Neurobiology of Aging 35 (2014) 442.e9e442.e16 Contents lists available at ScienceDirect 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, * a Reta Lila Weston Laboratories and Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK Laboratory of Neurogenetics, Department of Neurology, Faculty of Medicine, University of Thessaly, Larissa, Greece c Department of Neurology, Papageorgiou Hospital, Thessaloniki, Greece d Department of Neurology, Medical School, University of Crete, Heraklion, Crete, Greece e General Hospital of Syros, Syros, Greece f ‘Hygeia’ Hospital, Clinic of Neurodegenerative Disorders, Athens, Greece g Second Department of Neurology, National and Kapodistrian University of Athens Medical School, Athens, Greece h Neurogenetics Unit, 1st Department of Neurology, University of Athens Medical School, Eginition Hospital, Athens, Greece i Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA j Department of General Biology, Medical School, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece k Third Department of Neurology, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece l Department of Biological Anthropology, Temple University, Philadelphia, PA, USA m Division of Basic Neurosciences, Biomedical Research Foundation of the Academy of Athens, Athens, Greece b a r t i c l e i n f o a b s t r a c t Article history: Received 4 June 2013 Accepted 15 July 2013 Available online 27 September 2013 Genome-wide association studies (GWAS) have been shown to be a powerful approach to identify risk loci for neurodegenerative diseases. Recent GWAS in Parkinson’s disease (PD) have been successful in identifying numerous risk variants pointing to novel pathways potentially implicated in the pathogenesis of PD. Contributing to these GWAS efforts, we performed genotyping of previously identified risk alleles in PD patients and control subjects from Greece. We showed that previously published risk profiles for Northern 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 32 previously published risk variants with nominal p values <0.05. Genome-wide complex trait analysis revealed that known risk loci explain disease risk in 1.27% of Greek PD patients. Collectively, these results indicate that there is likely a substantial genetic component to PD in Greece, similarly to other worldwide populations, that remains to be discovered. Published by Elsevier Inc. Keywords: Parkinson’s disease GWAS GCTA Genetics Greece Risk profiles 1. Introduction The dissection of the genetic basis of Parkinson’s disease (PD) started with the identification of mutations in a-synuclein (SNCA) in 1997 (Polymeropoulos et al., 1997). Fifteen years later, the cause of most PD cases still remains unknown as Mendelian mutations collectively account for less than 5% of the disease (Pankratz et al., 2012). More recently, driven by the common disease-common * Corresponding author at: Molecular Genetics Section and Laboratory of Neurogenetics, NIA, NIH, Building 35, Room 1A1014, 35 Convent Drive, Bethesda, MD 20892, USA. Tel.: þ1 301 451 6079; fax: þ1 301-451 5466. E-mail address: Singleta@mail.nih.gov (A.B. Singleton). 0197-4580/$ e see front matter Published by Elsevier Inc. http://dx.doi.org/10.1016/j.neurobiolaging.2013.07.011 variant hypothesis (Reich and Lander, 2001), several PD genomewide 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 large scale meta-analyses (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) have shown that variants within 26 loci increase the risk for PD. Despite these advances, there is evidence that a large number of causative loci still remain to be discovered (Keller et al., 2012). It has been previously argued that studies in isolated populations with limited genetic heterogeneity are valuable for 442.e10 E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16 Table 1 Descriptive statistics of the Greek cohort Sample type Number of subjects passing QC All Case Control 1836 960 876 Sex, n (%) Mean age  SD Male Female 1036 (56.4) 553 (57.6) 483 (55.1) 800 (43.6) 407 (42.4) 393 (44.9) 63.18519  11.4321 64.04325  10.91366 (age at onset) 62.14231  11.95655 (age at study enrollment) PD family history, n (%)a Positive Negative 115 (17.2) 115 (17.2) NA 555 (82.8) 555 (82.8) 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. studying the genetic basis of disease (Hernandez et al., 2012) with an 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 discovery of C9orf72 repeat expansions as a major cause of ALS Fronto-temporal dementia (FTD) (DeJesus-Hernandez et al., 2011; Renton et al., 2011; Traynor, 2012). However, a recent PD GWAS completed in the Finnish population following a similar rationale failed to identify such high risk variants (Hernandez et al., 2012). Similarly to the Finnish population, there is evidence that the Greek population is an isolated population (Mok et al., 2012) with subtle genetic intricacies when compared with other European populations (International HapMap Consortium, 2003; Stathias et al., 2012). This, in combination with the location of Greece in the crossroad between Europe, Africa, and the Middle East, serving as 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 population promising and informative for other European populations. Motivated by these observations, we undertook a PD caseecontrol analysis targeting variants previously implicated in risk for PD by GWAS. 2. Methods All samples were collected in accordance with institutional ethical 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 and Supplementary Table 1). The total number of samples was from 1154 case and 997 control subjects. PD patients were diagnosed according to the Queen Square brain bank criteria (Gibb and Lees, 1988, 1989). Control subjects were healthy individuals with no signs or symptoms of parkinsonism whose close relatives were also free from parkinsonism based on self-report or available clinical data. All samples were genotyped as part of a larger study using the NeuroX Array (Illumina) which is an exome plus custom content genotyping array. The NeuroX contains 267,607 probes densely covering previously published PD GWAS-associated loci, rare variants identified through exome sequencing studies of neurodegenerative diseases, ancestry informative markers, markers for determination of identity by descent, X chromosome singlenucleotide polymorphisms (SNPs) for sex determination, candidate loci from neurodegenerative disease GWAS, and standard Illumina exome array content. After initial genotyping, genotypes were clustered using Illumina GenomeStudio with default parameters. For SNPs previously associated with PD, genotype clusters were manually inspected (see Supplementary Fig. 1). Sample quality control (QC) was slightly more rigorous than standard GWAS because of the use of an exome-based array with abundant rare variants and experimental content. All sample QC was based on SNPs with Illumina GenTrain scores >0.7, indicative of generally higher-quality genotyping. Initially, samples with less than 95% successful calls on a genome-wide scale, and sex as estimated from X chromosome heterogeneity not matching clinical reports of sex were excluded. X heterogeneity calculations were based on common SNPs from the International HapMap Project that had genotypes with missingness <5% and Hardy-Weinberg equilibrium (HWE) p values >1E-5. For further data cleaning, a subset of the genotype data was used, including only SNPs present in HapMap3 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 of heterozygosity, excluding any samples with observed heterozygosity divergence more than 3 standard deviations from the expected population mean. After this exclusion, samples were clustered using principal components analysis to evaluate European ancestry compared with HapMap3 populations at overlapping SNPs (International HapMap Consortium, 2003; Patterson et al., 2006; Price et al., 2006; Yang et al., 2011). At this stage, samples were excluded if they were outside of 6 standard deviations from the 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 identity by descent was quantified, allowing us to exclude any samples sharing proportionately more than 12.5% of alleles indicating cryptic relatedness at the level of cousins. Within related pairs, individuals were retained to maximize a 1:1 ratio of case to control subjects and preserve study power. At this time, 10 eigenvectors were estimated to account for population substructure and to be used as covariates in all analyses. When sample QC was completed, genotype data on all attempted SNPs was extracted for samples meeting inclusion criteria. At this point, we excluded all SNPs with minor allele frequency (MAF) <0.01, HWE p values <1E-5, differential missingness between case and control subjects at p values <1E-5, differential missingness according to haplotype at p values <1E-5, and GenTrain scores <0.7. SNPs at MAF < 0.01 were not retained because of concerns about study power. For analyses in this report, we used a working sample size of 960 case and 876 control subjects genotyped at 48,805 SNPs. The purpose of this project was to investigate whether known PD-associated SNPs contribute to PD risk in the Greek population through mining data generated on the single SNP level, and also using genetic risk profiling to aggregate risk across all known loci. We then attempted to estimate PD heritability in this population based on all available SNP data and also only focusing in on known GWAS loci. For all SNPs and samples passing QC, logistic regression analyses were used to estimate risk associated with each SNP, adjusted for eigenvectors 1e10 as covariates. All loci summarized in Keller et al. (2012) were also extracted to evaluate risk associated with previously discovered GWAS loci in our Greek cohort (Table 2). Loci that reached genome-wide significance in previously published PD GWAS were matched based on position to their corresponding 442.e11 E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16 Table 2 A summary of known PD risk loci in the Greek population (table continues on next page) Candidate gene Corresponding SNP NeuroX SNP Reference GBA GBA SYT11/RAB25 i400416 N370S (proxy) chr1:154105678 (proxy) rs708723 exm106217 NeuroX_rs71628662 exm-rs34372695_ver3 NMD3 rs947211 rs823156 rs6710823 (proxy) rs2102808 rs2390669 (proxy) rs34016896 MCCC1/LAMP3 MCCC1/LAMP3 rs10513789 rs11711441 exm-rs10513789_ver4 exm-rs11711441 GAK DGKQ BST1 rs6599389 rs11248060 rs11724635 exm-rs6599389 exm-rs11248060 NeuroX_rs11724635 STBD1 rs6812193 exm-rs6812193 SNCA SNCA HLA GPNMB rs356220 rs6532194 rs2395163 rs156429 (proxy) exm-rs356220 NeuroX_rs6532194 exm-rs2395163 NeuroX_rs199347 FGF20 rs591323 NeuroX_rs591323 ITGA8 LRRK2 rs7077361 rs1491942 exm-rs7077361 exm-rs1491942 LRRK2 CCDC62/HIP1R CCDC62/HIP1R STX1B rs34637584 rs12817488 (proxy) rs10847864 rs4889603 exm994671 NeuroX_dbSNP_ rs11060180_replicate_1 NeuroX_rs10847864 NeuroX_rs4889603 SREBF1/RAI1 MAPT MAPT RIT2/SYT4 USP25 rs11868035 rs2942168 rs12185268 rs4130047 rs2823357 exm-rs11868035 exm-rs2942168 exm1330895 exm-rs4130047 exm-rs2823357 RAB7LI/PARK16 RAB7LI/PARK16 SLC41A1 ACMSD STK39 STK39 CHR BP (HB37) Allele 1 (minor allele) Allele 2 1 1 1 155205634 155359992 156030037 C C T T T C 1 205739266 C T exm-rs947211 exm-rs823156 NeuroX_rs6430538 Do et al., 2011 Lill et al., 2012 Lill et al., 2012; Nalls et al., 2011 International Parkinson’s Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2), 2011 Lill et al., 2012 Do et al., 2011 Nalls et al., 2011 1 1 2 205752665 205764640 135539967 A G C G A T exm-rs2102808_ver4 NeuroX_rs1955337 Nalls et al., 2011 Lill et al., 2012 2 2 169117025 169129145 T T G G NeuroX_rs34016896 International Parkinson’s Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2), 2011 Do et al., 2011 Lill et al., 2012; Nalls et al., 2011 Do et al., 2011 Lill et al., 2012 Lill et al., 2012; Nalls et al., 2011 International Parkinson’s Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2), 2011 Do et al., 2011 Lill et al., 2012 Pankratz et al., 2012 International Parkinson’s Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2), 2011 International Parkinson’s Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2), 2011 Lill et al., 2012 Lill et al., 2012; Nalls et al., 2011 Do et al., 2011 Nalls et al., 2011 3 160992864 T C 3 3 182760073 182821275 G A T G 4 4 4 939113 964359 15737101 A T C G C A 4 77198986 T C 4 4 6 7 90641340 90780902 32387809 23293746 T T C C C C T T 8 16697091 A G 10 12 15561543 40620808 C C T G 12 12 40734202 123303586 A G G A 12 16 123326598 30982225 T A G G 17 17 17 18 21 17715101 43714850 43923683 40678235 16914905 A T G C A G C A T G NeuroX_rs708723 Lill et al., 2012 International Parkinson’s Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2), 2011 Do et al., 2011 Nalls et al., 2011 Do et al., 2011 Do et al., 2011 Do et al., 2011 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 on European samples available from http://1000genomes.org. For directionality comparisons, previously published minor alleles with corresponding ORs per minor allele dose 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. NeuroX probes. Before matching, Human Build 36 (release date March 2006) positions of published loci were converted to Human Build 37 (release date February 2009) using Single Nucleotide Polymorphism Database (dbSNP) (http://www.ncbi.nlm.nih.gov/ projects/SNP/) when an rsID was available, or else using UCSC genome lift (http://genome.ucsc.edu/cgi-bin/hgLiftOver). Suitable proxies were located using SNAP (http://www.broadinstitute.org/ mpg/snap/) or 1000 Genomes (http://www.1000genomes.org/) for published SNPs that were absent from the NeuroX, did not pass QC, or had nonsatisfactory cluster plots. Proxies selected fulfilled all of the following criteria: r2 > 0.5 and distance <500 kb from the SNP of interest as derived from calculations in the European ancestry populations with which imputations were conducted in the initial discovery GWAS, or the 1000 Genomes Project’s phase 1 alpha freeze if no imputation was used in the original report, or the imputation reference was unavailable. If more than 1 suitable proxy was located for a candidate SNP, proxies with the largest r2 and smallest distance were preferred. Previously published SNPs or their proxies with a MAF < 0.01 were included in the study if the MAF was similar to the one catalogued in 1000 Genomes and if the cluster plot was satisfactory. After this step, SNPs remaining without suitable proxies were excluded from the study. Power calculations were undertaken with the online tool CaTS (http:// www.sph.umich.edu/csg/abecasis/CaTS/index.html) (Skol et al., 442.e12 E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16 Table 2. (Continued) Genotype quality (GenTrain score) MAF MAF in 1000 Genomes Project, European OR per allele 1 (minor allele) dose (95% CI) 0.6468 0.9056 0.7682 0.9013 0.9262 0.8105 0.8185 0.712 0.9128 0.862 0.9032 0.8219 0.7941 0.9079 0.898 0.9278 0.8228 0.9136 0.7276 0.8652 0.8602 0.907 0.7824 0.7749 0.9306 0.8791 0.7141 0.7445 0.9049 0.7038 0.853 0.9151 0.005719 0.002996 0.005719 0.4011 0.2587 0.2072 0.2715 0 0.1394 0.3562 0.2075 0.146 0.1198 0.1168 0.4314 0.3597 0.3905 0.1155 0.1408 0.4319 0.2508 0.1443 0.2467 0.000545 0.4662 0.3453 0.4438 0.3769 0.2067 0.2033 0.3219 0.3736 NA 0.03 0.03 0.47 0.25 0.18 0.49 0.12 0.12 0.31 0.22 0.17 0.06 0.13 0.44 0.4 0.49 0.1 0.21 0.37 0.29 0.1 0.18 0 0.45 0.34 0.41 0.34 0.23 0.23 0.33 0.39 1.491 1.598 1.198 0.9454 0.8815 0.8822 1.035 NA 1.47 1.039 0.9603 1.007 1.258 1.221 0.9566 1.007 1.172 1.122 0.9702 0.9868 1.046 1.097 1.117 1.45Eþ09 0.9075 1.147 0.8985 0.9303 0.8524 0.8991 1.239 1.021 SE (0.6089e3.65) (0.4622e5.525) (0.5171e2.774) (0.8261e1.082) (0.7596e1.023) (0.7518e1.035) (0.8917e1.201) 0.4568 0.6329 0.4285 0.0688 0.07593 0.08163 0.07604 NA (1.212e1.783) (0.906e1.192) (0.8166e1.129) (0.8358e1.214) (1.03e1.537) (0.9917e1.503) (0.8382e1.092) (0.8793e1.153) (1.026e1.339) (0.9125e1.381) (0.774e1.216) (0.8651e1.126) (0.8982e1.219) (0.9128e1.318) (0.9619e1.297) (0eInf) (0.7963e1.034) (1.001e1.314) (0.7878e1.025) (0.8114e1.067) (0.6366e1.141) (0.6716e1.204) (1.08e1.422) (0.8938e1.167) 0.09843 0.06997 0.08271 0.0952 0.1022 0.1061 0.06739 0.06915 0.06791 0.1056 0.1153 0.06712 0.07793 0.09368 0.07629 17240 0.06668 0.06939 0.06708 0.06977 0.1489 0.1488 0.07014 0.06799 2006) for 3 levels of significance (0.05, 0.002, 5E-8) assuming a disease prevalence of 0.002 in an additive disease model. The power to detect association was calculated separately for each of the 32 variants included in our replication study using the smallest odds ratios and MAFs reported in previous PD GWAS meta-analyses or in the meta-analyses results cataloged in PDGene (http://www. pdgene.org/) (Lill et al., 2012). QQ plot and genomic inflation factor were also calculated for all SNPs passing QC (Supplementary Fig. 3). Risk profiles were calculated incorporating 30 of the 32 published SNPs (or their proxies) included in our study (Table 2), one monomorphic and a second near monomorphic SNP from the Greek dataset without sufficient proxies were excluded (rs2102808, rs34637584). For the SNPs from published GWAS, aggregate risk allele frequencies were calculated, weighted by the published odds ratio 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 all loci was generated. Per SNP, risk alleles were scaled by their published odds ratios, or using available data for proxy SNPs, giving larger weights to alleles with higher risk estimates. Overall trend estimates were used to evaluate the significance of the risk score’s association with PD status across the Greek cohort using logistic regression. At this stage, receiver operator curves were generated to assess the clinical predictability of PD associated with the cumulative risk score indicated by the area under the curve (Supplementary Fig. 4). In addition, the data set was divided into quintiles based on the genetic risk score. More logistic regression analyses were conducted comparing the lowest risk quintile to the 2nd p-value Published minor allele Published OR per minor allele dose (95% CI) Published p value Power for significance threshold a ¼ 0.05/ 0.002/5E-8, % 0.3821 0.4589 0.6739 0.4144 0.09669 0.1247 0.6507 NA 8.98E-05 0.5831 0.6244 0.9399 0.02478 0.05989 0.5099 0.9205 0.01923 0.2743 0.7928 0.8429 0.5603 0.3239 0.1468 0.999 0.1456 0.04871 0.1105 0.3004 0.2837 0.4749 0.00222 0.758 C C T C A G A T C T G A A T C T T T C G A C C A A T G A T G C A 4.048 3.37 1.67 0.89 0.87 0.827 1.4 1.28 1.14 1.08 0.803 0.84 1.311 1.22 0.86 0.89 1.285 1.23 0.81 0.89 0.89 0.86 1.2 9.615 1.17 1.12 1.14 0.851 0.78 0.769 1.161 1.149 5.17E-21 1.44E-14 2.35E-12 8.82E-15 8.00E-10 1.27E-07 1.35E-09 3.31E-11 1.37E-09 1.31E-06 2.67E-10 9.20E-10 3.87E-08 3.04E-12 1.87E-10 1.17E-17 2.29E-19 4.91E-11 3.00E-11 3.05E-13 1.92E-11 1.51E-08 6.44E-15 1.82E-28 4.43E-09 4.37E-17 6.98E-13 5.61E-08 1.62E-18 2.72E-14 2.44E-07 6.32E-07 99/89/7 100/97/26 69/26/0 99/86/11 44/10/0 47/12/0 98/82/8 75/33/0 28/4/0 19/2/0 58/18/0 32/6/0 63/21/0 50/13/0 90/12/0 79/5/0 96/74/4 87/51/1 66/23/0 41/9/0 35/7/0 25/4/0 51/14/0 100/100/35 61/20/0 55/16/0 40/8/0 63/21/0 82/42/1 92/60/2 57/17/0 54/15/0 (3.08e5.32) (2.67e4.25) (1.40e1.98) (0.85e0.92) (0.83e0.92) (0.77e0.89) (1.20e1.63) (1.19e1.38) (1.06e1.22) (1.02e1.14) (0.75e0.86) (0.80e0.89) (1.19e1.44) (1.13e1.32) (0.82e0.91) (0.85e0.93) (1.22e1.36) (1.14e1.32) (0.78e0.84) (0.86e0.93) (0.86e0.93) (0.79e0.93) (1.11e1.29) (6.43e14.37) (1.09e1.24) (1.07e1.18) (1.09e1.19) (0.80e0.90) (0.74e0.81) (0.72e0.82) (1.10e1.23) (1.09e1.21) through 5th highest risk quintiles, always using the lowest quintile as a reference group in the model (Table 3). All risk profiling analyses were adjusted for eigenvectors 1 and 2 to account for population substructure. To ascertain narrow sense heritability estimates from this outbred sample series, the restricted maximum likelihood method within the Genome-Wide Complex Trait Analysis package (version 1.11) was used (Lee et al., 2011, 2012; Yang et al., 2010, 2011, 2012). We calculated the variance in PD risk explained by all genotyped SNPs passing QC and the second modeling scenario based on a subset of all SNPs passing QC limited to those within 1 Megabase (Mb) of previously identified GWAS loci assuming a PD prevalence in the general population of 0.002 (Keller et al., 2012). These analyses were also adjusted for principal components 1e2 to account for population substructure. This allowed us to estimate heritability within the Greek population attributable to genome/exome-wide assayed variation and to known GWAS loci, respectively. Finally, to assess the contribution to PD risk of loci previously identified through candidate gene studies in the Greek population Table 3 Genetic risk profiles in the Greek cohort Profile based on Table 2 SNPs Quintile 1 2 3 4 5 Odds ratio Lower limit of the 95% CI Higher limit of the 95% CI Trend p value AUC 1 d d 7.82E-13 0.5934 1.06 0.79 1.42 d d 1.25 0.94 1.68 d d 1.59 1.18 2.12 d d 2.44 1.81 3.3 d d Key: AUC, predictive area under the curve; CI, confidence interval; SNP, singlenucleotide polymorphism. Table 4 Study of variants identified through previous candidate gene studies in the Greek population Gene SNP NeuroX SNP CHR BP (HB37) A1 (minor OR (95% CI) per copy of A1 SE allele) NeuroX_rs35873788 (proxy) 4 77096606 T SCARB2 rs6825004 NeuroX_dbSNP_rs6825004_replicate_1 4 77110365 G 0.8764 (0.763e1.007) SCARB2 rs6825004 NeuroX_dbSNP_rs6825004_replicate_2 4 77110365 G SCARB2 rs6825004 NeuroX_dbSNP_rs6825004_replicate_3 4 SCARB2 rs4241591 proxy to rs6824953 SCARB2 rs9991821 proxy to rs6825004 SCARB2 rs17234715 NeuroX_rs11097314 (proxy) 1.061 (0.9138e1.233) 0.07636 0.436 G 1.26 (1.01e1.57) 0.042 347/329 0.0707 0.06197 G 0.71 (0.56e0.90) 0.006 347/329 0.8764 (0.763e1.007) 0.0707 0.06197 G 0.71 (0.56e0.90) 0.006 347/329 77110365 G 0.8764 (0.763e1.007) 0.0707 0.06197 G 0.71 (0.56e0.90) 0.006 347/329 4 77121346 NA NA NA NA A 0.99 (0.79e1.23) 0.93 347/329 4 77130285 NA NA NA NA A 0.94 (0.72e1.24) 0.69 347/329 4 77187556 A 1.071 (0.938e1.222) 0.06752 0.3118 C 0.95 (0.74e1.22) 0.69 347/329 0.9141 (0.7935e1.053) 0.07222 0.2136 NA NA 0.05 217/221 LRRK2 rs10878258 NeuroX_rs6581622 (proxy) 12 40634158 C AKT1 rs2494743 NA 14 105251720 NA NA NA NA C 1.22 (0.85e1.75) 0.27 281/220 AKT1 rs2498788 NA 14 105253009 NA NA NA NA T 1.52 (1.0e2.32) 0.044 281/220 AKT1 rs2494746 NA 14 105257719 NA NA NA NA C 1.13 (0.79e1.62) 0.48 281/220 AKT1 rs1130214 NA 14 105259734 NA NA NA NA T 0.99 (0.74e1.32) 0.95 281/220 MAPT MAPT MAPT MAPT MAPT MAPT rs1467967 rs242557 rs3785883 rs2471738 del-In9 rs7521 NA exm-rs242557 NeuroX_dbSNP_rs116686818 NA NA NeuroX_rs7521 17 17 17 17 17 17 NA 1.044 (0.906e1.204) 1.069 (0.9062e1.261) NA NA 1.001 (0.8662e1.157) NA 0.0725 0.08429 NA NA 0.07395 NA 0.5493 0.4284 NA NA 0.9859 C A A A H2 G 0.89 1.28 1.42 0.97 0.81 0.82 0.43 0.09 0.05 0.88 0.19 0.15 100/94 100/94 100/94 100/94 100/94 100/94 43986179 44019712 44054433 44076063 44086651 44105395 NA A A NA NA G (0.67e1.18) (0.96e1.71) (0.99e2.03) (0.67e1.41) (0.59e1.11) (0.62e1.05) Michelakakis et al., 2012 Michelakakis et al., 2012 Michelakakis et al., 2012 Michelakakis et al., 2012 Michelakakis et al., 2012 Michelakakis et al., 2012 Michelakakis et al., 2012 Paisan-Ruiz et al., 2006 Xiromerisiou et al., 2008 Xiromerisiou et al., 2008 Xiromerisiou et al., 2008 Xiromerisiou et al., 2008 Fung et al., 2006 Fung et al., 2006 Fung et al., 2006 Fung et al., 2006 Fung et al., 2006 Fung et al., 2006 E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16 SCARB2 rs6824953 p-value Published Published OR (95% CI) Published Sample size Reference minor allele per copy of minor allele p value (cases/ controls), n 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. 442.e13 442.e14 E. Kara et al. / Neurobiology of Aging 35 (2014) 442.e9e442.e16 (Table 4), association results were extracted for suitable NeuroX SNPs or proxies selected for the known PD risk loci from the previously generated logistic regression dataset. 3. Results Based on previous knowledge, a number of recent GWASidentified loci show marginal associations at p values <0.05 (Table 2). This could technically be viewed as a form of replication if previous knowledge of these robust associations is considered, even though this study itself is immensely underpowered compared with the initial discovery and replication cohorts within the original reports. Although the astounding strength of the STK39 association is impressive, lower significance associations are seen at SNCA (p ¼ 0.019), RIT2/SYT4 (p ¼ 0.002), GAK (p ¼ 0.025), and CCDC62/HIP1R (p ¼ 0.048), all agreeable with the directionality of allelic 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 those published in Hernandez et al. (2012); International Parkinson’s Disease Genomics Consortium (IPDGC) and Wellcome Trust Case Control Consortium 2 (WTCCC2) (2011), and Nalls et al. (2011) (Table 3). We show a highly significant trend for risk profile scores calculated to assess the cumulative risk attributable to all known GWAS loci associated with PD (p < 1E-12), with an odds ratio of 2.44 associated with membership in the highest quintile of PD risk compared with those in the lowest quintile of PD risk. Like previous PD GWAS, the predictability of risk profiles based on GWAS data does not rise to the clinical utility we would have hoped for, with an area under the curve from receiver operator curve analyses being only 0.5934. Heritability analyses show roughly 1.27% of the variance in PD risk is attributable to the regions surrounding known GWAS loci. In contrast, heritability estimates from all assayed SNPs passing QC suggest that there is a total variance explained by the SNPs assayed on the NeuroX array to be approximately 17.55%. This suggests that future studies in larger samples sizes with dense sequencing data (among other sources of genetic data) might explain this remaining 16.28% genetic variation in risk similar to what was seen in Keller et al. (2012). We failed to replicate the results of previous candidate gene studies in the Greek population at a nominal significance level of <0.05 though the association was of similar directionality and effect size 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 effect size is unlikely in the Greek population based on this relatively underpowered analysis, we have replicated the association of 5 previously 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 nominal p values <0.05. There are 2 possible explanations for the failure to replicate the association for the remaining individual risk variants. First, our study had limited power to detect associations with variants of small MAF and effect size (Table 2). Second, because it is likely that the variants identified in previous GWAS are just proxies for the putative functional variants, population-specific differences in linkage disequilibrium patterns and allele frequencies could be responsible for the lack of replication (Singleton et al., 2013). We were able to replicate the previously reported risk profiles in the current data set, and the observed effects are relatively consistent 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 contribution of previously reported variants to PD risk in the Greek population. In conclusion, the results from Genome-Wide Complex Trait Analysis and the interpretation of our findings in the context of previous GWAS, coupled with positivity of family history for PD in 17.2% of our cases show that there is probably a substantial, unknown genetic component for PD in the Greek population which should 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 compliance with institutional regulations. Acknowledgements The authors thank the patients and their families for their essential help, Mark Gaskin (University College London; UCL) and Dr Matina Maniati (Bioacademy of Athens, Greece) for help with sample organization, Hallgeir Jonvik (UCL) and Rick Santmier (National Institutes of Health; NIH) for help with data management and IT support, and June Smalley (UCL), Joan Ward (NIH), and Kimberly Singleton (NIH) for administrative assistance. This work was supported by the Parkinson’s disease foundation (GX, HH), the Wellcome Trust, the Medical Research Council (MRC), and by the Wellcome Trust/MRC Joint Call in Neurodegeneration award (WT089698) to the UK Parkinson’s Disease Consortium whose members are from the UCL Institute of Neurology, the University of Sheffield, and the MRC Protein Phosphorylation Unit at the University of Dundee. This work was supported in part by the Intramural Research Programs of the National Institute on Aging, Z01 AG000949-02. The authors are grateful to The Queen Square brain bank, The Netherlands brain bank, The Harvard Brain Tissue Resource Centre, University of Maryland Brain and Tissue Bank for Developmental Disorders, and the MRC London Brain Bank. This study was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, Maryland (http://biowulf.nih.gov). This study was supported in part by Research Committee, University of Thessaly (code 2845; PI: GMH). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neurobiolaging. 2013.07.011. 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