Download Population Genetics Program on West Nile Virus

Genetic Association Studies
and GWAS
October 16, 2015
Topics
• Study Design
• Potential Threats to validity:
- sample recruitment
- genotyping error
-errors in data analysis
- replication
- population structure
West Nile Virus Transmission
Cycle
West Nile Outbreaks
•
•
•
•
•
•
Israel - 1951-1954, 1957
France - 1962
South Africa - 1974
Romania – 1996
Italy 1998
Russia - 1999
1999 West Nile Virus Activity NYC
Mosquitoes
Birds
Humans
Clinical Syndromes
• 80% asymptomatic
• 20% “West Nile Fever”
• 1 in 20 of symptomatic
patients develop
neuroinvasive disease
- Meningitis
- Encephalitis
- Acute Flaccid Paralysis
• Apart from increased age,
risk factors ill defined
Hypothesis
• WNV neuroinvasive disease is a
consequence of genetic factors that result
in increased WNV replication and
subsequent pathology
Q. Why detect Genes
associated with Disease ?
• Diagnosis
• Prognosis
• Therapeutics
• Basic Mechanisms of disease
Objectives
• To assess the association between
immune response genotype sets and
susceptibility to neuroinvasive disease
• To characterize the relationship between
gene polymorphisms, protein function, and
WNV infection
Q. What sort of evidence do you
look for to see if the question is
worthwhile?
Is there evidence for a ‘familial’ effect?
• Migration Studies
• Do immigrants
have disease
risk similar to
• Familial
Aggregation
studies
their native population or to the new
Disease Healthy
population?
SubjectsFH+
are relatives
Relative
of a case
FHRelative of a control
Disease
a
ca
c
• Sibling relative risk: ls
P(disease|sibling is affected)
P(disease)
• Familial aggregation if ls > 1
OR = ad/bc > 1 ?
Healthy
b
db
d
Is there evidence for a ‘genetic’ effect?
• Familial correlations in phenotype?
• Heritability can be thought of as the similarity
between related individuals that is due to shared
genes.
• If trait is heritable, individuals who share genes
should have higher correlation between trait
values than individuals who do not share genes
– Parent & offspring trait values should be correlated
– Identical twins should be more correlated than siblings
– Sibling values should be more correlated than cousins
Very similar
Phenotype similarity
(covariance)
Heritability – Familial Correlations
Heritable Trait
Non-Heritable Trait
Distant
Relatives
2nd
cousins
Cousins
Sibs/
DZ twins
MZ twins
Calculating Heritability Of A Disease
• Twin studies
• One way to study heritability
MZ twins share 100%
of the genome
DZ twins share 50% of
the genome, on average
• So if disease is genetically determined,
MZ > DZ
concordance MZ twins > concordance DZ twins
note:
• Any variation in phenotype between MZ twins must be due to
environmental variation
• Variation in phenotype among DZ twins due to environmental variation
AND genetic variation (they don’t necessarily have the same genes)
Genetic Epidemiology Questions
Is there familial clustering?
(Ycould be shared genes or
shared environments)
Is there evidence for a
particular genetic model?
(dominant, recessive,
polygenic)
Is there evidence for a genetic
effect?
(covariance structure may
indicate gene vs environment)
Where is the disease gene?
• Linkage
• Association
How does this gene contribute to disease in
the general population?
(variant frequency, risk magnitude, attributable
risk, environmental interactions)
Epidemic of Polio in North America
• In North America, although
sporadic epidemic disease
occurred in the first half of the
20th century, by the 1950s
epidemics of polio were
widespread in North America
• Prior to introduction of
vaccination, it has been
estimated that 600,000 cases
of paralytic poliomyelitis
occurred annually
Nathanson N. Amer J Epidemiol 2010;172: 1213-1229
Host Factors
• < 1% of individuals infected with poliovirus
developed paralytic polio in pre-vaccine
era
• In families with a clinical case of
poliomyelitis, ratio of inapparent to
apparent infection between 3:1 and 7:1
versus 100:1 in the general population
Other Evidence for Genetic Predisposition
Herndon and Jennings. AJHG 1951:3:17-46
Genetic Epidemiology Process - Methods
Familial clustering? – Familial Aggregation studies
Evidence for genetic effects? – Heritability studies
Based on phenotype
data
(don’t need DNA)
Mode of inheritance model? – Segregation Analyses
Where is the disease gene? - Disease gene identification
• Genome
• Genome
wide
wide
• Particular
• Particular
chromosomal
chromosomal
regions
regions
• Candidate
• Candidate
genes
genes
Linkage
Linkage
analysis
analysis
(families)
(families)
• Model-based
• Model-based
• Model-free
• Model-free
Association
studies
(families
Association
studies
(families
or or
population
population
samples)
samples)
• LD
• LD
• Direct
• Direct
Human Genetic Analysis
Families
Linkage Studies
Populations
Association Studies
C/C C/T
C/C C/T C/C C/T C/C C/C
C/T C/C C/T C/T C/C C/C
40% T, 60% C
Cases
15% T, 85% C
Controls
Simple Inheritance (Segregate)
Complex Inheritance (Aggregate)
Single Gene with Major Effect
Multiple Genes with Small Contributions
and Environmental Contexts
Variant Rare in the Population
Variant(s) Common in the Population
~600 Short Tandem Repeat Markers
Polymorphic Markers > 1,000,000
Single Nucleotide Polymorphisms (SNPs)
Q. What is the first step in
designing the study?
Define the phenotype!
•Relationship between genotype and diseaserelated phenotype is key concern in genetic
epidemiology!
•This can be very direct:
•Blood type A corresponds exactly to
genotypes AA and AO
•Or very complicated:
•Serum APOE levels may be a function of
APOE genotypes as well of other genes and
environments
Step 1: Define Phenotype – What is the trait?
• The ‘phenotype’ is an observable trait in people
• Phenotype must be measurable
• External ex:
Hair color (qualitative):
,
,
Height (quantitative): .....4ft.....5ft.....6ft..... 
• Biological measurement ex:
Protein isoform (qual): APOE2, APOE3, APOE4
Protein amount (quant): ...2copies……3.....100...
Blood antigen:
A, B, AB, O
Mendelian Genetics…..
-
+
+
-
A,a
A,a
A,a
+
+
+
-
AA
aA
Aa
aa
Dominant inheritance
AA
A,a
-
-
+
aA
Aa
aa
Recessive inheritance
+ Phenotype corresponds to:
2 genotypes:
A,A
A,a
1 genotype:
a,a
- Phenotype corresponds to:
1 genotype:
a,a
2 genotypes:
A,a
A,A
Family Pedigree
Dominant
Recessive
Quantitative
Complex
Trait/Disease:
Trait/Disease:
Trait/Disease:
Trait/Disease:
following gene ‘T’:
T
T t,
t
Gg
tttttt,
Gg
TTt t,
GG
Ttt tt,
GG
TTtt,
Gg
T T,
TTTT
GG
TTt t,
Gg
tT,
TTT
Gg
tT
Tt t,
t
Gg
TTt t,
Gg
Ex: Early-onset Alzheimer’s disease
Ex: Cystic Fibrosis
ttttt,
t
gg
ttt t,
t
Gg
TTt t,
Gg
T
T t,
tT
gg
Ttt tt,
t
Gg
tttttt,
Gg
Ttt tt,
t
gg
TTt t,
gg
Diagnostic Criteria
West Nile Meningitis
A. Clinical signs of meningeal inflammation
B. 1 or more of the following: T > 38 C or < 35 C, CSF cells, WBC > 10,000,
compatible CT or MRI results
West Nile Encephalitis
A. Encephalopathy ≥ 24 hrs
B. 2 or more of the following: T > 38 C or < 35 C, CSF pleocytosis, WBC >
10,000, compatible neuroimaging, focal neurologic deficit, meningismus,
EEG, seizures
Acute Flaccid Paralysis
A. Acute onset of limb weakness with progression ≥ 48 hrs
B. 2 or more of the following: asymmetric weakness, areflexia/hyporeflexia,
absence of pain, paresthesia, or numbness in affected limb, ≥ 5 leuk in CSF
and ≥ 48 protein,WBC > 10,000, compatible neuroimaging, or EMG
Resistance to WNV in Mice
• First demonstrated in 1920’s
• Resistant phenotype is
determined by a major locus WNV/FLv on chromosome 5
• Susceptibility completely
correlated to point mutation
resulting in truncation of the 2’5’ OAS L1 isoform
• Homologous region in human
chromosome 12q
Mashimo, PNAS 2002; 99:11311-11316
Clinical Syndromes
• 80% asymptomatic
• 20% “West Nile Fever”
• 1 in 20 of symptomatic
patients develop
neuroinvasive disease
- Meningitis
- Encephalitis
- Acute Flaccid Paralysis
• Apart from increased age,
risk factors ill defined
Q. How do you find genes
responsible for human disease?
Difficult:
• Many risk models (genotype-phenotype correlations do not follow
simple patterns – ‘complex disease’)
• Many possible genes (~30,000 human genes)
• Difficult challenge to find a disease gene: like finding a misspelled word
in a set of encyclopedias!
A
1
‘which
chromosome?’
Z
24
7
page
‘chromosomal
region’
This is a
sentence in a
paragraph…
This it a
sentence in a
paragraph…
‘gene’
‘mutation’
A
Z
page
‘chromosome’
‘region’
This is a
sentence in a
paragraph…
‘gene’
This it a
sentence in a
paragraph…
‘mutation’
• Too many words to ‘read’ the entire set of volumes (genome) for every individual
• Need ‘markers’ to represent sections
• Need study designs and statistical methods to find regions (sets of
markers) correlated with disease
• Then, ultimately look for specific disease-associated DNA variation
Definitions…
Genome –
• The entire sequence of DNA (across all chromosomes) of a particular
species.
Gene –
• A segment of DNA composed of a transcribed region and a regulatory
sequence that makes transcription possible.
Genetic locus –
• Loose term with several interpretations. Often: the specific location of a
gene on a chromosome. However, some use the term to refer to a location
of a putative gene. One definition: a region, or location, on the genome
harboring a particular sequence of interest (gene or several genes).
Genetic site –
• Loose term with several interpretations. One definition: a particular
.
nucleotide position on the genome
Definitions…
One possible visualization:
genome
locus
gene
site
Definitions…
Haplotype • Haploid – one copy of each chromosome
• Set of alleles on a particular chromosome transmitted from
parent to child (pink for haplotype from Mom, blue from Dad).
Diplotype –
• Diploid – two homologous copies of each chromosome
• Set of two haplotypes carried by an individual (one from each
parent), where phase is known.
Mom’s Dad’s
A
T
C
T
G
A
A
C
C
T
G
A
Mom’s Dad’s
A
T
C
T
G
A
A
C
C
A
G
A
Mom’s Dad’s
A
C
C
A
G
A
A
C
C
A
G
A
Phase –
• Knowledge of the orientation of alleles on a particular transmitted
chromosome
Illustration of Phase
Diploid person with
4 genotypes:
• Phase (orientation of alleles on particular
chromosomes) is unknown based solely on
these genotypes.
Two possibilities:
(T, C)
TC
(C, C)
CC
or
TC
CT
CC
CC
=
(T, A)
TA
TA
TA
(G, G)
GG
GG
GG
Diplotype 1:
Haplotypes: TCTG | CCAG
Diplotype 2:
Haplotypes: CCTG | TCAG
Broad Genetic Epidemiology Study Design Categories:
• Linkage Analysis
– Follows meiotic events through families for co-segregation of disease and
particular genetic variants
– Large Families
– Sibling Pairs (or other family pairs)
– Works VERY well for ‘Mendelian’ diseases
• Association Studies
– Detect association between genetic variants and disease across families:
exploits linkage disequilibrium
– Case-Control designs
– Cohort designs
– Parents – affected child trios (TDT)
– May be more appropriate for complex diseases
Q. What approaches exist for
association studies ?
Linkage
A,a
B,b
C,c
D,D
A,a
B,b
C,c
D,D
a,a
b,b
c,c
D,D
A,a
b,b
c,c
D,D
A,A
b,b
C,c
D,d
A,A
B,b
C,c
D,D
A,a
B,b
c, c
D,D
A,A
B,b
C,c
D,D
A,a
b, b
c, c
D,D
A,a
b,B
c,C
d,D
A,A
b,b
C,c
D,D
a,A
B,b
C,c
D,D
A,a
b,B
C,C
D,D
A,a
B,b
C,c
d,D
A,a
B,b
C,c
d,D
A,A
B,b
C,C
d,D
A,a
b,b
C,c
D,D
a,A
b,b
c,C
D,D
A,a
B,b
C,c
d,D
• All 4 loci are ‘linked’ to the (unobserved) disease allele WITHIN each of the
3 families
Linkage .vs. Linkage Disequilibrium (LD)
A,a
B,b
C,c
D,D
A,a
B,b
C,c
D,D
a,a
b,b
c,c
D,D
A,a
b,b
c,c
D,D
A,A
B,b
C,c
D,D
A,A
b,b
C,c
D,d
A,a
b,B
c,C
d,D
A,A
b,b
C,c
D,D
a,A
B,b
C,c
D,D
A,a
b,B
C,C
D,D
A,a
B,b
C,c
d,D
A,a
B,b
C,c
d,D
A,A
B,b
C,C
d,D
A,a
b,b
C,c
D,D
a,A
b,b
c,C
D,D
A,a
B,b
C,c
d,D
• All 4 loci are ‘linked’ to the (unobserved) disease allele WITHIN each of the
3 families
• Only alleles ‘B’ and ‘C’ are associated with the disease allele ACROSS
families (LD)
Genetic association
studies
– two
different concepts
Genetic
Association
Studies
1. Candidate locus testing (direct method)
–
–
–
Testing whether a particular locus allele is a disease
predisposing allele
Not really ‘LD mapping’, more like direct association test
normally seen with ‘exposure status’ in traditional epidemiology
Very applicable to studies of disease gene variant’s effect on
population levels of disease (risk and attributable risk
assessment)
2. ‘LD Mapping’ (indirect method)
–
–
Exploitation of relationship between linkage disequilibrium (LD)
and genetic distance
Testing for LD between marker(s) and (putative) disease allele
Genetic Association Studies –
Two Different Concepts
Known polymorphism
SNP has direct effect on protein and phenotype
Genetic Association Studies –
Two Different Concepts
1. Direct method
– Testing whether a particular allele is a disease predisposing (causative) allele
– ‘exposure status’ directly measured
Eg: A particular APOE allele (e4) changes protein isoform
APOE gene on c19
..GACTAAGGCCC CCGTTCAAGGAA..
C/T
• Genotype that particular site for association study
Fallin, L3a, 6/21/2005
slide # 46
Genetic Association Studies –
Two Different Concepts
Known polymorphism
SNP is a marker (proxy)in LD with allele
that has a direct effect
SNP with direct effect on
protein and risk
Unmeasured!
Genetic Association Studies –
Two Different Concepts
2. ‘LD Mapping’ (indirect method)
– ‘exposure status’ not directly measured
– Rely on MARKERS correlated with true exposure status
• This correlation is due to linkage disequilibrium
Eg: Genotype a nearby genetic marker among study participants
APOE gene on c19
..GACTAAGGCCC CCGTTCAAG…GA CCTG..
C/T
A/G
Rely on correlation (LD) between these alleles to detect association!
Marker-based Studies
• We often do not measure the genetic
variant of interest
• Instead, we genotype markers at known
locations in the genome
• Look for markers the may indicate close
proximity to a disease-related DNA variant
Candidate gene analysis
• Instead of genome-wide approach, many pursue particular
genes as ‘candidates’
– plausible biological role in the phenotype
– location in regions where prior evidence for linkage or
association has been observed (positional candidate)
Taken from: Makridakis and Reichardt, Molecular Epidemiology of Hormone-Metabolic Loci in
Prostate cancer. Epidemiologic Reviews, 23: 24-29.
Candidate Genes
•
•
•
•
•
•
CD209 (DC-SIGN)
VDR
Fc γ receptor II
TNF-, IL-10
HLA-A, HLA-B
TAP1, TAP2, and
CTLA-4
Case Control Comparison Groups
Genome Wide Association Studies
• Large number of individuals with disease
and a relevant comparison group
• DNA isolation and genotyping
• Statistical tests for associations between
the SNPs passing quality thresholds and
the disease/trait
• Replication of identified associations in an
independent population sample or
examination of functional implications
experimentally.
Lessons Learned from initial
G WA Studies
•
•
•
•
•
•
•
This actually works
Size and luck matter!
Replication matters
Collaboration matters
Controls matter, but can be shared sometimes
Non-coding SNPs matter
Current hypotheses regarding candidate genes and
pathways may not matter so much
• Several genes influence more than one disease
Genome-wide association study of 14,000 cases of seven common
diseases and 3,000 shared controls: Comparison of P values for 2
different controls
Q. What is the basis for LD?
Crossing over and recombination fraction
Diploid parent
Loci
.
Chromosome
duplication in
meiosis
Cross-overs
occur
Gamete
production
A
B
C
•
•
4 haploid gametes:
One is passed on to child
Crossing over between 2 genes is directly proportional to the distance between
them.
• Those sites closest together will have the least number of cross-overs
between them
Ex above:1 recombination between A & B
1 recombination between B & C
2 recombinations between A & C (further apart)
The frequency of recombination between sites is measure of ‘genetic distance’,
often expressed as the recombination fraction
LD Mapping Caveats: Other Reasons for
Observed Allelic Associations in Populations
• Population stratification / subdivision
• Recent admixture
• Genetic drift
• Selection
• Assortative mating
• Type 1 error
Q. What are key assumptions
made in genetic epidemiology
studies?
Random Mating
• Under random mating, all individuals of the opposite sex are equally likely to
mate, regardless of their genotype
• The combination of two individual genotypes that produce offspring is referred
to as a mating type
• If random mating, the probability of each mating type is the product of the two
genotype probabilities (frequencies) in the population:
Mom
Dad
Genotypes
AA
Aa
aa
AA
P(AA) x P(AA)
P(AA) x P(Aa)
P(AA x P(aa)
Aa
P(Aa) x P(AA)
P(Aa) x P(Aa)
P(Aa) x P(aa)
aa
P(aa) x P(AA)
P(aa) x P(Aa)
P(aa) x P(aa)
Random Mating…
• P(MT) = P(mom genotype) * P(dad genotype)
• There are 6 distinct mating types
Mom
Dad
Genotypes
AA
Aa
aa
AA
pAA2
pAA* pAa
pAA * paa
Aa
pAA* pAa
pAa2
pAa * paa
aa
pAA * paa
pAa * paa
paa2
There are 6 distinct mating types
(assuming parent gender doesn’t matter)
Mating Type
Probability of MT
AA x AA
pAA2
AA x Aa
2pAA* pAa
AA x aa
2pAA * paa
Aa x Aa
pAa2
Aa x aa
2pAa * paa
aa x aa
paa2
A Population-based Theoretical Example…
• With random mating we should have the following:
Mating
type
MT
Freq
Offspring conditional genotype
probability, P(g’|MT)
P(MT)
AA
Aa
aa
AA x AA
0.5 x 0.5
1
0
0
AA x aa
2(0.5 x 0.5)
0
1
0
aa x aa
0.5 x 0.5
0
0
1
• After one generation of random mating,
• P(AA) = SMT P(AA|MT)P(MT) = 1(.5*.5)+ 0 + 0 = .25
• P(Aa) = SMT P(Aa|MT)P(MT) = 0 + 1(2*.5*.5) + 0 = .5
• P(aa) = SMT P(aa|MT)P(MT) = 0 + 0 + 1(.5*.5) = .25
• Genotype frequencies will be:
p2 =P(AA), 2pq = P(Aa), q2= P(aa)
WNV study: Potential Gene
Categories
• Primary Response Modifiers (e.g. ISGs)
• Cytokines, Chemokines, Chemokine receptors,
MHC
• Signal Transduction Proteins (e.g. JAK Kinases)
• Transcription factors (e.g. IFN regulatory factors)
• Antiviral Effector Proteins (e.g. OAS)
WNV Study Methods: genotyping
• Whole genome screening
of non-synonymous
variants performed using
the Illumina HumanNS-12
Infinium array;
• 13,371 single nucleotide
polymorphisms (SNPs) in
~6000 genes;
• Mostly non-synonymous
coding, also includes
synonymous, UTR, tagSNPs (MHC).
Case-Control Study
• Cases from states/provinces with highest rates of WNV
infection
• Meet CDC criteria for WNV infection and have evidence
of neuroinvasive diease
• Controls are those who meet criteria for infection with
WNV but who did not develop neuroinvasive disease
Study Designs Used in Genome-wide
Association Studies
Pearson, T. A. et al. JAMA 2008;299:1335-1344.
Implementation
• State and provincial public health agencies
contact all WNV infected individuals in 20022008
• 4 Clinical Centers – Pennsylvania, Texas,
Nebraska, Ontario
• Whole blood is collected from participants and
sent to McGill Genome Center
Analysis
• Two stage design, retest the best candidates
in a second cohort
• 600 cases for each stage to detect alleles
with MAF > 0.05 for a two fold risk increase
• Unconditional LR to compute odds ratios and
95% CI adjusted for site
Samples: Genotyped and Phenotyped
Stage 1
Stage 2
Cases:
488 (445)
143 +
Controls:
858 (813)
142 +
SNP discovery is dependent on your sample population size
Fraction of SNPs Discovered
2 chromosomes
GTTACGCCAATACAGGATCCAGGAGATTACC
GTTACGCCAATACAGCATCCAGGAGATTACC
{
1.0
88
0.5
2
0.0
0.0
0.1
0.2
0.3
0.4
Minor Allele Frequency (MAF)
0.5
Replication A Must
Replication
Replication
Replication
Hirschhorn & Daly Nat. Genet. Rev. 6: 95, 2005
NCI-NHGRI Working Group on Replication Nature 447: 655, 2007
Examples of Multistage Designs in Genome-wide
Association Studies
Pearson, T. A. et al. JAMA 2008;299:1335-1344.
Copyright restrictions may apply.
Results
• Phenotypic data on 1371
patients
• 488 NI disease
• 858 controls
• 25 equivocal
Samples: age distribution
cases
controls
Samples: gender distribution
Genotyping: quality control
Out of 13,371 SNPs:
• 133 failed;
• 174 have call rate below 95% and were
considered failed;
• Average call rate is 99.8% for remaining 13,064
SNPs.
Out of 1,677 unique samples genotyped
• two failed (call rate < 88%);
• all others have call rate > 98% (average 99.7%).
Minor allele frequency spectrum
includes 1009 monomorphic SNPs
Hypothetical Quantile-Quantile Plots in
Genome-wide Association Studies
Pearson, T. A. et al. JAMA 2008;299:1335-1344.
Copyright restrictions may apply.
Population Structure
Pairs who share more alleles
(due to relatedness/identity by
descent)
Pairs who share less alleles
(due to different ancestries/
differences in allelic frequencies)
Population structure
Population structure
Population structure
Population structure
Cryptic relatedness
Cryptic relatedness
Hardy-Weinberg equilibrium
• For each SNP: test to evaluate if there
is an excess of heterozygous or
homozygous;
• After excluding markers that failed
HWE at p<0.0005 (48 SNPs)
Statistical Tests for HWP
• Q: Is an observed departure from HWP statistically
significant?
– Ho: DHW = 0 HA: DHW  0
• Methods:
– Chi-square goodness of fit (GOF)
• Ho: Do the data fit a model where genotype frequencies equal
expected values under HWP?
– Likelihood ratio test (LRT)
• Ho: Does a model assuming HWE fit the observed data better
than a model that does not assume HWE?
– I.e. compare likelihood of data, fixing genotype frequencies
to HWP (Lo) versus likelihood of the data without fixing
genotype frequencies to match HWP (L1)
Reasons for Departure from HWP
• Population allele frequencies can change from
generation to generation due to:
•
•
•
•
Migration / admixture
Chance, in small populations - genetic drift
Mutation
Selection - depends on fertility of parents and viability
of offspring
• Survival bias and gender proportions - Allele
frequencies can also change with age within a
generation, and could be sex dependent.
• Abnormal gene segregation (segregation distortion,
meiotic drive - all maternal and paternal gametic
contributions are not equally probable)
Why is the H-W model useful to Genetic
Epidemiology?
• Can use HWP assumption to calculate genotype
frequencies from observed phenotypes
• Can use HWP to obtain haplotype frequencies from
observed genotypes – useful for assessing inter-locus
equilibrium, later lectures…
• Can measure departures from HWP as an indication of
population genetic features in a sample:
– Inbreeding
– Migration / admixture
• Can judge potential genotyping errors
 Important to test for HWE!
Testing for association
After applying all QC filters:
• 445 neuroinvasive cases,
813 controls;
• 10,591 SNPs with MAF > 1% in controls
entered the analysis;
• Logistic model, adjusting for collection
center;
• X chromosome: risk of males = risk of
homozygous females; gender as additional
covariate.
Methods: samples
•
•
•
•
•
Data on 1371 patients, collected
in centers in USA and Canada;
All have been infected with the
WNv;
488 developed neuroinvasive
disease (meningitis, encephalitis,
acute flaccid paralysis);
858 did not (controls);
25 equivocal.
Methods: genotyping
• Whole genome screening of nonsynonymous variants performed using the
Illumina HumanNS-12 Infinium array;
• 13,371 single nucleotide polymorphisms
(SNPs);
• Mostly non-synonymous coding, also
includes synonymous, UTR, tag-SNPs
(MHC).
Preliminary results: Manhattan Plot
Testing for association
rs2066786
p = 1.67 x 10-6
RFC1
(4p14-p13)
Frq
Frq
Cases
Ctrls
Alberta
.76
.55
Colorado
.67
.50
Nebraska
.63
.55
Ontario/Manitoba
.65
.44
Saskatchewan
.57
.54
Texas
.59
.43
OR: 1.64 (1.34; 2.01)
RFC1
•
•
REPLICATION FACTOR C, 140-KD SUBUNIT -- 25 exons;
Has been shown to be essential for coordinated synthesis of both DNA strands
during simian virus 40 DNA replication in vitro;
•
•
rs2066786: coding synonymous (Pro847Pro) p = 1.67 x 10-6;
No other SNPs in RFC1 on the genotyping array.
RFC1
•
•
REPLICATION FACTOR C, 140-KD SUBUNIT -- 25 exons;
Has been shown to be essential for coordinated synthesis of both DNA strands
during simian virus 40 DNA replication in vitro;
•
•
rs2066786: coding synonymous (Pro847Pro) p = 1.67 x 10-6;
No other SNPs in RFC1 on the genotyping array.
•
SNPs in or near RFC1 (rs2066786, or in LD with it) are potentially regulatory
(p<2.78 x 10-9)
Testing for association
rs2298771
p = 1.73 x 10-4
SCN1A
(2q24)
Frq
Frq
Cases
Ctrls
Alberta
.50
.39
Colorado
.40
.34
Nebraska
.37
.27
Ontario/Manitoba
.28
.31
Saskatchewan
.52
.30
Texas
.31
.34
OR: 1.50 (1.21; 1.86)
SCN1A
•
•
SODIUM CHANNEL, NEURONAL TYPE I, ALPHA SUBUNIT -- 26 exons;
Shown to be associated with generalized epilepsy with febrile seizures, myoclonic
epilepsy, familial hemiplegic migraine;
•
•
rs2298771: coding non-synonymous (Ala1056Thr) p = 1.73 x 10-4;
No other SNPs in SCN1A on the genotyping array.
Testing for association
rs25651
p = 5.5 x 10-4
ANPEP
(15q26.1)
Frq
Frq
Cases
Ctrls
Alberta
.76
.69
Colorado
.64
.65
Nebraska
.72
.63
Ontario/Manitoba
.73
.63
Saskatchewan
.74
.67
Texas
.67
.57
OR: 1.47 (1.18; 1.83)
ANPEP
•
•
ALANYL AMINOPEPTIDASE -- 20 exons;
Serves as receptor for HCoV-229E (human coronavirus 229E); mediates human
cytomegalovirus (HCMV) infection;
•
•
rs25651: coding non-synonymous (Ser752Asn) p = 5.5 x 10-4;
rs8192297: coding non-synonymous (Ile603Met) p = 0.39.
Validation and replication panel
Genotyping
•
Panel of 33 SNPs was designed (Sequenom MassARRAY iPLEX Gold):
•
Top 12 SNPs from primary analysis for validation, replication;
•
TagSNPs in RFC1.
Results in primary samples
•
SNP reproducibility rate between Illumina/Sequenom: > 99.62%;
•
Tag-SNPs results in RFC1:
Replication samples
•
•
•
•
Data on 617 patients;
All have been infected with the
WNv;
277 developed neuroinvasive
disease (meningitis, encephalitis,
acute flaccid paralysis);
340 did not (controls).
SNP
Gene
Allele Freq
OR
Sample size required
rs2066786
RFC1
0.53
1.64
285 cases/285 controls
rs2298771
SCN1A
0.30
1.50
450 cases/450 controls
rs25651
ANPEP
0.65
1.47
530 cases/530 controls
80% power; p<0.001
Lack of replication
SNP
LOC56964-rs3738573
SCN1A-rs2298771
2'-PDE-rs2241988
RFC1-rs4974996
RFC1-rs11096990
RFC1-rs3733282
RFC1-rs17288828
RFC1-rs2306597
RFC1-rs2066786
RFC1-rs2066789
RFC1-rs13147094
RFC1-rs4975003
RFC1-rs3796517
RFC1-rs6835022
RFC1-rs6851075
RFC1-rs12644680
RFC1-rs13123782
na-rs9380006
TEX15-rs323347
CWF19L1-rs2270962
na-rs10778292
F7-rs6046
TLN2-rs3816988
LOC56964-rs7163367
ANPEP-rs25651
ANPEP-rs17240268
ANPEP-rs25653
GOT2-rs11076256
GRIN3B-rs2240154
XKR3-rs5748648
Chr
Pos
Allele1
Allele2
Primary
Pvalue
Replication
Pvalue
Joint
Pvalue
1
2
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
6
8
10
12
13
15
15
15
15
15
16
19
22
84636844
166601034
57517213
38903943
38963344
38965339
38966786
38973595
38978424
38984582
38987277
38989865
39013348
39030221
39044049
39047276
39053800
27764478
30825766
102006034
102784417
112821160
60898792
88061149
88136792
88148818
88150562
57309967
954172
15660822
2
2
4
2
4
3
1
1
4
3
1
2
3
4
2
2
1
2
3
4
2
1
2
1
4
1
2
4
4
1
3
4
2
1
2
1
3
3
2
1
3
3
1
1
4
4
2
1
1
2
4
3
4
4
2
3
4
2
2
3
0.003108
0.000299
0.001476
0.000006
0.001328
0.053486
0.583279
0.009005
0.000001
0.028359
0.036064
0.000011
0.003467
0.187690
0.003936
0.922901
0.000806
0.003029
0.000238
0.003019
0.001459
0.002125
0.004330
0.000446
0.000438
0.127363
0.074759
0.003654
0.001397
0.001685
0.53
0.66
0.42
0.60
0.65
0.66
0.87
0.21
0.56
0.50
0.95
0.44
0.82
0.61
0.94
0.31
0.57
0.13
0.13
0.81
0.83
0.83
0.69
0.45
0.88
0.59
0.89
0.98
0.86
0.89
0.007122
0.001455
0.000713
0.000103
0.004485
0.219224
0.484087
0.142987
0.000030
0.051783
0.106146
0.000114
0.012938
0.257738
0.020592
0.659812
0.005485
0.064231
0.045269
0.029862
0.011818
0.019520
0.012611
0.008170
0.003159
0.268328
0.187736
0.027808
0.007999
0.005334
Age distribution
Primary
Replication
Gender distribution
Primary
Replication
Neuroinvasive disease type
Primary
Replication
Ancestry: U.S. census 2000
Forest plots
Primary
Replication
Forest plots
Primary
Replication
Forest plots
Primary
Replication