Would you like to inspect the original subtitles? 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
Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.