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Human Molecular Genetics Advance Access originally published online on November 3, 2005
Human Molecular Genetics 2005 14(23):3549-3556; doi:10.1093/hmg/ddi376
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Comprehensive evaluation of common genetic variation within LRRK2 reveals evidence for association with sporadic Parkinson's disease

Lisa Skipper1, Yi Li2, Carine Bonnard1, Ratnagopal Pavanni3, Yuen Yih3, Eva Chua3,4, Wing-Kin Sung2, Louis Tan5, Meng-Cheong Wong5, Eng-King Tan3,4 and Jianjun Liu1,*

1Population Genetics and 2Information and Mathematical Sciences, Genome Institute of Singapore, Singapore 138672, 3Department of Neurology and 4Division of Research, SingHealth, Singapore General Hospital, Singapore 169611 and 5National Neuroscience Institute, Singapore, 308433

* To whom correspondence should be addressed at: Population Genetics, Genome, Genome Institute of Singapore, #02-01 60 Biopolis Street, Singapore 138672, Singapore. Tel: +65 64788088; Fax: +65 64789059; Email: liuj3{at}gis.a-star.edu.sg

Received August 15, 2005; Accepted September 30, 2005

GenBank accession no. AY792511


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Parkinson's disease (PD) is a complex neurodegenerative disorder whose aetiologies are largely unknown. To date, mutations in six genes have been found causal for some rare familial forms of the disease and common variation within at least three of these is associated with the more common sporadic forms of PD. LRRK2 is the most recently identified familial PD gene, although its role in sporadic disease is unknown. In this study, we have performed the first comprehensive evaluation of common genetic variation within LRRK2 and investigated its contribution to risk of sporadic PD. We first characterized the linkage disequilibrium within LRRK2 using a panel of densely spaced SNPs across the gene. We then identified a subset of tagging-SNPs (tSNP) that capture the majority of common variation within LRRK2. Both single tSNP and tSNP haplotype analyses, using a large epidemiologically matched sporadic case–control series comprising 932 individuals, yielded significant evidence for disease association. We identified a haplotype that dramatically increases disease risk when present in two copies (OR=5.5, 95%CI=2.1–14.0, P=0.0001). Thus, we provide the first evidence that common genetic variation within LRRK2 contributes to the risk of sporadic PD in the Chinese population.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Parkinson's disease (PD) is a complex neurodegenerative disorder currently thought to affect almost one million in the USA and as many as 1.7 million in China (1Go). Clinically, PD is characterized by resting tremor, muscle rigidity, bradykinesia and postural instability (2Go). Dopaminergic neuronal death within the substantia nigra pars compacta of the midbrain and the presence of proteinaceous intracellular aggregates (Lewy bodies) in surviving cells are defining pathological features (3Go), although the pathogenic mechanisms are poorly understood at present. The majority of cases are sporadic, although mutations in a handful of genes are responsible for a small proportion of rare familial forms of PD (4Go). Study of these familial PD genes has offered useful insight and is assisting efforts in understanding aetiologies in common forms of the disease.

The first familial PD gene identified was alpha-synuclein (SNCA). Mis-sense mutations are responsible for a small number of autosomal dominant PD cases (5Go–7Go). A central role for SNCA in PD stems from the finding that fibrillar forms of the protein are a major structural component of LBs in both familial and sporadic cases (8Go). Genomic multiplications leading to increased expression of SNCA found in other autosomal dominant PD families [PARK4 (MIM 605543 [OMIM] )] (9Go,10Go) suggest that protein levels may be important in pathogenesis. Polymorphic variability within the SNCA promoter is also associated with increased risk for sporadic PD (11Go).

Mutations in the parkin gene (PRKN) are responsible for about 50% of autosomal recessive (AR) PD cases [PARK2 (MIM 600166 [OMIM] )] (12Go). The genetic aberrations in this gene are numerous ranging from single base-pair substitutions and deletions to multiple exonic deletions and duplications. Although most parkin-linked disease is inherited in AR fashion, mutations have been described in sporadic cases and haploinsufficiency may also predispose to common PD (13Go). A common promoter variant that decreases PRKN transcription is also associated with sporadic PD (14Go). A function of the parkin protein is to act as an E3 ubiquitin protein ligase within the ubiquitin proteasomal pathway (UPP) (15Go); one of the principle mechanisms by which damaged/misfolded proteins are cleared in the cell. The protein product of another gene initially implicated in familial PD, UCH-L1 (16Go), also plays a role within the UPP. Impairment of UPP function is observed in sporadic cases (17Go) and a common polymorphism that alters UCH-L1 enzymatic activity is inversely associated with risk of sporadic PD (18Go).

The leucine-rich repeat kinase 2 gene (LRRK2) is the most recently identified causative gene for autosomal dominant PD (19Go). Although its biological function is unknown at present, peptide sequence analogy suggests the LRRK2 protein is a member of the ROCO family (20Go) that contains five conserved domains within the C-terminal half of the protein. Most LRRK2 mutations thus far identified are within exons encoding these putative functional domains and they account for up to 6% of autosomal dominant cases (21Go). The most common of these, G2019S, may also account up to 2% of sporadic cases (22Go). As variation in genes initially implicated in familial PD is becoming increasingly relevant to PD in the wider community, we sought to investigate whether common variation within LRRK2 may also predispose risk to sporadic PD. Here, we report our findings in the first study to address this question, using a large (n=932) epidemiologically matched sporadic case–control series.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
LRRK2 linkage disequilibrium and tagging-SNPs
Linkage disequilibrium (LD) is defined as the non-random association of alleles at adjacent loci. Loci and thus their allelic variants are expected to segregate independently in a randomly mating population. However, if a particular allele at one locus is found together on the same chromosome with a specific allele at a second locus, more often than expected by chance, then the loci are in linkage disequilibrium (23Go). Common measures of LD include D' and r2. D' values range from –1 to 1; 1 indicates ‘complete LD’ and is indicative of minimal recombination between loci (three out of a possible four haplotypes exist) and r2 ranges from 0 to 1; 1 indicates ‘perfect LD’ (only two of a possible four haplotypes exist). Perfect LD between two loci means that information for one of these is redundant. Extensive regions showing strong LD interspersed with (usually smaller) regions in equilibrium are typically manifested as regions of limited haplotype diversity (‘haplotype block’). The identification of haplotype blocks has allowed the use of haplotype ‘tagging-SNPs’ (tSNPs) in association studies, under the assumption that much of the variation can be identified with a smaller subset of SNPs, i.e. those that ‘tag’ a haplotype (24Go).

We characterized the LD and haplotype structure of LRRK2 (including 10 kb of sequence upstream from coding start, based on Genbank accession number AY792511) in 92 control individuals. Twenty-five SNPs (Table 1) with minor allele frequencies (MAF) ranging from 0.03 to 0.49 were genotyped, yielding an average density of 1 SNP/6 kb (Fig. 1B). Seventy-eight unique multilocus haplotypes were constructed and used to calculate pair-wise D' and r2 values (Fig. 1C and D).


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  		Table 1. SNP assay oligomer sequences
 


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  		Figure 1. Genomic organisation and LD within LRRK2. (A) Genomic organisation of LRRK2. Exons are shown as vertical lines. (B) Relative positions and frequencies of 25 SNPs used to determine tSNPs. Vertical bars represent the position within LRRK2 (x-axis) and the frequency in 94 Chinese control individuals (y-axis). tSNPs are denoted with an asterisk. (C) Pair-wise r2 and (D) pair-wise D' values between 25 SNPs. Values are shaded according to the key (E) such that lighter shading indicates low values (low LD) and darker shading indicates higher values (stronger LD).

 
Complete LD exists throughout much of the gene overall, suggestive of minimal recombination in this region. In addition, r2 values suggest that information for some SNPs is redundant. We therefore, sought to identify tSNPs that would efficiently capture common variation within LRRK2. As there is no unique definition of what constitutes a haplotype block and the number of tSNPs can be further reduced if inter-block disequilibrium is also considered (25Go), we used block-independent methods to identify six tSNPs (tSNPset) sufficient to uniquely distinguish 95% of all LRRK2 haplotypes with a frequency >0.01. We also evaluated the performance of our tSNPset to predict observed haplotypic variation (measured by Rh2) and unobserved genotypic variation (measured by r2) within LRRK2. Rh2 values were found to be >0.8 between the tSNPset and all common (frequency >0.05) full haplotypes (comprising the 25 SNPs). Average r2 was 0.94 between the tSNPset and all remaining common (MAF>0.05) genotypes. These performance levels exceed the criteria proposed by Ahmadi et al. (26Go) and indicate that our tSNPset affords only a modest reduction in power relative to genotyping all common SNPs within LRRK2.

Association analyses
The tSNPset was genotyped in all cases (n=466) and remaining control individuals (n=374). We initially carried out single marker tests of association for each LRRK2 tSNP, by calculating odds ratios under recessive, additive and dominant models (Table 2). There was a marginally significant association between rs10506151 and disease under a dominant model (OR=1.3, 95%CI=1.0–1.7, P=0.03). However, carrying two copies greatly increased disease risk under both recessive (OR=3.7, 95%CI=1.8–7.8, P=0.0001) and additive models (OR=4.0, 95%CI=1.9–8.3, P=0.0001). This association remains highly significant after a conservative Bonferroni correction for multiple testing (P=0.002).


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  		Table 2. LRRK2 tSNP association tests
 
We next performed tSNP haplotype association analysis, as haplotype analysis may provide more power to detect association than single marker analyses alone (27Go). tSNP haplotypes were constructed for all cases and controls and odds ratios for tSNP haplotypes (frequency >0.01) were calculated by comparing frequencies of each against all others present within the sample group (Table 3). Haplotype 2 is over-represented in the patient group (OR=1.4, 95%CI=1.1–1.8, P=0.005; corrected P=0.08); an effect partly because of signals emanating from the minor allele of rs10506151 (A) carried on this haplotype. Odds ratios for Haplotype 2 were calculated under recessive, additive and dominant models (Table 4). We observed a striking association between carrying two copies and disease status, under both recessive (OR=5.2, 95%CI=2.0–13.3, P=0.0002; corrected P=0.0008) and additive (OR=5.5, 95%CI=2.1–14.0, P=0.0001; corrected P=0.0004) models. We also investigated a possible age of onset effect related to Haplotype 2 dosage, using Kruskal–Wallis tests, although results were insignificant (data not shown).


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  		Table 3. LRRK2 tSNPset haplotype frequencies and ORs
 

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  		Table 4. Haplotype 2 frequencies and ORs
 
In addition to the identification of a risk haplotype, our results also raise the possibility that protective variants exist within LRRK2. Haplotype 3 (OR=0.8, 95%CI=0.6–0.9, P=0.04) and to a greater extent Haplotype 11 (OR=0.2, 95%CI=0.1–0.7, P=0.01) are both under-represented in the case group (Table 3).

In silico analysis of rs10506151
SNP rs10506151 is positioned within the intron following exon 16, 2203 bp downstream of exon 16 and 692 bp upstream of exon 17. In silico analysis shows that the wild-type allele (C) is conserved in vertebrates through to the rat. In addition, the SNP is within a putative 6-mer hnRNP K binding site (ACCCAA; SNP position shown in bold).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
This study is the first to comprehensively evaluate common genetic variation within LRRK2 and assess its potential role in sporadic PD. By using a powerful tSNP approach, we have identified a common haplotype that is highly over-represented within cases (P=0.005) and, when present in two copies, significantly increases disease risk (OR=5.5, 95%CI=2.1–14.0, P=0.0001). Interestingly, this haplotype carries a SNP (rs10506151) that is within a putative hnRNP K binding site and whose wild-type allele is conserved in vertebrates. hnRNP K is a component of the heterogeneous ribonucleoprotein (hnRNP) particle that is implicated in a variety of cellular processes including transcription and translation (28Go). It is possible that this SNP is a functional variant that alters the processing of LRRK2 and directly increases disease risk. However, given the level of LD, we observed within this gene, we postulate the association is indirect, emanating from a true risk allele in LD with rs10506151. This yet-to-be-identified variant could lie anywhere between rs10506151 and all 21 SNPs from rs11564115 through rs3761863 as LD is complete (D'=1) between these SNPs (Fig. 1D). However, the identification of a risk haplotype has enabled us to prioritize 25 individuals (cases carrying two copies of Haplotype 2) out of 189 cases (cases carrying at least one minor allele of rs10506151), who are likely to harbour a variant responsible for our remarkable observations. As none of the currently known LRRK2 mutations are present, detailed analysis of coding and regulatory sequences in these individuals is now justified. Our haplotype association analyses also suggest that protective variants may exist within LRRK2. However, these observations are based on marginal significance (for Haplotype 3) or small numbers (for Haplotype 11) and must be interpreted with caution.

We acknowledge the possibility that our findings may represent ‘spurious’ associations—a common pitfall of many association studies. However, we have taken care to minimize potential confounders, in particular by using a large case–control series well matched in terms of age, gender and ethnicity. This strategy is also believed sufficient to attenuate the possible, and debatable effect of population stratification (27Go).

Paisan-Ruiz et al. (29Go) recently reported negative association of four LRRK2 SNPs (two coding non-synonymous and two coding synonymous) in a smaller and unmatched Caucasian case–control series. However, patients in this study were selected on the basis of early onset and/or family history of PD and the level of LRRK2 variation assessed was extremely limited. It is also possible that sporadic disease risk associated with common variation in LRRK2 is influenced by other genetic and non-genetic factors, some of which may be population specific. It will therefore now be of value to investigate our findings in other populations.

In conclusion, our results strongly suggest that, in addition to the established role in familial disease, LRRK2 genetic variation may indeed make a significant contribution to common forms of PD in the general community. Isolation of the responsible factors will provide further clues to understanding the pathogenesis of this complex disorder and further study is warranted.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Samples
From the movement disorder clinics of two major tertiary institutions in Singapore (Singapore General Hospital and National Neuroscience Institute) 466 PD patients were included in this study. PD diagnosis was made in accordance with the UK PD Society Brain Bank Clinical Diagnostic Criteria of PD, by two movement disorders specialists (EK Tan and L Tan). The mean age of disease onset (AOO) was 60±12 years (range 24–81), mean current age was 65±11 years (range 30–91) and 56% were male. All patients were considered sporadic, as no family history of PD was evident. All were of Chinese ethnicity. None carry the common G2019S mutation (30Go) or any of the other 13 LRRK2 mutations reported to date (R793M, R1067Q, L1114L, I1122V, I1371V, R1441C, R1441G, R1441H, IVS31+3 A>G, IVS33+6 T>A, Y1699C, M1869T, I2020T); (EK Tan et al., manuscript in preparation). Each patient was matched with an unrelated control individual (mean age 60±11 years, range 26–93), free of neurological disease (n=466). Matching criteria included a similar age (maximum age difference was ±10 years), same sex and ethnicity. Institutional ethics committees approved this study and informed consent was obtained from all participants.

SNP selection and genotyping
Aiming for an average marker density of one SNP/5 kb, 30 SNPs with a MAF >0.02 in the Chinese population (according to HapMap Project population data, URL http://www.hapmap. org/) were selected for genotyping in 92 control samples. SNPs were genotyped using MALDI-TOF mass spectrometry using the Sequenom MassARRAYTM system (San Diego, CA, USA) as previously described (19Go). Briefly, multiplex genotyping assays were designed using the Sequenom DESIGNER software. Initial PCR (5 ng of genomic DNA) and primer extension reactions were carried out according to the Sequenom MassEXTEND protocol. Primers and allele codes are shown in Table 1. After purification, 15 nl of primer extension product was analysed with a MassARRAY Sequenom-Bruker Spectrometer (Bruker Biosciences). SNPs with overall call rates <0.85 (percentage of samples giving a reliable genotype call) and/or those that significantly deviated from Hardy–Weinberg equilibrium (at {alpha}=0.05) were excluded from further analyses (n=5).

LD characterization, tSNP selection and performance evaluation
Pair-wise D' and r2 values were calculated using multilocus haplotypes inferred from phase-unknown genotype data using PHASE version 2.1 (31Go). tSNPs were identified using the TSSA algorithm. This is a block-independent approach that finds a minimum set of tSNPs which uniquely distinguish a certain percentage of all possible haplotypes (25Go). {alpha} was set at 0.95 in order that the tSNPset returned was able to uniquely distinguish 95% of all possible haplotypes.

Performance of the selected tSNPset to capture haplotypic variation within LRRK2 was assessed by calculating Rh2 between tSNP haplotypes and common (frequency >0.05) full haplotypes (comprising 25 SNPs) (32Go). This value can range from 0 to 1, with 1 indicating that tSNP haplotypes can perfectly predict all common 25 SNP haplotypes. To evaluate tSNP performance in capturing unobserved genotypic variation within LRRK2 we performed the ‘leave-one-out’ analysis (33Go). In brief, each of the 25 genotyped SNPs (k) was dropped in turn and tSNPs were selected from the remaining SNPs (k–1). Haplotype r2 (possible value range 0–1) was then calculated between each tSNP set and remaining SNPs. This evaluation can provide an unbiased and accurate estimate of tSNP performance in predicting genotypes that are not directly assessed (34Go).

Statistical analysis
Odds ratios, 95%CIs and P-values were calculated using web-based simple interactive statistical analysis (SISA) tools (URL http://home.clara.net/sisa/index.htm). Kruskal–Wallis tests were carried out using Microsoft® Excel 2002.

In silico analysis
Sequence conservation around rs10506151 was analysed using conservation tracks available at the University of California at Santa Cruz Human Genome Browser Gateway (May 2004 assembly) (URL: http://genome.ucsc.edu/cgi-bin/hg). Intronic regulatory sequence analysis was performed using the web-based Alternative Splicing Workbench (URL: http://www. ebi.ac.uk/asd-srv/wb.cgi).


   ACKNOWLEDGEMENTS
 
The authors gratefully acknowledge all participants in this study. They also would like to thank Jason Ong and Seok Shin Ng for genotyping and Marie Wong for construction of Figure 1. This study was supported by funding from the Agency for Science and Technology and Research of Singapore.

Conflict of Interest statement: The authors declare no conflicts of interest.


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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

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