You are currently browsing the category archive for the ‘technology’ category.
The paper “Genomic-scale capture and sequencing of endogenous DNA from feces” by George H. Perry, John C. Marioni, Páll Melsted and Yoav Gilad is literally full of feces. The word ‘fecal’ appears 100 times.
Poop jokes aside, the paper presents an interesting idea that has legs. Perry et al. show that clever use of Agilent’s SureSelect allows for capturing nuclear genomic regions from fecal DNA. Intellectually, it is the predecessor of T. Mercer et al.‘s “Targeted RNA sequencing reveals deep complexity of the human transcriptome“, Nature Biotechnology, 2011 (another paper I like and for which I wrote a research highlight). Even though the Perry et al. paper does not have many citations, the Mercer et al. paper does (although unfortunately the authors forgot to cite Perry et al., which I think they should have). In other words, the Perry et al. paper is not as well known as it ought to be, and this post is an attempt to rectify that.
The ‘*’ in sh*t in my title is for *Seq. At a high level, the Perry et al. paper shows how high-throughput sequencing technology can be leveraged to sequence deeply a single genome from among a community of metagenomes. For this reason, and for convenience, I henceforth will refer to the Perry et al. paper as the Sh*t-Seq paper. The “*” is inserted in lieu of the “i” not as censorship, but to highlight the point that the method is general and applies not only to sequencing nuclear genome from fecal DNA, but also as Mercer et al. shows, for targeted transcriptome sequencing (one can imagine also many other applications).
The Sh*t-Seq protocol is conceptually simple yet complicated in practice. DNA was captured using the Agilent SureSelect target enrichment system coupled to the Illumina single-end sequencing platform library prep protocol (of note is that the paper is from 2010 and the kits are from 2009). However because of the very small amount of DNA targeted (the authors claim ~1.8%), a number of adjustments to standard SureSelect capture / Illumina library prep had to be implemented. To the authors credit, the paragraphs in the section “DNA Capture” are exemplary in their level of detail and presumably greatly facilitate replicability. I won’t repeat all the detail here. However there are two steps that caught my attention as possibly problematic. First, the the authors performed substantial PCR amplification of the adapter-ligated fecal DNA. This affects the computational analysis they discuss later and leads to a computational step they implemented that I have some issues with (more on this later). Second, they performed two rounds of capture as one round was insufficient for capturing the needed material for sequencing. This necessitated additional PCR, which also is possibly problematic.
The samples collected were from six chimpanzees. This is a fairly small n but the paper is a proof of principle and I think this is sufficient. Both fecal and blood samples were collected allowing for comparison of the fecally derived nuclear DNA to the actual genome of the primates. In what is clearly an attempt to channel James Bond, they collected fecal samples (2 g of stool) within 1 hour of defecation in tubes containing RNALater and these were then “shaken vigorously” (not stirred).
The next part of the paper is devoted to computational analyses to confirm that the Sh*t-Seq protocol can in fact be used to target nuclear endogenous DNA. As a sanity check, mtDNA was considered first. They noted too much diversity to align reads to a reference genome with BWA, and opted instead for de novo assembly using ABySS. This is certainly overkill, possible only because of the high copy count of mitochondrion reads. But I suppose it worked (after filtering out all the low coverage ABySS sequence, which was presumably junk). One interesting idea given more modern RNA-Seq assembly tools would be to assemble the resulting reads with an RNA-Seq de novo assembler that allows for different abundances of sequences. Ideally, such an assembly should indicate naturally the sought after enrichment.
Next, nuclear DNA was investigated, specifically the X chromosome and chromosome 21. Here the analyses is very pre-2014. First, all multi-mapping reads were removed. This is not a good idea for many reasons, and I am quite certain that with the new GRCh38 (with alternate sequence representation for variant regions) it is a practice that will rapidly be phased out. I’d like to give Perry et al. the benefit of the doubt for making this mistake since they published in 2010, but their paper appeared 6 months after the Cufflinks paper so they could have, in principle, known better. Having said that, while I do think multi-mapping would have allowed them to obtain much stronger results as to the accuracy and extent of their enrichment by avoiding the tossing of a large number of reads, their paper does manage to prove their principle so its not a big deal.
The removal of multi-mapping reads was just the first step in a series of “filters” designed to narrow down the nuclear DNA reads to regions of the chimpanzee genome that could be argued to be unambiguously representative of the target. I won’t go into details, although they are all in the paper. As with the experimental methods, I applaud the authors on publishing reproducible methods, especially computational methods, with all details included. But there was a final red flag for me in the computational methods: namely the selection of a single unique fragment (at random) for each genomic (start) site for the purposes of calling SNPs. This was done to eliminate problems due to amplification biases, which is indeed a serious concern, but if heterozygous sites appear due to the PCR steps, then there ought to have been telltale signatures. For example, a PCR “SNP’ would, I think, appear only in reads specific to a single position, but not in other overlapping reads of the site. It would have been very helpful if they would have done a detailed analysis of this issue, rather than just pick a single read at random for each genomic (start) site. They kicked the can down the road.
Having removed multiple mapping reads, repetitive regions, low coverage regions, etc. etc. Dayenu, they ended up estimating a false positive rate for heterozygous sites (using the X chromosome in males) at 0.0007% for fecal DNA and 0.0010% for blood DNA. This led them to conclude that incorrectly-identified heterozygous sites in their study were 0.8%, 2.0%, 1.1%, and 2.7% for fecal DNA chromosome 21, fecal DNA chromosome X in females, blood DNA chromosome 21, and blood DNA chromosome X in females, respectively. Such good news is certainly the result of their extraordinarily stringent filtering, but I think it does prove that they were able to target effectively. They give further proof using PCR and Sanger sequencing of 20 regions.
I have a final nitpick and it relates to Figure 4. It is a a companion to Figure 3 which shows the Chimpanzee phylogeny for their samples based on the mtDNA. As expected in that figure, the fecal and blood samples cluster together. Figure 4 shows two phylogenies, one based on chr 21, the other on chr X. My issue here is with the way that distances were constructed. Its a technical point, but it looks like they used hamming distance, and I don’t think that makes a lot of sense, not to mention the fact that neighbor-joining does not seem like the appropriate algorithm for building a tree in this setting (I plan to blog about neighbor-joining shortly). But this is a methodological point not really relevant to the main result of the paper, namely proof of principle for targeted sequencing of endogenous DNA from fecal matter.
I think Sh*t-Seq has a future. The idea of targeted capture coupled to high-throughput sequencing has more than an economic rationale. It provides the possibility to probe the “deep field” as discussed in the previously mentioned review on targeted RNA-Seq. This is a general principle that should be more widely recognized.
And of course, dung is just cool. Happy new year!
[Update April 6, 2014: The initial title of this post was “23andme genotypes are all wrong”. While that was and remains a technically correct statement, I have changed it because the readership of my blog, and this post in particular, has changed. Initially, when I made this post, the readers of the blog were (computational) biologists with extensive knowledge of genotyping and association mapping, and they could understand the point I was trying to make with the title. However in the past few months the readership of my blog has grown greatly, and the post is now reaching a wide public audience. The revised title clarifies that the content of this post relates to the point that low error rates in genotyping can be problematic in the context of genome-wide association reports because of multiple-testing.]
I have been reading the flurry of news articles and blog posts written this week about 23andme and the FDA with some interest. In my research talks, I am fond of displaying 23andme results, and have found that people always respond with interest. On the teaching side, I have subsidized 23andme testing for volunteer students in Math127 who were interested in genetics so that they could learn about personalized genomics first-hand. Finally, a number of my former and current students have worked at 23andme, and some are current employees.
Despite lots of opinions being expressed about the 23andme vs. FDA kerfuffle, I believe that two key points have been ignored in the discussions:
- All 23andme genotypes that have ever been reported to customers are wrong. This is the case despite very accurate genotyping technology used by 23andme.
- The interpretation of 23andme results involves examining a large number of odds ratios. The presence of errors leads to a huge multiple-testing problem.
Together, these issues lead to an interesting conundrum for the company, for customers, and for the FDA.
I always find it useful to think about problems concretely. In the case of 23andme, it means examining actual genotypes. Fortunately, you don’t have to pay the company $99 dollars to get your own- numerous helpful volunteers have posted their 23andme genotypes online. They can be viewed at openSNP.org where “customers of direct-to-customer genetic tests [can] publish their test results, find others with similar genetic variations, learn more about their results, get the latest primary literature on their variations and help scientists find new associations”. There are a total of 624 genotypes available at openSNP, many of them from 23andme. As an example, consider “samantha“, who in addition to providing her 23andme genotype, also provides lots of phenotypic information. Here is the initial part of her genotype file:
# This data file generated by 23andMe at: Wed Jul 20 20:37:11 2011 # # Below is a text version of your data. Fields are TAB-separated # Each line corresponds to a single SNP. For each SNP, we provide its identifier # (an rsid or an internal id), its location on the reference human genome, and the # genotype call oriented with respect to the plus strand on the human reference # sequence. We are using reference human assembly build 36. Note that it is possible # that data downloaded at different times may be different due to ongoing improvements # in our ability to call genotypes. More information about these changes can be found at: # https://www.23andme.com/you/download/revisions/ # # More information on reference human assembly build 36: # http://www.ncbi.nlm.nih.gov/projects/mapview/map_search.cgi?taxid=9606&build=36 # # rsid chromosome position genotype rs4477212 1 72017 AA rs3094315 1 742429 AG rs3131972 1 742584 AG rs12124819 1 766409 AA rs11240777 1 788822 AA rs6681049 1 789870 CC rs4970383 1 828418 CC rs4475691 1 836671 CC rs7537756 1 844113 AA rs13302982 1 851671 GG rs1110052 1 863421 GT ...
Anyone who has been genotyped by 23andme can get this file for themselves from the website (by clicking on their name, then on “Browse Raw Data” from the pull-down menu, and then clicking on “Download” in the top-right corner of the browser window). The SNPs are labeled with rsid labels (e.g. rs3094315) and correspond to specific locations on chromosomes (e.g. chr1:742429). Since every human is diploid, two bases are shown for every SNP; one came from mom and one from dad. The 23andme genotype is not phased, which means that you can’t tell in the case of rs3094315 whether the A was from mom and the G from dad, or vice versa (it turns out paternal origin can be important, but that is a topic for another post).
A key question the FDA has asked, as it does for any diagnostic test, is whether the SNP calls are accurate. The answer is already out there. First, someone has performed a 23andme replicate experiment precisely to assess the error rate. In an experiment in 2010 with two replicates, 85 SNPs out of about 600,000 were different. Today, Illumina types around 1 million SNPs, so one would expect even more errors. Furthermore, a replicate analysis provides only a lower bound, since systematic errors will not be detected. Another way to examine the error rate is to look at genotypes of siblings. That was written about in this blog post which concluded there were 87 errors. 23andme currently uses the Illumina Omni Express for genotyping, and the Illumina spec sheet claims a similar error rate to those inferred in the blog posts mentioned above. The bottom line is that even though the error rate for any individual SNP call is very very low (<0.01% error), with a million SNPs being called there is (almost) certainly at least one error somewhere in the genotype. In fact, assuming a conservative error rate leading to an average of 100 errors per genotype, the probability that a 23andme genotype has no errors is less than 10^(-40).
The fact that 23andme genotypes are wrong (i.e. at least one error in some SNP) wouldn’t matter if one was only interested in a single SNP. With very high probability, it would be some other SNPs that are the wrong ones. But the way people use 23andme is not to look at a single SNP of interest, but rather to scan the results from all SNPs to find out whether there is some genetic variant with large (negative) effect. The good news is that there isn’t much information available for the majority of the 1 million SNPs being tested. But there are, nevertheless, lots of SNPs (thousands) to look at. Whereas a comprehensive exam at a doctor’s office might currently constitute a handful of tests– a dozen or a few dozen at most– a 23andme test assessing thousands of SNPs and hundreds of diseases/traits constitutes more diagnostic tests on an individual at one time than have previously been performed in a lifetime.
To understand how many tests are being performed in a 23andme experiment, it is helpful to look at the Interpretome website. The website allows a user to examine information on SNPs without paying, and without uploading the data. I took a look at Samantha, and the Interpretome gave information about 2829 SNPs. These are SNPs for which there is a research article that has identified the SNP as significant in some association study (the website conveniently provides direct links to the articles). For example, here are two rows from the phenotype table describing something about Samantha’s genetic predisposition for large head circumference:
Samantha’s genotype at the locus is CC, the “risk” allele is T, the odds ratios are very small (0.05,0.07) and the p-values are apparently significant. Interpretome’s results differ from those of 23andme, but looking at the diversity of phenotypes reported on gives one a sense for the possibilities that currently exist in genetics, and the scope of 23andme’s reports.
From the estimates of error rates provided above, and using the back of an envelope, it stands to reason that about 1/3 of 23andme tested individuals have an error at one of their “interesting” SNPs. Not all of SNPs arising in association studies are related to diseases, but many of them are. I don’t think its unreasonable to postulate that a significant percentage of 23andme customers have some error in a SNP that is medically important. Whether such errors are typically false positives or false negatives is unclear, and the extent to which they may lead to significant odds ratios is another interesting question. In other words, its not good enough to know how frequently warfarin sensitivity is being called incorrectly. The question is how frequently some medically significant result is incorrect.
Of course, the issue of multiple testing as it pertains to interpreting genotypes is probably a secondary issue with 23andme. As many bloggers have pointed out, it is not even clear that many of 23andme’s odds ratios are accurate or meaningful. A major issue, for example, is the population background of an individual examining his/her genotype and how close it is to the population on which the GWAS were performed. Furthermore, there are serious questions about the meaning of the GWAS odds ratios in the case of complex traits. However I think the issue of multiple testing is a deeper one, and a problem that will only be exacerbated as more disease SNPs are identified. Having said that, there are also approaches that could mitigate errors and improve fidelity of the tests. As DECODE genetics has demonstrated, imputation and phasing can in principle be used to infer population haplotypes, which not only are useful for GWAS analyses, but can also be used to identify erroneous SNP calls. 23andme’s problem is that although they have many genotypes, they are from diverse populations that will be harder to impute and phase.
The issue of multiple testing arising in the context of 23andme and the contrast with classic diagnostics reminds me of the dichotomy between whole-genome analysis and classic single gene molecular biology. The way in which customers are looking at their 23andme results is precisely to look for the largest effects, i.e. phenotypes where they appear to have high odds of contracting a disease, or being sensitive to some drug. This is the equivalent of genome scientists picking the “low hanging fruit” out of genome-wide experiments such as those performed in ENCODE. In genomics, scientists have learned (with some exceptions) how to interpret genome-wide analyses after correcting for multiple-hypothesis testing by controlling for false discovery rate. But are the customers of 23andme doing so? Is the company helping them do it? Should it? Will the FDA require it? Can looking at ones own genotype constitute too much testing?
There are certainly many precedents for superfluous harmful testing in medicine. For example, the American Academy of Family Physicians has concluded that prostate cancer PSA tests and digital rectal exams have marginal benefits that are outweighed by the harm caused by following up on positive results. Similar arguments have been made for mammography screening. I therefore think that there are serious issues to consider about the implications of direct-to-consumer genetic testing and although I support the democratization of genomics, I’m glad the FDA is paying attention.
Don’t believe the anti-hype. They are saying that RNA-Seq promises the discovery of new expression events, but it doesn’t deliver:
Is this true? There have been a few papers comparing microarrays to RNA-Seq technology (including one of my own) that I’ll discuss below, but first a break-down of the Affymetrix “evidence”. The first is this figure (the poor quality of the images is exactly what Affymetrix provides, and not due to a reduction in quality on this site; they are slightly enlarged when clicked on):
The content of this figure is an illustration of the gene LMNB1 (Lamin protein of type B), used to argue that microarrays can provide transcript level resolution whereas RNA-Seq can’t!! Really? Affymetrix is saying that RNA-Seq users would likely use the RefSeq annotation which only has three isoforms. But this is a ridiculous claim. It is well known that RefSeq is a conservative annotation and certainly RNA-Seq users have the same access to the multiple databases Affymetrix used to build their annotation (presumably, e.g. Ensembl). It therefore seems that what Affymetrix is saying with this figure is that RNA-Seq users are dumb.
The next figure is showing the variability in abundance estimates as a function of expression level for RNA-SEq and the HTA 2.0, with the intended message being that microarrays are less noisy:
But there is a subtle trick going on here. And its in the units. The x-axis is showing RPM, which is an abbreviation for Reads Per Million. This is not a commonly used unit, and there is a reason. First, its helpful to review what is used. In his landmark paper on RNA-Seq, Ali Mortazavi introduced the units RPKM (note the extra K) that stands for reads per kilobase of transcript per million mapped. Why the extra kilobase term? In my review on RNA-Seq quantification I explain that RPKM is proportional to a maximum likelihood estimate of transcript abundance (obtained from a simple RNA-Seq model). The complete derivation is on page 6 ending in Equation 13; I include a summary here:
The maximum likelihood (ML) abundances are given by
In these equations is the length of transcript t (if reads are long it is necessary to modify the length due to edge effects, hence the tilde in the paper), the are the maximum likelihood estimates for the probabilities of selecting reads from transcripts (unnormalized by their length) and finally is the number of reads mapping to transcript t while N is the total number of mapped reads. The point is that RPKM (the rightmost formula for abundance) is really a unit for describing the maximum likelihood relative abundances () scaled by some factors.
RPKM as a unit has two problems. The first is that in current RNA-Seq experiments reads are paired so that the actual units being counted (in ) are fragments. For this reason we modified RPKM to FPKM in the Cufflinks paper (the “F” replaced “R” for fragment instead of read). A more serious problem, noted by Bo Li and Colin Dewey in their paper on RSEM, is that while FPKM is proportional to ML estimates of abundance, the proportionality constants may vary between experiments. For this reason they proposed TPM (transcripts per million) which is also proportional to the ML abundance estimates but with a proportionality constant (a million) that is the same between experiments. All of these units are used simply to avoid writing down the which are in many cases tiny numbers since they must all sum to 1.
Returning to the Affymetrix figure, we see the strange RPM units. In essence, this is the rightmost term in the equation above, with the length terms removed from the denominators. Therefore RPM is proportional to the . If a transcript is short, even if it is equally abundant to a longer transcript ,it will produce less RNA-Seq reads and therefore its will be (possibly considerably) smaller. The effect of displaying RPM for RNA-Seq vs. expression level for the HTA 2.0 arrays is therefore to mix apples and oranges. Since what is being displayed is a coefficient of variation, there is a bias caused by the relationship between length and expression (e.g. many highly expressed housekeeping genes are short).
To be fair to Affymetrix the conversion between the and the can be confusing (its explained in Lemma 14 in the Supplement of the Cufflinks paper). So maybe the discordant x-axes were unintentional…but then there is the third figure:
Here its a bit hard to tell what is going on because not all the information needed to decipher the figure is provided. For example, its not clear how the “expression of exons” was computed or measured for the RNA-Seq experiment. I suspect that as with the previous figure, read numbers were not normalized by length of exons, and moreover spliced reads (and other possibly informative reads from transcripts) were ignored. In other words, I don’t really believe the result.
Having said this, it is true that expression arrays can have an advantage in measuring exon expression, because an array measurement is absolute (as opposed to the relative quantification that is all that is possible with RNA-Seq). Array signal is based on hybridization, and it is probably a reasonable assumption that some minimum amount of RNA triggers a signal, and that this amount is independent of the remainder of the RNA in an experiment. So arrays can (and in many cases probably do) have advantages over RNA-Seq.
There are a few papers that have looked into this, for example the paper “A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae ” by Nookaew et al., Nucleic Acids Research 40 (2012) who find high reproducibility in RNA-Seq and consistency between arrays and RNA-Seq. Xu et al., in “Human transcriptome array for high-throughput clinical studies“, PNAS 108 (2011), 3707–3712 are more critical, agreeing with Affymetrix that arrays are more sensitive at the exon level. For disease studies, they recommend using RNA-Seq to identify transcripts relevant to the disease, and then screening for those transcripts on patients using arrays.
For the Cuffdiff2 paper describing our new statistical procedures for differential analysis of transcripts and genes, the Rinn lab performed deep RNA-Seq and array expression measurement on the same samples from a HOXA1 knowdown (the experiments included multiple replicates of both the RNA-Seq and the arrays). To my knowledge, it is the deepest and most comprehensive data currently available for comparing arrays and RNA-Seq. Admittedly, the arrays used were not Affymetrix but Agilent SurePrint G3, and the RNA-Seq coverage was deep, however we had two main findings very different from the Affymetrix claims. First, we found overall strong correlation between array expression values and RNA-Seq abundance estimates. The correlation remained strong for large regimes of expression even with very few reads (tested by sequencing fewer reads from a MiSeq). Second, we found that arrays were missing differentially expressed transcripts, especially at low abundance levels. In other words, we found RNA-Seq to have higher resolution. The following figure from our paper made the case (note the overall Spearman Correlation was 0.86):
There are definitely continued applications for arrays. Both in high-throughput screening applications (as suggested in the Xu et al. paper), and also in the development of novel assays. For example Mercer et al. “Targeted rNA sequencing reveals the deep complexity of the human transcriptome“, Nature Biotechnology 30 (2012) 99–104 show how to couple capture (with arrays) with RNA-Seq to provide ultra deep sequencing in subsets of the transcriptome. So its not yet the time to write off arrays. But RNA-Seq has many applications of its own. For example the ability to better detect allele-specific expression, the opportunity to identify RNA-DNA differences (and thereby study RNA editing), and the ability to study expression in non-model organisms where genomes sequences are incomplete and annotations poor. For all these reasons I’m betting on RNA-Seq.