ArticlePDF Available

A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci


Abstract and Figures

Common variant genome-wide association studies (GWASs) have, to date, identified >24 risk loci for Parkinson's disease (PD). To discover additional loci, we carried out a GWAS comparing 6,476 PD cases with 302,042 controls, followed by a meta-analysis with a recent study of over 13,000 PD cases and 95,000 controls at 9,830 overlapping variants. We then tested 35 loci (P < 1 × 10(-6)) in a replication cohort of 5,851 cases and 5,866 controls. We identified 17 novel risk loci (P < 5 × 10(-8)) in a joint analysis of 26,035 cases and 403,190 controls. We used a neurocentric strategy to assign candidate risk genes to the loci. We identified protein-altering or cis-expression quantitative trait locus (cis-eQTL) variants in linkage disequilibrium with the index variant in 29 of the 41 PD loci. These results indicate a key role for autophagy and lysosomal biology in PD risk, and suggest potential new drug targets for PD.
Content may be subject to copyright.
A meta-analysis of genome-wide association studies identifies
17 new Parkinson’s disease risk loci
Diana Chang1, Mike A Nalls2,3, Ingileif B Hallgrímsdóttir4,6, Julie Hunkapiller1, Marcel van
der Brug1,6, Fang Cai1, International Parkinson’s Disease Genomics Consortium5,
23andMe ResearchTeam5, Geoffrey A Kerchner1, Gai Ayalon1, Baris Bingol1, Morgan
Sheng1, David Hinds4, Timothy W Behrens1, Andrew B Singleton2, Tushar R Bhangale1,7,
and Robert R Graham1,7,iD
1Genentech, Inc., South San Francisco, California, USA
2Laboratory of Neurogenetics, National Institute on Aging, US National Institutes of Health,
Bethesda, Maryland, USA
3Data Tecnica International, Glen Echo, Maryland, USA
423andMe Inc., Mountain View, California, USA
Common variant genome-wide association studies (GWASs) have, to date, identified >24 risk loci
for Parkinson’s disease (PD). To discover additional loci, we carried out a GWAS comparing
6,476 PD cases with 302,042 controls, followed by a meta-analysis with a recent study of over
13,000 PD cases and 95,000 controls at 9,830 overlapping variants. We then tested 35 loci (
< 1 ×
10−6) in a replication cohort of 5,851 cases and 5,866 controls. We identified 17 novel risk loci (
< 5 × 10−8) in a joint analysis of 26,035 cases and 403,190 controls. We used a neurocentric
strategy to assign candidate risk genes to the loci. We identified protein-altering or
quantitative trait locus (
-eQTL) variants in linkage disequilibrium with the index variant in 29
of the 41 PD loci. These results indicate a key role for autophagy and lysosomal biology in PD
risk, and suggest potential new drug targets for PD.
Reprints and permissions information is available online at
Correspondence should be addressed to R.R.G. (
5A list of members appears in Supplementary Note 1
6Present addresses: Amgen, South San Francisco, California, USA (I.B.H.); E-Scape Bio, South San Francisco, California, USA
7These authors contributed equally to this work
Robert R Graham
URLs. PDGene,; LDScore,; INRICH,; GWAS
catalog,; GTEx portal,; STRING,
Note: Any Supplementary Information and Source Data files are available in the online version of the paper
D.C., M.A.N., I.B.H., the 23andMe Research Team, G.A.K., B.B., M.S., D.H., T.W.B., A.B.S., T.R.B., and R.R.G. contributed to the
study design. D.C., M.A.N., I.B.H., T.R.B., and D.H. contributed to analysis and methods. D.C., M.A.N., T.W.B., A.B.S., T.R.B., and
R.R.G. wrote the manuscript. D.C., M.A.N., I.B.H., J.H., M.v.d.B., F.C., the International Parkinson’s Disease Genomics Consortium
(IPDGC), the 23andMe Research Team, G.A.K., G.A., B.B., M.S., D.H., T.W.B., A.B.S., T.R.B., and R.R.G. reviewed the manuscript.
M.v.d.B., F.C., IPDGC and the 23andMe Research Team provided samples or data.
The authors declare competing financial interests: details are available in the online version of the paper.
HHS Public Access
Author manuscript
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Published in final edited form as:
Nat Genet
. 2017 October ; 49(10): 1511–1516. doi:10.1038/ng.3955.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
PD is the second most common neurodegenerative disorder1,2, with a prevalence of 3–4% in
individuals over 80 years of age3. PD is characterized by the loss of dopaminergic neurons
in the substantia nigra and the presence of Lewy bodies1,2. These neuropathologies manifest
in affected individuals primarily as motor-related symptoms, but the involvement of other
brain regions can lead to nonmotor symptoms4.
Early-onset, familial PD (onset at <60 years of age) accounts for a small fraction of cases5,
but the identified associated genes, including
, and
, provide insight into
disease pathogenesis6,7. For the later-onset, common form of PD, at least 24 loci have been
associated at a genome-wide significant level with disease risk in individuals of European
ancestry8. The narrow-sense heritability (
2) explained by the confirmed PD risk loci is low
(0.033)9; however, the heritability explained by common variants is estimated at 0.227 (s.d.:
0.08)9, which suggests that additional loci with smaller effect sizes remain to be discovered.
We carried out a GWAS of 6,476 subjects from a 23andMe PD cohort (PDWBS (Web-Based
Study of Parkinson’s Disease)) and 302,042 controls genotyped on custom Illumina arrays
(Fig. 1). The 6,476 PD cases of European ancestry were independent from those previously
reported8 but met the same inclusion criteria, except that carriers of the
mutation were not removed8,10. The 302,042 controls did not report having PD and were of
similar ancestry as the cases. The data were imputed with Minimac2 using 1000 Genomes
phase 1 haplotypes11,12. Single-nucleotide polymorphisms (SNPs) with low imputation
quality or that failed general quality control metrics were removed (Online Methods). After
correcting for age, sex, and the top principal components (Online Methods), we observed
minimal inflation for
values genome-wide (λgc = 1.057; λ1000 = 1.004; Supplementary
Fig. 1).
A total of 12 loci had
< 5 × 10−8 in the PDWBS analysis, including 11 of the loci that
were reported in a previous GWAS in individuals of European ancestry8 (Table 1). For the
remaining 13 previously reported loci, we observed
< 0.05 for 11 loci, with no significant
evidence for association observed in the PDWBS sample for
(rs115185635) or
(rs155399). The remaining novel locus in the PDWBS analysis, rs9468199 (
= 1.77 × 10−9), is more than 4 Mb from the nearest PD association in the HLA class II
region and is independent of rs9275326 (
conditional = 2.64 × 10−9).
Using genome-wide summary statistics from the PDWBS analysis, we estimated the
value for PD explained by common variants as 0.209 (95% confidence interval (CI): 0.148–
0.271, assuming a prevalence of 0.01), which is similar to the
2 value reported
previously9,10. Regions contributing to PD heritability were significantly enriched for
acetylation of histone H3 at lysine 27 (
= 0.001; Supplementary Table 1), a mark of active
regulatory regions. PD heritability was also enriched for histone marks in central nervous
system, adrenal, and pancreatic cell types (Supplementary Table 2), in agreement with a
previous study13.
We next carried out a meta-analysis between the PDWBS GWAS and results for the top
10,000 variants available from a large-scale meta-analysis for PD with over 13,000 cases and
95,000 controls8 (PDGene) (Fig. 1). For the 9,830 overlapping SNPs between the PDWBS
Chang et al. Page 2
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
and PDGene studies, we used an inverse-variance weighted method to combine association
statistics for meta-analysis14. The odds ratios and
values for the 9,830 overlapping SNPs
in the PDWBS and PDGene studies were correlated (ρ−log10(
value) = 0.85, ρOR = 0.58).
Furthermore, quantile–quantile (Q-Q) plots indicated an increase in the number of variants
with low
values (Supplementary Fig. 1), even after the exclusion of variants in regions
previously reported as associated with PD risk at a genome-wide significant level
(Supplementary Table 3).
The meta-analysis identified 35 loci associated at
< 1 × 10−6, including 15 loci with
< 5
× 10−8 (Fig. 2, Supplementary Figs. 2 and 3, Supplementary Table 4). Only two of the
previously reported loci (
rs14235 and
rs17649553) and 2 of the 20
suggestive loci (
rs5910) were in linkage disequilibrium (LD)
2 > 0.8) with variants associated (at
< 5 × 10−8) with any phenotype in the NHGRI
GWAS catalog15. Significant pleiotropy of PD risk loci with other complex diseases has not
been identified16, but this pleiotropy landscape may change as more modest effects are
We next sought validation of these 35 candidate loci in an independent cohort of 5,851 cases
and 5,866 controls of European ancestry genotyped with the semi-customized NeuroX
Illumina array8,17 (Fig. 1). Twenty-nine of the 35 loci either were directly genotyped on the
NeuroX array or had suitable proxies (
2 > 0.9 with the original SNP; Supplementary Table
5). Weaker proxies at four additional SNPs (
2 > 0.5) were available but were not used for
validation in this study (Supplementary Table 5). In a replication-phase joint analysis of
these 29 loci (meta-analysis of PDGene, PDWBS, and NeuroX), 16 had
< 5 × 10−8 (Table
2). Of these 16, all but 3 (rs4073221, rs10906923, and rs9468199) were also nominally
associated in the NeuroX study (one-sided
< 0.05). A genetic risk score8,18,19 defined by
these 16 loci, in addition to the previously reported loci, had a non-negligible ability to
predict PD case status (area under the curve, 0.6518; 95% CI, 0.6419–0.6616). This
represents a significant improvement over the predictive power of risk scores defined by
previously reported loci alone (
= 6 × 10−8) (Supplementary Note 1). In sum, we identified
16 independent PD risk loci with a joint
< 5 × 10−8 and 1 locus (rs601999) with
< 5 ×
10−8 in the discovery cohort with no suitable proxy for replication in the NeuroX cohort
(Table 2).
Overall, 11 of 17 novel loci were in high LD (
2 > 0.8) with at least one variant predicted to
affect transcription factor binding (Supplementary Table 6). Of the 17 novel loci and 24
previously reported loci, 10 contained residual associations with
< 1 × 10−3 after
conditioning on each region’s most significant SNP in the PDWBS data (Supplementary
Table 7). These regions included three of the four independent secondary signals reported by
et al.
8, as well as one variant previously reported at a non-genome-wide significant
level (
= 5.15 × 10−7)20.
We note that the HLA region association with PD is particularly complex. Two candidate
genes from the HLA region were nominated on the basis of support from either a protein-
coding variant or an eQTL (Fig. 3). This is in line with a previous study that suggested that
the PD association in the HLA region may point to multiple HLA factors, including
Chang et al. Page 3
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
independent regulatory factors21. The association pattern observed at this locus may be
reminiscent of the HLA association observed in schizophrenia and linked to C4 copy
The identification of the causal variants and genes underlying regions associated with
common, complex disease is a major challenge23. Several statistical methods have been
proposed for the fine-mapping of causal variants23–25. Alternatively, some studies have
narrowed down lists of candidate genes by combining multiple levels of evidence with
scoring-based strategies26,27. Here we implemented a neurocentric strategy to nominate
candidate genes for PD-associated loci.
We incorporated seven sources of data to annotate the index variant and linked variants from
PD-associated loci (including eQTLs and expression data from GTEx28, as well as
expression data from brain cell types in mice29; a full list is provided in the Online
Methods). We used a two-stage approach to assign candidate genes to each locus (see the
Online Methods for further details, and Supplementary Fig. 4 for a graphical visualization).
In the first stage, we assigned a gene to a locus if (i) the index SNP or linked variants (
2 >
0.6) altered the protein sequence or (ii) the index variant was a
-eQTL for the gene. When
no candidate genes were identified by the first stage, we ranked neighboring gene(s) on the
basis of neurologically related phenotypes and expression and assigned the gene with the
highest score to the locus (Online Methods).
With this strategy we identified a single candidate gene for 28 loci, and multiple candidate
genes with similar levels of supporting evidence for 13 loci (Fig. 3, Supplementary Figs. 5
and 6). The candidate-gene nomination strategy confirmed several known PD risk genes,
, and
. Among the 41 PD risk loci, a total of 29 loci
(71%) had either a protein-altering or a
-eQTL variant linked to the index SNP
(Supplementary Tables 8 and 9). In addition, we carried out a colocalization analysis to
determine whether the GWAS signal and the eQTL signal pointed to the same causal
variant30 (Supplementary Note 1). Seven candidate genes also had evidence for protein–
protein interaction (Online Methods, Supplementary Table 10). Further studies are needed to
experimentally determine the causal genes in the PD risk loci; however, the identification of
candidate genes provides testable hypotheses for functional studies.
To gain insight into the biology, we tested the identified candidate genes in the 41 PD risk
loci for association with any pathways or gene sets compared with a background gene list
(Online Methods). We investigated whether candidate genes were enriched for pathways
previously implicated in PD: autophagy, lysosomal, and mitochondrial biology1. PD-
associated signals were enriched (at a threshold of
< 0.05/3 = 0.017) for lysosomal and
autophagy genes (
= 3.35 × 10−6 and
= 5.71 × 10−3, respectively). The addition of
candidate genes more than doubled the number of lysosomal genes observed in PD loci and
improved the enrichment significance (
all_loci = 3.35 × 10−6,
novel_loci = 3.64 × 10−5). We
also observed that one previously identified gene (
) and two novel candidate genes
) mapped to the mitochondrial gene set (Supplementary Table 11).
Chang et al. Page 4
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Lysosomal biology and its role in the degradation of protein aggregates emerged as a highly
significant pathway in PD risk. Among the five candidate genes linked to lysosomal biology,
two were previously identified candidate genes (
(glucocerebrosidase) and
(transmembrane protein 175)), and three were newly identified candidate genes (
(cathepsin B),
(ATPase H+ transporting V0 subunit a1), and
(galactosylceramidase)). Glucocerebrosidase is required for normal lysosomal activity and
α-synuclein degradation. In addition,
loss-of-function alleles are a common PD risk
was recently shown to encode a potassium channel that can regulate
lysosomal function32, and the missense variant
M393T is strongly linked to the
index variant in the region (Supplementary Table 8).
is a lysosomal cysteine protease.
A PD risk allele is linked to a
-eQTL for
in multiple tissues (Supplementary Table
9), where the risk allele is associated with reduced levels of
mRNA. Double-knockout
mice for
(cathepsin L) show a tremor phenotype with cerebral and cerebellar
atrophy33. CTSB is also capable of degrading membrane-bound and soluble α-synuclein in
Autophagy is the catabolic process that targets long-lived proteins and dysfunctional
organelles for lysosomal degradation. Autophagy and lysosomal degradation have been
implicated in PD by rare familial and common GWAS-associated
variants. We note
that a strong
-eQTL for lysine acetyltransferase 8 (
) is associated with PD risk, with
lower levels of
mRNA linked to increased PD risk. Inhibition of KAT8 was recently
shown to decrease autophagic flux35.
Next, we used INRICH36 to investigate whether PD-associated regions were enriched for
gene sets in an unbiased fashion. Once again, we found significant enrichment of the
lysosomal pathway (
adjusted = 0.02) (Supplementary Table 12). We further examined the
expression of the PD candidate genes in a brain-specific cell-type expression data set in
mice29; however, we observed broad expression across the major brain cell types, and no
clear cell-type-specific pattern was evident (Supplementary Fig. 7).
Among the candidate genes newly identified in this study is
(SH3 domain-
containing GRB2-like 2, endophilin A1), a gene recently demonstrated to be phosphorylated
by LRRK2 and which may have a role in clathrin-mediated endocytosis of synaptic
vesicles37. Dysregulation of
(elongation of very long chain fatty acids protein 7) in
mice results in several neurological phenotypes, including inflammatory astrocytosis and
microgliosis in the brain, and neuronal degeneration38. Upregulation of the candidate gene
(sodium voltage-gated channel α-subunit 3) enhances neuronal excitability and is
associated with epilepsy in both humans and animal models39.
The new loci also encode three transcription factors: SATB1, ZNF184, and TOX3. TOX3
has been implicated in neuronal survival40, and SATB1 has been associated with T cell
function, particularly the development of regulatory T cells41.
Several of the PD candidate genes are within the ‘druggable’ genome42, including the
previously identified serine/threonine kinase 39 (
) and the novel candidate gene
inositol 1,4,5-trisphosphate kinase B (
). An in-frame deletion of
Chang et al. Page 5
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
(rs147889095) is linked to a PD-associated variant, and complete loss of ITPKB was
reported in a patient with common-variable immunodeficiency43. STK39 is a kinase linked
to hypertension44, regulation of K+ levels, and the cellular stress response.
In summary, this study presents what to our knowledge is the largest meta-analysis of PD so
far, involving a total of 26,035 cases and 403,190 controls. We identified 17 novel PD loci
and, using a neurocentric candidate-gene nomination pipeline, found that several of the
newly identified PD risk genes have a role in lysosomal biology and autophagy. The
identification of these candidate genes allows for the prioritization of functional studies to
determine causal genes for PD and possible therapeutic targets.
The PDWBS is a genome-wide analysis of 6,476 PD cases and 302,042 control subjects, all
of whom were customers of 23andMe Inc. and consented to participate in research. The
study protocol was approved by the external AAHRPP-accredited institutional review board,
Ethical and Independent Review Services (E&I Review). Cases and controls were
designated on the basis of surveys10. Controls were selected from 23andMe Inc. research
participants who did not self-report as having been diagnosed with PD. Although the use of
self-reported controls can result in a reduction of power, the effect of this on the current
study was probably minimal (Supplementary Note 1). Any samples present in the PDGene
study8 were removed from the PDWBS analysis. The average age of cases and controls was
67.6 and 50.8 years, respectively. The study also included 147 cases (2.3%) and 554 controls
(0.18%) that were
G2019S carriers. Removing
G2019S carriers from the
analysis removed genome-wide significant associations at the
DNA extraction and genotyping were performed on saliva samples by CLIA-certified CAP-
accredited clinical laboratories of the Laboratory Corporation of America. Samples were
genotyped on one of the following four platforms: V1 and V2, two variants of the Illumina
HumanHap550+ BeadChip, with ~25,000 custom SNPs and ~950,000 total SNPs; V3,
Illumina OmniExpress+BeadChip with custom SNPs to increase overlap with the V2 chip,
with a total of ~950,000 SNPs; and V4, a custom chip that included SNPs overlapping V2
and V3 chips, low-frequency coding variants and ~570,000 SNPs. Samples with a call rate
lower than 98.5% were reanalyzed, and research participants with samples that failed
repeatedly were re-contacted and asked to provide additional samples.
Research participants were restricted to those of mainly (>97%) European ancestry10,45. All
research participants in the study were also required to share <700 cM identity by descent
(IBD) (estimated by a segmental IBD estimation algorithm46), corresponding approximately
to the sharing expected between first cousins. We additionally excluded individuals who
shared >700 cM IBD with any 23andMe research participant whose data was used in the
PDGene GWAS. Data were imputed on 1000 Genomes phase 1 haplotypes (September 2013
release) with Minimac2 on default settings11,47. Imputation was run separately on data from
each genotyping platform.
Chang et al. Page 6
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
For genotyped SNPs, SNPs were removed if they were genotyped on only the V1 and/or the
V2 chip, if they failed a parent–offspring transmission test on trio data, if they were not in
Hardy–Weinberg equilibrium (
< 10−20), or if they had a call rate < 0.90. For imputed
SNPs, SNPs were removed if they had an average
2 < 0.5 or minimum
2 < 0.3 in any
imputation batch, or failed a test for imputation batch effect (testing imputation dosage with
imputation batch;
< 10−50).
We applied logistic regression assuming an additive model to test for association between
case/control status and either genotypes or imputed dosages (for imputed SNPs). Only SNPs
with minor allele frequency (MAF) > 0.1% were analyzed. Covariates were added to adjust
for age, sex, the first five principal components, and genotyping platform version. A total of
12,896,220 variants (11,933,700 SNPs) were analyzed. The genomic inflation factor was
calculated from the median
value of analyzed variants. Scaling of the genomic inflation
factor by sample size was carried out as described previously for 1,000 cases and 1,000
Meta-analysis of PD GWASs
Summary odds ratios, 95% CIs, and
values of the 10,000 most significant GWAS meta-
analysis results were obtained from PDGene (“URLs”). Cohort descriptions, quality control,
and meta-analysis for this study have been described previously8. SNP s.e. was derived from
the reported
values and odds ratios. More specifically, the
-statistic was calculated as the
square root of the inverse χ-square transformation of the
value, and the s.e. was calculated
as follows: s.e.m. = ln(odds ratio)/absolute(
There were 9,830 SNPs in common between the PDGene and the PDWBS data sets. A
fixed-effects model based on inverse-variance weighting, as implemented in METAL, was
used to combine summary statistics from the two studies14. Heterogeneity values (
2 and
were obtained with PLINK49. Novel signals of association were defined as genome-wide
significant associations in the meta-analysis that did not overlap loci associated with PD at
genome-wide significant thresholds in the PDGene data (35 loci with
< 1 × 10−6).
Joint analysis with NeuroX
The NeuroX cohort was previously described8,17. Briefly, 5,851 cases and 5,866 controls of
European ancestry were genotyped on a semi-custom NeuroX array. A logistic regression
was carried out to test for association, with covariates to adjust for age, sex, and population
ancestry (the first five principal components). Twenty-five of the 35 novel loci were directly
genotyped on the chip, and four additional SNPs had suitable proxies (
2 > 0.9). At these 29
SNPs, we carried out a fixed-effects inverse-variance weighted meta-analysis14 for all three
studies (PDGene, PDWBS, and NeuroX) as described above.
Conditional analysis
Conditional analysis was run on all 17 loci that were significantly associated with PD in the
joint meta-analysis (
< 5 × 10−8) and the 24 previously reported PD loci using the PDWBS
study. For each locus, SNPs within 500 kb of the index SNP (the SNP with the most
Chang et al. Page 7
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
value) were tested for association by the same methods as described above for
the PDWBS GWAS with the index SNP added as an additional covariate.
Heritability estimates
We used LD score regression (LDSC)50,51 to compute the narrow-sense heritability (
estimates of PD in the PDWBS GWAS data (described above). Several methods exist for
2 with GWAS data50–52. We used LDSC to estimate
2 in this study because it
requires only summary-level data and is more computationally efficient for larger data sets.
Reference LD scores were computed with the European ancestry subset of the 1000
Genomes data for SNPs within 500 kb of the SNP to be scored. Strict filtering was applied
to ensure the robustness of heritability estimates as recommended50,51. After filtering,
scores for 7,629,099 SNPs from the 23andMe study were used as input to LDSC. We further
used the stratified LD-score regression approach to partition heritability into 24 different
cell-type-agnostic annotation categories including conserved regions, histone marks, DNase
I hypersensitivity sites, ENCODE chromatin states, and enhancers51, as well as 10 different
cell-type-specific histone annotations. Significant enrichment was assessed at a strict
Bonferroni threshold of 0.0021 (0.05/24) for the 24 general categories, and 0.005 for the
cell-type-specific enrichment.
Pleiotropy analysis: overlap with EBI-NHGRI GWAS catalog
Data were downloaded from the EBI-NHGRI catalog15 (version available on 17 April
2016). If a variant in the meta-analysis was within 500 kb and in LD (
2 > 0.8) with an
association (
< 5 × 10−8) in the catalog, the meta-analysis signal was considered to be
overlapping the reported signal.
A neurocentric strategy to identify candidate causal variants and genes
Associated index SNPs were paired to candidate genes on the basis of two broad levels of
evidence: variant-level support and gene-level support (see Supplementary Fig. 4 for a
graphic representation). In the former category, index SNPs were paired with candidate
genes if there was evidence that the index SNP or an SNP in LD (
2 > 0.6) with the index
SNP was annotated with a putative high-impact variant (chromosome number variation,
exon loss variant, frame-shift variant, rare amino acid variant, splice donor or acceptor
variant, start-lost, stop-gained or stop-lost, and transcript ablation) or moderate-impact
variant (3or 5UTR truncation and exon loss, coding sequence variant, disruptive in-
frame deletion or insertion, in-frame deletion or insertion, missense variant, regulatory
region ablation, splice region variant, and transcription factor binding-site ablation). We
obtained variant annotations by running SnpEff53 on dbSNP build 142. A second source of
variant-level support consisted of
-eQTL evidence.
-eQTLs as pre-computed by GTEx
(v6)28 were downloaded directly from the GTEx portal (“URLs”). Although eQTL results
were available for 46 tissues, including ten regions from the brain, our search for eQTLs was
limited by the sampled tissues and cell types, and therefore we might have missed any
eQTLs that are cell-type or tissue specific, in addition to eQTLs that are present only under
certain stimuli (for example, ‘response’ eQTLs). The index SNP was tested for significant
association with any gene where the TSS was within 250 kb of the index SNP. As roughly
90% of eQTLs are within 250 kb of a gene28, it is likely that we captured the majority of
Chang et al. Page 8
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
eQTLs while missing rarer, more distal events. For brain eQTLs, a strict Bonferroni
correction was applied to the raw eQTL
values to adjust for multiple testing of genes
within 250 kb of the index SNP. For other tissues, only eQTLs with a false discovery rate of
<0.05 as determined by GTEx28 were considered. We weighted brain and non-brain eQTLs
Gene-level support was used when an index SNP had no candidate genes supported by
variant-level data as described above. A list of genes within 250 kb of the index SNP was
obtained (gene models used by GTEx were downloaded from the GTEx portal), and each
gene was scored for neurological relevant features or annotations. Genes were first weighted
for neurological relevant phenotypic annotations. Genes were (i) scored for being
differentially expressed between PD patients and healthy controls (see Supplementary Note
1 for further details) (311 genes total genome-wide); (ii) annotated with ‘neuro’-associated
phenotypes in FlyBase54 (1,521 genes); (iii) scored for behavioral, neurological, and
olfactory phenotypes annotated in MGI55 (3,890 genes); and (iv) annotated with any
phenotypes related to neurological disorders or the brain in OMIM56 (521 genes). Lastly,
genes were scored for being expressed (median expression across samples > 2 reads per
kilobase per million mapped reads) in any cohort of GTEx brain region samples (15,197
genes) or in at least one brain cell type in the mouse expression data set29 (astrocyte,
microglia, neuron, or oligodendrocyte) (12,092 genes). For the gene-level support, we used a
tiered scoring scheme to weight phenotypic annotations more heavily than expression in the
brain (scores demarked in Supplementary Fig. 4) to enrich for genes with demonstrated
neurological related roles. At each locus, the gene (or tied genes) with the highest score was
nominated as the candidate gene for the region.
Protein–protein and coexpression analysis
All protein-coding genes within 250 kb of PD-associated loci (Supplementary Table 13)
were used as input to STRING57. Gene pairs that were either coexpressed or involved in
experimentally validated protein–protein interactions with a medium score or higher (score ≥
0.4) are reported in Supplementary Table 10.
Pathway enrichment analysis
Previously reported PD loci and novel PD associations were tested for enrichment in
particular pathways or gene sets. First, the nominated candidate genes for these PD-
associated loci were tested for enrichment in several targeted gene sets by a hypergeometric
test. The background list of genes for comparison was matched to the neurological-centric
candidate-gene nomination pipeline. The background list thus consisted of genes that had
mouse knockout phenotypes, had fly mutant phenotypes, had OMIM-related phenotype
annotations, had nominally significant
-eQTLs in GTEx, were differentially expressed in
PD patients versus controls, and were expressed in GTEx brain tissue or mouse brain cell
types in the Barres data set.
We obtained mitochondrial genes from MitoMiner58 using the MitoCarta59,60 reference set
after excluding genes that mapped to the mitochondria (genes that map to the mitochondria
were not included in this metaanalysis). Lysosomal genes were obtained from the hlGDB61
Chang et al. Page 9
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
using only the proteomics and literature resources. Finally, we obtained autophagy genes
from the Human Autophagy Database62, as well as ten additional genes reported in a recent
siRNA screen of autophagic flux modulators35. A list of all genes in each pathway is
provided in Supplementary Note 1. The minimum
value per gene (for genes that an SNP
within the 9,830 variants assayed in this meta-analysis mapped to) is provided in
Supplementary Tables 14–16.
Second, we applied a non-targeted gene-set enrichment approach using INRICH36 to assess
whether regions associated with PD were enriched for genes in KEGG63 and Gene Ontology
(GO)64 gene sets. The 24 previously reported PD index variants and the 17 novel PD-
associated variants reported in this study were used as input into PLINK’s65 “show-tags”
function. The European 1000 Genomes12 samples were used for reference LD patterns. An
interval for each PD-associated variant was defined as the region from the leftmost tag
variant to the rightmost tag variant in the 1000 Genomes data.
We ran INRICH on these 41 intervals with the default settings, with the exception of
increasing the number of replicates and bootstraps to 5,000 (-r 5000 --q 5000) and setting
the pre-compute feature to false for software stability (-c). Enrichment for KEGG and GO
gene sets was assessed separately.
Data availability
A Life Sciences Reporting Summary for this paper is available. Summary statistics for the
9,830 variants presented in the discovery phase meta-analysis are available at http://research- The full GWAS summary statistics for PDWBS will be
made available through 23andMe and Genentech to qualified researchers under an
agreement with 23andMe that protects the privacy of the 23andMe participants and an
agreement with Genentech for data sharing. Please contact D.H. ( for
more information and to apply to access the data.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
We thank all of the subjects who donated their time and biological samples to be a part of this study. Funding
details and additional acknowledgments are provided in Supplementary Note 1.
1. Corti O, Lesage S, Brice A. What genetics tells us about the causes and mechanisms of Parkinson’s
disease. Physiol. Rev. 2011; 91:1161–1218. [PubMed: 22013209]
2. Verstraeten A, Theuns J, Van Broeckhoven C. Progress in unraveling the genetic etiology of
Parkinson disease in a genomic era. Trends Genet. 2015; 31:140–149. [PubMed: 25703649]
3. Nussbaum RL, Ellis CE. Alzheimer’s disease and Parkinson’s disease. N. Engl. J. Med. 2003;
348:1356–1364. [PubMed: 12672864]
4. Shulman JM, De Jager PL, Feany MB. Parkinson’s disease: genetics and pathogenesis. Annu. Rev.
Pathol. 2011; 6:193–222. [PubMed: 21034221]
Chang et al. Page 10
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
5. Klein C, Westenberger A. Genetics of Parkinson’s disease. Cold Spring Harb. Perspect. Med. 2012;
2:a008888. [PubMed: 22315721]
6. Hardy J. Genetic analysis of pathways to Parkinson disease. Neuron. 2010; 68:201–206. [PubMed:
7. Singleton AB, Farrer MJ, Bonifati V. The genetics of Parkinson’s disease: progress and therapeutic
implications. Mov. Disord. 2013; 28:14–23. [PubMed: 23389780]
8. Nalls MA, et al. Large-scale meta-analysis of genome-wide association data identifies six new risk
loci for Parkinson’s disease. Nat. Genet. 2014; 46:989–993. [PubMed: 25064009]
9. Keller MF, et al. Using genome-wide complex trait analysis to quantify ‘missing heritability’ in
Parkinson’s disease. Hum. Mol. Genet. 2012; 21:4996–5009. [PubMed: 22892372]
10. Do CB, et al. Web-based genome-wide association study identifies two novel loci and a substantial
genetic component for Parkinson’s disease. PLoS Genet. 2011; 7:e1002141. [PubMed: 21738487]
11. Fuchsberger C, Abecasis GR, Hinds DA. minimac2: faster genotype imputation. Bioinformatics.
2015; 31:782–784. [PubMed: 25338720]
12. Abecasis GR, et al. An integrated map of genetic variation from 1,092 human genomes. Nature.
2012; 491:56–65. [PubMed: 23128226]
13. Gagliano SA, et al. Genomics implicates adaptive and innate immunity in Alzheimer’s and
Parkinson’s diseases. Ann. Clin. Transl. Neurol. 2016; 3:924–933. [PubMed: 28097204]
14. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association
scans. Bioinformatics. 2010; 26:2190–2191. [PubMed: 20616382]
15. Welter D, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic
Acids Res. 2014; 42:D1001–D1006. [PubMed: 24316577]
16. Pickrell JK, et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat.
Genet. 2016; 48:709–717. [PubMed: 27182965]
17. Nalls MA, et al. NeuroX, a fast and efficient genotyping platform for investigation of
neurodegenerative diseases. Neurobiol. Aging. 2015; 36:1605.e7–1605.e12.
18. Nalls MA, et al. Imputation of sequence variants for identification of genetic risks for Parkinson’s
disease: a meta-analysis of genome-wide association studies. Lancet. 2011; 377:641–649.
[PubMed: 21292315]
19. International Parkinson’s Disease Genomics Consortium & Wellcome Trust Case Control
Consortium 2. A two-stage meta-analysis identifies several new loci for Parkinson’s disease. PLoS
Genet. 2011; 7:e1002142. [PubMed: 21738488]
20. Pankratz N, et al. Meta-analysis of Parkinson’s disease: identification of a novel locus. RIT2. Ann.
Neurol. 2012; 71:370–384. [PubMed: 22451204]
21. Wissemann WT, et al. Association of Parkinson disease with structural and regulatory variants in
the HLA region. Am. J. Hum. Genet. 2013; 93:984–993. [PubMed: 24183452]
22. Sekar A, et al. Schizophrenia risk from complex variation of complement component 4. Nature.
2016; 530:177–183. [PubMed: 26814963]
23. Kichaev G, et al. Integrating functional data to prioritize causal variants in statistical fine-mapping
studies. PLoS Genet. 2014; 10:e1004722. [PubMed: 25357204]
24. Maller JB, et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat.
Genet. 2012; 44:1294–1301. [PubMed: 23104008]
25. Chen W, et al. Fine mapping causal variants with an approximate Bayesian method using marginal
test statistics. Genetics. 2015; 200:719–736. [PubMed: 25948564]
26. Okada Y, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature.
2014; 506:376–381. [PubMed: 24390342]
27. Bentham J, et al. Genetic association analyses implicate aberrant regulation of innate and adaptive
immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 2015; 47:1457–
1464. [PubMed: 26502338]
28. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene
regulation in humans. Science. 2015; 348:648–660. [PubMed: 25954001]
29. Zhang Y, et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and
vascular cells of the cerebral cortex. J. Neurosci. 2014; 34:11929–11947. [PubMed: 25186741]
Chang et al. Page 11
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
30. Giambartolomei C, et al. Bayesian test for colocalisation between pairs of genetic association
studies using summary statistics. PLoS Genet. 2014; 10:e1004383. [PubMed: 24830394]
31. Sidransky E, et al. Multicenter analysis of glucocerebrosidase mutations in Parkinson’s disease. N.
Engl. J. Med. 2009; 361:1651–1661. [PubMed: 19846850]
32. Cang C, Aranda K, Seo YJ, Gasnier B, Ren D. TMEM175 is an organelle K+ channel regulating
lysosomal function. Cell. 2015; 162:1101–1112. [PubMed: 26317472]
33. Felbor U, et al. Neuronal loss and brain atrophy in mice lacking cathepsins B and L. Proc. Natl.
Acad. Sci. USA. 2002; 99:7883–7888. [PubMed: 12048238]
34. McGlinchey RP, Lee JC. Cysteine cathepsins are essential in lysosomal degradation of α-
synuclein. Proc. Natl. Acad. Sci. USA. 2015; 112:9322–9327. [PubMed: 26170293]
35. Hale CM, et al. Identification of modulators of autophagic flux in an image-based high content
siRNA screen. Autophagy. 2016; 12:713–726. [PubMed: 27050463]
36. Lee PH, O’Dushlaine C, Thomas B, Purcell SM. INRICH: interval-based enrichment analysis for
genome-wide association studies. Bioinformatics. 2012; 28:1797–1799. [PubMed: 22513993]
37. Arranz AM, et al. LRRK2 functions in synaptic vesicle endocytosis through a kinase-dependent
mechanism. J. Cell Sci. 2015; 128:541–552. [PubMed: 25501810]
38. Shin D, Shin JY, McManus MT, Ptácek LJ, Fu YH. Dicer ablation in oligodendrocytes provokes
neuronal impairment in mice. Ann. Neurol. 2009; 66:843–857. [PubMed: 20035504]
39. Tan NN, et al. Epigenetic downregulation of
expression by valproate: a possible role in its
anticonvulsant activity. Mol. Neurobiol. 2016; 54:2831–2842. [PubMed: 27013471]
40. Dittmer S, et al. TOX3 is a neuronal survival factor that induces transcription depending on the
presence of CITED1 or phosphorylated CREB in the transcriptionally active complex. J. Cell Sci.
2011; 124:252–260. [PubMed: 21172805]
41. Kondo M, et al. SATB1 plays a critical role in establishment of immune tolerance. J. Immunol.
2016; 196:563–572. [PubMed: 26667169]
42. Hopkins AL, Groom CR. The druggable genome. Nat. Rev. Drug Discov. 2002; 1:727–730.
[PubMed: 12209152]
43. Louis AG, Yel L, Cao JN, Agrawal S, Gupta S. Common variable immunodeficiency associated
with microdeletion of chromosome 1q42.1–q42.3 and inositol 1,4,5-trisphosphate kinase B
(ITPKB) deficiency. Clin. Transl. Immunology. 2016; 5:e59. [PubMed: 26900472]
44. Wang Y, et al. Whole-genome association study identifies
as a hypertension susceptibility
gene. Proc. Natl. Acad. Sci. USA. 2009; 106:226–231. [PubMed: 19114657]
45. Durand, EY., Do, CB., Mountain, JL., Macpherson, JM. Ancestry composition: a novel, efficient
pipeline for ancestry deconvolution. bioRxiv. 2014. Preprint at
46. Henn BM, et al. Cryptic distant relatives are common in both isolated and cosmopolitan genetic
samples. PLoS One. 2012; 7:e34267. [PubMed: 22509285]
47. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype
imputation in genome-wide association studies through prephasing. Nat. Genet. 2012; 44:955–959.
[PubMed: 22820512]
48. de Bakker PI, et al. Practical aspects of imputation-driven meta-analysis of genome-wide
association studies. Hum. Mol. Genet. 2008; 17:R122–R128. [PubMed: 18852200]
49. Purcell S, et al. PLINK: a tool set for whole-genome association and population-based linkage
analyses. Am. J. Hum. Genet. 2007; 81:559–575. [PubMed: 17701901]
50. Bulik-Sullivan BK, et al. LD score regression distinguishes confounding from polygenicity in
genome-wide association studies. Nat. Genet. 2015; 47:291–295. [PubMed: 25642630]
51. Finucane HK, et al. Partitioning heritability by functional annotation using genome-wide
association summary statistics. Nat. Genet. 2015; 47:1228–1235. [PubMed: 26414678]
52. Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR. Estimation of pleiotropy between complex
diseases using single-nucleotide polymorphism-derived genomic relationships and restricted
maximum likelihood. Bioinformatics. 2012; 28:2540–2542. [PubMed: 22843982]
Chang et al. Page 12
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
53. Cingolani P, et al. A program for annotating and predicting the effects of single nucleotide
polymorphisms, SnpEff: SNPs in the genome of
Drosophila melanogaster
strain w1118; iso-2;
iso-3. Fly (Austin). 2012; 6:80–92. [PubMed: 22728672]
54. dos Santos G, et al. FlyBase: introduction of the
Drosophila melanogaster
Release 6 reference
genome assembly and large-scale migration of genome annotations. Nucleic Acids Res. 2015;
43:D690–D697. [PubMed: 25398896]
55. Eppig JT, Blake JA, Bult CJ, Kadin JA, Richardson JE. The Mouse Genome Database (MGD):
facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 2015; 43:D726–
D736. [PubMed: 25348401]
56. McKusick VA. MENDELIAN Inheritance in Man and its online version, OMIM. Am. J. Hum.
Genet. 2007; 80:588–604. [PubMed: 17357067]
57. Szklarczyk D, et al. The STRING database in 2017: quality-controlled protein-protein association
networks, made broadly accessible. Nucleic Acids Res. 2017; 45:D362–D368. [PubMed:
58. Smith AC, Robinson AJ. MitoMiner v3.1, an update on the mitochondrial proteomics database.
Nucleic Acids Res. 2016; 44:D1258–D1261. [PubMed: 26432830]
59. Calvo SE, Clauser KR, Mootha VK. MitoCarta2.0: an updated inventory of mammalian
mitochondrial proteins. Nucleic Acids Res. 2016; 44:D1251–D1257. [PubMed: 26450961]
60. Pagliarini DJ, et al. A mitochondrial protein compendium elucidates complex I disease biology.
Cell. 2008; 134:112–123. [PubMed: 18614015]
61. Brozzi A, Urbanelli L, Germain PL, Magini A, Emiliani C. hLGDB: a database of human
lysosomal genes and their regulation. Database (Oxford). 2013; 2013:bat024. [PubMed:
62. Moussay E, et al. The acquisition of resistance to TNFα in breast cancer cells is associated with
constitutive activation of autophagy as revealed by a transcriptome analysis using a custom
microarray. Autophagy. 2011; 7:760–770. [PubMed: 21490427]
63. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res.
2000; 28:27–30. [PubMed: 10592173]
64. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015;
43:D1049–D1056. [PubMed: 25428369]
65. Chang CC, et al. Second-generation PLINK: rising to the challenge of larger and richer datasets.
Gigascience. 2015; 4:7. [PubMed: 25722852]
Chang et al. Page 13
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 1.
A flow chart of the two-stage meta-analysis design. In stage 1, we carried out a meta-
analysis of 9,830 SNPs between the PDWBS and PDGene studies. Thirty-five loci with
1 × 10−6 were carried forward into the replication-phase meta-analysis. In stage 2, we
carried out a meta-analysis between the two discovery-phase studies and the NeuroX study
for these 35 loci. Of these loci, 16 of the 29 available in NeuroX and 1 locus without
replication data were carried forward for downstream analyses (see the main text for further
Chang et al. Page 14
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 2.
Results of the Parkinson’s disease discovery-phase meta-analysis. The top SNPs in
associated regions are indicated by pink symbols. Candidate genes for previously associated
loci are labeled in black (
< 5 × 10−8 in the discovery phase) or gray text (
> 5 × 10−8 in
the discovery phase); candidate genes for newly identified loci are labeled in red. The
shows the two-sided unadjusted −log10(
) values for association with PD. SNPs with
< 1
× 10−25 are indicated by triangles.
Chang et al. Page 15
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 3.
The candidate genes for regions associated with Parkinson’s disease. The most likely
candidate gene is annotated for each region that was significantly associated with PD in the
final joint analysis. Black or gray text indicates previously reported loci that had
less than or greater than 5 × 10−8 in the discovery phase, respectively. Red text indicates
newly identified loci that were significantly associated with PD in the final joint analysis.
Gray lines at the outer edge spanning multiple genes indicate candidate genes within a single
locus. Chromosome numbers are shown in the gray shaded ring, and support for candidate
genes is indicated by color-coding in the inner rings. The innermost ring indicates
expression of the gene in brain cell types (in a mouse expression data set) or in human brain
regions (in GTEx), or differential expression between PD brains and healthy control brains.
Chang et al. Page 16
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Chang et al. Page 17
Table 1
Parkinson’s disease risk loci previously reported at genome-wide significance levels
CHR:BPaSNP Candidate geneb
Effect allele/
EAF in
Genomes EAFcases/controlscPPDGenedORPDGene PPDWBSeORPDWBS Pdiscovery ORdiscovery
95% CI
1:155135036 rs35749011
G/A 0.976 0.979/0.988 6.10 × 10−23 0.57 5.33 × 10−14 0.59 2.59 × 10−35 0.58 0.53–0.63
1:205723572 rs823118
C/T 0.467 0.419/0.443 1.96 × 10−16 0.89 8.78 × 10−9 0.90 1.12 × 10−23 0.89 0.87–0.91
1:232664611 rs10797576
T/C 0.137 0.145/0.135 1.76 × 10−10 1.13 7.4 × 810−4 1.10 8.41 × 10−13 1.12 1.09–1.15
2:135539967 rs6430538
T/C 0.488 0.426/0.450 3.35 × 10−19 0.88 1.5 × 410−6 0.91 8.24 × 10−24 0.89 0.87–0.91
2:169110394 rs1474055
C/T 0.881 0.855/0.874 7.11 × 10−16 0.82 1.11 × 10−11 0.83 5.68 × 10−26 0.83 0.80–0.86
C/G 0.036 0.040/0.039 2.2 × 10−8 1.79 0.182 1.08 1.22 × 10−4 1.21 1.10–1.33
3:182762437 rs12637471
A/G 0.219 0.175/0.198 5.38 × 10−22 0.84 4.27 × 10−10 0.86 2.11 × 10−30 0.85 0.82–0.87
4:951947 rs34311866
C/T 0.199 0.212/0.184 6.00 × 10−41 1.26 2.48 × 10−12 1.18 1.47 × 10−50 1.23 1.20–1.27
4:15737101 rs11724635
C/A 0.437 0.437/0.452 4.26 × 10−17 0.89 1.0 × 410−4 0.93 1.22 × 10−19 0.90 0.88–0.92
4:77198986 rs6812193
g FAM47E
T/C 0.398 0.351/0.370 1.85 × 10−11 0.91 1.24 × 10−4 0.93 1.43 × 10−14 0.92 0.90–0.94
4:90626111 rs356182
G/A 0.375 0.406/0.349 1.85 × 10−82 1.34 1.44 × 10−42 1.31 5.21 × 10−123 1.33 1.30–1.36
6:32666660 rs9275326
T/C 0.114 0.099/0.105 5.81 × 10−13 0.80 1.04 × 10−3 0.90 1.26 × 10−13 0.85 0.82–0.89
7:23293746 rs199347
G/A 0.368 0.389/0.412 5.62 × 10−14 0.90 8.66 × 10−6 0.92 3.51 × 10−18 0.91 0.89–0.93
8:16697091 rs591323
A/G 0.293 0.258/0.274 3.17 × 10−8 0.91 1.61 × 10−4 0.92 2.38 × 10−11 0.91 0.89–0.94
10:121536327 rs117896735
A/G 0.012 0.021/0.015 1.21 × 10−11 1.77 1.75 × 10−9 1.57 2.23 × 10−19 1.65 1.48–1.85
11:83544472 rs3793947
A/G 0.463 0.431/0.442 2.59 × 10−8 0.91 8.92 × 10−3 0.95 3.72 × 10−9 0.93 0.91–0.95
11:133765367 rs329648
T/C 0.327 0.369/0.351 8.05 × 10−12 1.11 9.16 × 10−4 1.07 1.11 × 10−13 1.09 1.07–1.12
12:40614434 rs76904798h
T/C 0.132 0.152/0.137 4.86 × 10−14 1.16 4.10 × 10−7 1.14 1.21 × 10−19 1.15 1.12–1.19
12:123303586 rs11060180
G/A 0.45 0.423/0.449 3.08 × 10−11 0.91 4.95 × 10−11 0.88 2.05 × 10−20 0.90 0.88–0.92
14:55348869 rs11158026
T/C 0.307 0.309/0.331 2.88 × 10−10 0.91 2.65 × 10−7 0.90 4.30 × 10−16 0.91 0.89–0.93
14:67984370 rs1555399
T/A 0.544 0.518/0.514 5.70 × 10−16 1.15 0.453 1.01 9.61 × 10−11 1.09 1.06–1.11
15:61994134 rs2414739
G/A 0.292 0.250/0.266 3.59 × 10−12 0.90 1.1 × 10−3 0.93 3.94 × 10−14 0.91 0.89–0.93
16:31121793 rs14235
A/G 0.397 0.388/0.378 3.63 × 10−12 1.10 0.0339 1.04 5.44 × 10−12 1.08 1.06–1.10
17:43994648 rs17649553
T/C 0.232 0.187/0.221 6.11 × 10−49 0.77 9.24 × 10−22 0.80 1.26 × 10−68 0.78 0.76–0.80
18:40673380 rs12456492
G/A 0.332 0.336/0.315 2.15 × 10−11 1.10 5.13 × 10−6 1.10 5.56 × 10−16 1.10 1.07–1.12
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Chang et al. Page 18
CHR:BPaSNP Candidate geneb
Effect allele/
EAF in
Genomes EAFcases/controlscPPDGenedORPDGene PPDWBSeORPDWBS Pdiscovery ORdiscovery
95% CI
rs62120679 LSM7 t/C 0.324 0.314/0.310 2.52 × 10−9 1.14 0.24O 1.03 6.64 × 10−7 1.08 1.05–1.11
rs8118008 DDRGK1 A/G 0.596 0.615/0.609 2.32 × 10−8 1.11 0.283 1.02 1.99 × 10−6 1.07 1.04–1.09
Rows in bold text refer to loci that did not pass the genome-wide significance threshold (5 × 10−8) in the discovery-phase meta-analysis.
Chromosome and physical position according to Hg19.
Details regarding the assignment of candidate genes are provided in the Online Methods.
Effect allele frequency (EAF) measured in PDWBS controls or cases.
value for SNP in the publicly available PDGene data (13,708 cases, 95,282 controls). Publicly available data for the following SNPs include an additional 5,450 cases and 5,798 controls genotyped on
NeuroX: rs115185635, rs35749011, rs117896735, rs62120679, rs9275326, rs3793947, rs1555399, rs1474055, and rs8118008.
value for SNP in PDWBS (6,476 cases, 302,042 controls).
The alternate SNP is genome-wide significant (rs12651582;
= 3.51 × 10−8).
The alternate SNP is genome-wide significant (rs76904798;
= 4.45 × 10−75).
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Chang et al. Page 19
Table 2
Seventeen novel regions associated with Parkinson’s disease at genome-wide significance levels
CHR:BPaSNP Candidate
Effect allele/
allele EAF in 1000
Genomes Pdiscovery ORdiscovery PNeuroX ORNeuroX Pjoint ORJoint
ORJoint (95%
1:226916078 rs4653767
C/T 0.315 2.40 × 10−10 0.92 0.017 0.93 1.63 × 10−11 0.92 0.90–0.94
2:102413116 rs34043159
C/T 0.352 3.83 × 10−8 1.07 1.91 × 10−4 1.11 5.48 × 10−11 1.08 1.06–1.10
2:166133632 rs353116
T/C 0.385 9.73 × 10−7 0.94 8.98 × 10−3 0.93 2.98 × 10−8 0.94 0.92–0.96
3:18277488 rs4073221
G/T 0.132 3.02 × 10−9 1.11 0.583 1.02 1.57 × 10−8 1.10 1.06–1.13
3:48748989 rs12497850
G/T 0.347 6.80 × 10−8 0.93 0.040 0.94 9.16 × 10−9 0.93 0.91–0.96
3:52816840 rs143918452
G/A 0.996 2.25 × 10−7 0.68 0.095 0.73 3.20 × 10−8 0.68 0.60–0.78
4:114360372 rs78738012
C/T 0.106 2.11 × 10−9 1.14 7.5 × 10−3 1.12 4.78 × 10−11 1.13 1.09–1.17
5:60273923 rs2694528
C/A 0.115 1.69 × 10−11 1.15 6.25 × 10−5 1.19 4.84 × 10−15 1.15 1.11–1.20
6:27681215 rs9468199
A/G 0.172 3.44 × 10−13 1.12 0.302 1.04 1.46 × 10−12 1.11 1.08–1.14
8:11707174 rs2740594
A/G 0.753 9.54 × 10−11 1.10 7.95 × 10−3 1.08 5.91 × 10−12 1.09 1.07–1.12
8:22525980 rs2280104
T/C 0.367 9.06 × 10−7 1.06 7.87 × 10−3 1.08 2.53 × 10−8 1.07 1.04–1.09
9:17579690 rs13294100
T/G 0.371 1.99 × 10−12 0.91 0.037 0.94 4.84 × 10−13 0.92 0.89–0.94
10:15569598 rs10906923
C/A 0.306 2.37 × 10−8 0.93 0.133 0.96 1.35 × 10−8 0.93 0.91–0.96
14:88472612 rs8005172
T/C 0.424 1.20 × 10−9 1.08 0.022 1.06 8.77 × 10−11 1.08 1.05–1.10
16:19279464 rs11343
T/G 0.454 1.46 × 10−9 1.07 0.019 1.06 9.13 × 10−11 1.07 1.05–1.10
16:52599188 rs4784227
T/C 0.265 8.29 × 10−8 1.08 1.47 × 10−4 1.12 9.75 × 10−11 1.09 1.06–1.12
17:40698158 rs601999
C/T 0.699 8.03 × 10−9 0.93 NA NA NA NA NA
Summary statistics are shown for the discovery cohort (PDWBS and PDGene), NeuroX (5,851 cases, 5,866 controls), and the joint meta-analysis of the discovery and NeuroX data.
Additional summary statistics for NeuroX and the joint meta-analysis are available in Supplementary Table 5. EAF, effect allele frequency.
Chromosome and physical position according to Hg19.
Details regarding the assignment of candidate genes are provided in the Online Methods.
NeuroX and joint statistics are shown for proxy SNP rs1293298.
Nat Genet
. Author manuscript; available in PMC 2018 February 14.
... In iPSC-derived LRRK2-G2019S astrocytes, colocalization between markers of autophagosomes (LC3) and lysosomes (LAMP1) Maintenance of lysosomal pH (around 4.5-5) is a crucial step for the activity of enzymes and degradation of lysosomal content . Interestingly, LRRK2 interacts with the ATP6V0A1 proton pump, a risk factor for PD (Chang et al., 2017). ATP6V0A1 is involved in the acidification of various organelles (Chang et al., 2017;Wallings et al., 2019). ...
... Interestingly, LRRK2 interacts with the ATP6V0A1 proton pump, a risk factor for PD (Chang et al., 2017). ATP6V0A1 is involved in the acidification of various organelles (Chang et al., 2017;Wallings et al., 2019). Interestingly, in cultured primary cortical neurons, the interaction between LRRK2-R1441C and ATPV60A1 is reduced, with reduced acidification of lysosomes followed by a reduction in lysosomal degradation (Wallings et al., 2019). ...
Parkinson’s disease (PD) is the most common neurodegenerative motor disease. Mutations in the leucine rich repeat kinase 2 (LRRK2) gene are linked to autosomal dominant parkinsonism, and genomic variation at the LRRK2 locus is associated with increased risk for sporadic PD. LRRK2 is a multi-phosphorylated protein and reduced phosphorylation is reported in PD patient brains as well as for some disease mutant forms of LRRK2. Dephosphorylation leads to alterations in LRRK2 interactions and subcellular localization. PD is characterized by impaired intracellular trafficking. However, the link between LRRK2 phosphorylation and membrane trafficking is not fully understood.The idea behind this project is to understand the consequences of phosphorylation or dephosphorylation of LRRK2 on its cellular functions.To this purpose, we generated LRRK2 phosphorylation site mutants and studied how these impacted LRRK2 catalytic activity, neurite growth, localization, protein binding and lysosomal physiology in cell models.We show that phosphorylation of RAB8a and RAB10 substrates are reduced with phosphomimicking forms of LRRK2, while RAB29 induced activation of LRRK2 kinase activity is enhanced for phosphodead forms of LRRK2 (LRRK2 S860/910/935/955/973/976A). Considering the hypotheses that PD pathology is associated to increased LRRK2 kinase activity, our results suggest that for its heterologous phosphorylation sites, LRRK2 phosphorylation correlates to protective phenotypes and LRRK2 dephosphorylation correlates to deleterious phenotypes.
... Since initial reports in 1996, mutations in the SNCA gene have been associated with PD, but people carrying the same mutation may show different degrees of severity in the phenotype, as is the case with A53T, suggesting the existence of genetic modifiers. Recently, the gene that encodes the ubiquitously expressed Inositol-trisphosphate 3-kinase B (ITPKB) was associated with Parkinson's disease (PD) in a genome-wide association study [12]. ITPKB phosphorylates inositol 1,4,5-trisphosphate (IP3) and converts it into inositol 1,3,4,5 tetrakisphosphate (IP4) via a Ca 2+ /Calmodulin-dependent mechanism [13]. ...
... So far, more than 20 genes and over 90 genetic risk variants have been associated with the insurgence and or progression of familial and sporadic PD [30,34], indicating that the disease is, at least in part, driven or modified by genetic factors. The identification of several of these factors in PD converge to common pathways such as neuronal networking, vesicular trafficking, autophagy, mitochondrial metabolism and the lysosomal pathway [12]. Of interest, rare variants located in different genes can be co-inherited in about 17% of familial/sporadic PD, pointing again to a polygenic contribution to the pathology [35]. ...
Full-text available
Autosomal dominant mutations in the gene encoding α-synuclein (SNCA) were the first to be linked with hereditary Parkinson’s disease (PD). Duplication and triplication of SNCA has been observed in PD patients, together with mutations at the N-terminal of the protein, among which A30P and A53T influence the formation of fibrils. By overexpressing human α-synuclein in the neuronal system of Drosophila, we functionally validated the ability of IP3K2, an ortholog of the GWAS identified risk gene, Inositol-trisphosphate 3-kinase B (ITPKB), to modulate α-synuclein toxicity in vivo. ITPKB mRNA and protein levels were also increased in SK-N-SH cells overexpressing wild-type α-synuclein, A53T or A30P mutants. Kinase overexpression was detected in the cytoplasmatic and in the nuclear compartments in all α-synuclein cell types. By quantifying mRNAs in the cortex of PD patients, we observed higher levels of ITPKB mRNA when SNCA was expressed more (p < 0.05), compared to controls. A positive correlation was also observed between SNCA and ITPKB expression in the cortex of patients, which was not seen in the controls. We replicated this observation in a public dataset. Our data, generated in SK-N-SH cells and in cortex from PD patients, show that the expression of α-synuclein and ITPKB is correlated in pathological situations.
... Cathepsin D is an aspartyl protease that is highly expressed in the brain and has been shown to play a central role in degradation of α-synuclein (Stoka et al. 2016;Aufschnaiter et al. 2017). Cathepsin B is another member of the lysosomal cathepsin family of proteases that has been classified as a risk locus for PD by genome wide association studies carried out on a PD cohort of more than 6 thousand subjects (Chang et al. 2018). ...
Full-text available
Parkinson’s disease (PD) is the second most common neurodegenerative disorder. The exact molecular mechanism of disease remains unclear. Several factors are proposed to play part including, but not limited to, decreased activity of mitochondrial complex I and lysosomal glucocerebrosidase enzymes and disrupted cellular antioxidant defence and lysosomal acidification. In addition, there is growing support for a role of organelle crosstalk between mitochondria and lysosome, the disruption of which is proposed to play part in PD pathology. The nature and consequence of this crosstalk remains unclear. The SH-SY5Y neuronal cell line model is commonly used to investigate PD mechanisms and potential therapeutics. However, functional analysis of the suitability of the cell line in its proliferative state or the necessity for differentiation remains unclear. Furthermore, iPSC-derived dopaminergic neurons are another commonly used model for PD and related diseases however, validating their functional dopamine metabolism is important to determine disease mechanism and test potential therapeutics. In this thesis, a host of biochemical tools, including HPLC measurement of neurotransmitter metabolites and enzyme activity assays, were used to elucidate the aforementioned ambiguities. The findings demonstrate that although there are similarities between proliferative and differentiated phenotypes of SH-SY5Y cells, there are also significant differences. Notably, the rate of dopamine turnover and the activity of lysosomal glucocerebrosidase were significantly higher in differentiated SH-SY5Y cells. In contrast, mitochondrial electron transport chain complexes’ activities were similar between the two phenotypes, despite a significant difference in mitochondrial content. Therefore, care should be taken when choosing either phenotype as a PD model. In addition, 4the findings demonstrate that inhibition of either mitochondrial complex I or lysosomal glucocerebrosidase affect both the ratio of pro-cathepsin D/cathepsin D protein expression and enzyme activity. Cathepsin D is one of the most ubiquitous lysosomal enzymes, the state of which can be used as reflection of the degree of lysosomal acidification. This shines a light on the potential involvement of both lysosomal glucocerebrosidase and mitochondrial complex I in maintenance of lysosomal acidification. This could be a consequence of a more dynamic crosstalk between mitochondria and lysosomes than previously thought. Moreover, the work presented provides a method for validation of the dysfunctional dopamine metabolism in iPSC derived dopaminergic neuronal disease models for aromatic amino acid decarboxylase deficiency and PD patients carrying mutations in PINK1. In addition, it provides a proof of concept for the effectiveness of both lentivirus-based gene therapy and levodopa treatment to restore dopamine metabolism in aromatic amino acid decarboxylase deficiency.
... 6 This makes the other components of the MSL and NSL complexes of potential interest, with the latter particularly important in Parkinson's disease as it contains KAT8 Regulatory NSL Complex Subunit 1 (KANSL1), another protein encoded by a Parkinson's disease candidate gene. 3,7 KANSL1 is contained within the 970kb inversion polymorphism on chromosome 17q21, located within a linkage disequilibrium (LD) block of approximately 2Mb which gives rise to H1/H2 haplotype variation. 8 The H1 haplotype has well established links to neurodegenerative disease, specifically progressive supranuclear palsy, Alzheimer's and Parkinson's disease. ...
Full-text available
Genetic variants conferring risk for Parkinsons disease have been highlighted through genome-wide association studies, yet exploration of their specific disease mechanisms is lacking. Two Parkinsons disease candidate genes, KAT8 and KANSL1, identified through genome-wide studies and a PINK1-mitophagy screen, encode part of the histone acetylating non-specific lethal complex. This complex localises to the nucleus, where it has a role in transcriptional activation, and to mitochondria, where it has been suggested to have a role in mitochondrial transcription. In this study, we sought to identify whether the non-specific lethal complex has potential regulatory relationships with other genes associated with Parkinsons disease in human brain. Correlation in the expression of non-specific lethal genes and Parkinsons disease-associated genes was investigated in primary gene co-expression networks utilising publicly available transcriptomic data from multiple brain regions (provided by the Genotype-Tissue Expression Consortium and UK Brain Expression Consortium), whilst secondary networks were used to examine cell-type specificity. Reverse engineering of gene regulatory networks generated regulons of the complex, which were tested for heritability using stratified linkage disequilibrium score regression and then validated in vitro using the QuantiGene multiplex assay. Significant clustering of non-specific lethal genes was revealed alongside Parkinsons disease-associated genes in frontal cortex primary co-expression modules. Both primary and secondary co-expression modules containing these genes were enriched for mainly neuronal cell types. Regulons of the complex contained Parkinsons disease-associated genes and were enriched for biological pathways genetically linked to disease. When examined in a neuroblastoma cell line, 41% of prioritised gene targets showed significant changes in mRNA expression following KANSL1 or KAT8 perturbation. In conclusion, genes encoding the non-specific lethal complex are highly correlated with and regulate genes associated with Parkinsons disease. Overall, these findings reveal a potentially wider role for this protein complex in regulating genes and pathways implicated in Parkinsons disease.
... In previous studies, we performed cDNA microarray analysis using hippocampal RNA isolated from C/EBPβ+/+ and C/EBPβ−/− mice and observed transcriptome modifications [19]. Based on these findings, we further studied published data on GWAS meta-analysis and RNA-seq in PD patients compared to controls [39][40][41][42][43]. We then searched for putative C/EBPβ binding sites in the −1300/+100 bp region from the transcription start site of selected genes by the use of UCSC genome browser [44], in order to find candidate genes involved in PD that could be regulated by this transcription factor. ...
Full-text available
Parkinson’s disease (PD) is a neurodegenerative disorder that results from the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc). Since there are only symptomatic treatments available, new cellular and molecular targets involved in the onset and progression of this disease are needed to develop effective treatments. CCAAT/Enhancer Binding Protein β (C/EBPβ) transcription factor levels are altered in patients with a variety of neurodegenerative diseases, suggesting that it may be a good therapeutic target for the treatment of PD. A list of genes involved in PD that can be regulated by C/EBPβ was generated by the combination of genetic and in silico data, the mitochondrial transcription factor A (TFAM) being among them. In this paper, we observed that C/EBPβ overexpression increased TFAM promoter activity. However, downregulation of C/EBPβ in different PD/neuroinflammation cellular models produced an increase in TFAM levels, together with other mitochondrial markers. This led us to propose an accumulation of non-functional mitochondria possibly due to the alteration of their autophagic degradation in the absence of C/EBPβ. Then, we concluded that C/EBPβ is not only involved in harmful processes occurring in PD, such as inflammation, but is also implicated in mitochondrial function and autophagy in PD-like conditions.
... PD is associated with both genetic and environmental factors (Di Monte, 2003;Dunn et al., 2019;Hipp et al., 2019). At present, numerous genetic variants associated with PD risk have been discovered (Chang et al., 2017;Nalls et al., 2019), however, the neural mechanisms underlying PD pathogenesis of these genetic variants are largely unknown. Currently, most researchers focus on the molecular mechanisms of risk genes in PD pathogenesis using cell-based or animal-based models (Nguyen et al., 2011;Fusco et al., 2017;Burmann et al., 2020;Watanabe et al., 2020;Wie et al., 2021), however, whether PD-associated risk genes contribute to brain abnormalities revealed by neuroimaging studies are poorly understood. ...
Background: Currently, over 90 genetic loci have been found to be associated with Parkinson's disease (PD) in genome-wide association studies, nevertheless, the effects of these genetic variants on the clinical features and brain structure of PD patients are largely unknown. Objective: This study investigated the effects of microtubule-associated protein tau (MAPT), rs17649553 (C>T), a genetic variant associated with reduced PD risk, on the functional and structural networks of PD patients. Methods: Totally 83 PD subjects from Parkinson's Progression Markers Initiative database were included for this study. They all received structural and functional magnetic resonance imaging and whole exome sequencing. The effects of MAPT rs17649553 on brain structural and functional networks were systematically assessed. Results: MAPT rs17649553 T allele was associated with better verbal memory in PD patients. In addition, MAPT rs17649553 significantly reshaped the topology of gray matter covariance network and white matter network but not that of functional network. Both the network metrics in gray matter covariance network and white matter network were correlated with verbal memory, however, the mediation analysis showed that it was the small-worldness topology in white matter network that mediated the effects of MAPT rs17649553 on verbal memory. Conclusion: In sum, we proposed that MAPT rs17649553 T allele was associated with superior structural network topology and better verbal memory in PD. Future studies are needed to determine the role of MAPT rs17649553 in PD initiation and progression.
Full-text available
Alternative polyadenylation (APA) plays an essential role in brain development; however, current transcriptome-wide association studies (TWAS) largely overlook APA in nominating susceptibility genes. Here, we performed a 3′ untranslated region (3′UTR) APA TWAS (3′aTWAS) for 11 brain disorders by combining their genome-wide association studies data with 17,300 RNA-seq samples across 2,937 individuals. We identified 354 3′aTWAS-significant genes, including known APA-linked risk genes, such as SNCA in Parkinson’s disease. Among these 354 genes, ~57% are not significant in traditional expression- and splicing-TWAS studies, since APA may regulate the translation, localization and protein-protein interaction of the target genes independent of mRNA level expression or splicing. Furthermore, we discovered ATXN3 as a 3′aTWAS-significant gene for amyotrophic lateral sclerosis, and its modulation substantially impacted pathological hallmarks of amyotrophic lateral sclerosis in vitro. Together, 3′aTWAS is a powerful strategy to nominate important APA-linked brain disorder susceptibility genes, most of which are largely overlooked by conventional expression and splicing analyses.
Over the last several decades, the environment of neuroinnovation has evolved on many fronts, in ways that have changed the ethical context of biomedical and translational research in general. Ethical frameworks of biomedical innovation are based not only on principles, but on assumptions about the locus of moral obligations, power dynamics, and interests. The post-World War II era, during which significant advances in biomedical research have occurred, has also seen major social changes that could be disruptive to neuroinnovation, for better and for worse. These changes include a shift of the center of gravity of research activity from the academic to the private sector, a blurring of the distinction between clinical research and clinical practice, and the increased empowerment of non-scientists in the research enterprise. At the same time, digital technologies are enabling the collection, distribution, and analysis of large amounts of neurological, behavioral, health, and other data. The broad availability of data further enhances the societal shifts, which together, could empower patients and the public to drive neuroinnovation towards more just purposes. However, the disruption of established ethical and regulatory frameworks, which are based on moral obligations of professionals, could lead to diminished safety and efficacy of new neurotechnologies.KeywordsBiomedical innovationBioethicsBig dataPatient advocacyCitizen science
Full-text available
The porcine gut is increasingly regarded as a useful translational model. The enteric nervous system in the colon coordinates diverse functions. However, knowledge of the molecular profiling of porcine enteric nerve system and its similarity to that of human is still lacking. We identified the distinct transcriptional programs associated with functional characteristics between inner submucosal and myenteric ganglia in porcine proximal and distal colon using bulk RNA and single-cell RNA sequencing. Comparative transcriptomics of myenteric ganglia in corresponding colonic regions of pig and human revealed highly conserved programs in porcine proximal and distal colon, which explained >96% of their transcriptomic responses to vagal nerve stimulation, suggesting that porcine proximal and distal colon could serve as predictors in translational studies. The conserved programs specific for inflammatory modulation were displayed in pigs with vagal nerve stimulation. This study provides a valuable transcriptomic resource for understanding of human colonic functions and neuromodulation using porcine model.
Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the common causes of inherited Parkinson's disease (PD) and emerged as a causative PD gene. Particularly, LRRK2-Gly2019Ser mutation was reported to alter the early phase of neuronal differentiation, increasing cell death. Selective inhibitors of LRRK2 kinase activity were considered as a promising therapeutic target for PD treatment. However, the development of effective brain-penetrant LRRK2 inhibitors remains challenging. Recently, we have developed a novel positron emission tomography (PET) radioligand for LRRK2 imaging and demonstrated preferable tracer properties in rodents. Herein, we evaluate [18F]PF-06455943 quantification methods in the nonhuman primate (NHP) brain using full kinetic modeling with radiometabolite-corrected arterial blood samples, and homologous blocking with two doses (0.1 and 0.3 mg/kg). Kinetic analysis results demonstrated that a two-tissue compartmental model and a Logan graphical analysis are appropriate for [18F]PF-06455943 PET quantification. In addition, we observed that total distribution volume (VT) values can be reliably estimated with as short as a 30 min scan duration. Homologous blocking studies confirmed the specific binding of [18F]PF-06455943 and revealed that the nonradioactive mass of PF-06455943 achieved 45-55% of VT displacement in the whole brain. This work supports the translation of [18F]PF-06455943 PET imaging for the human brain and target occupancy studies.
Full-text available
A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at
Full-text available
Objectives We assessed the current genetic evidence for the involvement of various cell types and tissue types in the etiology of neurodegenerative diseases, especially in relation to the neuroinflammatory hypothesis of neurodegenerative diseases. Methods We obtained large‐scale genome‐wide association study (GWAS) summary statistics from Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). We used multiple sclerosis (MS), an autoimmune disease of the central nervous system, as a positive control. We applied stratified LD score regression to determine if functional marks for cell type and tissue activity, and gene‐set lists were enriched for genetic heritability. We compared our results to those from two gene‐set enrichment methods (Ingenuity Pathway Analysis and enrichr). Results There were no significant heritability enrichments for annotations marking genes active within brain regions, but there were significant heritability enrichments for annotations marking genes active within cell types that form part of both the innate and adaptive immune systems. We found this for MS (as expected) and also for AD and PD. The strongest signals were from the adaptive immune system (e.g., T cells) for PD, and from both the adaptive (e.g., T cells) and innate (e.g., CD14: a marker for monocytes, and CD15: a marker for neutrophils) immune systems for AD. Annotations from the liver were also significant for AD. Pathway analysis provided complementary results. Interpretation For AD and PD, we found significant enrichment of heritability in annotations marking gene activity in immune cells.
Full-text available
We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at a false discovery rate of 10%) associated with multiple traits. Several loci are associated with multiple phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of the traits, including risk of schizophrenia (rs13107325: log-transformed odds ratio (log OR) = 0.15, P = 2 × 10(-12)) and Parkinson disease (log OR = -0.15, P = 1.6 × 10(-7)), among others. Second, we used these loci to identify traits that have multiple genetic causes in common. For example, variants associated with increased risk of schizophrenia also tended to be associated with increased risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased body mass index causally increases triglyceride levels.
Full-text available
Autophagy is the primary process for recycling cellular constituents through lysosomal degradation. In addition to nonselective autophagic engulfment of cytoplasm, autophagosomes can recognize specific cargo by interacting with ubiquitin-binding autophagy receptors such as SQSTM1/p62 (sequestosome 1). This selective form of autophagy is important for degrading aggregation-prone proteins prominent in many neurodegenerative diseases. We carried out a high content image-based siRNA screen (4 to 8 siRNA per gene) for modulators of autophagic flux by monitoring fluorescence of GFP-SQSTM1 as well as colocalization of GFP-SQSTM1 with LAMP2 (lysosomal-associated membrane protein 2)-positive lysosomal vesicles. GFP-SQSTM1 and LAMP2 phenotypes of primary screen hits were confirmed in 2 cell types and profiled with image-based viability and MTOR signaling assays. Common seed analysis guided siRNA selection for these assays to reduce bias toward off-target effects. Confirmed hits were further validated in a live-cell assay to monitor fusion of autophagosomes with lysosomes. Knockdown of 10 targets resulted in phenotypic profiles across multiple assays that were consistent with upregulation of autophagic flux. These hits include modulators of transcription, lysine acetylation, and ubiquitination. Two targets, KAT8 (K[lysine] acetyltransferase 8) and CSNK1A1 (casein kinase 1, α 1), have been implicated in autophagic regulatory feedback loops. We confirmed that CSNK1A1 knockout (KO) cell lines have accelerated turnover of long-lived proteins labeled with (14)C-leucine in a pulse-chase assay as additional validation of our screening assays. Data from this comprehensive autophagy screen point toward novel regulatory pathways that might yield new therapeutic targets for neurodegeneration.
Full-text available
Upregulation of sodium channel SCN3A expression in epileptic tissues is known to contribute to enhancing neuronal excitability and the development of epilepsy. Therefore, certain strategies to reduce SCN3A expression may be helpful for seizure control. Here, we reveal a novel role of valproate (VPA) in the epigenetic downregulation of Scn3a expression. We found that VPA, instead of carbamazepine (CBZ) and lamotrigine (LTG), could significantly downregulate Scn3a expression in mouse Neuro-2a cells. Luciferase assays and CpG methylation analyses showed that VPA induced the methylation at the -39C site in Scn3a promoter which decreased the promoter activity. We further showed that VPA downregulated the expression of methyl-CpG-binding domain protein 2 (MBD2) at the posttranscriptional level and knockdown of MBD2 increased Scn3a expression. In addition, we found that VPA induced the expression of fat mass and obesity-associated (FTO) protein and FTO knockdown abolished the repressive effects of VPA on MBD2 and Nav1.3 expressions. Furthermore, VPA, instead of other two anticonvulsant drugs, induced the expressions of Scn3a and Mbd2 and reduced Fto expression in the hippocampus of VPA-treated seizure mice. Taken together, this study suggests an epigenetic pathway for the VPA-induced downregulation of Scn3a expression, which provides a possible role of this pathway in the anticonvulsant action of VPA.
Ancestry deconvolution, the task of identifying the ancestral origin of chromosomal segments in admixed individuals, has important implications, from mapping disease genes to identifying candidate loci under natural selection. To date, however, most existing methods for ancestry deconvolution are typically limited to two or three ancestral populations, and cannot resolve contributions from populations related at a sub-continental scale. We describe Ancestry Composition, a modular three-stage pipeline that efficiently and accurately identifies the ancestral origin of chromosomal segments in admixed individuals. It assumes the genotype data have been phased. In the first stage, a support vector machine classifier assigns tentative ancestry labels to short local phased genomic regions. In the second stage, an autoregressive pair hidden Markov model simultaneously corrects phasing errors and produces reconciled local ancestry estimates and confidence scores based on the tentative ancestry labels. In the third stage, confidence estimates are recalibrated using isotonic regression. We compiled a reference panel of almost 10,000 individuals of homogeneous ancestry, derived from a combination of several publicly available datasets and over 8,000 individuals reporting four grandparents with the same country-of-origin from the member database of the personal genetics company, 23andMe, Inc., and excluding outliers identified through principal components analysis (PCA). In cross-validation experiments, Ancestry Composition achieves high precision and recall for labeling chromosomal segments across over 25 different populations worldwide.
Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia's strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.