Download Genetic Analysis in Human Disease

Genetic Analysis in Human Disease
Kim R. Simpfendorfer, PhD
Robert S.Boas Center for Genomics & Human Genetics
The Feinstein Institute for Medical Research
Learning Objectives



Describe the differences between a linkage
analysis and an association analysis
Identify potentially confounding factors in a
genetic study
Describe why a disease associated singlenucleotide polymorphism is not necessarily
the causal disease variant
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
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
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
Aims of Genetic Analysis in Human
Disease
McCarthy Nature Genetics Reviews
The contributions of genetic and
environmental factors to human diseases
Rare
Genetics simple
Unifactorial
High recurrence rate
Common
Genetics complex
Multifactorial
Low recurrence rate
Twin concordance to estimate heritability
Heritable and non-heritable factors
Heritable
factors
Shared
environmental
factors
Nonshared
environmental
factors
Castillo-Fernandez, Genome Medicine2014 6:60
The spectrum of genetic effects in complex diseases
Bush WS and Moore JH - Bush WS, Moore JH (2012) Chapter 11: Genome-Wide Association
Studies. PLoS Comput Biol 8(12)
Getting Started
Question to be answered
Which gene(s) are responsible for genetic
susceptibility for Disease A?

What is the measurable difference

Clinical phenotype


biomarkers, drug response, outcome
Who is affected

Demographics

male/female, ethnic/racial background, age
Genome Wide Study Design
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Linkage (single gene diseases: cystic fibrosis, Huntington’s
disease, Duchene's Muscular Dystrophy)


Families
Association (complex diseases: RA, SLE, breast cancer,
autism, allopecia, AMD, Alzheimer’s)


Families
Case - control
Linkage vs. Association Analysis
Ott Nat Rev Gen 2011
Linkage Studies- all in the family
Family based method to map location of disease
causing loci
Trios
Sib pairs
Multiplex families
Abo BMC Bioinformatics 2010
Genome-wide linkage analysis of an
autosomal recessive hypotrichosis
identifies a novel P2RY5 mutation
Petukhova Genomics 92 2008
Genome-wide linkage analysis of an
autosomal recessive hypotrichosis
identifies a novel P2RY5 mutation
Petukhova Genomics 92 2008
Genome-wide linkage analysis of an
autosomal recessive hypotrichosis
identifies a novel P2RY5 mutation
Petukhova Genomics 92 2008
Genome-wide linkage analysis of an
autosomal recessive hypotrichosis
identifies a novel P2RY5 mutation
Petukhova Genomics 92 2008
GWAS
Lasse Folkersen
Genome wide association study & meta-analysis
Case-control SLE
Meta-analysis RA
GWAS
So you have a hit: p< 5 x10-7

Validation/ replication

Dense mapping/Sequencing

Functional Analysis
Validation

Independent replication set



Genotyping platform



Same inclusion/exclusion subject criteria
Sample size
Same polymorphism
Analysis
Different ethnic group (added bonus)
Dense Mapping/Sequencing

Identifies the boundaries of your signal


close in on the target gene/ causal variant
find other (common or rare) variants
Imputation and haplotype analysis

Identifies the boundaries of your signal


close in on the target gene/ causal variant
find other (common or rare) variants
RA association in Europeans in BLK regulatory region
MTMR9
TDH
SLC35G5
P values
from
Stage 1 meta
GWAS Genetics
of rheumatoid arthritis
contributes to biology
and drug discovery.
Okada et al. 2013.
 FAM167A
C8orf12
BLK 
LINC00208
Association of the BLK risk haplotype with autoimmune
disease across ancestral groups
Controls
RA cases
n=2,134
n=2,526
Simpfendorfer et al. Arthritis &
Rheumatology 2015.
Systemic Lupus Erythematosus
Rheumatoid Arthritis
Dermatomyositis
Sjögren’s Syndrome
Systemic Sclerosis
Anti-phospholipid Syndrome
Kawasaki Disease
European / Caucasian
Chinese-Han
Japanese
African American
Hispanic
Asian
Korean
Candidate causal alleles in the BLK autoimmune
disease-risk haplotype
Histone mark
peaks from B
lymphocytes
Simpfendorfer et al. Arthritis &
Rheumatology 2015.
1bp insertion
1bp deletion
Functional Analysis

Does your gene make sense?

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
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pathway
function
cell type
expression
animal models
PTPN22: first non-MHC gene associated with RA (TCR signaling)
Autoimmunity risk genes/loci from GWAS
NHGRI
GWAS
catalog
Sharing of risk genes
between autoimmune
diseases indicates
involvement in a
shared autoimmune
disease development
mechanism
Perfect vs Imperfect Worlds

Perfect world


Linkage and/or GWAS – identify causative gene
polymorphism for your disease
Publish
Imperfect world


nothing significant
identify genes that have no apparent influence in
your disease of interest

Now what?
What Happened?

Disease has no genetic component.


Genetic effect is small and your sample size
wasn’t big enough to detect it.



Too many outliers
Wrong controls.


CDCV vs CDRV
Phenotype /or demographics too heterogeneous


Viral, bacterial, environmental
Population stratification; admixture
Genotyping platform does not detect CNVs
Not asking the right question.

wrong statistics, wrong model
Influence of Admixture

Not all Subjects are the same
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
Candidate gene association success
story: PCSK9
Cohen NEJM 2006
Genome-Wide Association Studies

The promise




Better understanding of biological processes
leading to disease pathogenesis
Development of new treatments
Identify non-genetic influences of disease
Better predictive models of risk
Genome-Wide Association Studies

The reality

Few causal variants have been identified


Clinical heterogeneity and complexity of disease
Genetic results don’t account for all of disease risk
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?

C) Replication/validation