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Identifying drug-targetable key drivers of disease
Published on Jul 18, 20161148 Views
In the last few years genome-wide association studies have revealed over 10,000 genetic risk factors for disease. For many disorders it is now clear that there are dozens of variants involved, preclud
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Identifying drug-targetable key drivers of disease00:00
To capture something small you need something big - 102:26
Dna02:48
To capture something small you need something big - 202:52
To capture something small you need something big - 303:05
Large amounts of data now available04:02
Goal: better diagnose and treat patients04:16
Seven years of GWAS studies05:01
Problem of life science community05:48
Expression quantitative trait locus (eQTL)07:25
Far majority of genetic risk factors affect gene expression11:05
Get larger sample-sizes: meta-analysis in 5,311 samples12:06
Goal18:44
Possible to identify all these downstream effects?21:58
Lifelines Deep22:46
The opportunities23:34
Trans-meQTL meta-analysis in 3,840 samples - 123:58
Trans-meQTL meta-analysis in 3,840 samples - 227:15
Trans-meQTL meta-analysis in 3,840 samples - 333:06
Detecting cell-type dependent eQTLs in whole blood38:40
Context specific cis-eQTL analysis in 2,116 samples - 142:15
Context specific cis-eQTL analysis in 2,116 samples - 243:12
Regulatory network reconstruction in 2,116 samples43:52
But is this relevant for my patients?45:14
But what about patients we see?45:51
Smart ways to filter?47:51
Transcriptome of the Netherlands - 148:09
Transcriptome of the Netherlands - 248:41
Remove non-genetic expression variation49:44
Strategies50:47
Amplifier can change many aspects of music51:38
A control panel that determines gene expression?52:37
800 ‘transcriptional components’: Component 1 - 5053:29
Component 1 and 253:43
Transcriptional component 354:17
Predicted gene functions: www.genenetwork.nl55:33
GeneNetwork gene function predictions55:54
DEPICT: New prioritisation algorithm for GWAS57:35
Components 51 - 80059:32
Some component show weird behaviour01:00:05
Detection cytogenetic aberration in expression data01:03:34
Identifying five chromosome duplications01:04:20
Comparison of arrayCGH and cytogenetic RNA profiles01:05:04
Known driver genes in amplification and deletion peaks01:05:53
Amount of cytogenetic aberrations01:07:19
Forest fire01:08:40
Complexity: Forest fire - 101:09:24
Complexity: Forest fire - 201:10:09
Complexity: Forest fire - 301:10:20
Complexity: Forest fire - 401:10:31
Complexity: Forest fire - 501:10:47
More accurate reference values for genes01:11:50
Explosion of publicly available RNA-seq data01:12:33
Derive SNP genotypes from RNA-seq data01:13:42
Calling genotypes in RNA-seq data - 101:14:40
Calling genotypes in RNA-seq data - 201:14:58
Tissue-specific eQTL mapping for free01:15:22
Allele specific effects for rare variants01:15:53
TRIM51BP likely causal gene01:16:03
Integration of different datasets01:17:30
Conclusions01:17:45
Acknowledgements01:18:00