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

Rare Variant Analysis: Aggregation Methods
Suzanne Leal, Baylor College of Medicine

Video Transcript

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JEFFREY KOPP: So, why don’t we move on to the first speaker of this session, which is Dr. Suzanne Leal, who’s a professor in the

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Department of Molecular and Human Genetics at the Baylor School of Medicine, and she’s an expert in statistical genetics and genetic

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epidemiology and I think she’s going to tell us more about rare variant analysis aggregation methods.

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SUZANNE LEAL: All right. So, this is my title that was kind of given to me and I plan to talk about more than just aggregate methods and one thing I

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would like to talk about is QC, and I can’t stress enough how important QC is and I could probably give a few hours talk on QC, and actually that’s

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when we do analysis of rare variants. I’d like to say up front, is what we spend the most time is on QC, and I’ll bring up some of the problems but

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it’s certainly not an exhaustive list of the problems we run into. So, I would say that GWAS have been a huge success in understanding the

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etiology of complex traits, but of course, common variants don’t explain all of the etiology and in most cases they explain less than 10% of the

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heritability of complex traits. And so therefore, there’s been a huge interest in looking at rare variants and for the analysis of rare variants,

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unlike common variants, we’re using direct mapping where we actually are analyzing the causal variants where, when we were looking at

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common variants and using these genome-wide association studies, we were using interact mapping in order to detect associations. So for

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rare variants, although they’re rare and these variants are much newer in the population and then the common variants which are old and

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widespread and you see quite a bit of differences in the rare variant distributions and types of rare variants in different populations. So,

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they do generally have a larger phenotypic effect than the common variants but we have to remember that these phenotypic effects are not

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huge. In most cases, we are not observing familial aggregation. So although they have stronger effects probably in most cases than in

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the common variants, we are not talking about effect sizes that would cause familial aggregation, and although the variants are quite

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rare, collectively they’re quite common. So, in order to analyze these rare variants we have to perform direct mapping, so we have to identify

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the rare variants within our sample, so one approach, of course, is by using next generation sequencing. Currently most of the studies that

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are being carried out—they’re exome studies—but I believe this is just a stop-gap until the price of whole genome sequencing falls to a price

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that’s reasonable where we could start sequencing relatively large samples. So, this was mentioned earlier by Steve about screening

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databases and I just would like to point this out even more that this is really not going to work for complex traits because, for complex traits, we

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really do expect to see those variants in the databases. In people who are perfectly healthy these variants are not fully penetrant, so

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perfectly healthy people are running around carrying these variants, too, and of course our databases also do contain cases, but even if you

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had a fully healthy population you would expect to see these causal variants in those healthy individuals. So, how can we go about analyzing

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these rare variants? Of course, we could go about using the same methods we use for common variants, using a variety of different

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tests, but what was shown is that, you know, you would need extremely large sample sizes in order to detect associations using these common

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variant methods and this is due to the low allele frequency and also the extreme allelic heterogeneity. So, there have been a number of

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tests that have been developed and these tests all work on aggregating variants across a region, which is usually a gene, and one big problem we

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have when we’re performing these aggregate tests is misclassification. So, there are two types of misclassification we have where we can

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include non-causal variants in our aggregate analysis, which of course attenuates our signal, and we can also have exclusion of causal

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variants. This can occur when we are not including regions that contain the causal variants, it can also occur because we’re filtering out

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variants either in our QC process or by using bioinformatic tools. I would really like to stress the point that bioinformatic tools are telling us

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information about functionality and not causality, and people frequently use these words as though they had exactly the same meaning. And

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yes, a variant has to be functional in order to be causal, but a variant can be functional and not be causal for your phenotype of interest, so I really

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would like to stress that. So, because we know we have this misclassification problem, we really need methods that are robust to misclassification,

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in particular, inclusion of non-causal variants. So, there are different types of aggregate analysis you can use and you can just analyze rare

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variants. I’m just using 1% frequency as an example of what would be considered rare—there’s many different definitions—or you could

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do a joint analysis of rare and low frequency variants, and then there’s also methods that were specifically developed to look at variants when

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you have…they’re bidirectional. So, some variants within your gene region are detrimental and others are protective, or for a quantitative

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trait they just go in two different directions. And so, I would like to go through some of the methods that have been developed to analyze

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rare variants. This is an abbreviated list; I guess I should say, maybe a very abbreviated list. I have to admit, I have a very difficult time keeping up

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with the rare variant methods. It seems like every week there’s a new one in the literature. But let’s just go through a few of them. So, one of the first

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tests was this combined multivariate and collapsing method. And so, this is just looking within an individual, be it the case or control or

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for quantitative trait, whether or not they have a rare variant within the gene region, so you’re only counting the observation ones, and then in the

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simplest form you could just perform a Fisher exact test or you can incorporate the 01 coding within a regression framework. Then there’s the

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weighted sum statistic, and this method is up-weighing the rare variants within a region, so you’re developing weights and then you’re

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aggregating the variants across the region, and because you’re using internal information, you need to empirically estimate the P values for this

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second test; you cannot analytically obtain the P values. The next one is a kernel based adaptive cluster method and this uses adaptive weighting

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based on the multivariate genotype within a region. Again, because we’re using internal information for these weights, the P values must

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be estimated empirically. The next test is a variable threshold method. You can use different coding for this particular method. You could use

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the…you could just code based on the CMC or I should have maybe put it…or in this particular coding used in the Morris and Zeggini test which

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is very similar to the CMC, but here you’re actually counting the number of rare variants which are within the gene region and not just whether or

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not an individual has a rare variant. But what the variable threshold does is it doesn’t depend on a very strict cutoff, so you’re not saying, “I’m only

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going to analyze variants, say, with 1% frequency or less or 5% frequency or less.” What it does is it maximizes the test statistic over

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a variant of allele frequencies. So, rare cover is also a maximization approach, but instead of just using different allele frequency cutoffs, it actually

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maximizes the test statistic over all variants within a genetic region. So both of these methods, the variable threshold method and the

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rare cover method, you both have to empirically estimate the P values because you have to adjust for multiple testing in both schemes, and of

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course, you do also pay a penalty for multiple testing here; but I actually rather like the variable threshold method because you’re not keeping to a

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very strict cutoff method. So, I mentioned already the MZ method and these last two tests, which there are more out there now, were both

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developed to look at when you have both protective and detrimental variants within a region. So, C-alpha is just limited to analyzing

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case-control data and it’s looking at deviates from the expected binomial distribution, where for SKAT, you can analyze also quantitative traits

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and also it allows you to weight based on sampled variant frequencies, and again here for both of these methods, the P values are

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estimated empirically. So, it’s very difficult when you go and read the literature to compare the power of the different methods. You know,

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everybody wants their method to be, you know, published in a top journal, and so unfortunately we’re all human so we tend to tweak our

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simulated data to make our method look better than all the other methods, even though that particular underlying model that you have there

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might not be realistic at all. So of course, when you look at the literature, everybody’s method is, of course, the best method out there and so you

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really kind of wonder, well, what is really the best method? So, we went about to compare the power of these different methods and I don’t

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have enough time to show you all the different power comparisons, and you know, in one talk I had I said this is probably the worst slide you’ve

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ever seen because all of the entire power curves are all on top of each other and what I’m trying to make is the point is most of these tests had very

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small differences in their power and, depending on the underlying model, the one that’s the most powerful tends to flip around. The one thing you

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do see a big difference in power, though, is between the tests that were developed to look at more all detrimental or all protective variants, to

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those tests that were developed to look at when you have effects that are bidirectional in the gene. And so, these tests that were developed to

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look at when you have bidirectional effects generally are not as powerful when all your variants are unidirectional as using one of these

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tests here, for example. So, there are also additional considerations when you’re choosing a test to carry out your analysis. We know that

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confounding can lead to spurious association, and one particular problem we have is population substructure, and this has been well-documented

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for common variants to be a potential problem of having spurious association. However, the problem is even greater for rare variants

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because we really have different allelic spectrums even within European populations for these rare variants. So, this should be an

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additional consideration when you’re doing your analysis that you are using a method that you can include covariates in the analysis to control for

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potential confoundings. So, how about choosing a lot of different tests and just hitting them all on your data and saying, “I’ll just take the best one?”

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Well, although people do this, this isn’t really the best thing to do at all. If you’re going to do this you really have to correct for multiple testing, and

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in performing this multiple testing correction you might have a loss in power instead of a gain in power. So, I know this trait is not related to this

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conference but I don’t really work on a trait related to the theme of this conference, but I would like to use this as an example of analyzing

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exome data. Right now one of the traits that we’re analyzing using exome data is age of menarche and this is part of the NHLBI ESP study

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that we have information of age and menarche. So, this is a study which you’ve heard about before and currently we have exome data on

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around 5,400 individuals, and it consists of both African and European Americans. These individuals were selected from a variety of

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cohorts. Also, as you’ve heard, many of the traits were sampled on extremes, so we have extremes of BMI, blood pressure, LDL. We also

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have dichotomous traits such as stroke, Type II diabetes, lung-related traits, early-onset MI, and then we also have controls—around 1,000

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controls—that were just phenotyped because they have phenotypic information available for a large variety of traits. So, the more you look at

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this data, the more you say, “Oh, no, another problem,” and this keeps on arising. And so one thing is, not only is the sequencing being done in

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two different centers, the Broad, which you heard, and the University of Washington, but they all decided that they were going to use different

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capture arrays. So, the Broad has been very consistent and stuck to this one array while the University of Washington has gone on to use

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three different arrays, and I don’t go into this detail, but not only do we have the different arrays, but during time they also changed

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sequencing machines. So, we have some of the samples done on genome analyzer 2 and also high-seek machines. So, this is all things to

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contend with. So, here are just the different arrays and we can see that the older arrays do capture less of the genome. And so, one thing

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we have to do is we have to do some QC on our data before we get started on even doing further QC. So, things that we filter on is that they fail

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this support vector machine that was developed by Gonçalo Abecasis and his group. Now this actually just pulls out bad variant sites or

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allegedly bad variant sites but it doesn’t pull out individual sites. So on top of using this support vector machine, it’s also good to filter on, say, a

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depth of 10, so for each individual they need at that particular variant site, you need at least 10 reads to call the variant, and first we were

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looking at it without using genotype quality score because we know this biases towards a homozygous wild-type, but after further looking

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at the data we decided maybe that wasn’t the best thing to do, so we got a little bit more stringent and used this GQ score; we cleaned

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out the data with a GQ score of less than 30. And so, we also have some data on duplicates, so this is just the concordance rates between the

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duplicates for different screenings. So, you see just on SVM and then you have SVM+depth 10 in the red…oops, sorry about that…an SVM with

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this genotype quality score of 20 here, so you’re getting much better concordancy. So, here’s a little bit of information in general about…we’re

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actually, in reality, analyzing more genes than this but this is the intersect of the different capture arrays, and so we have around…if you take the

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intersect of all capture arrays you have around 15,500 genes and you have the number of variant sites—current individual—vary drastically

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from 0 to over 500 in African Americans. There’s very nice Ti/Tv ratios on this particular data, which would tell you how good your data is, and

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we have around a ratio of 1.3. This is on an individual; it’s not if you look at your whole data set. You would not see this ratio if you looked at

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your entire data set. But on the average for an individual, we have a ratio of about 1.3:1 of synonymous to nonsynonymous variants, and

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we see more variants in African Americans than in European Americans but this ratio does hold. So, one of the first steps in our QC is to perform

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PC analysis, or MDS, and so this is to remove outliers and we also wanted to assign our individuals to an ethnic group to analyze African

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Americans and European Americans separately, and instead of just using self-report we wanted to use the information for this PCA analysis. And

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so the first thing we did is we trimmed our data to get rid of those variants that are in strong LD with each other and then we used those variants with

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a minor frequency of less than .1% for the PCA analysis. And we see that we definitely have two groups here. Over here is the European

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Americans and here is the African Americans, and there’s generally good agreement with the self-report, although we do see some European

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Americans right here in the middle of the African Americans. We also wanted to look in our sample for duplicate samples and related individuals and

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so we want to remove the related individuals and also cryptic duplicate samples, and so in order to do this we used this program called KING, and

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we can see that we have a number of duplicates here. We have first-degree relatives and some of these we knew about. A number of the samples

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were from small pedigrees that were included and then we have a number of second- and third-degree relatives. And so, here is our

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distribution of related individuals. And so, we took one of the duplicates based on one that had the most information and then we just limit it to one

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individual from a family, also based on the quality of their exome data and also the available phenotypic data for them. So, here’s about

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evaluating our phenotype data and other QC measures that we did, too, was looking at the sex of the individuals, looking at Hardy-Weinberg

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equilibrium, which isn’t very powerful because most of these variants are extremely rare and so you’ll have no power at all to detect a deviation

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from Hardy-Weinberg equilibrium, cleaning up the database on missing this by target because, of course, if we just took it out across all samples,

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there would be some sites that are just missing because they’re not included on a particular target, so that’s other QC that we included here.

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And so, here we’re going to evaluate our phenotypic data. So, our phenotype that I’m going to present here is age of menarche. We have a

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mean age of almost 13 in our women from this study with a medium of 13. We have almost 1,500 European Americans with this phenotype and

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close to 1,300 European Americans, and here is the distribution of the age of menarche within our women and it’s pretty normally distributed. We

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cleaned out the duplicates from those individuals, related individuals, the women with missing phenotypes or exome data, we also took out

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extreme outliers, and after that we had slightly over 2,200 women to work with. So, one thing I would like to point out here is that we had this

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little problem with the study design here where these individuals…most of them are not a random sample except those 1,000 individuals who were

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selected because they were well-phenotyped. All the other individuals were ascertained because of some other phenotype, either a phenotype or

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an extreme, as I mentioned before. And so, we wanted to look at the correlation between age and menarche and these different phenotypes,

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and lucky for us with this particular phenotype, it’s not particularly correlated. We’re also working with waist-to-hip ratio and that’s a totally different

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matter. As you’ll see in a minute, I include this as the covariate, this cohort of ascertainment as a covariate in the analysis and it does work okay

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here. I think it’s working fine for this particular phenotype because it’s not particularly correlated with the other phenotypes which were used for

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ascertainment. But I just would like to stress that this is not going to, in many circumstances, this is not going to get rid of the problem and you can

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definitely have an inflated Type 1 error when you do this because if the other trait is associated with a particular gene, then you can also pick up

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this association due to a correlation with a first trait; it’s not because of pleiotropy, it’s just because they’re correlated, those two traits. So,

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you have to be very careful. There are methods out there to adjust for this but it’s something you should be very aware of when you’re analyzing

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these secondary traits within a cohort. All right. One thing we noticed right away is that we had the age of baseline. So, this is the age of these

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women when they were ascertained in the study and you can see that we have a lot of older women, which isn’t surprising, because a lot of

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these women are coming from the Women’s Health Initiative where it’s a study of postmenopausal women, and so we have quite

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an older population. So, this is well-known that there’s a generational effect for age of menarche, and when we looked at our data with

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both the European American and the African American women combined, we just saw a very weak P value here. However, we don’t fully

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know or can’t fully explain that we see a very strong effect in the African American women but not in the European American women. Of course,

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the European American women are much older; maybe we don’t have the power to detect this. We also were wondering if this had something to

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do with BMI. We adjusted for BMI; we still saw the same effect. So, this is what we would expect, to see this generational effect, but also in

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European American women, which we’re just not seeing in our sample. So, we are analyzing the African American and European American

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women separately and this is to allow for adequate control of population substructure and then we can also model separately in African and

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European American women, and one thing we’ve noticed about when we look at the variants within the gene in African and European

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Americans, there’s not a huge overlap of the variants. So, I really think this is the right way to go in the analysis; to analyze them separately.

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Here we have the two models and our model is slightly different for African Americans and European Americans, as I mentioned. So, what

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we did is we went back and we recalculated our PCA component separately in the European Americans and the African Americans, and when

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we were building our model we saw that, in order to control for population substructure, it was adequate to include one PCA component in

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the European Americans, but in the African Americans we needed to include two, and then we also included this generational effect, which

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is their age at ascertainment into the model for the African Americans, but we didn’t put it in for the European Americans because it was not

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statistically significant. Okay. One thing we ran into from the start is we really didn’t have a way of analyzing this data; there was nothing out

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there. So, we went about developing our own association tool, which the current name is VAT—we seem to change this quite often—and so

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this analysis tool allows not only the single variant analysis, which you can perform with many of the current software out there such as

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PLINK and Gene Able, but it also performs these aggregate tests and it’s regression-based, so you can analyze both qualitative and quantitative

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trait and you can also include covariates in the analysis. So, we needed to analyze quantitative traits and also needed to include these covariates

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in our analysis. It also allows you to work with the VCF files, it allows you to do some QC measures, and also has annotation, and the

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annotation is done with this variant tools software. Another program that’s out there to analyze this sequence data to perform

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association analysis with sequence data is PSEQ. However, we could not use that in its current form because you can only analyze

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case-control data and it’s also not possible to control for covariates in the analysis. So, the first thing we did was we analyzed the more frequent

00:28:18,433 --> 00:28:29,899
variants individually. So, here we analyzed all the variants and our only cutoff was on their frequency and we used linear regression and

00:28:29,900 --> 00:28:39,100
then additive model, using those two models I showed you for the African and European Americans, and we don’t see anything in African

00:28:39,100 --> 00:28:48,900
Americans. I mean, we certainly know we don’t have a Type 1 error problem, but you would rather not see that type of Q-Q plot, but we do

00:28:48,900 --> 00:29:01,500
have some points here in the European Americans. Then we went on to do the aggregate analysis using European and African

00:29:01,500 --> 00:29:13,066
Americans, and here we limited ourselves to variants that had frequency of less than 1% and we only analyzed nonsynonymous variants,

00:29:13,066 --> 00:29:22,299
stop-loss/stop-gain, and splice sites when we performed the analysis. I wouldn’t do both of these, but just for comparison purposes, I’m

00:29:22,300 --> 00:29:30,766
showing both of them. This is the results in the European Americans. Again, I don’t think we have

00:29:37,800 --> 00:29:40,000
any inflation here. This is very nice on our Q-Q plot and then we have, like, four genes coming up; it’s the same four genes here. However,

00:29:40,000 --> 00:29:50,500
when we go to our African Americans again, our plots are totally flat here. Also, we combined the results from African Americans and European

00:29:50,500 --> 00:30:01,266
Americans using meta-analysis. I don’t show you the results but basically any results we have is pretty much just driven by one ethnic group or the

00:30:01,266 --> 00:30:13,832
other; we really don’t see that two of them are contributing to the analysis. Our future direction with this is we are going to try to replicate our

00:30:13,833 --> 00:30:23,899
findings using the exome chip data, and we have looked at those genes with the top hits there from the aggregate analysis, and they’re quite

00:30:23,900 --> 00:30:35,766
well-represented in the exome chip. So, in conclusion I would like just to acknowledge some of the people from my group who have been

00:30:35,766 --> 00:30:48,899
working on this project and also the NHLBI Exome Sequencing Project team who have been really helpful. We work very closely with Paul Auer and

00:30:48,900 --> 00:30:55,600
Gonçalo Abecasis and a few other people. And so, I would like to open the floor to questions.

00:30:55,600 --> 00:31:10,866
FEMALE: A beautiful talk, thank you very much. My question is with regard to the replication studies.

00:31:10,866 --> 00:31:16,166
SUZANNE LEAL: Yes? FEMALE: So, when you identify genes using

00:31:16,166 --> 00:31:20,666
collapsing methods, how do you replicate them in independent cohorts?

00:31:20,666 --> 00:31:29,232
SUZANNE LEAL: I mean, there are several ways you could go about this. You could go and you could sequence the genes in another cohort, you

00:31:29,233 --> 00:31:39,866
could genotype the variants you have found because most of these signals are being driven by the more common of the rare variants—I didn’t

00:31:39,866 --> 00:31:48,466
show that here. So, it’s not being driven by the singletons or the doubletons, so losing those variants is not going to, you know, reduce your

00:31:48,466 --> 00:31:58,399
power by very much so you could just genotype the variants that you’ve found. The reason why we’re considering using the exome chip is

00:31:58,400 --> 00:32:07,633
because basically we have cohorts available to us that are being exome chipped and they have our phenotype of interest. Probably, we would do

00:32:07,633 --> 00:32:15,733
better if we could actually genotype the variants within those genes, but we’re hoping that the loss of some of the variants will be made up by

00:32:15,733 --> 00:32:18,866
the sample size. FEMALE: And the second question, with regard

00:32:18,866 --> 00:32:26,832
to your software. So, we are all familiar with PLINK and it became very popular for the analysis of GWAS studies. Is there anything in the works

00:32:26,833 --> 00:32:32,533
that could be widely used as a gold standard for the analysis of rare variants?

00:32:32,533 --> 00:32:41,533
SUZANNE LEAL: It’s coming. It’s on its way. I mean, we developed that pipeline and there’s PSEQ, which I mentioned, which should be

00:32:41,533 --> 00:32:51,533
pronounced not “P-Seek” but “Seq” as Shaun told me. So yeah. It takes a little while but this is definitely going to be…those tools will be

00:32:51,533 --> 00:32:57,533
available. FEMALE: Thank you.

00:32:57,533 --> 00:33:03,766
MALE: You mentioned the problem with the control datasets having cases but I don’t actually see why that’s a problem, because your

00:33:03,766 --> 00:33:08,532
probands here—your study set—should be enriched in just cases…

00:33:08,533 --> 00:33:16,833
SUZANNE LEAL: I’m not saying…what I’m making the point is that you can’t screen databases; that’s my point. You have to carry out an

00:33:16,833 --> 00:33:31,899
association study and this data, like from this ESP project, it is going to be available through dbGap, and so you could use this dataset. So, I think we

00:33:31,900 --> 00:33:43,033
will see, you know, people using these so-called convenience control datasets, but we’ll have bigger problems with the sequence data—at least

00:33:43,033 --> 00:33:52,699
initially—than we had with the genotype data because there’s going to be great variability in the capture arrays, the depth of coverage—I didn’t

00:33:52,700 --> 00:34:00,966
discuss that. Those are quite variable. This is very high-def coverage. We have a medium coverage of 110X but some have lower

00:34:00,966 --> 00:34:12,266
coverage. There can be differences in the alignment, and so there’s going to be very big differences in the variants that are called within

00:34:12,266 --> 00:34:25,666
genes, so you’re going to have problems with false positives. So, I just don’t believe for complex traits that you can, you know, just be screening

00:34:25,666 --> 00:34:33,199
databases and finding things that way; I just don’t think that’s going to work at all.

00:34:33,200 --> 00:34:37,633
MATT SAMPSON: Thanks for that talk. Matt Sampson, University of Michigan. Given that a lot of variant callers seem to be performing equally

00:34:37,633 --> 00:34:45,966
well, I’m wondering if there are certain programs that work better for smaller sample sizes or depending on the array sequence that was used

00:34:45,966 --> 00:34:50,432
to generate the sequencing. SUZANNE LEAL: I’m not really the right person to

00:34:50,433 --> 00:35:03,133
ask about comparisons of variant callers. One thing I would like to say is that when you call your variants, sometimes you get files where it doesn’t

00:35:03,133 --> 00:35:13,766
really have information on some individuals. So, when you call your variants for this type of study or even if you’re using pedigrees, you really need

00:35:13,766 --> 00:35:23,232
to know the status for people who are homozygous wild-type. You need to know: are they homozygous wild-type or was this variant

00:35:23,233 --> 00:35:31,199
not called? We ran into this immediately with some trio data. We were like, “What is this?” because we were missing all these sites. We

00:35:31,200 --> 00:35:41,800
were like, “We can’t use this data; you have to give us something else.” So, that’s one thing, but there is some variability and their abilities to call

00:35:41,800 --> 00:35:53,400
indels and there’s also differences in filtering the data for QC.

00:35:53,400 --> 00:36:00,800
JIRONG LONG: Jirong Long from Vanderbilt University. Thank you very much for your very last talk. I totally agree about the call system. We

00:36:00,800 --> 00:36:10,600
have a lot of problems about the [---]. So, I’m wondering whether you have a website or you have a paper or…I mean, everybody has a paper

00:36:10,600 --> 00:36:21,500
to describe the detail or procedure for [---] for the [---] data, but because of GWAS and in the beginning we had a problem but later we saw

00:36:21,500 --> 00:36:29,766
publications and we have some ideas of how to do [---] so we want to see whether you have any suggestion or papers or…?

00:36:29,766 --> 00:36:38,666
SUZANNE LEAL: I don’t know of any papers. First, I kind of poo-pooed having a paper on this, but more and more that I see of the problems, I

00:36:38,666 --> 00:36:45,666
think it probably is a good idea, definitely, to write something up on this, yeah.

00:36:45,666 --> 00:36:58,799
MALE: I’m curious about…so you say you have different numbers of rare variants in the African American compared to the European set. I’m just

00:36:58,800 --> 00:37:09,900
curious, how much do rare variants then intersect? I mean, do you actually have a very strong separation of the rare variants or…how

00:37:09,900 --> 00:37:15,366
do they intersect, if at all? SUZANNE LEAL: They do intersect somewhat,

00:37:15,366 --> 00:37:26,732
but it’s less of an overlap than…there is some intersection but it’s not that great of an intersection, I guess, for the really rare variants.

00:37:26,733 --> 00:37:32,866
Yes? MALE: Thank you, Suzanne. That was a great

00:37:32,866 --> 00:37:40,066
talk. A lot of it, I have to say, went over my head because I don’t really work in this area directly. I was interested that, as part of QC, you exclude

00:37:40,066 --> 00:37:44,332
variants that are in linkage disequilibrium. SUZANNE LEAL: Oh, no, no. I’m sorry. That’s just

00:37:44,333 --> 00:37:52,433
for my PCA analysis that I exclude variants that are in linkage disequilibrium—when I perform my PCA—

00:37:52,433 --> 00:37:56,233
but they are not excluded from the analysis.

Date Last Updated: 9/18/2012

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