you can compare the overlap of the sequencing data with the GWAS data. So for example, in this dataset

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we have almost about 10,000 SNPs in the overlap, so I can compare the correlation and so it seems like, at least for our data, it seems like

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the correlation is very high. So, that is kind of reassuring.

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JOAN BAILEY-WILSON: Yeah, the Seider Lab is doing that and NISC is doing that now, too. If they bring in data they’re going to sequence, they just

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do a chip on the same sample so that they can do that kind of quality control comparison.

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MALE: First of all, I enjoyed this session very much and benefitted a lot from all the collective wisdom that you have accumulated at the

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forefront in this area, and my question is directly trying to tap into your collective wisdom. So, let’s assume or let’s pretend…or not pretend…but let’s

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take my…I have a cohort with 10,000 participants and interesting phenotypes and my dean says, “Here you go; next week I’ll give you a million to

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get us the hottest genomic data on your cohort,” and if I do exome sequencing I can probably, at this point in time, maybe sequence 1,000 of the

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10,000. If do the exome array I can do all of them. Now, what should I do?

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SUZANNE LEAL: I think…well, first of all the exome array probably to do the 10,000 would be less money than doing the 1,000, but also you

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have to take into consideration: are these European Americans? Are these Europeans? If they’re not Europeans, then I would not go with

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the exome chip, so that’s one consideration. Now, I think it’s probably…what?

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MALE: Could you explain that? SUZANNE LEAL: Oh, it’s just because of how

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the exome chip…what was used to the determine the SNPs that were put on the exome chip. So, the selection was done based on

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12,000 individuals and the vast majority of those individuals are European Americans. I think there was maybe 2,000 exomes that were African

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American and only maybe 500 Han Chinese and 500 Hispanic. Also, there was this criterion that the variants had to be seen at least in two

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studies, seen three times for most of the variants…three times in two studies and for, like, splice sites I believe it was only two times but in

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two studies. So in certain populations, that’s not going to work very well at all, but I think it’s hard to say because, you know, you’re going to lose

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some variants but you do have the trade-off where you can do a much larger sample size and I think we’ll start knowing a little bit better soon

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about that. You probably don’t have as much as…it’s not as problematic with calling the variants as with the sequencing, not only of it

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being able to do a much, much larger sample size. So, I would say it’s even more than, you know, 10 times the sample size you could do for the…it’s

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much more than 10 times the sample size you could for the same price. So, if you have a really large cohort, that might, right now, be the way to

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go, but I don’t know. I don’t think there’s such good odds for choosing one over the other right now, but if you have a smaller sample size, if you

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only had 1,000 individuals and you’re doing exome sequencing, you’re probably going to be very unpowered.

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FEMALE: Many speakers today mentioned gene-gene/gene-environment interactions but the power picture that they pictured was very

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pessimistic. They needed 6,000 samples more, and given that this was a power for main effects, and usually for interaction effects we need, like,

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4-fold number of samples. What do you think is the next step to be able to even start exploring gene-gene/gene-environment interactions?

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JOAN BAILEY-WILSON: When I was talking about gene-gene interactions I was really talking about the more common variants coming out of

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GWASs, because at this point in many of the GWAS studies that are out there, people are just now getting into consortia that are big enough

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that they have the kind of the power they need to really start querying these interactions because you do need larger samples, and so a lot of folks

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who’ve been doing GWASs and they’ve done their meta-analyses for the marginal effects—the effect of each SNP—in larger and larger and

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larger samples and they’re sort of feeling like they’ve found what they can find of those individual SNP effects, now they’re starting to

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look at the interactions and they are thinking that some of the missing heritability will be due to interactions of those more common things. So, I

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was really talking about analysis of data we already have, and let’s not go herring off after where variants are going to explain everything

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and forget to look at data that we already have that may help to explain some of it. I don’t think it’ll explain all of it but I think it will explain, perhaps,

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more. So, that’s where I was talking. Yeah, when you get into gene-gene-gene interactions with rare variants, then the sample sizes are going to

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have to be really huge and, you know, maybe it’ll happen in my lifetime, but I’m not sure about that because, you know, I’m old.

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XIHONG LIN: For the screening stage I would suggest what people to do is to, rather than fitting the main effect model, you fit the main effect for

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gene-environment interaction and you start with doing, like, testing the main effect on gene-environment interaction separately, and when

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you do the screening you do two-degree freedom test. So therefore, at the screening stage you will be able to pick up both the main

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effect and also the interaction, and then in the validation phase you can fine-tune it. So, that will help you to improve the screening power. And

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also for gene-gene action, I think that would probably be SNP by SNP interaction, right? So in that situation, because for the rare variants we

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focus on gene level analysis and so therefore, when you do the screening the one can account for SNP by SNP interaction. For example, in the

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SKAT method we can do the screening by allowing for SNP by SNP interaction in the model. FEMALE: Hi. I had a question about defining

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subsets of SNPs for rare variant analysis. I feel like it’s more obvious, you know, when we have exome sequencing data or gene sequencing

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data, in terms of defining subsets based on a functional unit, but as we move towards sequencing contiguous genomic regions, I was

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wondering about, you know, how to collapse rare variants. I know Dr. Lin had mentioned sort of a moving window approach as a possibility,

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but I wondered what’s known or what type of literature is there out there on this right now, and you know, if moving windows is the optimal

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approach, what size of a moving window? I don’t know. I was just hoping you guys might be able to comment on that.

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JOAN BAILEY-WILSON: Well, in addition to moving windows, people are using this sort of LD tiles. So, saying okay, if I’m going to go beyond

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genes where I have something where it does make sense that I could say, “I’m going to collapse within a gene because it’s a functional unit,” once

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you get out of it, you’re right. You don’t have something. And so, you can take just distance…so, like in SKAT you can say, “I want to do over

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3KB distance,” or something like that or you can do something where you’re collapsing based on the observed LD patterns in your data, and that’s

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another biologically attractive thing to do because then at least those things are perhaps close enough together that maybe there’s really one or

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two tagging SNPs in there that are really doing something, but it’s a difficult question and we don’t know. You may do better. Once we know

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more about the genome you might say, “All right, I know that this is a region that has an microRNA in it, so maybe I’m going to collapse there and here’s

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another region where I know there are some known microRNA seed sites, so maybe do I want to say that any microRNA seed site that works

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with this microRNA across the genome, I’m going to collapse any variants in them.” That’s sort of the pathway thing. So, there’s lot of different

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ways you can do it. I don’t think any of us know what’s going to be right. Someone was talking about…you…you were talking about, as you go

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across the genome, what’s right in this gene may not be what’s going on in this gene. In this gene, all the SNPs, all the rare variants may have a

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positive direction but in another gene maybe it is bidirectional; some increase risk, some decrease. We don’t know what it’s going to be and I suspect

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we’re all going to be experimenting for a while, but those are just some of my ideas of things you might collapse on. You guys probably have other ideas, too.

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SUZANNE LEAL: I think it’s a huge problem and I personally don’t have a solution. I’m kind of doubtful that a sliding window approach, just based on some, you know, criterion like size, is going to work because you wouldn’t know

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priority, what the size, you can’t try many different sizes and you’re going to have…it’s going to be a huge multiple testing problem. So,

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my answer is: I don’t have a clue, basically, and it’s a very important area of research.

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XIHONG LIN: Yeah, I don’t have a good answer, either. So, one way to do the sliding window, you can do this. Instead of doing it this way, this way,

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you can do this overlapping way. So therefore, that can help you a little bit, and the other is, as you mentioned, you can use the haplotype block

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to define the block, and the other way is to use a recombination hotspot to define the block. So, there are multiple ways but really we don’t know

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what’s the good way or the best way should be. I think that, probably, will take some time for us to figure it out. Maybe the bioinformatics could help,

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you know? FEMALE: My first question is about…

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JOAN BAILEY-WILSON: You need to talk louder. FEMALE: Okay. My first question is about the

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Q-Q plot that Suzanne showed for the age of menarche in African Americans. How do you explain why you see some signals in the

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Caucasian but not in African American from both the exome sequencing data?

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SUZANNE LEAL: Well, there’s many reasons. First of all, we do have a larger sample size for the European Americans. We might not have quite

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as much…it’s probably a little bit more homogenous sample in that they’re all older women, there’s less heterogeneity in the

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variants, so there’s many reasons but I really don’t know what explains it. The only thing I do know is we don’t have any inflation of Type 1

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error in our African Americans. FEMALE: My second question is about the

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imputation that Dr. Lin did. So, when you use 1000 Genome for imputation, which version of 1000 Genome did you use, and to impute, is your

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sample all Caucasian or…? XIHONG LIN: We used the most recent version of

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1000 Genome to do the imputation.

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FEMALE: So, do you have any comments about imputation against 1000 Genome for African Americans?

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XIHONG LIN: No, because we did not try that because all of our subjects are Caucasian, so I cannot answer that question.

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FEMALE: Okay. Thank you. FEMALE: So, I have a question about family

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studies. We probably all have pedigress of families from decades ago that we microsatellite linkage studies on. How informative do you think

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those will be for choosing…for example, if you just wanted to look at a targeted area of a genome or a family, rather than look across to

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whole exome or whole genome for the entire pedigree.

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SUZANNE LEAL: I think it’s extremely informative. In fact, I also work in nonsyndromic hearing impairment, where we have very large Mendelian

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families, and that’s what we do. We first do a linkage panel on those family members, then we have a much smaller region to look for the causal

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variants. I’m also working on this Mendelian genome project with the University of Washington and that’s one of our strategies, is first we’ll do a

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SNP array on the family members and we’ll actually use that information to inform us of which other best family members to select for

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exome sequencing because it could tell us if somebody’s in a phenocopy, it could help us select two individuals with the smallest overlap of

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their haplotypes, it also tells us in advance something about the DNA quality, which is, you know, you don’t want to push forward a sample

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that’s not good. So, I think it’s extremely useful. Most of the time you don’t have the luxury of having nice extended families, but if you have it

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you should definitely use it and it’s a very powerful tool.

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FEMALE: And you think you need extended, large pedigrees rather than the small families?

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SUZANNE LEAL: No, no, that’s the beauty of it because a lot of families that we really couldn’t use very well at all previously, like families maybe

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we had only a lot score of one point. Usually you want a lot score of 3.5 or greater in order to establish linkage. Before when we were using

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kind of the candidate gene approach, if we weren’t able to establish linkage, often you would have these peaks throughout the genome and

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there would be big regions with a lot of genes in it, and so that was very hard to follow-up with Sanger sequencing. So now, even if you have

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smaller families, of course you won’t have just one region but you would definitely reduce the number of regions that you could follow up. So,

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at one time these families weren’t that useful when we were doing Sanger sequence; unless you could combine families, you didn’t have a trait

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that was all that heterogenous and you could combine families to have one region. Now all of a sudden, these families are becoming extremely

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useful because you’re still reducing a lot [---] out of the genome but not as much as in a family that you could establish linkage. So, they are still very

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useful, these types of families, smaller families. JOAN BAILEY-WILSON: And I’m part of a

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hereditary prostate cancer consortium and also the lung cancer, and in both of those we have multiple aggregated families and we have many

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linkage peaks that are significant because some of the families have a peak here, and adding them all up it turns out to be significant. And then, some

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of the families have a peak somewhere else. And again, with Sanger we couldn’t even afford to do good sequencing under one peak in the families

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that are linked there, much less sequence multiple things. Then there are some families that maybe have a signal at two peaks. Well, then you’re

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going to have to sequence 200 genes instead of 100. So, what these tools are doing for us in these sort of consortia looking at highly

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aggregated families is letting us, in one go, take a look at the entire genome, and generally what we’re doing is taking distant relatives—the most

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distant relative pair we have in each family—sequencing them, and we can then filter to what are they sharing under the regions where they

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have some linkage information and then decide which variants we’re going to genotype in the remainder of the family, and then we can just

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make a custom genotyping array and genotype the whole dataset and help us then see which of these rare variants actually are segregating in the

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entire family, which is just back to linkage analysis again. She was telling…tell them your title of…

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SUZANNE LEAL: Oh, I have a talk that I give at one of my courses and it’s called “From Linkage Analysis to Next Generation Sequencing and

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Back Again.” So, you know, I really feel like for a long time people weren’t very interested in learning about linkage analysis, which I learned in

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graduate school and did a lot of it in graduate school, and the numbers in this course that I had with linkage analysis were really dwindling

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where everyone wanted to come to the GWAS course. But now, like, just last year, you know, we had a huge surge in the number of

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participants. So, there definitely is an interest and it is definitely a very useful tool

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FEMALE: Okay. My second question is filtering. Previously we filtered on dbSNP and now with the influx of variants in the dbSNP—that’s not a

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good filtering device—but supposing that you have a common disease that has a 4%-10% frequency in the population, quite common. How

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would you go about filtering for a disease like that? Would you just filter out everything that looked benign?

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SUZANNE LEAL: You can’t filter if you have something like that. You have to do an association…you can’t use filtering strategies;

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you have to use association testing. JOAN BAILEY-WILSON: I would agree. Yeah, we

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all three agree. XIHONG LIN: Or if you have some kind of

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functional information and you can use that to screen.

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FEMALE: Okay, thank you. JOAN BAILEY-WILSON: But given we all worry

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about how good the functional predictions are at this point, even if you think it doesn’t look functional you probably are going to want to do

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the association anyway, but you’ll up-weight. Have a really cool functional one. You’ll probably maybe up-weight your…

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XIHONG LIN: Yeah, exactly. So for example, for the weight function we can use the functional score to up-weight the variants with more

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function. Yeah. Another thing…I wanted to respond about the family studies. One advantage of the family study is it can control for population

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stratification but it’s more difficult to control than using the case-control studies.

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FEMALE: Okay and my third quick question is: what is your favorite tool for looking at functionality of SNPs? There are several of them

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out there and they seem to have little concordance.

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JOAN BAILEY-WILSON: That’s Jamie’s question. That’s not to us. That’s better to him.

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FEMALE: I think you have to look at a combination.

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JOAN BAILEY-WILSON: Yeah. I mean, we don’t just look at one; we look at several and sort of get a Gestalt. Is that how you do it? I mean, I

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know that’s what you guys do in Varsifter and I know that’s what NISC does.

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JAMIE TEER: I know some of the people have looked at that in our group and have found that the methods don’t overlap too well and, they’re

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each…they’re good, but the overlap isn’t great. So, I think looking at all of them and kind of considering that, is good and it’s a prediction. So,

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the more…you know, it’s a prediction. JOAN BAILEY-WILSON: And of course, we all

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hope, as we understand more and more about the genome, maybe those predictions will get better but right now they’re still kind of “iffy.”

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CHRISTY CHANG: Christy Chang from the University of Maryland. Going back to the function prediction to start my question, I just wanted to

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add that you have to be really committed to a variant to truly annotate function and figure out what’s going on, especially when it’s not in the

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coding region. We often make the assumption that if we found something intronic that’s really tagging something in the intron that’s going to alter

00:25:24,200 --> 00:25:30,633
the protein structure, when it’s just as likely when you found a coding variant that it’s actually a function of neutral and tagging something into

00:25:30,633 --> 00:25:38,666
intergenic and intronic region that has a regulatory function. A lot of people overlook that and then we found that to be true for monogenic

00:25:38,666 --> 00:25:47,566
disease over and over again. What we thought was amino acid substitution that killed the protein actually regulates splicing, for instance. But my

00:25:47,566 --> 00:25:56,799
question is, I’m very concerned that some of the GWAS results or many of the GWAS results will end up in the same graveyard with our linkage

00:25:56,800 --> 00:26:06,166
studies, that they are linkage peaks that showed up over and over again that we have not ever understood what biologically drove those linkage

00:26:06,166 --> 00:26:15,366
signals, and there are just as many GWAS results that have very compelling P values, but because they sit in intergenic regions or in the

00:26:15,366 --> 00:26:24,766
gene that’s poorly annotated or understood, we just don’t do anything with them. So, is there any way that, as a field, coming from clinician,

00:26:24,766 --> 00:26:34,299
genetic, epidemiologist, all the way to molecular biologist, that we will commit effort to understand those regions, to understand gene regulations

00:26:34,300 --> 00:26:47,033
and expression pathways and salvage all the biology from those signals before we jump in to sequence exomes and whole genomes and just

00:26:47,033 --> 00:26:53,999
end up with a bigger pile of things that we are not willing to understand at this point.

00:26:54,000 --> 00:27:03,700
JOAN BAILEY-WILSON: Well, I think this is one of the things Steve started talking about in his talk, was that one of the things they’re doing is they

00:27:03,700 --> 00:27:13,933
are following up their GWAS signals by doing really fine mapping in the regions and trying to identify what really is the functional variant that

00:27:13,933 --> 00:27:22,933
the GWAS SNPs tagged. Because what you have to remember is: what’s on those GWAS chips? They’re tag SNPs. The way they built

00:27:22,933 --> 00:27:32,466
those GWAS chips is they looked at the haplotype patterns, they went through and said, all right, in this haplotype block, what one or two

00:27:32,466 --> 00:27:45,366
or three SNPs do I need to recover all of the haplotype information? And so, those SNPs have very little likelihood of actually being the functional

00:27:45,366 --> 00:27:53,699
SNP that’s causing the association. I mean, probably in all the GWASs that have been done, yeah, probably some of them are, but most of

00:27:53,700 --> 00:28:02,733
them are just going to be in LD with functional variants. So, people are trying to find the functional variants that are responsible for those

00:28:02,733 --> 00:28:10,399
GWAS signals, and I agree with you, that’s critically important. And then also, I think if we’ve got something that we think is functional, we

00:28:10,400 --> 00:28:19,100
can’t just stop and say, “Oh, it looks cool, it’s a stop codon and it’s predicted to maybe be functional,” you’ve got to get out there and do the

00:28:19,100 --> 00:28:28,966
lab work. I mean, I’m not going to do it, we’re not going to do it, but our collaborators have to do the lab work. So if you’re doing cancer, well, you’re

00:28:28,966 --> 00:28:38,266
going to have mouse models and you’re going to look at zebrafish stuff. One of the faculty at NHGRI is just doing lots of really cool zebrafish

00:28:38,266 --> 00:28:48,932
mutations where he’s trying to, like, mutate everything and make a catalogue of what do all these things do. So, it’s going to take biology as

00:28:48,933 --> 00:28:55,766
well as statistics. Statistics takes you a certain way but then you’ve got to go to the biology.

00:28:55,766 --> 00:29:01,899
MATTHIAS KRETZLER: Matthias Kretzler, University of Michigan. I think I would strongly blog in that same area that we have an

00:29:01,900 --> 00:29:09,300
opportunity. So far we have talked about genetics and the phenotype, but obviously there are multi-levels of regulations—we’re a lot closer to the

00:29:09,300 --> 00:29:17,500
genes and phenotypes we have—and we have opportunities to capture those in our patients, and Eric Schadt will actually lead the Keystone

00:29:17,500 --> 00:29:25,866
conference in a week from now where the systems genetic approach will be discussed over five days to see how we can actually use these

00:29:25,866 --> 00:29:35,799
multidimensional data integrations to teach us which of these links might be those where it’s worthwhile to get into the real [---] of the

00:29:35,800 --> 00:29:43,800
coefficient mouse, which obviously is a lot of work and we will have to assemble very solid evidence before we engage our colleagues in

00:29:43,800 --> 00:29:53,833
that direction. And it’s also a pledge for cohort design…if we build our cohorts from the get-go that we have an opportunity to kind of have these

00:29:53,833 --> 00:30:02,566
additional scaffolds added to the genetic and phenotypic information we are currently focusing on.

00:30:02,566 --> 00:30:07,232
JOAN BAILEY-WILSON: I agree. GEORGIA DUNSTON: Quick question. I like the

00:30:07,233 --> 00:30:14,766
terminology. Joan, you mentioned it’s more like a treasure hunt and I’d like to know how…

00:30:14,766 --> 00:30:21,699
JOAN BAILEY-WILSON: The treasure hunt was in Genetic Analysis Workshop but it is a treasure hunt in our real stuff, too, right?

00:30:21,700 --> 00:30:25,366
GEORGIA DUNSTON: And I’m wondering…and I like the term but…

00:30:25,366 --> 00:30:30,899
JOAN BAILEY-WILSON: Actually, he’s the one that made up the term “treasure hunt.” I like that. I think I’m going to use for…

00:30:30,900 --> 00:30:39,433
GEORGIA DUNSTON: I want to know how are we going to distinguish, when we try to get support for these areas of pursuit, how do we

00:30:39,433 --> 00:30:52,333
distinguish a treasure hunt from a fishing expedition?

00:30:52,333 --> 00:31:00,633
JOAN BAILEY-WILSON: I’ve never liked fishing ex…well, I like to fish—my dad loved to fish—and I’m not averse to a fishing expedition, but I really

00:31:00,633 --> 00:31:09,166
like to think of it in terms of hypothesis generation. I mean, that’s what we’re really doing when we’re doing a lot of these genome-wide things,

00:31:09,166 --> 00:31:16,732
and a lot of people say, “Well, when you’re doing these genome-wide tests, it is a fishing expedition and you’re not testing hypotheses,”

00:31:16,733 --> 00:31:25,333
whereas statisticians like us say, “Wait, this is where we really are testing hypotheses.” You go do a lab experiment and maybe you’re not

00:31:25,333 --> 00:31:37,099
actually testing a hypothesis. We ARE testing hypotheses. Our null hypothesis is: there is no variant in the genome that explains this and we

00:31:37,100 --> 00:31:46,866
go out and try to reject that null hypothesis. So, most statisticians get a little upset when we’re told it’s a fishing expedition, and I know you

00:31:46,866 --> 00:31:53,032
believe that too, Georgia. [inaudible comment from audience]

00:31:53,033 --> 00:32:00,066
JOAN BAILEY-WILSON: Really. I mean, that’s the statistical answer; that’s what a statistician will tell you. I actually have a null hypothesis that’s

00:32:00,066 --> 00:32:08,232
based in biology and I’m trying to reject it with my data.

00:32:08,233 --> 00:32:17,633
FEMALE: I have two questions about indels. Many of you talked about two-step analyses with first GWAS and then next generation sequencing,

00:32:17,633 --> 00:32:27,399
but we know that indels are not covered by genotyping arrays, so probably some of them are in disequilibrium with stacked SNPs but not all of

00:32:27,400 --> 00:32:37,666
them. So, my question is more, for next generation sequencing, which tool is better to use to identify those indels, since we know that

00:32:37,666 --> 00:32:48,832
there is many problems of genotyping coding, especially…even in the last version of 1000 Genome? So, that’s my first question and my

00:32:48,833 --> 00:32:59,033
second question is about imputation methods of indels.

00:32:59,033 --> 00:33:06,899
JOAN BAILEY-WILSON: I haven’t done that myself. I know that NISC at NHGRI has been working really hard on developing some Bayesian

00:33:06,900 --> 00:33:17,366
algorithms that are helping them align correctly and then call indels better. I know there are a lot of people, other people working on such

00:33:17,366 --> 00:33:27,132
improvements to aligners and callers so that they call the indel correctly rather than messing it up. Do you have any favorites? Do you have any

00:33:27,133 --> 00:33:29,833
favorites? SUZANNE LEAL: No, I just know from the

00:33:29,833 --> 00:33:40,799
literature but I get the data from other people and I know it’s highly problematic and especially from exome sequence data it’s quite, you know, you

00:33:40,800 --> 00:33:49,933
can call them but it is problematic. As far as imputation, I mean, I think first we have to work out the bugs of calling them and then I think that

00:33:49,933 --> 00:33:54,899
will come in the future, but we certainly aren’t there yet at all.

00:34:01,733 --> 00:34:06,866
JEFFREY KOPP: There’s no more questions? Thank you, Suzanne, Joan, and Xi.

Date Last Updated: 9/18/2012

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