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. Author manuscript; available in PMC: 2022 Jan 3.
Published in final edited form as: Neurobiol Aging. 2017 Aug 2;59:220.e11–220.e18. doi: 10.1016/j.neurobiolaging.2017.07.009

Establishing the role of rare coding variants in known Parkinson’s disease risk loci

Iris E Jansen 1,2, J Raphael Gibbs 3, Mike A Nalls 3,4, T Ryan Price 5, Steven Lubbe 6, Jeroen van Rooij 7, André G Uitterlinden 7,8,9, Robert Kraaij 7,8,9, Nigel M Williams 10, Alexis Brice 11,12, John Hardy 13, Nicholas W Wood 14, Huw R Morris 14, Thomas Gasser 15, Andrew B Singleton 3, Peter Heutink 2,15, Manu Sharma 15,16,*; International Parkinson’s Disease Genomics Consortium*
PMCID: PMC8722270  NIHMSID: NIHMS897029  PMID: 28867149

Abstract

Many common genetic factors have been identified to contribute to PD susceptibility, improving our understanding of the related underlying biological mechanisms. The involvement of rarer variants in these loci have been poorly studied. Using International Parkinson’s Disease Genomics Consortium datasets, we performed a comprehensive study to determine the impact of rare variants in 26 previously published GWAS loci in PD. We applied Prixfixe to select the putative causal genes underneath the GWAS peaks, which was based on underlying functional similarities. The Sequence Kernel Association Test was used to analyze the joint effect of rare, common or both types of variants on PD susceptibility. All genes were tested simultaneously as a gene-set and each gene individually. We observed a moderate association of common variants, confirming the involvement of the known PD risk loci within our genetic datasets. Focusing on rare variants we identified additional association signals for LRRK2, STBD1, and SPATA19. Our study suggests an involvement of rare variants within several putatively causal genes underneath previously identified PD GWAS peaks.

Keywords: Parkinson’s disease, common risk loci, rare variants, whole exome sequencing, variant aggregation test

1. Introduction

Genetic factors play an important role in Parkinson’s disease (PD) pathogenesis. In addition to the discovery of rare variants using family-based linkage studies, resulting in the identification of, for example SNCA, LRRK2, parkin, DJ-1, PINK1 and VPS35, numerous genome-wide association studies (GWAS) have shown that common genetic variants increase PD risk (Bras, et al., 2015). The most recent and largest PD association study (Nalls, et al., 2014b) identified over 20 common risk variants, confirming many previously associated risk factors.

Nevertheless, heritability estimates indicate that additional genetic risk factors remain to be discovered since a relatively large fraction of PD heritability cannot be explained by known PD risk loci or Mendelian genes (Do, et al., 2011,Keller, et al., 2012,Pihlstrom and Toft, 2011). GWAS approaches are primarily designed to identify common risk variants by the usage of genotyping arrays. However, emerging evidence suggests that rare variants may explain part of the missing heritability (Manolio, et al., 2009,Zuk, et al., 2014). Rare variants in protein coding regions are more likely to affect the function of a gene than common variants which tag the causal variants via linkage disequilibrium (LD) and are often located in non-coding regions of the genome (Nelson, et al., 2012,Tennessen, et al., 2012). Therefore, rare variants might be of more importance to complex diseases than predicted by the Common Disease-Common Variant hypothesis (Botstein and Risch, 2003,Lander, 1996,Pritchard and Cox, 2002,Sharma, et al., 2014). In contrast to GWAS, exome sequencing studies aim at systematically analyzing coding regions of the genome to identify causal variants in complex diseases (Kiezun, et al., 2012). Exome studies have been proven to be effective for studying familial diseases (Bamshad, et al., 2011) but an increasing number of applications for populations-based studies have been developed (Cirulli, et al., 2015,Purcell, et al., 2014).

In PD, multiple genes have been shown to harbor both common and rare variants which contribute to disease pathogenesis. SNCA and LRRK2 contain both PD-risk associated rare variants with Mendelian effects as common variants that increase PD risk in sporadic patients (Edwards, et al., 2010,Nalls, et al., 2014b,Nalls, et al., 2011,Paisan-Ruiz, et al., 2004,Polymeropoulos, et al., 1997,Simon-Sanchez, et al., 2009,Zimprich, et al., 2004). GBA, for which an association was first seen in families with Gaucher’s disease and parkinsonism (Goker-Alpan, et al., 2004), is furthermore shown to play a role in PD by both rare coding variants and common risk variants (Do, et al., 2011,Nalls, et al., 2014b,Pankratz, et al., 2012). Thus, we hypothesize that rare coding variants in the known risk loci for sporadic PD are involved in the genetic etiology of PD. The combined effect of rare variants within recently identified PD risk loci will likely explain an additional portion of PD heritability. We aim to assess this hypothesis by determining the genetic burden of rare variants in the PD risk loci using two exome cohorts of the International Parkinson’s Disease Genomics Consortium (IPDGC).

2. Methods

2.1. Subjects

All PD cases included in this study have given written informed consent. Relevant local ethical committees for medical research approved involvement in genetic studies. The PD patients were diagnosed using the UK Brain Bank criteria (Hughes, et al., 1992).

2.2. Whole exome sequencing dataset

The whole exome sequencing (WES) dataset includes 1,167 PD cases and 1,685 controls (post QC) of European ancestry. The PD patients have a tendency towards a young age of onset with an average of 41.2 years (SD = 10.9). 1,201 controls originate from the Rotterdam Study version 1 (RSX1), as we merged the IPGDC WES data with the RSX1 WES data (Hofman, et al., 2015). The samples were sequenced in different batches with two exome capture kits: EZ Exome Library v2.0 (Roche/Nimblegen) and Truseq Exome Enrichment Kit targeting 44.1 Mb and 62 Mb, respectively (Supplementary Table 1). To account for putative technical differences between the different capture kits, we only considered variants that were targeted by both capture protocols and included preQC individual sample missingness (as a reference to sequencing coverage) as covariates during all genetic analyses.

On average, 94.4% of the exome was covered for at least 10x. The 100-bp paired-end reads were sequenced on a HiSeq2000 and aligned to the human reference genome (build hg19) using Barrow Wheeler Aligner (BWA)-MEM (Li and Durbin, 2009). Genome Analysis Toolkit (McKenna, et al., 2010) (GATK) called single nucleotide variants (SNVs) and small insertions/deletions (indels) for each sample, resulting in individual gVCF files. Genotypes of all IPDGC and RSX1 exome samples were then jointly called and recalibrated, allowing to merge the distinct WES datasets in a correct manner. Standard GATK filter steps were applied, together with a minimum genotype quality Phred-score of 20 and depth of 8, to only select high-quality variants. Only bi-allelic calls were considered that were located in regions targeted by both capture kits. Supplementary Table 2 reports the exons that have been excluded due to insufficient coverage within one of the exome capture protocols.

2.3. NeuroX dataset

The NeuroX dataset encompasses 6,801 PD cases and 5,970 controls (post QC) of European ancestry. Overlapping samples with the WES dataset were excluded. The average age of onset of the PD patients is 63.0 years (SD = 12.4). The Exome NeuroX array (Nalls, et al., 2014a) was used consisting of ~240,000 exonic variants standard to the Illumina HumanExome array v1.1 and ~25,000 variants focused on neurologic and neurodegenerative diseases.

2.4. Quality procedures

For individual QC in both the WES and the NeuroX datasets, samples were removed when showing gender ambiguity, dubious heterozygosity/genotype calls, evidence of relatedness, or being a population outlier. The latter two were calculated with LD-pruned common variants. Variant QC procedures were slightly different for the two different datasets. For the WES dataset, variants passed QC when having a minimum call rate > 85% and being in Hardy-Weinberg equilibrium (HWE p-values > 1e-8 based on controls). For the NeuroX dataset, variants were excluded for subsequent analyses with a minimum call rate < 95%, a HWE p-value < 1e-6, or with significant differences in missingness rate between cases and controls.

2.5. Causal gene selection within PD risk loci

Based on the most recent and largest GWAS (Lill, et al., 2012,Nalls, et al., 2014b) we selected 26 loci containing at least one top SNP nominated in meta-analysis with p < 5.00e-08 (as reported by pdgene.org). The published SNPs associated with PD are not the causal variants but rather tag the unknown causal variants with which they are in LD. As the causal variant (and therefore also the related gene) has not been determined for most of the PD risk loci, we explored the involvement of rare variants in PD susceptibility by using the PrixFixe strategy, which selects one gene per locus based on functional similarities of genes within the LD-blocks from the different loci.

The functional similarity is defined as the degree of shared biological function and is determined by overlapping biological features such as protein domains, transcription factor binding sites, gene-expression, phylogenetic profiles and protein-protein interactions. Based on these features, cofunction networks are generated which connect genes that are likely to share the same underlying molecular pathway. Genes that are strongly connected to other candidate genes obtain a higher PrixFixe score and therefore prioritized as causal gene. As this approach is based on genome-wide datasets and is not performed with disease-related biological assumptions, the PrixFixe strategy aims to prioritize genes without the usual text mining bias caused by literature-based knowledge (Edwards, et al., 2011).

The most significantly associated SNPs from the recent meta-analysis by Nalls et al. (Nalls, et al., 2014b) were used as seeding SNPs to define the LD region per PD locus. If a SNP was not applicable to be used as seeding SNP (not present in either the current dbSNP 137 or HapMap public resources), the next strongest associated SNP or a SNP in high LD (r2 > 0.8) within the same locus was used as a seed. We were unable to define a legitimate seeding SNP for 3 loci (rs71628662, rs591323, rs2414739). LD-regions were based on the CEU phase III population with a minimal R2 of 0.5. The final Prixfixe gene-set consists of 23 genes for downstream analyses (Table 1).

Table 1.

Selected set of genes.

Polymorphism Location (hg19) P -value Seeding SNP Prixfixe gene
rs71628662 chr1:155359992 6.86 × 10−28 NA
rs823118 chr1:205723572 1.96 × 10−16 rs823114 RAB7L1
rs10797576 chr1:232664611 1.76 × 10−10 rs2182431 SIPA1L2
rs6430538 chr2:135539967 3.35 × 10−19 rs6430538 ACMSD
rs1955337 chr2:169129145 1.67 × 10−20 rs2390669 STK39
rs12637471 chr3:182762437 5.38 × 10−22 rs12637471 LAMP3
rs11724635 chr4:15737101 4.26 × 10−17 rs11724635 FBXL5
rs6812193 chr4:77198986 1.85 × 10−11 rs6812193 STBD1
rs356182 chr4:90626111 1.85 × 10−82 rs356219 SNCA
rs34311866 chr4:951947 6.0 × 10−41 rs748483 MFSD7
rs9275326 chr6:32666660 5.81 × 10−13 rs9275311 HLA-DRB5
rs199347 chr7:23293746 5.62 × 10−14 rs199347 GPNMB
rs591323 chr8:16697091 3.17 × 10−8 NA
rs117896735 chr10:121536327 1.21 × 10−11 rs10886515 RGS10
rs329648 chr11:133765367 8.05 × 10−12 rs329648 SPATA19
rs3793947 chr11:83544472 2.59 × 10−08 rs1400313 DLG2
rs11060180 chr12:123303586 3.08 × 10−11 rs11060180 HIP1R
rs76904798 chr12:40614434 4.86 × 10−14 rs2708435 LRRK2
rs7155501 chr14:55347827 1.25 × 10−10 rs2878174 LGALS3
rs1555399 chr14:67984370 5.70 × 10−16 rs7155830 ARG2
rs2414739 chr15:61994134 3.59 × 10−12 NA
rs14235 chr16:31121793 3.63 × 10−12 rs14235 PRSS8
rs17649553 chr17:43994648 6.11 × 10−49 rs17649553 MAPT
rs12456492 chr18:40673380 2.15 × 10−11 rs12456492 RIT2
rs62120679 chr19:2363319 2.52 × 10−09 rs2074546 PLEKHJ1
rs55785911 chr20:3153503 3.30 × 10−10 rs2295545 AVP
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P -value = Meta p-value as reported on pdegene.org. Seeding SNP = input SNP for PrixFixe software. Prixfixe gene = genes selected based on underlying functional similarities, which is determined by overlapping biological features such as protein domains, transcription factor binding sites, gene expression, phylogenietic profiles and literature-based protein-protein interactions.

2.6. Variant selection

To enrich both genetic datasets for deleterious variants we selected multiple subsets of variants, differing in the method and stringency to select pathogenic variants. Based on variant annotation with ANNOVAR (Wang, et al., 2010), 3 distinct subsets of variants were created, including: 1) all exonic variants (disruptive, splicing, (non)synonymous and (non)frameshift indels), 2) amino-acid changing variants (same as previous except for synonymous) 3) amino-acid changing (AAchanging) variants that are predicted to be deleterious. The latter subset includes variants that are predicted to be pathogenic (CADD-score ≥ 12.37 (Amendola, et al., 2015)) by Combined Annotation Dependent Depletion (CADD) v1 (Kircher, et al., 2014). Figure 1 displays a workflow of the classification of the different variant subsets. The exonic subset was exclusively tested for the gene-set analysis to determine the involvement of the known PD risk loci in the WES and NeuroX dataset.

Figure 1.

Figure 1.

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Flowchart of variant subset classification. The variants undergo multiple analyses procedures, including quality control, selection of variants within PD loci and functional annotation. Each genetic dataset (WES and NeuroX) is tested for 6 different variant categories, differing in causal gene selection approach and functionality of variant.

2.7. Variant aggregation analysis

The Sequence Kernel Association Test (SKAT) (Ionita-Laza, et al., 2013,Wu, et al., 2011) was used to perform burden analyses. The MAF threshold, separating the rare and common variants, was based on the total sample size using the formula (T = 1/(√(2n))) suggested by SKAT (Ionita-Laza, et al., 2013), therefore resulting in the MAF thresholds of 0.013 and 0.006 for the WES dataset and NeuroX dataset, respectively. We performed polygenetic burden analyses for exclusively rare variants, exclusively common variants and both types of variants together. The common variants were pruned (PLINK (Purcell, et al., 2007) indep settings 50 5 1.5) aiming to only consider independent variants in our genetic analyses. For the gene-sets we performed a two-sided SKAT test allowing variants within a gene-set to have different directions and magnitudes off effects, which is in concordance with both damaging and protective effect estimates observed for the 26 published PD loci. To test individual genes we performed a one-sided burden test, as we hypothesized that variants in individual genes are likely to have the same direction of effect. We also performed a two-sided SKAT analysis per gene in case we were interested which genes are driven an observed rare variant association in the total gene-set.

To correct for confounding factors (e.g. population stratification and technical artifacts), we included 20 multi-dimensional scaling components, gender and individual missingness rate pre QC (as a reference to the individual WES coverage) for the WES dataset. As the NeuroX dataset is more homogeneous, we corrected for the first 4 MDS components and gender. Empirical p-values were calculated for significant sample results (p < 0.05). For the gene-set analysis, the original sample p-value of the gene-set of interest was compared to p-values of 1,000 randomly drawn gene-sets of the same size. For the individual gene associations, empirical p-values were calculated using the resampling method implemented by SKAT, by 10,000 permutations of the affection status. Empirical p-values are calculated by (n1+1)/(n+1), where n1 = the number of resampling p-values smaller than the original sample p-value and n = the number of resampling.

2.8. Power calculations

We estimated the power of our study design to detect rare variant associations. Supplementary Table 3 displays the parameters that were chosen for the calculations. For both datasets, the PD prevalence was set to 0.0057 (Pringsheim, et al., 2014). As approximately half of the loci in PDgene.org have an odds ratio below 1, the percentage protective effect was set to 50%. A thousand simulations (α = 0.025) were performed on a haplotype matrix of SKAT, mimicking linkage disequilibrium structure of European ancestry, comprising 10,000 haplotypes over 200 kb regions.

3. Results

3.1. WES and rare variants

First, we analyzed the WES dataset as it represents all exonic variants, of which the study design has 65% power to detect a rare variant association signal considering individual genes. Testing the aggregated effect of grouped variants within a gene-set has the potential to increase power. Supplementary Table 4 shows the results of the gene-set analyses in the WES dataset. Common exonic variants are moderately associated to PD. The nominal p-value is significant, but the empirical p-value exceeds 0.05. Although we anticipated a significant association of common variants, we attribute the moderate association to a relatively low sample size (compared to the original GWAS), and the selection of genes (by Prixfixe) with variants in moderate LD with the original highest SNP. The gene-set association is absent when focusing on the common amino-acid changing and CADD variants, which is probably due to a decrease in power as the number of variants drops.

No rare variant, or common & rare variant associations were observed for the gene-set in either of the functional variant categories (nominal p ≥ 0.223; Supplementary Table 4). An alternative approach to study the putative rare variant associations is to test each gene individually within the gene-set. Table 2 displays the 3 strongest associated genes per variant subset and approach. Using the AAchanging variants category, we observed a significant association for STBD1 (p = 0.046).

Table 2.

Gene-based rare variant association results for WES dataset.

Variant type Gene p-value (emp) n variants maf cases maf controls
AAchanging STBD1 0.018 (0.046) 8 0.05% 0.32%
HIP1R 0.082 20 0.61% 0.53%
STK39 0.126 4 0.20% 0.00%
CADD STBD1 0.105 5 0.05% 0.16%
SPATA19 0.122 4 0.19% 0.06%
GPNMB 0.141 18 0.85% 0.92%
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p -value = theoretical p-value; (emp.) = emperical p-value calculated by comparison to 10000 permutations of affection status. AAchanging = amino acid changing variants; CADD = variants predicted pathogenic

3.2. NeuroX and rare variants

The NeuroX dataset contains previously identified exonic variants, of which a large proportion is rare (Nalls, et al., 2014a). In contrast to the WES data sets, our NeuroX cohort has enough power (estimated at 96%), due to the larger sample size (6,804 cases 5,970 controls), to detect a rare variant association signal. Similarly, to the WES dataset, a moderate common variant association is detected (nominal p = 0.031). In contrast to the WES dataset, we do observe significant associations of the gene-set with PD, even when only considering rare variants (AAchanging = 0.007; CADD = 0.002; Supplementary Table 5a).

To discover whether specific genes drive this observed rare variant association as observed in our cohort, the variants were grouped per gene and two-sided tested for their association to PD. LRRK2 is the gene driving the association observed in the total gene-sets (Supplementary Table 6). Focusing on the CADD subset, this association (nominal p = 5.17 × 10−13) is considerably stronger than the second most significant SPATA19 (nominal p = 0.050). The NeuroX array custom content is primarily driven by neurodegenerative diseases; therefore, NeuroX chip biases towards capture of in-depth genetic variability within genes, which are known to cause disease pathogenesis.(Tennessen, et al., 2012) Likewise, NeuroX harbors many variants of the known PD genes. For example, NeuroX contains 32 harmful (predicted by CADD) LRRK2 variants, while only 2 harmful variants are present for SPATA19. The variants in LRRK2 are overrepresented and biasing the results of the total gene-sets. We, therefore, performed the same gene-set analyses on the NeuroX dataset excluding the variants of LRRK2 (Supplementary Table 5b), resulting in the absence of a rare variant association in the NeuroX dataset (nominal p ≥ 0.28). This suggests that the previously observed association of rare variants within the total gene-set to PD was solely driven by LRRK2.

The two-sided SKAT analysis per gene aimed at the discovery of genes driving the rare variant association in the total gene-set. Next, we were interested to explore the genetic burden of rare variants for each gene individually when assuming all rare variants to have the same direction of effect (one-sided BURDEN test). Table 3 shows again that LRRK2 is the strongest associated gene. Furthermore, SPATA19 (p =0.017) is significantly associated when specifically considering rare CADD variants.

Table 3.

Gene-based rare variant association results for neuroX dataset.

Variant type Gene p-value (emp) n variants maf cases maf controls
AAchanging LRRK2 0.0004 (0.0005) 48 1.70% 1.13%
RIT2 0.051 2 0.00% 0.03%
PRSS8 0.098 1 0.04% 0.01%
CADD LRRK2 0.0003 (0.0005) 32 1.38% 0.86%
SPATA19 0.014 (0.017) 2 0.05% 0.00%
RIT2 0.051 2 0.00% 0.03%
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p -value = theoretical p-value; (emp.) = emperical p-value calculated by comparison to 10000 permutations of affection status. AAchanging = amino acid changing variants; CADD = variants predicted pathogenic

3.3. Directionality of effect

We further explored the significant individual association signals (empirical p < 0.05) for LRRK2 and STBD1, and SPATA19. By focusing on the variant level we aimed to comprehend the direction of effect estimates. LRRK2 showed a significant burden of 32 rare damaging variants in the NeuroX dataset. Single-marker association analysis of LRRK2 variants revealed that the observed association (p = 3.17 × 10−13) is attributed to the p.G2019S (rs34637584), the most common cause of monogenetic forms of PD. Interestingly, this particular variant was present in 78 cases (MAF = 0.006). Performing the rare variant aggregation test on 31 pathogenic LRRK2 variants, excluding p.G2019S, resulted in no association (p = 0.98) to PD, and thus suggesting that the observed rare variant association in LRRK2 was solely driven by the p.G2019S variant. As this variant is only present in 7 cases in the WES dataset (MAF = 0.003) with a single-marker p-value of 0.002 (LRRK2 mutations generally observed in late-onset PD), it explains the discrepancy of results for LRRK2 locus as observed in the WES dataset, while it showed a strong association in the NeuroX dataset.

In addition to the rare variant association test in LRRK2, we explored the presence of the previously published common LRRK2 haplotype with a protective effect of 3 exonic variants (N551K-R1398H-K1423K) (Ross, et al., 2011). K1423K is not included in the NeuroX genotyping array, but is in high linkage-disequilibrium (r2 = 1.00) with R1398H. We therefore tested the N551K-R1398H (G-A) haplotype and confirmed the protective effect (OR = 0.89, p = 0.027) of this haplotype for the PD cases, showing a minor haplotype frequency of 6.2% in cases and 6.9% in controls. All 3 variants were detected in the WES dataset, allowing to test the full haplotype (G-A-A). Although the haplotype association was not significant in the WES dataset (OR = 0.81, p 0.223), the trend of effect is similar with a minor haplotype frequency of 7.0% in cases and 7.5% in controls. The smaller sample size of the WES dataset is a plausible reason for not obtaining a significant association.

Next, the WES-based STBD1 and NeuroX-based SPATA19 were investigated for their variant frequencies. Single-marker association analysis showed no significant results for the 8 variants within STBD1. It therefore appears that the observed rare variant association is not caused by one exclusive variant but is rather the effect of multiple rare variants. Seven of the 8 variants are control-specific as they are only present in 10 control individuals. In contrast, only 1 variant is present in a single case. The direction of effect of the variants that are generating the STBD1 gene association is therefore implied to be protective. The significant gene-based association for SPATA19 is relatively strong considering that it is driven by only 2 CADD variants that are present in 7 cases and 0 controls. The absence of SPATA19 CADD variants in controls suggests that the association signal is damaging.

4. Discussion

To establish the influence of rare variants in sporadic PD risk loci, we explored two independent PD datasets (WES and NeuroX) enriched for coding rare variants. We used the PrixFixe strategy to select the most likely causal genes underlying the PD loci peaks, which is based on overlapping biological functional similarities. We tested both the effect of rare variants in the gene-sets at once, as each gene individually. Aggregating variants simultaneously across a set of genes has the potential to increase power to detect an association signal, given that the selected genes are enriched for a group of genes that are genuinely involved in the disease pathogenesis.

The average age of onset within the cases of the WES dataset (~41 years) is 20 years younger than in the meta-analysis of the most recent PD GWAS (~61 years) where the PD risk loci were based on. As some rare genetic risk factors (DJ-1, parkin PINK1) (Bras, et al., 2015) are specific for young onset PD (YOPD), we acknowledge the putative existence of YOPD-specific common genetic risk factors within the WES dataset. However, risk factors related to late onset sporadic PD might also play a role in YOPD. PD risk loci, such as SNCA and GBA (Klein and Westenberger, 2012,Nalls, et al., 2014b) overlap between late and young onset. We therefore expect that our WES dataset is an adequate dataset to study the rare exonic variants in PD risk loci. Furthermore, YOPD is often genetically explained through rare variants (Bras, et al., 2015). The YOPD patient group in the WES dataset could therefore be enriched for cases which are genetically influenced by rare variants, possibly increasing the likelihood of detecting rare variant association.

Using gene-set approach in the WES dataset, we did not detect a burden of rare variants when comparing PD subjects to controls. However, it is undetermined whether the absence of a rare variant association is genuine or due to insufficient power. A genuine rare variant association might furthermore be impeded by the gene-set composition. By using PrixFixe, we increase the likelihood of selecting the truly involved PD genes underneath the known PD risk loci, yet unrelated genes might still be included, possibly diluting an association signal. In contrast, with the gene based association test for the genes selected with the Prixfixe strategy we observed a rare variant association for STBD1, implying that rare variants in this gene could decrease the risk to develop PD. STBD1 has its function in lysosomal-mediated autophagy to specifically guide glycogen to lysosomes for sequestration and degeneration (Jiang, et al., 2011). It, therefore, seems that variants in STBD1 could have beneficial effects for the removal of glycogen. The lysosomal-mediated autophagy has been implied to be involved in PD through the association of multiple genes, such as LRRK2, ATP13A2 and GBA (Trinh and Farrer, 2013). However, the involvement of STBD1 in PD pathogenesis has to be carefully considered, as we currently did not have an adequate independent dataset to replicate the association that was generally based on singletons. The NeuroX genotyping array typically includes variants that have been observed in previous datasets, minimizing the probability to detect similar singletons with an extremely low minor allele frequency. Only 3 of the 8 STBD1 variants of the WES dataset, were present within the NeuroX dataset reducing the power to detect the single gene association. Hence, further genetic validation studies are warranted to establish the role of STBD1 in PD. Once a legitimate replication is realized, functional assays on lysosomal-mediated autophagy should further decipher the contribution of STBD1, preferably in relation to well-established PD genes.

We detected a strong association of rare variants within the gene-sets for the NeuroX dataset. However, subsequent analyses showed that these associations were dominated by LRRK2 variants. Association analysis on variant level revealed that the LRRK2 gene signal was driven by the known p.G2019S variant. This observation highlights the importance of cataloguing the individual rare variants to fully resolve the impact of rare variants in disease susceptibility for PD. As shown for the LRRK2 association and even the total gene-set association, it is driven by only 1 variant, which also could have been detected with the performance of a simple single-marker association test. Besides the pathogenic association signal of rare variant G2019S, we observed a significant protective effect of a previously published common haplotype (Ross, et al., 2011). This observation supports the theory that other variants with opposite effects could interact and potentially influence the penetrance of pathogenic LRRK2 variants, such as G2019S. Besides LRRK2, we furthermore detected a NeuroX-based burden of rare CADD variants for SPATA19 that increases PD risk (p = 0.017). This association signal is relatively strong considering that it is driven by only 2 CADD variants that are present in 7 cases and 0 controls. As SPATA19 is involved in spermatogenesis (Nourashrafeddin, et al., 2014), and the GTEx portal displays specific high expression for the testis, it diminishes the likelihood that defects of this gene would contribute to neurodegeneration. Further genetic and functional studies are warranted to decipher a role of this gene in PD.

In contrast to selecting the physically closest gene to the strongest SNP within each PD locus, we followed a comprehensive strategy to define true causal gene, which is based on biological similarities. As we expect that only one gene per locus is the true causal gene, we did not define a gene-set including all the genes underneath the GWAS loci assuming the overrepresentation of non-causal genes would dilute a putative association signal. We acknowledge that the ultimate strategy to test the effect of rare variants in the PD loci would be to sequence all genes in a large cohort, and test the effect of rare variants in each gene individually. Furthermore, sequencing rather than genotyping will define novel rare variants and contribute to cataloguing the influence of rare variants underneath the PD risk loci. Acknowledging these caveats, our study suggests for the first time that, apart from LRRK2, SNCA and GBA, other common PD risk loci might harbor rare variants that contribute to PD risk.

Supplementary Material

1

Highlights.

  • Two genetic datasets comprising a total of 7,968 PD cases and 7,655 controls were used to study the exome

  • Rare variants in LRRK2, STBD1 and SPATA19 are suggested to play a role in PD

  • Larger sequencing studies are required in future for follow up.

Acknowledgements

This work was supported in part by the Prinses Beatrix Spierfonds (I.E.J. and P.H.). M.A.N.’s participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA. As a possible conflict of interest M.A.N. also consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare among others. M.S. is supported by the Michael J Fox Foundation, USA. T.G and P.H were supported by the Federal Ministry of Education and Research (BMBF) under the grant numbers 031A430A (TG) and 031A430D (PH) (e:Med Module II).

This work was supported in part by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, Department of Health and Human Services; project ZO1 AG000949.

The generation and management of the exome sequencing data for the Rotterdam Study was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Netherlands. The Exome Sequencing data set was funded by the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) sponsored Netherlands Consortium for Healthy Aging (NCHA; project nr. 050-060-810), by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, and by the and by a Complementation Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL; www.bbmri.nl ; project number CP2010-41). We thank Mr. Pascal Arp, Ms. Mila Jhamai, BSc and Mr. Marijn Verkerkj for their help in creating the RS-Exome Sequencing database.

The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists.

This study is also supported by the Courage-PD is an EU Joint Programme - Neurodegenerative Disease Research (JPND) project (M.S, T.G.) The project is supported through the following funding organizations under the aegis of JPND - www.jpnd.eu:

the Medical Research Council, United Kingdom

the French National Research Agency

the German Bundesministerium für Bildung und Forschung

the Italian Ministry of Health/Ministry of Education, Universities and Research

the Israeli Ministry of Health

the Luxembourgian National Research Fund

the Netherlands Organisation for Health Research and Development

the Research Council of Norway

the Portuguese Foundation for Science and Technology

the Spanish National Institute of Health Carlos III

Conflicts of interest

Mike A Nalls’ participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA. As a possible conflict of interest Dr. Nalls also consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare among others.

Footnotes

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