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

Introduction to Exome Studies: Approaches, Analysis, and Problems
Andrey Shaw, Washington University in St. Louis

Video Transcript

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ANDREY SHAW: …and I wanted to, even though I am listed as a co-organizer, I really wanted to say that Jeffrey has done 99.99% of the work,

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and he deserves all the credit for a really great meeting. So I have been asked to start this afternoon’s session where we are going to talk

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about exome sequencing. I’ve got 15 minutes, so I am just going to quickly go through what I think the major issues are, and then I am going to use

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an example of basically the data that we are generating, and then the problems that I am having right now as to figure out what to do with

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this data, and hopefully that will be a good jumping off point for the rest of the afternoon. So many of us here are mainly interested in one

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disease in the kidney, and that’s FSGS. FSGS is one of the leading causes of nephrotic syndrome and chronic kidney disease in both children and

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adults; responsible for about 5-10% of end-stage renal failure, and why it’s important to us today, it is thought to have a strong genetic component,

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especially in African Americans. Most of the progress, I would say, over the last 10 years has really established that FSGS is a disease of a

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single cell in the kidney called the podocyte. The podocyte has this really amazing architecture, where it’s an epithelial cell that basically coats the

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outside of the glomerular capillary, and is thought to play some role in glomerular filtration. So, I think many of us in this field still argue about this: is

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FSGS a Mendelian or a complex disease? I feel pretty sure that it’s a complex disease, but I know there are many in this audience who really think

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of it as a Mendelian disease, and how you approach this question, I think, really affects the way you analyze your data. So, we’ve talked a

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lot about this already, but I think the main points are that Mendelian diseases are going to be caused by highly penetrant alleles while complex

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diseases are going to be caused by very poorly pentrant alleles, and therefore will impart a low risk which makes them much more difficult to find.

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Mendelian diseases…with a good pedigree you can easily find the gene, but if it is a complex disease we are going to require many, many

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large numbers of patients. So, really what I think has transformed this area is really the development of this new technology, whole

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exome sequencing. I think most of us are aware of what it is. It’s basically a technique that focuses on only sequencing the 1% of the

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genome that’s exons that’s generally estimated at between 35-50 million base pairs. Comparison to whole genome sequencing, it actually has an

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extra labor step which makes it more expensive than it should be, and as sequencing costs drop that’s when whole genome sequencing will

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overtake whole exome sequencing, because it will eliminate this capture step. I think we should all admit that $1,000 is cheap, but still not cheap if

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the goal is to sequence thousands and thousands of patients that would be required if this was considered to be a complex disease. So

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one of the problems with whole exome sequencing is that we’re basically looking for variants, and I’ve just pulled this out of a paper. I

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think all of us have very different experiences here, but generally in the same area. But most people are going to have about 15,000-25,000

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variants, which will be divided into those variants that we call common, and those variants that we call rare or novel variants, and basically the big

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advantage of whole exome sequencing over GWAS is really this ability to focus on rare variants. So, why the focus on rare variants?

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We’ve talked about it this morning. The experience with GWAS suggests that there is significant missing heritability, and the current

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hypothesis, then, today is that missing heritability will be contained in the 400-800 rare variants that are present in all of us; and if that is what we are

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looking for then we basically can only see this today through exome sequencing. I think this raises another question that we can talk about, is

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what is a rare variant? We define it traditionally as not a common variant, and a common variant is defined as something that has an allele

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frequency of 1% or 2%, so anything less than 1% or 2% is a rare variant. Some rare variants are extremely, extremely rare; 1 in 10,000, 1 in

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50,000, are those still rare variants? I think there is a lot here that goes into deciding what we call rare variants. When the allele frequency gets

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very, very low, I think you really have to worry about whether you’re looking at a false negative, or false positive SNP call. So, as we accumulate

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greater and greater numbers of identification of rare variants, I think it becomes a formidable task to think about how we will validate all these rare

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SNPs. Okay, so this is a slide that comes from this great Altschuler, Daly, and Lander paper that really just addresses what sample sizes we

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need to basically generate, let’s say in this case, 90% power. So, just taking something that would have an odds ratio of 2.5, an allele frequency of

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1%, so this would be a relatively common rare SNP, according to this chart to basically get 90% power, we would need about 6,600 patients. If

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we assume that rare variants are even rarer, let’s say a rare frequency rate of .3%, we’re looking at huge numbers of patients. So, I think the task at

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using whole exome sequencing, if the focus and the hypothesis of whole exome sequencing is to focus on rare variants, is: how do we generate

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the statistical power to actually make any of this data meaningful? And so one method that has been developed, that we are going to hear some

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more about today, are collapsing methods where rather than consider each rare variant by itself, we basically take all the rare variants that are

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found in a particular gene and basically call that a single variant. The problem with this strategy is that many of these rare variants are probably

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benign and have no functional effect, and so clustering potentially significant variants with benign variants is probably going to decrease our

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association and power, and so that leads us into predictive tools like VAAST, SIFT, and PolyPhen that basically try to predict whether a rare variant

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has significance or not and whether these two techniques together can basically increase the statistical power of what we’re doing. Okay. So,

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our feeling was that if we are talking about sequencing thousands of patients to basically generate statistical power, that whole exome

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sequencing today is still out of our reach, and so we needed to develop a method that would be cheaper, allowing us to do this much more

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efficiently, and so our strategy was basically to sequence a smaller number of genes, basically targeted exome sequencing, and then figuring out

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a way to multiplex the samples. So, I am not going to go into this, but this seemed relatively straightforward to us because if FSGS is truly a

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podocyte disease, then theoretically we would only need to sequence the genes that are expressed in podocytes, and the microarray

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analyses suggest that that’s about 7,000. So, I have involved a lot of people in the selection of these genes, mostly Matthias Kretzler of

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Michigan, to really sit down and examine this list of genes, discard obvious housekeeping genes, and then using all of the human expression data

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that is out there try to make sure that every gene that is potentially a podocyte specific gene is included, and that basically gives us a list of

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2,400 genes; it’s about a tenth of the genome. We’ve developed a method that allows us to multiplex the captures that significantly reduces

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the labor, so instead of having to basically hybridize 100 samples, if we multiplex in groups of 10 we’d only be multiplexing 10 samples, so

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that would significantly reduce the cost. So right now, we think we can do this targeted exome of 2,400 genes for about $150 a patient, and one of

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the things that I thought would be better about this method is by reducing the number of tests we would reduce the Bonferroni correction, and

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that would increase the statistical significance of anything that we would find. Okay. So, this work was done in my lab by Ghaidan Shamsan, a

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technician who is here, and another technician, Chris Stander. I told you about the choosing of the genes. The patients basically come from Jeffrey

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Kopps’ FSGS trial that is basically, the cohort design would be the extremes—patients with HIV associated nephropathy with kidney disease,

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patients with HIV without any signs of renal insufficiency, and then Michelle Winn has provided about 90 patients, all with family history,

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all with pedigrees, and then Ania Koziell has provided some samples of pediatric FSGS. Okay, so I am not going to go through the details of our

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approach, but we validated it by pooling HapMap patient samples, going back sequencing and making sure that we basically make the same

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calls that are actually in the database. So, we’ve done about 200 and really the data that I could provide to you today was only from 131 patients,

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but basically what this chart is showing you is that out of about 200 patients that we have sequenced today, out of the 2,400 genes, we

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have identified rare variants in about 2,000 of them. What this chart is showing you is the number of patients that basically share a common

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variant in a specific gene. So, I told you about the patients; basically what we are seeing is about a total of 1,500 total SNPs per person. We remove

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common SNPs, and there is a whole discussion point there. We’ve removed all synonymous variants, and then just for the sake of our

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analysis, we have used PolyPhen and deleted anything that looks benign, and that leaves us about 60-80 what we call rare non-deleterious

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SNPs. This just kind of blows that up a little bit. So, what that is saying here is like, 68 of the 131 patients shared a rare variant in the same gene,

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while over here it would be 32 patients shared rare variants in 5 different genes. Okay. So, here is a list of the candidate genes. Here is that one

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that is at 68, and the 3 that are sitting out there and basically so on and so forth, and if you just look at this list, there are some interesting genes.

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MYH9 which we all know about, ALMS1 that was picked up in the GWAS CKD screen, NOTCH1 that we all know is important in kidney development,

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and here is even NPHS1 on nephron. So, is this significant? Is there anything here that tells us anything about the genetics of kidney disease? It

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really now depends on what are the controls. What is the frequency of rare variants in these genes in the general population? And so, this is

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where we have kind of been stuck, and so an issue that I wanted to talk about today, and that is, can we use the existing databases as

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controls? We could use 1,000 genomes, we could use all the genomes that have been sequenced at each of our private institutions, we

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can use the NHLBI GO ESP Project that we’ve heard about today. In the case of Michele’s patients, they are all in pedigrees so she is going

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to take the rare variants and basically go through her pedigrees, and she has uncovered two or three genes that potentially are linked to FSGS. Or

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will we need to sequence matched controls? The issues that I see here are that all of these databases basically declare rare variants, or

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common variants, using different SNP callers and different base callers. Are we all going to need to establish the same protocols, the same cutoffs,

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the same base callers and SNP callers to establish what these numbers of rare variants are? If we have to control for ethnicity, then

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actually if we are only having to look at one ethnic population—that 1,000 genomes—the size of that population now becomes very small, and

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that is actually not a large enough population for us to use as controls. So, that raises a different question: is the frequency of rare variants in a

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gene different in different ethnic populations, and when are we going to have to basically split out based on ethnicity? Even if we get targets, this is

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only going to be a statistical argument, probably a relatively low P value until we start reaching numbers of tens of thousands, and so really we

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are going to need new genetic and biological strategies to validate these genes, especially if we think they are multi-genic. I personally think a

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major part of the effort now should be trying to figure out ways to keep this data as open as possible so that as many people here in this room

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have an opportunity to try interrogate this data and try to come up with new methods for trying to figure out how to analyze it. And then that asks

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another question, and it is when we think about phenotype refinement, we are going to need to have attached to this data enough clinical data

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that will allow people to try to cluster this, and try to refine the sequencing data to potentially increase statistical power. So, Jeffrey asked me

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to add on this at the last minute, and just a discussion of this new Illumina exome BeadChip. So basically, this is potentially a method that

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would allow us to combine the two things that everybody has been doing: GWAS and exome. And basically what this Illumina exome chip is, it’s

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basically 250,000 SNPs that are only in the exons. So surprisingly, most of the GWAS chips have very, very little coverage of exons, and so

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the argument here is by having a new set of markers that is only in exons, that this will allow us to actually have better exonic coverage. The

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good thing about it is it is relatively cheap. The exciting part of it is that you can add up to 200,000 additional custom markers, but that

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actually doubles the cost, and I think cost becomes a major issue. As the cost of all these technologies that we are using changes, I think it

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changes what we want to do. Probably the most powerful part of this technology is to combine it with the current GWAS chips, giving you a total

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of about 5 million SNPs, but again, that is going to add to the total costs, and then the issue that I have is an issue that we talked about this

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morning. The focus on GWAS is primarily in intronic regions. Is that a totally different category of SNPs than the kind of SNPs that we are

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basically trying to see in exome sequencing, which are encoding regions? So, I think what I’ve tried to do just now is just kind of raise a bunch

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of issues that I hope will be answered in the next couple of sessions, and I look forward to it. So, thank you.

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MALE: Just a point of clarification since I was on the design team of the exome chip, we had 200,000 non-synonymous variants. There were

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about 14,000-15,000 splice variants, and 7,000 or so stop gain/loss of function variants. This came about because the exome sequencing project

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provided about 5,000 exomes, there was a Type II diabetes project providing exomes, autism project providing exomes, so the content comes

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from about 12,000 individual exomes; 9,000 of those are Caucasian, and about 2,000 African American. I think there were 500 Hispanic and

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500 Hahn Chinese. So, the distribution that you see is quite different. The other point is that the variants that are on the exome chip had to be

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seen at least twice or three times in two or more populations. So, in that sense they are not singletons or doubletons, and so you will get

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quite a different distribution. They are probably true variants, because they’ve had to be seen a couple of times, but they are not going to be the

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really rare variants, and the other content because, chip can contain 300,000 variants; there is all the GWAS hits from the NHGRI

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catalog, as well as the HLA and other types of variants. So, it is a useful chip, and I think Illumina has sold a million of them so far.

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ANDREY SHAW: I tried to find somebody at Wash U who has used them, and they said that they had been back ordered and everybody is waiting

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for them to come in.

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MALE: Yes well, the other point is that because most of these are rare variants, the clustering of these could be an issue because unlike normal

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GWAS chips where you see 3 genotypes, typically clustered, and the clustering works well, here because they are rare, you have a glob of

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the homozygous wild type and maybe 10 or 12 heterozygotes, and typically no homozygous variant, and so the clustering algorithms, typically,

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we don’t deal with that very well. We’ve run about 1,500 exome chips and they have performed remarkably well, and, knock on wood,

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but at least at this point they do well. The other point is that AlphaMetrix, just to be clear, AlphaMetrix has an exome chip as well, just so

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that not everyone goes rushing to Illumina. We try to have competition to keep the price down, but it doesn’t seem to work. If they get bought by

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Roche it probably will cause the price to go up even more.
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ANDREY SHAW: Well I just think an interesting point here is that as we get these new technologies, I was not aware of this technology

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at all until Jeffrey brought it up, and for me it was really just when do we use this technology over exome sequencing, or genome sequencing, and

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all of this for me is very confusing. Okay.

Date Last Updated: 9/18/2012

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