Session 4: How is Evolution Measured
Transcript of Part 5: What Can We Learn From Sequencing Our Genomes?
00:00:12.26 I'm David Haussler, scientific director 00:00:16.03 of the UC Santa Cruz Genomics Institute, 00:00:18.19 and Howard Hughes Medical Institute investigator. 00:00:21.13 It's my pleasure to be able to speak to you 00:00:24.26 on the great questions in biology. 00:00:27.13 I want to have you imagine 00:00:32.17 that you just got your genome sequenced. 00:00:35.06 Now, when we sequenced the first genome 00:00:37.21 in the year 2000, it cost about $300 million, 00:00:41.27 just for the sequencing reagents and activities. 00:00:44.25 But nowadays, it's a couple thousand bucks. 00:00:47.14 So let's assume, you went out and got your sequence done. 00:00:52.18 Now looking at that, it may be hard to interpret 00:00:58.23 but it's got to be a moving experience, 00:01:03.03 to look at that DNA sequence and think about 00:01:06.09 how it got there. 00:01:08.27 In particular the DNA in your genome 00:01:13.00 was passed on from generation to generation 00:01:16.11 for eons. 00:01:18.04 We all come out of the primordial ooze somewhere 00:01:21.24 billions of years ago 00:01:23.29 And it's stunning to think that a record 00:01:27.11 of many of those evolutionary events 00:01:29.24 is still present in the genomes today. 00:01:33.02 So what would you do? What would you look a 00:01:38.17 to start to understand where you came from? 00:01:41.19 And what is specifically interesting about your genome 00:01:46.14 versus all the other genomes on the planet? 00:01:49.09 Probably the first thing you would think about 00:01:54.16 in terms of the way that DNA is inherited 00:01:57.23 from parent to offspring is: 00:02:00.14 are there any new elements of your genome 00:02:04.03 that weren't even in your parents? 00:02:05.21 And statistics say there will be a few 00:02:09.00 So there will be a few changes in the way the DNA 00:02:12.16 was copied. You could think of them as errors, 00:02:16.01 but you could also think of them as fortuitous events 00:02:19.14 that caused something different in your genome 00:02:23.01 that wasn't in either of your parent's genome. 00:02:24.27 Those would be interesting, certainly. 00:02:27.18 And there are probably only a very small handful of those. 00:02:31.27 The next thing you would think about is 00:02:34.21 is there something I inherited from my parents 00:02:40.05 that's just specific to my family in some sense? 00:02:43.21 Maybe it's something special that happened in 00:02:47.12 a great grandparent and has been passed down 00:02:51.11 to me through all of these generations. 00:02:53.20 Now this is the very stuff of genetics. 00:02:57.20 To think about this, in particular, medical genetics 00:03:00.25 you would be very concerned if this actually 00:03:04.14 made you prone to a disease 00:03:05.18 You might also be protected from a disease 00:03:10.09 by a special version of a gene that's in your genome 00:03:13.13 that's specific, or private, to your specific family. 00:03:17.21 That would be exciting. 00:03:20.28 And as we start to get the era from just one genome 00:03:25.27 in the year 2000, to the coming era of millions of genomes, 00:03:29.25 we will be able to, by comparing genomes, 00:03:33.14 understand what's specific to certain families, 00:03:37.19 what's specific to certain ethnic groups, 00:03:39.24 what's specific to humans in general, but not 00:03:45.24 with other species. 00:03:47.09 It's an enormous computational problem 00:03:50.18 to compare all of these genomes, and this is probably 00:03:53.21 the most significant challenge facing the computational part 00:03:58.25 of genetics and genomics today. 00:04:01.20 If you understood what was specific to humans 00:04:05.21 that would be fascinating, you could start to think about 00:04:08.20 what happened since we diverged from our common ancestor 00:04:12.14 with our closest species, the neanderthal. 00:04:16.22 While the neanderthals are extinct, 00:04:19.17 we were able to sequence DNA from their bones 00:04:23.18 and hence, get an idea of their genomes looked like. 00:04:27.06 And we find that there are more than a million changes 00:04:31.27 that occurred in the human lineage 00:04:34.26 since we diverged from our common ancestor 00:04:37.18 to neanderthal. 00:04:38.28 What an exciting project at this point, to try to understand 00:04:42.05 those changes that almost everybody 00:04:47.24 almost all of humankind share, 00:04:49.22 as opposed to and distinguish them from the neanderthal. 00:04:54.23 Going back further, about 5-6 million years ago, 00:05:01.05 we shared a common ancestor with the chimpanzee 00:05:03.25 Since that time, there have been roughly 15 million changes 00:05:07.23 in our genome. 00:05:09.15 Which ones actually account for the difference 00:05:12.22 between a human and a chimp? 00:05:14.10 This is a substantial difference 00:05:16.22 and remarkably, we still know very little 00:05:19.29 about which of those changes actually make that huge difference 00:05:25.04 between a human and a chimp. 00:05:27.04 One thing that you'll run into, in this quest, 00:05:31.06 is the fact that most of these changes 00:05:35.16 are probably not important 00:05:39.03 in some sense. 00:05:40.18 If you look at the structure of your DNA 00:05:45.08 and in particular, if you look at it from this perspective 00:05:48.14 of going back and looking at where the DNA came from 00:05:52.16 what it's history is, 00:05:54.08 you see that there are some parts of your genome 00:05:56.28 that are shared with virtually all other life 00:06:00.11 on the planet. 00:06:01.21 The genes and the DNA in the genes 00:06:03.24 and the ribosome sequences, 00:06:05.20 DNA polymerase. Other fundamental molecules 00:06:11.07 that make life itself possible 00:06:13.02 are remarkably little changed 00:06:15.07 over the eons of evolutionary time. 00:06:17.26 And so when you look at one of those bases in your genome 00:06:21.22 you can sit back and say, "wow, that's a really ancient base!" 00:06:26.19 "That base of DNA was passed on to me 00:06:30.21 all the way back from the beginning 00:06:33.25 billions of years, copied faithfully 00:06:36.29 again and again and again. 00:06:38.15 And now it is a gift to me 00:06:41.18 so that my cells work." 00:06:45.01 In between that are all stages of evolutionary innovation. 00:06:51.00 So when you look at your genome 00:06:53.01 you'll find genes that were created essentially 00:06:58.12 as an evolutionary process in the bilaterian animals, 00:07:02.24 for example. 00:07:04.07 The set of animals that have bilateral symmetry 00:07:07.23 is a huge collection of animals on the planet 00:07:10.27 that they didn't exist 2 billion years ago. 00:07:13.17 So all of their genetic innovations happened 00:07:17.11 after that period. 00:07:19.06 If you look at vertebrates, 00:07:20.24 animals with a backbone, 00:07:22.12 they didn't exist 800 million years ago, but now 00:07:27.24 we can find all of the different innovations 00:07:31.03 that are specific to the vertebrates, 00:07:33.06 the backboned animals, that don't exist in other animals. 00:07:36.02 And each one of these beautiful genetic variations 00:07:39.20 happened at a particular time 00:07:42.05 in the marvelous history of life. 00:07:44.03 So when you're looking at every gene in your genome, 00:07:47.04 you can say "Aha! That's a bilaterian innovation." 00:07:51.02 "Oh, and this one was invented by vertebrates." 00:07:54.17 "And maybe this one was invented by primates." 00:07:57.27 "And maybe this one is specific to apes." 00:08:00.22 Understanding this, is probably the greatest challenge 00:08:07.16 to genomics, going forward at this point. 00:08:11.19 And we have an extraordinary opportunity 00:08:13.28 to look at every base in the genome 00:08:17.05 for the first time. 00:08:18.28 And more importantly, to compare it 00:08:22.23 to the bases in other genomes. 00:08:25.06 The lesson we've learned from first sequencing the human genome 00:08:30.04 in the year 2000, and subsequently looking 00:08:33.17 at the first chimpanzee genome, 00:08:35.09 the first mouse genome, 00:08:36.25 the first rat genome, 00:08:38.17 the first dog genome, 00:08:40.07 is that no genome is ever understandable 00:08:44.08 in isolation. 00:08:45.12 Every time we sequence the genome of a new species 00:08:48.25 we learn more about the genomes that we had previously 00:08:52.28 sequenced from other species. 00:08:55.08 And that is precisely because 00:08:57.20 we share a common heritage 00:08:59.26 and because we are sculpted by evolution. 00:09:03.07 By looking at these patterns of conservation, 00:09:06.27 and change within our genome, 00:09:08.18 we can often decode something about the function 00:09:13.07 of a region of DNA. 00:09:15.00 For example, if it codes for protein sequence, 00:09:19.03 then it has to have this triplet pattern of codons 00:09:22.25 and you'll find that while there are changes in the region 00:09:26.25 they preserve this fundamental property of being able to code 00:09:31.00 for a protein, and we can see that clearly 00:09:33.25 in the pattern of changes which are allowed 00:09:35.27 or not allowed. 00:09:38.00 So that gives us a window, 00:09:40.19 just by studying comparisons between many pieces of DNA, 00:09:44.19 into the function of those pieces of DNA. 00:09:47.17 But we're only seeing the tip of the iceberg here. 00:09:51.13 We're very much at the beginning 00:09:53.12 of a long journey, now that we're in the genomics era 00:09:57.13 and being able to look at all of these genomes 00:10:00.09 together, of decoding them through 00:10:03.11 their history, through comparison. 00:10:05.29 I hope you will consider taking this journey 00:10:09.03 with us. It's a journey that needs not only biologists, 00:10:14.16 but computer scientists. 00:10:15.24 Right now the world is struggling 00:10:18.16 to be able to write the software code 00:10:21.01 to create the computer architecture 00:10:22.29 to be able to compare the full genomes 00:10:26.01 from hundreds, or thousands, of different species 00:10:30.10 This is the very edge of our capabilities at this point. 00:10:35.13 And we can look forward to great innovations, 00:10:38.24 both in terms of computers and algorithms, 00:10:43.15 big data, cloud computing, all of these 00:10:46.18 things will have a say, along with traditional 00:10:50.17 fields like biology, molecular biology, biochemistry, 00:10:54.12 and evolution population genetics. 00:10:57.26 So it's a great time to be involved in this, 00:11:01.09 and I invite you to this wonderful adventure.