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The UK’s ambitious plan to sequence 100,000 whole genomes of NHS patients over the next 3-5 years, announced by the UK Prime Minister in December last year, sparked interest and curiosity throughout the UK genetics community. Undeterred by the enormity of the task, a new company, Genomics England Limited (GeL), was set up in June of this year by the Department of Health, tasked with delivering the UK100K genome project. Yesterday, they held what I’m sure will be the first of many ‘Town Hall’ engagement events, to inform and consult clinicians, scientists, patients and the public on their nascent plans.

So what did we learn? First, let’s be clear on the aims. GeL’s remit is to deliver 100,000 whole genome sequences of NHS patients by the end of 2017. No fewer patients, no less sequence. At its peak, GeL will produce 30,000 whole genome sequences per year. There’s no getting away from the fact that this is an extremely ambitious plan! But fortunately, the key people at GeL are under no illusions about the fact that theirs is a near impossible task. Continue reading ‘Genomics England and the 100,000 genomes’

Many of the Genomes Unzipped team are spending the week at the American Society of Human Genetics meeting in San Francisco. This year the coverage of the meeting on Twitter is more intense than ever before, and social media is becoming an increasingly mainstream component of the conference. Chris Gunter, Jonathan Gitlin, Jeannine Mjoseth, Shirley Wu and I will be presenting a workshop on social media use for scientists this evening, and we prepared these guidelines for those interested in live coverage of meetings.

1. Check the conference social media guidelines first.
   If there aren’t any, ask an organizer what the rules are. If there is no formal policy, you may want to take the initiative and ask speakers if they’re OK with their talks being tweeted.
2. Use the right #hashtag when you tweet.
   This ensures that everything written about a meeting is aggregated in a single channel. When you search a hashtag it filters those posts for you.
3. Remember that people are listening.
   Twitter is a public conversation. Don’t say anything you wouldn’t be prepared to tell the speaker to their face. Also, bear in mind that your boss and potential employers may be following.
4. Remember that people are listening who aren’t at the meeting.
   In general, leave off the conference hashtag for in-jokes and social chatter unless it’s likely to be genuinely entertaining to outsiders.
5. Be careful tweeting new findings.
   If a speaker is presenting unpublished data, don’t write about it unless you’re sure they’re happy to share.
6. Do your best to ensure that your tweets don’t misrepresent presented material.
   Add as much context as you can, and actively correct misunderstandings that arise about something you tweet.
7. Add value by contributing your specific area(s) of expertise to provide insight into presented material.
   Don’t just be the fifth person to tweet the easy soundbite from the plenary; instead, explain the unappreciated but profound scientific significance of their fourteenth slide.
8. At the same time, don’t tweet everything a speaker says.
   One to three key take-home messages per talk is usually enough, unless a presentation is particularly fascinating.
9. Don’t swamp the hashtag by quote-tweeting everyone else.
   Use the official retweet function, or “break the hashtag” (for instance, delete the # character) in your quote-tweets.
10. If you’re organizing a conference, be proactive with a social media policy.
    Make sure both the presenters and the audience at the meeting are aware in advance what this policy is.

This week sees the publication of a large study of the genetics of multiple sclerosis. A consortium of 23 research groups gathered together data on nearly 10,000 MS suffers, and discovered 29 new genetic variants that contribute to disease risk. Overall, genetic variants for MS can now explain around 20% of the overall heritability of the disease, and these genetic variants highlight pathways that are likely to be important in the disease (such as T-helper-cell differentiation). Notably, this study is published in Nature, which is pretty rare for genome-wide association studies such as this. Perhaps related to this is the wonderful degree of detail included in the figures, such as in the ancestry plots of individuals in the study (see left). It is also surprisingly readable, containing just 4 pages of main article, with the nitty-gritty relegated to 100 pages of supplemental text. [LJ]

The Genetics and Public Policy Center have released an updated version of their list of direct-to-consumer genetic testing companies. You can view the list as a rather user-unfriendly massive PDF matrix of companies versus diseases tested here. The list is certainly not as useful as it could be – for instance, there are no indications of test price or quality, and whole-genome sequencing companies are shown as not testing for any disease, rather than (effectively) testing for all diseases – but it would be a good starting point for a crowd-sourcing project to produce a more comprehensive database. Hmmm… [DM]

Konrad Karczewski and colleagues from Stanford have put together a very handy set of online tools for analysing personal genomic data. The tools work within your browser (Chrome and FireFox only, so the ~18% of you who continue to use Internet Explorer now have yet another incentive to change), meaning your genetic data never actually leaves your computer. They currently work with raw, unzipped data from 23andMe and Lumigenix. The tools were developed initially for use in Stanford’s pioneering Genomics and Personalized Medicine elective course for graduate and medical students, in which students had the opportunity to explore their own 23andMe or Lumigenix data. Karczewski has some background over at his personal blog.

Once you’ve pointed Interpretome to your raw data file (top right-hand corner) and assigned your ancestry you can start playing with the tools – for instance, you can calculate your type 2 diabetes risk or warfarin dose, or estimate the fraction of your genome inherited from Neandertal (see image above for my result). A caveat: I’m writing this post without carefully checking the output from any of these analyses, so as always in personal genomics, interpret your results with caution.

I suspect one of the more popular suites of tools will be the PCA package, which allow you to place your genetic data in the context of worldwide patterns of genetic variation. Here the authors have pre-calculated the crucial information (the PCA loadings) for each SNP in the 23andMe data-set, allowing them to very quickly calculate your position in a worldwide genetic map containing thousands of individuals. Here’s my 23andMe v3 data (black square) projected onto a genetic map of Europe created with POPRES samples. The picture isn’t quite as pretty as the one in the 2008 Nature paper using the same cohort – the Interpretome team haven’t applied the same extensive filters to remove extraneous features from the data have had to work with the smaller number of SNPs that overlap with the 23andMe v3 chip, and you need to plot PC1 vs PC4 before you start seeing something that resembles a map of Europe – but it’s enough to give you a sense as to where you fit. I was unsurprised to find myself sitting smack in the middle of the British cluster:

Anyway, go and check it out, and send it to your friends. We’re delighted to see such a handy package released free to the public – kudos to everyone involved in putting the website together. We’ll likely be posting a more thorough review of the site once we’ve had time to test the tools out on a range of Genomes Unzipped data-sets.

We’ve finally found the time to formally set up copyright for Genomes Unzipped. As you can see at the bottom of this post, most content on the Genomes Unzipped website is now available under a Creative Commons Attribution-ShareAlike 3.0 Unported License, meaning that it can be reused so long as proper attribution is given, and that the resulting product is distributed under the same or similar license. We’ve made an exception in one special case: all of our raw genetic data are now available under the Creative Commons CC0 public domain option. That means we waive copyright to our genetic information, so it can be used for any purpose without restriction or attribution.

One of our goals in this project is to encourage people to develop novel tools for mining information from genetic data, using our data as a testing ground. Adding the CC0 license makes it explicit that our data are intended to be a community resource: if you want to use them to test your new analysis tool, or to see how much data you can expect to get from a genetic test, or even to make an artwork, feel free.

Finally, a teaser: the pool of genetic data available from the group is set to expand over the next few weeks. Stay tuned…

Genomes Unzipped content is available for reuse under a Creative Commons Attribution-ShareAlike 3.0 Unported License.