-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathslides.html
500 lines (492 loc) · 20 KB
/
slides.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="generator" content="pandoc">
<meta name="author" content="Peter Humburg" />
<title>Rare variants</title>
<meta name="apple-mobile-web-app-capable" content="yes" />
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<link rel="stylesheet" href="/css/reveal.min.css"/>
<style type="text/css">code{white-space: pre;}</style>
<link rel="stylesheet" href="/css/theme/bug.css" id="theme">
<!-- If the query includes 'print-pdf', include the PDF print sheet -->
<script>
if( window.location.search.match( /print-pdf/gi ) ) {
var link = document.createElement( 'link' );
link.rel = 'stylesheet';
link.type = 'text/css';
link.href = '/css/print/pdf.css';
document.getElementsByTagName( 'head' )[0].appendChild( link );
}
</script>
<!--[if lt IE 9]>
<script src="/lib/js/html5shiv.js"></script>
<![endif]-->
<script src="/MathJax/MathJax.js?config=TeX-AMS-MML_HTMLorMML,local/local" type="text/javascript"></script>
</head>
<body>
<div class="reveal">
<div class="slides">
<section>
<h1 class="title">Rare variants</h1>
<h1 class="subtitle">Discovery and interpretation</h1>
<h2 class="author">Peter Humburg</h2>
<h3 class="date">18<sup>th</sup> November 2015</h3>
</section>
<section><section id="introduction" class="titleslide slide level1"><h1>Introduction</h1></section><section id="interpreting-patient-genomes" class="slide level2">
<h1>Interpreting patient genomes</h1>
<ul>
<li>Sequencing of patient genomes increasingly common</li>
<li>Can identify relevant variants</li>
<li>… amongst a large number of unrelated variants</li>
<li>… can be difficult to interpret</li>
<li><span class="fragment highlight-current-red"> Computational strategies critical to obtaining good set of candidates</span></li>
</ul>
<aside class="notes">
<p>Typical sequencing studies either focus on individual patients (or trios) or larger cohorts.</p>
<p>Obviously these serve different objectives, either individual diagnosis/treatment or generally gaining better understanding of phenotype.</p>
<p>Variants of interest may be rare or common. Will focus on rare variants in this talk. Some of this also applies to cancer genomes but won’t discuss that here.</p>
</aside>
</section><section id="finding-rare-disease-related-variants" class="slide level2">
<h1>Finding rare disease related variants</h1>
<p>Rare variants are of particular interest</p>
<ul>
<li>May have large effects</li>
<li><p>… but can be hard to find.</p></li>
<li>Need large sample sizes</li>
<li><p>… but may still struggle to identify causal variants.</p></li>
</ul>
<aside class="notes">
<p>No guarantee that there are rare variants with moderate/large effects in a given disease.</p>
<p>Even if they exist they likely to be only one of several factors involved in disease risk and may be rare even in the disease population.</p>
</aside>
</section></section>
<section><section id="identifying-novel-breast-cancer-risk-variants" class="titleslide slide level1"><h1>Identifying Novel Breast Cancer Risk Variants</h1></section><section id="motivation" class="slide level2">
<h1>Motivation</h1>
<ul>
<li>Several DNA repair genes implicated in breast and ovarian cancer susceptibility.</li>
<li>Strong evidence that rare loss-of-function variants confer increased risk.</li>
<li>Sequencing large number of patients not carrying known risk variants should lead to discovery of new ones.</li>
</ul>
</section><section id="study-design" class="slide level2">
<h1>Study design</h1>
<div class="twocolumn">
<ul>
<li>Exons of 507 DNA repair genes in 1,150 unrelated patients.</li>
<li>Pools of 24 individuals.</li>
<li>Included 79 individuals with known mutations in breast cancer predisposition genes as positive controls.</li>
</ul>
<div class="fragment">
<ul>
<li>No controls.</li>
<li>No barcoding.</li>
<li>Expect to do lots of Sanger sequencing in follow-up.</li>
</ul>
</div>
</div>
<aside class="notes">
<p>Note that no controls were sequenced and samples are pooled (no barcoding) to reduce amount of time and money required.</p>
<p>69 individuals also had ovarian cancer.</p>
</aside>
</section><section id="analysis-strategy" class="slide level2">
<h1>Analysis strategy</h1>
<ul>
<li>Sequence pools with <span class="fragment fade-out" data-fragment-index="1">GAIIx</span> <span class="fragment fade-in" data-fragment-index="1">HiSeq2000.</span></li>
<li>Call variants in pools with <a href="http://sourceforge.net/projects/syzygy/">Syzygy</a>.</li>
<li>Annotate variants to identify loss of function.</li>
<li>Validate variants of interest.</li>
<li>Sequence relevant genes in control panel.</li>
</ul>
<aside class="notes">
<ul>
<li>Aim for <span class="math">\(\gt\)</span> 10<span class="math">\(\times\)</span> coverage per genome in target regions.</li>
<li>Should be able to detect singletons in pool.</li>
<li>But of course coverage isn’t uniform.</li>
</ul>
</aside>
</section><section id="achieved-coverage" class="slide level2 small">
<h1>Achieved coverage</h1>
<figure>
<img src="figure/coverage.png" />
</figure>
<p><span class="math">\(\gt\)</span> 480<span class="math">\(\times\)</span> coverage in 90% of target region</p>
<aside class="notes">
<ul>
<li>filtered reads to exclude
<ul>
<li>ambiguous alignments (MQ == 0)</li>
<li>masked bases with quality < 22</li>
</ul></li>
<li>poor performance for pool 22</li>
</ul>
</aside>
</section><section id="variant-calling" class="slide level2">
<h1>Variant calling</h1>
<ul>
<li>Syzygy called 34,564 variants in target region.</li>
</ul>
<div class="fragment">
<ul>
<li>Performance for known variants:
<ul>
<li>439/439 common SNPs</li>
<li>24/26 rare SNPs</li>
<li>51/54 rare (short) indels
<div>
</li>
</ul></li>
</ul>
<aside class="notes">
<ul>
<li>Sensitivity for rare SNPS: 92%</li>
<li>Sensitivity for rare indels: 94%</li>
</ul>
</aside>
</section></section>
<section><section id="aside-annotating-variants" class="titleslide slide level1"><h1>Aside: Annotating variants</h1></section><section id="a-simple-plan" class="slide level2">
<h1>A simple plan</h1>
<ul>
<li>Use EnsEMBL annotations (via Perl API)</li>
<li>Identify protein truncating variants</li>
<li>Group variants by gene to identify candidates for follow-up</li>
</ul>
<div class="fragment">
<p>But it isn’t that easy…</p>
<aside class="notes">
<ul>
<li>VEP was in its infancy at the time, using Perl API allowed for much more flexibility.</li>
<li>In hindsight probably not the best choice. API not stable, hard to maintain code based on it. Also pretty slow.</li>
</ul>
</aside>
</div>
</section><section id="beware-of-transcript-annotations" class="slide level2">
<h1>Beware of transcript annotations</h1>
<p><a href="http://grch37.ensembl.org/Homo_sapiens/Location/View?r=11:8149771-8149831;db=core", target="_blank"> <img src="figure/transcripts.png" /> </a></p>
<div class="footnote">
<p><a href="http://www.genomemedicine.com/content/6/3/26" target="_blank"> McCarthy <em>et al.</em> Genome Medicine 2014 6:26 </a></p>
</div>
<aside class="notes">
<ul>
<li>EnsEMBL contains large number of transcripts.</li>
<li>Not all transcripts well supported by evidence.</li>
<li>Reporting the most severe consequence will enrich for false positives.</li>
<li>Including transcripts that aren’t expressed massively increases false positive annotations for PTV.</li>
</ul>
</aside>
</section><section id="beware-of-edge-effects" class="slide level2">
<h1>Beware of edge effects</h1>
<p><a href="http://grch37.ensembl.org/Homo_sapiens/Variation/Explore?db=core;r=6:30557978-30558977;v=rs72545970;vdb=variation;vf=116290482", target="_blank"> <img src="figure/indel.png" /> </a></p>
<div class="footnote">
<p><a href="http://www.genomemedicine.com/content/6/3/26" target="_blank"> McCarthy <em>et al.</em> Genome Medicine 2014 6:26 </a></p>
</div>
<aside class="notes">
<ul>
<li>At the time of the study EnsEMBL incorrectly annotated this as <em>stop loss</em>.</li>
<li>More recent versions of VEP correctly note that this is <em>frame shift/stop retained</em>.</li>
<li>But note that the most severe consequence still is <em>frame shift</em>.</li>
</ul>
</aside>
</section><section id="beware-of-misaligned-indels" class="slide level2">
<h1>Beware of misaligned indels</h1>
<p><a href="http://www.genomemedicine.com/content/7/1/76/figure/F1", target="_blank"> <img src="figure/misaligned_indel.jpg" /> </a></p>
<div class="footnote">
<p><a href="http://www.genomemedicine.com/content/7/1/76" target="_blank">Münz <em>et al.</em> Genome Medicine 2015 7:76</a></p>
</div>
<aside class="notes">
<ul>
<li>Indel position may be ambiguous.</li>
<li>Variant callers typically report the <em>left most</em> position, i.e. position closest to 5’ end of forward strand.</li>
<li>Really should report position closest to 3’ end of transcript.</li>
</ul>
</aside>
</section></section>
<section><section id="back-to-the-breast-cancer-study" class="titleslide slide level1 unnumbered"><h1>Back to the Breast Cancer Study</h1></section><section id="selecting-candidate-genes" class="slide level2">
<h1>Selecting candidate genes</h1>
<div class="twocolumn">
<ul>
<li>Identified 1,044 PTVs</li>
<li>Ranked genes by number of truncating mutations observed.</li>
<li>Identify candidate genes</li>
</ul>
<div class="fragment">
<ul>
<li>Top ranking genes were BRCA2, CHEK1, ATM, BRCA1, …</li>
<li>Partially driven by positive controls.</li>
<li>First interesting gene on list was PPM1D with 5 PTV.</li>
<li>None of these PTVs present in 1000 Genomes.</li>
</ul>
</div>
</div>
</section><section id="investigating-ppm1d" class="slide level2" data-transition="none">
<h1>Investigating PPM1D</h1>
<figure>
<img src="figure/ppm1d_2.png" />
</figure>
<ul>
<li>PPM1D is a phosphatase</li>
<li>Phosphatase domain encoded by first 5 exons</li>
</ul>
</section><section id="investigating-ppm1d-1" class="slide level2" data-transition="none">
<h1>Investigating PPM1D</h1>
<figure>
<img src="figure/ppm1d_3.png" />
</figure>
<p>All identified truncating mutations validated with Sanger sequencing.</p>
</section><section id="phase-2-case-control-study" class="slide level2" data-transition="none">
<h1>Phase 2: Case-control study</h1>
<figure>
<img src="figure/ppm1d_4.png" />
</figure>
<ul>
<li>Sequenced PPM1D an additional 2456 cases and 1347 controls.</li>
<li>Identified 10 additional PTVs (none in controls)</li>
</ul>
<aside class="notes">
<ul>
<li>All PTVs clustering in last exon</li>
<li>proceeded to only sequence this region.</li>
</ul>
</aside>
</section><section id="phase-2-case-control-study-1" class="slide level2" data-transition="none">
<h1>Phase 2: Case-control study</h1>
<figure>
<img src="figure/ppm1d_5.png" />
</figure>
<ul>
<li>Sequenced final exon only in 5325 cases and 4514 controls.</li>
<li>Identified 15 additional PTVs in cases (1 in controls)</li>
</ul>
</section><section id="case-control-summary" class="slide level2">
<h1>Case-control summary</h1>
<table>
<thead>
<tr class="header">
<th style="text-align: left;"></th>
<th style="text-align: center;">Breast cancer</th>
<th style="text-align: center;">Ovarian cancer</th>
<th style="text-align: center;">controls</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Sequenced</td>
<td style="text-align: center;">6,912</td>
<td style="text-align: center;">1,121</td>
<td style="text-align: center;">5,861</td>
</tr>
<tr class="even">
<td style="text-align: left;">with PTV</td>
<td style="text-align: center;">18</td>
<td style="text-align: center;">12</td>
<td style="text-align: center;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">relative risk</td>
<td style="text-align: center;">2.7</td>
<td style="text-align: center;">11.5</td>
<td style="text-align: center;"></td>
</tr>
<tr class="even">
<td style="text-align: left;">95% CI</td>
<td style="text-align: center;">1.3 - 5.3</td>
<td style="text-align: center;">4.3 - 30.4</td>
<td style="text-align: center;"></td>
</tr>
</tbody>
</table>
</section><section id="how-does-it-work" class="slide level2">
<h1>How does it work?</h1>
<figure>
<img src="figure/pathway.png" />
</figure>
<div class="fragment">
<p>Cells expressing truncated versions of PPM1D show reduced activation of p53 in response to ionizing radiation.</p>
<aside class="notes">
<p>Suggests truncated PTV is hyperactive.</p>
</aside>
</div>
</section></section>
<section><section id="the-plot-thickens" class="titleslide slide level1 unnumbered"><h1>The Plot Thickens</h1></section><section id="a-complication" class="slide level2">
<h1>A complication</h1>
<div class="twocolumn">
<ul>
<li>Read counts for variant alleles appear low.</li>
<li>Difficult to assess in pools but also visible in trace data.</li>
<li>Consistently low frequency of PTVs.</li>
</ul>
<div class="fragment">
<figure>
<img src="figure/trace.png" />
</figure>
</div>
</div>
</section><section id="somatic-variation" class="slide level2">
<h1>Somatic variation?</h1>
<div class="twocolumn">
<ul>
<li>Could indicate that these are somatic mutations</li>
<li>If these are germ line variants we should see them in children of carriers.</li>
</ul>
<div class="fragment" data-fragment-index="1">
<figure>
<img src="figure/family.png" />
</figure>
</div>
</div>
<aside class="notes">
<ul>
<li>No guarantee that there are informative families.</li>
<li>Got lucky and found a few.</li>
</ul>
</aside>
</section><section id="further-complications" class="slide level2">
<h1>Further complications</h1>
<div class="twocolumn">
<ul>
<li>PPM1D truncating mutations appear to be mosaic in lymphocytes.</li>
<li>What does PPM1D look like in the tumour tissue?
<ul>
<li>Deep sequencing of DNA from tumour, stromal tissue and blood in four cases.</li>
<li>Found expected mutations in blood <span class="fragment highlight-red" data-fragment-index="1"><span class="fragment" data-fragment-index="1">but not in tumour or stroma</span></span></li>
</ul></li>
</ul>
<div class="fragment" data-fragment-index="1">
<figure>
<img src="figure/tumour_seq.png" />
</figure>
</div>
</div>
</section></section>
<section><section id="discussion" class="titleslide slide level1"><h1>Discussion</h1></section><section id="possible-interpretations" class="slide level2">
<h1>Possible interpretations</h1>
<ul>
<li>Are these mutations present in cell of cancer origin but lost later?</li>
<li>Is oncogenesis driven by lymphocytes?</li>
<li>Are the PPM1D mutations only symptoms of an underlying problem that leads to cancer development in other tissues?</li>
<li>Are PPM1D mutations and cancer unrelated?</li>
</ul>
</section><section id="loss-during-cancer-development" class="slide level2">
<h1>Loss during cancer development</h1>
<ul>
<li>Evidence for loss of heterozygosity at PPMID locus.</li>
<li>The lost haplotype is the one carrying the PTV in lymphocytes.</li>
<li>Unclear whether the mutation was present prior to LOH event.</li>
<li>Loss of heterozygosity in this region is common in breast and ovarian cancers.</li>
</ul>
<aside class="notes">
<ul>
<li>Lends some support to the hypothesis that mutations were initially present</li>
<li>LOH may well be unrelated</li>
<li>Evidence is inconclusive</li>
</ul>
</aside>
</section><section id="oncogenesis-driven-by-lymphocytes" class="slide level2 small">
<h1>Oncogenesis driven by lymphocytes</h1>
<ul>
<li>Only real evidence is absence of mutation in tumour.</li>
<li>Unclear what the mechanism would be.</li>
</ul>
</section><section id="symptom-of-a-bigger-problem" class="slide level2">
<h1>Symptom of a bigger problem</h1>
<ul>
<li>Could be a sign of general genome instability.</li>
<li>This might lead to clonal expansion of cells with PPM1D PTVs as well as cancers.</li>
<li>Unclear what the driver of this would be.</li>
</ul>
</section><section id="simply-unrelated" class="slide level2">
<h1>Simply unrelated</h1>
<ul>
<li>Evidence supporting some relationship between PPM1D PTVs and cancer seems strong.</li>
<li>Observation has been replicated by <a href="http://jnci.oxfordjournals.org/content/early/2013/11/18/jnci.djt323.full">Akbari <em>et al.</em>, 2013</a></li>
<li>Somatic PPM1D PTVs have been found in cancers (<a href="http://jcb.rupress.org/content/201/4/511.full">Kleiblova <em>et al.</em>, 2013</a>, <a href="http://www.nature.com/ng/journal/v46/n7/full/ng.2995.html">Zhang <em>et al.</em>, 2014</a>)</li>
</ul>
</section></section>
<section><section id="lessons-learned" class="titleslide slide level1"><h1>Lessons Learned</h1></section><section id="finding-rare-variants" class="slide level2">
<h1>Finding rare variants</h1>
<ul>
<li>Strategy to sequence as many cases as possible paid off.</li>
<li>Would not have found PPM1D PTVs if we had split initial sequencing between cases and controls.</li>
<li>A lot, but very focused, follow-up required.</li>
<li>Focus on candidate gene panel paid off for similar reasons</li>
<li>… but means we have no easy way to check for other shared genomic variation amongst PPM1D PTV carriers.</li>
</ul>
</section><section id="somatic-variation-1" class="slide level2">
<h1>Somatic variation</h1>
<div class="twocolumn">
<ul>
<li>Were lucky that study design was suited to discovery of somatic variation.</li>
<li>Can find somatic variants through deep sequencing</li>
<li>but proving that a variant is somatic can be difficult in absence of control tissue.</li>
<li>Variant frequency in gDNA and RNA may differ markedly.</li>
</ul>
<figure>
<img src="figure/gdna_cdna.png" />
</figure>
</div>
</section><section id="variant-annotation" class="slide level2" data-transition="none">
<h1>Variant annotation</h1>
<ul>
<li>Be careful with automated annotations.</li>
<li>Have improved a lot over the last few years</li>
<li>… but can still be misleading or incomplete.</li>
<li>Consider PPM1D PTVs</li>
</ul>
</section><section id="variant-annotation-1" class="slide level2" data-transition="none">
<h1>Variant annotation</h1>
<ul>
<li>Be careful with automated annotations.</li>
<li>Have improved a lot over the last few years</li>
<li>… but can still be misleading or incomplete.</li>
<li>Consider PPM1D PTVs
<ul>
<li>Truncation of final exon.</li>
<li>(Correctly) predicted to escape nonsense mediated decay.</li>
<li>So not loss of function.</li>
<li>Doesn’t mean we should ignore it!</li>
</ul></li>
</ul>
</section></section>
<section><section id="acknowledgements" class="titleslide slide level1" data-transition="none"><h1>Acknowledgements</h1></section><section id="acknowledgements-1" class="slide level2" data-transition="none">
<h1>Acknowledgements</h1>
<div class="twocolumn">
<h3 id="wtchg">WTCHG</h3>
<p><strong>Peter Donnelly</strong></p>
<p>Manuel Rivas</p>
<p>Andrew Rimmer</p>
<p>Davis McCarthy</p>
<h3 id="icr">ICR</h3>
<p><strong>Nazneen Rahman</strong></p>
<p>Elise Ruark</p>
<p>Katie Snape</p>
</div>
</section></section>
</div>
</div>
<script src="/lib/js/head.min.js"></script>
<script src="/js/reveal.min.js"></script>
<script>
// Full list of configuration options available here:
// https://github.com/hakimel/reveal.js#configuration
Reveal.initialize({
controls: true,
progress: true,
history: true,
center: true,
slideNumber: true,
theme: 'bug', // available themes are in /css/theme
transition: Reveal.getQueryHash().transition || 'default', // default/cube/page/concave/zoom/linear/fade/none
// Optional libraries used to extend on reveal.js
dependencies: [
{ src: '/lib/js/classList.js', condition: function() { return !document.body.classList; } },
{ src: '/plugin/zoom-js/zoom.js', async: true, condition: function() { return !!document.body.classList; } },
{ src: '/plugin/notes/notes.js', async: true, condition: function() { return !!document.body.classList; } },
// { src: '/plugin/search/search.js', async: true, condition: function() { return !!document.body.classList; } },
// { src: '/plugin/remotes/remotes.js', async: true, condition: function() { return !!document.body.classList; } },
{ src: '/plugin/math/math.js', async: true, condition: function() { return !!document.body.classList; }}
]});
// Reveal.configure({slideNumber:true});
</script>
</body>
</html>