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applications of genome
sequencing projects
1) Molecular Medicine
2) Energy sources and environmental
applications
3) Risk assessment
4) Bioarchaeology, anthropology, evolution,
and human migration
5) DNA forensics
6) Agriculture, livestock breeding, and
bioprocessing
http://www.ornl.gov/hgmis/project/benefits.html
Molecular medicine
improved diagnosis of disease
eearlier detection of genetic predisposition to disease
rational drug design
gene therapy and control systems for drugs
ppharmacogenomics "custom drugs"
sequence variation within species
• Alleles
– any variations in the genome at a particular location (locus)
• Polymorphic
– two or more alleles at a locus
• Polymorphism
– the particular variation
• DNA marker
– polymorphic locus useful for mapping studies, disease
diagnosis
• Anonymous locus
– position on genome with no known function
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 polymorphism) 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)-Sites
resulting from a single change in individual bp.
RFLPs
- Amplify fragment
- Expose to restriction
enzyme
- Gel electrophoresis
e.g., sickle-cell
genotyping with a
PCR based protocol
Fig. 11.7 – genetics/ Hartwell
SSLPs
Similar principles used in detection of RFLPs
However, no change in restriction sites
Changes in length of repeats
SNPs (single nucleotide polymorphisms)
Sites resulting from a single change in individual bp
SNP detection using allele-specific oligonucleotides
(ASOs)
• Very short probes (<21 bp) specific which
hybridize to one allele or other
• Such probes are called ASOs
Fig. 11.8
ASOs can
determine
genotype at
any SNP
locus
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
Mendelian inheritance patterns
Lungs affected in cystic
fibrosis
Pappenheimer bodies in
thalassemias
Huntington's Chorea
Complex traits
Skin colour
cancer
Incomplete penetrance
– when a mutant genotype does not
always cause a mutant phenotype
• No environmental factor associated with
likelihood of breast cancer
• Positional cloning identified BRCA1 as one
gene causing breast cancer.
– Only 66% of women who carry BRCA1 mutation
develop breast cancer by age 55
• Incomplete penetrance hampers linkage
mapping and positional cloning
– Solution – exclude all nondisease individuals form
analysis
– Requires many more families for study
Variable expressivity - Expression of a mutant
trait differs from person to person
• Phenocopy
– Disease phenotype is not caused by any
inherited predisposing mutation
– e.g. BRCA1 mutations
• 33% of women who do not carry BRCA1 mutation
develop breast cancer by age 55
Genetic heterogeneity
Mutations at more than one locus cause same
phenotype
e.g. thalassemias
– Caused by mutations in
either the a or b-globin
genes.
– Linkage analysis studies
therefore always
combine data from
multiple families
• Polygenic inheritance
– Two or more genes interact in the expression of
phenotype
• QTLs, or quantitative trait loci
– Unlimited number of transmission patterns for QTLs
» Discrete traits – penetrance may increase with number
of mutant loci
» Expressivity may vary with number of loci
– Many other factors complicate analysis
» Some mutant genes may have large effect
» Mutations at some loci may be recessive while others
are dominant or codominant
Polygenic inheritance
E.g heart attacks or cholesterol levels
Sudden cardiac death (SCD)
Haplotype association analysis
• specific combination of 2 or more DNA marker
alleles situated close together on the same DNA
molecule (homolog).
• SNPs most commonly used markers in haplotypes.
• series of closely linked mutations accumulate
over time in the surviving generation derived
from a common ancestor.
• powerful genetic tool for identifying ancient
genetic relationships.
• Alleles at separate loci that are associated with
each other at a frequency that is significantly
higher than that expected by chance, are said to
be in linkage disequilibrium
Formation of haplotypes over time
Ancient disease loci are
associated with haplotypes
• Start with population genetically isolated for a long
time such as Icelanders or Amish
• Collect DNA samples from subgroup with disease
• Also collect from equal number of people without
disease
• Genotype each individual in subgroups for
haplotypes throughout entire genome
• Look for association between haplotype and disease
phenotype
• Association represents linkage disequilibrium
• If successful, provides high resolution to narrow
parts of chromosomes
Haplotype analysis provides high
resolution gene mapping
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 genes
2 broad strategies have been used
• A. Position independent approach (based on
knowledge of gene function)
1) biochemical approach
2) candidate gene approach
3) animal model approach
• B. Position dependent approach (based on
mapped position)
Position independent approach
1) biochemical approach
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.
Fig.11.16
11.16d d
2) Candidate gene approach
based on previously isolated human genes that may
have a role in disease using expression array
experiments (mRNA samples different in patients)
Disease locus candidates identified based on
 candidate
possible
role
inhavedisease
physiology or
Mapping
genes: 2 broad
strategies
been used
A.
Position independent approach (based on knowledge of gene function)
1) biochemical approach
 map
to the same chromosomal area & encode most
2) candidate
gene approach
3) animal model approach
likely
protein
B.
Position
dependent approach
(based on mapped position)
reverse genetics / positional cloning
e.g. hereditary retinal degeneration
 several genes encoding proteins involved in
phototransduction identified
 choice of candidate - rhodopsin gene
mutations identified in patients with retinitis pigmentosa
3) Animal model approach
compares animal mutant models in a phenotypically
similar human disease.
Identification of the SOX10 gene in human
Waardenburg syndrome4 (WS4)
Dom (dominant megacolon) mutant mice shared
phenotypic traits (Hirschsprung disease, hearing loss
and pigment abnormalities) similar to these human
patients.
WS4 patients screened for SOX10 mutations
confirmed the role of this gene in WS4.
B) Positional dependent
approach
Positional 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
Positional 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 Steps
Step 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 contig of clones across the region using readymade contigs from the HG database
Step 4 - Establish a transcript map, cataloguing all the genes in the
region using either
 Combination of database searching and transcript mapping
(Treacher Collins syndrome)
 Chromosomal aberrations (Duschenne muscular dystrophy)
 Linkage disequilibrium (Cystic fibrosis)
Step 4 - Identify potential candidate genes
Step 5 – screen for mutations among affected families
• Once region of
chromosome is
identified, a
high resolution
mapping is
performed with
additional
markers to
narrow down
region where
gene may lie
Fig. 11.17
Positional cloning –
identifying candidate genes
• Once region of chromosome has been
narrowed down by linkage analysis to 1000
kb or less, all genes within are identified
• Candidate genes
– Usually about 17 genes per 1000 kb fragment
– Identify coding regions
• Computational analysis to identify conserved
sequences between species
• Computational analysis to identify exon-like sequences
by looking for codon usage, ORFs, and splice sites
• Appearance on one or more EST clones derived from
cDNA
Computational analysis of genomic
sequences to identify candidate genes
Fig. 11.19
Gene expression patterns can
pinpoint candidate genes
• Look in public database of EST
sequences representing certain
tissues
• Northern blot
– RNA transcripts in the cells of a
particular tissue (e.g., with disease)
separated by electrophoresis and probed
with candidate gene sequence
– Expression array analysis
Northern blot example showing SRY candidate
for testes determining factor is expressed in
testes, but not lung, overy, or kidney
Fig. 11.20
Positional cloning – Find the gene
responsible for the phenotype
– Expression patterns
• RNA expression assayed by Northern blot or
PCR amplification of cDNA with primers
specific to candidate transcript
• Look for misexpression (no expression,
underexpression, overexpression)
– Sequence differences
• Missense mutations identified by sequencing
coding region of candidate gene from normal
and abnormal individuals
– Transgenic modification of phenotype
• Insert the mutant gene into a model organism
Transgenic analysis can prove
candidate gene is disease locus
Fig. 11.21
Northern blot analysis reveals only one of candidate
genes is expressed in lungs and pancreas
CF gene
Fig. 11.22 b
Reading
HMG3 by T Strachan & AP Read : Chapter 14
AND/OR
Genetics 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.
•
Mapping complex disease loci in whole genome studies by
CS Carlson et al pp446-452