Tag Archive for 'personal genomics'

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Using 23andMe to confirm identical twinnery, and a chance to tell the FDA what you think about DTC genetics

Over at Daily Kos, Michael Convente shares a fascinating story of using 23andMe data to pin down the precise relationship he has with his twin brother Matt. When Mike and Matt were born, the obstetrician told their mother that the presence of two separate placentas indicated that the brothers were non-identical (fraternal) twins – yet their incredibly similar appearance while growing up (see photo on left) suggested otherwise. Testing with 23andMe confirmed what the brothers had always suspected: that they are in fact identical twins. This is a useful reminder of the non-medical value of accessible genetic information: when it comes to unravelling these kinds of family mysteries, direct access to large-scale genetic data can be a powerful tool. [DM]

Readers who care about access to genetic information (i.e. all of you) and who are concerned about the potential effects of regulation on this access and on innovation in the field in general will soon have an opportunity to make their voices heard. Thanks to the efforts of Dan Vorhaus and others, the FDA has agreed to reopen the opportunity for public submissions while it deliberates on its next move following the agency-sponsored meeting on direct-to-consumer genetics last month. The submissions docket is apparently due to reopen today, and will remain open to submissions until the 2nd of May – so you all have a month to get your opinions in there. You’ll hear more from us about the process of submitting to the docket over the next week or so. [DM]

On a related note, genetic counsellor Christine Patch and academic (and Unzipped guest blogger) Barbara Prainsack have penned a response to the above-mentioned FDA meeting for BioNews. Patch and Prainsack provide a welcome note of nuance to the discussion; their final two paragraphs are worth quoting in full:
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Analysing your own genome, bloggers respond to the FDA and more reporting on bogus GWAS results

Razib Khan, more known for his detailed low-downs of population biology and history, has written an important post on Gene Expression, explaining in careful detail exactly how to run some simple population genetic analysis on public genomes, as well as on your own personal genomics data. The outcome of the tutorial is an ADMIXTURE plot (like the one to the left), showing what proportion of your genome comes from different ancestral populations. This sort of analysis is not difficult, but it can often be hard to know how to start, so Razib’s post gives a good landing point for people who want to dig deaper into their own genomes.

This tutorial also ties in to some political ideas that Razib has been talking about since the recent call to allow access to genomic information only via prescription. If you are worried about losing access to your genome, one option is to ensure that you do not require companies to generate and interpret your genome. As sequencing, genotyping and computing prices fall, DIY genetics becomes more and more plausible. Learn to discover things about your own genome, and no-one will be able to take that away from you. [LJ]

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Guest post by Razib Khan: My personal genome

I’ve been following Razib Khan’s scholarly and analytical exploration of his family’s genetic history – using data from 23andMe – over at Gene Expression with increasing fascination. When last week he noted that his findings appeared to be (finally) converging on a consensus, I asked if he’d be willing to summarise his journey for Genomes Unzipped readers. Here it is. –DM.

I’ve always been interested in genetics, anthropology, and history. Many may perceive me to be a collector of obscure facts, but summing up infinitesimals does produce something substantial in aggregation. One of the most influential books in my life has been History and Geography of Genes. So with that, the shift from classical markers to uniparental lineages, and now to the dense SNP-chips, has been a boon for my own intellectual interests which reside in part at the intersection of history and population genetics.

However, I’ve never been deeply curious as to the history of my own personal genome. I’m not adopted. All four of my grandparents were ethnic Bengalis, albeit from relatively diverse communal backgrounds. I look typically South Asian. Genealogy has never been a family fascination, and I’m going to be honest and admit that until five years ago I didn’t even know the names of my grandparents (in the Bengali language there are distinctive terms for maternal and paternal grandparents, so this wasn’t needed). Both sides of my family are from the Comilla district of Bengal, and that’s all I really cared about (and I didn’t care that much, I don’t put much stock in “heritage” as determinative).

As for other yields of personal genomics, I was skeptical. My parents have many siblings, and many, many, cousins. I had a general sense of my risks for diseases through an inspection of the pedigree of my family and their medical histories. Additionally, many of the risk alleles have been identified in European study populations, and I wasn’t totally sure about the between-population portability of these inferences. And I won’t even address the fact that effect size of many of the markers isn’t something to shout home about.

But last spring Daniel alerted me to the 23andMe “DNA Day” sale. It was affordable, and at that point enough of the readers of my weblog had been typed that I kept getting questions as to my own background (e.g., my family has the title Khan, so there was a question as to whether I carried the “Genghis Khan haplotype”). So I bit. At the time I recall emailing Dan and being excited that I’d be told I likely had brown eyes and was 75% “European” and 25% “Asian.” When my results came back, I was in for a mild surprise. The proportion to the left are calculated by 23andMe’s “ancestry painting” algorithm. As you can see, I’m more than 25% “Asian.” My initial reaction was that this seemed a touch high, but no worries, I would ask around and see which other South Asians had such a high value. After dozens of instances of “gene sharing,” the answer came back: none.
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A decade of genomics, 60 new genomes, parenthood and sharing genetic data, and more on data return

To celebrate 10 years since the back-to-back publications of complete human genomes in Science and Nature, Science has published series of articles looking back at the last 10 years of genomics, and forward to the future. The article contains short essays from Francis Collins and Craig Venter, the former talking about some of the successes of medical sequencing (including giving a name and photograph to the exome-sequenced IBD patient I discussed a few weeks ago), and the latter discussing how far we still have to go before genomics can reach its potential. Baylor’s Richard Gibbs talks about how the large-scale technical discipline of genomics and the biological subject of genetics are starting to re-merge, after the Human Genome Project saw the two diverging, and there is an oddly inspiring comment from theologian Ronald Cole-Turning about how genomics is redefining our vision of humanity.

Of particular interest is an article by Eliot Marshall on why genomics hasn’t yet had a large effect on medical practice, and what needs to be done to allow the genomic revolution to trickle into medical care. He argues that scientists and doctors need to meet each other half way; scientists need to focus more on showing the direct clinical utility of genomics, whereas doctors need to be more ready to accept new technologies and discoveries, and adapt the way they practice medicine to make full use of them. [LJ]

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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…

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