Tag Archive for 'personal genomics'

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Digging deeper into my disease risk

When Daniel first asked me if I wanted to be involved in Genomes Unzipped, I was one of the more hesitant participants.  I weighed up the pros and cons, but in the end what sold me was that after almost a decade of curiosity I finally had the opportunity to find out my genotype for the hereditary haemochromatosis (HH) variants in the gene HFE.  But things didn’t unfold quite how I’d expected, and I’m still left with some unanswered questions about HH in my family.

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Saturday Links

Due to a communication breakdown, no-one wrote a Friday Links post yesterday, so today we have a Saturday Links to make up for it.

Steve Hsu has a very appropriately named post, News from the future, about the Beijing Genomics Institute. The BGI is the largest genome sequencing center in China, and one of the largest in the world, and is growing faster than any other, and loading up on a shedload of high-tech HiSeq machines.

Steve reports that the BGI are claiming that their sequencing rate will soon be at 1000 genomes per day, with a cost of about $5k (£3.2k) each. To put a slight downer on these amazing numbers, he clarifies that this might be referring to 10X genomes, which would realistically mean ~300 high quality genomes a day, at $15k (£9.6). Either way, if you want to keep an eye on how fast whole-genome sequencing is progressing, perhaps with an eye to when you’re ready to shell out to get your own done.

A question for the comments: how cheap would a whole-genome sequence have to get before you’d order one?

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Finding the holes in our genomes

In a previous post I discussed copy number variation, a form of genetic variation not broadly reported by DTC companies. In today’s post I provide a very simple program that allows one to identify potential deletions on the basis of high density SNP genotypes from a parent-offspring trio, and report on the results of running this program on data from my own family.

The program uses an approach that I applied as a graduate student to mine deletions from the very first release of data from the International HapMap Project in 2004.  The idea, explained in my last post, is to look for stretches of homozygous genotypes interspersed with mendelian errors, which might indicate the transmission of a large deletion. Let’s be clear, this is a simple analysis that most programmers and computational biologists would find straightforward to implement. It is probably a good practice problem for graduate students and would-be DIY personal genomicists.

I obtained 23andMe data from both my mom and dad, and, with their consent, ran the three of us through the program. I was mildly surprised to find only two potential deletions; I had previously speculated that one would find 5-10 deletions per trio with the 550K platform used by 23andMe.

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Friday Links

This will be somewhat of an introspective Friday Links, looking at what other people have had to say about our recent announcement. We’ll resume our regular programming next week.

It’s been a big week here at Genomes Unzipped, with the announcement that all of the group members have released their genetic data publicly. The announcement was accompanied by a story by Mark Henderson in The Times (subscription only, unfortunately, but also syndicated here) along with commentary from Misha Angrist, Linda Avey and Christine Patch.

You can also listen to Daniel talk about the project on the BBC World Service (starts 19m30s), and Carl on BBC Radio Scotland (starts 38m). Finally, Luke and Daniel were on CBC Radio’s The Current today.

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Getting even with the odds ratio

In the recent report from the US Government Accountability Office on direct-to-consumer genetic tests, much was made of the fact that risk predictions from DTC genetic tests may not be applicable to individuals from all ethnic groups. This observation was not new to the report – it has been commented on by numerous critics ever since the inception of the personal genomics industry.

So, why does risk prediction accuracy vary between individuals and what can be done to combat this? Are the DTC companies really to blame?

To explore these questions it is first necessary to understand what is meant by the odds ratio (OR). In genetic case-control association studies the OR typically represents the ratio of the odds of disease if allele A is carried compared to if allele B is carried. If all else is equal, genetic loci with a higher OR are more informative for disease prediction – so getting an accurate estimate is extremely important if prediction underpins your business model. However, getting an accurate estimate of OR is far from easy because many, often unmeasured, factors can cause OR estimates to vary. In this post I will try to break down the concept of a single, fixed odds ratio for a disease association, and highlight a number of factors that can cause odds ratios to vary using examples from the scientific literature.

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Estimating the size of the DTC genomics market

Over the last few years, I’ve found that the same question keeps cropping up again and again at meetings whenever we talk about direct-to-consumer genetic tests:  “How many people are actually buying these tests?”. And because the companies (for whatever reason) have thus far been rather reticent about telling us how many kits they’ve sold, until recently the answer has simply been “I don’t know”. Yet if we’re going to talk about their sociological impact, their knock-on effect on health systems, and re-writing our regulatory laws around them, surely this is something we ought to have a handle on.

So… how many people have actually bought these tests then?

The problem is how to go about estimating a market size when there is precious little data, and the companies are all privately owned? First, we teamed up with some enthusiastic MBA students, who came up with the simple but elegant idea of using website hits as a proxy for market share. Using Compete.com, we found that the ‘big three’ – 23andMe, deCODEme and Navigenics – together had just over 662,000 unique hits during 2009, of which 23andMe received the lion’s share at nearly 80%. (They received fairly constant internet traffic throughout the year, with an average of around 43, 4 and 8 thousand unique visitors per month respectively). Pathway had only just launched when we did the analysis, resulting initially in a large transient spike in internet traffic, so we left it out.

Second, fortunately for us, in October 2009 23andMe stated publicly that their database contained “30,000 active genomes”, which were either sold or given away at a substantially reduced rate. (This rose to 50,000 in June 2010, but that doesn’t really alter the calculations). So, assuming a steady rate of uptake, this equates to perhaps 15,000 genome scans sold during 2009. Combining this with the internet traffic data, we estimate in this month’s Genetics in Medicine [Wright CF, Gregory-Jones S. Genet Med. (2010) 12: 594] that around 20-30,000 genome scans were sold in 2009, at a cost of between $300-1,000, which probably equates to a commercial value of around $10-20 million.

Is that really true?

Obviously there are substantial margins of error in any estimate made from such limited data, and caveats include the fact that we only considered tests sold during 2009 and we ignored (as much as possible) non-medical tests like paternity and ancestry testing. Nonetheless, this seems like a realistic ballpark figure, and importantly it is neither millions of people, nor hundreds of millions of dollars. We don’t know (yet) how big the market for whole genome sequences will be, or what impact preconception carrier testing might have, but at the moment it is clear that the market for DTC genetic testing is much smaller than expected or than one might surmise from all the media attention. Which means that the alleged harms to consumers, and the reputed knock-on effects on health systems, must necessarily be limited.

Nonetheless, I would dearly love to hear from any DTC genomics companies out there willing to share some more concrete data…

Getting Serious About Personal Genomics’ Risks

When we launched Genomes Unzipped three months ago, we promised a focus on the “budding industry of personal genomics.” Recent developments, however, have demonstrated that this emerging field is susceptible to critics who may be more focused on generating controversy than in engaging in a thoughtful discussion about the balance of risks and benefits in personal genomics. The University of California Berkeley’s short-lived proposal to provide a voluntary and educational genetic testing program for its incoming freshman class highlight this concern.

Over at the Genomics Law Report, in Getting Serious About Personal Genomics’ Risks, I review the Berkeley example and argue that we must carefully examine where and why we restrict the ability of individuals to participate in personal genomics. The failure to do so threatens not only the future of personal genomics but the autonomy of the individuals involved.

For more, please see the complete post at the Genomics Law Report.

Friday Links

Two exciting-looking new science blogging collectives have been announced this week. The Public Library of Science launched a new blogging collective, including personal genomics blogger Misha Angrist, and the Guardian newspaper has launched its Guardian Science Blogs network, including Dr Evan Harris, ex-MP for Oxford West and long time supporter of the role of science in public policy. I’m pretty excited about these new blogs, but it does stand to increase my RSS load significantly. [LJ]

In this month’s issue of European Journal of Human Genetics, Yang, Visscher and Wray contribute to the discussion around the aetiology of common complex diseases.  They demonstrate that the existence of a large number of sporadic cases (instances where a patient has no first, second, or third-degree relatives with the disease) is not incompatible with a polygenic model of disease.  A little less hot-off-the-press are two opinion pieces on genetic testing regulation from the August issue of Nature.  Arthur Beaudet argues that stringent government regulation should be applied to genotyping/sequencing, and interpretation should be the exclusive domain of the medical profession.  Gail Javitt takes a different view, arguing that genetic tests to be treated in the same way as other medical tests and that the level of regulation imposed should be determined by medical relevance of the outcome. [KIM]

Finally, Procreation News; our very own Daniel MacArthur and Ilana Fisher have recently given birth to a baby boy (the picture to the left may be a little out of date). Daniel made the announcement on Twitter, and also had this to say:

After careful inspection, I’ve decided that my six-day-old son is the most remarkable human being to have ever lived.

Due to double blinding, neither we nor Daniel know whether he has an actual or placebo baby, so we can’t yet assess the significance of this claim. Watch this space! [LJ]

Dude, where are my copy number variants?

The genome scans currently offered by major personal genomics companies provide information about only one kind of genetic variation: single nucleotide polymorphisms, or SNPs. However, SNPs are just one end of a size spectrum of variation, reaching all the way up to large duplications or deletions of DNA known as copy number variants (CNVs). Over the last decade we have learned that CNVs are a surprisingly common form of variation in humans, and they span a formidable chunk of the genome. While there are about 3M-3.5M bases of variation due to SNPs within an individual genome (in say, a typical person of European descent), there are at least 50-60M variable bases due to CNVs.

For the personal genome enthusiast with their SNP chip data from 23andMe or deCODEme in hand, there are two important practical questions: (1) can I learn about my CNVs using SNP chip data; and (2) will that information be useful?

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Excessive regulation of DTC genomics will come at a cost

Caroline and I have an opinion piece in this week’s New Scientist arguing that regulators should consider the benefits of personal genomics (in terms of increasing genetic literacy and innovation) before imposing excessive regulation on the direct-to-consumer genetic testing industry. Here’s the take-home message:

We don’t yet know what role personal genomics will play in the future of medicine. However, we do know that it has great potential for innovation and education, and we must ensure that neither excessive regulation nor medical paternalism get in the way.

Unfortunately due to space restrictions several sections of our argument were cut – for instance, we spent some time arguing against the idea of arbitrary divisions of tests into “medical” and “non-medical” categories, with the former requiring supervision of a medical professional to receive results. Here’s the original wording:

Unlike many commentators, including the HGC, we do not support an unsustainable division of tests into separate categories, one for health-related tests that require counselling from a medical professional and another for everything else with no such requirement. It is hard to justify the argument that results suggesting a mildly elevated predisposition to obesity require a professional intermediary to provide support, while those indicating unexpected paternity do not.

In general, we would argue that people should be free to access their own genetic data unless there is good reason to believe that doing so will cause them real harm – and as long as the information is accurate and transparent. Companies should ensure that customers have access to expert advice if they want it, but medical supervision should not be a requirement for access to your own genome.

In a similar vein, if you missed the editorial on DTC genetics in last week’s Economist, you should go read it now. The final two paragraphs warrant quoting in full:

But three things argue against wholesale regulation. First, the level of interference needs to be based on the level of risk a test represents. The government does not need to be involved if someone decides to trace his ancestry or discover what type of earwax he has. Second, the laws on fraud should be sufficient to deal with the snake-oil salesmen who promise to predict, say, whether a child might be a sporting champion. And third, science is changing very fast. Fairly soon, a customer’s whole genome will be sequenced, not merely the parts thought to be medically relevant that the testing companies now concentrate on, and he will then be able to crank the results through open-source interpretation software downloadable from anywhere on the planet. That will create problems, but the only way to stop that happening would be to make it illegal for someone to have his genome sequenced—and nobody is seriously suggesting that illiberal restriction.

Instead, then, of reacting in a hostile fashion to the trend for people to take genetic tests, governments should be asking themselves how they can make best use of this new source of information. Restricting access to tests that inform people about bad reactions to drugs could do harm. The real question is not who controls access, but how to minimise the risks and maximise the rewards of a useful revolution.

It’s great to see these issues getting sensible discussion in the wider media; let’s hope the FDA is paying attention.


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