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These are the user uploaded subtitles that are being translated: 0 00:00:00,000 --> 00:00:01,090 1 00:00:01,090 --> 00:00:01,900 PETER REDDIEN: OK. 2 00:00:01,900 --> 00:00:03,550 So now, let's come back to our cross, 3 00:00:03,550 --> 00:00:07,330 with this concept in mind, and think about what 4 00:00:07,330 --> 00:00:08,964 happens to the next generation. 5 00:00:08,964 --> 00:00:13,220 6 00:00:13,220 --> 00:00:14,810 We do this cross with the sibling. 7 00:00:14,810 --> 00:00:24,220 8 00:00:24,220 --> 00:00:25,870 And we need to predict, then, what 9 00:00:25,870 --> 00:00:38,070 happens in this F2 generation, with respect to the genotypes. 10 00:00:38,070 --> 00:00:41,460 Now, how do we predict what happens? 11 00:00:41,460 --> 00:00:44,430 Now, to do this, we're going to use Mendel's first law, 12 00:00:44,430 --> 00:00:47,870 the law of equal segregation. 13 00:00:47,870 --> 00:00:53,900 Equal segregation refers to this principle from Mendel 14 00:00:53,900 --> 00:00:56,450 that allele pairs separate equally 15 00:00:56,450 --> 00:00:58,820 during gamete formation, and then 16 00:00:58,820 --> 00:01:01,310 unite randomly at fertilization. 17 00:01:01,310 --> 00:01:04,879 18 00:01:04,879 --> 00:01:07,130 So when this individual is producing gametes-- 19 00:01:07,130 --> 00:01:08,990 if it's a male, sperm-- 20 00:01:08,990 --> 00:01:11,990 then it's sort of random sampling of these two 21 00:01:11,990 --> 00:01:14,080 alleles of the gene. 22 00:01:14,080 --> 00:01:16,600 So, it's 50% chance at a given gamete-- 23 00:01:16,600 --> 00:01:19,240 sperm cell, say-- gets this allele, 50% chance 24 00:01:19,240 --> 00:01:21,350 it gets that allele. 25 00:01:21,350 --> 00:01:25,558 Same thing for gamete formation in the female, 26 00:01:25,558 --> 00:01:27,350 and then when they unite, it doesn't really 27 00:01:27,350 --> 00:01:28,850 matter what the nature of the allele 28 00:01:28,850 --> 00:01:31,610 is for the probability of uniting. 29 00:01:31,610 --> 00:01:33,770 OK, so let's just write this out a little bit more. 30 00:01:33,770 --> 00:01:45,770 31 00:01:45,770 --> 00:01:50,600 So our F1 gametes, we can look at the probability of a given 32 00:01:50,600 --> 00:01:51,283 gamete. 33 00:01:51,283 --> 00:01:53,450 We can say, what's the probability of a given gamete 34 00:01:53,450 --> 00:01:58,642 here, having the TS allele? 35 00:01:58,642 --> 00:02:01,100 And, what's the probability of having the wild-type allele? 36 00:02:01,100 --> 00:02:11,000 37 00:02:11,000 --> 00:02:12,890 Probability, if you sample a random gamete, 38 00:02:12,890 --> 00:02:14,060 would be 0.5 for each. 39 00:02:14,060 --> 00:02:15,740 So, these are the expected frequencies 40 00:02:15,740 --> 00:02:20,460 of these gamete classes would be being produced. 41 00:02:20,460 --> 00:02:20,960 OK. 42 00:02:20,960 --> 00:02:24,710 43 00:02:24,710 --> 00:02:26,270 So, we're going to make these gametes 44 00:02:26,270 --> 00:02:28,560 and then unite them, randomly. 45 00:02:28,560 --> 00:02:30,935 So, let's think about the combinations that could emerge. 46 00:02:30,935 --> 00:02:35,610 47 00:02:35,610 --> 00:02:38,835 Let's say this is the male, and this is the female. 48 00:02:38,835 --> 00:02:41,410 49 00:02:41,410 --> 00:02:44,500 We could get a para(TS) allele from the sperm, 50 00:02:44,500 --> 00:02:47,080 and a para(TS) allele from the egg. 51 00:02:47,080 --> 00:02:48,543 That's one possibility. 52 00:02:48,543 --> 00:02:51,880 53 00:02:51,880 --> 00:02:53,710 That's one possible F2 genotype. 54 00:02:53,710 --> 00:02:57,010 We could get a para(TS) allele from the sperm, 55 00:02:57,010 --> 00:02:58,870 and a para wild-type allele from the egg. 56 00:02:58,870 --> 00:03:04,887 57 00:03:04,887 --> 00:03:06,470 We could get the para wild-type allele 58 00:03:06,470 --> 00:03:08,900 from the sperm, para(TS) from the egg. 59 00:03:08,900 --> 00:03:16,192 60 00:03:16,192 --> 00:03:18,400 And finally, we could get two para wild-type alleles. 61 00:03:18,400 --> 00:03:26,400 62 00:03:26,400 --> 00:03:28,830 Those are all the possible combinations 63 00:03:28,830 --> 00:03:31,630 of what could happen. 64 00:03:31,630 --> 00:03:34,170 And then, how do we determine our expected frequencies 65 00:03:34,170 --> 00:03:37,400 of these classes? 66 00:03:37,400 --> 00:03:40,370 Well, using this principle of equal segregation 67 00:03:40,370 --> 00:03:43,340 where they're randomly uniting, then we 68 00:03:43,340 --> 00:03:46,730 would expect these classes to emerge at equal frequency. 69 00:03:46,730 --> 00:03:48,950 One way of looking at this would be 70 00:03:48,950 --> 00:03:56,480 to say, what is the probability of event A happening, 71 00:03:56,480 --> 00:04:02,590 and the probability of event B happening. 72 00:04:02,590 --> 00:04:04,340 So, if what has happened is, you inherited 73 00:04:04,340 --> 00:04:08,660 a para(TS) allele from the father and a para(TS) allele 74 00:04:08,660 --> 00:04:14,030 from the mother, then this would be event A and event B. 75 00:04:14,030 --> 00:04:17,240 And if event A and B are independent events, then 76 00:04:17,240 --> 00:04:20,329 the probability of getting event A and B 77 00:04:20,329 --> 00:04:23,270 is equal to the probability of event A times 78 00:04:23,270 --> 00:04:32,590 the probability of event B. 79 00:04:32,590 --> 00:04:36,280 This is the product rule of probability. 80 00:04:36,280 --> 00:04:41,933 81 00:04:41,933 --> 00:04:43,600 So it's like, if you're flipping a coin, 82 00:04:43,600 --> 00:04:46,140 you've got a 50% chance of being heads or tails. 83 00:04:46,140 --> 00:04:47,790 You say, what are the odds of getting 84 00:04:47,790 --> 00:04:50,860 a heads on the first slip and a heads on the second flip? 85 00:04:50,860 --> 00:04:52,680 0.5 chance of getting a heads. 86 00:04:52,680 --> 00:04:55,170 On the second flip, 0.5 chance of getting a heads. 87 00:04:55,170 --> 00:05:00,060 So it'd be 0.5 times 0.5. 88 00:05:00,060 --> 00:05:01,080 OK. 89 00:05:01,080 --> 00:05:05,780 Similarly, here, the probability of getting para(TS), 90 00:05:05,780 --> 00:05:15,310 para(TS) is 0.5 times 0.5, equals 0.25. 91 00:05:15,310 --> 00:05:24,780 Same for all of these classes, 0.25. 92 00:05:24,780 --> 00:05:25,280 OK. 93 00:05:25,280 --> 00:05:27,890 94 00:05:27,890 --> 00:05:37,180 Now, you will notice that these two classes are essentially 95 00:05:37,180 --> 00:05:38,110 the same genotype. 96 00:05:38,110 --> 00:05:45,630 97 00:05:45,630 --> 00:05:46,140 OK? 98 00:05:46,140 --> 00:05:50,430 para(TS), para wild-type, one copy of each allele. 99 00:05:50,430 --> 00:05:56,640 And so, we see that these genotypes 100 00:05:56,640 --> 00:06:02,070 exist at relative frequencies of 1 to 2 to 1. 101 00:06:02,070 --> 00:06:06,030 102 00:06:06,030 --> 00:06:07,470 So we'll have a 1 to 2 to 1 ratio 103 00:06:07,470 --> 00:06:11,440 of these different genotype classes expected. 104 00:06:11,440 --> 00:06:14,290 OK. 105 00:06:14,290 --> 00:06:22,520 Now, if we think about the phenotypes, 106 00:06:22,520 --> 00:06:24,380 we see that all of these-- 107 00:06:24,380 --> 00:06:28,290 108 00:06:28,290 --> 00:06:32,490 what would the phenotype of this first class be? 109 00:06:32,490 --> 00:06:33,758 Anybody? 110 00:06:33,758 --> 00:06:34,550 STUDENT: Paralyzed. 111 00:06:34,550 --> 00:06:35,592 PETER REDDIEN: Paralyzed. 112 00:06:35,592 --> 00:06:37,340 This second class? 113 00:06:37,340 --> 00:06:38,810 Not paralyzed. 114 00:06:38,810 --> 00:06:39,960 This class, not paralyzed. 115 00:06:39,960 --> 00:06:45,050 OK, so all three of these are not paralyzed. 116 00:06:45,050 --> 00:06:52,340 117 00:06:52,340 --> 00:06:53,420 OK. 118 00:06:53,420 --> 00:06:57,875 So, our expected phenotype ratios then are 1 to 3. 119 00:06:57,875 --> 00:07:01,200 120 00:07:01,200 --> 00:07:01,740 OK? 121 00:07:01,740 --> 00:07:05,790 These three are expected to have the same phenotype. 122 00:07:05,790 --> 00:07:08,600 123 00:07:08,600 --> 00:07:12,500 And in our example data, we got a 1 to 3 ratio. 124 00:07:12,500 --> 00:07:14,920 All right. 125 00:07:14,920 --> 00:07:16,780 So, that's sort of what we would expect 126 00:07:16,780 --> 00:07:19,930 with this hypothesis one. 127 00:07:19,930 --> 00:07:22,630 And you'll see that this phenotype class 128 00:07:22,630 --> 00:07:27,430 of not paralyzed by this model, by this hypothesis, 129 00:07:27,430 --> 00:07:28,600 is sort of-- 130 00:07:28,600 --> 00:07:31,630 this 1 to 3 ratio-- is sort of masking a true 1 to 2 to 1 131 00:07:31,630 --> 00:07:35,950 ratio of genotypes, where these not paralyzed flies could 132 00:07:35,950 --> 00:07:38,140 fall into two categories. 133 00:07:38,140 --> 00:07:42,790 Can anyone describe, or propose an experiment 134 00:07:42,790 --> 00:07:45,460 that you could do that would distinguish between these two 135 00:07:45,460 --> 00:07:46,240 genotype classes? 136 00:07:46,240 --> 00:07:48,568 137 00:07:48,568 --> 00:07:50,360 Keep in mind, we have two breeding strains. 138 00:07:50,360 --> 00:07:51,652 We talked about this last time. 139 00:07:51,652 --> 00:07:55,730 So yeah, you could breed the not paralyzed flies 140 00:07:55,730 --> 00:07:59,692 to a paralyzed strain. 141 00:07:59,692 --> 00:08:02,150 And what you see is, they'd break down into two categories. 142 00:08:02,150 --> 00:08:04,025 I'll let you work this out for yourself after 143 00:08:04,025 --> 00:08:05,610 lecture some time. 144 00:08:05,610 --> 00:08:09,000 One class would give only not paralyzed flies. 145 00:08:09,000 --> 00:08:11,760 The other class would get 50% of the progeny to be paralyzed. 146 00:08:11,760 --> 00:08:13,130 OK. 147 00:08:13,130 --> 00:08:14,400 OK, good. 148 00:08:14,400 --> 00:08:17,120 149 00:08:17,120 --> 00:08:20,380 So that's hypothesis one. 9895

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