© 2016 Nature America, Inc. All rights reserved.
Nature GeNetics ADVANCE ONLINE PUBLICATION 1
L E T T E R S
To elucidate the genetic architecture of amyotrophic lateral
sclerosis (ALS) and find associated loci, we assembled a
custom imputation reference panel from whole-genome-
sequenced patients with ALS and matched controls (n = 1,861).
Through imputation and mixed-model association analysis in
12,577 cases and 23,475 controls, combined with 2,579 cases
and 2,767 controls in an independent replication cohort, we
fine-mapped a new risk locus on chromosome 21 and identified
C21orf2 as a gene associated with ALS risk. In addition, we
identified MOBP and SCFD1 as new associated risk loci.
We established evidence of ALS being a complex genetic trait
with a polygenic architecture. Furthermore, we estimated
the SNP-based heritability at 8.5%, with a distinct and
important role for low-frequency variants (frequency 1–10%).
This study motivates the interrogation of larger samples
with full genome coverage to identify rare causal variants
that underpin ALS risk.
ALS is a fatal neurodegenerative disease that affects 1 in 400 people,
with death occurring within 3 to 5 years of the onset of symptoms
1
.
Twin-based studies estimate heritability to be around 65%, and 5–10%
of patients with ALS have a positive family history
1,2
. Both of these
features are indicative of an important genetic component in ALS
etiology. Following initial discovery of a risk-associated C9orf72 locus
in ALS genome-wide association studies (GWAS)
3–5
, identification of
a pathogenic hexanucleotide-repeat expansion in this locus revolu-
tionized the field of ALS genetics and biology
6,7
. The majority of ALS
heritability, however, remains unexplained, and only two additional
risk loci have since been identified robustly
3,8
.
To discover new genetic risk loci and elucidate the genetic archi-
tecture of ALS, we genotyped 7,763 new cases and 4,669 controls and
additionally collected genotype data from published GWAS of ALS. In
total, we analyzed 14,791 cases and 26,898 controls from 41 cohorts
(Supplementary Table 1 and Supplementary Note). We combined
these cohorts on the basis of genotyping platform and nationality to form
27 case–control strata. In total, 12,577 cases and 23,475 controls passed
quality control (Online Methods and Supplementary Tables 2–5).
For imputation purposes, we obtained high-coverage (~43.7×)
whole-genome sequencing data from 1,246 patients with ALS and 615
controls from the Netherlands (Online Methods and Supplementary
Fig. 1). After quality control, we constructed a reference panel including
18,741,510 single-nucleotide variants (SNVs). Imputing this cus-
tom reference panel into Dutch ALS cases considerably increased
the imputation accuracy for low-frequency variants (minor allele
frequency (MAF) = 0.5–10%) in comparison to commonly used
reference panels from 1000 Genomes Project Phase 1 (ref. 9) and
Genome of the Netherlands
10
(Fig. 1a). Improvement was also
observed when imputing into ALS cases from the UK (Fig. 1b). To
benefit from the global diversity of haplotypes, the custom and 1000
Genomes Project panels were combined, which further improved
imputation. Given these results, we used the merged reference panel
to impute all strata in our study.
In total, we imputed 8,697,640 variants passing quality control
into the 27 strata and tested the strata separately for association with
ALS risk by logistic regression. We then included the results in an
inverse-variance-weighted, fixed-effects meta-analysis, which identi-
fied four loci associated at genome-wide significance (P < 5 × 10
−8
)
(Fig. 2a). The previously reported C9orf72 (rs3849943)
3–5,8
, UNC13A
(rs12608932)
3,5
and SARM1 (rs35714695)
8
loci all reached genome-
wide significance, as did a new association for a nonsynonymous
variant in C21orf2 (rs75087725, P = 8.7 × 10
−11
; Supplementary
Tables 6–10). This variant was present on only 10 haplotypes in the
1000 Genomes Project reference panel (MAF = 1.3%), whereas it was
present on 62 haplotypes in our custom reference panel (MAF = 1.7%).
As a result, more strata passed quality control for this variant by pass-
ing the allele frequency threshold of 1% (Supplementary Table 11).
This result demonstrates the benefit of the merged reference panel
with ALS-specific content, which improved imputation and resulted
in the identification of a genome-wide significant association.
Linear mixed models (LMMs) can improve power while controlling
for sample structure
11
, which would be particularly important in our
study that included a large number of imperfectly balanced strata.
Even though LMM analysis for ascertained case–control data poten-
tially results in a small loss of power in comparison to meta-analysis
11
,
we judged the advantage of combining all strata while controlling the
false positive rate to be more important than this potential loss and
therefore jointly analyzed all strata in an LMM to identify additional
risk loci. There was no overall inflation of the LMM test statistics in
comparison to the meta-analysis test statistics (Supplementary Fig. 2).
We observed modest inflation of test statistics in the quantile–
quantile plot (
λ
GC
= 1.12,
λ
1,000
= 1.01; Supplementary Fig. 3).
LD score regression yielded an intercept of 1.10 (standard error
Genome-wide association analyses identify new risk
variants and the genetic architecture of amyotrophic
lateral sclerosis
A full list of authors and affiliations appears at the end of the paper.
Received 7 January; accepted 20 June; published online 25 July 2016; doi:10.1038/ng.3622
© 2016 Nature America, Inc. All rights reserved.
2 ADVANCE ONLINE PUBLICATION Nature GeNetics
L E T T E R S
of 7.8 × 10
−3
). Although an LD score regression intercept higher
than 1.0 can indicate the presence of residual population stratifica-
tion, which is fully corrected for in an LMM, this can also reflect a
distinct genetic architecture where most causal variants are rare or a
noninfinitesimal architecture
12
. The LMM identified all four genome-
wide-significant associations from the meta-analysis. Furthermore,
three additional loci—MOBP at 3p22.1 (rs616147), SCFD1 at 14q12
(rs10139154) and a long noncoding RNA at 8p23.2 (rs7813314)—
were associated at genome-wide significance (Fig. 2b, Table 1 and
Supplementary Tables 12–14). SNPs in the MOBP locus have been
reported to be associated in a GWAS on progressive supranuclear palsy
(PSP)
13
and to act as a modifier for survival in frontotemporal demen-
tia (FTD)
14
. The putative pleiotropic effects of variants in this locus
suggest that ALS, FTD and PSP share a neurodegenerative pathway.
We also found that rs74654358 at 12q14.2 in the TBK1 gene approxi-
mated genome-wide significance (MAF = 4.9%, odds ratio (OR) = 1.21
for the A allele, P = 6.6 × 10
−8
). This gene was recently identified as an
ALS risk gene through exome sequencing
15,16
.
In the replication phase, we genotyped the newly discovered
associated SNPs in nine independent replication cohorts, totaling
2,579 cases and 2,767 controls. In these cohorts, we replicated the
signals for the C21orf2, MOBP and SCFD1 loci, with lower P values
in the combined analysis than in the discovery phase (combined
P value = 3.08 × 10
−10
, 4.19 × 10
−10
and 3.45 × 10
−8
for rs75087725,
rs616147 and rs10139154, respectively; Table 1 and Supplementary
Fig. 4)
17
. The combined signal for rs7813314 was less significant
because the effects for the discovery and replication phases were in
opposite directions, indicating non-replication. Although replication
yielded an effect estimate for rs10139154 similar to that obtained
in the discovery phase, this effect was not statistically significant
(P = 0.09) in the replication phase alone. This lack of significance
reflects the limited sample size of our replication phase, a feature that
is inherent to studies of ALS because of its low prevalence. Even larger
sample sizes are warranted to replicate this signal robustly.
There was no evidence of residual association in each locus
after conditioning on the top SNP, indicating that all the risk loci are
independent signals. Apart from the C9orf72, UNC13A and SARM1
loci, we found no evidence of associations previously described in
smaller GWAS (Supplementary Table 15).
The association of the low-frequency nonsynonymous SNP in
C21orf2 suggested that this gene could be directly involved in ALS
risk. Indeed, we found no evidence that linkage disequilibrium
(LD) between this SNP and sequenced variants beyond the boundaries
of C21orf2 explained the association of this locus (Supplementary
Fig. 5). In addition, we investigated the burden of rare coding muta-
tions in C21orf2 in a set of whole-genome-sequenced cases (n = 2,562)
and controls (n = 1,138). After quality control, these variants were
tested for association using pooled association tests for rare variants
and applying correction for population structure (tests T5 and T1
for alleles with 5% and 1% frequency, respectively; Supplementary
Note). This approach demonstrated an excess of nonsynonymous and
loss-of-function mutations in C21orf2 among ALS cases that per-
sisted after conditioning on rs75087725 (P
T5
= 9.2 × 10
−5
, P
T1
= 0.01;
Supplementary Fig. 6), further supporting the notion that C21orf2
contributes to ALS risk.
In an effort to fine-map the other loci to pinpoint susceptibility genes,
we searched for SNPs in these loci with cis expression quantitative
0.005
0.010
0.020
0.050
0.100
0.200
0.500
1.000
0
0.2
0.4
0.6
0.8
1.0
Dutch ALS cases
Allele frequency
Aggregate r
2
1000GP (n = 1,092)
GoNL (n = 499)
ALS-enriched panel (n = 1,861)
1000GP + ALS (n = 2,953)
0
0.2
0.4
0.6
0.8
1.0
UK ALS cases
Allele frequency
Aggregate r
2
0.005
0.010
0.020
0.050
0.100
0.200
0.500
1.000
1000GP (n = 1,092)
GoNL (n = 499)
ALS-enriched panel (n = 1,861)
1000GP + ALS (n = 2,953)
a b
Figure 1 Comparison of imputation accuracy. (a,b) Aggregate r
2
values
between imputed and sequenced genotypes on chromosome 20 are shown
when using different reference panels for imputation. Allele frequencies
were calculated from the Dutch samples included in the Genome of
the Netherlands (GoNL) cohort. The highest imputation accuracy was
achieved when imputing from the merged custom and 1000 Genomes
Project (1000GP) panel. The difference in accuracy was most pronounced
for low-frequency alleles (frequency 0.5–10%) in ALS cases from both the
Netherlands (a) and the UK (b).
Meta-analysis
−log
10
(P value)
C9orf72
SARM
UNC13A
C21orf2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
0
5
10
15
20
25
LMM
Chromosome
Chromosome
MOBP
LOC101927815
C9orf72
SARM
SCFD1
UNC13A
C21orf2
TBK1
0
5
10
15
20
25
−log
10
(P value)
a
b
Figure 2 Meta-analysis and LMM associations. (a) Manhattan plot for
the meta-analysis results. This approach yielded four genome-
wide-significant associations. The associated SNP in C21orf2 is a
nonsynonymous variant not found to be associated in previous GWAS.
(b) Manhattan plot for the LMM results. This analysis yielded three loci
in addition to those identified by meta-analysis with associations that
reached genome-wide significance (MOBP, LOC101927815 and SCFD1).
The association for SNPs in the previously identified ALS risk gene TBK1
approached genome-wide significance (P = 6.6 × 10
−8
).
As the C21orf2 SNP was removed from a Swedish stratum because
of MAF <1%, this SNP was tested separately, but it is presented here
together with all SNPs with MAF >1% in all strata. LOC101927815
is shown in gray because the association for this locus could not be
replicated. Loci are labeled by the name of the nearest gene. The dotted
lines correspond to the significance threshold of P = 5 × 10
–8
.
© 2016 Nature America, Inc. All rights reserved.
Nature GeNetics ADVANCE ONLINE PUBLICATION 3
L E T T E R S
trait locus (cis-eQTL) effects observed in brain and other tissues
(Supplementary Table 16 and Supplementary Note)
18
. We found
overlap with previously identified brain cis-eQTLs for five regions
(Supplementary Fig. 7, Supplementary Table 17 and Supplementary
Data Set). In the C9orf72 locus, we found that proxies of rs3849943
(LD r
2
= 0.21–0.56) only had a brain cis-eQTL effect on C9orf72
(minimal P = 5.27 × 10
−7
), which harbors the hexanucleotide-repeat
expansion that drives this GWAS signal. Additionally, we found that
rs12608932 and its proxies in the UNC13A locus had an exon-level
cis-eQTL effect on KCNN1 in frontal cortex (P = 1.15 × 10
−3
)
19
.
Another overlap was observed in the SARM1 locus where rs35714695
and its proxies had the strongest exon-level cis-eQTL effect on
POLDIP2 in multiple brain tissues (P = 2.32 × 10
−3
). In the SCFD1
locus, rs10139154 and its proxies had a cis-eQTL effect on SCFD1 in
cerebellar tissue (P = 7.71 × 10
−4
). For the MOBP locus, rs1768208 and
its proxies had a cis-eQTL effect on RPSA (P = 7.71 × 10
−4
).
To describe the genetic architecture of ALS, we generated polygenic
scores, which can be used to predict phenotypes for traits with a poly-
genic architecture
20
. We calculated SNP effects using an LMM in 18 of
the 27 strata and subsequently assessed predictive ability in the other
9 independent strata. This analysis showed that a significant albeit
modest proportion of the phenotypic variance could be explained
by all SNPs (Nagelkerke r
2
= 0.44%, r
2
= 0.15% on the liability scale,
P = 2.7 × 10
−10
; Supplementary Fig. 8). This finding adds to the exist-
ing evidence that ALS is a complex genetic trait with a polygenic archi-
tecture. To further quantify the contribution of common SNPs to ALS
risk, we estimated SNP-based heritability using three approaches, all
assuming a population baseline risk of 0.25% (ref. 21). GCTA-REML
estimated the SNP-based heritability at 8.5% (s.e.m. = 0.5%). Haseman–
Elston regression yielded a very similar estimate of 7.9%, and LD
score regression estimated the SNP-based heritability at 8.2% (s.e.m.
= 0.5%). The heritability estimates for each chromosome were signifi-
cantly correlated with chromosome length (r
2
= 0.46, P = 4.9 × 10
−4
;
Fig. 3a), again indicative of a polygenic architecture in ALS.
We found that the genome-wide-significant loci only explained 0.2%
of heritability, and the bulk of the heritability (8.3%, s.e.m. = 0.3%)
was thus captured by SNPs with associations below genome-wide
significance. This finding implies that many genetic risk variants have
yet to be discovered. Understanding where these unidentified risk
variants remain across the allele frequency spectrum will inform the
design of future studies to identify these variants. We therefore esti-
mated heritability partitioned by MAF. Furthermore, we contrasted
these results with those for common polygenic traits studied in GWAS
such as schizophrenia. We observed a clear trend indicating that most
variance is explained by low-frequency SNPs (Fig. 3b). Exclusion of
the C9orf72 locus, which harbors the rare pathogenic repeat expan-
sion, and the other genome-wide-significant loci did not affect this
trend (Supplementary Fig. 9). This architecture is different from that
expected for common polygenic traits and reflects a polygenic rare
variant architecture observed in simulations
22
.
To gain better insight into the biological pathways that explain
the associated loci found in this study, we looked for enriched
pathways using DEPICT
23
. This analysis identified SNAP recep-
tor (SNARE) activity as the only enriched category (false discovery
rate (FDR) < 0.05; Supplementary Fig. 10). SNARE complexes have
a central role in neurotransmitter release and synaptic function
24
,
which are both perturbed in ALS
25
.
Although the biological role of C21orf2, a conserved leucine-rich-
repeat protein, remains poorly characterized, this protein is part of
the ciliome and is required for the formation and/or maintenance
of primary cilia
26
. Defects in primary cilia are associated with vari-
ous neurological disorders, and cilia numbers are decreased in mice
expressing the Gly93Ala mutant of human SOD1, a well-characterized
ALS model
27
. C21orf2 has also been localized to mitochondria in
immune cells
28
and is part of the interactome of the protein prod-
uct of NEK1, which has previously been associated with ALS
15
. Both
proteins seem to be involved in DNA repair mechanisms
29
. Although
future studies are needed to dissect the function of C21orf2 in ALS
pathophysiology, we speculate that defects in C21orf2 may lead to
primary cilium and/or mitochondrial dysfunction or inefficient DNA
repair and thereby result in adult-onset disease. The other associated
loci will require more extensive studies to fine-map causal variants.
SARM1 has been suggested to be a susceptibility gene for ALS, mainly
because of its role in Wallerian degeneration and its interaction with
UNC13A
8,30
. Although these are indeed interesting observations, the
brain cis-eQTL effect for SNPs in this locus on POLDIP2 suggests
Table 1 Discovery and replication of new genome-wide significant loci
Discovery Replication Combined
SNP
MAF
cases
MAF
controls
OR P
meta
P
LMM
MAF
cases
MAF
controls
OR P P
combined
I
2
rs75087725 0.02 0.01 1.45 8.65 × 10
−11
2.65 × 10
−9
0.02 0.01 1.65 3.89 × 10
−3
3.08 × 10
−10
0.00*
rs616147 0.30 0.28 1.10 4.14 × 10
−5
1.43 × 10
−8
0.31 0.28 1.13 2.35 × 10
−3
4.19 × 10
−10
0.00*
rs10139154 0.34 0.31 1.09 1.92 × 10
−5
4.95 × 10
−8
0.33 0.31 1.06 9.55 × 10
−2
3.45 × 10
−8
0.05*
rs7813314 0.09 0.10 0.87 7.46 × 10
−7
3.14 × 10
−8
0.12 0.10 1.17 7.75 × 10
−3
1.05 × 10
−5
0.80**
Genome-wide-significant loci from the discovery phase including 12,557 cases and 23,475 controls were directly genotyped and tested for association in the replication phase
including 2,579 cases and 2,767 controls. The three top associated SNPs in the MOBP (rs616147), SCFD1 (rs10139154) and C21orf2 (rs75087725) loci replicated with
associations in the same direction as in the discovery phase and an association in the combined analysis that exceeded that in the discovery phase. Cochrane’s Q test, *P > 0.1,
**P = 4.0 × 10
−6
. MAF, minor allele frequency; OR, odds ratio, P
meta
, meta-analysis P value; P
LMM
, linear mixed-model P value; P
combined
, P value from meta-analysis of the
associations in the discovery and replication phase.
Heritability by MAF
Heritability (proportion)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.01−0.1
0.1−0.2
0.2−0.3
0.3−0.4
0.4−0.5
MAF
ALS (merged panel)
ALS (HM3)
SCZ (HM3)
Heritability by chromosome
Chromosome length (Mb)
Heritability
50 100 150 200 250
0
0.002
0.004
0.006
0.008
0.010
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
r
2
= 0.46
a b
Figure 3 Partitioned heritability. (a) Heritability estimates for each
chromosome were significantly correlated with chromosome length
(P = 4.9 × 10
−4
). (b) For ALS, there was a clear trend where more
heritability was explained by the low-frequency alleles. This effect was
still observed when, for a fair comparison between ALS and a previous
study partitioning heritability for schizophrenia (SCZ) using identical
methods
22
, SNPs present in HapMap 3 (HM3) were included. Error bars
correspond to standard errors.
© 2016 Nature America, Inc. All rights reserved.
4 ADVANCE ONLINE PUBLICATION Nature GeNetics
L E T T E R S
that POLDIP2 and not SARM1 could in fact be the causal gene in
this locus. Similarly, KCNN1, which encodes a neuronal potassium
channel involved in neuronal excitability, could be the causal gene
either through a direct eQTL effect or rare variants in LD with the
associated SNP in UNC13A.
In conclusion, we have identified a key role for rare variation in
ALS and discovered SNPs in new complex loci. Our study therefore
informs future study design in ALS genetics, promoting the combina-
tion of larger sample sizes, full genome coverage and targeted genome
editing experiments, leveraged together to fine-map new loci, identify
rare causal variants and thereby elucidate the biology of ALS.
METHODS
Methods and any associated references are available in the online
version of the paper.
Accession codes. The GWAS summary statistics and sequenced vari-
ants are publicly available through the Project MinE data browser at
http://databrowser.projectmine.com/.
Note: Any Supplementary Information and Source Data files are available in the
online version of the paper.
ACKNOWLEDGMENTS
The work of the contributing groups was supported by various grants
from governmental and charitable bodies. Details are provided in the
Supplementary Note.
AUTHOR CONTRIBUTIONS
A.V., N.T., K.L., B.R., K.V., M.R.-G., B.K., J.Z., L.L., L.D.G., S.M., F.S., V.M., M.d.C.,
S. Pinto, J.S.M., R.R.-G., M.P., S. Chandran, S. Colville, R.S., K.E.M., P.J.S., J.H.,
R.W.O., A. Pittman, K.S., P.F., A. Malaspina, S.T., S. Petri, S. Abdulla, C.D., M.S.,
T. Meyer, R.A.O., K.A.S., M.W.-P., C.L.-H., V.M.V.D., J.Q.T., L.E., L. McCluskey,
A.N.B., Y.P., T. Meitinger, P.L., M.R.-B., C.R.A., C. Maurel, G. Bensimon, B.L., A.B.,
C.A.M.P., S.S.-D., A.D., N.W.W., L.T., W.L., A.F., M.R., S. Cichon, M.M.N., P.A.,
C. Tzourio, J.-F.D., A.G.U., F.R., K.E., A.H., C. Curtis, H.M.B., A.J.v.d.K., M.d.V.,
A.G., M.W., C.E.S., B.N.S., O.P., C. Cereda, R.D.B., G.P.C., S.D’A., C.B., G.S.,
L. Mazzini, V.P., C.G., C. Tiloca, A.R., A. Calvo, C. Moglia, M.B., S. Arcuti, R.C.,
C.Z., C.L., S. Penco, N.R., A. Padovani, M.F., B.M., R.J.S., PARALS Registry,
SLALOM Group, SLAP Registry, FALS Sequencing Consortium, SLAGEN
Consortium, NNIPPS Study Group, I.B., G.A.N., D.B.R., R.P., M.C.K., J.G., O.W.W.,
T.R., B.S., I.K., C.A.H., P.N.L., F.C., A. Chìo, E.B., E.P., R.T., G.L., J.P., A.C.L., J.H.W.,
W.R., P.V.D., L.F., T.P., R.H.B., J.D.G., J.E.L., O. Hardiman, P.M.A., P.C., P.V., V.S.,
M.A.v.E., A.A.-C., L.H.v.d.B. and J.H.V. were involved in phenotyping, sample
collection and management. W.v.R., A.S., A.M.D., R.L.M., F.P.D., R.A.A.v.d.S.,
P.T.C.v.D., G.H.P.T., M.K., A.M.B., W.S., A.R.J., K.P.K., I.F., A.V., N.T., R.D.S.,
W.J.B., A.V., K.V., M.R.-G., B.K., L.L., S. Abdulla, K.S., E.P., F.P.D., J.M., C. Curtis,
G. Breen, A.A.-C. and J.H.V. prepared DNA and performed SNP array
hybridizations. W.v.R., S.L.P., K.P.K., K.L., A.M.D., P.T.C.v.D., G.H.P.T., K.R.v.E.,
P.I.W.d.B. and J.H.V. were involved in the next-generation sequencing analyses.
W.v.R., K.R.v.E., A. Menelaou, P.I.W.d.B., A.A.-C. and J.H.V. performed the
imputation. W.v.R., A.S., F.P.D., R.L.M., S.L.P., S.d.J., I.F., N.T., W.S., A.R.J., K.P.K.,
K.R.v.E., K.S., H.M.B., P.I.W.d.B., M.A.v.E., C.M.L., G. Breen, A.A.-C., L.H.v.d.B.
and J.H.V. performed GWAS analyses. W.v.R., A.M.D., R.A.A.v.d.S., R.L.M.,
C.R.A., M.K., A.M.B., R.D.S., E.P.M., J.A.F., C. Tunca, H.H., K.Z., P.C., P.V. and
J.H.V. performed the replication analyses. W.v.R., A.S., R.L.M., M.R.R., J.Y.,
N.R.W., P.M.V., C.M.L., A.A.-C. and J.H.V. performed polygenic risk scoring
and heritability analyses. S.d.J., U.V., L.F., T.H.P., W.v.R., O. Harschnitz, G. Breen,
R.J.P. and J.H.V. performed biological pathway analyses. U.V., L.F., W.v.R. and
J.H.V. performed eQTL analyses. W.v.R., A.S., A.A.-C., L.H.v.d.B. and
J.H.V. prepared the manuscript with contributions from all authors. A.A.-C.,
L.H.v.d.B. and J.H.V. directed the study.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Reprints and permissions information is available online at http://www.nature.com/
reprints/index.html.
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Nature GeNetics ADVANCE ONLINE PUBLICATION 5
L E T T E R S
Wouter van Rheenen
1,123
, Aleksey Shatunov
2,123
, Annelot M Dekker
1
, Russell L McLaughlin
3
, Frank P Diekstra
1
,
Sara L Pulit
4
, Rick A A van der Spek
1
, Urmo Võsa
5
, Simone de Jong
6,7
, Matthew R Robinson
8
, Jian Yang
8
,
Isabella Fogh
2,9
, Perry TC van Doormaal
1
, Gijs H P Tazelaar
1
, Max Koppers
1,10
, Anna M Blokhuis
1,10
,
William Sproviero
2
, Ashley R Jones
2
, Kevin P Kenna
11
, Kristel R van Eijk
1
, Oliver Harschnitz
1,10
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Raymond D Schellevis
1
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1
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1
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4
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12,13
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9,14
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Kuang Lin
2
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15,16
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17
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17,18
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19
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19
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Lea Leonardis
19
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19
, Stéphanie Millecamps
20
, François Salachas
20–22
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23,24
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Mamede de Carvalho
25,26
, Susana Pinto
25,26
, Jesus S Mora
27
, Ricardo Rojas-García
28,29
, Meraida Polak
30,31
,
Siddharthan Chandran
32,33
, Shuna Colville
32
, Robert Swingler
32
, Karen E Morrison
34
, Pamela J Shaw
35
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John Hardy
36
, Richard W Orrell
37
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36,38
, Katie Sidle
37
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39
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40,41
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Simon Topp
2
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42
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43
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44
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44
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45
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Roel A Ophoff
46–48
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48
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49
, Catherine Lomen-Hoerth
50
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51
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John Q Trojanowski
51
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52
, Leo McCluskey
52
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53
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53
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53
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Yesim Parman
54
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55
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55
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55
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56
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Cindy Maurel
56
, Gilbert Bensimon
57–59
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60
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61–65
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57,59
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66
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67
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68
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69
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69
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70
,
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71
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72–76
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72,73
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77
, Christophe Tzourio
78
,
Jean-François Dartigues
78
, Andre G Uitterlinden
79,80
, Fernando Rivadeneira
79,80
, Karol Estrada
79
, Albert Hofman
80,81
,
Charles Curtis
6,7
, Hylke M Blauw
1
, Anneke J van der Kooi
82
, Marianne de Visser
82
, An Goris
83
, Markus Weber
84
,
Christopher E Shaw
2
, Bradley N Smith
2
, Orietta Pansarasa
85
, Cristina Cereda
85
, Roberto Del Bo
86
,
Giacomo P Comi
86
, Sandra D’Alfonso
87
, Cinzia Bertolin
88
, Gianni Sorarù
88
, Letizia Mazzini
89
, Viviana Pensato
90
,
Cinzia Gellera
90
, Cinzia Tiloca
9
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9,14
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91,92
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91,92
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91,92
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93
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93
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93
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94
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95
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96
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97
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97
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98
, Robbert Jan Stuit
98
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99
,
SLALOM Group
99
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99
, FALS Sequencing Consortium
99
, SLAGEN Consortium
99
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99
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Ian Blair
100
, Katharine Zhang
100
, Emily P McCann
100
, Jennifer A Fifita
100
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100,101
,
Dominic B Rowe
100
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102
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103
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104
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104
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104
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107
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107
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93
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60
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60
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83,110,111
,
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83,110,111
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5
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112–116
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11
, Jonathan D Glass
30,31
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11
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12,13
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60,117
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56,118,119
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56
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9,14
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8
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8,120
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4,121
, Michael A van Es
1
,
R Jeroen Pasterkamp
10
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6,122
, Gerome Breen
6,7
, Ammar Al-Chalabi
2,124
,
Leonard H van den Berg
1,124
& Jan H Veldink
1,124
1
Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands.
2
Maurice Wohl Clinical Neuroscience Institute,
Department of Basic and Clinical Neuroscience, King’s College London, London, UK.
3
Population Genetics Laboratory, Smurfit Institute of Genetics, Trinity College
Dublin, Dublin, Ireland.
4
Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
5
Department of
Genetics, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
6
MRC Social, Genetic and Developmental Psychiatry Centre,
Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK.
7
NIHR Biomedical Research Centre for Mental Health, Maudsley Hospital
and Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK.
8
Queensland Brain Institute, University of Queensland, Brisbane,
Queensland, Australia.
9
Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milan, Italy.
10
Department of Translational
Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands.
11
Department of Neurology, University of Massachusetts
Medical School, Worcester, Massachusetts, USA.
12
Academic Unit of Neurology, Trinity College Dublin, Trinity Biomedical Sciences Institute, Dublin, Ireland.
13
Department of Neurology, Beaumont Hospital, Dublin, Ireland.
14
Department of Pathophysiology and Tranplantation, ‘Dino Ferrari’ Center, Università degli Studi di
Milano, Milan, Italy.
15
Department of Biotechnology, Jožef Stefan Institute, Ljubljana, Slovenia.
16
Biomedical Research Institute BRIS, Ljubljana, Slovenia.
17
Department of Molecular Genetics, Institute of Pathology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
18
Institute of Biochemistry, Faculty of
Medicine, University of Ljubljana, Ljubljana, Slovenia.
19
Ljubljana ALS Centre, Institute of Clinical Neurophysiology, University Medical Centre Ljubljana, Ljubljana,
Slovenia.
20
Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Université Paris 06, UMRS 1127,
Paris, France.
21
Centre de Référence Maladies Rares SLA Ile de France, Département de Neurologie, Hôpital de la Pitié-Salpêtrière, Paris, France.
22
GRC-UPMC
SLA et Maladies du Motoneurone, Paris, France.
23
Ramsay Generale de Santé, Hôpital Peupliers, Paris, France.
24
Réseau SLA Ile de France, Paris, France.
25
Institute of Physiology, Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal.
26
Department of Neurosciences, Hospital
de Santa Maria–CHLN, Lisbon, Portugal.
27
Department of Neurology, Hospital San Rafael, Madrid, Spain.
28
Neurology Department, Hospital de la Santa Creu i Sant
Pau de Barcelona, Autonomous University of Barcelona, Barcelona, Spain.
29
Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain.
30
Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA.
31
Emory ALS Center, Emory University School of Medicine, Atlanta,
Georgia, USA.
32
Euan MacDonald Centre for Motor Neuron Disease Research, Edinburgh, UK.
33
Centre for Neuroregeneration and Medical Research Council