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course, for most of history, genetics was unknown territory. This | :00:00. | :00:00. | |
is the world Society of medicine in central London. And this institution | :00:00. | :00:08. | |
has been at the forefront of demoting innovation and the sharing | :00:09. | :00:11. | |
of information through the medical community. And in 2003, a community | :00:12. | :00:19. | |
received an explosion of information. The human gene project | :00:20. | :00:28. | |
was declared complete. And this knowledge paves the way for a far | :00:29. | :00:31. | |
deeper understanding of which genes cause which diseases. These days, | :00:32. | :00:37. | |
the talk is all about personalised medicine. But how useful is this | :00:38. | :00:48. | |
genetic information at the moment? After meeting a leading geneticist | :00:49. | :00:54. | |
in San Francisco, our reporter decided to embark on her own genetic | :00:55. | :00:59. | |
discovery journey. If you could unlock all of the secrets of your | :01:00. | :01:03. | |
health, how long you will live, what diseases right risk of developing, | :01:04. | :01:09. | |
would you? Stand to profit in a department is hoping that this day | :01:10. | :01:13. | |
will one day be a reality. It is studying 100 healthy people and | :01:14. | :01:16. | |
sequencing their DNA to see if they can predict when they will get sick | :01:17. | :01:22. | |
before they do. Leading the research is this professor. I'm always | :01:23. | :01:27. | |
keeping my devices very well charged. Here's a 1-man tracking | :01:28. | :01:31. | |
machine and along with sequencing his Geno, he wears nine different | :01:32. | :01:35. | |
devices every day to monitor his health outputs, including three | :01:36. | :01:38. | |
smart watchers and the radiation monitor. I have it continual glucose | :01:39. | :01:46. | |
monitor that sits just on top of my skin that continuously measures my | :01:47. | :01:52. | |
glucose levels. The Professor, it is already been a success of sorts. He | :01:53. | :01:56. | |
found he had a genetic predisposition to type 2 diabetes, | :01:57. | :01:59. | |
despite showing no typical signs of the condition. My gin and predicted | :02:00. | :02:05. | |
a number of risks, one of which was type 2 diabetes. As we doing all of | :02:06. | :02:10. | |
these medicines analyses, we discovered that my sugar which have | :02:11. | :02:14. | |
been running on perfectly fine actually shot through the roof, and | :02:15. | :02:20. | |
basically to the poor grass asked as fighters diabetic. The professor | :02:21. | :02:24. | |
keeps track of his Geno and to this personalised system. It looks | :02:25. | :02:28. | |
complex, but it is showing changes happening in his gingers everyday. | :02:29. | :02:33. | |
The outside affects my gene representation of my gin, and all | :02:34. | :02:38. | |
the different changes I have relative to the inner line here. | :02:39. | :02:45. | |
Inspired by Professor Snider, I signed up for 23 and May. Is one of | :02:46. | :02:52. | |
the better-known and cheap services that offers you insights to your | :02:53. | :02:57. | |
genes. Rather than looking at your whole Geno, it looks of the ones | :02:58. | :03:04. | |
that are significant. Civilly send a sample and you're left with this. Is | :03:05. | :03:11. | |
to getting back 3 billion bits of info, I received 100. These included | :03:12. | :03:17. | |
risk factors for indicators of Alzheimer's disease and are very | :03:18. | :03:20. | |
name breast cancer syndrome. The roles of 41 genetic variants that | :03:21. | :03:25. | |
produced different things from lactose intolerance to eye colour. I | :03:26. | :03:30. | |
was lucky to find out I didn't have anything significant to report. In | :03:31. | :03:34. | |
fact, the most interesting thing I found out with sales likely lactose | :03:35. | :03:37. | |
intolerant. I've changed my diet accordingly and that his made a | :03:38. | :03:42. | |
difference to my life. This small discovery increase my appetite for | :03:43. | :03:47. | |
more results. After reading forums, I discovered a site. It says it can | :03:48. | :03:56. | |
unlock more data for only $5. So I went for it. And rather wished I | :03:57. | :04:03. | |
hadn't. In ten minutes I was flooded with information on 20,000 Geno and. | :04:04. | :04:08. | |
These are marked as non- said, good and bad. And whatever this is? | :04:09. | :04:14. | |
Instead of nothing to report I seem to have hundreds of bad genes and | :04:15. | :04:18. | |
knows that a high risk of developing type 2 diabetes and various cancer. | :04:19. | :04:23. | |
Put my results to a clinical geneticists. I'm absolutely baffled | :04:24. | :04:30. | |
by the information that is in this report. You find any of this | :04:31. | :04:36. | |
information useful to me. As a clinical genesis would be looking at | :04:37. | :04:40. | |
your risk of disease, I would say there is nothing in here that we | :04:41. | :04:43. | |
would find clinically actionable in terms of setting up screening or | :04:44. | :04:51. | |
modifying your lifestyle. It may tell you something about where you | :04:52. | :04:55. | |
are and that spectrum of normality. Adam explained that the percentages | :04:56. | :04:58. | |
that scared me actually showed that these genes were fairly common in | :04:59. | :05:03. | |
the general population. It is also just one genetic aspect out of 3 | :05:04. | :05:10. | |
billion molecules and make up your Geno and and that single molecule is | :05:11. | :05:17. | |
not going to change that much. What you think my GP would say to me if I | :05:18. | :05:23. | |
brought them this? I think your GP was struggle to find anything in | :05:24. | :05:26. | |
here that they would find useful in managing your health. And more | :05:27. | :05:32. | |
comrades of insight into our genes may come from the NHS's 100,000 GM | :05:33. | :05:36. | |
project. Participants include people with rare diseases and their family. | :05:37. | :05:40. | |
The NHS wants us to form the base for a genomic medical service, | :05:41. | :05:44. | |
potentially offering new and more effective treatments and diagnoses. | :05:45. | :05:50. | |
And while it may be many years before we can access useful | :05:51. | :05:52. | |
information about her Geno 's cheaply on a smartphone, a future | :05:53. | :05:57. | |
when a greater role in the healthcare seems increasingly | :05:58. | :06:02. | |
possible. Will that was Jennifer, and this is Tony Young who was the | :06:03. | :06:08. | |
leader of innovation at the NHS, but you're also a surgeon. That is | :06:09. | :06:13. | |
correct. What do you make of giving a load of raw genetic data to the | :06:14. | :06:19. | |
public to read through. Because she seemed quite freaked out when she | :06:20. | :06:24. | |
read that. I can understand that and there are more and more of these | :06:25. | :06:29. | |
offerings coming from the private sector around doing some element of | :06:30. | :06:33. | |
your genomic screening and when you have that data, what do you do with | :06:34. | :06:37. | |
the? What sense can you make of the? And the explicit she had was one of | :06:38. | :06:40. | |
very confusion and there is an enormous mass of data. It is not | :06:41. | :06:44. | |
just the public who is confused, many clinicians as well don't know | :06:45. | :06:49. | |
what to do with large swathes of data coming out. And that is one of | :06:50. | :06:53. | |
the reasons in 2012, our Prime Minister launched 100,000 GM | :06:54. | :06:59. | |
project, which was a world first because it was a larger scale effort | :07:00. | :07:02. | |
the country had undertaken to that point to screen 100,000 whole Geno | :07:03. | :07:10. | |
and throw population to look at both cancer risk and rare genetic | :07:11. | :07:13. | |
disorders, so we at the NHS could say, the results of confusing data | :07:14. | :07:18. | |
but actually, we're going to take a major first step in a. Not relying | :07:19. | :07:21. | |
on a commercial company to give you some advice on the risk of diabetes | :07:22. | :07:26. | |
or our son is that may or may not be relevant. We learn very recently | :07:27. | :07:35. | |
that Google is using the deep mine project to analyse health data from | :07:36. | :07:38. | |
the NHS patients. The programme that you mentioned with a deep mind is | :07:39. | :07:43. | |
all around acute kidney injury. So patients are going to hospital and | :07:44. | :07:49. | |
have an altered blood test or a Syrian correction, but the early | :07:50. | :07:53. | |
stages of that. And you are still very well and your kidneys are very | :07:54. | :07:56. | |
well, but I can deteriorate over time. But you're waiting for a human | :07:57. | :08:02. | |
to look at that blood test result, and the whole point of using a | :08:03. | :08:05. | |
machine learning and artificial intelligence is that can we use is | :08:06. | :08:09. | |
to actually pick that up much earlier to prevent that person | :08:10. | :08:12. | |
getting kidney damage and renal failure. The data is there we're | :08:13. | :08:19. | |
just not using it. So we can deliver safer and better care. I think it is | :08:20. | :08:32. | |
really exciting. Over 47 million people in the world are suffering | :08:33. | :08:36. | |
from dementia and an ageing population means that that figure is | :08:37. | :08:39. | |
only likely to increase. So I've been looking at the technology | :08:40. | :08:42. | |
hoping to better the lives of those with the condition. This week, see | :08:43. | :08:52. | |
hero launches, a game designed to appeal to gamers but beneath the | :08:53. | :08:58. | |
surface is real science. While you may in think of the main feature | :08:59. | :09:00. | |
being memory loss, or the early things to be affected is actually | :09:01. | :09:06. | |
spatial awareness. So after collecting data about how healthy | :09:07. | :09:11. | |
minded players navigate the game and comparing that to how someone with | :09:12. | :09:14. | |
dementia plays, at benchmark can be created to both diagnose and assess | :09:15. | :09:21. | |
progression. Just two minutes spent on the app will generate the same | :09:22. | :09:26. | |
amount of data is five hours in a research lab. The design of the game | :09:27. | :09:33. | |
was built from the perspective of a scientist and what data they needed | :09:34. | :09:39. | |
to understand how people navigate in 3-D space. We should provide not | :09:40. | :09:44. | |
only a standardised measure of quantifying cells condition, and | :09:45. | :09:49. | |
also the condition to do several only. The key with this research is | :09:50. | :09:53. | |
understanding what goes wrong with spatial navigation and orientation. | :09:54. | :09:57. | |
To having understood that with this experiment in this big set of data, | :09:58. | :10:01. | |
we're armed to go on and do new research tell people working with | :10:02. | :10:06. | |
drug trials through example and to investigate particular drugs and how | :10:07. | :10:11. | |
they will have a good idea as to how people navigate. The boy was now a | :10:12. | :10:19. | |
man. The hope is that now in a crowded market of smartphone games, | :10:20. | :10:22. | |
the Apple appeal to enough people to make this mission possible. Bring | :10:23. | :10:30. | |
them back to his beloved see hero. That was Lara. And that is if the | :10:31. | :10:36. | |
shortcut of click this week. GOTO I play if you like to see the | :10:37. | :10:39. | |
full-length version. Jonas Twitter throughout the week. They differ | :10:40. | :10:42. | |
watching, see you soon. | :10:43. | :10:46. |