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Neurobiology of Aging 35 (2014) 442.e9e442.e16

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Neurobiology of Aging

journal homepage: www.elsevier .com/locate/neuaging

Assessment of Parkinson’s disease risk loci in Greece

Eleanna Kara a, Georgia Xiromerisiou b,c, Cleanthe Spanaki d, Maria Bozi e,f,g, Georgios Koutsis h,Marios Panas h, Efthimios Dardiotis b, Styliani Ralli b, Jose Bras a, Christopher Letson i, Connor Edsall i,Hannah Pliner i, Sampath Arepalli i, Kallirhoe Kalinderi j, Liana Fidani j, Sevasti Bostantjopoulou k,Margaux F. Keller i, l, Nicholas W. Wood a, John Hardy a, Henry Houlden a, Leonidas Stefanis g,m,Andreas Plaitakis d, Dena Hernandez a,i, Georgios M. Hadjigeorgiou b, Mike A. Nalls i,Andrew B. Singleton i,*

aReta Lila Weston Laboratories and Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UKb Laboratory of Neurogenetics, Department of Neurology, Faculty of Medicine, University of Thessaly, Larissa, GreececDepartment of Neurology, Papageorgiou Hospital, Thessaloniki, GreecedDepartment of Neurology, Medical School, University of Crete, Heraklion, Crete, GreeceeGeneral Hospital of Syros, Syros, Greecef ‘Hygeia’ Hospital, Clinic of Neurodegenerative Disorders, Athens, Greeceg Second Department of Neurology, National and Kapodistrian University of Athens Medical School, Athens, GreecehNeurogenetics Unit, 1st Department of Neurology, University of Athens Medical School, Eginition Hospital, Athens, Greecei Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USAjDepartment of General Biology, Medical School, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greecek Third Department of Neurology, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, GreecelDepartment of Biological Anthropology, Temple University, Philadelphia, PA, USAmDivision of Basic Neurosciences, Biomedical Research Foundation of the Academy of Athens, Athens, Greece

a r t i c l e i n f o

Article history:Received 4 June 2013Accepted 15 July 2013Available online 27 September 2013

Keywords:Parkinson’s diseaseGWASGCTAGeneticsGreeceRisk profiles

* Corresponding author at: Molecular Genetics Secrogenetics, NIA, NIH, Building 35, Room 1A1014, 35 C20892, USA. Tel.: þ1 301 451 6079; fax: þ1 301-451 5

E-mail address: Singleta@mail.nih.gov (A.B. Singlet

0197-4580/$ e see front matter Published by Elsevierhttp://dx.doi.org/10.1016/j.neurobiolaging.2013.07.011

a b s t r a c t

Genome-wide association studies (GWAS) have been shown to be a powerful approach to identify riskloci for neurodegenerative diseases. Recent GWAS in Parkinson’s disease (PD) have been successful inidentifying numerous risk variants pointing to novel pathways potentially implicated in the pathogenesisof PD. Contributing to these GWAS efforts, we performed genotyping of previously identified risk allelesin PD patients and control subjects from Greece. We showed that previously published risk profiles forNorthern European and American populations are also applicable to the Greek population. In addition,although our study was largely underpowered to detect individual associations, we replicated 5 of 32previously published risk variants with nominal p values <0.05. Genome-wide complex trait analysisrevealed that known risk loci explain disease risk in 1.27% of Greek PD patients. Collectively, these resultsindicate that there is likely a substantial genetic component to PD in Greece, similarly to other worldwidepopulations, that remains to be discovered.

Published by Elsevier Inc.

1. Introduction

The dissection of the genetic basis of Parkinson’s disease (PD)startedwith the identification of mutations in a-synuclein (SNCA) in1997 (Polymeropoulos et al., 1997). Fifteen years later, the cause ofmost PD cases still remains unknown as Mendelian mutationscollectively account for less than 5% of the disease (Pankratz et al.,2012). More recently, driven by the common disease-common

tion and Laboratory of Neu-onvent Drive, Bethesda, MD466.on).

Inc.

variant hypothesis (Reich and Lander, 2001), several PD genome-wide association studies (GWAS) (Edwards et al., 2010;Hernandez et al., 2012; Pihlstrom et al., 2013; Saad et al., 2011;Satake et al., 2009; Simon-Sanchez et al., 2009, 2011) and largescale meta-analyses (Do et al., 2011; International Parkinson’sDisease Genomics Consortium [IPDGC] and Wellcome Trust CaseControl Consortium 2 [WTCCC2], 2011; Lill et al., 2012; Nallset al., 2011; Pankratz et al., 2012) have shown that variants within26 loci increase the risk for PD. Despite these advances, there isevidence that a large number of causative loci still remain to bediscovered (Keller et al., 2012).

It has been previously argued that studies in isolated pop-ulations with limited genetic heterogeneity are valuable for

Table 1Descriptive statistics of the Greek cohort

Sample type Number of subjectspassing QC

Sex, n (%) Mean age � SD PD family history, n (%)a

Male Female Positive Negative

All 1836 1036 (56.4) 800 (43.6) 63.18519 � 11.4321 115 (17.2) 555 (82.8)Case 960 553 (57.6) 407 (42.4) 64.04325 � 10.91366 (age at onset) 115 (17.2) 555 (82.8)Control 876 483 (55.1) 393 (44.9) 62.14231 � 11.95655 (age at study enrollment) NA NA

Key: NA, not applicable; PD, Parkinson’s disease; QC, quality control.a Family history statistics calculation was based on 670 PD cases and 0 control subjects with available data.

E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16442.e10

studying the genetic basis of disease (Hernandez et al., 2012) withan illustrative example being the Finnish population (Kere, 2001;Peltonen et al., 1999) in which amyotrophic lateral sclerosis(ALS) GWAS (Laaksovirta et al., 2010) paved the road to the dis-covery of C9orf72 repeat expansions as a major cause ofALS Fronto-temporal dementia (FTD) (DeJesus-Hernandez et al.,2011; Renton et al., 2011; Traynor, 2012). However, a recent PDGWAS completed in the Finnish population following a similarrationale failed to identify such high risk variants (Hernandezet al., 2012).

Similarly to the Finnish population, there is evidence that theGreek population is an isolated population (Mok et al., 2012) withsubtle genetic intricacies when compared with other Europeanpopulations (International HapMap Consortium, 2003; Stathiaset al., 2012). This, in combination with the location of Greece inthe crossroad between Europe, Africa, and the Middle East, servingas a “genetic pool” for transiting populations (Di Giacomo et al.,2004; Hughey et al., 2013; King et al., 2011; Semino et al., 2004;Stathias et al., 2012) renders genetic studies in the Greek popula-tion promising and informative for other European populations.Motivated by these observations, we undertook a PD caseecontrolanalysis targeting variants previously implicated in risk for PD byGWAS.

2. Methods

All samples were collected in accordance with institutionalethical procedures after written informed consent was provided.Individuals originated from 4 geographic locations in Greece(Athens, Crete, Syros, and Thessaly; for details see Table 1 andSupplementary Table 1). The total number of samples was from1154 case and 997 control subjects. PD patients were diagnosedaccording to the Queen Square brain bank criteria (Gibb and Lees,1988, 1989). Control subjects were healthy individuals with nosigns or symptoms of parkinsonismwhose close relatives were alsofree from parkinsonism based on self-report or available clinicaldata.

All samples were genotyped as part of a larger study using theNeuroX Array (Illumina) which is an exome plus custom contentgenotyping array. The NeuroX contains 267,607 probes denselycovering previously published PD GWAS-associated loci, rare vari-ants identified through exome sequencing studies of neurodegen-erative diseases, ancestry informative markers, markers fordetermination of identity by descent, X chromosome single-nucleotide polymorphisms (SNPs) for sex determination, candi-date loci from neurodegenerative disease GWAS, and standardIllumina exome array content. After initial genotyping, genotypeswere clustered using Illumina GenomeStudio with default param-eters. For SNPs previously associated with PD, genotype clusterswere manually inspected (see Supplementary Fig. 1).

Sample quality control (QC) was slightly more rigorous thanstandard GWAS because of the use of an exome-based array withabundant rare variants and experimental content. All sample QCwas based on SNPs with Illumina GenTrain scores>0.7, indicative of

generally higher-quality genotyping. Initially, samples with lessthan 95% successful calls on a genome-wide scale, and sex as esti-mated from X chromosome heterogeneity not matching clinicalreports of sex were excluded. X heterogeneity calculations werebased on common SNPs from the International HapMap Project thathad genotypes with missingness <5% and Hardy-Weinberg equi-librium (HWE) p values>1E-5. For further data cleaning, a subset ofthe genotype data was used, including only SNPs present in Hap-Map3 populations with genotype missingness <5%, HWE p values>1E-5, and a pairwise r2 < 0.5 across sliding windows of 50 SNPs.Using this reduced dataset we estimated genome-wide rates ofheterozygosity, excluding any samples with observed heterozy-gosity divergence more than 3 standard deviations from the ex-pected population mean. After this exclusion, samples wereclustered using principal components analysis to evaluate Euro-pean ancestry compared with HapMap3 populations at overlappingSNPs (International HapMap Consortium, 2003; Patterson et al.,2006; Price et al., 2006; Yang et al., 2011). At this stage, sampleswere excluded if they were outside of 6 standard deviations fromthe means of eigenvectors 1 or 2 based on the combined CEPH(Utah Residents with Northern and Western European Ancestry)and Tuscan reference samples (see Supplementary Fig. 2).Confirmed European ancestry samples were extracted and identityby descent was quantified, allowing us to exclude any samplessharing proportionately more than 12.5% of alleles indicatingcryptic relatedness at the level of cousins. Within related pairs,individuals were retained to maximize a 1:1 ratio of case to controlsubjects and preserve study power. At this time, 10 eigenvectorswere estimated to account for population substructure and to beused as covariates in all analyses.

When sample QC was completed, genotype data on allattempted SNPs was extracted for samples meeting inclusioncriteria. At this point, we excluded all SNPs with minor allele fre-quency (MAF) <0.01, HWE p values <1E-5, differential missingnessbetween case and control subjects at p values <1E-5, differentialmissingness according to haplotype at p values <1E-5, and Gen-Train scores <0.7. SNPs at MAF < 0.01 were not retained because ofconcerns about study power. For analyses in this report, we used aworking sample size of 960 case and 876 control subjects geno-typed at 48,805 SNPs.

The purpose of this project was to investigate whether knownPD-associated SNPs contribute to PD risk in the Greek populationthrough mining data generated on the single SNP level, and alsousing genetic risk profiling to aggregate risk across all known loci.We then attempted to estimate PD heritability in this populationbased on all available SNP data and also only focusing in on knownGWAS loci.

For all SNPs and samples passing QC, logistic regression analyseswere used to estimate risk associated with each SNP, adjusted foreigenvectors 1e10 as covariates. All loci summarized in Keller et al.(2012) were also extracted to evaluate risk associated with previ-ously discovered GWAS loci in our Greek cohort (Table 2). Loci thatreached genome-wide significance in previously published PDGWAS were matched based on position to their corresponding

Table 2A summary of known PD risk loci in the Greek population (table continues on next page)

Candidate gene CorrespondingSNP

NeuroX SNP Reference CHR BP (HB37) Allele 1(minorallele)

Allele 2

GBA i400416 exm106217 Do et al., 2011 1 155205634 C TGBA N370S (proxy) NeuroX_rs71628662 Lill et al., 2012 1 155359992 C TSYT11/RAB25 chr1:154105678

(proxy)exm-rs34372695_ver3 Lill et al., 2012;

Nalls et al., 20111 156030037 T C

RAB7LI/PARK16 rs708723 NeuroX_rs708723 International Parkinson’s Disease Genomics Consortium(IPDGC) andWellcome Trust Case Control Consortium 2(WTCCC2), 2011

1 205739266 C T

RAB7LI/PARK16 rs947211 exm-rs947211 Lill et al., 2012 1 205752665 A GSLC41A1 rs823156 exm-rs823156 Do et al., 2011 1 205764640 G AACMSD rs6710823

(proxy)NeuroX_rs6430538 Nalls et al., 2011 2 135539967 C T

STK39 rs2102808 exm-rs2102808_ver4 Nalls et al., 2011 2 169117025 T GSTK39 rs2390669

(proxy)NeuroX_rs1955337 Lill et al., 2012 2 169129145 T G

NMD3 rs34016896 NeuroX_rs34016896 International Parkinson’s Disease Genomics Consortium(IPDGC) andWellcome Trust Case Control Consortium 2(WTCCC2), 2011

3 160992864 T C

MCCC1/LAMP3 rs10513789 exm-rs10513789_ver4 Do et al., 2011 3 182760073 G TMCCC1/LAMP3 rs11711441 exm-rs11711441 Lill et al., 2012;

Nalls et al., 20113 182821275 A G

GAK rs6599389 exm-rs6599389 Do et al., 2011 4 939113 A GDGKQ rs11248060 exm-rs11248060 Lill et al., 2012 4 964359 T CBST1 rs11724635 NeuroX_rs11724635 Lill et al., 2012;

Nalls et al., 20114 15737101 C A

STBD1 rs6812193 exm-rs6812193 International Parkinson’s Disease Genomics Consortium(IPDGC) andWellcome Trust Case Control Consortium 2(WTCCC2), 2011

4 77198986 T C

SNCA rs356220 exm-rs356220 Do et al., 2011 4 90641340 T CSNCA rs6532194 NeuroX_rs6532194 Lill et al., 2012 4 90780902 T CHLA rs2395163 exm-rs2395163 Pankratz et al., 2012 6 32387809 C TGPNMB rs156429

(proxy)NeuroX_rs199347 International Parkinson’s Disease Genomics Consortium

(IPDGC) andWellcome Trust Case Control Consortium 2(WTCCC2), 2011

7 23293746 C T

FGF20 rs591323 NeuroX_rs591323 International Parkinson’s Disease Genomics Consortium(IPDGC) andWellcome Trust Case Control Consortium 2(WTCCC2), 2011

8 16697091 A G

ITGA8 rs7077361 exm-rs7077361 Lill et al., 2012 10 15561543 C TLRRK2 rs1491942 exm-rs1491942 Lill et al., 2012;

Nalls et al., 201112 40620808 C G

LRRK2 rs34637584 exm994671 Do et al., 2011 12 40734202 A GCCDC62/HIP1R rs12817488

(proxy)NeuroX_dbSNP_rs11060180_replicate_1

Nalls et al., 2011 12 123303586 G A

CCDC62/HIP1R rs10847864 NeuroX_rs10847864 Lill et al., 2012 12 123326598 T GSTX1B rs4889603 NeuroX_rs4889603 International Parkinson’s Disease Genomics Consortium

(IPDGC) andWellcome Trust Case Control Consortium 2(WTCCC2), 2011

16 30982225 A G

SREBF1/RAI1 rs11868035 exm-rs11868035 Do et al., 2011 17 17715101 A GMAPT rs2942168 exm-rs2942168 Nalls et al., 2011 17 43714850 T CMAPT rs12185268 exm1330895 Do et al., 2011 17 43923683 G ARIT2/SYT4 rs4130047 exm-rs4130047 Do et al., 2011 18 40678235 C TUSP25 rs2823357 exm-rs2823357 Do et al., 2011 21 16914905 A G

Risk estimates are based on the dosage of allele 1 (minor allele). MAF refers to the minor allele frequency, comparisons with 1000 Genomes Project data were based onEuropean samples available from http://1000genomes.org. For directionality comparisons, previously published minor alleles with corresponding ORs per minor alleledose and p values are listed. Power to detect association at 3 significance levels for each variant individually is also listed.Key: BP, base pair; CHR, chromosome; CI, confidence interval; HB, Human Build; MAF, minor allele frequency; NA, not applicable; OR, odds ratio; PD, Parkinson’s disease;SE, standard error; SNP, single-nucleotide polymorphism.

E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16 442.e11

NeuroX probes. Before matching, Human Build 36 (release dateMarch 2006) positions of published loci were converted to HumanBuild 37 (release date February 2009) using Single NucleotidePolymorphism Database (dbSNP) (http://www.ncbi.nlm.nih.gov/projects/SNP/) when an rsID was available, or else using UCSCgenome lift (http://genome.ucsc.edu/cgi-bin/hgLiftOver). Suitableproxies were located using SNAP (http://www.broadinstitute.org/mpg/snap/) or 1000 Genomes (http://www.1000genomes.org/) forpublished SNPs that were absent from the NeuroX, did not pass QC,or had nonsatisfactory cluster plots. Proxies selected fulfilled all ofthe following criteria: r2 > 0.5 and distance <500 kb from the SNPof interest as derived from calculations in the European ancestry

populations with which imputations were conducted in the initialdiscovery GWAS, or the 1000 Genomes Project’s phase 1 alphafreeze if no imputation was used in the original report, or theimputation reference was unavailable. If more than 1 suitable proxywas located for a candidate SNP, proxies with the largest r2 andsmallest distance were preferred. Previously published SNPs ortheir proxies with a MAF < 0.01 were included in the study if theMAF was similar to the one catalogued in 1000 Genomes and if thecluster plot was satisfactory. After this step, SNPs remainingwithout suitable proxies were excluded from the study. Powercalculations were undertaken with the online tool CaTS (http://www.sph.umich.edu/csg/abecasis/CaTS/index.html) (Skol et al.,

Table 3Genetic risk profiles in the Greek cohort

Profile based on Table 2 SNPs Quintile

1 2 3 4 5

Odds ratio 1 1.06 1.25 1.59 2.44Lower limit of the 95% CI d 0.79 0.94 1.18 1.81Higher limit of the 95% CI d 1.42 1.68 2.12 3.3Trend p value 7.82E-13 d d d d

AUC 0.5934 d d d d

Key: AUC, predictive area under the curve; CI, confidence interval; SNP, single-nucleotide polymorphism.

Genotypequality(GenTrainscore)

MAF MAF in 1000GenomesProject,European

OR per allele 1(minor allele)dose (95% CI)

SE p-value Publishedminorallele

Published ORper minor alleledose (95% CI)

Publishedp value

Power for significancethresholda ¼ 0.05/0.002/5E-8, %

0.6468 0.005719 NA 1.491 (0.6089e3.65) 0.4568 0.3821 C 4.048 (3.08e5.32) 5.17E-21 99/89/70.9056 0.002996 0.03 1.598 (0.4622e5.525) 0.6329 0.4589 C 3.37 (2.67e4.25) 1.44E-14 100/97/260.7682 0.005719 0.03 1.198 (0.5171e2.774) 0.4285 0.6739 T 1.67 (1.40e1.98) 2.35E-12 69/26/00.9013 0.4011 0.47 0.9454 (0.8261e1.082) 0.0688 0.4144 C 0.89 (0.85e0.92) 8.82E-15 99/86/110.9262 0.2587 0.25 0.8815 (0.7596e1.023) 0.07593 0.09669 A 0.87 (0.83e0.92) 8.00E-10 44/10/00.8105 0.2072 0.18 0.8822 (0.7518e1.035) 0.08163 0.1247 G 0.827 (0.77e0.89) 1.27E-07 47/12/00.8185 0.2715 0.49 1.035 (0.8917e1.201) 0.07604 0.6507 A 1.4 (1.20e1.63) 1.35E-09 98/82/80.712 0 0.12 NA NA NA T 1.28 (1.19e1.38) 3.31E-11 75/33/00.9128 0.1394 0.12 1.47 (1.212e1.783) 0.09843 8.98E-05 C 1.14 (1.06e1.22) 1.37E-09 28/4/00.862 0.3562 0.31 1.039 (0.906e1.192) 0.06997 0.5831 T 1.08 (1.02e1.14) 1.31E-06 19/2/00.9032 0.2075 0.22 0.9603 (0.8166e1.129) 0.08271 0.6244 G 0.803 (0.75e0.86) 2.67E-10 58/18/00.8219 0.146 0.17 1.007 (0.8358e1.214) 0.0952 0.9399 A 0.84 (0.80e0.89) 9.20E-10 32/6/00.7941 0.1198 0.06 1.258 (1.03e1.537) 0.1022 0.02478 A 1.311 (1.19e1.44) 3.87E-08 63/21/00.9079 0.1168 0.13 1.221 (0.9917e1.503) 0.1061 0.05989 T 1.22 (1.13e1.32) 3.04E-12 50/13/00.898 0.4314 0.44 0.9566 (0.8382e1.092) 0.06739 0.5099 C 0.86 (0.82e0.91) 1.87E-10 90/12/00.9278 0.3597 0.4 1.007 (0.8793e1.153) 0.06915 0.9205 T 0.89 (0.85e0.93) 1.17E-17 79/5/00.8228 0.3905 0.49 1.172 (1.026e1.339) 0.06791 0.01923 T 1.285 (1.22e1.36) 2.29E-19 96/74/40.9136 0.1155 0.1 1.122 (0.9125e1.381) 0.1056 0.2743 T 1.23 (1.14e1.32) 4.91E-11 87/51/10.7276 0.1408 0.21 0.9702 (0.774e1.216) 0.1153 0.7928 C 0.81 (0.78e0.84) 3.00E-11 66/23/00.8652 0.4319 0.37 0.9868 (0.8651e1.126) 0.06712 0.8429 G 0.89 (0.86e0.93) 3.05E-13 41/9/00.8602 0.2508 0.29 1.046 (0.8982e1.219) 0.07793 0.5603 A 0.89 (0.86e0.93) 1.92E-11 35/7/00.907 0.1443 0.1 1.097 (0.9128e1.318) 0.09368 0.3239 C 0.86 (0.79e0.93) 1.51E-08 25/4/00.7824 0.2467 0.18 1.117 (0.9619e1.297) 0.07629 0.1468 C 1.2 (1.11e1.29) 6.44E-15 51/14/00.7749 0.000545 0 1.45Eþ09 (0eInf) 17240 0.999 A 9.615 (6.43e14.37) 1.82E-28 100/100/350.9306 0.4662 0.45 0.9075 (0.7963e1.034) 0.06668 0.1456 A 1.17 (1.09e1.24) 4.43E-09 61/20/00.8791 0.3453 0.34 1.147 (1.001e1.314) 0.06939 0.04871 T 1.12 (1.07e1.18) 4.37E-17 55/16/00.7141 0.4438 0.41 0.8985 (0.7878e1.025) 0.06708 0.1105 G 1.14 (1.09e1.19) 6.98E-13 40/8/00.7445 0.3769 0.34 0.9303 (0.8114e1.067) 0.06977 0.3004 A 0.851 (0.80e0.90) 5.61E-08 63/21/00.9049 0.2067 0.23 0.8524 (0.6366e1.141) 0.1489 0.2837 T 0.78 (0.74e0.81) 1.62E-18 82/42/10.7038 0.2033 0.23 0.8991 (0.6716e1.204) 0.1488 0.4749 G 0.769 (0.72e0.82) 2.72E-14 92/60/20.853 0.3219 0.33 1.239 (1.08e1.422) 0.07014 0.00222 C 1.161 (1.10e1.23) 2.44E-07 57/17/00.9151 0.3736 0.39 1.021 (0.8938e1.167) 0.06799 0.758 A 1.149 (1.09e1.21) 6.32E-07 54/15/0

Table 2. (Continued)

E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16442.e12

2006) for 3 levels of significance (0.05, 0.002, 5E-8) assuming adisease prevalence of 0.002 in an additive disease model. The po-wer to detect association was calculated separately for each of the32 variants included in our replication study using the smallestodds ratios andMAFs reported in previous PD GWASmeta-analysesor in the meta-analyses results cataloged in PDGene (http://www.pdgene.org/) (Lill et al., 2012). QQ plot and genomic inflation fac-tor were also calculated for all SNPs passing QC (SupplementaryFig. 3).

Risk profiles were calculated incorporating 30 of the 32 pub-lished SNPs (or their proxies) included in our study (Table 2), onemonomorphic and a second near monomorphic SNP from theGreek dataset without sufficient proxies were excluded (rs2102808,rs34637584). For the SNPs from published GWAS, aggregate riskallele frequencies were calculated, weighted by the published oddsratio in a method described in detail elsewhere (Hernandez et al.,2012; International Parkinson’s Disease Genomics Consortium[IPDGC] and Wellcome Trust Case Control Consortium 2[WTCCC2], 2011; Nalls et al., 2011; Ripatti et al., 2010). In brief,risk allele dosages were counted and a composite score across allloci was generated. Per SNP, risk alleles were scaled by their pub-lished odds ratios, or using available data for proxy SNPs, givinglarger weights to alleles with higher risk estimates. Overall trendestimates were used to evaluate the significance of the risk score’sassociation with PD status across the Greek cohort using logisticregression. At this stage, receiver operator curves were generated toassess the clinical predictability of PD associated with the cumu-lative risk score indicated by the area under the curve (Supple-mentary Fig. 4). In addition, the data set was divided into quintilesbased on the genetic risk score. More logistic regression analyseswere conducted comparing the lowest risk quintile to the 2nd

through 5th highest risk quintiles, always using the lowest quintileas a reference group in the model (Table 3). All risk profiling ana-lyses were adjusted for eigenvectors 1 and 2 to account for popu-lation substructure.

To ascertain narrow sense heritability estimates from thisoutbred sample series, the restricted maximum likelihood methodwithin the Genome-Wide Complex Trait Analysis package (version1.11) was used (Lee et al., 2011, 2012; Yang et al., 2010, 2011, 2012).We calculated the variance in PD risk explained by all genotypedSNPs passing QC and the second modeling scenario based on asubset of all SNPs passing QC limited to those within 1 Megabase(Mb) of previously identified GWAS loci assuming a PD prevalencein the general population of 0.002 (Keller et al., 2012). These ana-lyses were also adjusted for principal components 1e2 to accountfor population substructure. This allowed us to estimate heritabilitywithin the Greek population attributable to genome/exome-wideassayed variation and to known GWAS loci, respectively.

Finally, to assess the contribution to PD risk of loci previouslyidentified through candidate gene studies in the Greek population

Table 4Study of variants identified through previous candidate gene studies in the Greek population

Gene SNP NeuroX SNP CHR BP (HB37) A1 (minorallele)

OR (95% CI) per copy of A1 SE p-value Publishedminor allele

Published OR (95% CI)per copy of minor allele

Publishedp value

Sample size(cases/controls), n

Reference

SCARB2 rs6824953 NeuroX_rs35873788 (proxy) 4 77096606 T 1.061 (0.9138e1.233) 0.07636 0.436 G 1.26 (1.01e1.57) 0.042 347/329 Michelakakis et al.,2012

SCARB2 rs6825004 NeuroX_dbSNP_rs6825004_replicate_1 4 77110365 G 0.8764 (0.763e1.007) 0.0707 0.06197 G 0.71 (0.56e0.90) 0.006 347/329 Michelakakis et al.,2012

SCARB2 rs6825004 NeuroX_dbSNP_rs6825004_replicate_2 4 77110365 G 0.8764 (0.763e1.007) 0.0707 0.06197 G 0.71 (0.56e0.90) 0.006 347/329 Michelakakis et al.,2012

SCARB2 rs6825004 NeuroX_dbSNP_rs6825004_replicate_3 4 77110365 G 0.8764 (0.763e1.007) 0.0707 0.06197 G 0.71 (0.56e0.90) 0.006 347/329 Michelakakis et al.,2012

SCARB2 rs4241591 proxy to rs6824953 4 77121346 NA NA NA NA A 0.99 (0.79e1.23) 0.93 347/329 Michelakakis et al.,2012

SCARB2 rs9991821 proxy to rs6825004 4 77130285 NA NA NA NA A 0.94 (0.72e1.24) 0.69 347/329 Michelakakis et al.,2012

SCARB2 rs17234715 NeuroX_rs11097314 (proxy) 4 77187556 A 1.071 (0.938e1.222) 0.06752 0.3118 C 0.95 (0.74e1.22) 0.69 347/329 Michelakakis et al.,2012

LRRK2 rs10878258 NeuroX_rs6581622 (proxy) 12 40634158 C 0.9141 (0.7935e1.053) 0.07222 0.2136 NA NA 0.05 217/221 Paisan-Ruiz et al.,2006

AKT1 rs2494743 NA 14 105251720 NA NA NA NA C 1.22 (0.85e1.75) 0.27 281/220 Xiromerisiou et al.,2008

AKT1 rs2498788 NA 14 105253009 NA NA NA NA T 1.52 (1.0e2.32) 0.044 281/220 Xiromerisiou et al.,2008

AKT1 rs2494746 NA 14 105257719 NA NA NA NA C 1.13 (0.79e1.62) 0.48 281/220 Xiromerisiou et al.,2008

AKT1 rs1130214 NA 14 105259734 NA NA NA NA T 0.99 (0.74e1.32) 0.95 281/220 Xiromerisiou et al.,2008

MAPT rs1467967 NA 17 43986179 NA NA NA NA C 0.89 (0.67e1.18) 0.43 100/94 Fung et al., 2006MAPT rs242557 exm-rs242557 17 44019712 A 1.044 (0.906e1.204) 0.0725 0.5493 A 1.28 (0.96e1.71) 0.09 100/94 Fung et al., 2006MAPT rs3785883 NeuroX_dbSNP_rs116686818 17 44054433 A 1.069 (0.9062e1.261) 0.08429 0.4284 A 1.42 (0.99e2.03) 0.05 100/94 Fung et al., 2006MAPT rs2471738 NA 17 44076063 NA NA NA NA A 0.97 (0.67e1.41) 0.88 100/94 Fung et al., 2006MAPT del-In9 NA 17 44086651 NA NA NA NA H2 0.81 (0.59e1.11) 0.19 100/94 Fung et al., 2006MAPT rs7521 NeuroX_rs7521 17 44105395 G 1.001 (0.8662e1.157) 0.07395 0.9859 G 0.82 (0.62e1.05) 0.15 100/94 Fung et al., 2006

Key: BP, base pair; CHR, chromosome; CI, confidence interval; HB, Human Build; NA, not applicable; OR, odds ratio; SE, standard error; SNP, single-nucleotide polymorphism.

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(Table 4), association results were extracted for suitable NeuroXSNPs or proxies selected for the known PD risk loci from the pre-viously generated logistic regression dataset.

3. Results

Based on previous knowledge, a number of recent GWAS-identified loci show marginal associations at p values <0.05(Table 2). This could technically be viewed as a form of replication ifprevious knowledge of these robust associations is considered,even though this study itself is immensely underpoweredcompared with the initial discovery and replication cohorts withinthe original reports. Although the astounding strength of the STK39association is impressive, lower significance associations are seen atSNCA (p ¼ 0.019), RIT2/SYT4 (p ¼ 0.002), GAK (p ¼ 0.025), andCCDC62/HIP1R (p ¼ 0.048), all agreeable with the directionality ofallelic effect as seen in previous studies (Do et al., 2011;International Parkinson’s Disease Genomics Consortium [IPDGC]and Wellcome Trust Case Control Consortium 2 [WTCCC2], 2011;Lill et al., 2012; Nalls et al., 2011).

Our risk profiling analysis yielded results quite similar to thosepublished in Hernandez et al. (2012); International Parkinson’sDisease Genomics Consortium (IPDGC) and Wellcome Trust CaseControl Consortium 2 (WTCCC2) (2011), and Nalls et al. (2011)(Table 3). We show a highly significant trend for risk profilescores calculated to assess the cumulative risk attributable to allknown GWAS loci associated with PD (p < 1E-12), with an oddsratio of 2.44 associated with membership in the highest quintile ofPD risk compared with those in the lowest quintile of PD risk. Likeprevious PD GWAS, the predictability of risk profiles based onGWAS data does not rise to the clinical utility wewould have hopedfor, with an area under the curve from receiver operator curveanalyses being only 0.5934.

Heritability analyses show roughly 1.27% of the variance in PDrisk is attributable to the regions surrounding known GWAS loci. Incontrast, heritability estimates from all assayed SNPs passing QCsuggest that there is a total variance explained by the SNPs assayedon the NeuroX array to be approximately 17.55%. This suggests thatfuture studies in larger samples sizes with dense sequencing data(among other sources of genetic data) might explain this remaining16.28% genetic variation in risk similar to what was seen in Kelleret al. (2012).

We failed to replicate the results of previous candidate genestudies in the Greek population at a nominal significance level of<0.05 though the associationwas of similar directionality and effectsize for all 3 previously identified significant SNPs (Fung et al.,2006; Michelakakis et al., 2012) (Table 4).

4. Discussion

Even though the existence of common variants of large effectsize is unlikely in the Greek population based on this relativelyunderpowered analysis, we have replicated the association of 5previously reported variants of lower effect size within the SNCA,STK39, RIT2/SYT4, GAK, and CCDC62/HIP1R loci (Do et al., 2011;International Parkinson’s Disease Genomics Consortium [IPDGC]and Wellcome Trust Case Control Consortium 2 [WTCCC2], 2011;Lill et al., 2012; Nalls et al., 2011; Pankratz et al., 2012) with nom-inal p values<0.05. There are 2 possible explanations for the failureto replicate the association for the remaining individual risk vari-ants. First, our study had limited power to detect associations withvariants of small MAF and effect size (Table 2). Second, because it islikely that the variants identified in previous GWAS are just proxiesfor the putative functional variants, population-specific differences

in linkage disequilibrium patterns and allele frequencies could beresponsible for the lack of replication (Singleton et al., 2013).

Wewere able to replicate the previously reported risk profiles inthe current data set, and the observed effects are relativelyconsistent with previous work (Hernandez et al., 2012;International Parkinson’s Disease Genomics Consortium [IPDGC]and Wellcome Trust Case Control Consortium 2 [WTCCC2], 2011;Nalls et al., 2011) indicating that there probably is a contributionof previously reported variants to PD risk in the Greek population.

In conclusion, the results from Genome-Wide Complex TraitAnalysis and the interpretation of our findings in the context ofprevious GWAS, coupled with positivity of family history for PD in17.2% of our cases show that there is probably a substantial, un-known genetic component for PD in the Greek population whichshould be addressed in future studies.

Disclosure statement

The authors have no potential or actual conflicts of interest.All samples were collected in accordance with institutional

ethical procedures after written informed consent was provided.Human work on blood materials was been carried out in compli-ance with institutional regulations.

Acknowledgements

The authors thank the patients and their families for theiressential help,MarkGaskin (University College London; UCL) andDrMatinaManiati (BioacademyofAthens,Greece) forhelpwith sampleorganization, Hallgeir Jonvik (UCL) and Rick Santmier (National In-stitutes of Health; NIH) for help with data management and ITsupport, and June Smalley (UCL), Joan Ward (NIH), and KimberlySingleton (NIH) for administrative assistance. This work was sup-ported by the Parkinson’s disease foundation (GX, HH), the Well-come Trust, the Medical Research Council (MRC), and by theWellcome Trust/MRC Joint Call in Neurodegeneration award(WT089698) to the UK Parkinson’s Disease Consortium whosemembers are from the UCL Institute of Neurology, the University ofSheffield, and the MRC Protein Phosphorylation Unit at the Univer-sity of Dundee. This work was supported in part by the IntramuralResearch Programs of the National Institute on Aging, Z01AG000949-02. The authors are grateful to The Queen Square brainbank, The Netherlands brain bank, The Harvard Brain TissueResource Centre, University of Maryland Brain and Tissue Bank forDevelopmental Disorders, and the MRC London Brain Bank. Thisstudy was supported by the National Institute for Health ResearchUniversity College London Hospitals Biomedical Research Centre.This study used the high-performance computational capabilities ofthe Biowulf Linux cluster at the National Institutes of Health,Bethesda, Maryland (http://biowulf.nih.gov). This study was sup-ported in part by Research Committee, University of Thessaly (code2845; PI: GMH).

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.neurobiolaging.2013.07.011.

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