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ARTICLE
Genome-wide association study in 79,366
European-ancestry individuals informs the genetic
architecture of 25-hydroxyvitamin D levels
Xia Jiang et al.
#
Vitamin D is a steroid hormone precursor that is associated with a range of human traits and
diseases. Previous GWAS of serum 25-hydroxyvitamin D concentrations have identified four
genome-wide significant loci (GC, NADSYN1/DHCR7, CYP2R1, CYP24A1). In this study, we
expand the previous SUNLIGHT Consortium GWAS discovery sample size from 16,125 to
79,366 (all European descent). This larger GWAS yields two additional loci harboring
genome-wide significant variants (P=4.7×10−9at rs8018720 in SEC23A, and P=1.9×10−14 at
rs10745742 in AMDHD1). The overall estimate of heritability of 25-hydroxyvitamin D serum
concentrations attributable to GWAS common SNPs is 7.5%, with statistically significant loci
explaining 38% of this total. Further investigation identifies signal enrichment in immune and
hematopoietic tissues, and clustering with autoimmune diseases in cell-type-specific analysis.
Larger studies are required to identify additional common SNPs, and to explore the role of
rare or structural variants and gene–gene interactions in the heritability of circulating 25-
hydroxyvitamin D levels.
DOI: 10.1038/s41467-017-02662-2 OPEN
Correspondence and requests for materials should be addressed to E.H. (email: elina.hypponen@unisa.edu.au) or to P.K. (email: pkraft@hsph.harvard.edu)
or to D.P.K. (email: kiel@hsl.harvard.edu)
#A full list of authors and their affliations appears at the end of the paper
NATURE COMMUNICATIONS | (2018) 9:260 |DOI: 10.1038/s41467-017-02662-2 |www.nature.com/naturecommunications 1
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Vitamin D is an essential fat soluble vitamin and steroid
pro-hormone that plays an important role in muscu-
loskeletal health. Vitamin D deficiency has been linked to
autoimmune1,2and infectious disease3, cardiovascular disease4,
cancer5, and neurodegenerative conditions6. Serum 25-
hydroxyvitamin D, a primary circulating form of vitamin D
and a measure that best reflects vitamin D stores, is influenced by
many factors including sun exposure, age, body mass index7,
dietary intake of certain foods such as fortified dairy products and
oily fish, supplements, and genetic factors8. The concentration of
25-hydroxyvitamin D has been reported to be highly heritable,
with heritability estimates of 50–80% from classical twin
studies9,10.
A genome-wide association study (GWAS) meta-analysis of
serum 25-hydroxyvitamin D11 in 4501 participants of European
ancestry and replication in 2221 samples identified variants in
three loci (group component (GC), 7-dehydrochlesterol reductase
(NADSYN1/DHCR7), and 25-hydroxylase (CYP2R1)). A larger
GWAS conducted by the SUNLIGHT consortium in 16,125
European ancestry individuals, with a replication sample of
17,871, replicated these three loci and discovered one additional
locus (CYP24A1)8. However, despite these loci being in or near
genes encoding proteins involved in vitamin D synthesis, the
associated variants collectively explain only a small fraction of the
variance in 25-hydroxyvitamin D concentrations (~5%)8,11,12.
Therefore, to extend our previous findings and better understand
the genetic architecture underlying serum 25-hydroxyvitamin D,
as well as test for interactions between dietary vitamin D intake
and genetic factors, we conducted a large-scale GWAS meta-
analysis on this important vitamin.
Our GWAS with a 79,366 discovery sample and a 40,562
replication sample replicates four previous loci and identifies two
new genetic loci for serum levels of 25-hydrovxyvitamin D. We
further find evidence for a shared genetic basis between circu-
lating 25-hydroxyvitamin D and autoimmune diseases. Our
analyses suggest a relatively modest SNP-heritability rate of 25-
hydroxyvitamin D when considering only common variants.
Larger studies are required to identify additional common SNPs,
and to explore the role of rare or structural variants. The genetic
instruments identified by our results could be used in future
Mendelian Randomization analyses of the association between
vitamin D and complex traits.
Results
Study description and GWAS. This study represents an expan-
sion of our previous SUNLIGHT consortium GWAS8. Here, we
combine the 5 discovery cohorts and 5 in-silico replication
cohorts from that study, and augment these with 21 additional
cohorts that have joined the SUNLIGHT consortium since 2010
(study characteristics are described in Supplementary Table 1,
Supplementary Note 1). In contrast to the previous meta-analysis
which involved discovery, in-silico and de-novo genotyping
stages, we performed a first stage discovery meta-analysis on a
total of up to 79,366 individuals and replicated novel findings in
two independent separate in-silico data sets (40,562 individuals
collected by EPIC and 2195 individuals collected by SOCCS). To
assess and control for population stratification, we examined QQ-
plots and genomic control inflation factors for each contributing
cohort prior to meta-analysis. We did not observe evidence for
widespread inflation (median λ
GC
=0.92; only 1/31 samples with
λ
GC
>1.01), indicating that our GWAS results were not inflated
by population stratification or cryptic relatedness (Supplementary
Fig. 1). Despite the slightly deflated λ
GC
observed in some of the
constituent cohorts most probably due to over-correction of test
statistics, our λ
GC
of 0.99 in all samples indicated appropriate
control for population stratifications and confounders. We
identified six susceptibility loci harboring genome-wide sig-
nificant SNPs, confirming four previously reported loci at GC
(P=4.7×10−343 at rs3755967), NADSYN1/DHCR7 (P=3.8×10−62
at rs12785878), CYP2R1 (P=2.1×10−46 at rs10741657), CYP24A1
(P=8.1×10−23 at rs17216707), and two novel loci at AMDHD1
(P=1.9×10−14 at rs10745742) and SEC23A (P=4.7×10−9at
rs8018720) (Table 1; Manhattan plots and QQ-plots for overall
samples are presented in Fig. 1, and regional association plots are
presented in Supplementary Fig. 2). The associations at both
novel loci were confirmed in the two independent in-silico
replication cohorts (EPIC: P=1.21×10−8at rs10745742, P=
5.24×10−4at rs8018720; SOCCS: P=0.03 at rs10745742, P=0.04
at rs8018720) with consistent direction of effect but slightly larger
effect sizes and wider confidence intervals observed in SOCCS
which could be due to the reduced sample size. When analyzing
the two replication data sets together with the discovery data set,
the P-values in pooled samples became more significant (P
pooled
=
2.10×10−20 at rs10745742, P
pooled
=1.11×10−11 at rs8018720)
(Table 1). We also found more than one distinct signal arising
from variants at the GC,CYP2R1, and AMDHD1 loci through
conditional and joint analysis, but not for the NADSYN1/DHCR7,
CYP24A1, and SEC23A loci where only one primary associated
SNP was identified (Supplementary Table 2).
SNP by dietary vitamin D intake interaction. In addition to
performing the marginal effect meta-analysis using all samples,
we also tested a model with SNPs and dietary vitamin D intake as
main effects and a term for their interaction in a subset of
samples. Diet questionnaires, including vitamin D intake, were
available for a subset of 13 cohorts and an additional 2 cohorts
that were not included in the overall meta-GWAS analysis (a total
of 15 cohorts, N=41,981). We performed a GWAS explicitly
allowing for an interaction between vitamin D intake and SNP
genotypes, in which dietary vitamin D was coded as a continuous
variable. We performed two tests: (i) a 1 degree-of-freedom
interaction test between each SNP and vitamin D intake, and (ii)
a 2 degree-of-freedom joint test of main genetic and interaction
effects. However, for comparison purposes, we also performed,
(iii) a standard test of marginal genetic effect after adjusting for
vitamin D intake in the same sub-samples (Supplementary Fig. 3,
and Supplementary Fig. 4). The marginal genetic effect analyses
confirmed existing association signals at GC (lead SNP rs2282679,
in complete linkage disequilibrium with the lead SNP rs3755967
identified from meta-GWAS using all individuals), CYP2R1,
NADSYN1/DHCR7 (lead SNP rs4944062, in complete linkage
disequilibrium with the lead SNP rs12785878 identified from
meta-GWAS using all individuals) and CYP24A1, as well as the
novel association at AMDHD1 (P=5.7×10−9, Supplementary
Table 3). The joint analysis also identified the five genes above,
but with less significant P-values. For instance, the association at
AMDHD1 achieved only suggestive genome-wide significance (P
=1.2×10−7, Table 2). The interaction test did not identify any
variants at genome-wide significance level. Among the 5 SNPs
significant in marginal effect tests, the lead SNP in CYP2R1
showed nominal significance for interaction with dietary vitamin
D intake (rs10741657, P=0.028), but no interactions were
observed for the other SNPs (rs2282679, P=0.45; rs4944062, P=
0.74; rs10745742, P=0.64; rs17216707, P=0.46). Repeating the
analysis using a tertile coding for vitamin D instead of a con-
tinuous coding did not qualitatively change the results.
SNP-heritability of 25-hydroxyvitamin D. We further evaluated
the SNP-heritability, defined as the heritability explained by
GWAS SNPs of 25-hydroxyvitamin D, using LD score regression
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02662-2
2NATURE COMMUNICATIONS | (2018) 9:260 |DOI: 10.1038/s41467-017-02662-2 |www.nature.com/naturecommunications
(see Methods). The overall observed heritability of 25-
hydroxyvitamin D estimated by using all common SNPs
(2,579,296 after QC) was 7.54% (standard error (SE): 1.88%).
After excluding genome-wide significant SNPs (533 SNPs with
P≤5×10−8) from six loci and all SNPs within ±500 kb of those
loci, the heritability decreased to 4.70% (SE: 0.72%). The estimate
further decreased to 1.73% (SE: 0.32%) after excluding all SNPs
that reached nominal significance (156,675 SNPs with P≤0.05).
These results indicate that common variants tagged by GWAS
chips explain a modest fraction of overall variability in circulating
30
GC NADSYN1/DHCR7
CYP2R1
AMDHD1
SEC23A
CYP24A1
Know
Novel
25
20
–log10 (p – value)
15
10
5
200 = 0.99
01234
Expected –log10 corrected p-value
56
Observed –log10 corrected p-value
100
50
0
1
2
3
4
5
6
7
8
Chromosome
9
10
11
12
13
14
15
16
17
18
19
20
21
22
a
b
Fig. 1 Genome-wide association of circulating 25-hydroxyvitamin D graphed by chromosome positions and −log10 P-value (Manhattan plot), and quantile-
quantile plot of all SNPs from the meta-analysis (QQ-plot). aManhattan plot: The P-values were obtained from the single stage fixed-effects inverse
variance weighted meta-analysis. The Yaxis shows −log
10
P-values, and the Xaxis shows chromosome positions. Horizontal gray dash line represents the
thresholds of P=5×10−8for genome-wide significance. Known loci were colored coded as red, and novel loci were color coded as green. bQQ-plot: The Y
axis shows observed −log
10
P-values, and the Xaxis shows the expected −log
10
P-values. Each SNP is plotted as a black dot, and the dash line indicates null
hypothesis of no true association. Deviation from the expected P-value distribution is evident only in the tail area, with a lambda of 0.99, suggesting that
population stratification was adequately controlled
Table 1 Single nucleotide polymorphisms identified in genome-wide analyses for circulating 25-hydroxyvitamin D concentrations
Gene SNP Chromosome: Position Effect/reference
allele
Allele frequency Meta-GWAS estimates
Effect (Beta) Standard Error P-value
First stage discovery meta-GWAS (N=79,366)
GC rs3755967 4:72828262 T/C 0.28 −0.089 0.0023 4.74E–343
NADSYN1/ DHCR7 rs12785878 11:70845097 T/G 0.75 0.036 0.0022 3.80E–62
CYP2R1 rs10741657 11:14871454 A/G 0.4 0.031 0.0022 2.05E–46
CYP24A1 rs17216707 20:52165769 T/C 0.79 0.026 0.0027 8.14E–23
AMDHD1 rs10745742 12:94882660 T/C 0.4 0.017 0.0022 1.88E–14
SEC23A rs8018720 14:38625936 C/G 0.82 −0.017 0.0029 4.72E–09
Replication data set 1: samples collected by EPIC (N=40,562)
AMDHD1 rs10745742 12:94882660 T/C 0.41 0.041 0.0071 1.21E–08
SEC23A rs8018720 14:38625936 C/G 0.83 −0.032 0.0093 5.24E–04
Replication data set 2: additional control samples collected by SOCCS (N=2195)
AMDHD1 rs10745742 12:94882660 T/C 0.37 0.045 0.021 0.03
SEC23A rs8018720 14:38625936 C/G 0.81 −0.051 0.026 0.04
Pooled analysis (discovery meta-GWAS + replication 1 + replication 2) (N=122,123)
AMDHD1 rs10745742 12:94882660 T/C 0.39 0.019 0.002 2.10E–20
SEC23A rs8018720 14:38625936 C/G 0.82 −0.019 0.0027 1.11E–11
In the pooled analysis, P
heterogeneity
=0.003 for AMDHD1 rs10745742; P
heterogeneity =
0.14 for SEC23A rs8018720.
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25-hydroxyvitamin D, and that an appreciable proportion of this
SNP-heritability is explained by the six genetic regions of asso-
ciated SNPs identified through GWAS.
Partitioning the total heritability of 25-hydroxyvitamin D.We
next partitioned the heritability by functional elements using
baseline model with 24 publicly available annotations (see
Methods), and observed large and significant enrichment for
several functional categories (Fig. 2, Supplementary Table 4). For
example, we found the largest enrichment in weak enhancers,
with 2.1% of SNPs explaining 42.3% of the overall heritability
(20-fold enrichment, P=0.02), followed by conserved regions
(13.8-fold enrichment, P=0.03), open chromatin (as reflected by
DHS, 8.5-fold enrichment, P=0.02), transcription factor binding
sites (5.7-fold enrichment, P=0.048), super-enhancers (1.9-fold
enrichment, P=0.04), and all four histone marks were enriched
(both versions of H3K27ac (one version processed by Hnisz et al.,
and another version used by the Psychiatric Genomics Con-
sortium (PGC), H3K4me1, H3K4me3 (500 bp), H3K29ac (500
bp)). We also observed depletion for repressed regions (0.06-fold
enrichment, P=0.006). However, none of those annotations
withstood multiple-testing corrections (Bonferroni corrected P-
threshold: 0.05/24) except for the active enhancer histone mark
H3K27ac (PGC) (4.2-fold enrichment, P=8×10−4) and
H3K4me1 (1.8-fold enrichment, P=0.0019).
We subsequently performed cell-type-specific analysis by using
10 broad cell-type groups. As shown in Table 3, the top three
enrichments were in the immune and hematopoietic tissues (4.3-
fold enrichment, P=2.2×10−5), gastrointestinal tissues (4.4-fold
enrichment, P=0.0017), and CNS (3.6-fold enrichment, P=
0.0039). There was also significant enrichment for liver, kidney,
and connective and bone tissues, but these results did not survive
multiple-testing corrections. When further analyzing 220 cell-
type-specific annotations, we observed the most significant
enrichment in CD19 cells (approximately 8-fold enrichment,
P~0.001), followed by CD20 cells (6.4-fold enrichment, P=0.003)
and CD3 cells (7.8-fold enrichment, P=0.01) (Supplementary
Data 1).
Genetic correlations between 25-hydroxyvitamin D and traits.
We continued to assess the genetic correlation between 25-
hydroxyvitamin D and each of the 37 traits with publicly available
GWAS summary statistics data (Supplementary Table 5). None of
the genetic correlations remained significant after Bonferroni
correction (corrected P-threshold: 0.05/37, Fig. 3). Without
multiple-testing correction, there were some correlations with
nominal statistical significance. For example, ever smoking
(rg(SE): −0.17 (0.073), P=0.019), primary biliary cirrhosis
(rg(SE): −0.18 (0.076), P=0.019) and BMI adjusted waist-hip-
ratio (rg(SE): −0.10 (0.050), P=0.042) were observed to be
inversely correlated with 25-hydroxyvitamin D; whereas lung
function (rg(SE): 0.14 (0.046), P=0.0036) showed a positive
correlation with 25-hydroxyvitamin D. Subsequent directional
genetic correlation analysis did not reveal any apparent putative
causal relationship of 25-hydrovyvitamin D with other traits,
except for a potential link between 25-hydroxyvitamin D and
Table 2 Results from the SNP-by-dietary vitamin D intake interaction analysis
Gene SNP Chromosome:
Position
Effect/
Reference
Allele
Allele
Frequency
SNP-by-dietary vitamin D intake Interaction analysis
Main
Genetic
Effect
Interaction
Effect
P-value for
interaction
P-value
for joint
test
Effect
(Beta_G)
Standard
Error
Effect
(Beta_Int)
Standard
Error
First stage discovery meta-GWAS(N=79,366)
GC rs3755967 4:72828262 T/C 0.28 −0.082 0.0042 −2.01E–05 1.67E–05 0.23 2.92E–171
rs2282679* 4:72827247 T/G 0.28 0.085 0.004 1.20E–05 1.60E–05 0.45 1.40E–187
NADSYN1/
DHCR7
rs12785878 11:70845097 T/G 0.75 0.033 0.0039 6.61E–06 1.62E–05 0.68 3.52E–29
rs4944062* 11:70864942 T/G 0.75 0.034 0.004 5.30E–06 1.60E–05 0.74 1.90E–31
CYP2R1 rs10741657 11:14871454 A/G 0.4 0.03 0.0035 3.21E–05 1.46E–05 0.028 2.23E–38
CYP24A1 rs17216707 20:52165769 T/C 0.79 0.025 0.0048 1.39E–05 1.88E–05 0.46 1.32E–14
AMDHD1 rs10745742 12:94882660 T/C 0.4 0.016 0.0036 −7.05E–06 1.49E–05 0.64 1.20E–07
SEC23A rs8018720 14:38625936 C/G 0.82 −0.013 0.0051 −2.40E–05 2.06E–05 0.24 1.94E–05
* Top SNPs identified in the SNP-by-dietary vitamin D intake interaction analysis, performed in a subset of individuals. For GC and NADSYN1/DHCR7, the top SNPs identified through the marginal effect
regression meta-analysis using all individuals were in high linkage disequilibrium with the top SNPs identified through the SNP-by-dietary vitamin D intake interaction analysis using a subset of individuals
(r2for rs3755967 and rs2282679: 1.0; r2for rs12785878 and rs4944062: 1.0). Beta_G indicates the main effect of the SNP, Beta_Int indicates the interaction effect of SNP-by-dietary vitamin D intake
20
Type
24annotations
24annotations_500 bp
Enrichment=Prop.H2/Prop.SNPs
10
0
Weak enhancer
Conserved
TSS
Fetal DHS
TFBS
H3K27ac (PGC)
H3K27ac
H3K4me1
H3K4me3
Super enhancer
H3K27ac (Hnisz)
Repressed
Functional categories
**
**
Fig. 2 Heritability enrichment of the top 12 genomic functional elements.
We partitioned the SNP-heritability of serum 25-hydroxyvitamin D
concentrations into 24 publicly available genomic functional elements using
LD-score regression. We plotted the enrichment (Yaxis) for each of the 12
top annotations (as shown in Xaxis) into a bar chart. Gray bars and blue
bars represent the annotations with and without the 500 base-pair
windows. The height of each bar represents magnitude of enrichment.
Significant estimates of enrichment that passed Bonferroni corrections
(P-value for enrichment <0.05/24) are marked with double stars. TSS
transcription start sites, DHS DNase I hypersensitive sites, TFBS
transcription factor binding sites, Repressed repressed regions
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02662-2
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HDL (Supplementary Table 6). However, with only six
25-hydroxyvitamin D associated SNPs included in the analysis,
we consider an overall null directional correlation as our main
finding, and further well-designed large-scale Mendelian rando-
mization analyses are warranted.
Finally, we analyzed the 220 cell-type-specific annotations in
each of the 37 traits and compared the cell-type-specific
enrichments for 25-hydroxyvitamin D to the enrichments for
these traits. The enrichment pattern for 25-hydroxyvitamin D
differed notably from the patterns for psychiatric diseases and
metabolic related traits. Psychiatric diseases showed enrichment
for histone marks specific to CNS cell types, and metabolic
diseases showed enrichment for gastrointestinal cell types, while
these annotations were depressed in 25-hydroxyvitamin D.
Conversely, 25-hydroxyvitamin D showed similar patterns with
autoimmune inflammatory diseases, where multiple immune cell
types were enriched. We consistently observed that 25-
hydroxyvitamin D was clustered with autoimmune diseases
(Supplementary Fig. 5).
Discussion
Vitamin D inadequacy has been linked to many diseases such as
cancer, autoimmune disorder and cardiovascular conditions in
addition to musculoskeletal diseases, which has led to substantial
interest in the determinants of vitamin D status, especially its
genetic components. We have performed a large 25-
hydroxyvitamin D meta-GWAS involving 31 studies with a
total of 79,366 individuals. Our results recapitulated several pre-
viously reported findings. First of all, we confirmed the role for
common genetic variants in regulation of circulating 25-
hydroxyvitamin D concentrations. Our study validated three
loci, GC,NADSYN1/DHCR7, and CYP2R1, all were established
25-hydroxyvitamin D risk loci identified through two earlier
GWASs8,11. In addition, we were able to confirm the association
of a locus containing CYP24A1 with 25-hydroxyvitamin D con-
centrations using our large sample size, which highlights the
importance of this protein in the degradation of vitamin D
molecule, by catalyzing hydroxylation reactions at the side chain
of 1,25-dihydroxyvitamin D, the physiologically active form
(hormonal form) of vitamin D. Significant finding at this locus
was only shown in the pooled analyses involving both discovery
and replication samples in an earlier GWAS8.
We extended previously reported findings by identifying two
additional new loci. SEC23A (Sec23 Homolog A, coat protein
complex II (COPII) component) encodes a member of
SEC23 subfamily. In eukaryotic cells, secreted proteins are syn-
thesized in the endoplasmic reticulum (ER), packaged into
COPII-coated vesicles, and traffic to the Golgi apparatus. As part
of COPII complex, SEC23 plays a role in promoting ER-Golgi
protein trafficking. SEC23A mutations have been reported to
cause craniolenticulosutural dysplasia, a disease characterized by
craniofacial and skeletal malformation such as delay in closure of
fontanels, sutural cataracts and facial dysmorphisms, due to
defective collagen secrection13,14. The second novel locus is
AMDHD1 (amidohydrolase domain containing 1). This gene
encodes an enzyme involved in the histidine, lysine, phenylala-
nine, tyrosine, proline and tryptophan catabolic pathway. Muta-
tions in AMDHD1 are found to be associated with atypical
lipomatous tumor, a cancer of connective tissues that resemble fat
cells15.
Our SNP-heritability results suggest that 25-hydroxyvitamin D
has a modest overall heritability due to common genome-wide
SNPs of 7.5%, and that an appreciable proportion (2.84% out of
7.5%, i.e., 38%) of this total could be explained by known genetic
regions identified through GWAS. Our findings are in line with a
previous published report (by Hiraki et al.12) which estimated the
variance in circulating 25-hydroxyvitamin D explained by SNPs
in a total of 5575 individuals12. According to that report, by
employing a linear mixed model fitting the additive genetic
matrix created from all genotyped and imputed SNPs, the pro-
portion of variance explained was 8.9%; by employing a polygenic
score approach comprised of the then GWAS-discovered SNPs
(GC, CYP2R1, DHCR7/NADSYN1), the proportion of variance
explained was 5%. Both of these estimates were close to ours. In
Hiraki et al., the known 25-hydroxyvitamin D associated envir-
onmental factors such as age, BMI, season of blood drawn,
vitamin D dietary intake, vitamin D supplement intake, region of
residence and ethnicity, explained ~18% of the observed var-
iance12. Our results, in agreement with these findings, suggest
that although there appears to be some polygenic signals outside
of the identified regions, the remaining common effects may be
small. There also may be low frequency variants with larger
effects that were not investigated here. For example, while this
paper was under review, a related study identified low-frequency
(MAF =2.5%) synonymous coding variant rs117913124_A at
CYP2R1 conferring a large effect on 25-hydroxyvimtain D levels,
which was four times greater in magnitude and independent of a
previously described association for a common variant
(rs10741657) near CYP2R116.
Results of twin and familial studies have revealed a substantial
genetic basis in the variability of circulating 25-hydroxyvitamin D
levels, with estimates of heritability reaching as high as
86%9,10,17–19. These estimates, however, seem to be influenced by
environmental conditions. For example, in a study conducted by
Orton et al. with 40 monozygotic and 59 dizygotic twin pairs,
bloods were collected at the end of winter and a heritability of
77% was reported10. Similarly, the study conducted by Karohl
et al. with 310 monozygotic and 200 dizygotic male twins
observed a heritability of 70% during winter, whereas in summer,
serum 25-hydroxyvitamin D concentrations appeared to be
Table 3 Heritability enrichment of ten grouped cell types
Category Proportion of SNPs (%) Proportion of h2
g(%) Enrichment (standard errors) P-value
Kidney 4.26 27.27 6.4 (2.44) 0.027
Liver 7.22 37.68 5.22 (1.55) 0.01
Gastrointestinal 16.77 72.88 4.35 (0.97) 0.0017
Immune and hematopoietic 23.34 100.17 4.29 (0.76) 2.20E-05
Central nervous system 14.88 54.09 3.64 (0.87) 0.0039
Cardiovascular 11.11 35.74 3.22 (1.26) 0.078
Connective tissue/bone 11.5 35.65 3.1 (1.04) 0.037
Adrenal/pancreas 9.36 26.17 2.8 (1.31) 0.18
Other 20.27 56.68 2.8 (0.98) 0.076
Skeletal Muscle 10.38 14.29 1.38 (1.25) 0.76
Black bold font indicates significant P-values after multiple corrections (P<0.05/10)
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entirely determined by non-genetic factors (heritability: 0%)9.
Comparable estimates were also identified in a slightly larger
study conducted by Mills et al. (winter: 90% vs. summer: 56%)18.
Consistent with season dependency, sex differences were also
observed (males: 86% vs. females: 17%)17. While these estimates
should be treated with caution due to small samples and related
imprecision, they confirm the substantial variation in 25-
hydroxyvitamin D levels by season (as shown previously20) and
illustrate that heritability estimates derived from a homogenous
source may be highly inflated. In a relevantly well-powered twin
study with a total of ~2100 female twins, the heritability of 25-
hydroxyvitamin D was calculated to be 40%, indicating a larger
proportion of variance explained by non-genetic factors21. Her-
itability estimates obtained using GWAS SNPs have typically
been found to be approximately half of those from classical twin
studies9,10, but our estimate of 7.5%, calculated using common
genome-wide SNPs, is far lower than reported heritability from
twin and family based studies. In addition to potentially inflated
estimates from twin studies, the difference may reflect the pro-
portion of heritability explained by rare SNPs or structural var-
iants that were not included in our data, and the potential gene-
gene interactions that remain to be identified. The combination of
our samples from all seasons is also likely to decrease the prob-
ability of finding genetic variants, and hence deflate heritability
estimates.
Through partitioning the SNP-heritability of serum 25-
hydroxyvitamin D levels, we observed a significant enrichment
in immune and hematopoietic tissues; likewise, the cell-type-
specific analysis revealed clustering of 25-hydroxyvitamin D and
autoimmune diseases, indicating that these traits share a majority
Traits Genetic correlation
(95%CI)
Autoimmune/inflammatory diseases
Age-related macular degeneration –0.02 (–0.1, 0.06)
p-value Significance
*
*
*
*
0.57
0.89
0.25
0.98
0.21
0.019
0.44
0.59
0.9
0.91
0.71
0.39
0.7
0.64
0.63
0.49
0.61
1
0.25
0.66
0.98
0.21
0.36
0.18
0.26
0.54
0.019
0.13
0.25
0.45
0.63
0.69
0.0036
0.64
0.078
0.8
0.042
–0.02 (–0.17, 0.15)
–0.07 (–0.19, 0.05)
0 (–0.15, 0.14)
–0.17 (–0.45, 0.1)
–0.18 (–0.33, –0.03)
0.05 (–0.08, 0.19)
0.04 (–0.1, 0.18)
–0.01 (–0.11, 0.1)
–0.01 (–0.12, 0.1)
–0.02 (–0.13, 0.09)
–0.08 (–0.28, 0.11)
–0.03 (–0.15, 0.1)
0.03 (–0.11, 0.17)
0.04 (–0.11, 0.18)
0.04 (–0.08, 0.17)
0.02 (–0.07, 0.11)
0 (–0.08, 0.08)
0.14 (–0.1, 0.38)
–0.03 (–0.16, 0.1)
0 (–0.12, 0.12)
0.07 (–0.04, 0.19)
0.06 (–0.07, 0.18)
–0.06 (–0.16, 0.03)
–0.08 (–0.22, 0.06)
0.02 (–0.05, 0.1)
–0.17 (–0.32, –0.03)
–0.07 (–0.02, 0.15)
–0.07 (–0.19, 0.05)
–0.03 (–0.11, 0.05)
0.02 (–0.06, 0.09)
–0.01 (–0.08, 0.06)
0.14 (0.04, 0.23)
–0.02 (–0.09, 0.05)
0.06 (–0.01, 0.12)
–0.01 (–0.08, 0.06)
–0.1 (–0.2, 0)
–0.5 –0.25 00.25 0.5
Celiac Disease
Crohn’s Disease
Lupus
Multiple Sclerosis
Primary biliary cirrhosis
Rheumatoid Arthritis
Ulcerative Colitis
UKBiobank Asthma
UKBiobank Eczema
Inflammatory Bowel Disease
Alzheimer’s
Psychiatric disorder/traits
Metabolism related traits
Coronary Artery Disease
Fasting Glucose
HDL
LDL
Triglycerides
Type 2 Diabetes
UKBiobank Hypertension
Ever Smoked
UKBiobank Age at Menarche
UKBiobank Age at Menopause
UKBiobank BMI
UKBiobank Diastolic
UKBiobank FEV1FVC
UKBiobank FVC
UKBiobank Heel TScore
UKBiobank Height
UKBiobank Systolic
UKBiobank Waist-Hip Ratio
Others
Anorexia
Autism
Bipolar Disorder
Depressive Syndrome
Neuroticism
Schizophrenia
Subject Well Being
Fig. 3 Genetic correlations between 25-hydroxyvitamin D and 37 traits. We collected GWAS summary statistics of 37 diseases and traits spanning a wide
range of phenotypes (autoimmune inflammatory diseases, psychiatric disorders, metabolic traits, and anthropometric index) from publicly available
resources, and estimated their shared genetic similarities with serum 25-hydroxyvitamin D levels. We plotted the genetic correlation together with 95%
confidence intervals using a blue square and gray horizontal lines. Red vertical line indicates no genetic correlation (rg=0). Statistical significance was
defined as P-value <0.05. None of the pairwise correlations passed Bonferroni corrections
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02662-2
6NATURE COMMUNICATIONS | (2018) 9:260 |DOI: 10.1038/s41467-017-02662-2 |www.nature.com/naturecommunications
of common cell types. The link between vitamin D deficiency and
increased risk for autoimmune inflammatory diseases has long
been recognized by epidemiological investigations22,23. Although
the underlying mechanisms remain unclear, it is now evident that
vitamin D is involved in many biological processes that regulate
both innate and adaptive immune responses, through ligand-
receptor binding, activation, interaction with response elements
in the promoter regions of different genes, and eventually lead to
functional changes in a wide variety of immune cells including
Th1, Th2, Th17, T regulatory and natural killer T cells22,23. The
shared cell type enrichments between vitamin D and autoimmune
diseases observed in our study, further suggest that vitamin D not
only affects autoimmune diseases through its direct effect (as a
ligand), but also through their shared genetic etiology. Thus,
individuals with vitamin D deficiency may be more susceptible to
these disorders, both because of environmental and genetic
influences.
Our genome-wide interaction analysis between genetic variants
and dietary intake of vitamin D did not identify new signals. All
significant associations observed in the joint test of main genetic
and interaction effects were of equal or higher significance level
(i.e., lower P-values) in the GWAS of marginal genetic effect
performed in the same individuals, indicating no major con-
tribution of interaction effects at these loci. Indeed, only one of
the top 5 loci from the overall marginal GWAS showed nominally
significant interaction effect, and none passed Bonferroni cor-
rections. While smaller gene-diet interaction effects remain to be
discovered, our results provide some evidence against large
interactions between common SNPs and dietary vitamin D
intake. Still, one cannot completely rule out the possibility of
interaction, but only conclude that genetic effects appear stable
within vitamin D intake range in the populations studied. Indeed,
as for any gene-environment interaction tests, statistical power is
highly dependent on the variance of exposure in the samples
analyzed24, and interactions would remain unobserved if the
exposure is homogeneous among individuals. Also, we were not
able to capture vitamin D supplementation adequately to include
this in the dietary intake variable, and were not able to estimate
sunlight exposure as a source of vitamin D production in the skin.
Serum 25-hydroxyvitamin D concentrations are mainly
determined by modifiable environmental factors, and contrary to
estimates from previous twin studies, our large-scale analyses
suggest a SNP-heritability rate that is relatively modest in mag-
nitude when considering common variants. Our study also
showed that common genetic variants are unlikely to have a
strong modifying effect on increases in 25-hydroxyvitamin D
following typical dietary intakes, suggesting that consideration of
genetic background is not required when determining population
based vitamin D intake recommendations. However, our results
support the role of vitamin D in immunological diseases as we
observed from cell-type-specific analysis for clustering of vitamin
D and autoimmune diseases, and the evidence for signal
enrichment for immune and hematopoietic tissues. These find-
ings are in line with previous Mendelian Randomization studies
which found a putative causal association between vitamin D and
autoimmune diseases such as multiple sclerosis1,2and type 1
diabetes25. The additional genetic instruments identified by our
results could also be used in future Mendelian Randomization
analyses of the association between vitamin D and complex traits.
Methods
Study cohorts. We expanded our previous SUNLIGHT consortium GWAS, and
undertook a large, multicenter, genome-wide association study of 31 cohorts in
Europe, Canada and USA. Our first stage discovery meta-analysis consisted of
79,366 samples of European descent drawn from 31 epidemiological cohorts.
Among those 31 cohorts, ten were used as discovery and in-silico replication
samples in our previous GWAS publication (the 1958 British Birth Cohort
(1958BC), the Cardiovascular Health Study (CHS), the Framingham Heart Study
(FHS), the Gothenburg Osteoporosis and Obesity Determinants study (GOOD),
the Health, Aging, and Body Composition study (Health ABC), the Indiana
Women cohort, the North Finland Birth Cohort 1966 (NFBC1966), the Old Order
Amish Study (OOA), the Rotterdam Study (RS), and the TwinsUK), and an
additional 21 cohorts were included for the current analysis (the Alpha-Toco-
pherol, Beta-Carotene Cancer Prevention Study (ATBC), the Atherosclerosis Risk
in Communities Study (ARIC), the AtheroGene registry, B-vitamins for the Pre-
vention Of Osteoporotic Fractures (B-PROOF), the Epidemiology of Diabetes
Interventions and Complications (EDIC), the Case-Control Study for Metabolic
Syndrome (GenMets), the Helsinki Birth Cohort Study (HBCS), the Health Pro-
fessional Follow-up Study (HPFS, nested a coronary heart disease case-control
study), the Invecchiare in Chianti Study (InChianti), the Cooperative Health
Research in the region Augsburg (KORA), the Leiden Longevity Study (LLS), the
Ludwigshafen Risk and Cardiovascular Health Study (LURIC), the Multi-Ethnic
Study of Atherosclerosis (MESA), the Nijmegen Biomedische Studie (NBS), the
Nurses’Health Study (NHS, nested a breast cancer case-control study, and a type2
diabetes case-control study), the Orkney Complex Disease Study (ORCADES), the
Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), the
PROspective Study of Pravastatin in the Elderly at Risk (PROSPER), the Study of
Health in Pomerania (SHIP), the Scottish Colorectal Cancer Study (SOCCS), the
Cardiovascular Risk in Young Finns Study (YFS), and more samples from the RS
(RSI, RSII, and RSIII)). Full descriptions of all participating cohorts, details of
genotyping platforms used, number of SNPs, and the measurements of serum 25-
hydroxyvitamin D concentrations in each cohort are shown in Supplementary
Table 1and Supplementary Note 1. Written informed consent was obtained from
all participants in the included cohorts, and the study protocols were reviewed and
approved by local institutional review boards.
Power calculation. Our large sample size provided good statistical power for
association analysis. At the genome-wide significance threshold of 5×10−8, with a
discovery sample size of 75,000, our study had 85% power to detect a genetic
variant (single nucleotide polymorphism, SNP) accounting for 0.06% of the total
variance in serum 25-hydroxyvitamin D concentrations, and 99% power to detect a
variant that explained 0.1% of the total variance. We also had power to detect gene-
environment interaction effects even smaller than the observed marginal effects. In
the case where a SNP has no marginal effect on circulating 25-Hydroxyvitamin D
concentrations (and so could not have been discovered via the marginal GWAS),
we had 80% power to detect an interaction that explained 0.07% of the total
variance in 25-hydroxyvitamin D concentrations.
Association analysis. Genome-wide analyses were performed within each cohort
according to a uniform analysis plan. We fit additive genetic models using linear
regression on natural-log transformed 25-hydroxyvitamin D, and adjusted the
models for month of sample collection (12 categories), age, sex, and body mass
index, and principal components capturing genetic ancestry. Further adjustments
included cohort-specific variables, such as geographical location and assay batch,
where relevant. For participating studies with a case-control design, we analyzed
cases and controls separately. We performed a fixed-effects inverse variance
weighted meta-analysis across the contributing cohorts, as implemented in the
software METAL26, with control for population structure within each cohort and
quality control thresholds of minor allele frequency (MAF) >0.05, imputation info
score >0.8, Hardy-Weinberg equilibrium (HWE) >1×10−6, and a minimum of
two studies and 10,000 individuals contributing to each reported SNP-phenotype
association. We regarded P-values <5×10−8as genome-wide significant.
Replication study. We replicated the identified novel loci in two independent data
sets for which genotype data were available: the European Prospective Investigation
into Cancer and Nutrition (EPIC) study with 40,562 individuals across two nested
case-control studies (EPIC-InterAct and EPIC-CVD) and the cohort-wide EPIC-
Norfolk study (Supplementary Note 1); and a cohort of 2195 individuals (all
controls) additionally collected as part of the SOCCS that were not included in our
discovery stage. As for the phenotype, EPIC individuals were assayed for plasma
25-hydroxyvitamin D
3
and SOCCS individuals were assayed for total 25-
hydroxyvitamin D. We performed the association analysis in a similar manner,
adjusted for age, sex, time of sample collection, and study center where relevant.
We regarded P-value <0.05 in the replication samples, and P-value <5×10−8in the
pooled analysis as successful replication.
Conditional analysis. After identifying the primary associated variant at each locus
selected according to the strength of its association, we further tested whether there
were any other SNPs significantly associated with 25-hydroxyvitamin D after
accounting for the effect of lead SNP. We thus performed a stepwise model
selection procedure for those chromosomes where a significant variant was pre-
viously identified. We started with the most significantly associated SNP, scanning
through the whole chromosome, selecting additional independently associated
SNPs using a stepwise procedure, one at a time, based on their conditional P-
values. Finally, we fit all selected SNP into one model to estimate their joint effects.
NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02662-2 ARTICLE
NATURE COMMUNICATIONS | (2018) 9:260 |DOI: 10.1038/s41467-017-02662-2 |www.nature.com/naturecommunications 7
We used GCTA-COJO software to accommodate our summary level GWAS
data27, and the Cancer Genetic Marker of Susceptibility (CGEMS) GWAS with
2287 individuals of European descent and 2,543,887 genotyped and imputed
(HapMap22) SNPs as reference panel.
SNP-by-diet interaction. We performed a genome-wide association screening of
circulating 25-hydroxyvitamin D while accounting for potential interaction effect
between SNP and dietary vitamin D intake. Our tests incorporating gene-diet
interaction were based on the following model:
ln 25 OH
ðÞ
D
ðÞ
¼β0þβ1´Gþβ2´Eþβ3´G´EþβZ´Z
where Gis a SNP that was coded additively, Eis the raw vitamin D intake,
measured on a continuous scale. The parameters β0,β1,β2, and β3are the
intercept, the main effect of SNP, the main effect of dietary vitamin D intake, and
the interaction effect between Gand E. The model also included the same
covariates Zas for the marginal effect screening, the effects of which were captured
in the parameter βZ. We considered both a standard 1 degree-of-freedom test of
interaction effect (i.e., null hypothesis of β3¼0), and a joint 2 degree-of-freedom
test of main genetic effect plus gene-by-diet interaction (i.e., null hypothesis of
β1¼0 and β3¼0). For comparison purposes, we also considered a model
adjusting for vitamin D intake but not modeling interaction (i.e., not including the
β3´G´Eterm) using the same subset of individuals.
Vitamin D intake was available for 15 cohorts on a total of 41,981 individuals. It
included both the population based cohorts (ARIC, 1958BC, B-PROOF, FHS,
Health ABC, MESA, NFBC, RS, RS III, and YFS as part of the overall Meta-GWAS,
plus two additional cohorts, the Prospective Investigation of the Vasculature in
Uppsala Seniors (PIVUS), and the Uppsala Longitudinal Study of Adult Men
(ULSAM), genotyped on custom array that were not included in the overall meta-
GWAS but were included in this SNP-by-diet interaction analysis), and case-control
studies (HPFS (HPFS_CHD), NHS (NHS_BRCA, NHS_T2D), and SOCCS). For the
latter studies, all analyses were performed separately in cases and controls. The
aforementioned interaction model was applied to each of the included cohorts, and
study-specific results were meta-analyzed using inverse-variance weighted sum of
effect estimates as implemented in METAL26. For the 2 degree-of-freedom test we
used joint framework described in two previous published papers28,29. Quality
control filtering was performed on each study before meta-analysis. Only SNPs with
imputation info score >0.8, MAF >0.05, HWE >1×10−6, and a minimum of total
sample size in the meta-analysis >10,000 were retained.
Linkage Disequilibrium score regression. We performed linkage disequilibrium
score regression (LDSC) analysis to estimate the SNP-heritability of serum 25-
hydroxyvitamin D concentrations30,31. This method is based on a validated rela-
tionship between LD score and χ2-statistics:
Eχ2
j
hi
Njh2
g
Mljþ1
where Eχ2
j
hi
denotes the expected χ2-statistics for the association between
outcome and SNP j,N
j
is the study sample size available for SNP j, M is the total
numbers of variants and ljdenotes the LD score of SNP j defined as lj¼P
k
r2ðj;kÞ.
LDSC calculates heritability using only summary-level data instead of individual
genotypes, and is computationally cost effective at large sample sizes. We used the
summary statistics from 25-hydroxyvitamin D meta-GWAS results, with SNPs
available in at least 2 studies and a sample size of at least 10,000. We first analyzed
the SNP-heritability by using (1) SNPs across the entire genome that passed quality
control; (2) SNPs excluding the top associations (SNPs reaching genome-wide
significance, P≤5×10−8), as well as all SNPs within ±500 kb of the top hits in the
region; and (3) SNPs excluding the nominally significant associations (P≤0.05). We
subsequently partitioned the heritability through three different models: (1) a full
baseline model including the 24 publicly available main annotations that are not
specific to any cell type, the 500-bp windows around each annotation, as well as
100-bp windows around chromatin immunoprecipitation and sequencing peaks
(ChIP-seq) when appropriate. This resulted in a total of 52 overlapping functional
categories in the full baseline model; (2) a cell-type-specific model including 10 cell
type groups: adrenal and pancreas, central nervous system (CNS), cardiovascular,
connective and bone, gastrointestinal, immune and hematopoietic, kidney, liver,
skeletal muscle, and other; (3) a cell-type-specific model including 220 cell-type-
specific annotations for the four histone marks with putative enhancer or promoter
functions, H3K4me1, H3K4me3, H3K9ac, and H3K27ac.
Details of the 24 publicly available annotations, the 220 cell-type-specific
annotations, as well as the 10 cell type groups were described by Finucane et al.31.
Briefly, the 24 annotations included coding, UTR (3′UTR and 5′UTR), promoter
and intronic regions, acquired from the UCSC Genome Browser32 and post-
processed by Gusev et al.33; the three histone marks (mono-methylation
(H3K4me1) of histone H3 at lysine 4, tri-methylation (H3K4me3) of histone H3 at
lysine 4, and acetylation of histone H3 at lysine 9 (H3K9ac) processed by Trynka
et al.34–36 and two versions of acetylation of histone H3 at lysine 27 (H3K27ac, one
version processed by Hnisz et al.37, another version used by the Psychiatric
Genomics Consortium (PGC)38); open chromatin, as reflected by DNase I
hypersensitivity sites (DHSs and fetal DHSs)33, obtained as a combination of
Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics data, and
processed by Trynka et al.36; combined chromHMM and Segway predictions
obtained from Hoffman et al.39, which leverage on many annotations to partition
the genome into seven underlying chromatin states (the CCCTC-binding factor
(CTCF), promoter-flanking, transcribed region, transcription start site (TSS),
strong enhancer, weak enhancer, and the repressed region); regions that are
conserved in mammals, provided by Lindblad-Toh et al.40 and post-processed by
Ward and Kellis41; super-enhancers, which are large groups of putative enhancers
with high levels of activity, provided by Hnisz et al.37; FANTOM5 enhancers
mapped by using cap analysis of gene expression in the FANTOM5 panel of
samples, obtained from Andersson et al.42; digital genomic footprint (DGF) and
transcription factor binding site (TFBS) annotations downloaded from ENCODE35
and post-processed by Gusev et al.33. We included 500-bp windows around each of
the 24 main annotations in the baseline model, and 100-bp windows around ChIP-
seq when appropriate, to prevent upward bias of estimates generated by
enrichment in the nearby regions.
In addition to the baseline model using 24 main annotations, we also performed
cell-type-specific analyses using annotations of the four histone marks (H3K4me1,
H3K4me3, H3K9ac and H3K27ac). Each cell-type-specific annotation corresponds
to a histone mark in a single cell type (for example, H3K27ac in adipose nuclei
tissues), and there was a total of 220 such annotations. We further subdivided these
220 cell-type-specific annotations into 10 categories by aggregating the cell-type-
specific annotations within each group (for example, SNPs related with any of the
four histone modifications in any hematopoietic and immune cells were considered
as one big category). When generating the cell-type-specific models, we added each
annotation individually (one at a time) to the baseline model, creating separate
models to control for overlap with the genomic functional elements in the full
baseline model but not overlap with the other cell types.
We additionally assembled the summary statistics from GWAS of 37 traits or
diseases performed in individuals of European descent, which are publicly
available38,43–55 or applied from the UK Biobank. These studies span a wide range
of phenotypes, from anthropometric indices such as height, weight, BMI, to mental
disorders (for example depressive syndrome and schizophrenia) to autoimmune
and inflammatory diseases (for example rheumatoid arthritis and celiac diseases).
We calculated the pairwise genetic correlation (rg, cross trait heritability) between
25-hydroxyvitamin D and each of the 37 traits. We further conducted the same
cell-type-specific analysis for each trait, and plotted beta-coefficient z-score matrix,
constructed from the total 220 annotations by 37 traits, into four heat-maps based
on the four histone marks.
Finally, in addition to the genetic correlation analysis which reflects shared
genetic factors across different traits but does not inform direction, we also
attempted to identify directions of such correlation using an algorithm proposed by
Pickrell et al.56. The method adopts a similar intuition as the Mendelian
Randomization approach, where, if a trait X influences trait Y, then SNPs
influencing X should also influence Y, and the SNP-specific effect sizes for the two
traits should be correlated. Further, since Y does not influence X, but could be
influenced by mechanisms independent of X, genetic variants that influence Y do
not necessarily influence X. Based on this intuition, the method proposes two
“causal”models and two “non-causal”models, and calculates the relative likelihood
ratio of the best non-causal model compared to the best causal model. We
determined significant SNPs for each given trait by selected genome-wide
significant (P<5×10−8) SNPs and pruned the numbers based on their LD-pattern
in the European populations in Phase1 of 1000 Genome Project. We scanned
through all pairs of 25-hydroxyvitamin D and traits to identify directional
correlations. We consider pairs of traits with likelihood ratio
non-causal vs. causal
<
0.05 as having evidence of directional correlations.
Data availability. The GWAS summary statistics on serum circulating vitamin D
concentrations is available at dbGap https://drive.google.com/drive/folders/
0BzYDtCo_doHJRFRKR0ltZHZWZjQ; all relevant data are available from the
authors upon request.
Received: 21 July 2017 Accepted: 15 December 2017
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Acknowledgements
A full list of acknowledgements can be found in Supplementary Note 2.
Author contributions
C.P., E.H., M.I.M., E.A.S., P.L.L., E.B., D.A., S.J.W., N.D.F., W.H., L.C.P.G.M.D.G.,
N.M.V.S., N.v.V., J.B.R., B.K., T.J.W., D.P.K., R.S.V., C.O., M.L., J.G.E., S.B.K., M.B., E.S.,
I.H.D.B., J.I.R., S.S.R., M.J., P.K., J.F.W., L.L., K.M., L.Z., H.C., E.T., S.M.F., M.G.D., T.L.,
M.K., O.T.R., V.M., M.A.I., J.W.J., N.J.W., C.L., N.G.F., K.K., A.B., and J.D. designed and
managed individual studies. C.P., E.H., M.I.M., E.A.S., P.L.L., D.A., S.J.W., W.H.,
N.M.V.S., A.E., J.B.R., R.S.V., C.O., L.V., M.L., J.G.E., S.B.K., M.B., D.V.H., I.H.D.B.,
J.I.R., S.S.R., J.F.W., L.L., E.I., K.M., H.C., E.T., S.M.F., M.G.D., T.L., M.K., O.T.R., V.M.,
M.A.I., N.S., J.W.J., N.J.W., C.L., N.G.F., K.K., A.B., and J.D. collected data. E.B., A.E.,
C.O., M.L., Y.L., M.B., E.M.K., J.I.R., S.S.R., J.F.W., L.L., E.I., K.M., S.T., S.M.F., M.G.D.,
T.L., L.L., J.W.J., N.J.W., C.L., A.B., and J.D. performed the genotyping. C.P., D.B., E.H.,
M.I.M., W.T., E.B., N.M.V.S., A.E., J.D., C.O., L.V., M.L., K.K.L., J.D., E.M.K., J.I.R.,
S.S.R., J.F.W., C.H., E.I., K.M., S.T., A.G.U., F.R., L.Z., S.M.F., M.G.D., A.M.V., T.L., L.L.,
M.A.I., N.S., N.J.W., C.L., A.B., and J.D. prepared the genotype data. D.B., E.H., M.I.M.,
E.A.S., P.L.L., A.E., J.B.R., S.B., R.S.V., C.O., L.V., M.L., J.G.E., D.K.H., D.V.H., E.M.K.,
I.H.D.B., A.C.W., J.F.W., L.L., K.M., M.C.Z., A.G.U., F.R., L.Z., E.T., S.M.F., M.G.D.,
A.M.V., E.T., L.L., N.J.W., C.L., N.G.F., A.B., and J.D. prepared the phenotype data. D.B.,
E.H., M.I.M., J.B.R., T.J.W., D.P.K., Y.H., C.L., A.C.W., C.R.C., P.F.O., M.J., X.J., H.A.,
N.J.W., C.L., N.G.F., A.B., and J.D. developed the analysis plan. A.Z., D.B., E.H., M.I.M.,
NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02662-2 ARTICLE
NATURE COMMUNICATIONS | (2018) 9:260 |DOI: 10.1038/s41467-017-02662-2 |www.nature.com/naturecommunications 9
P.L.L., W.T., L.C.P.G.M.D.G., N.v.V., A.E., J.B.R., B.K., T.J.W., J.D., D.P.K., Y.H., C.L.,
D.K.H., I.H.D.B., A.C.W., J.I.R., S.S.R., C.R.C., P.F.O., M.J., X.J., H.A., M.C.Z., A.G.U.,
F.R., L.B., N.J.W., C.L., and N.G.F reviewed the analysis plan. D.B., E.H., M.I.M., L.Y.,
W.T., A.E., J.B.R., Y.H., C.L., Y.Z., K.K.L., J.D., A.C.W., P.F.O., A.C., X.J., H.A., P.K.J.,
C.H., S.T., L.B., L.Z., E.T., E.T., L.L., J.Z., and M.T analyzed the data. D.B., Y.H., P.F.O.,
and H.A performed the meta-analysis. Y.H. and X.J. performed the pathway and other
analyses. C.P., E.H., M.I.M., E.A.S., P.L.L., L.Y., W.T., E.D.M., E.B., L.C.P.G.M.D.G.,
N.v.V., A.E., J.B.R., T.J.W., J.D., D.P.K., Y.H., P.F.O., M.J., X.J., H.A., E.I., K.M., S.T.,
M.C.Z., A.G.U., F.R., L.B., L.Z., E.T., N.J.W., and N.G.F interpreted results. E.H., T.J.W.,
D.P.K., L.A.C., R.S.V., Y.H., I.H.D.B., J.I.R., S.S.R., M.J., P.K., A.G.U., F.R., N.S., and
J.W.J. supervised the overall study design. E.H., J.B.R., T.J.W., D.P.K., Y.H., P.F.O., M.J.,
X.J., and H.A. wrote the manuscript. C.P., E.H., M.I.M., E.A.S., P.L.L., L.Y., W.T., E.D.M.,
E.B., D.A., S.J.W., N.D.F., W.H., L.C.P.G.M.D.G., N.M.V.S., N.v.V., J.B.R., B.K., T.J.W.,
J.D., D.P.K., D.K., S.B., R.S.V., Y.H., C.L., Y.Z., C.O., L.V., M.L., J.G.E., M.K.S., D.K.H.,
M.P., M.J.E., E.M.K., S.P., I.H.D.B., A.C.W., J.I.R., S.S.R., C.R.C., P.F.O., M.J., P.K., X.J.,
H.A., P.K.J., J.F.W., C.H., L.L., E.I., K.M., S.T., H.V., H.W., L.Z., E.T., T.S., E.T., T.L., L.L.,
M.K., O.T.R., V.M., M.A.I., N.S., J.W.J., N.J.W., C.L., N.G.F., J.Z., T.G., K.K., J.L., A.B.,
J.D., E.S., C.G., W.M., M.d.H., and M.T. reviewed the manuscript. E.H., M.I.M., E.A.S., T.
J.W., D.P.K., L.A.C., R.S.V., L.F., M.P., M.J., P.K., M.C.Z., A.G.U., F.R., L.B., T.S., and M.
A.I. oversee the consortium.
Additional information
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
017-02662-2.
Competing interests: The authors declare no competing financial interests.
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Program in Genetic Epidemiology and Statistical Genetics. Department of Epidemiology, Harvard T.H.Chan School of Public Health, 677 Huntington
Avenue, Boston 02115, MA, USA.
2
Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Nobels vagen 13,
Stockholm 17177, Sweden.
3
Department of Social Genetic & Developmental Psychiatry, King’s College London, Institute of Psychiatry, De Crespigny
Park, London SE5 8AF, UK.
4
Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris 75724, France.
5
Institute
for Aging Research, Hebrew SeniorLife, 1200 Centre Street, Boston, MA 02131, USA.
6
Department of Medicine, Beth Israel Deaconess Medical
Center and Harvard Medical School, Boston, MA 02115, USA.
7
Broad Institute of Harvard and Massachusetts Institute of Technology, Boston, MA
02142, USA.
8
Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, 3755 Côte Ste-Catherine Road, Suite H-413 Montréal,
Québec H3T 1E2, Canada.
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Department of Biostatistics, Boston University School of Public Health, Crosstown Center. 801 Massachusetts Avenue
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Framingham Heart Study, 73 Mt. Wayte Avenue, Framingham, MA 01702, USA.
11
Department of Medicine,
Division of Cardiovascular Medicine, Stanford University School of Medicine Stanford, Stanford, CA 94305, USA.
12
Department of Medical
Sciences, Uppsala University, 751 85 Uppsala, Sweden.
13
Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical
University of Graz, Auenbruggerplatz 15, 8036 Graz, Austria.
14
Population, Policy and Practice, University College London, Great Ormond Street,
Institute of Child Health, London WC1E 6BT, UK.
15
Kidney Research Institute, Division of Nephrology, 325 Ninth Avenue, Seattle, WA 98104, USA.
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MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.
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Department of Public Health & Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort’s Causeway, Cambridge CB1 8RN,
UK.
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Genetics and Personalized Medicine Program, University of Maryland School of Medicine, Howard Hall Room 567, Baltimore, MD 21201,
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Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17489 Greifswald, Germany.
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DZHK (German Centre for Cardiovascular Research), Partner Site, Greifswald, 13316 Berlin, Germany.
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Chemistry, Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere 33014,
Finland.
77
Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland.
78
Department of Clinical Physiology, Finnish
Cardiovascular Research Center Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere 33014, Finland.
79
Department of
Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland.
80
Research Centre of Applied and Preventive
Cardiovascular Medicine, University of Turku, Turku 20014, Finland.
81
Science Adviser at Academy of Finland, Hakaniemenranta 6, PO Box 131,
FI-00531 Helsinki, Finland.
82
BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, University Avenue, Glasgow G12 8QQ, UK.
83
Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.
84
Vitas AS, Gaustadaleen 21, N-0349 Oslo, Norway.
85
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10
NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02662-2 ARTICLE
NATURE COMMUNICATIONS | (2018) 9:260 |DOI: 10.1038/s41467-017-02662-2 |www.nature.com/naturecommunications 11
1SA, UK.
86
Division of Cardiovascular Medicine, Vanderbilt Heart and Vascular Institute, 2220 Pierce Avenue 383 Preston Research Building,
Nashville, TN 37232-6300, USA. Xia Jiang, Paul F. O’Reilly, Hugues Aschard and Yi-Hsiang Hsu contributed equally to this work. Thomas J. Wang,
Elina Hyppönen, Peter Kraft and Douglas P. Kiel jointly supervised this work.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02662-2
12 NATURE COMMUNICATIONS | (2018) 9:260 |DOI: 10.1038/s41467-017-02662-2 |www.nature.com/naturecommunications
