genetic association studies and gwas october 16, 2015

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 NYC1999 West Nile Virus Activity NYC

MosquitoesMosquitoes

BirdsBirds

HumansHumans

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 their native population or to the new population?

• Migration Studies• Familial Aggregation studies

Disease Healthy

FH+ a b

FH- c d

Subjects are relatives Disease Healthy

Relative of a case a b

Relative of a control c d

OR = ad/bc > 1 ?

• Sibling relative risk: s

P(disease|sibling is affected)

P(disease)

• Familial aggregation if s > 1

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

Heritability – Familial CorrelationsP

heno

type

sim

ilarit

y (c

ovar

ianc

e)

Distant Relatives

2nd cousins

Cousins Sibs/DZ twins

MZ twins

Heritable Trait

Non-Heritable Trait

Ve

ry s

imila

rN

o s

imi la

ri ty

Calculating Heritability Of A Disease

• So if disease is genetically determined,MZ > DZ

concordance MZ twins > concordance DZ twins

MZ twins share 100% of the genome

DZ twins share 50% of the genome, on average

• Twin studies• One way to study heritability

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? (Ycould be shared genes or

shared environments)

Is there evidence for a genetic effect?

(covariance structure may indicate gene vs environment)

Is there evidence for a particular genetic model?

(dominant, recessive, polygenic)

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

Where is the disease gene? - Disease gene identification

Evidence for genetic effects? – Heritability studies

Mode of inheritance model? – Segregation Analyses

Based on phenotype data

(don’t need DNA)

• Genome wide

• Particular chromosomal regions

• Candidate genes

Linkage analysis (families)• Model-based• Model-free

Association studies (families or population samples)

• LD• Direct

• Genome wide


• Candidate genes



• LD• Direct

• Genome wide


• Candidate genes



• LD• Direct

• Genome wide


• Candidate genes



• LD• Direct

Cases Controls

40% T, 60% C 15% T, 85% C

C/C C/T

C/C C/T C/C

C/C

C/TC/C C/C

C/T C/CC/TC/TC/C

Multiple Genes with Small Contributions and Environmental Contexts

Variant(s) Common in the Population

Polymorphic Markers > 1,000,000Single Nucleotide Polymorphisms (SNPs)

Single Gene with Major Effect

Variant Rare in the Population

~600 Short Tandem Repeat Markers

Human Genetic Analysis

FamiliesLinkage Studies

Populations Association Studies

Simple Inheritance (Segregate) Complex Inheritance (Aggregate)

Q. What is the first step in designing the study?

Define the phenotype!

•Relationship between genotype and disease-related 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

• 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,

APOE4Protein amount (quant): ...2copies……

3.....100... Blood antigen: A, B, AB, O

Step 1: Define Phenotype – What is the trait?

Mendelian Genetics…..

A,a

AA aA Aa aa

A,a A,a

AA aA Aa aa

A,a

Dominant inheritance Recessive inheritance

+ Phenotype corresponds to:

2 genotypes: 1 genotype: A,A a,a A,a

- Phenotype corresponds to:

1 genotype: 2 genotypes: a,a A,a

A,A

+ +

++ + +-

- -

---

T t T t

T t T T t t T t

t t

T T T t t t

t t

T t T t

t t

T t t t t t T t

Family Pedigree following gene ‘T’:

T t T t

T t T t t t T T

t t

T T T t t t

t t

T t T t

t t

T t T t T t T t

Family Pedigree following gene ‘T’:

T t T t

T t T T t t T tt t

t t T t T t

T T T t t t

t t

T t t t t t T t

Dominant Trait/Disease:Ex: Early-onset Alzheimer’s disease

Recessive Trait/Disease:

T t T t

T t T T t t T tt t

t t T t

T T T t t t

t t

T t t t t t T t

Ex: Cystic Fibrosis T t, Gg

T t, Gg

T t, GG

T T,Gg

t t, Gg

T t, g g

t t, Gg

t t, GG

T t, Gg

T t, Gg

T T, GG

T t, Gg

t t, gg

t t, Gg

T t, Gg

t t, Gg

t t, gg

T t, gg

Quantitative Trait/Disease:

T t, Gg

T t, Gg

T t, GG

T T, Gg

t t, Gg

T t, g g

t t, Gg

t t, GG

T t, Gg

T t, Gg

T T, GG

T t, Gg

t t, gg

t t, Gg

T t, Gg

t t, Gg

t t, gg

T t, gg

Complex Trait/Disease:

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 EncephalitisA. Encephalopathy ≥ 24 hrsB. 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 ParalysisA. Acute onset of limb weakness with progression ≥ 48 hrsB. 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)

page

‘chromosomal region’

This is a sentence in a paragraph…

‘gene’

This it a sentence in a paragraph…

‘mutation’

• Difficult challenge to find a disease gene: like finding a misspelled word in a set of encyclopedias!

‘which chromosome?’

Vol 7

A1

Z247

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…

genome locus gene site

One possible visualization:

ATCTGA

ACCTGA

ATCTGA

ACCAGA

ACCAGA

ACCAGA

Mom’s Dad’s Mom’s Dad’sMom’s Dad’s

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.

Definitions…

Phase – • Knowledge of the orientation of alleles on a particular transmitted

chromosome

Illustration of Phase

(T, C)

(C, C)

(T, A)

(G, G)

T C

C C

T A

G G

Diploid person with 4 genotypes:

• Phase (orientation of alleles on particular chromosomes) is unknown based solely on these genotypes.

Two possibilities:

Diplotype 1:Haplotypes: TCTG | CCAG

C T

C C

T A

G G

T C

C C

T A

G G

or =

Diplotype 2:Haplotypes: CCTG | TCAG

• 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

Broad Genetic Epidemiology Study Design Categories:

Q. What approaches exist for association studies ?

• All 4 loci are ‘linked’ to the (unobserved) disease allele WITHIN each of the 3 families

A,aB,bC,cd,D

A,aB,bC,cd,D

a,Ab,bc,CD,D

A,aB,bC,cd,D

A,AB,bC,Cd,D

A,ab,bC,cD,D

Linkage

A,Ab,bC,cD,d

A,ab,Bc,Cd,D

A,ab,BC,CD,D

A,Ab,bC,cD,D

a,AB,bC,cD,D

A,aB,bC,cD,D

A,ab,bc,cD,D

A,aB,bC,cD,D

A,AB,bC,cD,D

a,ab,bc,cD,D

A,aB,bc, cD,D

A,AB,bC,cD,D

A,ab, bc, cD,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)

A,aB,bC,cD,D

A,Ab,bC,cD,d

A,ab,Bc,Cd,D

A,ab,BC,CD,D

A,Ab,bC,cD,D

A,aB,bC,cd,D

A,aB,bC,cd,D

a,Ab,bc,CD,D

A,aB,bC,cd,D

A,AB,bC,Cd,D

A,ab,bc,cD,D

a,AB,bC,cD,D

A,ab,bC,cD,D

A,aB,bC,cD,D

A,AB,bC,cD,D

a,ab,bc,cD,D

Linkage .vs. Linkage Disequilibrium (LD)

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

Genetic Association Studies – Two Different Concepts

SNP has direct effect on protein and phenotype

Known polymorphism

Fallin, L3a, 6/21/2005 slide # 46

1. Direct method

– Testing whether a particular allele is a disease - predisposing (causative) allele

– ‘exposure status’ directly measured


..GACTAAGGCCC CCGTTCAAGGAA..

C/T

APOE gene on c19

Eg: A particular APOE allele (e4) changes protein isoform

• Genotype that particular site for association study


SNP with direct effect on protein and risk

SNP is a marker (proxy)in LD with allele that has a direct effect

Known polymorphism

Unmeasured!

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


..GACTAAGGCCC CCGTTCAAG…GA CCTG..

C/T A/G

APOE gene on c19

Eg: Genotype a nearby genetic marker among study participants

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

• 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 themEx 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

.

Diploid parent

Chromosome duplication in meiosis

Cross-overs occur

Gamete production

4 haploid gametes: One is passed on to child

Loci

A

B

C

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

Genotypes AA Aa aa

AA P(AA) x P(AA) P(AA) x P(Aa) P(AA x P(aa)

Dad 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

Genotypes AA Aa aa

AA pAA2 pAA* pAa pAA * paa

Dad 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

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

A Population-based Theoretical Example…• With random mating we should have the following:

• After one generation of random mating, • P(AA) = MT P(AA|MT)P(MT) = 1(.5*.5)+ 0 + 0 = .25• P(Aa) = MT P(Aa|MT)P(MT) = 0 + 1(2*.5*.5) + 0 = .5• P(aa) = MT 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, tag-SNPs (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

Pearson, T. A. et al. JAMA 2008;299:1335-1344.

Study Designs Used in Genome-wide Association Studies

Implementation

• State and provincial public health agencies contact all WNV infected individuals in 2002-2008

• 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

GTTACGCCAATACAGGTTACGCCAATACAGGGATCCAGGAGATTACCATCCAGGAGATTACCGTTACGCCAATACAGGTTACGCCAATACAGCCATCCAGGAGATTACCATCCAGGAGATTACC{{2 chromosomes2 chromosomes

0.0 0.2 0.3 0.4 0.50.10.0

0.5

1.0

Minor Allele Frequency (MAF)

Fra

ctio

n o

f S

NP

s D

isco

vere

d

2

888

Replication A Must

Replication

Replication

Replication

Hirschhorn & Daly Nat. Genet. Rev. 6: 95, 2005

NCI-NHGRI Working Group on Replication Nature 447: 655, 2007

Copyright restrictions may apply.


Examples of Multistage Designs in Genome-wide Association Studies

Results

• Phenotypic data on 1371

patients

• 488 NI disease

• 858 controls

• 25 equivocal

Samples: age distribution

casescontrols

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

Copyright restrictions may apply.


Hypothetical Quantile-Quantile Plots in Genome-wide Association Studies

Population Structure

Pairs who share less alleles(due to different ancestries/

differences in allelic frequencies)

Pairs who share more alleles(due to relatedness/identity bydescent)

Population structure

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)

• 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)

Reasons for Departure from HWP

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 non-synonymous 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 associationrs2066786p = 1.67 x 10-6

RFC1 (4p14-p13)

Frq

Cases

Frq

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)


SCN1A(2q24)

Frq

Cases

Frq

Ctrls

Alberta .50 .39

Colorado .40 .34

Nebraska .37 .27



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.


ANPEP(15q26.1)

Frq

Cases

Frq

Ctrls

Alberta .76 .69

Colorado .64 .65

Nebraska .72 .63



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 replicationPrimary Replication Joint

SNP Chr Pos Allele1 Allele2 Pvalue Pvalue Pvalue

LOC56964-rs3738573 1 84636844 2 3 0.003108 0.53 0.007122SCN1A-rs2298771 2 166601034 2 4 0.000299 0.66 0.0014552'-PDE-rs2241988 3 57517213 4 2 0.001476 0.42 0.000713RFC1-rs4974996 4 38903943 2 1 0.000006 0.60 0.000103RFC1-rs11096990 4 38963344 4 2 0.001328 0.65 0.004485RFC1-rs3733282 4 38965339 3 1 0.053486 0.66 0.219224RFC1-rs17288828 4 38966786 1 3 0.583279 0.87 0.484087RFC1-rs2306597 4 38973595 1 3 0.009005 0.21 0.142987RFC1-rs2066786 4 38978424 4 2 0.000001 0.56 0.000030RFC1-rs2066789 4 38984582 3 1 0.028359 0.50 0.051783RFC1-rs13147094 4 38987277 1 3 0.036064 0.95 0.106146RFC1-rs4975003 4 38989865 2 3 0.000011 0.44 0.000114RFC1-rs3796517 4 39013348 3 1 0.003467 0.82 0.012938RFC1-rs6835022 4 39030221 4 1 0.187690 0.61 0.257738RFC1-rs6851075 4 39044049 2 4 0.003936 0.94 0.020592RFC1-rs12644680 4 39047276 2 4 0.922901 0.31 0.659812RFC1-rs13123782 4 39053800 1 2 0.000806 0.57 0.005485na-rs9380006 6 27764478 2 1 0.003029 0.13 0.064231TEX15-rs323347 8 30825766 3 1 0.000238 0.13 0.045269CWF19L1-rs2270962 10 102006034 4 2 0.003019 0.81 0.029862na-rs10778292 12 102784417 2 4 0.001459 0.83 0.011818F7-rs6046 13 112821160 1 3 0.002125 0.83 0.019520TLN2-rs3816988 15 60898792 2 4 0.004330 0.69 0.012611LOC56964-rs7163367 15 88061149 1 4 0.000446 0.45 0.008170ANPEP-rs25651 15 88136792 4 2 0.000438 0.88 0.003159ANPEP-rs17240268 15 88148818 1 3 0.127363 0.59 0.268328ANPEP-rs25653 15 88150562 2 4 0.074759 0.89 0.187736GOT2-rs11076256 16 57309967 4 2 0.003654 0.98 0.027808GRIN3B-rs2240154 19 954172 4 2 0.001397 0.86 0.007999XKR3-rs5748648 22 15660822 1 3 0.001685 0.89 0.005334

Age distribution

Primary Replication

Gender distribution

Primary Replication

Neuroinvasive disease type

Primary Replication

Ancestry: U.S. census 2000

Forest plots

Primary

Replication

genetic association studies and gwas october 16, 2015

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