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