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These are the user uploaded subtitles that are being translated: 0 00:00:00,000 --> 00:00:00,318 1 00:00:00,318 --> 00:00:01,860 PETER REDDIEN: What we're going to do 2 00:00:01,860 --> 00:00:08,340 is work with something that has some characteristic that 3 00:00:08,340 --> 00:00:09,630 looks-- 4 00:00:09,630 --> 00:00:12,990 instead of being some kind of continuous characteristic, 5 00:00:12,990 --> 00:00:16,620 something that's a state, like a discrete state, where 6 00:00:16,620 --> 00:00:18,450 individuals either have the state or they 7 00:00:18,450 --> 00:00:19,903 don't have this state, all right? 8 00:00:19,903 --> 00:00:21,570 So that's one choice we're going to make 9 00:00:21,570 --> 00:00:25,480 to try to understand some of these core genetic principles. 10 00:00:25,480 --> 00:00:28,890 So what we're going to look at is the phenotype 11 00:00:28,890 --> 00:00:31,842 of an organism, which I'm going to begin some 12 00:00:31,842 --> 00:00:33,300 of our definitions today that we'll 13 00:00:33,300 --> 00:00:34,810 be using throughout the class. 14 00:00:34,810 --> 00:00:36,840 So phenotype refers to the characteristics 15 00:00:36,840 --> 00:00:51,610 of an organism, essentially all the characteristics 16 00:00:51,610 --> 00:00:52,840 of an organism. 17 00:00:52,840 --> 00:00:55,120 Its common usage will be referring 18 00:00:55,120 --> 00:00:59,740 to some characteristic under observation. 19 00:00:59,740 --> 00:01:05,290 So we'll start with some example that we'll use today. 20 00:01:05,290 --> 00:01:08,340 So let's say we want to figure this out 21 00:01:08,340 --> 00:01:12,540 and we decide to work with fruit flies. 22 00:01:12,540 --> 00:01:16,440 So we go outside, let's say, and we look for some trait. 23 00:01:16,440 --> 00:01:19,560 We need something that's variant between different individuals 24 00:01:19,560 --> 00:01:21,810 in a population. 25 00:01:21,810 --> 00:01:23,730 If everything looks the same, then we're 26 00:01:23,730 --> 00:01:26,230 never going to be able to figure anything out. 27 00:01:26,230 --> 00:01:28,350 So we look for some discrete state 28 00:01:28,350 --> 00:01:31,980 that looks different in some of the flies, all right? 29 00:01:31,980 --> 00:01:37,870 So now let's say we find a fly that is not moving very well. 30 00:01:37,870 --> 00:01:44,160 So we'll have a fruit fly that is paralyzed. 31 00:01:44,160 --> 00:01:48,750 32 00:01:48,750 --> 00:01:51,790 We see it moving a little bit so we know it's still alive, 33 00:01:51,790 --> 00:01:54,033 but it's not moving very well. 34 00:01:54,033 --> 00:01:55,950 So now we're going to try to-- the idea is now 35 00:01:55,950 --> 00:01:59,250 we have something different in this group of flies. 36 00:01:59,250 --> 00:02:02,460 And we want to use that to try to figure out 37 00:02:02,460 --> 00:02:04,845 how we explain this trait. 38 00:02:04,845 --> 00:02:07,680 39 00:02:07,680 --> 00:02:09,330 So what-- first, let's start with this. 40 00:02:09,330 --> 00:02:12,120 So why could the fly be paralyzed? 41 00:02:12,120 --> 00:02:14,990 Any hypotheses? 42 00:02:14,990 --> 00:02:17,080 Yeah. 43 00:02:17,080 --> 00:02:19,330 It could be injured. 44 00:02:19,330 --> 00:02:20,140 Any other ideas? 45 00:02:20,140 --> 00:02:23,444 46 00:02:23,444 --> 00:02:25,092 Yeah, in the back. 47 00:02:25,092 --> 00:02:26,588 STUDENT: [INAUDIBLE]. 48 00:02:26,588 --> 00:02:27,630 PETER REDDIEN: Say again. 49 00:02:27,630 --> 00:02:29,777 STUDENT: Mutations in actin. 50 00:02:29,777 --> 00:02:31,360 PETER REDDIEN: So it could be-- you've 51 00:02:31,360 --> 00:02:32,790 got a very specific hypothesis. 52 00:02:32,790 --> 00:02:34,900 It could be a mutation in actin filament protein. 53 00:02:34,900 --> 00:02:36,010 But we could generalize that and say 54 00:02:36,010 --> 00:02:37,968 it could have some mutation in something that's 55 00:02:37,968 --> 00:02:39,190 important for movement. 56 00:02:39,190 --> 00:02:42,910 So, yeah, it could have a mutation or multiple mutations. 57 00:02:42,910 --> 00:02:45,038 Anything else? 58 00:02:45,038 --> 00:02:45,538 Yeah. 59 00:02:45,538 --> 00:02:47,848 STUDENT: Side effect of some virus. 60 00:02:47,848 --> 00:02:48,890 PETER REDDIEN: Infection. 61 00:02:48,890 --> 00:02:50,348 It could be some kind of infection. 62 00:02:50,348 --> 00:02:53,720 So maybe this fly was infected by some virus or something 63 00:02:53,720 --> 00:02:56,000 else. 64 00:02:56,000 --> 00:02:56,952 Could be old. 65 00:02:56,952 --> 00:02:58,910 So anyway there's a number of things that could 66 00:02:58,910 --> 00:03:00,620 have happened to this fly. 67 00:03:00,620 --> 00:03:03,950 And we could group a number of these categories 68 00:03:03,950 --> 00:03:09,350 as environmental influences, like infection, accident, 69 00:03:09,350 --> 00:03:11,550 things like that. 70 00:03:11,550 --> 00:03:13,820 And then we could have a set of hypotheses 71 00:03:13,820 --> 00:03:18,560 around genetic, heritable-based mechanisms 72 00:03:18,560 --> 00:03:20,690 to explain this difference. 73 00:03:20,690 --> 00:03:23,990 And one of the complexities in real world genetics 74 00:03:23,990 --> 00:03:28,460 is that environmental exposures to things, experiences 75 00:03:28,460 --> 00:03:30,440 can influence some phenotypes. 76 00:03:30,440 --> 00:03:34,370 Nonetheless, most phe-- attributes of an organism 77 00:03:34,370 --> 00:03:36,960 have a very strong heritable component. 78 00:03:36,960 --> 00:03:41,350 We'll get into this later in the course. 5531

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