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

Authors:
  • Glostrup-Rigshospitalet, Faculty of Medicine, University of Copenhagen

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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.
ARTICLE OPEN ACCESS
Migraine polygenic risk score associates with
ecacy of migraine-specicdrugs
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-specic acute
treatment (odds ratio [OR] = 1.25 [95% condence interval (CI) = 1.051.49]). The asso-
ciation between migraine risk and migraine-specic acute treatment was replicated in an in-
dependent cohort consisting of 5,616 triptan users with prescription history (OR = 3.20
[95% CI = 1.268.14]). No association was found for acute treatment with nonmigraine-
specic weak analgesics and prophylactic treatment response.
Conclusions
The migraine PRS can signicantly identify subgroups of patients with a higher-than-average
likelihood of a positive response to triptans, which provides a rst 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´
eUniversit¨
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 eect 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%
79
and a worldwide prevalence of 18%.
10
The
acute treatment of migraine is dominated by the highly
receptor-specic triptans. Approximately 25% of patients with
migraine do not respond to triptans. In case of a high fre-
quency of migraine attacks, many dierent nonspecic 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, dened by a PRS derived from the recent meta-
analysis on migraine,
12
is associated with acute and pro-
phylactic migraine treatment.
Methods
Study populationthe target sample
The study population consisted of 2,591 patients with mi-
graine who were recruited at the Danish Headache Center in
19992002, 20052006, and 20102011.
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 classication committee Prof. Jes Olesen to phenotype and
classify migraine diagnosis according to the International Clas-
sication 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 eect of migraine treatment. Acute treatment eect 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 eect 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 eciency of (1) triptans and (2) ergotamine,
which are both migraine-specic drugs, and (3) weak anal-
gesics, which is nonspecic for migraine treatment. For pro-
phylactic treatment, the patient was asked about the eciency
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-
cically for treatment of migraine. For all questions, the an-
swer Do not knowwas 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 eect 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 ltering 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 = condence 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 eects 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 eects.
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 signicantly 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 signicantly (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 rst subtracting the mean PRS
from each subjects 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 rst 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
stratication. As triptan and ergotamine are both migraine-
specic 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-specicacute,mi-
graine nonspecic 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 eect on treatment response. In case they were statistically
signicantly 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 signicant dierence 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 dierence between the 2 AUCs was tested using
the DeLong test in the partial Receiver Operating Caracteristic
R-package. ORs were presented with 95% condence 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 = 3.184.98). 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-
ed 5,616 users of migraine-specic treatment (1,372 males
and 4,244 females). Positive triptan responders were dened 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 rst 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 signicant dierence 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 dierence in response rates implies a potential dierence
in association with the PRS across migraine subtypes, and
therefore, we tested whether such dierence was evident.
There was a signicantly 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 dierent classes
of drugs: migraine-specic and nonspecic drugs (gure 1).
The PRS was statistically signicantly associated with positive
migraine-specic 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 = 1.051.49, p
adj
=
1.25 × 10
2
). Although the PRS was statistically signicantly
associated with acute treatment response, there was no sta-
tistically signicant 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 = 0.5690.637),
and the model including all covariates except the PRS was
0.598 (95% CI = 0.5630.633). No statistically signicant
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 [1.363.57],
p= 1.55 × 10
3
) but not for females (OR = 1.15 [0.951.39],
p= 0.16).
To ensure that the signal we are detecting is between
migraine-specic drugs and the genetic load of migraine, we
used migraine nonspecic drugs (weak analgesics) as negative
control and saw no signicant association. As a secondary
analysis, we split the migraine-specic drugs into triptans and
ergotamine; we saw only a statistically signicant association
for triptans (OR = 1.27 [1.071.51], 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 [1.313.39], p= 2.43 × 10
3
) and not for females (OR =
1.17 [0.971.42], 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 (gure 2). We did not
nd a statistically signicant 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 = 0.901.27).
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 0.320.51 5.29 × 10
14
Triptan 80.9 (1,828) 86.6 (1,113) 72.0 (715) 0.40 0.310.50 9.99 × 10
15
Ergotamine 40.0 (255) 43.1 (102) 37.9 (153) 0.80 0.481.34 0.40
Weak analgesics 27.6 (1,626) 21.2 (848) 34.5 (778) 1.92 1.562.43 2.54 × 10
9
Prophylactic treatment response
b
54.2 (1,106) 53.8 (651) 55.0 (455) 1.05 0.821.33 0.70
β-blocker 29.9 (782) 30.0 (460) 29.8 (322) 0.99 0.731.35 0.96
Ca2+ antagonist 15.9 (201) 15.4 (117) 16.7 (84) 1.10 0.512.36 0.81
Ang. II receptor antagonist 41.2 (580) 42.2 (358) 39.6 (222) 0.90 0.641.27 0.55
ACE inhibitors 25.5 (102) 26.8 (56) 23.9 (46) 0.86 0.352.11 0.74
Anticonvulsants 27.9 (495) 25.9 (293) 30.7 (202) 1.26 0.851.88 0.25
Antidepressants 24.3 (136) 18.1 (83) 34.0 (53) 2.33 1.055.17 0.04
Hormone treatment 37.0 (81) 37.8 (45) 36.1 (36) 0.93 0.382.31 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 signicant 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 signicantly 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 signi-
cantly associated with anticonvulsants (p= 8.88 × 10
3
). We
found no statistically signicant 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
signicant association between the PRS of migraine and
triptan response with an OR of 1.78 (95% CI = 1.202.64,
p= 3.36 × 10
2
). We found an association for both males and
females (OR = 3.20 [1.268.14] and 1.63 [1.052.53], re-
spectively). Although the OR was higher for males, the dif-
ference was not statistically signicant. The prediction
showed an increased rate of triptan response among the
individuals with higher genetic load for migraine (gure 3).
Discussion
We show that the genetic burden of migraine is associated
with the response to pharmacologic treatment. The PRS for
migraine was statistically signicantly associated with re-
sponse to migraine-specic treatment: triptans and/or er-
gotamine. Neither the response to weak analgesics nor the
response to prophylactic treatment, which are not migraine
specic, 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 signicant association
was found between the response to clozapine and PRS of
schizophrenia, although not signicant.
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 aective 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 = 1.031.15) for
acute treatment response, but no signicant 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 eect 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 dened. Many factors may aect treatment response,
e.g., polypharmacy, comorbidity, and body mass index. To
obtain enough power, we have analyzed the dierent drugs
collectively, which may not be optimal, as a patient may not
have tried all drugs questioned and, therefore, may be in-
correctly dened as a nonresponder. Furthermore, although
we used a semistructured interview, recall bias and negativity
bias are inherent limitations. Our PRS is based on the eect
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
eect estimates. On the other hand, a high genetic burden of
migraine may be associated with specic 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 eects
that potentially have a higher predictive power to predict the
genetic risk score of patients with migraine.
While currently the eect 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 aected by noise (sensitivity of 82% and
aspecicity 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 dene more
homogeneous groups of patients benetting from specic
treatments using genetic data. With the arrival of new migraine
treatments, such as the novel but expensive calcitonin gene-
related peptide antibodies, a genetic classier to identify
patients who are likely to benet 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 nanced 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 nal 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´
eUniversit¨
atsmedizin,
Berlin, Germany
Author Acquisition
and analysis of
data
Stephan
Ripke, PhD
Charit´
eUniversit¨
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,
KingsCollege
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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Appendix 3 (continued)
Name Location Role Contribution
Davor Lessel University Medical
Center Hamburg-
Eppendorf, Germany
Member of
the IHGC
Acquisition of
data
Lannie
Ligthart
VU University,
Amsterdam, The
Netherlands
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the IHGC
Acquisition of
data
Nadia
Litterman
23andMe Inc.,
Mountain View
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the IHGC
Acquisition of
data
Arn van den
Maagdenberg
Leiden University
Medical Centre, The
Netherlands
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the IHGC
Acquisition of
data
Alfons
Macaya
Vall dHebron Research
Institute, Barcelona,
Spain
Member of
the IHGC
Acquisition of
data
Rainer Malik Institute for Stroke and
Dementia Research,
Munich, Germany
Member of
the IHGC
Acquisition of
data
Massimo
Mangino
Department of Twin
Research and Genetic
Epidemiology, Kings
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
Member of
the IHGC
Acquisition of
data
Priit Palta Institute for Molecular
Medicine Finland
(FIMM), University of
Helsinki, Finland
Member of
the IHGC
Acquisition of
data
Linda
Pedersen
Oslo University Hospital
and University of Oslo,
Norway
Member of
the IHGC
Acquisition of
data
Nancy
Pedersen
Karolinska Institutet,
Stockholm, Sweden
Member of
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 dHebron
Research Institute,
Universitat Aut`
onoma
de Barcelona, Spain &
Headache Unit,
Neurology Department,
Vall dHebron
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
Georges, 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 dHebron 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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Neurology.org/NG Neurology: Genetics | Volume 5, Number 6 | December 2019 11
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
... Triptans, which are 5-HT 1B/1D receptor agonists, are widely used migraine-specific medications to abort acute migraine attacks (6). Even though generic products have emerged, sumatriptan is still the most widely prescribed acute treatment medication for migraine (7,8). Additionally, clinical trials and post-marketing experience have shown its efficacy and tolerability since the introduction of sumatriptan in the 1990s (9,10). ...
... To date, the variability in the treatment response is not fully understood (12), and only a few studies have identified the predictors for triptan response in migraine. Current evidence showed that a lower pretreatment pain severity and a higher polygenic risk score were associated with a better response to triptans (7,8). An early study suggested that triptans' efficacy is less optimal after a patient develops allodynia, but new controlled studies have shown conflicting results (9,10). ...
... One early study in 2004 found that pretreatment pain severity is a reliable predictor for the response to sumatriptan (7). Another recent study used genome-wide association studies and found a higher polygenic risk score for migraine associated with the sumatriptan response, which implies that a higher genetic burden of migraine is associated with a better response to migrainespecific treatment (8). To our knowledge, the present study identified left hippocampal volume as a new predictor for the response to triptans in migraine. ...
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Objectives To identify the neuroimaging predictors for the responsiveness of patients to sumatriptan and use an independent cohort for external validation. Methods Structuralized headache questionnaire and 3-Tesla brain magnetic resonance imaging were performed in migraine patients. Regional brain volumes were automatically calculated using FreeSurfer version 6.0, including bilateral amygdala, anterior cingulated cortex, caudate, putamen, precuneus, orbitofrontal cortex, superior frontal gyri, middle frontal gyri, hippocampus, and parahippocampus. A sumatriptan-responder was defined as headache relief within 2 h after the intake of sumatriptan in at least two out of three treated attacks. We constructed a prediction model for sumatriptan response using the regional brain volume and validated it with an independent cohort of migraine patients. Results A total of 105 migraine patients were recruited, including 73 sumatriptan responders (69.5%) and 32 (30.5%) non-responders. We divided the migraine patients into derivation ( n = 73) and validation cohorts ( n = 32). In the derivation cohort, left hippocampal volume was larger in sumatriptan responders (responders vs. non-responders: 3,929.5 ± 403.1 vs. 3,611.0 ± 389.9 mm ³ , p = 0.002), and patients with a larger left hippocampal volume had a higher response rate to sumatriptan (>4,036.2 vs. ≤4,036.2 mm ³ : 92.0 vs. 56.3%, p = 0.001). Based on the findings, we constructed a prediction model using the cutoff value of 4,036.2 mm ³ , and we found that patients with a left hippocampal volume >4,032.6 mm ³ had a higher response rate to sumatriptan than those with a left hippocampal volume ≤4,032.6 mm ³ (84.6 vs. 42.1%, odds ratio [OR] = 7.6 [95% confidence interval = 1.3–44.0], p = 0.013) in the validation cohort. Conclusion Our study showed that left hippocampal volume is helpful to identify sumatriptan non-responders. This proof-of-concept study shows that left hippocampal volume could be used to predict the treatment response to sumatriptan in migraine patients.
... GWAS have become the standard approach to unravel genetic variations underlying complex diseases and subsequently to generate polygenic risk scores (PRSs). PRSs calculate the additive effect of several SNPs of disease (Kogelman et al. 2019a). The PRS approach relies on the theory that phenotypic variation explained by genetic components is caused by an additive effect of multiple common gene variants with small individual effect sizes (polygenic effect) that is traditionally identified by GWAS. ...
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Migraine is a common and complex neurologic disorder that affects approximately 15–18% of the general population. Although the cause of migraine is unknown, some genetic studies have focused on unravelling rare and common variants underlying the pathophysiological mechanisms of this disorder. This review covers the advances in the last decade on migraine genetics, throughout the history of genetic methodologies used, including recent application of next-generation sequencing techniques. A thorough review of the literature interweaves the genomic and transcriptomic factors that will allow a better understanding of the mechanisms underlying migraine pathophysiology, concluding with the clinical utility landscape of genetic information and future consideration to creating a new frontier toward advancing the field of personalized medicine.
... To investigate which genetic factors (variants) affect gene expression changes during the migraine attack, we integrated genotype data with the transcriptomic profiles. Genotyping was performed using the Illumina HumanOmniExpress 24v1 chip and quality control was performed as described previously 22 . First, we calculated the change in gene expression by deducting the gene expression profile 'after treatment' from the gene expression profile 'during attack' , both in TPM and in log(TPM). ...
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Migraine attacks are delimited, allowing investigation of changes during and outside attack. Gene expression fluctuates according to environmental and endogenous events and therefore, we hypothesized that changes in RNA expression during and outside a spontaneous migraine attack exist which are specific to migraine. Twenty-seven migraine patients were assessed during a spontaneous migraine attack, including headache characteristics and treatment effect. Blood samples were taken during attack, two hours after treatment, on a headache-free day and after a cold pressor test. RNA-Sequencing, genotyping, and steroid profiling were performed. RNA-Sequences were analyzed at gene level (differential expression analysis) and at network level, and genomic and transcriptomic data were integrated. We found 29 differentially expressed genes between ‘attack’ and ‘after treatment’, after subtracting non-migraine specific genes, that were functioning in fatty acid oxidation, signaling pathways and immune-related pathways. Network analysis revealed mechanisms affected by changes in gene interactions, e.g. ‘ion transmembrane transport’. Integration of genomic and transcriptomic data revealed pathways related to sumatriptan treatment, i.e. ‘5HT1 type receptor mediated signaling pathway’. In conclusion, we uniquely investigated intra-individual changes in gene expression during a migraine attack. We revealed both genes and pathways potentially involved in the pathophysiology of migraine and/or migraine treatment.
... Statin therapy was shown to lead to greater risk reduction in those with high genetic risk for the first coronary event [116]; and a high PRS for coronary artery disease (>90th percentile) was associated with a greater reduction (37% versus 13%) in major adverse cardiovascular events compared with a lower PRS (≤90th percentile) upon treatment with alirocumab/anti-PCSK9 [117]. Recently, a PRS constructed for migraine was able to identify subgroups of individuals with a higher likelihood of responding to triptans when looking for associations between migraine PRS and migraine-specific drug efficacy [118]. ...
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Human genetics plays an increasingly important role in drug development and population health. Here we review the history of human genetics in the context of accelerating discovery of therapies, present examples of how human genetics evidence supports successful drug targets and discuss how polygenic risk scores could be beneficial in various clinical settings. We highlight the value of direct‐to‐consumer platforms in the era of fast‐paced big data biotechnology, and how diverse genetic and health data can benefit society. This article is protected by copyright. All rights reserved.
... Imputation was based on The 1000 Genomes Project reference panel [29] The quality control of genotypes was performed using Plink2 v.1.90beta5.4. Details about genotyping and quality control is described elsewhere [30] After filtering and quality control, 6,101,288 SNPs and 1053 patients were retained for analyses. ...
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Background The transition from episodic migraine to chronic migraine, migraine chronification, is usually a gradual process, which involves multiple risk factors. To date, studies of the genetic risk factors for chronic migraine have focused primarily on candidate gene approaches using healthy individuals as controls. Aims/methods In this study, we use a large cohort of migraine families and unrelated migraine patients (n>2200) with supporting genotype and whole genome sequencing data. We evaluate whether there are any genetic variants, common or rare, with specific association to chronic migraine compared with episodic migraine. Results We find no aggregation of chronic migraine in families with a clustering of migraine. No specific rare variants give rise to migraine chronification, and migraine chronification is not associated with a higher polygenic risk score. Migraine chronification is not associated with allelic associations with an odds ratio above 2.65. Assessment of effect sizes with genome‐wide significance below an odds ratio of 2.65 requires a genome‐wide association study of at least 7500 chronic migraine patients. Conclusion Our results suggest that migraine chronification is caused by environmental factors rather than genetic factors.
Article
Polygenic scores (PGSs) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGSs for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n = 30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGSs were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g., schizophrenia-derived PGSs for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGSs, with between 3 and 6.6 million variants included in the PGSs. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGSs deposited on the Polygenic Score Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.
Article
Migraine is a complex brain disorder explained by the interaction of genetic and environmental factors. In monogenic migraines, including familial hemiplegic migraine and migraine with aura associated with hereditary small-vessel disorders, the identified genes encode proteins expressed in neurons, astrocytes or vessels, which all increase the susceptibility to cortical spreading depression. Study of monogenic migraines showed that the neurovascular unit plays a prominent role in migraine. Genome-wide association studies have identified multiple susceptibility variants that only cause a small increase of the global migraine risk. The variants belong to several complex networks of "pro-migraine" molecular abnormalities, which are mainly neuronal or vascular. Genetics has also underscored the importance of genetic factors shared between migraine and its major co-morbidities including depression and high blood pressure. Further studies are still needed to map all of the susceptibility loci for migraine and then to understand how these genomic variants lead to migraine cell phenotypes. Thanks to the advent of new technologies such as induced pluripotent stem cells, genetic data will hopefully finally be able to lead to therapeutic progress.
Article
Migraine is a disabling neurological disorder, diagnosis of which is based on clinical criteria. A shortcoming of these criteria is that they do not fully capture the heterogeneity of migraine, including the underlying genetic and neurobiological factors. This complexity has generated momentum for biomarker research to improve disease characterisation and identify novel drug targets. In this Series paper, we present the progress that has been made in the search for biomarkers of migraine within genetics, provocation modelling, biochemistry, and neuroimaging research. Additionally, we outline challenges and future directions for each biomarker modality. We also discuss the advances made in combining and integrating data from multiple biomarker modalities. These efforts contribute to developing precision medicine that can be applied to future patients with migraine.
Chapter
Systems toxicology is the integration of classical toxicology approaches with quantitative analysis of large sets of molecular and functional changes occurring across multiple levels of biological organization. Systems toxicology–based research enables identification of the biological mechanisms and molecular pathways that are affected by exposure to cigarette smoke (CS) and, hence, provides a more comprehensive understanding of the causal link between exposure-induced molecular changes and the ensuing toxicity endpoints and adverse outcomes. This knowledge can then be used to quantify the biological effects of electronic nicotine delivery product aerosols relative to those of CS. This chapter provides a short description of the key methodologies used in systems toxicology.
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Purpose To establish a cohort that enables identification of genomic factors that influence human health and empower increased blood donor health and safe blood transfusions. Human health is complex and involves several factors, a major one being the genomic aspect. The genomic era has resulted in many consortia encompassing large samples sizes, which has proven successful for identifying genetic factors associated with specific traits. However, it remains a big challenge to establish large cohorts that facilitate studies of the interaction between genetic factors, environmental and life-style factors as these change over the course of life. A major obstacle to such endeavours is that it is difficult to revisit participants to retrieve additional information and obtain longitudinal, consecutive measurements. Participants Blood donors (n=110 000) have given consent to participate in the Danish Blood Donor Study. The study uses the infrastructure of the Danish blood banks. Findings to date The cohort comprises extensive phenotype data and whole genome genotyping data. Further, it is possible to retrieve additional phenotype data from national registries as well as from the donors at future visits, including consecutive measurements. Future plans To provide new knowledge on factors influencing our health and thus provide a platform for studying the influence of genomic factors on human health, in particular the interaction between environmental and genetic factors.
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Background: Precision medicine may offer new strategies to treat migraine, and access to existing large cohorts may be a key resource to increase statistical power. Treatment response data is not routinely collected for large cohorts; however, such information could be extracted from pharmacy databases. Using a clinical migraine sample with treatment effect data, we assessed whether treatment response can be predicted based on the number of drug purchases. Methods: A clinical cohort including 1913 migraineurs were interviewed using a semi-structured interview to retrieve treatment response data for acute and prophylactic migraine drugs. The purchase history was obtained from the Danish national pharmacy database. We assessed whether number of purchases at different thresholds could predict the specificity and sensitivity of treatment response. Results: Purchase history of drugs was significantly associated with treatment response. For triptan treatment the specificity and sensitivity were above 80% for individuals with at least ten purchases. For prophylactic treatment (beta-blockers, angiotensin II antagonists or antiepileptic) we observed a sensitivity and specificity above 80% and 50% for individuals purchasing any prophylactic drug at least four times. In the Danish pharmacy database, 73% of the migraine patients have purchased at least ten triptans, while 55-63% have purchased at least one of the four prophylactic drugs. Conclusion: Pharmacy databases are a valid source for identification of treatment response. Specifically for migraine drugs, we conclude that ten purchases of triptans or four purchases of prophylactic drugs are sufficient to predict a positive treatment response. Precision medicine may be accelerated with the use of pharmacy databases.
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Importance Lithium is a first-line mood stabilizer for the treatment of bipolar affective disorder (BPAD). However, the efficacy of lithium varies widely, with a nonresponse rate of up to 30%. Biological response markers are lacking. Genetic factors are thought to mediate treatment response to lithium, and there is a previously reported genetic overlap between BPAD and schizophrenia (SCZ). Objectives To test whether a polygenic score for SCZ is associated with treatment response to lithium in BPAD and to explore the potential molecular underpinnings of this association. Design, Setting, and Participants A total of 2586 patients with BPAD who had undergone lithium treatment were genotyped and assessed for long-term response to treatment between 2008 and 2013. Weighted SCZ polygenic scores were computed at different P value thresholds using summary statistics from an international multicenter genome-wide association study (GWAS) of 36 989 individuals with SCZ and genotype data from patients with BPAD from the Consortium on Lithium Genetics. For functional exploration, a cross-trait meta-GWAS and pathway analysis was performed, combining GWAS summary statistics on SCZ and response to treatment with lithium. Data analysis was performed from September 2016 to February 2017. Main Outcomes and Measures Treatment response to lithium was defined on both the categorical and continuous scales using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. The effect measures include odds ratios and the proportion of variance explained. Results Of the 2586 patients in the study (mean [SD] age, 47.2 [13.9] years), 1478 were women and 1108 were men. The polygenic score for SCZ was inversely associated with lithium treatment response in the categorical outcome, at a threshold P < 5 × 10⁻². Patients with BPAD who had a low polygenic load for SCZ responded better to lithium, with odds ratios for lithium response ranging from 3.46 (95% CI, 1.42-8.41) at the first decile to 2.03 (95% CI, 0.86-4.81) at the ninth decile, compared with the patients in the 10th decile of SCZ risk. In the cross-trait meta-GWAS, 15 genetic loci that may have overlapping effects on lithium treatment response and susceptibility to SCZ were identified. Functional pathway and network analysis of these loci point to the HLA antigen complex and inflammatory cytokines. Conclusions and Relevance This study provides evidence for a negative association between high genetic loading for SCZ and poor response to lithium in patients with BPAD. These results suggest the potential for translational research aimed at personalized prescribing of lithium.
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Editorial summary Genome-wide association studies have made strides in identifying common variation associated with disease. The modest effect sizes preclude risk prediction based on single genetic variants, but polygenic risk scores that combine thousands of variants show some predictive ability across a range of complex traits and diseases, including neuropsychiatric disorders. Here, we consider the potential for translation to clinical use.
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Migraine is a debilitating neurological disorder affecting around one in seven people worldwide, but its molecular mechanisms remain poorly understood. There is some debate about whether migraine is a disease of vascular dysfunction or a result of neuronal dysfunction with secondary vascular changes. Genome-wide association (GWA) studies have thus far identified 13 independent loci associated with migraine. To identify new susceptibility loci, we carried out a genetic study of migraine on 59,674 affected subjects and 316,078 controls from 22 GWA studies. We identified 44 independent single-nucleotide polymorphisms (SNPs) significantly associated with migraine risk (P < 5 × 10(-8)) that mapped to 38 distinct genomic loci, including 28 loci not previously reported and a locus that to our knowledge is the first to be identified on chromosome X. In subsequent computational analyses, the identified loci showed enrichment for genes expressed in vascular and smooth muscle tissues, consistent with a predominant theory of migraine that highlights vascular etiologies.
Article
Complex traits, including migraine, often aggregate in families, but the underlying genetic architecture behind this is not well understood. The aggregation could be explained by rare, penetrant variants that segregate according to Mendelian inheritance or by the sufficient polygenic accumulation of common variants, each with an individually small effect, or a combination of the two hypotheses. In 8,319 individuals across 1,589 migraine families, we calculated migraine polygenic risk scores (PRS) and found a significantly higher common variant burden in familial cases (n = 5,317, OR = 1.76, 95% CI = 1.71–1.81, p = 1.7 × 10⁻¹⁰⁹) compared to population cases from the FINRISK cohort (n = 1,101, OR = 1.32, 95% CI = 1.25–1.38, p = 7.2 × 10⁻¹⁷). The PRS explained 1.6% of the phenotypic variance in the population cases and 3.5% in the familial cases (including 2.9% for migraine without aura, 5.5% for migraine with typical aura, and 8.2% for hemiplegic migraine). The results demonstrate a significant contribution of common polygenic variation to the familial aggregation of migraine. Gormley et al. use polygenic risk scores to show that common variation, captured by genome-wide association studies, in combination contributes to the aggregation of migraine in families. The results may have similar implications for other complex traits in general.
Article
Treatment-resistant schizophrenia (TRS) affects around one-third of individuals with schizophrenia. Although a number of sociodemographic and clinical predictors of TRS have been identified, data on the genetic risk of TRS are sparse. We aimed to investigate the association between a polygenic risk score for schizophrenia and treatment resistance in patients with schizophrenia. We conducted a nationwide, population-based follow-up study among all Danish individuals born after 1981 and with an incident diagnosis of schizophrenia between 1999 and 2007. Based on genome-wide data polygenic risk scores for schizophrenia were calculated in 862 individuals with schizophrenia. TRS was defined as either clozapine initiation or at least 2 periods of different antipsychotic monotherapies and still being hospitalized. We estimated hazard rate ratios (HRs) for TRS in relation to the polygenic risk score while adjusting for population stratification, age, sex, geographical area at birth, clinical treatment setting, psychiatric comorbidity, and calendar year. Among the 862 individuals with schizophrenia, 181 (21.0%) met criteria for TRS during 4674 person-years of follow-up. We found no significant association between the polygenic risk score and TRS, adjusted HR = 1.13 (95% CI: 0.95-1.35). Based on these results, the use of the polygenic risk score for schizophrenia to identify individuals with TRS is at present inadequate to be of clinical utility at the individual patient level. Future research should include larger genetic samples in combination with non-genetic markers. Moreover, a TRS-specific developed polygenic risk score would be of great interest towards early prediction of TRS.
Article
We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
Article
Objective Specific acute treatments of migraine are 5HT1B/D receptor agonists; triptans and ergotamine, but only two-thirds of patients respond well without side effects. No migraine-prophylactic drugs are specific to migraine. Prophylactic drugs are selected by time-consuming âtrial and error.†Personalized treatment is therefore much needed. The objective of this study was to test the effect of 12 single nucleotide polymorphisms (SNPs) significantly associated with migraine on migraine drug responses. Methods Semi-structured migraine interviews including questions on drug responses, blood samples and genotyping were performed on 1806 unrelated migraine cases recruited from the Danish Headache Center. Association analyses were carried out using logistic regression, assuming an additive model for the genetic effect. The effect on drug responses was tested for a combined genetic score and for each of the 12 SNPs. Significant findings were subsequently tested in an independent replication sample of 392 unrelated Danish migraine cases. Results A single risk variant, rs2651899 in PRDM16, was significantly associated with efficacy of triptans with an odds ratio (OR) of treatment success of 1.3, and a higher combined genetic score was significantly associated with efficacy of triptans with an OR of success of up to 2.6. A number of SNPs showed nominal preferential association with the efficacy of triptans and others with prophylactic drugs. Analyses of triptans and ergotamine complemented each other and gave a stronger signal when analyzed together. The associations between response to triptans and genetic load and rs2651899 were partially confirmed in the independent sample. Conclusion We show for the first time an association between genetic constitution and migraine drug response. This is a first step toward future individualized medicine.