Migraine polygenic risk score associates with efficacy of migraine-specific drugs

Abstract

Objective:
To assess whether the polygenic risk score (PRS) for migraine is associated with acute and/or prophylactic migraine treatment response.

Methods:
We interviewed 2,219 unrelated patients at the Danish Headache Center using a semistructured interview to diagnose migraine and assess acute and prophylactic drug response. All patients were genotyped. A PRS was calculated with the linkage disequilibrium pred algorithm using summary statistics from the most recent migraine genome-wide association study comprising ∼375,000 cases and controls. The PRS was scaled to a unit corresponding to a twofold increase in migraine risk, using 929 unrelated Danish controls as reference. The association of the PRS with treatment response was assessed by logistic regression, and the predictive power of the model by area under the curve using a case-control design with treatment response as outcome.

Results:
A twofold increase in migraine risk associates with positive response to migraine-specific acute treatment (odds ratio [OR] = 1.25 [95% confidence interval (CI) = 1.05-1.49]). The association between migraine risk and migraine-specific acute treatment was replicated in an independent cohort consisting of 5,616 triptan users with prescription history (OR = 3.20 [95% CI = 1.26-8.14]). No association was found for acute treatment with non-migraine-specific weak analgesics and prophylactic treatment response.

Conclusions:
The migraine PRS can significantly identify subgroups of patients with a higher-than-average likelihood of a positive response to triptans, which provides a first step toward genetics-based precision medicine in migraine.

Full text

ARTICLE OPEN ACCESS
Migraine polygenic risk score associates with
efficacy of migraine-specificdrugs
Lisette J.A. Kogelman, PhD, Ann-Louise Esserlind, MD, PhD, Anne Francke Christensen, MD, PhD,
Swapnil Awasthi, MSc, Stephan Ripke, PhD, Andres Ingason, PhD, Olafur B. Davidsson, MSc,
Christian Erikstrup, PhD, Henrik Hjalgrim, PhD, DMSc, Henrik Ullum, PhD, Jes Olesen, DMSc, and
Thomas Folkmann Hansen, PhD,DBDS Genomic Consortium,The InternationalHeadache Genetics Consortium
Neurol Genet 2019;5:e364. doi:10.1212/NXG.0000000000000364
Correspondence
Dr. Folkmann Hansen
Thomas.hansen@regionh.dk
Abstract
Objective
To assess whether the polygenic risk score (PRS) for migraine is associated with acute and/or
prophylactic migraine treatment response.
Methods
We interviewed 2,219 unrelated patients at the Danish Headache Center using a semistructured
interview to diagnose migraine and assess acute and prophylactic drug response. All patients
were genotyped. A PRS was calculated with the linkage disequilibrium pred algorithm using
summary statistics from the most recent migraine genome-wide association study comprising
;375,000 cases and controls. The PRS was scaled to a unit corresponding to a twofold increase
in migraine risk, using 929 unrelated Danish controls as reference. The association of the PRS
with treatment response was assessed by logistic regression, and the predictive power of the
model by area under the curve using a case-control design with treatment response as outcome.
Results
A twofold increase in migraine risk associates with positive response to migraine-specific acute
treatment (odds ratio [OR] = 1.25 [95% confidence interval (CI) = 1.05–1.49]). The asso-
ciation between migraine risk and migraine-specific acute treatment was replicated in an in-
dependent cohort consisting of 5,616 triptan users with prescription history (OR = 3.20
[95% CI = 1.26–8.14]). No association was found for acute treatment with non–migraine-
specific weak analgesics and prophylactic treatment response.
Conclusions
The migraine PRS can significantly identify subgroups of patients with a higher-than-average
likelihood of a positive response to triptans, which provides a first step toward genetics-based
precision medicine in migraine.
RELATED ARTICLE
Editorial
Headaches and
polygenic scores
Page e368
From the Danish Headache Center (L.J.A.K., A.- L.E., A.F.C., O.B.D., J.O., T.F.H.), Department of Neu rology, Rigshospitalet Glostrup, Denmar k; Department of Psychiatry and Psy-
chotherapy (S.A., S.R.), Charit´
e–Universit¨
atsmedizin, Berlin, Germany; Analy tic and Translational Genetics U nit (S.R.), Massachusetts General Hospital, Boston; Stanley Cen ter for
Psychiatric Research (S.R.), Broad Ins titute of MIT and Harvard, Cambridge, MA; Mental Health Centre Sct Hans (A .I.), Institute of Biological Psychiatry, Roskilde; Departme nt of Clinical
Immunology (C.E.), Aarhus University Hos pital; Department of Epidemiology Research (H.H.), Statens Seru mIns titut, Copenhagen; and Department of Clinical Immunology (H.U.), th e
Blood Bank, Rigshospitalet, Copenhagen University Hospital, Denmark.
Go to Neurology.org/NG for full disclosures. Funding information is provided at the end of the article.
The Article Processing Charge was funded by the authors.
The DBDS Genomic Consortium and the International Headache Genetics Consortium coinvestgators are listed in appendices 2 and 3 at the end of the article.
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivativesLicense 4.0 (CC BY-NC-ND), which permits downloading
and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. 1

For complex diseases, there is an expected interindividual
variation in the response to pharmacologic treatment. The
current trend in medical science focuses on precision medi-
cine, tailoring treatments to subsets of patients. Treatment
can be improved by considering individual genomic pre-
diction relating to drug metabolism.
1,2
Genome-wide associ-
ation studies (GWASs) have been used in many complex
diseases to detect genetic variants associated with diseases,
and subsequently to generate polygenic risk scores (PRSs),
which includes the additive effect of all variants of the disease.
To date, PRS analysis is gaining ground in disease risk pre-
diction,
3
identifying and quantifying comorbidities and
endophenotypes,
4
and drug responses.
5,6
Migraine is a polygenic disorder with an estimated heritability
of 40%–60%
7–9
and a worldwide prevalence of 18%.
10
The
acute treatment of migraine is dominated by the highly
receptor-specific triptans. Approximately 25% of patients with
migraine do not respond to triptans. In case of a high fre-
quency of migraine attacks, many different nonspecific pro-
phylactic drugs may be prescribed. It is unknown to what
degree this variation in treatment response is related to ge-
netic variants.
11
We aim to test whether the genetic burden of migraine risk
variants, defined by a PRS derived from the recent meta-
analysis on migraine,
12
is associated with acute and pro-
phylactic migraine treatment.
Methods
Study population—the target sample
The study population consisted of 2,591 patients with mi-
graine who were recruited at the Danish Headache Center in
1999–2002, 2005–2006, and 2010–2011.
13,14
All patients
with migraine were interviewed face to face or by telephone by
a trained physician or trained senior medical student using
a semistructured interview. The interview was designed by head
of classification committee Prof. Jes Olesen to phenotype and
classify migraine diagnosis according to the International Clas-
sification of Headache Disorders, second edition.
15
Migraine drug response
The semistructured interview included questions covering the
necessary clinical data for migraine diagnoses and information
on the effect of migraine treatment. Acute treatment effect was
considered to be positive in cases where the patient reported
at least 50% pain reduction within 2 hours after taking med-
ication. Prophylactic treatment effect was considered to be
positive in cases where the patient reported a reduction of
over 50% in migraine attacks. For acute treatment, the patient
was asked about efficiency of (1) triptans and (2) ergotamine,
which are both migraine-specific drugs, and (3) weak anal-
gesics, which is nonspecific for migraine treatment. For pro-
phylactic treatment, the patient was asked about the efficiency
of (1) β-blockers, (2) Ca
2+
antagonists, (3) angiotensin II
receptor blockers, (4) angiotensin-converting enzyme (ACE)
inhibitors, (5) anticonvulsants, (6) antidepressants, and (7)
hormone treatment. Both generic and commercial names
were mentioned, where the interviewer used pro.medicin.dk
as reference. The questioned drug needed to be taken spe-
cifically for treatment of migraine. For all questions, the an-
swer “Do not know”was considered as missing data.
Genotyping
All patients with migraine were genotyped on the Illumina
HumanOmniExpress 12v1 (n = 2,152) or Illumina Human-
OmniExpress 24v1 (n = 439) chip. For each data set, the
quality control of genotypes was performed using PLINK
1.9.
16
We used genotypes for 2,766 ethnicity-sensitive single
nucleotide polymorphisms (SNPs) common to all Illumina
SNP arrays to estimate European, Asian, and African ancestry
probabilities with STRUCTURE
17
and excluded individuals
with <90% European ancestry. SNPs with <0.95 genotyping
rate, <0.01 minor allele frequency, or p<1×10
−6
for Hardy-
Weinberg Equilibrium were excluded, and individuals with
<0.98 genotype rate were removed. Next, we created a subset
of markers independent of each other with respect to linkage
disequilibrium (LD) using a window size of 100 markers
shifting by 25 markers at a time and removed 1 half of every
SNP pair with genotypic r
2
> 0.1. This was performed to avoid
overestimating the effect by including mutually dependent
SNPs, i.e., SNPs in LD. Using this subset of markers, we
calculated heterozygosity (HET) and sex and removed (1) all
individuals with outlying HET values (>5 SD from the me-
dian of the whole sample) and (2) all individuals where sex
determined from genotype did not match reported sex. We
then removed all A/T and C/G markers to avoid strand
issues. Related individuals were detected based on their ge-
notype data, and 1 random individual per related couple
(Pihat > 0.10) was removed. After filtering and quality con-
trol, 542,168 SNPs and 2,219 individuals were retained for
analyses. The total data set of 2,219 patients with migraine
consisted of 1,201 patients who had migraine without aura
(MO) and 1,018 patients who had migraine with aura (MA).
Cases with probable migraine (with or without aura) were
included in the analysis, and in case the individual had both
MA and MO, they were assigned to the MA subgroup.
Glossary
ACE = angiotensin-converting enzyme; AUC = area under the curve; CI = confidence interval; DBDS = Danish Blood Donor
Study; GWAS = genome-wide association study; HET = heterozygosity; IHGC = International Headache Genetics
Consortium; LD = linkage disequilibrium; MA = migraine with aura; MO = migraine without aura; OR = odds ratio; PC =
principal component; PRS = polygenic risk score; SNP = single nucleotide polymorphism.
2Neurology: Genetics | Volume 5, Number 6 | December 2019 Neurology.org/NG

PRS calculation
The PRS was calculated using LDpred, which adjusts for LD
between markers and further rescales allelic effects based on
the likelihood of each marker belonging to the fraction of
markers assumed to be causal.
18
We calculated PRSs using the
default models for causal variant fraction in LDpred (i.e., 1,
0.3, 0.1, 0.03, 0.01, 0.003, and 0.001). The LD information
was retrieved from the subjects with migraine and 929 un-
related Danish controls, who were genotyped on the same
genotype chip (Illumina HumanOmniExpress 12v1). To
calculate PRSs for migraine, we used pvalues and log
10
odds
ratios (ORs) from a subset of the International Headache
Genetics Consortium (IHGC) migraine GWAS meta-analysis
(n
case
= 59,674; n
control
= 316,078)
12
from which all individ-
uals of Danish origin (1,771 cases and 1,000 controls) had
been removed to avoid overlap between the discovery and
target sample and a resulting overestimation of allelic effects.
To investigate which fraction of causal variants gives the best
prediction of migraine, we compared the PRSs of our mi-
graine sample (n = 2,219) with the 929 Danish controls.
Migraine was most significantly predicted with a model as-
suming the fraction of causal variants to be 0.03 (p= 6.91 ×
10
−27
). The PRS generated under this model predicted both
MO and MA significantly (p= 3.69 × 10
−26
and 4.98 × 10
−17
).
Next, we investigated whether the PRSs of the migraine
subtypes could predict the respective subtype better than the
PRS of migraine, using the GWAS on the clinical subset
(5,557 MA and 7,352 MO) of the IHGC meta-analysis.
13
This
was not the case, likely because of the limited sample size of
the discovery cohort; the PRS of MO was predicted with a p
value of 2.70 × 10
−12
and the PRS of MA predicted MA with
apvalue of 1.61 × 10
−3
. Therefore, all analyses were con-
ducted using the PRS of migraine. We then rescaled the mi-
graine PRS to a mean of zero and a unit corresponding to
a twofold genetic increased risk for migraine in the target
population; this was done by first subtracting the mean PRS
from each subject’s PRS and then multiplying it by log(OR)/
log(2), where the OR was extracted from the model pre-
dicting migraine using the 2,219 cases and 929 controls.
Statistical analysis
The rescaled PRS for migraine was tested for its association
with drug response using a logistic regression model including
age, sex, genotype chip, and the first 10 principal components
(PCs) of the genotypes as covariates. The PCs were calculated
in PLINK
16
and included in the model to correct for population
stratification. As triptan and ergotamine are both migraine-
specific drugs used for acute treatment and act through the same
serotonin receptors (5-HT
1B
and 5-HT
1D
), they were analyzed
together to increase the statistical power. The mode of action of
prophylactic treatments is unknown; therefore, they were ana-
lyzed together. The association of migraine-specificacute,mi-
graine nonspecific acute, and prophylactic treatment with the
PRS was corrected for multiple testing (n = 3) using Bonferroni
correction resulting in adjusted pvalues (p
adj
). As prophylactic
treatment is potentially confounded by comorbid hypertension
or epilepsy, we tested whether these comorbidities had a signif-
icant effect on treatment response. In case they were statistically
significantly associated with treatment response, they were in-
cluded as covariates.
All analyses were performed for the complete set of patients
with migraine. Subsequently, it was tested whether there was
a statistically significant difference between the migraine sub-
types by including an interaction term between the PRS and
migraine subtype. We presented the area under the curve
(AUC), representing the prediction accuracy and ORs, using
the partial Receiver Operating Caracteristic R-package.
19
The
AUC was calculated for both the model including only the
covariates and the full model including the PRS and the
covariates. The difference between the 2 AUCs was tested using
the DeLong test in the partial Receiver Operating Caracteristic
R-package. ORs were presented with 95% confidence intervals
(CIs). All analyses were performed in R (version 3.4.3.).
20
Replication cohort
The Danish Blood Donor Study (DBDS) genomic cohort
(n = 79,595) was used as the replication cohort (see detailed
description elsewhere).
21
The PRS for migraine was calculated
as done for the clinical cohort. Using a subpopulation of the
DBDS genomic cohort (n = 17,222) with information on self-
reported migraine (n
migraine
= 3,906), we estimated the OR for
migraine within the DBDS genomic cohort (OR = 3.98, 95%
CI = 3.18–4.98). The OR for migraine was used for subsequent
normalization of the PRS as done for the clinical cohort. Using
the prescription register of the 79,595 participants, we identi-
fied 5,616 users of migraine-specific treatment (1,372 males
and 4,244 females). Positive triptan responders were defined as
having 10 or more purchases of triptans, as previously sug-
gested to be a reliable indicator of positive treatment re-
sponse.
22
This resulted in 1,246 triptan responders (213 males
and 1,033 females). In the regression model, age, sex, and the
10 first PCs were included as covariates.
Standard protocol approval, registrations, and
patient consents
Written informed consent was obtained from all participants.
The study was approved by the Danish Ethical Standards
Committee (H-2-2010-122) and the Danish Data Protection
Agency (01080/GLO-2010-10).
Data availability
Summary statistics of the GWAS are available upon agreement
with the IHGC due to embargo with 23andMe. Genotype data
of our clinical cohort are available upon agreement with the
senior author and upon material transfer agreement.
Results
Sample characteristics
Our data set consists of 2,219 patients with migraine including
1,201 MO and 1,018 MA patients. The male:female ratio in
patients with migraine was 1:4.7; this was slightly lower in
Neurology.org/NG Neurology: Genetics | Volume 5, Number 6 | December 2019 3

MO (1:5.8) than in MA (1:3.8) (p= 2.7 × 10
−4
). The patients
with migraine were on average 44.2 years old with an SD of
12.8. There was no significant difference in age (SD) between
MO and MA (44.0 [12.1] years and 44.4 [13.6] years, re-
spectively). A higher response rate was found for MO than
MA in acute and prophylactic treatment response (table 1).
The difference in response rates implies a potential difference
in association with the PRS across migraine subtypes, and
therefore, we tested whether such difference was evident.
There was a significantly higher response rate for female
patients with migraine than male patients with migraine for
acute treatment (p= 0.03). Furthermore, among the res-
ponders to prophylactic treatment, there were a higher
number of patients with migraine with hypertension (table 2).
Association with acute treatment response
Acute treatment response was assessed by 2 different classes
of drugs: migraine-specific and nonspecific drugs (figure 1).
The PRS was statistically significantly associated with positive
migraine-specific acute treatment response: a unit increase in
the PRS (corresponding to a twofold increased migraine risk)
was associated with an OR of 1.25 (95% CI = 1.05–1.49, p
adj
=
1.25 × 10
−2
). Although the PRS was statistically significantly
associated with acute treatment response, there was no sta-
tistically significant improvement of the accuracy when added
to a model that included treatment covariates (p= 0.50): the
AUC for the full model was 0.603 (95% CI = 0.569–0.637),
and the model including all covariates except the PRS was
0.598 (95% CI = 0.563–0.633). No statistically significant
interaction was present between the PRS and migraine sub-
types, age, or sex. However, testing the association of acute
treatment response with genetic load for each sex separately
showed a strong signal for males (OR = 2.17 [1.36–3.57],
p= 1.55 × 10
−3
) but not for females (OR = 1.15 [0.95–1.39],
p= 0.16).
To ensure that the signal we are detecting is between
migraine-specific drugs and the genetic load of migraine, we
used migraine nonspecific drugs (weak analgesics) as negative
control and saw no significant association. As a secondary
analysis, we split the migraine-specific drugs into triptans and
ergotamine; we saw only a statistically significant association
for triptans (OR = 1.27 [1.07–1.51], p= 7.60 × 10
−3
). The
stronger signal between the PRS and treatment response
among males was still present among triptan response (OR =
2.08 [1.31–3.39], p= 2.43 × 10
−3
) and not for females (OR =
1.17 [0.97–1.42], p= 0.10).
Association with prophylactic treatment response
Migraine can be preventively treated with β-blockers, calcium
antagonists, angiotensin II receptor antagonists, ACE inhib-
itors, antiepileptics, antidepressants, and by hormone treatment.
Because their mode of action on migraine is unknown, we an-
alyzed all prophylactic treatments together (figure 2). We did not
find a statistically significant association between the migraine
PRS and a positive prophylactic treatment response: a unit in-
crease in the PRS (corresponding to a twofold increased mi-
graine risk) resulted in an OR of 1.07 (95% CI = 0.90–1.27).
Table 1 Response rates of the investigated acute and prophylactic drugs in all patients with migraine, patients with
migraine without aura, and patients with migraine with aura
% (Total number of patients) Migraine without vs with aura
Migraine MO MA OR 95% CI pValue
Acute treatment response
a
81.5 (1,840) 87.0 (1,116) 73.1 (724) 0.41 0.32–0.51 5.29 × 10
−14
Triptan 80.9 (1,828) 86.6 (1,113) 72.0 (715) 0.40 0.31–0.50 9.99 × 10
−15
Ergotamine 40.0 (255) 43.1 (102) 37.9 (153) 0.80 0.48–1.34 0.40
Weak analgesics 27.6 (1,626) 21.2 (848) 34.5 (778) 1.92 1.56–2.43 2.54 × 10
−9
Prophylactic treatment response
b
54.2 (1,106) 53.8 (651) 55.0 (455) 1.05 0.82–1.33 0.70
β-blocker 29.9 (782) 30.0 (460) 29.8 (322) 0.99 0.73–1.35 0.96
Ca2+ antagonist 15.9 (201) 15.4 (117) 16.7 (84) 1.10 0.51–2.36 0.81
Ang. II receptor antagonist 41.2 (580) 42.2 (358) 39.6 (222) 0.90 0.64–1.27 0.55
ACE inhibitors 25.5 (102) 26.8 (56) 23.9 (46) 0.86 0.35–2.11 0.74
Anticonvulsants 27.9 (495) 25.9 (293) 30.7 (202) 1.26 0.85–1.88 0.25
Antidepressants 24.3 (136) 18.1 (83) 34.0 (53) 2.33 1.05–5.17 0.04
Hormone treatment 37.0 (81) 37.8 (45) 36.1 (36) 0.93 0.38–2.31 0.88
Abbreviations: ACE = angiotensin-converting enzyme; Ang. II receptor antagonist = angiotensin II receptor antagonist; CI = confidence interval; MA = migraine
with aura; MO = migraine without aura; OR = odds ratio.
Presented ORs and pvalues are for MO vs MA, with MO as the reference level for presented ORs.
a
Acute treatment response only includes triptans and ergotamine (both 5-HT
1B/D
receptor antagonists).
b
Prophylactic treatment response includes all medications questioned.
4Neurology: Genetics | Volume 5, Number 6 | December 2019 Neurology.org/NG

Again, we did not see any statistically significant interaction
between the PRS and migraine subtypes.
To test whether treatment of comorbidities was masking the
association between the PRS and treatment response, we
tested each drug separately. Comorbid hypertension was
statistically significantly associated with angiotensin II re-
ceptor blockers and ACE inhibitors (p= 2.85 × 10
−4
and 4.51
×10
−2
, respectively), and epilepsy was statistically signifi-
cantly associated with anticonvulsants (p= 8.88 × 10
−3
). We
found no statistically significant associations between any
prophylactic treatment response and the PRS, although it
should be noted that prophylactic treatments were used only
by a relatively small proportion of the patients.
Replication of the association with triptan response
As it has been shown that pharmacy databases are a valuable
source to identify treatment responders, we used the DBDS
Genomic Cohort to replicate the association between genetic
load of migraine and triptan response. We found a statistically
significant association between the PRS of migraine and
triptan response with an OR of 1.78 (95% CI = 1.20–2.64,
p= 3.36 × 10
−2
). We found an association for both males and
females (OR = 3.20 [1.26–8.14] and 1.63 [1.05–2.53], re-
spectively). Although the OR was higher for males, the dif-
ference was not statistically significant. The prediction
showed an increased rate of triptan response among the
individuals with higher genetic load for migraine (figure 3).
Discussion
We show that the genetic burden of migraine is associated
with the response to pharmacologic treatment. The PRS for
migraine was statistically significantly associated with re-
sponse to migraine-specific treatment: triptans and/or er-
gotamine. Neither the response to weak analgesics nor the
response to prophylactic treatment, which are not migraine
specific, was associated with the PRS.
Figure 1 Association of the polygenic risk score with acute treatment response
ORs and the 95% CI are shown on the left-hand
side, and the corresponding forest plot is shown
on the right-hand side. An OR below 1 represents
a lower response rate to the drug. An OR above 1
represents a higher response rate to the drug.
Migraine-specific acute treatment includes both
triptan and ergotamine, as both are acting on the
same 5-HT
1B/D
receptors. Migraine nonspecific
treatment is represented by the weak analgesics.
CI = confidence interval; OR = odds ratio.
Table 2 Descriptive statistics of potential confounding factors
Nonresponders Responders OR 95% CI pValue
Acute treatment
Age (mean [SD]) 41.71 (13.09) 44.87 (12.05) −3.16
a
−3.53 to −2.79 5.28 × 10
−5
Sex (M: F ratio) 1:4.31 1:6.11 1.42 1.04 to 1.93 0.03
Migraine subtype (% MO) 42.65 64.73 2.47 1.94 to 3.14 5.3 × 10
−14
Epilepsy (%) 3.62 2.74 0.75 0.38 to 1.50 0.42
Hypertension (%) 15.00 17.69 1.22 0.88 to 1.69 0.24
Prophylactic treatment
Age (mean [SD]) 43.49 (13.27) 45.17 (12.15) −1.68
a
−2.03 to −1.33 0.03
Sex (M: F ratio) 1:4.82 1:5.82 1.21 0.87 to 1.67 0.25
Migraine subtype (% MO) 59.49 58.33 0.95 0.75 to 1.21 0.70
Epilepsy (%) 2.96 3.17 1.07 0.54 to 2.13 0.85
Hypertension (%) 15.61 23.04 1.60 1.19 to 2.20 1.97 × 10
−3
Abbreviations: CI = confidence interval; MO = migraine without aura; OR = odds ratio.
a
As this is a continuous variable, we showed the difference in age between nonresponders and responders, instead of an OR.
For all ORs the nonresponders are used as reference.
Neurology.org/NG Neurology: Genetics | Volume 5, Number 6 | December 2019 5

Genomics play an important role in the variability of drug
response, which is best understood in relation to
pharmacokinetics.
23,24
Recently, PRS studies have pre-
dicted drug response in psychiatric diseases. A PRS of major
depressive disorder explained 1.2% of the antidepressant re-
sponse.
25
In schizophrenia, no statistically significant association
was found between the response to clozapine and PRS of
schizophrenia, although not significant.
26
The PRS could not
predict treatment-resistant schizophrenia,
6
but a lower PRS for
schizophrenia was associated with a positive response to lithium
in bipolar affective disorder.
5
A better understanding of the ge-
netic contribution in migraine drug response could pave the road
to personalized treatment of migraine or deepen our un-
derstanding of the underlying pathophysiology. Recently, a PRS
of migraine has been associated with migraine (OR = 1.76),
migraine subtypes (OR MO = 1.57; OR MA = 1.85), and se-
verity of migraine (OR = 1.29).
27
Although information about
migraine treatment response was not available in their cohort,
they report a higher PRS among individuals who had self-
reported use of triptans (OR = 1.12).
Previously, the association between the cumulative genetic
risk score, based on the count of number of risk alleles of 12
migraine-associated SNPs, and migraine drug response was
investigated.
14
The OR was 1.09 (95% CI = 1.03–1.15) for
acute treatment response, but no significant correlation of the
cumulative genetic risk score with prophylactic treatment was
found. In the current study, we found higher estimates than
previously for acute treatment response. This may be a con-
sequence of increased sample size in the discovery sample
resulting in improved accuracy of the effect size of the genetic
variants. Furthermore, the cumulative genetic risk score pre-
viously used is not comparable with this study, as we used
a weighted risk score and included all genotyped SNPs.
Treatment response shows, for migraine as well as other
conditions, a large inter-individual variation and, therefore,
a precise measurement of positive treatment response is not
easily defined. Many factors may affect treatment response,
e.g., polypharmacy, comorbidity, and body mass index. To
obtain enough power, we have analyzed the different drugs
collectively, which may not be optimal, as a patient may not
have tried all drugs questioned and, therefore, may be in-
correctly defined as a nonresponder. Furthermore, although
we used a semistructured interview, recall bias and negativity
bias are inherent limitations. Our PRS is based on the effect
sizes of common SNPs that explain an estimated 14.63% of
the overall of the migraine phenotype.
12
Patients with mi-
graine with a low PRS might nevertheless have a high genetic
burden if they carry rare genetic variants with relatively high
effect estimates. On the other hand, a high genetic burden of
migraine may be associated with specific symptoms of mi-
graine or, for example, severity of migraine. Patients in this
study were recruited from the Danish Headache Center,
which is a tertiary referral center. Patients therefore have
Figure 3 Replication of the association of the polygenic risk
score (PRS) with acute treatment response
Odds ratio by PRS within each 20 percentiles for n = 5,616 triptan users in the
Danish Blood Donor Study replication cohort.
Figure 2 Associations of the polygenic risk score with prophylactic treatment response
ORs and the 95% CI are shown on the left-hand
side, and the corresponding forest plot is shown
on the right-hand side. An OR below 1 represents
a lower response rate to the drug. An OR above 1
represents a higher response rate to the drug.
Acute treatment includes both triptan and er-
gotamine, as both are acting on the same 5-HT
1B/
D
receptors. Prophylactic treatment response
includes all questioned treatments summed up
(positive response to any prophylactic drug vs
response to none). ACE = angiotensin-converting
enzyme; CI = confidence interval; OR = odds ratio.
6Neurology: Genetics | Volume 5, Number 6 | December 2019 Neurology.org/NG

a relatively severe migraine. In other diseases, genes in the
monoamine oxidase (MAO) A and cytochrome P450 su-
perfamily are associated with drug response,
2,28
showing the
genetic contribution of drug metabolic pathways. There is no
evidence that those genes are interacting with migraine-
associated genes, and therefore, those are not represented by
the PRS. Future and larger studies may focus on other models,
such as Bayesian methods extensively used in plant and animal
breeding, and/or they may include epistatic interaction effects
that potentially have a higher predictive power to predict the
genetic risk score of patients with migraine.
While currently the effect size is too small to be clinically im-
portant, the study provides an important proof of concept.
Furthermore, we were able to replicate the association in an
independent cohort of Danish blood donors, although the re-
sponse phenotype is affected by noise (sensitivity of 82% and
aspecificity of 66%). We expect that we will see increased
predictive power with an increased sample size in the migraine
GWAS and/or a future GWAS focusing on migraine treatment
response. Thus, future studies might enable us to define more
homogeneous groups of patients benefitting from specific
treatments using genetic data. With the arrival of new migraine
treatments, such as the novel but expensive calcitonin gene-
related peptide antibodies, a genetic classifier to identify
patients who are likely to benefit from the treatment could have
great clinical impact.
Acknowledgment
The authors thank all participating patients and the employees at
the Danish Headache Center for their help during the
recruitment of the patients.
Study funding
This project was financed by a grant from Candys Foundation
“CEHEAD”(Prof. Jes Olesen).
Disclosure
Disclosures available: Neurology.org/NG.
Publication history
Received by Neurology: Genetics March 21, 2019. Accepted in final form
September 4, 2019.
Appendix 1 Authors
Name Location Role Contribution
Lisette J.A.
Kogelman,
PhD
Rigshospitalet Glostrup,
Denmark
Author Conception and
design of the
study; acquisition
and analysis of
data; and drafting
asignificant
portion of the
manuscript or
figures
Ann-Louise
Esserlind, MD,
PhD
Rigshospitalet Glostrup,
Denmark
Author Acquisition and
analysis of data
Appendix 1 (continued)
Name Location Role Contribution
Anne Francke
Christensen,
MD, PhD
Rigshospitalet Glostrup,
Denmark
Author Acquisition and
analysis of data
Swapnil
Awasthi, MSc
Charit´
e–Universit¨
atsmedizin,
Berlin, Germany
Author Acquisition
and analysis of
data
Stephan
Ripke, PhD
Charit´
e–Universit¨
atsmedizin,
Berlin, Germany;
Massachusetts General
Hospital, Boston; Broad
Institute of MIT and
Harvard, Cambridge
Author Acquisition and
analysis of data
Andres
Ingason, PhD
Mental Health Centre Sct
Hans, Roskilde, Denmark
Author Conception and
design of the
study and
acquisition and
analysis of data
Olafur B.
Davidsson,
MSc
Rigshospitalet Glostrup,
Denmark
Author Acquisition and
analysis of data
Christian
Erikstrup,
PhD
Department of Clinical
Immunology, Aarhus
University Hospital,
Denmark
Author Acquisition and
analysis of data
Henrik
Hjalgrim, PhD,
DMSc
Department of Epidemiology
Research, Statens Serum
Institut, Copenhagen,
Denmark
Author Acquisition and
analysis of data
Henrik Ullum,
PhD
Department of Clinical
Immunology, the Blood Bank,
Rigshospitalet, Copenhagen
University Hospital,
Denmark
Author Acquisition and
analysis of data
Jes Olesen,
DMSc
Rigshospitalet Glostrup,
Denmark
Author Conception and
design of the
study; acquisition
and analysis of
data; and drafting
a significant
portion of the
manuscript or
figures
Thomas
Folkmann
Hansen, PhD
Rigshospitalet Glostrup,
Denmark
Author Conception and
design of the
study; acquisition
and analysis of
data; and drafting
a significant
portion of the
manuscript or
figures
Appendix 2 Members of the DBDS Genomic Consortium
(DBDS-GC)
Name Location Role Contribution
Daniel
Gudbjartsson
deCODE Genetics,
Reykjavik, Iceland
Member
of the
DBDS-GC
Acquisition of
data
Omar
Gastafsson
deCODE Genetics,
Reykjavik, Iceland
Member
of the
DBDS-GC
Acquisition of
data
Continued
Neurology.org/NG Neurology: Genetics | Volume 5, Number 6 | December 2019 7

Appendix 2 (continued)
Name Location Role Contribution
Kari
Stefansson
deCODE Genetics,
Reykjavik, Iceland
Member
of the
DBDS-
GC
Acquisition of
data
Hreinn
Stefansson
deCODE Genetics,
Reykjavik, Iceland
Member
of the
DBDS-
GC
Acquisition of
data
Unnur
Þorsteinsd´
ottir
deCODE Genetics,
Reykjavik, Iceland
Member
of the
DBDS-
GC
Acquisition of
data
Steffen
Andersen
Department of Finance,
Copenhagen Business
School, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Karina Banasik Novo Nordisk
Foundation Center for
Protein Research,
Faculty of Health and
Medical Sciences,
University of
Copenhagen, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Søren Brunak Novo Nordisk
Foundation Center for
Protein Research,
Faculty of Health and
Medical Sciences,
University of
Copenhagen, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Alfonso Buil Institute of Biological
Psychiatry, Mental
Health Centre Sct. Hans,
Copenhagen University
Hospital, Roskilde,
Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Kristoffer
Burgdorf
Department of Clinical
Immunology, the Blood
Bank, Rigshospitalet,
Copenhagen University
Hospital, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Christian
Erikstrup
Department of Clinical
Immunology, Aarhus
University Hospital,
Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Thomas
Folkmann
Hansen
Danish Headache
Center, Department of
Neurology
Rigshospitalet, Glostrup
& Institute of Biological
Psychiatry, Mental
Health Centre Sct. Hans,
Copenhagen University
Hospital, Roskilde,
Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Henrik
Hjalgrim
Department of
Epidemiology Research,
Statens Serum Institut,
Copenhagen, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Jemec Gregor Department of Clinical
Medicine, Sealand
University Hospital,
Roskilde, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Appendix 3 Members of the International Headache
Genetics Consortium (IHGC)
Name Location Role Contribution
Verneri
Anttila
Broad Institute of MIT
and Harvard,
Cambridge
Member of
the IHGC
Acquisition of
data
Ville Artto Department of
Neurology, Helsinki
University Central
Hospital, Finland
Member of
the IHGC
Acquisition of
data
Andrea
Carmine
Belin
Karolinska Institute,
Stockholm, Sweden
Member of
the IHGC
Acquisition of
data
Irene de Boer Leiden University
Medical Centre, The
Netherlands
Member of
the IHGC
Acquisition of
data
Appendix 2 (continued)
Name Location Role Contribution
Poul Jennum Department of Clinical
Neurophysiology at
University of
Copenhagen, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Kasper Rene
Nielsen
Department of Clinical
Immunology, Aalborg
University Hospital,
Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Mette
Nyegaard
Department of
Biomedicine, Aarhus
University, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Helene
Mariana
Paarup
Department of Clinical
Immunology, Odense
University Hospital,
Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Ole Birger
Pedersen
Department of Clinical
Immunology, Naestved
Hospital
Member
of the
DBDS-
GC
Acquisition of
data
Erik Sørensen Department of Clinical
Immunology, the Blood
Bank, Rigshospitalet,
Copenhagen University
Hospital, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Henrik Ullum Department of Clinical
Immunology, the Blood
Bank, Rigshospitalet,
Copenhagen University
Hospital, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
Thomas Werge Institute of Biological
Psychiatry, Mental
Health Centre Sct. Hans,
Copenhagen University
Hospital, Roskilde,
Denmark & Department
of Clinical Medicine,
University of
Copenhagen, Denmark
Member
of the
DBDS-
GC
Acquisition of
data
8Neurology: Genetics | Volume 5, Number 6 | December 2019 Neurology.org/NG

Appendix 3 (continued)
Name Location Role Contribution
Dorret I.
Boomsma
VU University,
Amsterdam, The
Netherlands
Member of
the IHGC
Acquisition of
data
Sigrid Børte Oslo University Hospital
and University of Oslo,
Norway
Member of
the IHGC
Acquisition of
data
Daniel I
Chasman
Harvard Medical
School, Boston
Member of
the IHGC
Acquisition of
data
Lynn Cherkas Department of Twin
Research and Genetic
Epidemiology,
King’sCollege
London, UK
Member of
the IHGC
Acquisition of
data
Anne Francke
Christensen
Danish Headache
Center, Department of
Neurology,
Rigshospitalet,
Glostrup Hospital,
University of
Copenhagen, Denmark
Member of
the IHGC
Acquisition of
data
Bru Cormand University of Barcelona,
Spain
Member of
the IHGC
Acquisition of
data
Ester Cuenca-
Leon
Broad Institute of MIT
and Harvard,
Cambridge
Member of
the IHGC
Acquisition of
data
George
Davey-Smith
Medical Research
Council (MRC)
Integrative
Epidemiology Unit,
University of Bristol, UK
Member of
the IHGC
Acquisition of
data
Martin
Dichgans
Institute for Stroke and
Dementia Research,
Munich, Germany
Member of
the IHGC
Acquisition of
data
Cornelia van
Duijn
Erasmus University
Medical Centre,
Rotterdam, The
Netherlands
Member of
the IHGC
Acquisition of
data
Tonu Esko Estonian Genome
Center, University of
Tartu, Estonia
Member of
the IHGC
Acquisition of
data
Ann-Louise
Esserlind
Danish Headache
Center, Department of
Neurology,
Rigshospitalet,
Glostrup Hospital,
University of
Copenhagen, Denmark
Member of
the IHGC
Acquisition of
data
Michel
Ferrari
Leiden University
Medical Centre, The
Netherlands
Member of
the IHGC
Acquisition of
data
Rune R.
Frants
Leiden University
Medical Centre, The
Netherlands
Member of
the IHGC
Acquisition of
data
Tobias
Freilinger
University of
Tuebingen, Germany
Member of
the IHGC
Acquisition of
data
Nick Furlotte 23andMe Inc.,
Mountain View
Member of
the IHGC
Acquisition of
data
Padhraig
Gormley
Broad Institute of MIT
and Harvard,
Cambridge
Member of
the IHGC
Acquisition of
data
Appendix 3 (continued)
Name Location Role Contribution
Lyn Griffiths Institute of Health and
Biomedical Innovation,
Queensland University
of Technology,
Brisbane, Australia
Member of
the IHGC
Acquisition of
data
Eija
Hamalainen
Institute for Molecular
Medicine Finland
(FIMM), University of
Helsinki, Finland
Member of
the IHGC
Acquisition of
data
Thomas
Folkmann
Hansen
Danish Headache
Center, Department
of Neurology,
Rigshospitalet, Glostrup
Hospital, University of
Copenhagen, Denmark
Member of
the IHGC
Acquisition of
data
Marjo
Hiekkala
Folkh¨
alsan Institute of
Genetics, Helsinki,
Finland
Member of
the IHGC
Acquisition of
data
M. Arfan
Ikram
Erasmus University
Medical Centre,
Rotterdam, The
Netherlands
Member of
the IHGC
Acquisition of
data
Andres
Ingason
deCODE Genetics Inc.,
Reykjavik, Iceland
Member of
the IHGC
Acquisition of
data
Marjo-Riitta
J¨
arvelin
University of Oulu,
Biocenter, Finland
Member of
the IHGC
Acquisition of
data
Risto Kajanne Institute for Molecular
Medicine Finland (FIMM),
University of Helsinki
Member of
the IHGC
Acquisition of
data
Mikko Kallela Department of
Neurology, Helsinki
University Central
Hospital, Finland
Member of
the IHGC
Acquisition of
data
Jaakko Kaprio Institute for Molecular
Medicine Finland
(FIMM), University of
Helsinki, Finland
Member of
the IHGC
Acquisition of
data
Mari
Kaunisto
Folkh¨
alsan Institute of
Genetics, Helsinki,
Finland
Member of
the IHGC
Acquisition of
data
Lisette J.A.
Kogelman
Danish Headache
Center, Department of
Neurology,
Rigshospitalet,
Glostrup Hospital,
University of
Copenhagen, Denmark
Member of
the IHGC
Acquisition of
data
Christian
Kubisch
University Medical
Center Hamburg-
Eppendorf, Germany
Member of
the IHGC
Acquisition of
data
Mitja Kurki Broad Institute of MIT
and Harvard,
Cambridge
Member of
the IHGC
Acquisition of
data
Tobias Kurth Harvard Medical
School, Boston
Member of
the IHGC
Acquisition of
data
Lenore
Launer
National Institute on
Aging, Bethesda
Member of
the IHGC
Acquisition of
data
Terho
Lehtimaki
School of Medicine,
Unviersity of 3
Tampere, Finland
Member of
the IHGC
Acquisition of
data
Continued
Neurology.org/NG Neurology: Genetics | Volume 5, Number 6 | December 2019 9

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Name Location Role Contribution
Davor Lessel University Medical
Center Hamburg-
Eppendorf, Germany
Member of
the IHGC
Acquisition of
data
Lannie
Ligthart
VU University,
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Netherlands
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data
Nadia
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the IHGC
Acquisition of
data
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the IHGC
Acquisition of
data
Massimo
Mangino
Department of Twin
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Epidemiology, King’s
College London, UK
Member of
the IHGC
Acquisition of
data
George
McMahon
Medical Research
Council (MRC) Integrative
Epidemiology Unit,
University of Bristol, UK
Member of
the IHGC
Acquisition of
data
Bertram
Muller-
Myhsok
Max Planck Institute of
Psychiatry, Munich,
Germany
Member of
the IHGC
Acquisition of
data
Benjamin M.
Neale
Broad Institute of MIT
and Harvard,
Cambridge
Member of
the IHGC
Acquisition of
data
Carrie
Northover
23andMe Inc.,
Mountain View
Member of
the IHGC
Acquisition of
data
Dale R.
Nyholt
Institute of Health and
Biomedical Innovation,
Queensland University
of Technology,
Brisbane, Australia
Member of
the IHGC
Acquisition of
data
Jes Olesen Danish Headache
Center, Department of
Neurology,
Rigshospitalet, Glostrup
Hospital, University of
Copenhagen, Denmark
Member of
the IHGC
Acquisition of
data
Aarno Palotie Broad Institute of MIT
and Harvard,
Cambridge
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the IHGC
Acquisition of
data
Priit Palta Institute for Molecular
Medicine Finland
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the IHGC
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data
Linda
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Oslo University Hospital
and University of Oslo,
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the IHGC
Acquisition of
data
Nancy
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Karolinska Institutet,
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the IHGC
Acquisition of
data
Danielle
Posthuma
VU University,
Amsterdam, The
Netherlands
Member of
the IHGC
Acquisition of
data
Appendix 3 (continued)
Name Location Role Contribution
Patricia Pozo-
Rosich
Headache Research
Group, Vall d’Hebron
Research Institute,
Universitat Aut`
onoma
de Barcelona, Spain &
Headache Unit,
Neurology Department,
Vall d’Hebron
University Hospital,
Barcelona, Spain
Member of
the IHGC
Acquisition of
data
Alice
Pressman
Sutter Health,
Sacramento
Member of
the IHGC
Acquisition of
data
Olli Raitakari Department of
Medicine, University of
Turku, Finland
Member of
the IHGC
Acquisition of
data
Markus
Sch¨
urks
Harvard Medical
School, Boston
Member of
the IHGC
Acquisition of
data
Celia Sintas University of Barcelona,
Spain
Member of
the IHGC
Acquisition of
data
Kari
Stefansson
deCODE Genetics Inc.,
Reykjavik, Iceland
Member of
the IHGC
Acquisition of
data
Hreinn
Stefansson
deCODE Genetics Inc.,
Reykjavik, Iceland
Member of
the IHGC
Acquisition of
data
Stacy
Steinberg
deCODE Genetics Inc.,
Reykjavik, Iceland
Member of
the IHGC
Acquisition of
data
David
Strachan
Population Health
Research Institute, St
George’s, University of
London, Cranmer
Terrace, London, UK
Member of
the IHGC
Acquisition of
data
Gisela
Terwindt
Leiden University
Medical Centre, The
Netherlands
Member of
the IHGC
Acquisition of
data
Marta Vila-
Pueyo
Vall d’Hebron Research
Institute, Barcelona,
Spain
Member of
the IHGC
Acquisition of
data
Maija
Wessman
Folkh¨
alsan Institute of
Genetics, Helsinki,
Finland
Member of
the IHGC
Acquisition of
data
Bendik S.
Winsvold
Oslo University Hospital
and University of Oslo,
Norway
Member of
the IHGC
Acquisition of
data
Huiying Zhao Institute of Health and
Biomedical Innovation,
Queensland University
of Technology,
Brisbane, Australia
Member of
the IHGC
Acquisition of
data
John-Anker
Zwart
Oslo University Hospital
and University of Oslo,
Norway
Member of
the IHGC
Acquisition of
data
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DOI 10.1212/NXG.0000000000000364
2019;5; Neurol Genet
Lisette J.A. Kogelman, Ann-Louise Esserlind, Anne Francke Christensen, et al.
Migraine polygenic risk score associates with efficacy of migraine-specific drugs
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is an official journal of the American Academy of Neurology. Published since April 2015, it isNeurol Genet