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These are the user uploaded subtitles that are being translated: 0 00:00:00,000 --> 00:00:03,610 PETER REDDIEN: Now you and I, all individuals in this room 1 00:00:03,610 --> 00:00:09,300 will vary roughly about one nucleotide for every 1,000 2 00:00:09,300 --> 00:00:10,470 nucleotides. 3 00:00:10,470 --> 00:00:12,660 So humans are very, very similar to one another 4 00:00:12,660 --> 00:00:14,280 at the DNA sequence level. 5 00:00:14,280 --> 00:00:18,290 One in every 1,000 nucleotides, on average, are different. 6 00:00:18,290 --> 00:00:24,537 But nonetheless, we have a 3.2 billion nucleotide genome. 7 00:00:24,537 --> 00:00:26,370 and there could be heterozygosity and so on. 8 00:00:26,370 --> 00:00:29,180 So there's roughly 4 to 5 million sequence differences 9 00:00:29,180 --> 00:00:32,369 between to any two individuals. 10 00:00:32,369 --> 00:00:33,242 So that's a lot. 11 00:00:33,242 --> 00:00:34,700 So if you do the sequencing, you've 12 00:00:34,700 --> 00:00:38,580 got 4 to 5 million differences to sort through. 13 00:00:38,580 --> 00:00:41,450 So it's literally a needle in a haystack to try to find this. 14 00:00:41,450 --> 00:00:44,010 15 00:00:44,010 --> 00:00:45,675 So how in the world do we do it? 16 00:00:45,675 --> 00:00:49,170 There's plenty of human disease genes that have been found. 17 00:00:49,170 --> 00:00:51,750 You could take model organisms, like our paralyzed fly, 18 00:00:51,750 --> 00:00:54,150 and you can find the gene and study it, 19 00:00:54,150 --> 00:00:55,210 try to do things with it. 20 00:00:55,210 --> 00:00:57,910 So how do we do it? 21 00:00:57,910 --> 00:01:01,210 Sequence lots of individuals. 22 00:01:01,210 --> 00:01:05,080 Well, so all the individuals are going 23 00:01:05,080 --> 00:01:07,930 to have a lot of variants, and if there's some clonal origin, 24 00:01:07,930 --> 00:01:13,480 let's say, of the variant like one paralyzed fly, or one 25 00:01:13,480 --> 00:01:16,420 mutated individual, then all of those variants 26 00:01:16,420 --> 00:01:19,540 will be present in all the descendants, 27 00:01:19,540 --> 00:01:22,270 depending upon the nature of crosses that have happened. 28 00:01:22,270 --> 00:01:23,990 But you could start working with that, 29 00:01:23,990 --> 00:01:27,680 the nature of crosses that have happened, to think about this. 30 00:01:27,680 --> 00:01:29,680 But if you sequence a lot of clonal individuals, 31 00:01:29,680 --> 00:01:31,360 you just find the same polymorphisms over and over 32 00:01:31,360 --> 00:01:32,240 again. 33 00:01:32,240 --> 00:01:35,860 But there is something to what you're getting at. 34 00:01:35,860 --> 00:01:37,900 Any other suggestions? 35 00:01:37,900 --> 00:01:40,150 So the suggestion is look at how far apart things are. 36 00:01:40,150 --> 00:01:41,110 But what things are you looking at 37 00:01:41,110 --> 00:01:42,360 to see how far apart they are? 38 00:01:42,360 --> 00:01:45,360 You're kind of on the right track. 39 00:01:45,360 --> 00:01:47,100 OK, so look at other things that are 40 00:01:47,100 --> 00:01:49,600 different in that individual that 41 00:01:49,600 --> 00:01:51,100 might be going with that trait? 42 00:01:51,100 --> 00:01:51,400 STUDENT: Yeah. 43 00:01:51,400 --> 00:01:52,030 PETER REDDIEN: OK. 44 00:01:52,030 --> 00:01:53,643 So that's good, and in general, that's 45 00:01:53,643 --> 00:01:55,810 the approach taken in a lot of human genetics, which 46 00:01:55,810 --> 00:01:57,160 we'll get into. 47 00:01:57,160 --> 00:02:00,080 Did you have a-- 48 00:02:00,080 --> 00:02:03,050 So the suggestion was, if the location of another gene, 49 00:02:03,050 --> 00:02:03,800 can you use that? 50 00:02:03,800 --> 00:02:05,000 OK, that's great. 51 00:02:05,000 --> 00:02:07,910 So what you could do, so you remember the genetic mapping 52 00:02:07,910 --> 00:02:12,410 with yellow and vermillion and stuff, miniature wings on the X 53 00:02:12,410 --> 00:02:13,920 chromosome. 54 00:02:13,920 --> 00:02:16,730 Let's say you find, oh, I'm one centimorgan away 55 00:02:16,730 --> 00:02:18,800 from one of these, and the other one 56 00:02:18,800 --> 00:02:22,250 has been discovered from other work. 57 00:02:22,250 --> 00:02:24,410 Well, then I might roughly know the area 58 00:02:24,410 --> 00:02:26,630 of the genome this is in, because you've 59 00:02:26,630 --> 00:02:30,260 placed some genetic marker onto a physical map, 60 00:02:30,260 --> 00:02:31,850 a reference sequence. 61 00:02:31,850 --> 00:02:34,430 And I'm in between these known-- 62 00:02:34,430 --> 00:02:37,010 from the genetic crosses, these known genetic markers. 63 00:02:37,010 --> 00:02:38,927 And I know their position on the physical map, 64 00:02:38,927 --> 00:02:41,360 so I know I must be in this region of the physical map. 65 00:02:41,360 --> 00:02:42,420 So that's good. 66 00:02:42,420 --> 00:02:44,640 That can start narrowing it down. 67 00:02:44,640 --> 00:02:46,790 Great. 68 00:02:46,790 --> 00:02:50,220 So I think we're getting on the right track. 69 00:02:50,220 --> 00:02:57,140 Let's now dive in to trying to do this in as efficient a way 70 00:02:57,140 --> 00:02:59,290 as we can think of. 5160

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