Transcript Chapter 13 – Genetic Mapping of Mendelian Characters

Complex Disease and
Susceptibility
Gene
Gene
Gene
Gene
Environment
Disease A
Disease B
Disease C
Multifactorial disorders
Complex Disease and
Susceptibility
• Single gene disorders
–
–
–
–
–
–
Huntington’s
Fragile X
SCA1
DMD
Werner’s syndrome
Cystic fibrosis
• Multifactorial
–
–
–
–
–
–
–
Heart disease
Cancer
Stroke
Asthma
Diabetes
Alzheimer’s
Parkinson’s
Cancer Statistics
68% of new cases involve individuals 60 years and
older
Why does cancer incidence
increase with age?
Cancer is the natural endpoint of a multicellular
animal
Balance between mutation rate and losing control
Genetic Mutations Leading to Cancer
“6-hit model”
10-7 mutations per gene per cell generation
1013 cells in a human
For one cell to collect 6 mutations:
10-42 x 1013 = 10-29
Thus, 1 in 1029 chance
Then why do we get cancer?
Genetic Mutations Leading to Cancer
Multistage evolution model
Successive mutations provide a growth
advantage, expanding that population of
mutants
Genomic instability occurs when DNA repair
mechanisms are mutated
Genes altered in Cancer
Oncogenes
EGFR
PDGFR
ABL
SRC
PI3K
Akt
Bcl2
b-catenin
Genomic
Stability
ATM
BRCA1
BRCA2
BARD
XPA
Tumor
Suppressors
APC
Axin
p53
PTEN
Rb
TSC1,2
p16 INK4A
p53 – Guardian of the Genome
G1
Rb
M
G1/S
G2/M
p53
S
G2
Apoptosis
Genes altered in Cancer
Oncogenes
EGFR
PDGFR
ABL
SRC
PI3K
Akt
Bcl2
b-catenin
Genomic
Stability
ATM
BRCA1
BRCA2
BARD
XPA
Tumor
Suppressors
APC
Axin
p53
PTEN
Rb
TSC1,2
p16 INK4A
By Clark et al; Part B Cropped from original Fig 1 by SLE346_B3 [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)]
Ceshi Chen, Arun K. Seth and Andrew E. Aplin
Complex Disease and
Susceptibility
• Single gene disorders
–
–
–
–
–
–
Huntington’s
Fragile X
SCA1
DMD
Werner’s syndrome
Cystic fibrosis
• Multifactorial
–
–
–
–
–
–
–
Heart disease
Cancer
Stroke
Asthma
Diabetes
Alzheimer’s
Parkinson’s
Genetic Component in Complex
Disorders
• Relative risk
lr= frequency in relative of affected person
Population frequency
Genetic Component in Complex
Disorders
• Family Studies
Class of relative
Proportion of genes
shared
Examples
First degree
50%
Parent/child, siblings
Second degree
25%
Grandparent/grandchild, aunt/niece
Third degree
12.5%
Cousins
Genetic Component in Complex
Disorders
Congenital
Malformations
Cleft lip
Pyloric stenosis
General population
0.001
0.001
First degree relatives
X40 (0.04)
X10 (0.01)
Second degree relatives
X7
X5
Third degree relatives
X3
X1.5
• Problem of environmental impact
Genetic Component in Complex
Disorders
Disorder
Breast cancer
Type I diabetes
Type II diabetes
Multiple sclerosis
Peptic ulcer
Rheumatoid arthritis
Tuberculosis
Monozygotic
6.5
30
50
20
64
50
51
Dizygotic
5.5
5
30
6
44
8
22
Genetic Component in Complex
Disorders
Disorder
Alcoholism
Autism
Schizophrenia
Alzheimer’s
Dyslexia
Monozygotic
40
60
44
58
64
Dizygotic
20
7
16
26
40
Genetic Component in Complex
Disorders
• In polygenic diseases, risk (susceptibility)
alleles increase the phenotypic value
• Traits may appear continuously variable
• Traits may appear discontinuous
Genetic Component in Complex
Disorders
•
How to find susceptibility gene?
– Four main approaches
1.
2.
3.
4.
Candidate gene
Parametric linkage analysis
Non-parametric linkage analysis
Population association studies
Candidate gene
• Before searching the whole genome, think
about what genes may be involved
– Eg., Type I diabetes
– Some genes involved in cell-mediated
immunity are located on chromosome 6
(Human leukocyte antigen region)
– Linkage between Type I diabetes and HLA
was closely examined
• After a small genomic region is isolated,
determine best candidate gene
Parametric Linkage Analysis
• Standard LOD score analysis, as used for
single-gene disorders
Parametric Linkage Analysis
• Eg., breast cancer susceptibility genes
• Collect family history of >1500 breast cancer
patients
– Some family histories showed multiple cases
occurring at early ages – could be a Mendelian allele
segregating
– Best model suggested a dominant single-gene allele
with a population frequency of 0.0006 – this
suggested about 5% of total breast cancers
Parametric Linkage Analysis
• Eg., breast cancer susceptibility genes
• Collect family history of >1500 breast cancer
patients
– Now, look for families with multiple breast cancer
cases with early onset
– Genotype family members and look for linkage
– Linkage (significant LOD score) to breast cancer was
found to a marker on 17q21
Parametric Linkage Analysis
• Eg., breast cancer susceptibility genes
• Collect family history of >1500 breast cancer
patients
– The gene involved was cloned, like other single-gene
disorders
– Breast cancer (BRCA) 1 gene– tumor suppressor
gene involved in genomic stability
– LOH leads to high penetrance of breast cancer, as
well as ovarian cancer
Parametric Linkage Analysis
• Eg., breast cancer susceptibility genes
• Collect family history of >1500 breast cancer
patients
– However, examination of BRCA1 mutations outside of
affected families suggests lower penetrance
Parametric Linkage Analysis
• Other successes in finding Mendelian risk
factors in polygenic diseases
– HNPCC – non-polyposis colon cancer
• MSH1, MLH1, PMS1, PMS2
– FAP – familial polyposis colon cancer
• APC
– Premature heart disease - hypercholesterolemia
• Mutation of the LDL receptor
Parametric Linkage Analysis
• Familial hypercholesterolemia
– Autosomal dominant
Parametric Linkage Analysis
• Familial hypercholesterolemia
• 200 mg/dl - 350 mg/dl - dietary, common
• 400 mg/dl - 600 mg/dl - heterozygous,
uncommon
• >600 mg/dl - homozygous, rare
Parametric Linkage Analysis
• Familial hypercholesterolemia
• Autosomal dominant; allele frequency about
1:150
Parametric Linkage Analysis
• Spectacular misfires as well:
– Bi-polar disease (manic depression)
– Initial linkage to HRAS and INS on
chromosome 11
– LOD scores of 4.08 and 2.63
– Two individuals in extended family
misdiagnosed
– Lowered LOD score to 1.03 and 1.75
Non-parametric Linkage Analysis
• Genomic regions surrounding risk alleles will be
inherited from a common ancestor in affected
individuals to a greater frequency than by
chance – also called autozygosity mapping
• Search for commonly inherited regions by
polymorphic microsatellites, SNP’s, etc.
• High throughput analysis critical
Non-parametric Linkage Analysis
• Common to use Affected Sib-Pairs (ASP)
• Collect genotypic data for 100’s of ASP
• 300+ microsatellite markers genotyped for 10cM
coverage
• Look for significant IBD (>chance occurrence)
Non-parametric Linkage Analysis
• IBD: if parental alleles differ at locus, then
sibs that have both alleles in common are
identical by decent
• IBS: if parental alleles are not know, then
we can only say sibs are identical by state
Population association studies
• Association studies are carried out on
populations
• Look for alleles that segregate with the
disease in a whole population
– Direct causation
– Natural selection
– Linkage disequilibrium
Population association studies
• Linkage disequilibrium
• Combination of alleles at two closely
linked loci occur more often than expected
by chance from population frequencies
• Recombination reduces linkage
disequilibrium
Population association studies
• Linkage disequilibrium vs. Linkage
Mapping
– Mapping is performed on families with few
informative meiosis; LD is determined on
populations after many generations
– Mapping will show linkage over large
distances; LD is visible only over short
distances
Genetic Component in Complex
Disorders
• How to find susceptibility genes?
– Four main approaches
1.
2.
3.
4.
Candidate gene
Parametric linkage analysis
Non-parametric linkage analysis
Population association studies
Alzheimer’s Disease (AD)
• North America – 0.1% at 60, 10% at 80, 30% at
90
• Early onset: <60
• Neurofibrillary tangles in the cerebral cortex and
amyloid plaques in the brain
• Neuronal apoptosis occurs in the hippocampus
and cerebral cortex – memory and learning
Alzheimer’s Disease (AD)
• Neurofibrillary
tangles –
polymerized tau
protein
• Amyloid plaques
– deposition of
the b-amyloid
protein
Alzheimer’s Disease (AD)
• Apoptosis of neuronal cells
– Sometimes called “Programmed cell death”
– Energy-utilizing program of orderly selfdestruction
– Organized dismantling of the cell to avoid
autoimmune reaction
Apoptosis
Apoptosis
• Activation of proteases (cysteine-aspartic
acid specific; called Caspases)
• Cascade of “irreversable” proteolysis
• Activation of endonuclease – chops up the
cells DNA – no going back now!
Apoptosis
• Apoptosis occurs:
– During development
– Removal of immunological cells
– In cells with DNA damage
– Defeated in cancer cells
• Neuronal cells maintain survival by
exposure to “neurotrophins”
Search for Susceptibility Alleles for
Alzheimer’s Disease
• Some clues as to causative agents of AD
– Down syndrome individuals develop clinical
features of AD when they live >30 years
– Suggested that chromosome 21 may be
involved in AD
– Parametric linkage analysis located a locus
on chromosome 21q in early-onset familial AD
Causative genes in AD
• Amyloid precursor protein (APP) overabundant in Alzheimer’s and Down
syndrome individuals
• Amyloid precursor protein gene mapped to
chromosome 21
• Trisomy 21 causes a over-expression of
genes from chromsome 21, including APP
Causative genes in AD
• APP – a causative agent of AD and
involved in pathology of Downs syndrome
• Large transmembrane protein processed
by a, b or g-secretase
• a-secretase generates Aa40 protein –
non-toxic and the main protein in normal
brain
Causative genes in AD
• b and g-secretase generates Ab42 protein
– toxic and insoluble – which forms
plaques
• After APP was found by parametric
linkage, mutations were found
• In familial AD, mutations in APP increased
the amount of Ab42 cleavage
Causative genes in AD
• More parametric linkage analysis within
families of early-onset AD
– Presenilin I and II were discovered on
chromosome 14 and 2
– Presenilin I is a g-secretase – leading to
increased Ab42 secretion
Causative genes in AD
• 1% of AD is familial, and shows strong
Mendelian inheritance of altered Ab42
generation
• What about risk alleles in sporadic AD? –
99% of cases
Causative genes in AD
• Non-parametric linkage analysis was
performed on Affected Pedigree Member
(APM)
• 32 families in which 87 of 293 members
showed AD
• Linkage with locus on chromosome 19
Causative genes in AD
• In this region was the gene for Apolipoprotein E.
• ApoE was found in plaques and tangles
– Good candidate
• A population association study was performed
• Three alleles of ApoE were identified:
– ApoE2 (6%), ApoE3 (78%) and ApoE4 (16%)
• Strong LD was found for allele ApoE4 and
several nearby SNP’s
Causative genes in AD
• ApoE4 is a risk factor Alzheimer’s disease
ApoE4
dose
0
1
2
% affected
20
46.6
91.3
Relative
Risk
1
2.84
8.07
Age of
onset
84.3
75.5
68.4
Summary
• Family, adoption and twin studies provide evidence of
genetic component to complex disease
• Risk of disease is the combined effect of polygenes
influenced by environment, thus termed multifactorial
• Combined affect of many common alleles each providing
a small effect, or of a few uncommon alleles with large
effect
• Candidate gene, parametric and non-parametric linkage
analysis, and population association analysis are used to
find risk factors for multifactorial disease