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These are the user uploaded subtitles that are being translated: 0 00:00:00,000 --> 00:00:01,583 PETER REDDIEN: Let's move forward then 1 00:00:01,583 --> 00:00:11,140 to human Mendelian traits, part two of the lecture. 2 00:00:11,140 --> 00:00:22,250 3 00:00:22,250 --> 00:00:24,200 All right, so here's our idea. 4 00:00:24,200 --> 00:00:26,093 At this point, we've looked at pedigrees, 5 00:00:26,093 --> 00:00:28,010 and we're going to move forward with pedigrees 6 00:00:28,010 --> 00:00:31,730 and try to use them to identify the genetic basis of diseases. 7 00:00:31,730 --> 00:00:33,230 So let's just start at the beginning 8 00:00:33,230 --> 00:00:40,250 here and say we have some trait or disease. 9 00:00:40,250 --> 00:00:41,900 What is our real goal here? 10 00:00:41,900 --> 00:00:43,415 Our real goal is to find the cause. 11 00:00:43,415 --> 00:00:46,515 12 00:00:46,515 --> 00:00:48,140 If we know the cause, there's something 13 00:00:48,140 --> 00:00:51,183 we could do to intervene. 14 00:00:51,183 --> 00:00:53,600 The first question we might want to ask is, is it genetic? 15 00:00:53,600 --> 00:01:01,820 16 00:01:01,820 --> 00:01:03,800 And let's say we find that it is. 17 00:01:03,800 --> 00:01:05,570 There is some genetic basis for it. 18 00:01:05,570 --> 00:01:09,250 What we're going to be doing is to try 19 00:01:09,250 --> 00:01:12,370 to go from this information that it is genetic 20 00:01:12,370 --> 00:01:16,570 in some way with human populations 21 00:01:16,570 --> 00:01:21,260 to find the gene or genes involved. 22 00:01:21,260 --> 00:01:25,137 OK, that's going to be our goal. 23 00:01:25,137 --> 00:01:26,720 Now, how would we know if something is 24 00:01:26,720 --> 00:01:29,540 genetic in human populations? 25 00:01:29,540 --> 00:01:32,180 Any ideas? 26 00:01:32,180 --> 00:01:34,110 So we're going to-- we can look at pedigrees. 27 00:01:34,110 --> 00:01:35,240 So that's one suggestion. 28 00:01:35,240 --> 00:01:40,355 29 00:01:40,355 --> 00:01:41,980 And that can give you some information. 30 00:01:41,980 --> 00:01:43,980 We're going to come back to that. 31 00:01:43,980 --> 00:01:48,610 But that will only apply to certain cases. 32 00:01:48,610 --> 00:01:49,980 There can be a genetic-- 33 00:01:49,980 --> 00:01:51,540 there can be genetic basis for traits 34 00:01:51,540 --> 00:01:52,860 where you don't see a clear inheritance 35 00:01:52,860 --> 00:01:53,830 patterns in pedigrees. 36 00:01:53,830 --> 00:01:56,740 So that wouldn't be a definitive way to know. 37 00:01:56,740 --> 00:01:59,450 So any other ideas? 38 00:01:59,450 --> 00:02:01,550 OK, looking at the genome of everyone 39 00:02:01,550 --> 00:02:04,190 affected and see if you see something in common. 40 00:02:04,190 --> 00:02:06,530 OK, I'm going to come back to that. 41 00:02:06,530 --> 00:02:08,400 That is doable. 42 00:02:08,400 --> 00:02:09,800 It has some limits. 43 00:02:09,800 --> 00:02:11,690 And the core part of the limit is 44 00:02:11,690 --> 00:02:15,080 that there's 3 to 5 million variants, sequence variants, 45 00:02:15,080 --> 00:02:16,680 between any two individuals. 46 00:02:16,680 --> 00:02:18,830 And so there's a lot to sift through. 47 00:02:18,830 --> 00:02:22,970 The genome is 3 billion bases, 3.2 billion bases. 48 00:02:22,970 --> 00:02:28,580 So, somewhere in there is, if it's genetic, we think, 49 00:02:28,580 --> 00:02:30,170 is what's causing this trait. 50 00:02:30,170 --> 00:02:36,670 51 00:02:36,670 --> 00:02:39,307 So that's the trick. 52 00:02:39,307 --> 00:02:40,890 That's the challenge we have before us 53 00:02:40,890 --> 00:02:43,130 is, how do we sift through these 3 billion bases 54 00:02:43,130 --> 00:02:44,330 to try to actually find it? 55 00:02:44,330 --> 00:02:46,730 Now, we talked about some concepts 56 00:02:46,730 --> 00:02:49,283 of how to do that in organisms where 57 00:02:49,283 --> 00:02:50,450 you can study it in the lab. 58 00:02:50,450 --> 00:02:52,020 And I'll mention this again as we go through. 59 00:02:52,020 --> 00:02:53,930 But here we're just stuck with what has 60 00:02:53,930 --> 00:02:58,280 happened in human populations. 61 00:02:58,280 --> 00:03:00,020 OK, so any other ideas to determine 62 00:03:00,020 --> 00:03:02,760 if something is genetic? 63 00:03:02,760 --> 00:03:06,120 You can try to study and look for alternative hypotheses 64 00:03:06,120 --> 00:03:08,880 and see if you find support for those. 65 00:03:08,880 --> 00:03:09,740 Yeah? 66 00:03:09,740 --> 00:03:11,912 STUDENT: Wouldn't it be possible to do a twin study? 67 00:03:11,912 --> 00:03:13,245 PETER REDDIEN: OK, twin studies. 68 00:03:13,245 --> 00:03:21,420 69 00:03:21,420 --> 00:03:24,030 OK, twin studies are very useful for determining 70 00:03:24,030 --> 00:03:27,000 if there's a trait that's heritable 71 00:03:27,000 --> 00:03:29,370 where identical twins, monozygotic twins, 72 00:03:29,370 --> 00:03:33,390 share 100% of their alleles, whereas dizygotic twins, 73 00:03:33,390 --> 00:03:36,210 fraternal twins, share 50% of their alleles. 74 00:03:36,210 --> 00:03:39,300 So you can compare those two, though, they're in similar-- 75 00:03:39,300 --> 00:03:42,120 you can hope-- similar environments 76 00:03:42,120 --> 00:03:43,810 and try to distinguish the genetic 77 00:03:43,810 --> 00:03:45,060 from the environmental causes. 78 00:03:45,060 --> 00:03:47,860 So that's a powerful way to do it. 79 00:03:47,860 --> 00:03:49,720 I'll leave it at that for now. 80 00:03:49,720 --> 00:03:51,930 We may have more to say about that later. 81 00:03:51,930 --> 00:03:55,800 OK, so let's say we have some information that something 82 00:03:55,800 --> 00:03:58,140 is genetic then. 83 00:03:58,140 --> 00:03:58,815 So, yes. 84 00:03:58,815 --> 00:04:02,220 85 00:04:02,220 --> 00:04:06,120 The next thing we want to know is, is it 86 00:04:06,120 --> 00:04:14,760 Mendelian or a simple, single-gene inheritance 87 00:04:14,760 --> 00:04:16,029 pattern? 88 00:04:16,029 --> 00:04:18,140 Do we see an autosomal dominant type of pattern? 89 00:04:18,140 --> 00:04:21,730 Do we see an autosomal recessive pattern? 90 00:04:21,730 --> 00:04:24,080 Sex linked? 91 00:04:24,080 --> 00:04:26,715 So, often, the answer is-- 92 00:04:26,715 --> 00:04:27,715 often, the answer is no. 93 00:04:27,715 --> 00:04:31,685 94 00:04:31,685 --> 00:04:33,310 The twin studies tell you it's genetic. 95 00:04:33,310 --> 00:04:35,110 But you look at the pedigrees, and it 96 00:04:35,110 --> 00:04:38,380 doesn't look to be a simple Mendelian inheritance pattern. 97 00:04:38,380 --> 00:04:39,928 And that's what I kind of alluded 98 00:04:39,928 --> 00:04:41,470 to at the very beginning in the class 99 00:04:41,470 --> 00:04:43,840 that, often, a lot of traits and diseases 100 00:04:43,840 --> 00:04:47,830 are complex where there's many genes contributing. 101 00:04:47,830 --> 00:04:54,156 So examples are height or schizophrenia. 102 00:04:54,156 --> 00:04:59,870 103 00:04:59,870 --> 00:05:01,780 There's many, many examples where 104 00:05:01,780 --> 00:05:04,870 you see a high degree of heritability, 105 00:05:04,870 --> 00:05:07,180 but you don't see it tracking through pedigrees 106 00:05:07,180 --> 00:05:09,850 in a very clear way. 107 00:05:09,850 --> 00:05:12,610 OK, we're not going to deal with these now. 108 00:05:12,610 --> 00:05:14,065 These are for later in the class. 109 00:05:14,065 --> 00:05:22,442 110 00:05:22,442 --> 00:05:25,860 But we will come back to how to try to crack 111 00:05:25,860 --> 00:05:27,180 these kinds of cases too. 112 00:05:27,180 --> 00:05:29,932 There are genetic ways to do that. 113 00:05:29,932 --> 00:05:31,390 For now, where we are in the class, 114 00:05:31,390 --> 00:05:34,950 we're focusing on Mendelian, so scenarios 115 00:05:34,950 --> 00:05:38,580 where, yes, we see some kind of Mendelian pattern in pedigrees. 116 00:05:38,580 --> 00:05:46,000 117 00:05:46,000 --> 00:05:50,770 OK, all right, so now our goal then-- so now the answer 118 00:05:50,770 --> 00:05:51,280 is yes. 119 00:05:51,280 --> 00:05:52,600 We know it's Mendelian. 120 00:05:52,600 --> 00:05:54,710 Now, we want to find the gene. 121 00:05:54,710 --> 00:05:58,870 OK, so how are we going to do that? 122 00:05:58,870 --> 00:06:01,500 123 00:06:01,500 --> 00:06:03,390 It has been done many times. 124 00:06:03,390 --> 00:06:05,860 It is possible. 125 00:06:05,860 --> 00:06:07,952 So how are we going to do it? 126 00:06:07,952 --> 00:06:08,910 What do you guys think? 127 00:06:08,910 --> 00:06:13,460 128 00:06:13,460 --> 00:06:15,920 We've had one suggestion already. 129 00:06:15,920 --> 00:06:17,480 So one suggestion was sequence. 130 00:06:17,480 --> 00:06:22,230 131 00:06:22,230 --> 00:06:24,000 Sequence the individuals in the pedigree. 132 00:06:24,000 --> 00:06:25,500 And I've talked about the challenges 133 00:06:25,500 --> 00:06:28,485 there that there's a lot of variants to sift through. 134 00:06:28,485 --> 00:06:38,977 135 00:06:38,977 --> 00:06:40,560 Nonetheless, there are some approaches 136 00:06:40,560 --> 00:06:41,935 with sequencing that can be done. 137 00:06:41,935 --> 00:06:45,670 And I'll get into that in a moment. 138 00:06:45,670 --> 00:06:48,630 Any other ideas? 139 00:06:48,630 --> 00:06:50,340 Great, that's it. 140 00:06:50,340 --> 00:06:51,750 We use linkage information. 141 00:06:51,750 --> 00:06:59,280 142 00:06:59,280 --> 00:07:03,460 OK, recall, that's how we've done it already in this class. 143 00:07:03,460 --> 00:07:07,800 You've seen how, in, say, the Drosophila X chromosome, 144 00:07:07,800 --> 00:07:09,990 you could figure out the miniature wing 145 00:07:09,990 --> 00:07:11,880 locus, what it's between. 146 00:07:11,880 --> 00:07:16,230 And then we extended upon that to take sequence information 147 00:07:16,230 --> 00:07:21,420 and use polymorphisms to get map distances 148 00:07:21,420 --> 00:07:26,730 between some gene causing a trait and some polymorphism. 149 00:07:26,730 --> 00:07:28,950 The polymorphisms, we know where they are. 150 00:07:28,950 --> 00:07:32,270 By definition, they're sequence variants in the genome. 151 00:07:32,270 --> 00:07:34,650 And so that's what we're going to do here. 152 00:07:34,650 --> 00:07:36,430 OK, so we're going to know the pedigree. 153 00:07:36,430 --> 00:07:38,440 We can take advantage of these variants that 154 00:07:38,440 --> 00:07:39,952 exist between individuals. 155 00:07:39,952 --> 00:07:41,410 And what we're going to do is we're 156 00:07:41,410 --> 00:07:43,210 going to ask which of those variants 157 00:07:43,210 --> 00:07:46,780 segregate through a pedigree together 158 00:07:46,780 --> 00:07:50,860 with whatever mutation is causing the trait. 159 00:07:50,860 --> 00:07:52,970 And, if we can find which ones go together, 160 00:07:52,970 --> 00:07:55,210 then we'll know where in the genome 161 00:07:55,210 --> 00:07:57,930 our disease-causing gene is. 11023

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