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.