Brian Canada Academic Computing Fellow and PhD Candidate in Integrative Biosciences
Disease Gene Candidate Prioritization by Integrative Biology Table of contents:
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Transcript of Disease Gene Candidate Prioritization by Integrative Biology Table of contents:
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Disease Gene Candidate Prioritization by Integrative Biology
Table of contents:
Background
Networks – deducing functional relationships from PPI data networksProtein interaction networksFunctional modules / network clusters
Phenotype associationGrouping disorders based on their phenotype.Biological implications of phenotype clusters.
Method and examplesIntegrating protein interaction data and phenotype associations in an automated
large scale disease gene finding platform
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Background
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Background
Finding genes responsible for major genetic disorders can lead to diagnostics, potential drug targets, treatments and large amounts of information about molecular cell biology in general.
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BackgroundMethods for disease gene finding post genome era (>2001):
Mircodeletions Translocations
http://www.med.cmu.ac.th/dept/pediatrics/06-interest-cases/ic-39/case39.html
http://www.rscbayarea.com/images/reciprocal_translocation.gif
Linkage analysis
Fagerheim et al 1996.
1q21-1q23.1
chr1:141,600,00-155,900,000
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BackgroundAutomated methods for disease gene finding int the post genome era (>2001):
?
(Perez-Iratxeta, Bork et al. 2002) (Freudenberg and Propping 2002)(van Driel, Cuelenaere et al. 2005)(Hristovski, Peterlin et al. 2005)
Grouping:
Tissues, Gene Ontology, Gene Expression, MeSH terms …….
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Disease Gene Finding.
Summery
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Protein interactionnetworksFunctional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.Status of pipeline / infrastructure
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Networks and functional modules
Deducing functional relationships from protein interaction networks
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daily
weekly
monthly
(de Licthenberg et al.)
Networks
Social Networks, The CBS interactome
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daily
weekly
monthly
(de Licthenberg et al.)
Social Networks, The CBS interactome
Networks
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Protein interaction networks of physical interactions.
(Barabasi and Oltvai 2004).
Networks
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Extracting functional data from protein interaction networks
InWeb
Homo Sapiens
The Ach receptor involved in Myasthenic Syndrome.
Dynamic funcional module:
Eg:
Cell cycle regulation
Metabolism
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Trans-organism protein interaction network
Orthologs?
Orthologous genes are direct descendants of a gene in a common ancestor:
(O'Brien K, Remm et al. 2005)
S.Cerevisiae
D. Melanogaster
H.Sapiens
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D. Melanogaster Experim.
C. Elegans Experim.
S. Cerevisiae Experim.
H.Sapiens MOSAIC
Trans-organism protein interaction network
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Infrastructure status
BIND
IntAct
DIP
MINT
HPRD
Hand-curated
sets
PPI – pred.
GRID
InWeb
Homo Sapiens
Trans-organism ppi
pipeline>122.000 int.
> 22.000 genes
Scoring
A) Topological
B) No publ.
Extraction
perl modules
Direct SQL access
XML or SIF output
Web serverOpis
Command lineInweb.pl
CBS Datawarehouse
Download/reformat db’s
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Protein interaction networks scoring the interactions
Number of methods that have shown the same interaction
Number of independent studies that have shown the same interaction
Number of common interaction partners
Cluster issues
Large scale / small scale issues
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Disease Gene Finding.
Summery
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Protein interactionnetworksFunctional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.Status of pipeline / infrastructure
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Phenotype association
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Phenotype association
Absent liver peroxisomesHepatomegalyIntrahepatic biliary dysgenesisProlonged neonatal jaundicePyloric hypertrophyPatent ductus arteriosusVentricular septal defectsBell-shaped thoraxSmall adrenal glandsAbsent renal peroxisomesClitoromegalyCryptorchidismHydronephrosisHypospadiasRenal cortical microcystsFailure to thriveAbnormal electroretinogramAbnormal helicesAnteverted naresBrushfield spotsCataractsCorneal clouding
Epicanthal foldsFlat faciesFlat occiputGlaucomaHigh arched palateHigh foreheadHypertelorismLarge fontanellesMacrocephalyMicrognathiaNystagmusPale optic diskPigmentary retinopathyPosteriorly rotated earsProtruding tongueRedundant skin folds of neckRound faciesSensorineural deafnessTurribrachycephalyUpward slanting Hyporeflexia or areflexiaHypotonia
PolymicrogyriaSeizuresSevere mental retardationSubependymal cystsPulmonary hypoplasiaCubitus valgusDelayed bone ageMetatarsus adductusRocker-bottom feetStippled epiphyses (especially patellar and acetabular regions)Talipes equinovarusTransverse palmar creaseUlnar deviation of handsWide cranial suturesTransverse palmar creaseHeterotopias/abnormal migrationHypoplastic olfactory lobes
Zelwegger syndrome
palpebral fissuresAutosomal recessiveAlbuminuriaAminoaciduriaDecreased dihydroxyacetone phosphate acyltransferase (DHAP-AT) activityDecreased plasmologenElevated long chain fatty acidsElevated serum iron and iron binding capacityIncreased phytanic acidPipecolic acidemiaBreech presentationDeath usually in first year of lifeGenetic heterogeneityInfants occasionally mistaken as having Down syndromeAgenesis/hypoplasic corpus collosum
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Phenotype association
Word vectors
Phenotype Sim. Score
Adrenoleukodystrophy (202370) 0.781
Hyperpipecolatemia (239400) 0.703
Cerebrohepatorenal Syndr. (214110) 0.682
Refsum Disease (266510) 0.609
Reference : Zelwegger Syndrome (214100)
A relationship between the infantile form of Refsum disease and Zellweger syndrome was suggested by the observations of Poulos et al. (1984) in 2 patients. In the infantile form of Refsum disease, as in Zellweger syndrome, peroxisomes are deficient and peroxisomal functions are impaired (Schram et al., 1986). Clinically, infantile Refsum disease, ZWS, and adreno-leukodystrophy have several overlapping features. (Stokke et al., 1984).(http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=266510)
214100 202370
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Phenotype association
Word vectorsPhenotype association network
Cerebro-Hepato-
renal
Zelwegger
Refsum
Adrenoleuko-dystrophy
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Disease Gene Finding.
Summery
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Protein interactionnetworksFunctional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.
![Page 22: Disease Gene Candidate Prioritization by Integrative Biology Table of contents:](https://reader036.fdocuments.in/reader036/viewer/2022081520/56814e3f550346895dbbac87/html5/thumbnails/22.jpg)
Method –
Proof of concept
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Method
InWeb
Homo Sapiens
Word vectors
Phenotype clustering
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Results - Benchmark
MIM RANK GENE Probability TRUE
278800 1 ENSG00000032514 0.300326793109544 *278800 2 ENSG00000188611 0.0125655342047565278800 2 ENSG000001382970.0125655342047565278800 2 ENSG000001654060.0125655342047565278800 3 ENSG000001966930.0121357313793756278800 3 ENSG000001855320.0121357313793756278800 4 ENSG000001979100.00680983722337082278800 4 ENSG000001653830.00680983722337082278800 4 ENSG000001725380.00680983722337082. . . .. . . .. . . .. . . .. . . .278800 4 ENSG000001655110.00680983722337082278800 4 ENSG000001823540.00680983722337082278800 4 ENSG000001726610.00680983722337082278800 4 ENSG000001655070.00680983722337082278800 4 ENSG000001784400.00680983722337082278800 4 ENSG000001382990.00680983722337082278800 4 ENSG000001977040.00680983722337082278800 4 ENSG000000127790.00680983722337082278800 4 ENSG000001973540.00680983722337082278800 4 ENSG000001890900.00680983722337082278800 4 ENSG000001075510.00680983722337082278800 4 ENSG000001265420.00680983722337082278800 4 ENSG000001983640.00680983722337082278800 4 ENSG000001858490.00680983722337082278800 4 ENSG000001501650.00680983722337082278800 4 ENSG000001288150.00680983722337082278800 4 ENSG000001786450.00680983722337082278800 4 ENSG000001382930.00680983722337082278800 4 ENSG000001768330.00680983722337082278800 4 ENSG000001792510.00680983722337082278800 4 ENSG000001698260.00680983722337082278800 4 ENSG000001726780.00680983722337082278800 4 ENSG000001977520.00680983722337082278800 5 ENSG000001076430.00412573091718715278800 6 ENSG000001657330.000263885640603109
278800 7 ENSG00000169813 6,63E+07
DE SANCTIS-CACCHIONE SYNDROME
Gene map locus 10q11 >12MB area, 103 ranked genes
CLINICAL FEATURES
De Sanctis and Cacchione (1932) reported a condition, which they called 'xerodermic idiocy,' in which patients had xeroderma pigmentosum, mental deficiency, progressive neurologic deterioration, dwarfism, and gonadal hypoplasia.http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=278800
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Results – Benchmarking
DE SANCTIS-CACCHIONE
SYNDROME Ranked 1
Probability: 0.300326793109544
DNA excision repair
protein ERCC-6
Eukaryotic translation initiation factor 4E (eIF4E)
DNA excision repair protein ERCC-2
Eukaryotic initiation factor 4A-I (eIF4A-I)
*126340 DNA REPAIR DEFECT EM9 OF CHINESE HAMSTER OVARY CELLS, COMPLEMENTATION OF; EM9
#133540 COCKAYNE SYNDROME CKN2
#278730 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP D
#278800 DE SANCTIS-CACCHIONE SYNDROME
#601675 TRICHOTHIODYSTROPHY
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Results – Benchmarking
DE SANCTIS-CACCHIONE
SYNDROME Ranked 2
Probability 0.0125655342047565
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Disease Gene Finding.
Summery
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Protein interactionnetworksFunctional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associationsin an automated large scale disease gene finding platformproof of concept.
![Page 28: Disease Gene Candidate Prioritization by Integrative Biology Table of contents:](https://reader036.fdocuments.in/reader036/viewer/2022081520/56814e3f550346895dbbac87/html5/thumbnails/28.jpg)
Acknowledgments
Disease Gene Finding :
Olga RiginaOlof Karlberg
Zenia M. Størling Páll Ísólfur Ólason
Kasper LageAnders GormAnders HinsbyYves Moreau
Niels TommerupSøren Brunak