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Nature Genetics
nature genetics
https://doi.org/10.1038/s41588-023-01538-0Article
Rare variants with large effects provide
functional insights into the pathology of
migraine subtypes, with and without aura
Migraine is a complex neurovascular disease with a range of severity
and symptoms, yet mostly studied as one phenotype in genome-wide
association studies (GWAS). Here we combine large GWAS datasets from
six European populations to study the main migraine subtypes, migraine
with aura (MA) and migraine without aura (MO). We identied four new
MA-associated variants (in PRRT2, PALMD, ABO and LRRK2) and classied
13 MO-associated variants. Rare variants with large eects highlight three
genes. A rare frameshift variant in brain-expressed PRRT2 confers large
risk of MA and epilepsy, but not MO. A burden test of rare loss-of-function
variants in SCN11A, encoding a neuron-expressed sodium channel with a key
role in pain sensation, shows strong protection against migraine. Finally, a
rare variant with cis-regulatory eects on KCNK5 confers large protection
against migraine and brain aneurysms. Our ndings oer new insights with
therapeutic potential into the complex biology of migraine and its subtypes.
Migraine is a complex neurovascular disease characterized by
recurrent, disabling headache attacks that are difficult to treat. It
is among the most common pain disorders worldwide, with preva-
lence of up to 20% in adult populations and affecting three times
more females than males1. Two main subtypes are clinically dis-
tinguished, migraine with aura (MA) and migraine without aura
(MO)
2
. MO is characterized by severe headache attacks accompa-
nied by nausea and hypersensitivity to light and sound, whereas
MA is characterized by gradually spreading, fully reversible focal
neurological symptoms, collectively called aura, that are usually fol-
lowed by headache
1
. An estimated 30% of migraineurs have MA, and
the most frequently experienced aura involves visual disturbances
(for example, flashes of bright light and blurred vision)
3
. During
MA attacks, characteristic regional brain blood flow changes indi-
cate that MA is caused by cortical spreading depression, a transient
wave of neuronal depolarization of the cortex4,5. Such findings are
not observed in MO6,7, suggesting divergent pathogenesis of these
migraine subtypes. A rare and clinically distinct subtype of MA is
familial hemiplegic migraine (FHM)
2
. Three genes have been linked
to FHM—one encoding a membrane protein involved in maintaining
gradients of sodium and potassium ions across plasma membranes
(ATP1A2), and two genes encoding sodium and calcium channels
expressed in brain (SCN1A and CACNA1A, respectively)8.
More is known about the genetics and biology of migraine than
any other pain disorder, leading to recent treatment advances such as
those targeting the calcitonin gene-related peptide (CGRP) activation
of the trigeminovascular system9,10. The largest genome-wide asso-
ciation studies (GWAS) meta-analysis of migraine to date identified
123 migraine risk loci, among them a locus including genes encod-
ing CGRP (CALCA and CALCB)11. However, the pathophysiology of
migraine is not fully understood, and a substantial subset of patients
has treatment-resistant migraine
12
. In the study reporting 123 common
(minor allele frequency (MAF) > 2%) migraine variants, subtype analysis
showed that 5 associate specifically with migraine subtypes—3 with MA
(in or near CACNA1A, HMOX2 and MPPED2) and 2 with MO (near SPINK2
and FECH)11,13. These findings suggest that the genetics of MA and MO
should be studied separately and with more emphasis on detecting
rare variants.
To identify both distinct and common biological underpinnings
of these migraine subtypes, we performed GWAS meta-analyses of
clinically defined MA, MO and overall migraine, using six datasets and
analyzing variants down to 0.001% in frequency. We used samples from
Received: 2 December 2022
Accepted: 18 September 2023
Published online: xx xx xxxx
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e-mail: gyda.bjornsdottir@decode.is; kari.stefansson@decode.is
A list of authors and their afiliations appears at the end of the paper
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
BRH. In all, we report 12 new migraine variants (regional plots shown
in Supplementary Figs. 1 and 2).
Using cross-trait linkage disequilibrium (LD) score regression
22
,
we calculated genetic correlations in nonoverlapping samples
(Methods) showing that VD correlates genetically with clinically
defined MA (rg = 0.65, P = 4.0 × 10−23) but not MO (rg = −0.09, P = 0.21),
and BRH correlate strongly with clinically defined migraine (rg = 0.85,
P = 7.4 × 10−91; Supplementary Table 8 and Supplementary Fig. 3). Fur-
ther supporting VD as an MA proxy, the GWAS meta-analysis of VD
reveals an association with a variant (rs11085837-A) in high LD (r2 = 0.96)
with the reported MA variant in CACNA1A, rs10405121-A11 (Fig. 1 and
Table 1). Its VD effect (odds ratio (OR) = 0.926, P = 8.8 × 10
−14
) is consist-
ent with its MA effect (OR = 0.930, P = 1.8 × 10−9), and no association is
detected with MO (OR = 0.983, P = 0.22). In Supplementary Table 9,
we list associations with all migraine phenotypes of the current study
with the recently published 123 migraine variants
11
, finding support
(P < 0.05) in our data for all but 9 variants (Supplementary Note 1).
A rare loss-of-function PRRT2 variant associates with MA
The top MA association is with a rare insertion in PRRT2 leading
to frameshift (rs587778771-GCC, p.Arg217ProfsTer8; OR = 5.446,
P = 5.6 × 10−16). This variant also associates with VD (OR = 3.634,
P = 0.0037) but not MO (P = 0.97; Table 3). It is detected in only three
cohorts, with a founder effect observed in Iceland (frequency = 0.117%),
compared to UK and US (frequency = 0.013% and 0.0051%, respec-
tively). It is detected at even lower frequencies in samples from Den-
mark, with no carriers detected in Norway or Finland. This variant has
been reported in case studies of rare neurological disorders, includ-
ing benign infantile seizures and paroxysmal kinesigenic dyskinesia
(PKD)23. In a few carriers, FHM has also been detected8. Among six
Danish heterozygous carriers identified, five are in the same family,
of which three have FHM.
The p.Arg217ProfsTer8 insertion is located in an unsta-
ble DNA site24,25 where we find another rarer (0.024%) deletion
about 1.3 million individuals, of which 12,000 have MO, 17,000 have
MA and 80,000 have migraine. Because migraine and especially its
subtypes are considerably underdiagnosed14, and to obtain measures
of specific symptoms and severity, we also assessed self-reported
proxy phenotypes representing severe and recurrent migraine head-
aches (52,000 cases) as well as migraine’s most distinctive subtype,
headaches preceded by visual aura (30,000 cases). Here we report
4 new MA-associated variants and show that 13 known migraine vari-
ants associate with MO over MA. In all, we observed associations with
44 lead variants, 12 of which are new for migraine, and we found func-
tional evidence implicating 22 genes—3 in MA, 3 in MO and the remain-
der in overall migraine. Among the findings are rare variants with large
effects providing new insights into biological underpinnings of distinct
characteristics of migraine, with and without aura.
Results
We conducted GWAS meta-analyses of clinically defined migraine,
MA and MO, using datasets from Iceland (deCODE Genetics), Den-
mark (Copenhagen Hospital Biobank (CHB)15 and Danish Blood Donor
Study (DBDS)16), the United Kingdom (UK; UK Biobank17), the United
States (US; Intermountain Health
18
), Norway (the Hordaland Health
Study (HUSK)19) and Finland (FinnGen20). We also performed GWAS
meta-analyses of two self-reported proxy phenotypes available in three
datasets (Iceland, UK and Denmark)—an MA proxy represented by expe-
riencing visual disturbances (VD) preceding headaches, and a severe
migraine proxy represented by bad and recurrent headaches (BRH).
In total, we analyzed data on 1.3 million individuals, including 16,603
with MA, 11,718 with MO, 79,495 with any migraine, 30,297 with VD
and 51,803 with BRH (Methods; Supplementary Table 1). We analyzed
up to 85 million variants, and using a significance threshold weighted
by variant impact21, we found associations with 44 lead variants at 39
loci (Fig. 1, Tables 1 and 2 and Supplementary Tables 2–7). Two variants
associate with MA (one new), five with the MA-proxy VD (four new) and
six with MO. The remaining variants associate with overall migraine or
PRDM16
PALMD
TGFBR3
ADAMSTL4
TSPAN, NGF
WDR12
ARAP2
MEF2D
A3GALT2, ZNF362/MANEAL/PPCS
GPR149
PHACTR1
FHL5
TRPM8
KCNK5
LATS1
TGFBR2
SCN11A
HACD4, IFNB1
SUGCT
PRRT2
PLCE1
ASTN2
ABO
FGF6
LRRK2 LRP1
MRVI1
CFDP1
CACNA1A
SLC24A3
MRPS6
ONECUT2
HORMAD2
GJA1
FXN
HTRA1
0
12 3 4 5 6 7 8
Chromosome
9 10 11 12 13 14 15 16 18 20 22 X
20
–log10(P)
40
Nonsignificant VD BRH
MAAll migraine MO
Fig. 1 | Manhattan plot of GWAS meta-analysis results for all studied
phenotypes. The graph shows data for migraine (ncase/control = 74,495/1,259,808),
MA (ncase/control = 16,603/1,336,517), MO (ncase/control = 11,718/1,330,747), VD
(ncase/control = 30,297/86,134) and BRH (ncase/control = 51,803/123,732). See
Supplementary Table 1 for ncase/control for each cohort. On the x axis, variants are
plotted along the 22 autosomes and the X chromosome. On the y axis is the
statistical significance of their association with the respective phenotypes from
meta-analyses using a fixed-effects inverse-variance method based on effect
estimates and s.e. under the additive model, in which each dataset was assumed
to have a common OR but allowed to have different population frequencies for
alleles and genotypes. Gray dots are not significant variants. Variant associations
that reach the P threshold weighted by variant annotation21 are represented by
color-coded dots. Adjacent chromosomes are presented in different shades of
gray. Known migraine loci are represented by gene names in black text, and new
loci are represented by gene names in blue text.
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
(p.Arg217GlufsTer12) that also leads to premature PRRT2 trunca-
tion25. This variant also shows a founder effect in Iceland, being
tenfold more frequent than in the UK (frequency of 0.0025%), and
not detected in other cohorts. It was previously reported in a single
case study of a homozygous carrier with severe PKD that responded
to carbamazepine, an epilepsy drug that reduces the generation
of rapid action potentials in the brain
26
and is also used to treat
migraine. We found p.Arg217GlufsTer12 in 38 heterozygous car-
riers in Iceland, mainly in two families where it segregates with
migraine and epilepsy. Of 38 carriers, 11 (29%) are diagnosed with
migraine (without subtype), six (16%) with epilepsy and one with MA
and epilepsy.
For these rare variants, we looked for associations with other
phenotypes. Apart from the MA and migraine associations, p.Arg217-
ProfsTer8 associates only with epilepsy (OR = 7.077, P = 1.9 × 10
−35
;
Table 3 and Supplementary Table 10). We find epilepsy moderately
genetically correlated with migraine (r
g
= 0.28, P = 9.4 × 10
−6
) and VD
(r
g
= 0.28, P = 2.8 × 10
−4
), but not with MO (r
g
= 0.05, P = 0.90). We tested
30 epilepsy variants27 in our data and found that only two also impact
migraine (at P < 3.3 × 10−4 = 0.05/30 variants × 5 phenotypes). The
common (23.3%) intron variant rs59237858-T in SCN1A that confers
protection against epilepsy27 confers risk of migraine (OR = 1.031,
P = 8.6 × 10
−6
) in our data, and rs62151809-T (44.7%) near TMEM182 con-
fers risk of epilepsy27 and of VD in our data (OR = 1.047, P = 8.5 × 10−6).
None of the 30 epilepsy variants associate with MO or BRH (Supple-
mentary Table 11). Conversely, of the 44 variants reported here, only
p.Arg217ProfsTer8 associates with epilepsy.
GWAS meta-analysis of MA-proxy phenotype yields new
MA-associated loci
Besides the known MA-associated variant in CACNA1A, we found four
other variants associating with the MA-proxy VD, all new to migraine
(Table 1). The first, rs11166276-C, is in a TF-binding site near PALMD
(OR = 0.926, P = 5.1 × 10
−14
). It is in complete LD with rs7543130 that also
associates protectively with aortic valve stenosis28. Secondly, in ABO,
the frameshift variant rs8176719-TC associates with VD (OR = 1.081,
P = 3.0 × 10−13). This variant contributes to determining the non-O
blood groups
29
, and variants in high LD associates with various coagu-
lation factors and risk of venous thromboembolism (Supplementary
Table 12). This variant associates with MA (OR = 1.030, P = 0.015) and
overall migraine (OR = 1.020, P = 1.5 × 10
−3
; Supplementary Table 7).
Thirdly, a variant upstream of LRRK2, rs10748014-T, associates with VD
(OR = 1.073, P = 5.6 × 10−12). LRRK2 encodes leucine-rich repeat kinase
2, a gene harboring common risk variants for inherited Parkinson’s
disease (PD)
30
, none of which are in LD with rs10748014 (Supplementary
Table 12). This variant also associates with MA (OR = 1.065, P = 8.4 × 10
−8
)
and weakly with overall migraine (OR = 1.012, P = 0.048), and we
detected no association with MO or PD. Finally, in a regulatory
region near HACD4/IFNB1 is an association with rs77778288-C (fre-
quency = 12.9%, OR = 1.097, P = 4.9 × 10−10). IFNB1 encodes interferon β
1, which is used to treat multiple sclerosis and can induce headaches
31
.
We compared the effects of these VD variants on MA and all
migraine in effect–effect plots (Fig. 2). Based on the slope derived
from a weighted regression through the origin, overall MA and migraine
effect estimates are 73% and 29%, respectively, of VD effect estimates,
Table 1 | Lead variants associated with migraine subtypes and headache-related visual disturbances (MA proxy)
Phenotypes Locus Position hg38 Variants OA EA EAF (%) Nearest genes Variant
annotation
OR (95% CI) P Pbonf Phet SNP previously
reported at locus
(r2 if correlated SNP)
MA 16p11.2 29813694 rs587778771 GC GCC 0.05 PRRT2 Frameshift 5.446
(3.626, 8.148)
5.6×10−16 6.7×10−10 – –
MA 19p13.13 13228314 rs10405121 G A 27. 3 CACNA1A Intron 0.927
(0.905, 0.949)
2.5×10−10 0.03 0.24 rs10405121a
VD 1p21.2 99579683 rs11166276 T C 49.0 PALMD TF-binding
site
0.926
(0.907, 0.945)
5.1×10−14 2.2×10−6 0.87 –
VD 9p21.3 21047562 rs77778288 A C 12.9 HACD4/IFNB1 Regulatory
region
1.097
(1.065, 1.129)
4.9×10−10 0.02 0.77 –
VD 9q34.2 133257521 rs8176719 TTC 32.9 ABO Frameshift 1.08 1
(1.059, 1.104)
3.0×10−13 1.2×10−7 0.07 –
VD 12q12 40221267 rs10748014 C T 46.8 LRRK2 Upstream 1.073
(1.052, 1.094)
5.6×10−12 0.00012 0.11 –
VD 19p13.13 13234712 rs11085837 G A 45.6 CACNA1A Intron 0.926
(0.907, 0.945)
8.8×10−14 3.7×10−6 0.18 rs10405121 (0.96)a
MO 1q22 156460957 rs750439cT C 33.9 MEF2D Downstream 1.09 2
(1.062, 1.123)
8.7×10−10 0.017 0.06 rs2274319 (0.64) a,
rs1925950 (0.86)b
MO 2q37.1 233937757 rs12470426cG A 9.1 TRPM8 Intron 0.853
(0.812, 0.897)
6.0×10−10 7.6×10−2 0.56 rs10166942 (0.39)a,
rs10166942 (0.96)b
MO 6p24.1 12903725 rs9349379cA G 41.7 PHACTR1 3 UTR 0.904
(0.879, 0.929)
3.8×10−13 8.0×10−6 0.35 rs9349379a,b
MO 6q16.1 96610677 rs2273621cA G 32.3 FHL5 Missense 1.0 96
(1.065, 1.128)
3.0×10−10 1.9×10−3 0.61 rs11153082 (1.0)a,
rs67338227 (0.54)b
MO 12p13.32 4418156 rs2160875cT C 49.1 FGF6 Regulatory
region
1.088
(1.059, 1.118)
8.2×10−10 3.4×10−2 0.85 rs2160875a,
rs1024905 (0.83)b
MO 12q13.3 57132863 rs4759276cG A 39.9 LRP1 Intron 0.889
(0.865, 0.914)
9.8×10−17 1.3×10−8 0.32 rs11172113 (0.84)a,b
Supplementary Table 1 shows ncase/control per cohort and Supplementary Table 7 shows associations of these variants with all migraine. Discovery phenotype is in the irst column: MA,
headache-related VD proxy for MA, MO. Effect allele frequency (EAF) is the average frequency of EA in the cohorts studied (Supplementary Table 1; Methods). OR and P value for
inverse-variance weighted meta-analysis of association results for all cohorts. Pbonf is the P value after a variant class-speciic Bonferroni adjustment21. Phet is the heterogeneity P value from
a likelihood ratio test. Results per cohort and for all phenotypes are in Supplementary Tables 2–7. Associations of these and correlated variants (r2>0.8) with various traits listed in the GWAS
Catalog (https://www.ebi.ac.uk/gwas/) are in Supplementary Table 12. Bold are variants that associate primarily with MO, over MA or VD (Fig. 3). CI, conidence interval; EA, effect allele; OA,
other allele. aSNPs previously reported in ref. 11. bSNPs previously reported in ref. 69. cMO-associated variants that also correlate with migraine variants in Table 2; rs750439 (r2=0.64) with
rs1925950, rs12470426 (r2=0.39) with rs1003540, rs2160875 (r2=1.0) with rs7957385, rs4759276 (r2=0.84) with rs11172113 and rs9349379 and rs2273621 also associate with migraine.
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
Table 2 | Variants identiied in association with all migraine (M) or migraine proxy (BRH)
Pheno-
types
Locus Position
hg38
Variants OA EA EAF
(%)
Nearest
genes
Variant
annotation
OR (95% CI) P Pbonf Phet SNP previously
reported at locus
(r2 if correlated
SNP)
M1p36.32 3155918 rs10797381 T A 22.7 PRDM16 Intron 1.09 4
(1.079, 1.109)
2.1×10−37 8.7×10−30 0.08 rs10218452 (1.0)a,
rs2651899 (0.37)b
M1p36.1 33302206 rs933718575 A G 0.01 A3GALT2 Downstream 11.032
(5.11, 23.8)
9.7×10−10 0.020 –
M1p34.3 37790755 rs71642605 T C 25.3 MANEAL Upstream 1.042
(1.028, 1.056)
1.1×10−9 0.023 0.89 –
M1p34.2 42465863 rs11799356 G A 34.2 PPCS Downstream 1.039
(1.026, 1.052)
6.0×10−10 0.013 0.67 –
M1p22.1 91731541 rs12070846 T C 23.0 TGFBR3 Intron 1.044
(1.030, 1.058)
6.8×10−10 0.029 0.16 rs11165300
(0.88)a
M1p13.2 115135325 rs12134493cC A 12.0 TSPAN2 TF-binding
site
1.112
(1.092, 1,132)
1.7×10−30 7.2×10−23 0.03 rs2078371 (1.0)a,
rs12134493b
M1p13.2 115286692 rs6330cG A 46.7 NGF Missense 1.035
(1.023–1.048)
2.1×10−8 0.041 0.06 –
M1q21.1 150538184 rs6693567 T C 26.0 ADAMSTL4 Regulatory
region
1.044
(1.031, 1.058)
1.1×10−10 0.0046 0.64 rs6693567a,b
M1q22 156480948 rs1925950 A G 36.0 MEF2D Synonymous 1.047
(1.034, 1.059)
6.3×10−14 1.3×10−6 0.08 rs2274319 (1.0)a,
rs3790455 (1.0)b
M2q33.2 202901033 rs35212307 T C 12.6 WDR12 Missense 0. 949
(0.933, 0.966)
6.7×10−9 0.013 0.54 rs149163995
(0.99)b
M2q37.1 233917239 rs1003540 A G 19.4 TRPM8 Upstream 0.923
(0.910, 0.937)
3.3×10−26 6.9×10−19 0.50 rs10166942 (1.0)a
M3p24.1 30424073 rs4955309 C A 31.9 TGFBR2 Intergenic 1.042
(1.030, 1.055)
4.0×10−11 0.005 0.10 rs7371912 (0.91)a,
rs7640543
(0.97)b
M3p22.2 38894643 rs33985936 C T 25.0 SCN11A Missense 1.041
(1.027, 1.054)
3.4×10−9 0.0065 0.32 –
M3q25.2 154572157 rs13078967 A C 3.5 GPR149 Regulatory
region
0.892
(0.862, 0.922)
1.6×10−11 0.00066 0.27 rs13078967a,b
M4p15.1 35563301 rs74992952 G A 17. 9 ARAP2 Intergenic 0.949
(0.935, 0.963)
8.8×10−12 0.00037 0.45 rs73805934
(0.92)a
M6p24.1 12903725 rs9349379 A G 41.9 PHACTR1 3 UTR 0.92 8
(0.917, 0.939)
1.9×10−35 3.9×10−28 0.43 rs9349379a,b
BRH 6p21.2 39280316 rs72854118 A G 0.67 KCNK5 TF-binding
site
0.697
(0.634, 0.766)
7.6×10−14 3.2×10−6 0.10 –
M6q16.1 96610677 rs2273621 A G 32.3 FHL5 Missense 1.08 2
(1.069, 1.096)
1.1×10−36 2.1×10−30 0.14 rs11153082
(0.99)a,
rs11759769
(0.55)b
M6q22.31 121487928 rs7743275 G A 19.9 GJA1 Regulatory
region
1.060
(1.044, 1.077)
9.7×10−14 1.2×10−5 0.17 rs28455731
(0.73)a,b
M6q25.1 149721026 rs1359155039 TAAAA
AAAA
TAAAA
AAAAA
32.8 LATS1 Upstream 0.958
(0.945, 0.971)
8.1×10−10 0.017 0.38 rs9383843
(0.87)a
M7p14.1 40367277 rs186166891 A T 10.4 SUGCT Intron 1.084 (1.062,
1.106)
1.1×10−14 1.4×10−6 0.40 rs10234636
(0.91)a,
rs4379368
(0.91)b
BRH 9q21.11 69099647 rs34965002 G A 43.3 FXN/TJP2 Regulatory
region
1.056
(1.039, 1.072)
6.6×10−12 0.000277 0.19 rs7034179 (0.87 )a
M9q33.1 116479356 rs12684144dT C 22.3 ASTN2 Intron 1.055
(1.041, 1.070)
1.3×10−14 5.4×10−7 0.01 rs3891689
(0.91)a, rs6478241
(0.57)b
M10q23.33 94279840 rs2274224 G C 41.5 PLCE1 Missense 0.959
(0.948, 0.970)
2.7×10−12 5.1×10−6 0.04 rs2274224a,
rs11187838 (1.0)b
BRH 10q26.13 122470997 rs12252027 G T 11.4 HTRA1 Intron 0.926
(0.904, 0.948)
1.2×10−10 0.0149 0.73 –
M11p15.4 10652192 rs4909945 C T 33.0 MRVI1 Missense 0.945
(0.934, 0.957 )
3.1×10−19 5.9×10−13 0.61 rs4910165 (1.0)a,b
M12p13.32 4416380 rs7957385 G A 48.6 FGF6 Intergenic 1.0 45
(1.03 3, 1.0 57)
8.2×10−14 1.0×10−5 0.03 rs2160875 (1.0)a,
rs140668749
(1.0) b
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Article https://doi.org/10.1038/s41588-023-01538-0
and no associations were detected for MO, which is in line with our
estimates of genetic correlation between these traits.
Migraine subtype classification of lead variants
We used a similar approach discussed in ref. 11 to study the effects of
43 lead variants on the migraine subtypes adjusting for sample overlap
(PRRT2 excluded as it has larger effects than other variants and is shown
to be an MA-associated variant; Methods). We find that the new variants
in ABO, LRRK2 and PALMD, and the previously reported
11
MA-associated
variant in CACNA1A are classified as MA-associated variants, and 13
variants are classified as MO-associated variants (bold in Tables 1 and 2;
Fig. 3 and Supplementary Fig. 4). All MO-associated variants are
in known migraine loci except the new MO-associated variant
rs71642605-C in MANEAL. We find that one of the MO-associated vari-
ants, rs12684144-C in ASTN2, confers protection against VD (OR = 0.956,
P = 0.00017) but risk of MO (OR = 1.073, P = 1.5 × 10−5). In line with only
30% of migraineurs experiencing aura3, its association with overall
migraine confers risk (OR = 1.055, P = 1.3 × 10−14).
Protein-altering variants in NGF and SCN11A
Among new variants associated with overall migraine is the common
missense variant rs6330-A (p.Ala35Val) in NGF (OR = 1.035, P = 2.1 × 10−8).
NGF encodes nerve growth factor that is involved in regulating growth
and differentiation of sympathetic and certain sensory neurons (https://
www.ncbi.nlm.nih.gov/gene). NGF is at 1p13.2 and nearby is TSPAN2, har-
boring a previously reported
11
migraine-associated variant (rs2078371)
that is, however, uncorrelated (r2 = 0.02) with rs6330. Conditional analy-
sis shows that the effects of rs6330-A on migraine are significant when
adjusting for rs2078371 (Table 2).
In SCN11A, another common (25%) missense variant, rs33985936-T
(p.Val909Ile), associates with overall migraine (OR = 1.041,
P = 3.4 × 10
−9
). SCN11A encodes Na
v
1.9, which is highly expressed in
nociceptive neurons of dorsal root and trigeminal ganglia
32,33
. Rare
loss-of-function (LOF) variants in SCN11A can lead to both extremely
painful and completely pain-insensitive disorders32,33. We looked for
LOF variants in SCN11A and found them at very low frequency in all
datasets studied, with the highest in the UK at a combined frequency of
0.13%, which is two orders of magnitude higher than in other cohorts.
We used a genome-wide burden test combining the effects of these
rare variants on migraine in the UK, and at a threshold of P = 2.5 × 10−6
(P = 0.05/20,000 genes34 tested), they associate with strong protec-
tion against overall migraine (OR = 0.650, P = 3.9 × 10−7) and other
severe headaches and are not driven by a single variant (Table 4 and
Supplementary Note 2).
A rare variant targeting KCNK5 with protective effects
In the GWAS meta-analysis of BRH, there is an association with a large
protective effect (OR = 0.697, P = 7.6 × 10−14) with the rare (0.67%)
intergenic variant rs72854118-G located in a regulatory region
between two potassium channel genes, KCNK5 and KCNK17. The
variant also protects against clinically defined migraine (OR = 0.836,
P = 9.7 × 10−7), but does not associate with migraine subtypes, MA,
MO or VD (P > 0.05). Two additional variants in high LD are at this
locus, rs72854120 and rs72851880 (Supplementary Fig. 2). A com-
mon (28.1%) intronic variant in KCNK5 was previously reported11 to be
associated with migraine (rs10456100, OR = 1.051, P = 9.2 × 10−19), but
is uncorrelated with rs72854118 (r
2
= 0.002). rs72854118-G is reported
in weak association with decreased diastolic blood pressure (β = −0.07,
P = 2.7 × 10
−7
)
35
, and in a GWAS meta-analysis of self-reported migraine
and headaches combined, one of two correlated SNPs, rs72854120-C,
shows borderline association, more so with headaches than migraine
(Z
migraine
= −2.68, Z
headache
= −5.49, P = 2.8 × 10
−8
)
36
. Inspection of effect–
effect plots of BRH versus clinically defined migraine for all 44 lead
variants shows that rs72854118-G effects on BRH far exceed its migraine
effects (Fig. 4 and Supplementary Fig. 5). We performed a phenos-
can in 1,000 GWAS meta-analyses at deCODE Genetics (P thresh-
old = 0.05/1,000 = 5.0 × 10−5) and observed that rs72854118-G also
confers substantial protection against brain aneurysms (OR = 0.470,
P = 1.8 × 10−8) and coronary artery disease (CAD) requiring bypass
surgery (OR = 0.725, P = 9.3 × 10−8), but associates more weakly with
CAD in general (OR = 0.900, P = 1.9 × 10−5) and systolic blood pressure
(effect = −0.054 s.d., P = 2.0 × 10−5; Supplementary Table 15). Of 17
known brain aneurysm variants
37
, 3 are in migraine loci (FHL5, SLC24A3
and PLCE1). Plotting effects of the brain aneurysm variants (including
rs72854118) on brain aneurysms versus effects on migraine and BRH,
we find this variant is an outlier in both and confers larger protective
effects against brain aneurysms than other brain aneurysm variants
(Supplementary Fig. 5).
Pheno-
types
Locus Position
hg38
Variants OA EA EAF
(%)
Nearest
genes
Variant
annotation
OR (95% CI) P Pbonf Phet SNP previously
reported at locus
(r2 if correlated
SNP)
M12q13.3 57133500 rs11172113 T C 42.3 LRP1 Intron 0.912
(0.901, 0.923)
1.8×10−53 7.4×10−46 0.42 rs11172113a,b
M16p11.2 29813694 rs587778771 GC GCC 0.05 PRRT2 Frameshift 3.038
(2.320, 3.977)
6.6×10−16 1.0×10−9 0.83 –
M16q23.1 75289942 rs17685540 C T 41.0 CFDP1 Downstream 1.037
(1.025, 1.049)
1.4×10−9 0.029 0.31 rs8046696
(0.98)a,
rs77505915
(0.91)b
M18q21.31 57494932 rs7233335 C G 20.4 ONECUT2 Downstream 0.954
(0.941, 0.968)
1.8×10−10 0.0038 0.01 rs8087942 (0.45)a
M20p11.23 19494370 rs3827986 G A 24.5 SLC24A3 Intron 1.05 0
(1.036, 1.064)
4.6×10−13 1.9×10−5 0.52 rs4814864 (1.0)a
M21q22.11 34221526 rs28451064 G A 13.6 MRPS6 Regulatory
region
0.943
(0.927, 0.959)
1.8×10−11 0.00075 0.2 rs28451064a
M22q12.2 30076759 rs5753008 T C 35.6 HORMAD2 Upstream 1.039
(1.027, 1.051)
4.0×10−10 0.0084 0.5 –
Effect allele frequency (EAF) is the average frequency of EA in the cohorts studied. OR and P value for inverse-variance-weighted meta-analysis of association results for all cohorts
(Supplementary Table 1; Methods). Pbonf is the P value after a variant class-speciic Bonferroni adjustment 21. Phet is the heterogeneity P value from a likelihood ratio test. Bold are variants that
associate primarily with MO, not MA or VD, or with larger effects on MO than on MA or VD (Fig. 3). aSNPs previously reported in ref. 11. bSNPs previously reported in ref. 69. cResults presented
are after adjusting for the respective effects of these uncorrelated (r2=0.02) variants at this locus. Results per cohort for all studied phenotypes are in Supplementary Tables 2–7. drs12684144-C
confers protection against VD and risk against MO.
Table 2 (continued) | Variants identiied in association with all migraine (M) or migraine proxy (BRH)
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
Colocalization highlights new migraine and aura genes
We performed systemic functional annotation of the 44 lead variants
and variants in high LD (r
2
≥ 0.8) and studied their association with
mRNA sequence data (expression quantitative trait loci (eQTL)) and
with protein levels in plasma38 (protein quantitative trait loci (pQTL);
Methods; Supplementary Tables 16–19). Results are summarized in
Supplementary Fig. 6. For the lead variants, we find 144 eQTLs, of
which 16 implicate a specific gene (Supplementary Table 17). Vari-
ant rs4768221-G, in complete LD with rs10748014-T (VD association
OR = 1.073, P = 1.2 × 10−12) upstream of LRRK2, consistently associates
with VD and is the top ranking eQTL for this gene in blood. The allele
associated with increased risk of VD associates with reduced LRRK2
expression in blood (β = −0.74 s.d., P = 1.3 × 10−1,260).
The lead BRH variant near KCNK5 rs72854118, but not the other
correlated variants at this locus, is found within a distal enhancer-like
sequence (dELS) as defined by ENCODE’s catalog of candidate
cis-regulatory elements39, and the gene target for this regulatory ele-
ment is KCNK5 (Supplementary Tables 20 and 21 and Supplemen-
tary Note 3). The variant is too rare to be studied in Genotype-Tissue
Expression (GTEx, which includes only three carriers; Supplementary
Fig. 7), and its expression coverage in tissues available to us is too low
for conclusive results.
Three variants (or variants with r2 ≥ 0.8) represent top cis pQTLs
at their respective loci in Icelandic SomaScan plasma protein asso-
ciation data and two variants in the UK Olink data (Supplementary
Table 19). These proteomic methods differ in protein profiles, but in
both datasets are pQTL variants correlating with the migraine variant
rs1359155039-TAAAAAAAAA upstream of LATS1 that associates with
reduced migraine risk and increased LRP11 plasma levels (β = 0.58 s.d.,
P = 10−1,140 and β = 0.59 s.d., P = 10−2,140 in Iceland and UK, respectively).
LRP11 is predicted to be located in plasma membrane and involved in
several processes, including response to heat and cold (https://www.
ncbi.nlm.nih.gov/gene).
We do not have RNA expression or protein data for enough carriers
of the rare PRRT2 variants to detect transcription or protein associa-
tions. However, on the basis of previous functional studies
40
, the gene’s
known function as a key component of the Ca
2+
-dependent neurotrans-
mitter release machinery41, and its reported links to rare paroxysmal
brain disorders including infantile convulsions, the movement disorder
PKD and FHM
42
, in addition to the findings in this current study, we
conclude that PRRT2 is also a risk gene for the common forms of MA
and epilepsy. Finally, we scanned the GWAS catalog (https://www.ebi.
ac.uk/gwas/) for associations with lead variants identified in this study
(or r2 ≥ 0.8). Results are presented in Supplementary Table 12.
Pathway analysis highlights NGF-related processes
For the 22 genes with evidence supporting their role in migraine or
subtypes, we performed a protein network analysis (https://reactome.
org). Among the top 67 relevant pathways identified, 13 involve NGF
processing, including TrkA activation by NGF, previously studied in
the context of pain and pain therapeutics
43
. Interestingly, pathways
involved in phase-4 resting potential and cardiac conduction involve
the products of both KCNK5 and SCN11A, with the products of both
LRRK2 and LRPI interacting in the cardiac conduction pathway (Sup-
plementary Data and Supplementary Table 22).
Genetic drug target analysis
We performed a genetic drug target analysis for the 22 genes for
which we have evidence of function pointing to the gene in addi-
tion to the established MA gene CACNA1A. Drugs at various levels of
development target four genes that associate with MA (PRRT2, ABO,
LRRK2 and CACNA1A), none associated with MO, and four genes that
associate with overall migraine or severe headaches (KCNK5, NGF,
SCN11A and TRPM8; Supplementary Table 23 and Supplementary
Note 5). Targeting PRRT2 is bryostatin, a powerful protein kinase C
agonist that was originally developed to prevent tumor growth, but
in preclinical studies has also shown promising effects as a restorative
synapse drug that is currently in trials to treat Alzheimer’s disease44.
Several voltage-gated Ca
+2
channel blockers have been developed
against CACNA1A, but have not been tested in migraine. Targeting
TRPM8, cutaneous menthol treatment has been found to alleviate
migraine headaches
45
. Targeting SCN11A (and other voltage-gated
sodium transporter genes), intranasal lidocaine can be effective in
treating acute migraine46, and intravenous lidocaine infusion is sug-
gested for treating refractory chronic migraine
47
. Drugs targeting
other genes have not been tested for migraine, but β-nerve growth
factor inhibitors (antibodies) that target NGF (fasinumab, tanezumab
and fulranumab) are widely studied in the context of various other
chronic pain conditions (for example, sciatica, low back pain and
abdominal pain; www.ClinicalTrials.gov).
–0.05
0
0.05
0.10
–0.05
0
0.05
0.10
–0.05
0
0.05
0.10
VD log(OR)
MA log(OR)
Slope: 0.731 (0.100)
VD log(OR)
Migraine log(OR)
Slope: 0.291 (0.046)
0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12
VD log(OR)
MO log(OR)
Slope: –0.026 (0.115)
Fig. 2 | Effects of SNPs associated with self-reported headache-related
VD in clinically defined MA, overall migraine and MO. The x axis
(VD, ncase/control = 30,297/86,134) and the y axis (MA, ncase/control = 16,603/1,336,517;
migraine, ncase/control = 74,495/1,259,808 and MO, ncase/control = 11,718/1,330,747)
show the logarithmic estimated odds ratios, log(OR), for the associations with
the respective phenotypes from meta-analyses using a fixed-effects inverse-
variance method based on effect estimates and s.e. under the additive model,
in which each dataset was assumed to have a common OR but allowed to have
different population frequencies for alleles and genotypes. All effects are
shown for the VD risk allele, and black crosses indicate 95% CIs. The dashed
red lines represent slope (s.d.) based on a simple linear regression through the
origin using 1/s.e. as weights. Effect estimates are 73%, 29% and 0% of VD effect
estimates for MA, migraine and MO, respectively.
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
Discussion
Whether MA and MO are different diseases or part of a migraine con-
tinuum has long been debated48,49. Little is known about the genetics
underlying migraine subtypes as most prior studies have focused on
migraine in general. Here we have identified several new associations
supporting the distinct pathogenesis of MA and MO. In terms of MA,
variants in PRRT2, PALMD, CACNA1A, ABO and LRRK2 associate with
MA (VD) over MO. Of these, two genes have the highest expression in
the cerebellum (PRRT2 and CACNA1A), and in both are rare autosomal
dominant variants reported to cause rare forms of movement disorders
and hemiplegic migraine (https://www.omim.org/). This is of interest
in light of the characteristic cortical spreading depression observed in
MA but not MO
4,5
. Both ABO and PALMD are widely expressed in tissues,
and both harbor variants associated with cardiovascular disorders.
Indeed, the link between migraine and cardiovascular disease is well
established
50
. Drugs targeting these genes are in various phases of
development, but for indications other than migraine. Five drugs target
CACNA1A for seven indications, including anxiety, insomnia and car-
diovascular disease, and targeting LRRK2 is a trial drug DNL201 (Clini-
calTrials.gov identifier: NCT0371070, https://clinicaltrials.gov/study/
NCT03710707) that shows promising therapeutic potential against PD51.
LRRK2 is especially abundant in dopamine-innervated areas and dopa-
minergic neurons of the substantia nigra
30
. Increased LRRK2 kinase
activity is thought to impair lysosomal function and thus contribute
to the pathogenesis of PD
52
. However, consistent with our results show-
ing that the variant in LRRK2 associates with increased risk of VD (MA)
and with reduced LRRK2 mRNA expression, the main adverse effects
of this LRRK2 inhibitor in healthy individuals were headache (40% of
participants) and nausea (13%), the main symptoms of migraine, and
dizziness (in 13%)51. While LRRK2’s expression is highest in brain areas
associated with PD pathology, it is also expressed in other neurons and
glial cells of the human brain
53
. Considerable pleiomorphism can occur
LRRK2
0
0
0.05
0.05
0.10
0.10
MA log(OR)
MO log(OR) MO log(OR)
0.15
0.15
0.20 MA > MO
MO > MA
Other
0.20
0
0
0.05
0.05
–0.05
–0.05
0.10
0.10
VD log(OR)
0.15
0.15
0.20
0.20
CACNA1A
LRP1
PHACTR1
MEF2D
FGF6
ABO PALMD
LRRK2
CACNA1A
ARAP2
PHACTR1
LRP1
SUGCT
SLC24A3
MRVI1
ADAMSTL4
MANEAL
PLCE1 FGF6 MRPS6
MEF2D
ASTN2
a b VD > MO
MO > VD
Other
Fig. 3 | Subtype classification of lead variants. Effect plots for all lead variants
except the MA variant in PRRT2. Effects are from meta-analyses using a fixed-
effects inverse-variance method based on effect estimates and s.e. under the
additive model, in which each dataset was assumed to have a common OR but
allowed to have different population frequencies for alleles and genotypes. Data
are presented as additive effect estimates (center) with 95% CI (crosses) for the
annotated variants. a, Axes show logarithm of odds ratios (log(OR)) for MO
(x axis; ncase/control = 11,718/1,330,747) and MA (y axis; ncase/control = 16,603/1,336,517).
b, Axes show MO (x axis; ncase/control = 11,718/1,330,747) and VD (y axis;
ncase/control = 30,297/86,134). log(OR) is calculated for the effect allele. The effects
of variants that have been colored and annotated with gene names differ between
the migraine subtypes at a significance threshold of 0.0012 = 0.05/43. The 95%
CIs for the log(ORs) are shown for annotated variants. Effects are adjusted with
sample overlap (rij) estimated from counts of cases, controls and the counts of
overlaps in these groups between phenotypes70 from all cohorts except FinnGen
(for which we only have summary statistics). The parameter representing sample
overlap between MO and MA is rij = 0.023 and MO and VD is rij = 0.012. Dashed
lines show the coordinate axes, the diagonal and a line through the origin with
slope = 1 (Methods; see Supplementary Tables 13 and 14 and Supplementary
Fig. 4 for VD versus MA plot).
Table 3 | GWAS meta-analysis results for PRRT2 frameshift variant (p.Arg217ProfsTer8)
Phenotypes Iceland (MAF=0.117%) UK (MAF=0.013%) US (MAF=0.0051%) Combined
OR (95% CI) POR (95% CI) POR (95% CI) POR (95% CI) P Phet
Epilepsy 7.482 (5.398, 10.370) 1.3×10−33 4.284 (1.548, 11.859) 0.0051 5.455 (0.407, 73.054) 0.20 7.077 (5.197, 9.635) 1.9×10−35 0.58
MA 5.534 (3.631, 8.434) 1.7×10−15 3.019 (0.283, 32.163) 0.36 5.869 (0.712, 48.348) 0.10 5.446 (3.626, 8.148) 5.6×10−16 0.88
Migraine 3.129 (2.333, 4.196) 2.6×10−14 2.482 (1.202, 5.125) 0.014 3.553 (0.489, 25.791) 0.21 3.038 (2.320, 3.977) 6.6×10−16 0.83
BRH 5.276 (2.104, 13.227) 3.9×10−4 1.981 (0.857, 4.581) 0.11 – – 3.091 (1.664, 5.742) 3.6×10−4 0.12
VD 8.344 (1.952, 35.662) 4.2×10−3 2.274 (0.764, 6.771) 0.14 – – 3.634 (1.519, 8.696) 3.7×10−3 0.16
MO 1.025 (0.283, 3.712) 0.97 0.017 (0.000, 4357619.32) 0.68 0.017 (0.000, 15176.02) 0.56 0.972 (0.271, 3.489) 0.97 0.78
The table shows OR with 95% CI and two-sided P values from GWAS results derived from a logistic regression of selected phenotypes in the three cohorts where p.Arg217ProfsTer8 was
detected at suficient frequency. Combined OR and two-sided P are results from inverse-variance-weighted meta-analyses of GWAS results. P values after a variant class-speciic Bonferroni
adjustment21. Phet is the heterogeneity P value from a likelihood ratio test. See Supplementary Table 1 for cohort descriptions and Supplementary Table 10b for other neurological associations
with both rare PRRT2 frameshift variants (p.Arg217ProfsTer8 and p.Arg217GlufsTer12).
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
among LRRK2 carriers sharing the same pathogenic variant, even within
the same family54. Indeed, LRRK2 has been dubbed the ‘Rosetta stone’
of Parkinsonism, perhaps providing a common link between various
neurological diseases55.
Our GWAS meta-analysis identified six variants associated with
MO, all in previously reported migraine loci. However, by the subtype
stratification of all lead variants, we detect 13 variants that impact
MO over MA. These MO-associated variants are in or near genes with
various functions, such as muscle cell development and differentiation
(MEF2D, FGF6 and LRP1) and intracellular calcium homeostasis (MRVI1
and SLC24A3). Several are in genes highly expressed in arteries (MEF2D,
LRP1, ADAMTSL4, SUGCT, MRVI1 and MRPS6) and in brain (MEF2D,
ARAP2, PHACTR1 and SLC24A3). Of these, only LRP1 is currently a drug
target (https://platform.opentargets.org). LRP1 encodes low-density
lipoprotein receptor-related protein 1, and an LRP1 binding agent is in
trials to treat various brain tumors.
Our results highlight three genes in or near which rare vari-
ants show large and informative effects. Firstly, the rare insertion
(p.Arg217ProfsTer8) in PRRT2 that associates with large effects on epi-
lepsy and MA provides new insights into these comorbid
56
and geneti-
cally correlated diseases. PRRT2 is a four-exon gene that encodes a 340
amino acid protein with two predicted transmembrane domains25.
Both the insertion and rarer deletion lead to premature termination of
around one-third of PRRT2, resulting in nonsense-mediated decay40.
Due to the founder effect in Iceland, we have power to show the pleio-
tropic effect of these LOF variants. Not only can they lead to rare neu-
rological disorders, but they also confer substantial risk of common
forms of MA and epilepsy, both of which are paroxysmal brain diseases
frequently experienced with aura
57,58
. PRRT2 is widely expressed in the
brain, particularly in the cerebellum25,59. It is enriched in presynaptic
terminals, is regulated by Ca+2 release and interacts with SNAP-25 and
synaptogamin
41
. The mutant PRRT2 of the truncating variants leads
to increased glutamate release and subsequent neuronal hyperexcit-
ability60. A study of three Nav1 subunits (Nav1.1 encoded by SCN1A,
Na
v
1.2 encoded by SCN2A and Na
v
1.6 encoded by SCN8A) expressed
in human embryonic kidney cell lines (HEK-293) demonstrated that
PRRT2 directly interacts with and negatively modulates Na
v
1.2 and
Nav1.6, which generate action potentials in excitatory neurons, but
does not affect Na
v
1.1 channels, which generate action potentials in
inhibitory neurons
61
. Lack of PRRT2 leads to hyperactivity of Na
v
1.2
and Nav1.6 in homozygous PRRT2 knockout (human and mouse) neu-
rons
61
. The authors of that study suggest that the lack of PRRT2 effects
on Na
v
1.1 may enhance excitation/inhibition imbalance and trigger
hyper-synchronized activity in neuronal networks
61
. Interestingly, we
find that the only epilepsy variant in our data that also associates with
migraine is rs59237858 in SCN1A, the gene that encodes Nav1.1.
Secondly, in the context of Na
v
1 channels, it is of interest that we
find both common and rare variants in SCN11A that impact migraine
risk. SCN11A encodes Nav1.9 that is expressed in primary sensory
neurons in peripheral and trigeminal ganglia62 and is known to have
a substantial role in pain perception
62
. Compared to other sodium
channels, Nav1.9 generates a persistent current regulated by G-protein
pathways63. Whether Nav1.9 is also affected by PRRT2, like Nav1.2 and
Na
v
1.6 (ref. 61), is not known. Currently in various stages of devel-
opment are 63 drugs targeting SCN11A (most unspecific blockers
of all Na
v
subtypes), with 341 indications, including headache, epi-
lepsy and pain in general (https://genetics.opentargets.org/gene/
ENSG00000168356). Increasing specificity of Nav subtype channel
blockers and studying their protein interactions seems key to harness-
ing their therapeutic potential64,65.
Table 4 | Results of SCN11A LOF variant burden tests in the respective cohorts for association with migraine
Cohorts Unique LOF variants
combined Combined frequency (%) OR P value ncases ncontrols
UK Biobank 127 0.129 0.650 3.90×10−7 22,082 408,965
US 26 0.0254 0.751 0.63 7,42 7 50,785
Denmark 26 0.0183 0.629 0.43 14,371 266,473
Iceland 58.79×10−3 0.882 0.83 24,604 319,066
–Combined 0.0454 0.660 2.90×10−7 68,484 1,045,289
The table shows a number of unique LOF variants tested in each cohort. We classiied as high-impact variants those predicted as start-lost, stop-gain, stop-lost, splice donor, splice acceptor
or frameshift. We used logistic regression under an additive model to test for association between LOF gene burdens and phenotypes using likelihood ratio test to compute two-sided P values
(Methods; see Supplementary Note 2 for other headache associations in UK Biobank data).
rs72854118
0
0.2
0.4
0 0.1 0.2 0.3 0.4 0.5
BRH log(OR)
Migraine log(OR)
Pheno
Migr
MA
MO
BRH
VD
Fig. 4 | Rare variant rs72854118 in regulatory region targeting KCNK5
associates with BRH. Effect–effect plot of clinically defined migraine
(ncase/control = 74,495/1,259,808) vs. self-reported BRH (ncase/control = 51,803/123,732)
effects for 42 lead variants identified in this study (excluding high-impact
variants in PRRT2 and A3GALT2; see Supplementary Table 7 for their associations
with the respective phenotypes). Effects are from meta-analyses using a fixed-
effects inverse-variance method based on effect estimates and s.e. under the
additive model, in which each dataset was assumed to have a common OR but
allowed to have different population frequencies for alleles and genotypes. The
x axis and the y axis show the logarithmic estimated ORs for the associations with
the respective phenotypes. Error bars represent 95% CI. The dashed red lines
represent slope (s.d.) based on a simple linear regression through the origin using
1/s.e. as weights. Cohort descriptions are in Supplementary Table 1. Variants are
colored according to their primary associations in this study. The red dot outlier
depicts the variant rs72854118-G near KCNK5, its effects on BRH exceeding its
effects on all migraine. Pheno, phenotype; Migr, migraine.
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
Thirdly, the rare intergenic rs72854118-G near KCNK5 and KCNK17
is another variant providing insight into the pathogenesis of migraine.
Previous studies have assigned this variant to KCNK17 and reported
weak associations with reduced blood pressure35 and protection
against self-reported headaches and migraine
36
. However, we find
that rs72854118, but not its correlated variants at this locus, is in a
cis-regulatory region targeting KCNK5. KCNK5 encodes TWIK-related
acid-sensitive potassium channel 2, primarily expressed in kidney
(GTEx, https://gtexportal.org) but also in T cells, suggesting a role in
the immune system
66
. We find that the variant also confers protection
against brain aneurysms and severe occlusive CAD, but associates
weakly with blood pressure. Although hypertension is a risk factor for
both aneurysms and CAD, it is not a conclusive risk factor for migraine67.
The observed association with brain aneurysms begs the question
whether in some cases undetected brain aneurysms could be misclas-
sified as migraine68. According to the Open Targets Platform, no drugs
are in development that target KCNK5.
In all, our findings are consistent with the results of previous GWAS
analyses that have established migraine as a complex neurovascular
brain disorder13,69. However, our results also highlight several distinct
biological pathways involved in MA and MO that warrant further study.
In summary, we contribute new insights into both general and spe-
cific mechanisms underlying migraine and its subtypes, especially
to the visual aura associated with migraine attacks. Our results also
emphasize the importance of assessing disease subtypes and proxies
to improve understanding of complex genetic signals.
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acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41588-023-01538-0.
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© The Author(s) 2023
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
1deCODE Genetics/Amgen, Inc., Reykjavik, Iceland. 2Danish Headache Center, Department of Neurology, Copenhagen University Hospital,
Rigshospitalet-Glostrup, Copenhagen, Denmark. 3Reykjavik University, School of Technology, Reykjavik, Iceland. 4School of Engineering and
Natural Sciences, University of Iceland, Reykjavik, Iceland. 5Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
6Intermountain Heart Institute, Salt Lake City, UT, USA. 7Intermountain Healthcare, Saint George, UT, USA. 8Faculty of Physical Sciences, School of
Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland. 9NORMENT, Centre for Mental Disorders Research, Division of Mental Health
and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 10Department of Global Public Health and
Primary Care, University of Bergen, Bergen, Norway. 11Department of Health and Social Science, Centre for Evidence-Based Practice, Western Norway
University of Applied Science, Bergen, Norway. 12Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway. 13Department of
Biomedicine, University of Bergen, Bergen, Norway. 14Division of Psychiatry, Haukeland University Hospital, Bergen, Norway. 15Novo Nordisk Foundation
Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 16Department of Clinical
Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. 17Department of Clinical Immunology, Odense University Hospital,
Odense, Denmark. 18Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark. 19Department of Clinical Medicine Health, Aarhus
University, Aarhus, Denmark. 20Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark. 21Department of Clinical Medicine,
Aalborg University, Aalborg, Denmark. 22Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark. 23Department of Clinical
Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 24Statens Serum Institut, Copenhagen, Denmark.
25Department of Pediatrics, Landspitali University Hostpital, Reykjavik, Iceland. 26Heilsuklasinn Clinic, Reykjavik, Iceland. 27Laeknasetrid Clinic, Reykjavik,
Iceland. 28Department of Neurology, Landspitali University Hospital, Reykjavik, Iceland. 33These authors contributed equally: Gyda Bjornsdottir,
Mona A. Chalmer. 34These authors jointly supervised this work: Thomas F. Hansen, Kari Stefansson. *A list of members and afiliations appears at the
end of the paper. e-mail: gyda.bjornsdottir@decode.is; kari.stefansson@decode.is
DBDS Genetic Consortium
Karina Banasik15, Jakob Bay22, Jens K. Boldsen18, Thorsten Brodersen22, Søren Brunak15, Kristoffer Burgdorf15,16,
Mona A. Chalmer2,33, Maria Didriksen16, Khoa M. Dinh18, Joseph Dowsett16, Christian Erikstrup18,19, Bjarke Feenstra16,24,
Frank Geller16,24, Daniel F. Gudbjartsson1,4, Thomas F. Hansen2,15,34, Lotte Hindhede18, Henrik Hjalgrim29, Rikke L. Jacobsen16,
Gregor Jemec30, Katrine Kaspersen18, Bertram D. Kjerulf18, Lisette J. A. Kogelman2, Margit A. H. Larsen16, Ioannis Louloudis15,
Agnete Lundgaard15, Susan Mikkelsen18, Christina Mikkelsen16, Kaspar R. Nielsen20,21, Ioanna Nissen16, Mette Nyegaard31,
Sisse R. Ostrowski16,23, Ole B. Pedersen22,23, Alexander P. Henriksen15, Palle D. Rohde31, Klaus Rostgaard29, Michael Swinn16,
Kari Stefansson1,5,34, Hreinn Stefansson1, Erik Sørensen16, Unnur Thorsteinsdottir1,5, Lise W. Thørner16, Mie T. Bruun17,
Thomas Werge23,32 & David Westergaard15
29Danish Cancer Society Research Center, Copenhagen, Denmark. 30Department of Dermatology, Zealand University Hospital, Roskilde, Denmark.
31Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark. 32Institute of Biological Psychiatry, Mental
Health Centre, Sct. Hans, Copenhagen University Hospital, Roskilde, Denmark.
Gyda Bjornsdottir 1,33 , Mona A. Chalmer 2,33, Lilja Stefansdottir1, Astros Th. Skuladottir 1, Gudmundur Einarsson 1,
Margret Andresdottir1, Doruk Beyter1, Egil Ferkingstad 1, Solveig Gretarsdottir 1, Bjarni V. Halldorsson 1,3,
Gisli H. Halldorsson 1,4, Anna Helgadottir 1, Hannes Helgason1,4, Grimur Hjorleifsson Eldjarn 1, Adalbjorg Jonasdottir1,
Aslaug Jonasdottir1, Ingileif Jonsdottir 1,5, Kirk U. Knowlton6, Lincoln D. Nadauld7, Sigrun H. Lund 1,8,
Olafur Th. Magnusson1, Pall Melsted1,4, Kristjan H. S. Moore 1, Asmundur Oddsson 1, Pall I. Olason1, Asgeir Sigurdsson1,
Olafur A. Stefansson 1, Jona Saemundsdottir1, Gardar Sveinbjornsson 1, Vinicius Tragante 1, Unnur Unnsteinsdottir1,
G. Bragi Walters 1, Florian Zink1, Linn Rødevand9, Ole A. Andreassen 9, Jannicke Igland10,11, Rolv T. Lie 10,12,
Jan Haavik 1 3,14, Karina Banasik 15, Søren Brunak 15, Maria Didriksen 16, Mie T. Bruun 17, Christian Erikstrup 1 8,19,
Lisette J. A. Kogelman 2, Kaspar R. Nielsen20,21, Erik Sørensen16, Ole B. Pedersen 22,23, Henrik Ullum24, DBDS Genetic
Consortium*, Gisli Masson1, Unnur Thorsteinsdottir1,5, Jes Olesen 2, Petur Ludvigsson25, Olafur Thorarensen25,
Anna Bjornsdottir26, Gudrun R. Sigurdardottir27, Olafur A. Sveinsson27,28, Sisse R. Ostrowski 16,23, Hilma Holm 1,
Daniel F. Gudbjartsson 1,4, Gudmar Thorleifsson 1, Patrick Sulem 1, Hreinn Stefansson1, Thorgeir E. Thorgeirsson1,
Thomas F. Hansen 2,15,34 & Kari Stefansson 1,5,34
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
Methods
Ethics statement
All human research was approved by the relevant ethics review boards
and conducted according to the Declaration of Helsinki. All participants
provided written and informed consent as described per the study
population below.
Study populations
Cases and controls were defined from six study populations.
Iceland. About 155,000, or close to half of the Icelandic population of
340,000, have participated in an ongoing nationwide research program
at deCODE Genetics
71,72
. Participants donated blood or buccal samples
after signing informed consents allowing the use of their samples and
data in various studies approved by the National Bioethics Committee
(NBC). The data used here were analyzed under a study on the genet-
ics of migraine (NBC; 19-158-V3, VSNb2019090003/03.01) following
review by the Icelandic Data Protection Authority.
Denmark. Danish samples and data were obtained in collaboration
with the Copenhagen Hospital Biobank Study
15
and the DBDS
16
. CHB is
a research biobank, which contains samples obtained during diagnos-
tic procedures on hospitalized and outpatients in the Danish Capital
Region hospitals. Data analysis within this study was performed under
the ‘Genetics of pain and degenerative diseases’ protocol, approved
by the Danish Data Protection Agency (P-2019-51) and the National
Committee on Health Research Ethics (NVK-18038012). The DBDS
Genomic Cohort is a nationwide study of ~110,000 blood donors
16
.
The Danish Data Protection Agency (P-2019-99) and the National
Committee on Health Research Ethics (NVK-1700407) approved the
studies under which data on DBDS participants were obtained for
this study.
UK. Since 2006, the UK Biobank resource has collected extensive
phenotype and genotype data from ~500,000 participants recruited
in the age range of 40–69 from across the UK after signing an informed
consent for the use of their data in genetic studies
17
. The North West
Research Ethics Committee reviewed and approved the UK Biobank’s
scientific protocol and operational procedures (REC Reference: 06/
MRE08/65). This study was conducted using the UK Biobank Resource
(application 42256).
Finland. The FinnGen study20 consists of samples collected from the
Finnish biobanks and phenotype data collected at Finland’s national
health registers. The Coordinating Ethics Committee of the Helsinki
and Uusimaa Hospital District evaluated and approved the FinnGen
research project. The project complies with existing legislation (in
particular the Biobank Law and the Personal Data Act). The official
data controller of the study is the University of Helsinki. The summary
statistics for FinnGen’s migraine GWAS were imported from a source
available to consortium partners (Release 6: https://r6.finngen.fi/).
US. Participants from the US were recruited via ongoing studies con-
ducted at Intermountain Healthcare (https://intermountainhealthcare.
org). These studies include the Intermountain Inspire Registry and the
HerediGene: Population study
18
. The latter is a large-scale collaboration
between Intermountain Healthcare, deCODE Genetics and Amgen.
The Intermountain Healthcare Institutional Review Board approved
this study, and all participants provided written informed consent and
samples for genotyping.
Norway. Data on Norwegian migraine cases and controls were obtained
from the HUSK study, a population-based study carried out in Horda-
land county in Western Norway19. In 1992–1993, all Hordaland County
residents born between 1950 and 1952, all Bergen residents born
between 1925 and 1927 and three neighboring municipalities and a
random sample of individuals born between 1926 and 1949 were invited
to participate. In total, 18,044 individuals participated, of which 17,561
provided blood samples for genotyping, of which 10,000 were geno-
typed at deCODE Genetics. All participants signed informed consents,
and the study was approved and carried out by the National Health
Screening Service, Oslo (now the Norwegian Institute of Public Health)
in cooperation with the University of Bergen19.
Phenotype definitions
Cases with migraine and the migraine subtypes with and without aura
were in all cohorts but Norway (using self-reported migraine from
questionnaires), mainly defined by International Classification of
Diseases 10th Revision (ICD-10) codes (or comparable codes from
earlier versions of ICD) representing MA (code G43.1, MO (G43.0)
and overall migraine (G43). Diagnostic codes were assigned by physi-
cians and captured through both inpatient and outpatient diagnostic
registries. As triptan medications (Anatomical Therapeutic Chemical
code N02CC) are used to prevent/treat migraine attacks, individuals
who had received triptan subscriptions were identified in data from
drug registries (Iceland, Denmark, Finland and the UK) and added to
migraine cases (without subtype).
Both proxy phenotypes used in this study were based on validated
questionnaire items selected for the headache section of UK Biobank’s
pain questionnaire (https://biobank.ctsu.ox.ac.uk/crystal/ukb/docs/
pain_questionnaire.pdf), which was designed in consultation with a
group of leaders in pain research. The headache section is based on
questions used in the American Migraine Prevalence and Prevention
study73. For the MA-proxy phenotype used in this study (VD preceding
headaches), we defined cases and controls from questionnaire data
obtained in the studies conducted in Iceland, Denmark and the UK
Biobank. Questions used in Icelandic and Danish cohorts were com-
parable to the question answered by participants in the UK Biobank
(data field 120065: data description: visual changes before or near
the onset of headaches, Question: ‘I develop visual changes such as
spots, lines and heat waves or graying out of my vision’). Responses
‘Yes’ were compared to responses ‘No.’ Such defined cases with, and
controls without, headache-related VD had all previously responded
‘Yes’ to a question on headaches as asked in the UK Biobank survey
(data field 120053: data description: bad and/or recurring headaches
at any time in life, Question: ‘Have you ever had bad and/or recurring
headaches at any time in your life?’). We used this UK Biobank data field
120053 as a migraine proxy, defining comparable severity qualified
headache questions in Icelandic and Danish questionnaire datasets
for the GWAS meta-analysis.
Genotyping and whole-genome sequencing
Iceland. At deCODE Genetics, 63,118 Icelandic samples have been
whole-genome sequenced (WGS) using GAIIx, HiSeq, HiSeqX and
NovaSeq Illumina technology
71,72
to a mean depth of 38×. Genotypes
of single-nucleotide polymorphisms (SNPs) and insertions/deletions
(indels) were identified and called jointly by Graphtyper
74
. The effects
of sequence variants on protein-coding genes were annotated using
the variant effect predictor (VEP) using protein-coding transcripts
from RefSeq. Including all sequenced samples, 155,250 samples from
Icelandic participants have been genotyped using various Illumina
SNP arrays
71,72
. The chip-typed individuals were long-range phased
75
,
and the variants identified in the WGS Icelanders imputed into the
chip-typed individuals. Additionally, genotype probabilities for
285,644 ungenotyped close relatives of chip-typed individuals were
calculated based on extensive encrypted genealogy data compiled
by deCODE Genetics (an unencrypted version is publicly available
to all Icelandic citizens at https://www.islendingabok.is/english).
All variants tested were required to have imputation information
over 0.8.
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
identify subsets of individuals with similar ancestry was performed for
the Danish, Intermountain and Norwegian datasets separately. ADMIX-
TURE (v1.23)
81
was run in supervised mode using the 1000 Genomes
populations
82
CEU (Utah residents with Northern and Western Euro-
pean ancestry), CHB (Han Chinese in Beijing, China), ITU (Indian Telugu
in the UK), PEL (Peruvian in Lima, Peru) and YRI (Yoruba in Ibadan,
Nigeria) as training samples. These training samples had themselves
been filtered for ancestry outliers using principal component analysis
(PCA) and unsupervised ADMIXTURE.
For the Danish and Intermountain datasets, samples assigned
<0.93 CEU were excluded. We performed a different filtering procedure
for the Norwegian dataset to include individuals with Finnish and Saami
ancestry, who are common in Norway83. To identify such individuals,
we first selected candidates those assigned between 0.5 and 0.93 CEU
ancestry. We then merged these individuals with the Human Origins
dataset and calculated F statistics
84
of the form f
3
(Mbuti; candidate
individual, X), where X was each of the Human Origins populations
Nganasan, Pima, Han and Norwegian. In these F3 statistics, we identi-
fied a clear cluster of individuals with excess affinity to Nganasan and
Norwegian over Pima and Han. In available metadata, we observed that
these individuals were highly enriched for locations of residence in
Finnmark and officially designated Saami villages. These genetic and
demographic features match expectations for individuals of Saami
or Finnish ancestry. Except for this cluster, we excluded all other Nor-
wegian individuals assigned <0.93 CEU ancestry. Genetic principal
components for use as covariates in association analysis were obtained
using bigsnpr85.
Association testing and meta-analysis
Using software developed at deCODE Genetics
72
, we applied logis-
tic regression assuming an additive model to test for genome-wide
associations between sequence variants and migraine phenotypes.
Association results from FinnGen were imported (Release 6: http://
r6.finngen.fi). For the Icelandic data, the model included sex, county of
birth, current age or age at death (first-order and second-order terms
included), blood sample availability for the individual and an indicator
function for the overlap of the lifetime of the individual with the time
span of phenotype collection. To include imputed but ungenotyped
individuals, we used county of birth as a proxy covariate for the first
PCs in our analysis because county of birth has been shown to be in
concordance with the first PC in Iceland86. For the Danish, Norwegian,
UK and US data, the covariates were sex, age, expected allele count and
20 PCs to adjust for population stratification. The association analysis
of the imported Finnish data was adjusted for sex, age, the genotyping
batch and the first ten PCs. We used LD score regression intercepts
22
to
adjust the χ
2
statistics and avoid inflation due to cryptic relatedness and
stratification, using a set of 1.1 million variants. P values were calculated
from the adjusted χ2 results. All statistical tests were two-sided unless
otherwise indicated.
For the meta-analyses, we combined GWASs from the respective
cohorts with summary statistics from Finland using a fixed-effects
inverse-variance method based on effect estimates and s.e. in which
each dataset was assumed to have a common OR but allowed to have
different population frequencies for alleles and genotypes. The total
number of variants included in the meta-analyses was between 68
and 80 million variants. Sequence variants were mapped to the NCBI
Build 38 and matched on position and alleles to harmonize the data-
sets. The threshold for genome-wide significance was corrected for
multiple testing with a weighted Bonferroni adjustment that con-
trols for the family-wise error rate, using as weights the enrichment
of variant classes with predicted functional impact among associa-
tion signals21. The significance threshold then becomes 2.5 × 10−7
for high-impact variants (including stop-gained, frameshift, splice
acceptor or donor), 5.0 × 10
−8
for moderate-impact variants (includ-
ing missense, splice-region variants and in-frame indels), 4.5 × 10
−9
Denmark. Danish samples from both CHB and DBDS were genotyped
at deCODE Genetics using Illumina Infinium Global Screening Array.
Individual genotype arrays were discarded if the total yield was below
98%. Variants were derived from sequencing 25,215 Scandinavian sam-
ples (8,360 Danish) using NovaSeq Illumina technology. Only samples
with a genome-wide average coverage of over 20× were used. The geno-
types of SNPs and indels were called jointly by Graphtyper74. Variants
with a missing rate >2% were discarded. The genotyped samples were
phased using Eagle (version 2.4.1) and high-quality variants imputed
into 270,627 genotyped Danes using haplotype sharing in a Hidden
Markov Model based on a Li and Stephens model76 similar to the one
used in IMPUTE2 (ref. 77).
UK. In the UK Biobank dataset, the first 50,000 participants were geno-
typed using a custom-made Affymetrix chip, UK BiLEVE Axiom78, and
the remaining participants using the Affymetrix UK Biobank Axiom
array
17
. We used existing long-range phasing of the SNP chip-genotyped
samples
17
. We excluded SNP and indel sequence variants in which at
least 50% of samples had no coverage (genotype quality (GQ) score = 0),
if the Hardy–Weinberg P value was <10
−30
or if heterozygous excess
<0.05 or >1.5. At deCODE Genetics, a collaborative effort was recently
performed to whole-genome sequence 150,119 samples from the UK
Biobank, allowing us to create a haplotype reference panel, which was
then imputed into the UK Biobank chip-genotyped dataset, as previ-
ously described elsewhere79.
US. Samples from the US (Intermountain dataset) were genotyped
using Illumina Global Screening Array chips (n = 28,279) and WGS
using NovaSeq Illumina technology (n = 16,621). Samples were fil-
tered on 98% variant yield and any duplicates were removed. Over
245 million high-quality sequence variants and indels, sequenced
to a mean depth of 20×, were identified using Graphtyper74.
Quality-controlled chip genotype data were phased using SHAPEIT4
(ref. 80). A phased haplotype reference panel was prepared from the
sequence variants using the long-range phased chip-genotyped sam-
ples using in-house tools and methods described previously71,72.
Norway. Norwegian samples were genotyped on Illumina SNP arrays
(OmniExpress or Global Screening Array). The chip-genotyping QC
and imputation of the Norwegian dataset were performed at deCODE
Genetics in Iceland using the same methods as described above for the
Icelandic samples. The imputation for Norwegian samples is based on
a haplotype reference panel of 25,215 samples of European ancestry,
of which 3,336 are Norwegian.
Finland. A custom-made FinnGen ThermoFisher Axiom array
(>650,000 SNPs) was used to genotype FinnGen samples at the Thermo
Fisher Scientific genotyping service facility in San Diego. Genotype calls
were made with the AxiomGT1 algorithm (https://finngen.gitbook.io/
documentation/methods/genotype-imputation). The FinnGen Release
6 used in this study contains 260,405 genotyped individuals after qual-
ity control (QC). Individuals with ambiguous sex, high genotype miss-
ingness (>5%), excess heterozygosity (±4 s.d.) or non-Finnish ancestry
were excluded, as were variants with high missingness (>2%), low Hardy–
Weinberg equilibrium (<1 × 10
−6
) or minor allele count (<3). Imputa-
tion was performed using the Finnish population-specific and high
coverage (25–30 times) WGS backbone and the population-specific
SISu v3 imputation reference panel with Beagle 4.1. More than 16 mil-
lion variants have been imputed in the Finnish dataset (https://
www.finngen.fi/en/access_results).
Genetic ancestry filtering and principal components
For the UK Biobank, we used a British–Irish ancestry subset defined
previously
79
. Procedures to account for ancestry in FinnGen
20
and Ice-
land72 have also been previously described. Genetic ancestry analysis to
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
for low-impact variants, 2.3 × 10
−9
for other DNase I hypersensitivity
sites (DHS) variants and 7.5 × 10
−10
for other non-DHS variants
21
. In a
random-effects method, a likelihood ratio test was performed in all
genome-wide associations to test the heterogeneity of the effect esti-
mate in the four datasets; the null hypothesis is that the effects are the
same in all datasets, and the alternative hypothesis is that the effects
differ between datasets.
The primary signal at each genomic locus was defined as the
sequence variant with the lowest Bonferroni-adjusted P value using
the adjusted significance thresholds described above. Conditional
analysis was used to identify possible secondary signals within 500 kb
from the primary signal. This was done using genotype data for the
Icelandic, Norwegian, Danish, UK and US datasets and an approximate
conditional analysis implemented in GCTA software87 for the Finnish
summary data. Adjusted P values and ORs were combined using a
fixed-effects inverse-variance method. Class-specific genome-wide
significance thresholds were also used for the secondary signals. Man-
hattan plots were generated using topr package in R.
For burden testing, we used the UK Biobank whole-exome
sequenced dataset, consisting of 400,912 whole-exome sequenced
White British (individuals identified by PCA analyses)88,89 who enrolled
in the study between 2006 and 2010 throughout the UK and were
aged 38–65 years at recruitment. A wide range of phenotypic data has
been provided by the UK Biobank primarily from hospital records and
increasingly from general practitioners from the UK. For the Icelandic,
US and Danish cohorts, we used the phenotypes and WGS and imputa-
tion data previously described.
We used VEP
90
to attribute predicted consequences to the variants
sequenced in each dataset. We classified as high-impact variants those
predicted as start-lost, stop-gain, stop-lost, splice donor, splice accep-
tor or frameshift, collectively called LOF variants. For case–control
analyses, we used logistic regression under an additive model to test
for association between LOF gene burdens and phenotypes, in which
disease status was the dependent variable and genotype counts as the
independent variable, using likelihood ratio test to compute two-sided
P values. Individuals were coded 1 if they carried any of the LOF vari-
ants in the autosomal gene being tested and 0 otherwise. For the UK
Biobank association testing, 20 PCs were used to adjust for population
substructure, and age and sex were included as covariates in the logistic
regression model. We further included variables indicating sequencing
batches to remove batch effects. For these analyses, we used software
developed at deCODE Genetics72.
Genetic correlations
Using cross-trait LD score regression22, we estimated the genetic cor-
relation between each of the migraine and proxy (BRH) and migraine
subtype phenotypes (MO, MA and VD) defined in this study, in addi-
tion to epilepsy. In this analysis, we used results for about 1.2 million
well-imputed variants, and for LD information, we used precomputed
LD scores for European populations (downloaded from https://data.
broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2).
To avoid bias due to sample overlap, we used the Icelandic and Dan-
ish cohorts combined to test for correlation with the respective
phenotypes in the other remaining datasets combined. Finally, we
meta-analyzed the results of the two correlation analyses for each cor-
relation for a combined correlation estimation. The significance level
for the correlation estimates was determined using a simple Bonferroni
correction for the number of meta-analyzed correlations, and hence
significance was set at P < 0.0033 (0.05/15).
Identification and confirmation of rare PRRT2 variants
The variants in the PRRT2 gene are in a stretch of nine C’s, with one extra
C in carriers of the insertion (p.Arg217ProfsTer8) and one missing C in
carriers of the deletion (p.Arg217GlufsTer12). This imposes a technical
challenge for accurate whole-genome sequence calling. Therefore, all
potential carriers of both variants were analyzed with Sanger sequenc-
ing. Primers were designed using Primer 3 software. Following PCR,
cycle sequencing reactions were performed in both directions on MJ
Research PTC-225 thermal cyclers, using the BigDye Terminator Cycle
Sequencing Kit v3.1 (Life Technologies) and Ampure XP and CleanSeq
kits (Agencourt) for cleanup of the PCR products and cycle sequencing
reactions. Sequencing products were loaded onto the 3730 XL DNA
Analyzer (Applied Biosystems) and analyzed with Sequencher 5.0
software (Gene Codes Corporation). Based on the sequencing results,
the variants were then re-imputed into the respective cohorts.
Migraine subtype analysis of lead variants
To classify our lead variants by migraine subtype, we plotted their
effects on MA versus MO and VD versus MO using the method applied
in ref. 11. This method requires a correlation parameter between MO
and MA (MO and VD) to account for sample overlap, and previously
this parameter was estimated from GWAS summary statistics11, using
empirical Pearson correlation of effect size estimates of common
variants (MAF > 0.05), which do not show a strong association with
either of the migraine subtypes studied (P > 1 × 10
−4
)
91
. In our data,
this estimate of the correlation parameter was r
ij
= 0.59 between MO
and MA and r
ij
= 0.198 between MO and VD (estimated using 7,858,264
markers), which is considerably larger than if we estimated the sam-
ple overlap directly using counts of cases, controls and the counts
of overlaps in these groups between phenotypes
70
(from all cohorts
except the summary statistics from FinnGen), where we get rij = 0.023
for MO and MA and r
ij
= 0.012 for MO and VD. As the latter estimates
are more conservative, we used those in the subtype analysis. Finally,
we tested whether the effect sizes between MA and MO (and VD and
MO) were equal at a Bonferroni corrected significance threshold of
P = 0.05/43 (as we excluded from the 44 lead variants the MA variant
in PRRT2) performed by using normal approximation and accounting
for the correlation in effect size difference estimators. As pointed out
in ref. 11, this subtype classification method takes into account the
different statistical power of the migraine subtype GWASs, which is
an advantage compared to simply comparing subtype effects. For the
subtype analysis, we followed the R code available at https://github.
com/mjpirinen/migraine-meta.
Functional data and colocalization analysis
To highlight genes whose products potentially mediate the observed
associations with migraine and migraine subtypes, we annotated the
associations detected in this study (Tables 1 and 2) as well as variants in
high LD (r2 ≥ 0.8 and within ±1 Mb) that are predicted to affect coding
or splicing of a protein (VEP using RefSeq gene set), mRNA expression
(top local eQTL, cis-eQTL) in multiple tissues from deCODE, GTEx
(https://www.gtexportal.org) and other public datasets (see Supple-
mentary Table 18 for eQTL data sources) and/or plasma protein levels
(top pQTL) identified in large proteomic datasets from Iceland and the
UK. The Icelandic proteomics data were analyzed using the SomaLogic
SOMAscan proteomics assay that scans 4,907 aptamers, measuring
4,719 proteins in samples from 35,559 Icelanders with the genetic
information available at deCODE Genetics38. Plasma protein levels were
standardized and adjusted for year of birth, sex and year of sample col-
lection (2000–2019)
38
. The UK proteomics dataset was analyzed using
the Olink proteomics assay characterizing 1,463 proteins in 54,306
participants in the UK Biobank92.
RNA sequencing was performed on whole blood from 17,848 Ice-
landers and on subcutaneous adipose tissue from 769 Icelanders,
respectively
38
. Gene expression was computed based on personalized
transcript abundances using kallisto
93
. Association between sequence
variants and gene expression (cis-eQTL) was tested using a general-
ized linear regression, assuming additive genetic effect and normal
quantile gene expression estimates, adjusting for measurements of
sequencing artifacts, demographic variables, blood composition
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01538-0
and PCs94. The gene expression PCs were computed per chromosome
using a leave-one-chromosome-out method. All variants within 1 Mb
of each gene were tested.
We performed gene-based enrichment analysis using the GEN-
E2FUNC tool in FUMA95. The genes were tested for over-representation
in different gene sets, including Gene Ontology cellular components
(MsigDB c5) and GWAS Catalog-reported genes.
Genetic drug target analysis
Using sources from the Drug-Gene Interaction Database96, Open Tar-
gets
97
and the National Institutes of Health’s Illuminating the Druggable
Genome
98
, we performed a genetic drug target analysis for the 22 genes
for which we have evidence of function pointing to the gene (Sup-
plementary Fig. 6), in addition to the established MA gene CACNA1A.
Reporting summary
Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.
Data availability
Our previously described Icelandic population whole-genome
sequence data have been deposited at the European Variant Archive
under accession PRJEB15197. The GWAS summary statistics for the
migraine GWAS meta-analyses are available at https://www.decode.
com/summarydata/. FinnGen data are publicly available and were
downloaded from https://www.finngen.fi/en/access_results. The UKB
data were downloaded under application 42256. Proteomics data and
protein mapping to UniProt identifiers and gene names were provided
by SomaLogic and Olink. Other data generated or analyzed in this study
are included in the article and its Supplementary Information. URLs
for other external data used are as follows: precomputed LD scores for
European populations, https://data.broadinstitute.org/alkesgroup/
LDSCORE/eur_w_ld_chr.tar.bz2; GWAS Catalog, https://www.ebi.ac.uk/
gwas/; GTEx project, https://gtexportal.org/home/. URL sources for
expression data can be found in Supplementary Table 18.
Code availability
We used publicly available software that is available on request under
the following URLs: GraphTyper (v2.0-beta, GNU GPLv3 license),
https://github.com/DecodeGenetics/graphtyper; Eagle (version
2.4.1), http://www.hsph.harvard.edu/alkes-price/software/; SHA-
PEIT4, https://odelaneau.github.io/shapeit4/; ADMIXTURE (v1.23),
https://dalexander.github.io/admixture/; BOLT-LMM (v.2.1),
http://www.hsph.harvard.edu/alkes-price/software/; R (version
3.6.3), https://www.r-project.org/; R package ggplot for visualization
(version 3.3.3), https://ggplot2.tidyverse.org/; Ensembl v.87,
https://www.ensembl.org/index.html; IMPUTE2 v.2.3.1, https://math-
gen.stats.ox.ac.uk/impute/impute_v2.html; dbSNP v.140, http://www.
ncbi.nlm.nih.gov/SNP/; kallisto v.0.46, https://github.com/pachterlab/
kallisto; for subtype stratification analysis, we used R code available
at https://github.com/mjpirinen/migraine-meta; MAGMA (v1.08),
http://ctglab.nl/software/magma; VEP (release 100), https://
github.com/Ensembl/ensembl-vep; FUMA, https://fuma.ctglab.nl/;
Sequencher 5.0, https://sequencher.software.informer.com/5.0/;
NCBI Build 38, https://www.ncbi.nlm.nih.gov/. No custom code was
written for this study.
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Acknowledgements
We thank all participants who contributed data and samples
used in this study. Their contributions are essential for research
such as reported here. We thank all investigators and colleagues
who collaborated on the many aspects of this study, including
data collection, sample handling, phenotypic characterization of
clinical samples, genotyping and analysis of the whole-genome
association data. We acknowledge participants and investigators
of the FinnGen study20 and the UK Biobank study. This research
has been conducted using the UK Biobank Resource, a major
biomedical database (application 42256, https://www.ukbiobank.
ac.uk/). The inancial support from the European Commission to the
painFACT project to T.E.T. (H2020-2020-848099) is acknowledged,
as is support from the Novo Nordisk Foundation, DBDS Consortium
(grants NNF17OC0027594 and NNF14CC0001). The Genotype-Tissue
Expression (GTEx) Project was supported by the Common Fund
of the Oice of the Director of the National Institutes of Health
(commonfund.nih.gov/GTEx). Additional funds were provided by the
NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. Donors were enrolled
at Biospecimen Source Sites funded by NCI\Leidos Biomedical
Research, Inc. subcontracts to the National Disease Research
Interchange (10XS170), GTEx Project March 5, 2014 version Page 5
of 8 Roswell Park Cancer Institute (10XS171), and Science Care, Inc.
(X10S172). The Laboratory, Data Analysis, and Coordinating Center
(LDACC) was funded through a contract (HHSN268201000029C) to
the The Broad Institute, Inc. Biorepository operations were funded
through a Leidos Biomedical Research, Inc. subcontract to Van
Andel Research Institute (10ST1035). Additional data repository
and project management were provided by Leidos Biomedical
Research, Inc. (HHSN261200800001E). The Brain Bank was
supported supplements to University of Miami grant DA006227.
Statistical Methods development grants were made to the University
of Geneva (MH090941 and MH101814), the University of Chicago
(MH090951, MH090937, MH101825 and MH101820), the University
of North Carolina - Chapel Hill (MH090936), North Carolina State
University (MH101819), Harvard University (MH090948), Stanford
University (MH101782), Washington University (MH101810) and to the
University of Pennsylvania (MH101822). The datasets used for the
analyses described in this manuscript were obtained from dbGaP at
http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number
phs000424.v9.p2.
Author contributions
O.B.P. (olbp@regionsjaelland.dk) is the representative for the DBDS
Genetic Consortium. G.B., M.A.C., L.S., A.Th.S., G.E., E.F., S.G., B.V.H.,
A.H., Adalbjorg Jonasdottir, Aslaug Jonasdottir, I.J., G.M., K.H.S.M.,
O.Th.M., P.I.O., A.S., O.A. Stefansson, G.S., V.T., U.U., G.B.W., F.Z., U.T.,
S.R.O., H. Holm, D.F.G., G.T., P.S., H.S., T.E.T., T.F.H. and K.S. designed
the study, analyzed data and interpreted results. G.B., M.A., A.H.,
I.J., A.O., J.S., U.U., G.B.W., U.T., H. Holm, D.F.G., P.S., H.S., T.E.T. and
K.S. collected and analyzed Icelandic phenotypes and samples for
the study. G.B., A.Th.S., D.B., E.F., G.H.H., H. Helgason, S.H.L., P.M.,
A.S., O.A. Stefansson, H. Holm, G.H.E., D.F.G., G.T., P.S., H.S., T.E.T.,
T.F.H. and K.S. performed and/or interpreted results from functional
studies, transcriptomics, proteomics and gene set enrichment.
O.A.A., J.H., J.I., R.T.L. and L.R. designed, collected, contributed and
interpreted Norwegian study data. The DBDS Genetic Consortium,
M.A.C., K.B., S.B., M.D., M.T.B., C.E., L.J.A.K., K.R.N., E.S., O.B.P.,
H.U., J.O., S.R.O. and T.F.H. designed, collected, contributed and
interpreted Danish study data. L.D.N. and K.U.K designed, collected,
contributed and interpreted the US study data. G.B., M.A.C., L.S.,
A.Th.S., E.F., S.G., A.H., Adalbjorg Jonasdottir, Aslaug Jonasdottir,
A.S., A.B., A.O., G.R.S., P.L., O.T., O.A. Sveinsson, H. Holm, G.T., P.S.,
H.S., T.E.T., T.F.H. and K.S. drafted the manuscript with input and
comments from other authors who all reviewed and contributed to
the inal version of the manuscript.
Competing interests
Authors ailiated with deCODE Genetics/Amgen declare competing
inancial interests as employees. The remaining authors declare no
competing inancial interests.
Additional information
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41588-023-01538-0.
Correspondence and requests for materials should be addressed to
Gyda Bjornsdottir or Kari Stefansson.
Peer review information Nature Genetics thanks Guy Rouleau and
Ynte Ruigrok for their contribution to the peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
