Author Archive for Joe Pickrell

Review of the Lumigenix “Comprehensive” personal genome service

03/02/2012
Categories: Products and Test Review
Written by Daniel MacArthur, Luke Jostins, Katherine Morley, Joe Pickrell, and Dan Vorhaus

This is the first of a new format on Genomes Unzipped: as we acquire tests from more companies, or get data from others who have been tested, we’ll post reviews of those tests here. The aim of this series is to help potential genetic testing customers to make an informed decision about the products on the market. We’re still tweaking the format, so if you have any suggestions regarding additional analyses or areas that should be covered in more detail, let us know in the comments.

Overview

Lumigenix is a relative newcomer to the personal genomics scene: the Australian-based company launched back in March this year, offering a SNP chip-based genotyping service similar in concept to those provided by 23andMe, deCODEme and Navigenics.

The company kindly provided Genomes Unzipped with 12 free “Comprehensive” kits, which provide genotypes at over 700,000 positions in the genome, to enable us to review their product. We note that the company offers several other services, including a lower-priced “Introductory” test that covers fewer SNPs, and whole-genome sequencing for the more ambitious personal genomics enthusiast. This review should be regarded as entirely specific to the Comprehensive test.
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Size matters, and other lessons from medical genetics

23/11/2011
Categories: Journal Club
Written by Joe Pickrell, Jeff Barrett, Daniel MacArthur, and Luke Jostins

Size really matters: prior to the era of large genome-wide association studies, the large effect sizes reported in small initial genetic studies often dwindled towards zero (that is, an odds ratio of one) as more samples were studied. Adapted from Ioannidis et al., Nat Genet 29:306-309.

[Last week, Ed Yong at Not Exactly Rocket Science covered a paper positing an association between a genetic variant and an aspect of social behavior called prosociality. On Twitter, Daniel and Joe dismissed this study out of hand due to its small sample size (n = 23), leading Ed to update his post. Daniel and Joe were then contacted by Alex Kogan, the first author of the study in question. He kindly shared his data with us, and agreed to an exchange here on Genomes Unzipped. In this post, we expand on our point about the importance of sample size; Alex’s reply is here.

Edit 01/12/11 (DM): The original version of this post included language that could have been interpreted as an overly broad attack on more serious, well-powered studies in psychiatric disease genetics. I've edited the post to reduce the possibility of collateral damage. To be clear: we're against over-interpretation of results from small studies, not behavioral genetics as a whole, and I apologise for any unintended conflation of the two.]

In October of 1992, genetics researchers published a potentially groundbreaking finding in Nature: a genetic variant in the angiotensin-converting enzyme ACE appeared to modify an individual’s risk of having a heart attack. This finding was notable at the time for the size of the study, which involved a total of over 500 individuals from four cohorts, and the effect size of the identified variant–in a population initially identified as low-risk for heart attack, the variant had an odds ratio of over 3 (with a corresponding p-value less than 0.0001).

Readers familiar with the history of medical association studies will be unsurprised by what happened over the next few years: initial excitement (this same polymorphism was associated with diabetes! And longevity!) was followed by inconclusive replication studies and, ultimately, disappointment. In 2000, 8 years after the initial report, a large study involving over 5,000 cases and controls found absolutely no detectable effect of the ACE polymorphism on heart attack risk. In the meantime, the same polymorphism had turned up in dozens of other association studies for a wide range of traits ranging from obstet­ric cholestasis to menin­go­­coccal disease in children, virtually none of which have ever been convincingly replicated.
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Revisiting RNA-DNA sequence differences

12/10/2011
Categories: Journal Club
Written by Joe Pickrell

A few months ago, I discussed a paper by Li and colleagues reporting a large number of sequence differences between mRNA and DNA from the same individual [1]. While some such differences are expected due to known mechanisms of RNA editing (e.g. A->I editing, see [2]), Li et al. reported an astonishingly high number of them, including thousands of events inconsistent with any known regulatory mechanism. These results implied at least one, and probably many, new mechanisms of gene regulation, and called into question some basic assumptions in molecular biology.

An alternative explanation for the observations of Li et al. is less exciting–imagine two genes with similar (but not identical) sequences, which produce similar (but not identical) mRNAs. If you accidentally attributed both mRNA sequences to the same gene, you could erroneously conclude that one of the two sequences arose via RNA editing of the other. According to a new paper in by Schrider and colleagues [3], this banal artifact accounts for the majority of the reported RNA-DNA sequence differences in Li et al.

Schrider et al. show that RNA-DNA mismatches are enriched in genes with close paralogs or copy number variants, both of which are consistent with the technical artifact mentioned above. However, their most striking result is that, at many of the putative RNA editing sites, the “edited” base from the mRNA is actually present in genomic DNA. To show this, Schrider et al. took advantage of the fact that low-coverage DNA sequencing data is available for the individuals used in the Li et al. study. They searched through these data to find genomic sequences matching the “edited” mRNA form. If these sites were truly due to RNA editing, they shouldn’t find any. Instead, at ~75% of the tested sites, they could find a genomic match to the “edit” in at least one individual. There are some potential complications with the interpretation of this number (as they note, the genomic data could include sequencing errors that happen to be the same base as the “edit”), but this observation strongly suggests that a majority of the sites identified by Li et al. are false positives due to this single technical issue.

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[1] Li et al. (2011) Widespread RNA and DNA Sequence Differences in the Human Transcriptome. Science. doi: 10.1126/science.1207018

[2] Levanon et al. (2004) Systematic identification of abundant A-to-I editing sites in the human transcriptome. Nature Biotechnology. doi:10.1038/nbt996

[3] Schrider et al. (2011) Very Few RNA and DNA Sequence Differences in the Human Transcriptome. PLoS One. doi:10.1371/journal.pone.0025842


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The week that I worried I had a rare genetic disease

15/09/2011
Categories: Disease Risk
Written by Joe Pickrell

I recently had a series of moderately unpleasant health problems, which eventually led to my being tested for a rare, and potentially very serious, genetic disease (for worried parties: the test was negative). I thought I would share this anecdote because, first, it’s the only time I’ve wished I had more genetic information about myself in a medical setting, and second, because it illustrates the sorts of gaps in medical knowledge that could be aided by routine genome sequencing.


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Why publish science in peer-reviewed journals?

13/07/2011
Categories: Opinion
Written by Joe Pickrell

The recent announcement of a new journal sponsored by the Howard Hughes Medical Institute, the Max Planck Society, and the Wellcome Trust generated a bit of discussion about the issues in the scientific publishing process it is designed to address—arbitrary editorial decisions, slow and unhelpful peer review, and so on. Left unanswered, however, is a more fundamental question: why do we publish scientific articles in peer-reviewed journals to begin with? What value does the existence of these journals add? In this post, I will argue that cutting journals out of scientific publishing to a large extent would be unconditionally a good thing, and that the only thing keeping this from happening is the absence of a “killer app”.

Google Scholar in 2015?

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Visualizing the impact of APOE4 on ageing

05/07/2011
Categories: Disease Risk
Written by Joe Pickrell

Recently, Luke reported that I am a carrier of the E4 allele at the gene APOE; this gives me approximately double the average risk for late-onset Alzheimer’s disease. I didn’t think too much about this–it’s only double the risk, and in any case I’m 28 years old. But I recently came across the below plot, by Nick Eriksson (I’ve re-plotted it). It shows the frequency of the APOE4 allele plotted against average age in 15 cohorts of “cognitively normal elders” (data from here).

If we assume that these 15 cohorts are all from relatively similar populations, the interpretation of this is that, between the ages of 70 and 85, people with my genotype go from being cognitively normal elders to not (due to Alzheimer’s, another form of dementia, or death) at a rate about twice that of people who don’t carry the E4 allele [1]. This, of course, is exactly what I knew before (that E4 carriers have double the risk of Alzheimer’s), but seeing this visually is quite striking.

[1] Could the drop in APOE4 allele frequency could be mostly due to E4/E4 homozygotes (i.e. people not of my genotype)? If we assume an initial allele frequency of 20% and Hardy-Weinburg equilibrium, then a fifth of the APOE4 alleles are present in homozygotes. So even if all of these individual developed Alzheimer’s, then this would drop the allele frequency from 20% to ~16%. The observed drop in allele frequency is much greater than that.

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Notes on the evidence for extensive RNA editing in humans

20/05/2011
Categories: Analysis, DIY and Journal Club
Written by Joe Pickrell

The “central dogma” of molecular biology holds that the information present in DNA is transferred to RNA and then to protein. In a paper published online at Science yesterday, Li and colleagues report a potentially extraordinary observation: they show evidence that, within any given individual, there are tens of thousands of places where transcribed RNA does not match the template DNA from which it is derived [1]. This phenomenon, called RNA editing, is generally thought to be limited (in humans) to conversions of the base adenosine to the base inosine (which is read as guanine by DNA sequencers), and occasionally from cytosine to uracil. In contrast, these authors report that any type of base can be converted to any other type of base.

If these observations are correct, they represent a fundamental change in how we view the process of gene regulation. However, in this post I am going to point out a couple of technical issues that, if not properly taken into account, have the potential to cause a large number of false positives in this type of data. The main point can be summarized like this: RNA editing involves the production of two different RNA and/or protein sequences from a single DNA sequence. To infer RNA editing from the presence of two different RNA and/or protein sequences, then, one must be very sure that they derive from the same DNA sequence, rather than from two different copies of the DNA (due to, for example, paralogs or copy number variants). Although this issue has the potential to be a large source of false positives in a study like this, I will discuss an additional technical problem that could also result in false positives.

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How do variants outside genes influence disease risk?

14/04/2011
Categories: Journal Club
Written by Joe Pickrell

Over the last several years, the number of genetic variants unambiguously associated with disease risk has grown dramatically. However, interpreting these signals has been extremely difficult—most of the identified variants do not disrupt genes, and indeed many don’t fall anywhere near genes (this observation has even led some to discount these signals entirely). To an investigator interested in following up on these signals, this is somewhat depressing: how can we hope to explore how polymorphisms affect disease risk if they don’t seem to fall in any sort of genome annotation that we understand?

In this context, I thought I’d point to an important paper that, among many other things, gives the first systematic evidence that variants which influence disease are not just randomly scattered across the genome, but instead tend to fall in particular regions—in particular, enhancer elements (regions where DNA-binding proteins interact with DNA to influence gene expression).

The authors rely on the fact that, in the cell, DNA is wrapped around proteins called histones, which control how accessible the DNA is to things like transcription factors (see above figure). These proteins can be chemically modified, and it is now clear that particular patterns of modifications are predictive of the function of the DNA in the region—some modifications indicate transcribed genes, others regions of enhancer activity, others repressed regions, etc.

What the authors did in this study was generate genome-wide maps of several histone modifications in nine different cell types, and use this data to predict the function of each 200 base pair segment of the human genome in each cell type. There are a number of interesting analyses of these “maps” of genome function in the paper, but for our purposes here there’s one of particular interest: the authors took sets of SNPs associated with various diseases and simply asked, are these variants enriched in regions with any particular functional prediction? And indeed, for several phenotypes, there is a striking enrichment of association signals in enhancers elements in a relevant cell type. For example, SNPs which influence lipid levels are enriched in enhancers in a liver cancer cell line, and SNPs which influence the autoimmune disease lupus are enriched in enhancers in a lymphoblastoid cell line.

As these types of functional maps are generated in more cell types, I imagine there will be more stories like this. The problem with interpreting disease association studies, it seems likely, is largely due to our lack of understanding of genome function.

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Citation: Ernst et al. (2011) Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. doi:10.1038/nature09906

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People Have A Right To Access Their Own Genetic Information

11/03/2011
Categories: Law and Regulation and Opinion
Written by Caroline Wright, Daniel MacArthur, Joe Pickrell, Katherine Morley, Luke Jostins, Ilana Fisher, Carl Anderson, Don Conrad, Dan Vorhaus, and Vincent Plagnol

This week has seen another FDA meeting seeking guidance on how to regulate direct-to-consumer (DTC) genetic tests in the US. The meeting itself has been covered by GNZ bloggers Daniel at Genetic Future and Dan at Genomics Law Report, and its apparent outcome has sparked furious debate elsewhere. The discussion among the “independent” panel convened at the meeting appeared to converge on the proposal that all health-related genomic tests should be ordered and reported through physicians. However, the outcomes of the meeting in terms of FDA policy remain unclear, and one FDA official has indicated that decisions about the availability of genetic tests will be made on a test-by-test basis.

There is no doubt that the appropriate regulation of personal genomics tests is a complex issue, and there is a diversity of opinion about how best to achieve it within GNZ (as there is throughout the genomics community). However, there are several points we agree on:

  • Individuals have a fundamental right to access information about themselves, including genetic information. While it is important to also consider the accuracy, interpretation, validity and utility of tests, this underlying principle should guide policy.
  • There is currently no evidence that DTC genetic tests pose a danger to consumers. A recent study of over 2,000 participants in DTC testing concluded that “testing did not result in any measurable short-term changes in psychological health”. In the absence of any evidence of harm there is no justification for restricting individual autonomy.
  • DNA does not have magical powers, and does not require special treatment simply by virtue of being DNA. Genetic exceptionalism – the idea that genetics must be treated as special under the law – is an inappropriate basis for policy-making. Tests should be regulated appropriately based on their predictive power, utility and potential for harm, all of which are related concepts.
  • As DNA sequencing becomes cheaper, the line between medical and non-medical testing will continue to blur. Excessive regulation of health-related genetic tests could also unncessarily hinder the ability of people to access their entire genome sequences for other purposes (such as genetic genealogy).
  • Most clinicians do not have the appropriate knowledge to interpret genomic tests, particularly in healthy individuals. This point is almost universally agreed, even by the FDA, and has certainly been the experience of some of the GNZ members upon taking our genetic results to doctors. Physicians in general are therefore a strange choice for ‘guardians of the genome’.
  • Most early adopters of DTC genetic tests are sufficiently well-informed to understand the implications of a genomic test and interpret the results correctly. Putting a general physician between these informed individuals and their own genomes is paternalistic and unnecessary.

While the outcome of the FDA’s deliberations remain uncertain, it is clear that there will be intensive lobbying against any attempt at excessive legislation. In the worst case scenario, the fledgling and innovative personal genomics market could be crushed by the FDA. However, there is still plenty of room for a measured approach that enforces test accuracy, punishes false claims and promotes informed choices by consumers, without reducing the ability of responsible companies to continue to operate and innovate.

We urge others in the genomics community to make their voices heard on these issues. Let the FDA – and, if you’re based in the USA, your political representatives – know that regulation of genetic testing should be based on evidence, not fear, and that any attempt to unreasonably restrict your access to your own genetic information is unacceptable.


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Why DTC genetic testing is good for research

10/03/2011
Categories: In The News, Law and Regulation and Opinion
Written by Joe Pickrell

I’ve been reading with interest Daniel’s coverage of the recent FDA hearings into DTC genetic testing. In this context, both he and Razib Khan are incensed by a video which seemingly shows an FDA official misleading Congress about the research done by 23andme:

You can think what you want about the value of the research done to date by 23andme [1], but in my mind, there’s one simple reason why the sorts of participant-driven research they’re doing can only be a good thing: all research is driven by curiosity, and the people most curious about a disease or trait are those who have it. While people may think of the academic research community as a machine with endless resources and limitless motivation, it’s not. People work on things they think are interesting; they sometimes follow “trendy” topics, or move into fields with more grant money, or get bored of a given problem and move on. So if the research in the trait you’re most interested in isn’t moving fast enough for you, well, tough luck.

Recall that one of the key players in the discovery of the gene for Huntington’s disease was a foundation started by a man whose wife had the disease (startlingly, the current president of the foundation apparently accused DTC companies of “raping” the human genome during the present FDA hearing). Recall also that James Lupski, curious about the cause of his Charcot-Marie-Tooth disease, simply sequenced his own genome to find it. These are simply well-connected and trained people driven to find a gene involved in a disease. Patient communities that currently exist are also curious and driven, but in many cases are dealing with complex diseases that are amenable to genetics only with large sample sizes and extensive organization; what these communities can now do is outsource, in a sense, their research to 23andme (see, eg., 23andme’s Parkinson’s study). For scientific knowledge, this can only be a good thing.

[1] To date, the novel associations discovered by 23andme are in hair morphology, freckling, photic sneeze reflex, and “asparagus anosmia”. What these things have in common is that they’re biologically interesting, but not particularly medically interesting; it’s pretty much only curiosity that would drive you to map these traits. Medical researchers tend to scoff at this sort of thing; I think it’s actually pretty cool.


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