Tag Archives: disease

David Goldstein Proves Himself Wrong

A recent paper in PLoS Biology by David Goldstein’s group is being seen as another ‘death of GWAS’ moment (again?). I have a lot of issues with this paper, but I will be brief and stick to my main objection; the authors attempt to demonstrate that common associations can be caused by sets of rare variants, and in doing so inadvertantly show they most of them are not.

The Paper and the Press

This is another example of a scientific paper being careful to make no solid, controversial claims, but being surrounded by a media story that is not justified by the paper itself. The only real solid claim in the paper is that, if you do not include rare SNPs in your genome-wide association study, and rare SNPs of large effect are contributing to disease, then you will sometimes pick up more common SNPs as associated, because they are in Linkage Disequilibrium with the rare SNPs. Pretty uncontroversial, in so far as it goes. The paper makes no attempt to say whether this IS happening, just says that it CAN happen, and that we should be AWARE of it.

However, in the various articles around the internet, this paper is being received as if it makes some fundamental claim about complex disease genetics; that this somehow undermines Genome-Wide Association Studies, or shows their results to be spurious. David Goldstein is quoted on Nature News:

…many of the associations made so far don’t seem to have an explanation. Synthetic associations could be one factor at play. Goldstein speculates that, “a lot, and possibly the majority [of these unexplained associations], are due to, or at least contributed to, by this effect”.

Another author is quoted here as saying

We believe our analysis will encourage genetics researchers to reinterpret findings from genome-wide association studies

Much of the coverage conflates this paper with the claim that rare variants may explain ‘missing heritability’, which is an entirely different question; Nature News opens with the headline “Hiding place for missing heritability uncovered”. Other coverage can be found on Science Daily, Gene Expression and GenomeWeb.

Does this actually happen?

Is all this fuss justified? How common is this ‘synthetic hit’ effect; are a lot of GWAS hits caused by it, or hardly any? There are many ways that you could test this; for instance, you could make some predictions about what distribution of risk you’d expect to see in the many fine mapping experiments that have been done as follow ups to Genome-Wide Association Studies (this would be trivially easy to do using the paper’s simulations).

However, there is an even easier way to test the prevalence of the effect. If most GWAS hits are tagging relatively common variants, then you would expect to see most disease associated SNPs with a frequency in the 10% to 90% range (the range for which GWAS are best powered). However, a SNP with a frequency of 50% is less likely than one with a frequency of 10% to tag a SNP with frequency 0.5%, so if most GWAS hits are tagging rare variants, then you would expect to see most associated SNPs with a frequency skewed towards the very rare or the very common.

In fact, the paper makes an explicit calculation of the expected frequency distribution of GWAS hits, under their synthetic model. In-double-fact, the paper plots this distribution against the distribution of know GWAS hits. And here is that plot, taken directly from the paper (Figure 5):

The green line is the expected frequency distribution of ‘synthetic’ associations; the red line is the actual distribution. We can see that the GWAS hits we do see fail to follow the distribution for synthetic associations; in fact, they follow pretty much exactly the distribution we’d expect if most common associations are tagging common causal SNPs.

The paper manages to pretty conclusively show both that demonstrate that synthetic SNPs can occur, but they rarely do.


Dickson, S., Wang, K., Krantz, I., Hakonarson, H., & Goldstein, D. (2010). Rare Variants Create Synthetic Genome-Wide Associations PLoS Biology, 8 (1) DOI: 10.1371/journal.pbio.1000294

Cargo Cult Science and NT Factor®

A recent blog post on Chronic Fatigue Syndrome linked in passing to a ‘treatment’ called Mitochondria Ignite™ with NT Factor®. This product caught my attention as an example of what Richard Feynman called ‘Cargo Cult Science’; a company dressing up like scientists, using chemical names and precise sounding figures, without actually having any science underlying it.

However, the product is not arguably not exactly pure Cargo Cult Science; there is a small amount of science content present. The product page contains a number of references, some of which point to peer review journals, and some of which are actually studies of the effect of some of the contents of the drug on humans. Of cours,e taking apart the studies shows that the product is still unproven, despite the thin glaze of real science; I can’t help but feel that this sort of thing has slightly grim implications for the future of accurate consumer information.

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The Economist Mangles Disease Genetics

The Economist has a rather distressingly bad article by the evolutionary psychologist Geoffrey Miller, about the supposed general failure in human disease genetics over the last 5 years. The thesis is that Genomes Wide Association Studies (GWAS) for common diseases have been a failure that geneticists are trying to keep hidden, and that the new techniques required to solve the problem of disease genetics will raise ‘politically awkward and morally perplexing facts’ about the different traits and evolutionary histories of races. The former claim is pretty much the same as Steve Jones Telegraph article earlier this year, and is just as specious. I will look at both claims separately.

A quick point of terminology: Miller uses ‘GWAS’ to refer to studies that look for disease association in common variants using a genotyping chip, and acts as if sequencing studies are not, in fact, GWAS. In fact, a sequencing association study is just another type of GWAS, just looking at a larger set of variants.
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ASHG: Quantifying Relatedness and Active Subjects in Genome Research

Well, the American Society of Human Genetics Annual Meeting is coming to a close for another year. My talk is done and dusted, so I no longer have to lie awake at night worrying that I will forget everything other then the words to “Stand By Your Man” when confronted by the crowd. My white suit is now more of an off-white suit, with regions of very-off-white and pretty-much-entirely-out-of-sight-of-white. I’m looking forward to getting back home to catch up on my sleep.

For the last time, I’m going to give a little summary of talks today that I thought were interesting, or gave some indication of where genetics may be heading in the future. I will write up some more general thoughts about the meeting in the next few days, as soon as the traveling is out of the way and my mind has recharged.

If you would like some second opinions on the conference, GenomeWeb has a number of articles, including a couple of short summaries, as well as a nice mid-length article about the 1000 Genomes session; there are also a number of articles over at In The Field, the Nature network conference blog.
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ASHG: Finding Mendelian Mutations and Inclusive Population Genetics

Third day down, one to go. I am starting to suffer from conference fatigue somewhat. I’m not going to any other talks this evening, so I am going to try and get some relaxation time in from this point on. But first; the summary of Day 3.

Today I saw a lot of talks over three sessions, and many of them were very interesting. However, I won’t talk about everything, or even my favourite talks. I’ll go for the talks that seem to tie together into nice stories about a few directions genetics seems to be heading in.
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ASHG: Statistical Genomics and Beyond GWAS in Complex Disease

The second day of the American Society of Human Genetics Annual Meeting is drawing to a close; here’s a lowdown of what talks I’ve enjoyed today.

Remember, follow @lukejostins on Twitter if you want more up-to-the-minute details on the ASHG talks.
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How Much Health Information Is In A Person’s Genome?

How much information can we get from a genome scan? Many companies, such as 23andMe and deCODE Genetics sell genetic tests that allow you to determine parts of your DNA sequence: one selling point is that it can tell you how susceptible you are to various diseases. But how much can a genome really tell you?

In general, people say ‘not much’, and cite the importance of the environment, social, cultural factors, and our lack of knowledge of disease genetics: these are all valid and important points. But, can we put some figures on exactly how much a genome scan can tell us? Can we calculate exactly how much the average person’s predicted probability of getting a disease will change after they get their DNA scanned?

In this post, we will take three important diseases of decreasing rarity, and take all the genetic variants that are known to influence them. We will see exactly how much we expect this information to change someone’s likelihood of getting the disease.
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Research Interests Translated

I recently updated the information on my website, and in doing so I decided to produce two versions of my research interests. The first is for other scientists, and the second is a translation for lay people. I would be interested to know how people think this is pitched; is the lay-information too confusing, or is it too simple and patronising?

I think every scientist should try and do this at some point. It is an interesting exercise to see how well you can communicate and summarise the entirety of your research in a way that doesn’t use the shared lingo and knowledge base that you have access to when taking to other scientists. Plus, of course, communicating your work to the world outside of academia is generally A Good Thing.
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On the UK’s DNA Database, Part 2

This is the second part of a double post in the UK National DNA Database.

In the first part of this double post I talked about what information the DNA database holds, and who it holds it on. In this second part, I will discuss what this information is used for, what it could be used for in the wrong hands, and how bad this could be.
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On Stuffed Venereal Diseases

I’m currently visiting my parents, and my female spawner, always keen to spoil me, presented me with a couple of biology-related presents (including this novelty t-shirt). One particularly cool thing, picked up in CyberDog in Camden Market, is this stuffed toy:

hiv

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