Download Phenotype association

Disease Gene Finding.
Table of contents:
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Networks
Biological networks
Functional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.
Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associations in an automated large
scale disease gene finding platform
Proof of concept.
Abstract
Aim
Find new disease genes.
Means
Use protein interaction networks and phenotype association
networks for inferring phenotype gneotype relationships.
Proof
Interesting candidates are reported to experimental
collaborators who perform mutational analysis in patient
material.
Background
Background
Aim
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.
Background
Methods for disease gene finding post genome era (>2001):
Mircodeletions
http://www.med.cmu.ac.th/dept/pediatrics/06interest-cases/ic-39/case39.html
Translocations
http://www.rscbayarea.com/image
s/reciprocal_translocation.gif
Linkage analysis
Fagerheim et al 1996.
1q21-1q23.1
chr1:141,600,00-155,900,000
Which is Keyser Söze ?
Background
Bioinformatic methods for disease gene finding post genome era (>2001):
?
Grouping:
Tissues, Gene Ontology, Gene
Expression, MeSH terms …….
(Perez-Iratxeta, Bork et al. 2002)
(Freudenberg and Propping 2002)
(van Driel, Cuelenaere et al. 2005)
(Hristovski, Peterlin et al. 2005)
Disease Gene Finding.
Table of contents:
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Networks
Biological networks
Functional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.
Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associations in an automated large
scale disease gene finding platform
Proof of concept.
Networks and functional modules
Deducing functional relationships from network theory
Networks and functional modules
Deducing functional relationships from network theory
Network theory is boooooooooring
Networks
Text mining of full text corpora e.g PubMed Central
http://www.biosolveit.de/ToPNet/screenshots/fig1.html
Networks
Protein interaction networks of physical interactions.
(Barabasi and Oltvai 2004).
Networks
daily
Social Networks, The CBS interactome
weekly
monthly
(de Licthenberg et al.)
Genetically heterogeneous disorders and protein interactions
(de Licthenberg et al.)
http://www.biosolveit.de/ToPNet/screenshots/fig1.html
(Barabasi and Oltvai 2004).
(Barabasi and Oltvai 2004).
Genetically heterogeneous disorders and protein interactions
(de Licthenberg et al.)
http://www.biosolveit.de/ToPNet/screenshots/fig1.html
(Barabasi and Oltvai 2004).
(Barabasi and Oltvai 2004).
Genetically heterogeneous disorders and protein interactions
Degree (k) :
Number of connections
Protein : Number of interaction partners
Social : Number of collaborators / friends
Degree distribution P(k) :
The probability that a selected node
has exactly k links:
Protein : probability of k interaction partners
Social : Probability of k collaborators / friends
(Barabasi and Oltvai 2004).
Genetically heterogeneous disorders and protein interactions
Clustering coefficient C(k)
Average clustering coefficient of all
nodes with k links.
The average tendency of nodes to form clusters or
groups.
Protein : Tendency of interaction partners to
interact with each other
Social : Tendency of collaborators / friends to be
friends / collaborators of each other.
Hubs, connect distant parts of the network.
Ultra small world
(Barabasi and Oltvai 2004).
Genetically heterogeneous disorders and protein interactions
daily
Social Networks, The CBS interactome
weekly
monthly
(de Licthenberg et al.)
Genetically heterogeneous disorders and protein interactions
daily
Social Networks, The CBS interactome
weekly
monthly
(de Licthenberg et al.)
Genetically heterogeneous disorders and protein interactions
Genetically heterogeneous disorders and protein interactions
Network clustering
Functional modules
Genetically heterogeneous disorders and protein interactions
Network clustering
Functional modules
The Ach receptor
involved in
Myasthenic
Syndrome.
Edge/physical interaction
Node/protein
Dynamic
funcional
module:
Eg:
Cell cycle
regulation
Metabolism
Genetically heterogeneous disorders and protein interactions
•Grouping of proteins
that are functionally
undescribed. (30% of
proteins in completely
sequenced geneomes
cannot be appointed
to a specific biological
function).
•70-80% of interacting
proteins share at least
one function.
Edge/physical interaction
Node/protein
•Grouping of proteins
based on function not
biochemistry/sequenc
e alignment.
•Correlation between
mutation in
interacting proteins
and phenotype.
•Disease gene
finding!!
Disease Gene Finding.
Table of contents:
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Networks
Biological networks
Functional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.
Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associations in an automated large
scale disease gene finding platform
Proof of concept.
Phenotype association
Phenotype association
Smith-Lemi-Opitz Syndrome
Constipation
Malrotation
Poor suck
Pyloric stenosis
Vomiting
Atrial septal defect
Coarctation of aorta
Patent ductus arteriosus
Ventricular septal defect
Ambiguous genitalia
Bifid scrotum
Cryptorchidism
Cystic kidneys
Hydronephrosis
Hypoplastic scrotum
Hypospadias
Micropenis
Microurethra
Renal agenesis
Single kidney
Ureteropelvic junction
obstruction
Birth weight <2500gm
Failure to thrive
Short stature
Anteverted nares
Bitemporal narrowing
Broad alveolar margins
Broad, flat nasal bridge
Cataracts
Cleft palate
Dental crowding
Epicanthal folds
Hypertelorism
Hypoplastic tongue
Large central front teeth
Low-set ears
Microcephaly
Micrognathia
Posteriorly rotated ears
Ptosis
Strabismus
Autosomal recessive
Elevated 7dehydrocholesterol
Low cholesterol
Allelic with Rutledge lethal multiple
congenital anomaly syndrome
Estimated incidence 1/20,000 1/40,000
Caused by mutations in the delta-7dehydrocholesterol reductase gene
Abnormal sleep pattern
Aggressive behavior
Frontal lobe hypoplasia
Hydrocephalus
Hypertonia (childhood)
Hypotonia (early infancy)
Mental retardation
Periventricular gray matter
heterotopias
Seizures
Self injurious behavior
Breech presentation
Decreased fetal movement
Hypoplastic lungs
Incomplete lobulation of
the lungs
Hip dislocation
Hip subluxation
Limb shortening
Metatarsus adductus
Overriding toes
Postaxial polydactyly
Proximally placed
thumbs
Short thumbs
Short, broad toes
Stippled epiphyses
Syndactyly of second
and third toes
Talipes calcaneovalgus
Blonde hair
Eczema
Facial capillary
hemangioma
Severe photosensitivity
Shrill screaming
Phenotype association
Word vectors
(Brunner and van Driel 2004)
Phenotype association
Word vectors
(Brunner and van Driel 2004)
Phenotype association
Word vectors
The Ach receptor
involved in
Myasthenic
Syndrome.
Phenotype association
Word vectors
Phenotype association
Word vectors
ACHOO SYNDROME 100820
Gastric Sneezing
(Brunner and van Driel 2004)
137130 0.441407
Disease Gene Finding.
Table of contents:
Background
Why do we want to find disease genes, how has it been done until now?
Networks – deducing functional relationships from network theory
Networks
Biological networks
Functional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.
Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associations in an automated large
scale disease gene finding platform
Proof of concept.
Method –
Proof of concept
Method
Method
Proof of Concept
Input all critical intervals in OMIM (Approx 900)
%125480 MAJOR AFFECTIVE DISORDER 1
%132800 MULTIPLE SELF-HEALING SQUAMOUS EPITHELIOMA
%137100 IMMUNOGLOBULIN A DEFICIENCY SUSCEPTIBILITY 1
%137580 GILLES DE LA TOURETTE SYNDROME
%143850 ORTHOSTATIC HYPOTENSIVE DISORDER
%156240 MESOTHELIOMA, MALIGNANT
%157900 MOEBIUS SYNDROME 1
%177900 PSORIASIS SUSCEPTIBILITY 1
%209850 AUTISM
%252350 MOYAMOYA DISEASE 1
%608631 ASPERGER SYNDROME, SUSCEPTIBILITY TO, 2 ;;ASPG2
%301845 BAZEX SYNDROME; BZX
%608389 BRANCHIOOTIC SYNDROME 3
%600175 SPINAL MUSCULAR ATROPHY
%600318 DIABETES MELLITUS, INSULIN-DEPENDENT, 3; IDDM3 ;;INSULIN-DEPENDENT DIABETES MELLITUS 3
%601042 CHOREOATHETOSIS/SPASTICITY
%601388 DIABETES MELLITUS, INSULIN-DEPENDENT, 12; IDDM12 ;;INSULIN-DEPENDENT DIABETES MELLITUS 12
%601493 CARDIOMYOPATHY, DILATED, 1C; CMD1C
%603694 DIABETES MELLITUS, NONINSULIN-DEPENDENT, 3 ;;NIDDM3;; NONINSULIN-DEPENDENT DIABETES US 3
%604288 CARDIOMYOPATHY, DILATED, 1H; CMD1H
Proof of Concept
Input all critical intervals in OMIM (Approx 900)
%125480 MAJOR AFFECTIVE DISORDER 1
%132800 MULTIPLE SELF-HEALING SQUAMOUS EPITHELIOMA
%137100 IMMUNOGLOBULIN A DEFICIENCY SUSCEPTIBILITY 1
%137580 GILLES DE LA TOURETTE SYNDROME
%143850 ORTHOSTATIC HYPOTENSIVE DISORDER
%156240 MESOTHELIOMA, MALIGNANT
%157900 MOEBIUS SYNDROME 1
%177900 PSORIASIS SUSCEPTIBILITY 1
%209850 AUTISM
%252350 MOYAMOYA DISEASE 1
%608631 ASPERGER SYNDROME, SUSCEPTIBILITY TO, 2 ;;ASPG2
%301845 BAZEX SYNDROME; BZX
%608389 BRANCHIOOTIC SYNDROME 3
14q23.1
SIX1
%600175 SPINAL MUSCULAR ATROPHY
%600318 DIABETES MELLITUS, INSULIN-DEPENDENT, 3; IDDM3 ;;INSULIN-DEPENDENT DIABETES MELLITUS 3
%601042 CHOREOATHETOSIS/SPASTICITY
%601388 DIABETES MELLITUS, INSULIN-DEPENDENT, 12; IDDM12 ;;INSULIN-DEPENDENT DIABETES MELLITUS 12
%601493 CARDIOMYOPATHY, DILATED, 1C; CMD1C 10q21-q23 VINC_HUMAN
%603694 DIABETES MELLITUS, NONINSULIN-DEPENDENT, 3 ;;NIDDM3;; NONINSULIN-DEPENDENT DIABETES US 3
%604288 CARDIOMYOPATHY, DILATED, 1H; CMD1H
%608389 BRANCHIOOTIC SYNDROME 3
14q23.1
SIX1
Proof of Concept
SIX1 mutations cause branchio-oto-renal syndrome by
disruption of EYA1-SIX1-DNA complexes.
Ruf RG, Xu PX, Silvius D, Otto EA, Beekmann F, Muerb UT, Kumar S, Neuhaus TJ, Kemper MJ, Raymond
RM Jr, Brophy PD, Berkman J, Gattas M, Hyland V, Ruf EM, Schwartz C, Chang EH, Smith RJ, Stratakis CA,
Weil D, Petit C, Hildebrandt F.
Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA.
Urinary tract malformations constitute the most frequent cause of chronic renal failure in the first two decades of
life. Branchio-otic (BO) syndrome is an autosomal dominant developmental disorder characterized by hearing loss.
In branchio-oto-renal (BOR) syndrome, malformations of the kidney or urinary tract are associated.
Haploinsufficiency for the human gene EYA1, a homologue of the Drosophila gene eyes absent (eya), causes BOR
and BO syndromes. We recently mapped a locus for BOR/BO syndrome (BOS3) to human chromosome 14q23.1.
Within the 33-megabase critical genetic interval, we located the SIX1, SIX4, and SIX6 genes, which act within a
genetic network of EYA and PAX genes to regulate organogenesis. These genes, therefore, represented excellent
candidate genes for BOS3. By direct sequencing of exons, we identified three different SIX1 mutations in four
BOR/BO kindreds, thus identifying SIX1 as a gene causing BOR and BO syndromes. To elucidate how these
mutations cause disease, we analyzed the functional role of these SIX1 mutations with respect to protein-protein
and protein-DNA interactions. We demonstrate that all three mutations are crucial for Eya1-Six1 interaction, and
the two mutations within the homeodomain region are essential for specific Six1-DNA binding. Identification of SIX1
mutations as causing BOR/BO offers insights into the molecular basis of otic and renal developmental diseases in
humans.
PMID: 15141091 [PubMed - indexed for MEDLINE]
%604288 CARDIOMYOPATHY, DILATED, 1C; CMD1C
10q21-q23 VINC_HUMAN
Proof of Concept
Metavinculin mutations alter actin interaction in dilated
cardiomyopathy.
Olson TM, Illenberger S, Kishimoto NY, Huttelmaier S, Keating MT, Jockusch BM.
Department of Pediatrics and the Division of Cardiology, University of Utah, Salt Lake City, Utah, USA.
[email protected]
BACKGROUND: Vinculin and its isoform metavinculin are protein components of intercalated discs, structures that
anchor thin filaments and transmit contractile force between cardiac myocytes. We tested the hypothesis that
heritable dysfunction of metavinculin may contribute to the pathogenesis of dilated cardiomyopathy (DCM).
METHODS AND RESULTS: We performed mutational analyses of the metavinculin-specific exon of vinculin in 350
unrelated patients with DCM. One missense mutation (Arg975Trp) and one 3-bp deletion (Leu954del) were
identified. These mutations involved conserved amino acids, were absent in 500 control individuals, and
significantly altered metavinculin-mediated cross-linking of actin filaments in an in vitro assay. Ultrastructural
examination was performed in one patient (Arg975Trp), revealing grossly abnormal intercalated discs. A potential
risk-conferring polymorphism (Ala934Val), identified in one DCM patient and one control individual, had a less
pronounced effect on actin filament cross-linking. CONCLUSIONS: These data provide genetic and functional
evidence for vinculin as a DCM gene and suggest that metavinculin plays a critical role in cardiac structure and
function. Disruption of force transmission at the thin filament-intercalated disc interface is the likely mechanism by
which mutations in metavinculin may lead to DCM.
How well does it work ?
How well does the score work ?
Is it unbiased ?
Reveals novel global aspect of
human diseases
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
Networks
Biological networks
Functional modules / network clusters
Phenotype association
Grouping disorders based on their phenotype.
Biological implications of phenotype clusters.
Method and examples
Combining network theory and phenotype associations
in an automated large scale disease gene finding platform
Proof of concept.
Hopes & Dreams for the future
Disease Genes -Treasures in
a genomic jungle.
CBS - the Lara Croft of
disease gene finding.
Acknowledgements
Disease Gene Finding group at CBS:
Olga Rigina
: Database handling, Computer Scientist
Olof Karlberg
: Programmer, Pharmacologist
Zenia M. Larsen : Expert in diabetes and related disorders, Engineer
Páll Ísólfur Ólason : Engineer, data flow, text mining.
Kasper Lage
: Proteomics, genomics, diseases, Human Biologist
Anders Hinsby : Proteomics, mass spec. expert, Human Biologist