applications of genome sequencing projects
DESCRIPTION
Applications of genome sequencing projects. 1) Molecular Medicine 2) Energy sources and environmental applications 3) Risk assessment. 4) Bioarchaeology, anthropology, human evolution, human migration 5) DNA forensics 6) Agriculture, livestock breeding, and bioprocessing. - PowerPoint PPT PresentationTRANSCRIPT
Applications of genome sequencing projects
http://www.ornl.gov/hgmis/project/benefits.html
4) Bioarchaeology, anthropology, human evolution, human migration 5) DNA forensics 6) Agriculture, livestock breeding, and bioprocessing
1) Molecular Medicine 2) Energy sources and environmental applications 3) Risk assessment
Molecular medicine
improved diagnosis of disease
• earlier detection of genetic predisposition to disease
• Rational drug design• Gene therapy and control systems for drugs • pharmacogenomics "custom drugs"
DefinitionsDNA polymorphism: A DNA sequence that occurs in two or more
variant formsAlleles: any variations in genes at a particular location (locus)Haplotype: combination of alleles at multiple, tightly-linked loci that
are transmitted together over many generations Anonymous locus : position on genome with no known functionDNA marker: polymorphic locus useful for mapping studiesRFLP Variation in the length of a restriction fragment due to nucleotide
changes at a restriction site, detected by a particular probe / PCR.SNP: presence of two different nucleotides at the same loci in genomic
DNA from different individualsDNA fingerprinting: Detection of genotype at a number of unlinked
highly polymorphic loci using one probeGenetic testing: Testing for a pathogenic mutation in a certain gene
in an individual that indicate a person’s risk of developing or transmitting a disease
The spectrum of human diseases
Cystic fibrosis thalassemia
Huntington’s
cancer
<5%
‘Mendelian’ diseases (<5%)
Autosomal dominant inheritance: e.g huntington’s disease
Autosomal codominant inheritance e.g Hb-S sickle cell disease
Autosomal recessive inheritance: e.g cystic fibrosis, thalassemias
X-linked inheritance: e.g Duchenne muscular dystrophy (DMD)
How to identify disease genes
• Identify pathology• Find families in which the disease is
segregating• Find ‘candidate gene’• Screen for mutations in segregating
families
How to map candidate genes2 broad strategies have been used
A. Position independent approach (based on knowledge of gene function) 1) biochemical approach
2) animal model approach
B. Position dependent approach (based on mapped position)
Position independent approachPosition independent approach1) Biochemical: when the causative protein
has been identified E.g. Factor VIII haemophilia
Blood-clotting cascade in
which vessel damage causes a
cascade of inactive
factors to be converted to active factors
Blood tests determine if active form of each factor in the
cascade is present
Fig. 11.16 c
Techniques used to purify Factor VIII and clone the gene
Fig. 11.16 dFig. 11.16 d Hartwell
2) Animal model approachcompares animal mutant models for a phenotypically similar human disease. E.g. Identification of the SOX10 gene in human Waardenburg syndrome4 (WS4)
Dom (dominant megacolon) mutant mice shared phenotypic traits similar to human patient with WS4 (Hirschsprung disease, hearing loss, pigment abnormalities)
WS4 patients screened for SOX10 mutations
confirmed the role of this gene in WS4.
Dom mouse
Hirschsprung
Waardenburg
B) Positional dependent approachB) Positional dependent approachPositional cloning
identifies a disease gene based on only approximate chromosomal location. It is used when nature of gene product / candidate genes is unknown.
Candidate genes can be identified by a combination of their map position and expression, function or homology
B) Positional Cloning StepsStep 1 – Collect a large number of affected
families as possible Step 2 - Identify a candidate region based
on genetic mapping (~ 10Mb or more)Step 3 - Establish a transcript map,
cataloguing all the genes in the region Step 4- Identify potential candidate genesStep 5 – confirm a candidate gene and
screen for mutations in affected families
Step 2 - Identifying a candidate regionGenetic map of <1Mb
Genetic markers: RFLPs, SSLPs, SNPs
Linkage association: Lod scores (log of the odds): ratio of the odds that 2 loci are linked or not linkedneed a lod of 3 to prove linkage and a lod of -2 against linkage
Chromosmal abnormalities
Halpotype association
HapMap published in Oct27 2005 Nature
DNA markers/polymorphisms
RFLPs (restriction fragment length polymorphisms)
- Size changes in fragments due to the loss or gain of a restriction site
SSLPs (simple sequence length polymorphisms) or
microsatellite repeats. Copies of bi, tri or tetra nucleotide repeats of differing lengths e.g. 25 copies of a CA repeat can be detected using PCR analysis.
SNPs (single nucleotide polymorphisms)- presence of two different nucleotides at the same loci in genomic DNA from different individuals
RFLPs
Fig. 11.7 – genetics/ Hartwell
- Amplify fragment- Expose to
restriction enzyme- Gel
electrophoresis
e.g., sickle-cell genotyping with a PCR based protocol
SSLPs Similar principles used in detection of RFLPs However, no change in restriction sitesChanges in length of repeats
SNPs (single nucleotide polymorphisms)
SNP detection using allele-specific oligonucleotides (ASOs)
Very short probes (<21 bp) specific which hybridize to one allele or other
ASOs can determine genotype at any SNP locus
Fig. 11.8
presence of two different nucleotides at the same loci in genomic DNA from different individuals
Fig. 11.9 a-c
Hybridized and labeled with ASO for allele 1
Hybridized and labeled with ASO for allele 2
Fig. 11.9 d, e
Step 2 – identifying candidate regions
Chromosomal abnormalities: Rare patients who show chromosomal abnormalities linked to an unexplained phenotype. E.g DMD
Boy’BB’ with a single large Xp21 deletion who had- Duschenne’s muscular dystrophy (DMD gene)- Chronic granulomatoses disease (CYBB gene)- retinitis pigmentosa (RPGR gene)- McLeod phenotype (XK gene)
Step 3 – transcript map which defines all genes within the
candidate region Search browsers e.g. Ensembl Computational analysis
– Usually about 17 genes per 1000 kb fragment– Identify coding regions, conserved sequences
between species, exon-like sequences by looking for codon usage, ORFs, and splice sites etc
Experimental checks – double check sequences, clones, alignments etc
Direct searches – cDNA library screen
Step 4 – identifying candidate genes
Expression: Gene expression patterns can pinpoint candidate genes
Northern blot analysis reveals only one of candidate genes is expressed in lungs and pancreas
RNA expression by Northern blot or RT-PCR or microarrays
Look for misexpression (no expression, underexpression, overexpression)
CFTR gene
Step 4 – identifying candidate genes
Function: Look for obvious function or most likely function based on sequence analysis
e.g. retinitis pigmentosa
Candidate gene RHO part of phototransduction pathway
Linkage analysis mapped disease gene on 3q (close to RHO)Patient-specific mutations identified in a year
Step 4 – identifying candidate genes
Homology: look for homolog (paralog or ortholog)
Both mapped to 5q
Beals syndromefibrillin gene FBN2
Marfan syndrome fibrillin gene FBN1
Step 4 – identifying candidate genes
Animal models: look for homologous genes in animal models especially mouse
e.g. Waardenburg syndrome type 1Linkage analysis localised WS1 to 2q
Splotch mouse mutant showed similar phenotype
Could sp and WS1 be orthologous genes?
Pax-3 mapped to sp locusHomologous to HuP2
Splotch mouse WS type1
Step 5 – confirm a candidate gene
Mutation screeningSequence differences
- Missense mutations identified by sequencing coding region of candidate gene from normal and abnormal individualsTransgenic model- Knockout / knockin the mutant gene into a model organism
Modification of phenotype
Transgenic analysis can prove candidate gene is disease locus
Fig. 11.21
ReadingHMG3 by T Strachan & AP Read : Chapter 14
AND/ORGenetics by Hartwell (2e) chapter 11
Optional Reading on Molecular medicine Nature (May2004) Vol 429 Insight series• human genomics and medicine pp439 (editorial)• predicting disease using medicine by John Bell pp 453-
456.