Transcript Genetic Analysis in Human Disease

Genetic Analysis in Human Disease
Learning Objectives
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Describe the differences between a linkage
analysis and an association analysis
Identify potentially confounding factors in a
genetic study
Define missing heritability
Question:
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1) You have a grant to do a genetics study of
the disease of your choice. What are 3
aspects you need to consider when recruiting
subjects?
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A) Phenotype, gender and age
B) Phenotype, gender and income
C) Gender, age and income
D) Age, income and education
Question:
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2) You’ve analyzed 1,000 cases and 1,000
controls for an association study but found
nothing significant. What went wrong?
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A) Recruited too many subjects
B) Population was too homogeneous
C) Not enough subjects
D) Genotyped using only one platform
Question:
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3) You’ve made it to the big time. From your
GWAS analysis you have significant hits in
known genes. What’s the next step?
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A) End of story, move on to the next study
B) Develop new drugs
C) Replication/validation
D) Patent the SNPs
Power of Genetic Analysis
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Success stories
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Age-related Macular Degeneration
Crohn’s Disease
Allopecia Areata
Type1 Diabetes
Not so successful
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Ovarian Cancer
Obesity
The spectrum of genetic effects in complex diseases
Getting Started
Question to be answered
Which gene(s) are responsible for genetic
susceptibility for Disease A?
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What is the measurable difference
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Clinical phenotype
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biomarkers, drug response, outcome
Who is affected
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Demographics
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male/female, ethnic/racial background, age
Study Design
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Linkage (single gene diseases: cystic fibrosis, Huntington’s
disease, Duchene's Muscular Dystrophy)
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Families
Association (complex diseases: RA, SLE, breast cancer,
autism, allopecia, AMD, Alzheimer’s)
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Case - control
Linkage vs. Association Analysis
5M
Linkage Studies- all in the family
Family based method to map location of disease
causing loci
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Families
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Multiplex
Trios
Sib pairs
Staged Genetic Analysis - RA
Linkage/Association/Candidate Gene
Association Studies – numbers game
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Genome-Wide Association Studies (GWAS)
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Tests the whole genome for a statistical
association between a marker and a trait in
unrelated cases and controls
Affecteds
Controls
Staged Genetic Analysis - RA
Linkage/Association/Candidate Gene
So you have a hit: p< 5 x10
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Validation/ replication
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Dense mapping/Sequencing
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Functional Analysis
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Validation
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Independent replication set
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Genotyping platform
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Same inclusion/exclusion subject criteria
Sample size
Same polymorphism
Analysis
Different ethnic group (added bonus)
Staged Genetic Analysis - RA
Linkage/Association/Candidate Gene
Dense Mapping/Sequencing
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Identifies the boundaries of your signal
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close in on the target gene/ causal variant
find other (common or rare) variants
Functional Analysis
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Does your gene make sense?
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pathway
function
cell type
expression
animal models
PTPN22: first non-MHC gene associated with RA (TCR signaling)
Perfect vs Imperfect Worlds
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Perfect world
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Linkage and/or GWAS – identify causative gene
polymorphism for your disease
Publish
Imperfect world
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nothing significant
identify genes that have no apparent influence in
your disease of interest
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Now what?
What Happened?
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Disease has no genetic component.
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Genetic effect is small and your sample size
wasn’t big enough to detect it.
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Too many outliers
Wrong controls.
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CDCV vs CDRV
Phenotype /or demographics too heterogeneous
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Viral, bacterial, environmental
Population stratification; admixture
Not asking the right question.
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wrong statistics, wrong model
Meta-Analysis – Bigger is better
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Meta-analysis - combines genetic data from
multiple studies; allows identification of new
loci
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Rheumatoid Arthritis
Lupus
Crohn’s disease
Alzheimer’s
Schizophrenia
Autism
Influence of Admixture
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Not all Subjects are the same
Missing heritability
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Except for a few diseases (AMD, T1D)
genetics explains less than 50% of risk.
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Large number of genes with small effects
Other influences?
Other Contributors
Any change in gene expression can influence disease
state- not always related directly to DNA sequence
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Environmental
Epigenetic
MicroRNA
Microbiome
Copy Number Variation
Gene-Gene Interactions
Alternative splice sites/transcription start sites
Genome-Wide Association Studies
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The promise
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Better understanding of biological processes
leading to disease pathogenesis
Development of new treatments
Identify non-genetic influences of disease
Better predictive models of risk
GWAS – what have we found?
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3800 SNPs identified for 427 diseases and traits
Only 7% in coding regions
>50% in DNAse sensitive sites, presumed regulatory regions
Genome-Wide Association Studies
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The reality
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Few causal variants have been identified
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Clinical heterogeneity and complexity of disease
Genetic results don’t account for all of disease risk
Genome-Wide Association Studies
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The potential clinical applications
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Risk prediction
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Disease subtyping/classification
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MODY: HNF1A- C- reactive protein biomarker
Drug development
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Type 1 Diabetes (MHC and 50 loci)
Ribavirin- induced anemia: ITPA variants protective
Drug toxicity/ adverse effects
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MCR4 SNPs and extreme SGA-induced weight gain
(Manolio 2013)
Question:

1) You have a grant to do a genetics study of
the disease of your choice. What are 3
aspects you need to consider when recruiting
subjects?




A) Phenotype, gender and age
B) Phenotype, gender and income
C) Gender, age and income
D) Age, income and education
Answer:

1) You have a grant to do a genetics study of
the disease of your choice. What are 3
aspects you need to consider when recruiting
subjects?

A) Phenotype, gender and age
Question:

2) You’ve analyzed 1,000 cases and 1,000
controls for an association study but found
nothing significant. What went wrong?




A) Recruited too many subjects
B) Population was too homogeneous
C) Not enough subjects
D) Genotyped using only one platform
Answer:

2) You’ve analyzed 1,000 cases and 1,000
controls for an association study but found
nothing significant. What went wrong?

C) Not enough subjects
Question:

3) You’ve made it to the big time. From your
GWAS analysis you have significant hits in
known genes. What’s the next step?




A) End of story, move on to the next study
B) Develop new drugs
C) Replication/validation
D) Patent the SNPs
Answer:

3) You’ve made it to the big time. From your
GWAS analysis you have significant hits in
known genes. What’s the next step?
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C) Replication/validation