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These are the user uploaded subtitles that are being translated: 0 00:00:00,000 --> 00:00:01,290 PETER REDDIEN: It's expensive to sequence 1 00:00:01,290 --> 00:00:02,832 the genome of an individual, but it's 2 00:00:02,832 --> 00:00:05,282 getting cheaper and cheaper. 3 00:00:05,282 --> 00:00:06,615 So we could sequence the genome. 4 00:00:06,615 --> 00:00:11,360 5 00:00:11,360 --> 00:00:21,250 It's about $1,000 to $2,000 per person. 6 00:00:21,250 --> 00:00:25,340 Which you could do this for the individuals in a pedigree. 7 00:00:25,340 --> 00:00:27,340 You could also sequence what's called the exome. 8 00:00:27,340 --> 00:00:31,330 9 00:00:31,330 --> 00:00:34,870 So the exome are just the exons of the genome, which is 10 00:00:34,870 --> 00:00:37,540 about 1% to 2% of the genome. 11 00:00:37,540 --> 00:00:42,080 So genes exist in these exons separated by introns, 12 00:00:42,080 --> 00:00:44,630 and there's lots of repeats and other stuff. 13 00:00:44,630 --> 00:00:46,280 And you could say, well, let's just 14 00:00:46,280 --> 00:00:50,210 guess that the mutation is going to be in an exon that's 15 00:00:50,210 --> 00:00:51,140 causing the trait. 16 00:00:51,140 --> 00:00:52,070 It doesn't have to be. 17 00:00:52,070 --> 00:00:55,330 It could be somewhere else. 18 00:00:55,330 --> 00:00:57,740 And we'll talk about that later. 19 00:00:57,740 --> 00:00:59,303 But let's just for now say, well, 20 00:00:59,303 --> 00:01:00,470 let's guess it's in an exon. 21 00:01:00,470 --> 00:01:02,137 And wouldn't it be nice if we could just 22 00:01:02,137 --> 00:01:04,160 sequence those things, and that cuts 23 00:01:04,160 --> 00:01:05,990 by a couple of orders of magnitude 24 00:01:05,990 --> 00:01:07,977 the size of what you're sequencing. 25 00:01:07,977 --> 00:01:09,060 And this is a bit cheaper. 26 00:01:09,060 --> 00:01:10,520 So it's about $500 per person. 27 00:01:10,520 --> 00:01:17,850 28 00:01:17,850 --> 00:01:20,100 And the way you do it is by taking 29 00:01:20,100 --> 00:01:24,925 genomic DNA from an individual and making lots 30 00:01:24,925 --> 00:01:26,050 of little fragments for it. 31 00:01:26,050 --> 00:01:28,140 So you chop it up into fragments. 32 00:01:28,140 --> 00:01:30,990 And let's say these dark blue fragments are sequences 33 00:01:30,990 --> 00:01:33,330 that happen to come from exons. 34 00:01:33,330 --> 00:01:36,240 Well, you can hybridize those to synthesized 35 00:01:36,240 --> 00:01:39,030 pieces of DNA, that's these orange ones, that 36 00:01:39,030 --> 00:01:41,550 correspond to the exons throughout the genome. 37 00:01:41,550 --> 00:01:44,060 So these would be synthesized. 38 00:01:44,060 --> 00:01:48,200 And then you can hybridize by base pair complementarity 39 00:01:48,200 --> 00:01:51,830 the regions of DNA of interest, and then you can sort of 40 00:01:51,830 --> 00:01:55,400 pull these out, washing away that sequences you don't want, 41 00:01:55,400 --> 00:01:58,340 and then sequence the library from it. 42 00:01:58,340 --> 00:02:01,910 These exons will be known from prior efforts 43 00:02:01,910 --> 00:02:04,470 in annotating the genome. 44 00:02:04,470 --> 00:02:08,070 So there's lots of ways to annotate a genome. 45 00:02:08,070 --> 00:02:11,009 You can look for open reading frames present in exons. 46 00:02:11,009 --> 00:02:15,420 You can look for expressed genes. 47 00:02:15,420 --> 00:02:17,010 There's experimental ways to find 48 00:02:17,010 --> 00:02:20,400 the sequence of expressed genes and then map that information 49 00:02:20,400 --> 00:02:21,000 onto a genome. 50 00:02:21,000 --> 00:02:25,020 So work in annotating the genome leads to this information. 51 00:02:25,020 --> 00:02:30,150 All right, so one place in which this is applied, 52 00:02:30,150 --> 00:02:32,340 either genome or exome sequencing, 53 00:02:32,340 --> 00:02:33,510 is for rare diseases. 54 00:02:33,510 --> 00:02:39,917 55 00:02:39,917 --> 00:02:41,500 There are a lot of rare diseases where 56 00:02:41,500 --> 00:02:45,070 someone has something wrong and it's really hard 57 00:02:45,070 --> 00:02:46,240 to explain what it is. 58 00:02:46,240 --> 00:02:49,930 It's not matching some known disease. 59 00:02:49,930 --> 00:02:52,480 Someone comes into the clinic with this. 60 00:02:52,480 --> 00:02:53,770 And what do you do? 61 00:02:53,770 --> 00:02:55,390 Trying to figure out what it is. 62 00:02:55,390 --> 00:02:57,670 You might not even have-- if it's not a known disease, 63 00:02:57,670 --> 00:02:59,712 you don't even have this heritability information 64 00:02:59,712 --> 00:03:01,422 from twin studies. 65 00:03:01,422 --> 00:03:02,380 So it can be very hard. 66 00:03:02,380 --> 00:03:05,475 And there's a lot of these types of things. 67 00:03:05,475 --> 00:03:06,850 And one thing that could be tried 68 00:03:06,850 --> 00:03:10,480 is to try to sequence the individual. 69 00:03:10,480 --> 00:03:13,420 And an approach that's often taken 70 00:03:13,420 --> 00:03:17,770 is to sequence what are called trios 71 00:03:17,770 --> 00:03:20,330 where you sequence the individual and the individual's 72 00:03:20,330 --> 00:03:20,830 parents. 73 00:03:20,830 --> 00:03:33,940 74 00:03:33,940 --> 00:03:37,720 Now what do you do with that information? 75 00:03:37,720 --> 00:03:40,660 Well, you could look up a database of known variants 76 00:03:40,660 --> 00:03:44,900 that exist in human populations and you could say, well, 77 00:03:44,900 --> 00:03:47,750 do these three individuals carry some variant 78 00:03:47,750 --> 00:03:52,400 within an exon that's not known previously? 79 00:03:52,400 --> 00:03:54,050 Maybe the parents are heterozygous 80 00:03:54,050 --> 00:03:55,760 and the individual is homozygous. 81 00:03:55,760 --> 00:03:58,460 So you try to get some candidate genes this way. 82 00:03:58,460 --> 00:04:04,380 83 00:04:04,380 --> 00:04:07,040 And an example, a powerful approach, 84 00:04:07,040 --> 00:04:09,050 is to look for de novo mutations. 85 00:04:09,050 --> 00:04:20,750 86 00:04:20,750 --> 00:04:23,090 By de novo, I mean the parents don't have it. 87 00:04:23,090 --> 00:04:35,250 88 00:04:35,250 --> 00:04:39,930 So a mutation that arose in the generation of the gametes 89 00:04:39,930 --> 00:04:42,150 or early in the development of this individual that 90 00:04:42,150 --> 00:04:45,510 has this disease. 91 00:04:45,510 --> 00:04:47,400 So you can identify these de novo mutations 92 00:04:47,400 --> 00:04:49,680 and identify candidate genes that way. 93 00:04:49,680 --> 00:04:52,890 Then if you find something, you have some knowledge 94 00:04:52,890 --> 00:04:55,440 of these genes, you could also go look in other individuals 95 00:04:55,440 --> 00:04:57,023 and try to find other individuals that 96 00:04:57,023 --> 00:04:59,280 might have a similar rare trait and see do you ever 97 00:04:59,280 --> 00:05:02,250 see mutations in that gene in those individuals. 98 00:05:02,250 --> 00:05:05,440 99 00:05:05,440 --> 00:05:07,500 There are a lot of human Mendelian diseases. 100 00:05:07,500 --> 00:05:09,960 I list some famous ones here. 101 00:05:09,960 --> 00:05:13,290 Huntington's disease, inherited forms of risk 102 00:05:13,290 --> 00:05:14,850 for breast cancer. 103 00:05:14,850 --> 00:05:17,880 Not all forms of breast cancer display a heritable risk, 104 00:05:17,880 --> 00:05:19,500 but some do. 105 00:05:19,500 --> 00:05:23,000 Polycystic kidney disease, Lou Gehrig's disease, 106 00:05:23,000 --> 00:05:27,170 or ALS, cystic fibrosis, sickle cell anemia, hemophilia, 107 00:05:27,170 --> 00:05:29,220 and many others. 108 00:05:29,220 --> 00:05:32,290 So I'm mentioning there's lots of traits and diseases 109 00:05:32,290 --> 00:05:34,040 that are non-Mendelian, but there are also 110 00:05:34,040 --> 00:05:37,560 a lot of Mendelian ones. 111 00:05:37,560 --> 00:05:40,410 Here are some examples of pedigrees with human Mendelian 112 00:05:40,410 --> 00:05:41,760 traits. 113 00:05:41,760 --> 00:05:45,360 Polydactyly displays the autosomal dominant pattern 114 00:05:45,360 --> 00:05:47,850 of inheritance, which you can see in pedigrees. 115 00:05:47,850 --> 00:05:51,880 Hemophilia displays X-linked recessive inheritance. 116 00:05:51,880 --> 00:05:55,110 This is an inheritance from the royal family of England 117 00:05:55,110 --> 00:05:56,940 and through Europe. 118 00:05:56,940 --> 00:05:59,910 We can see Queen Victoria here passing on an allele 119 00:05:59,910 --> 00:06:01,500 to the son Leopold, and so on. 120 00:06:01,500 --> 00:06:05,040 121 00:06:05,040 --> 00:06:07,890 You can go and look up statistics on Mendelian traits 122 00:06:07,890 --> 00:06:10,560 in humans in the Online Mendelian 123 00:06:10,560 --> 00:06:12,210 Inheritance in Man database. 124 00:06:12,210 --> 00:06:14,880 So I looked this up yesterday, where 125 00:06:14,880 --> 00:06:20,280 they say there are 6,900 something phenotypes for which 126 00:06:20,280 --> 00:06:22,230 the molecular basis is known. 127 00:06:22,230 --> 00:06:25,290 That's a lot, and it's been going up rapidly. 128 00:06:25,290 --> 00:06:28,340 129 00:06:28,340 --> 00:06:31,790 And then depending on how you break down those phenotypes, 130 00:06:31,790 --> 00:06:34,790 if you just-- if you look at some set of them, 131 00:06:34,790 --> 00:06:37,280 some Mendelian phenotypes, you can say, how are we 132 00:06:37,280 --> 00:06:38,540 doing with identifying them. 133 00:06:38,540 --> 00:06:43,960 So there's 8,500 in this data, Mendelian phenotypes. 134 00:06:43,960 --> 00:06:46,790 So just focusing on Mendelian phenotypes here. 135 00:06:46,790 --> 00:06:50,700 And about 67% of those have a known molecular basis. 136 00:06:50,700 --> 00:06:52,168 So most. 137 00:06:52,168 --> 00:06:53,210 These numbers are rising. 138 00:06:53,210 --> 00:06:57,470 Of course, we're identifying new traits and diseases, 139 00:06:57,470 --> 00:06:59,990 phenotypes, I guess, and then more and more getting 140 00:06:59,990 --> 00:07:01,640 identified all the time. 141 00:07:01,640 --> 00:07:04,280 Now there's lots of news stories about this kind of approach, 142 00:07:04,280 --> 00:07:07,100 using sequencing to try to identify the molecular basis 143 00:07:07,100 --> 00:07:10,470 of rare traits. 144 00:07:10,470 --> 00:07:13,600 So you can see some example titles here 145 00:07:13,600 --> 00:07:16,570 where you take one example here, like this one. 146 00:07:16,570 --> 00:07:19,657 It was a de novo mutation in this child. 147 00:07:19,657 --> 00:07:21,490 The parents didn't have it and they found it 148 00:07:21,490 --> 00:07:26,010 by sequencing the parents and the child. 149 00:07:26,010 --> 00:07:29,530 Here's an example paper where, if you look at the methods, 150 00:07:29,530 --> 00:07:32,580 how did they do it, where they found de novo variants 151 00:07:32,580 --> 00:07:35,880 in some gene causing some neuropathy. 152 00:07:35,880 --> 00:07:38,370 They say three patients carrying de novo variants 153 00:07:38,370 --> 00:07:42,055 were identified by a diagnostic trio exome sequencing. 154 00:07:42,055 --> 00:07:43,680 So now if you see that kind of wording, 155 00:07:43,680 --> 00:07:45,790 you know what it means. 156 00:07:45,790 --> 00:07:50,290 Now individuals that are displaying some rare trait 157 00:07:50,290 --> 00:07:52,180 can participate in studies. 158 00:07:52,180 --> 00:07:55,570 This is a study the Rare Genomes Project being conducted 159 00:07:55,570 --> 00:07:58,090 at the Broad Institute here at MIT, in collaboration 160 00:07:58,090 --> 00:08:00,730 with Mass General Hospital and Brigham and Women's 161 00:08:00,730 --> 00:08:02,680 where here's the process. 162 00:08:02,680 --> 00:08:06,190 You submit your DNA, they extract it and process it 163 00:08:06,190 --> 00:08:07,810 for either exome or genome sequencing, 164 00:08:07,810 --> 00:08:12,100 depending upon the details of the study. 165 00:08:12,100 --> 00:08:14,170 And they say in particular, participation 166 00:08:14,170 --> 00:08:15,670 from both of the patient's parents 167 00:08:15,670 --> 00:08:17,753 will increase our ability to find a genetic cause, 168 00:08:17,753 --> 00:08:20,130 and this is the reason why. 12279

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