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Review Article
Human Genetic Variation and Parkinson’s Disease
Sun Ju Chung
Journal of Movement Disorders 2010;3(1):1-5.
Published online: April 30, 2010

Department of Neurology, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, Korea

Corresponding author: Sun Ju Chung, MD, PhD, Department of Neurology, Asan Medical Center, University of Ulsan, College of Medicine, 86 Asanbyeongwon-gil, Songpa-gu, Seoul 138-736, Korea, Tel +82-2-3010-3440, Fax +82-2-474-4691, E-mail
• Received: March 9, 2010   • Accepted: April 10, 2010

Copyright © 2010 The Korean Movement Disorder Society

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Parkinson’s disease (PD) is a chronic neurodegenerative disorder with multifactorial etiology. In the past decade, the genetic causes of monogenic forms of familial PD have been defined. However, the etiology and pathogenesis of the majority of sporadic PD cases that occur in outbred populations have yet to be clarified. The recent development of resources such as the International HapMap Project and technological advances in high-throughput genotyping have provided new basis for genetic association studies of common complex diseases, including PD. A new generation of genome-wide association studies will soon offer a potentially powerful approach for mapping causal genes and will likely change treatment and alter our perception of the genetic determinants of PD. However, the execution and analysis of such studies will require great care.
In 2001, two reference versions of the human genome were published.1,2 One human genome sequence was reported by the Human Genome Sequencing Consortium and reflected the assembly of sequences derived from numerous donors,1 whereas the other genome sequence, released by Celera Genomics, was a consensus sequence derived from five individuals. 2 However, both versions of the genome sequence represented the human genome as a haploid sequence, and generic variation was not annotated. Therefore, many researchers have studied how genetic variants contribute to phenotype diversity and have conducted large-scale studies to identify and catalogue nucleotides that differ among individuals. Initial studies focused largely on understanding the range of patterns and frequencies of single nucleotide polymorphisms (SNPs).35 As their prevalence and contribution to human traits and biology were realized, several consortia were formed, and systemic studies were performed to improve our understanding of diverse human genomic variants.6,7
The first complete human genome sequence of a single individual, Levy et al.8 was published in 2007. Shortly thereafter, the second complete genome sequence of an individual, Watson, determined with next-generation sequencing technology, was published.9 Subsequently, three additional genomes from anonymous individuals were sequenced: one Han Chinese (Asian), one Nigerian (African), and one Korean (Asian).1012 Although these data have rapidly increased our knowledge of the various forms of human genetic variation, our understanding of the location and frequencies of structural variants across the genome is still limited. However, an enormous amount of effort is being expended to identify the common genetic variations that contribute to the development of common complex diseases.
This review is a general overview of human genetic variation and its contribution to Parkinson’s disease (PD).
Common vs. rare variants
Human genetic variants are typically referred to as either common or rare to denote the frequency of the minor allele in the human population. Common variants are synonymous with polymorphisms, defined as genetic variants with a minor allele frequency of at least 1% in the population, whereas rare variants have a minor allele frequency of less than 1%.
Single nucleotide polymorphisms
A SNP is a single base change in the DNA sequence at a particular point compared with the “common” or “wild-type” sequence. SNPs are the most prevalent class of genetic variation among individuals. It has been estimated that the human genome contains at least 11 million SNPs, with about 7 million of these occurring with minor allele frequencies exceeding 5% and the remaining having minor allele frequencies between 1 and 5%.
Structural variants
Structural variants are defined as all base pairs that differ between individuals and that are not single nucleotide variants. These include insertion-deletion variants (indels), block substitutions, inversions of DNA sequences, and copy number differences. The technical ability to detect structural variants in the human genome has only recently emerged.6,13
Investigators conducting genetic association studies may target genes for investigation according to the known or postulated biology and previous results, an approach known as candidate gene association. As a large-scale candidate gene association study, Chung et al. investigated the association of common variants in PARK loci and related genes with PD susceptibility and age at onset in an outbred population (unpublished data: correspondence to Dr. Maraganore at NorthShore University Health System, Chicago, USA). They matched 1,103 PD cases from the upper Midwest, USA, individually with unaffected siblings (n = 654) or unrelated controls (n = 449) from the same region. Using a sequencing approach in 25 cases and 25 controls, SNPs in species-conserved regions of PARK loci and related genes were detected. Additional tag SNPs were selected from the HapMap. A total of 235 SNPs and two variable-number tandem repeats (VNTRs) in the ATP13A2, DJ1, LRRK1, LRRK2, MAPT, Omi/HtrA2, PARK2, PINK1, SNCA, SNCB, SNCG, SPR, and UCHL1 genes were genotyped in all 2,206 subjects. Case-control analyses were performed to study the association with PD susceptibility, whereas case-only analyses were used to study the association with age at onset. Only MAPT SNP rs2435200 was associated with PD susceptibility after correcting for multiple testing [odds ratio (OR) = 0.74, 95% confidence interval (CI) = 0.64–0.86, uncorrected p < 0.0001, log additive model]; however, 16 additional MAPT variants, seven SNCA variants, and one LRRK2, PARK2, and UCHL1 variant each had significant uncorrected p-values (Table 1). No significant associations were found for age at onset after correcting for multiple testing. These results confirmed the association of the MAPT and SNCA genes with PD susceptibility, but showed limited association of other PARK loci and related genes with PD.
Alternatively, we may screen the entire genome for association, an approach that has recently transformed the field of genetic association studies. Such a “genome-wide association study (GWAS)” is hypothesis-free, as there is no bias or presumptive list of candidate genes that are being tested. GWAS has greatly accelerated the pace of discovery of genetic association.
As testing so many potential genes simultaneously carries the risk of finding many spurious associations, genetic variants that seem to have strong or suggestive statistical signals in an initial GWAS need to be tested for replication in other large data sets or studies.
The boundaries between candidate gene studies and GWAS can become blurred, and the two types of study are not mutually exclusive.
Six GWAS of PD have been published (Table 2).1419 The study by Maraganore et al. included 775 PD cases and 775 matched controls. This study genotyped 198,345 informative genomic SNPs, and found that a SNP within the semaphorin 5A gene (SEMA5A) had the lowest combined p-value (p = 7.62 × 10−6).14 The authors also reported some suggestive findings for MAPT and SNCA, as well as other PARK loci and related genes. However, none of the findings was significant after correcting for multiple testing. The study by Fung et al.15 examined more SNP markers (408,000 SNPs), but also failed to observe an association of any genetic variation with PD susceptibility after correcting for multiple testing; however, that study included only 276 PD cases and 276 unmatched controls. The study by Pankratz et al.16 enrolled 857 familial PD cases and 867 controls, and observed suggestive associations for the GAK/DGKQ region on chromosome 4 (additive model: OR = 1.69; p = 3.4 × 10−6), MAPT SNPs (recessive model: OR = 0.56; p = 2.0 × 10−5), and the SNCA SNPs (additive model: OR = 1.35; p = 5.5 × 10−5). Despite enriching their sample for genetic load (familial PD cases), none of the SNPs was significant after correcting for multiple testing.
Recently, three GWAS confirmed that common variants in SNCA and MAPT genes increase PD susceptibility.1719 The study by Satake et al.17 (2,011 cases and 18,381 controls) reported strong associations at SNCA on 4q22 (rs11931074, OR = 1.37, p = 7.35 × 10−17), PARK16 on 1q32 (p = 1.52 × 10−12), BST1 on 4q15, (p = 3.94 × 10−9), and LRRK2 on 12q12 (p = 2.72 × 10−8). The study by Simón-Sánchez et al.18 (5,074 cases and 8,551 controls) observed two strong association signals in the SNCA gene (rs2736990, OR = 1.23, p = 2.24 × 10−16) and the MAPT locus (rs393152, OR = 0.77, p = 1.95 × 10−16). Note that the two studies analyzed distinct two human populations (Japanese and European), and data were exchanged so that each group could replicate the other’s findings. The two GWAS of PD reported consistent significant findings at three loci (SNCA, LRRK2, and PARK16). The BST1 gene was associated with PD only in the Japanese population, whereas multiple variants within and near the MAPT gene were associated with PD exclusively in subjects of European ancestry. The most recent study by Edwards et al.19 (1,752 cases and 1,745 controls) observed that the SNCA SNP (rs2736990, OR = 1.29, p = 6.7 × 10−8) and the MAPT region (rs11012, OR = 0.70, p = 5.6 × 10−8) were genome-wide significant. Importantly, the SNCA SNP rs2736990 is the same SNCA SNP that showed the second highest nominally significant association with PD susceptibility in the large-scale candidate association study of Chung et al. The definite evaluation of the functions of these genetic variations awaits further investigation.
The GWAS approach still has substantial limitations. Enormous gaps remain in the ability to provide a biological explanation for why a genomic interval tracks with a complex trait. Although a tag SNP for a linkage disequilibrium (LD) bin is statistically associated with a trait, we have no idea of the precise variants in the bin that have a causal role in contributing to variation in the trait. Moreover, tag SNPs are in LD not only with other SNPs, but also with common structural variants, the majority of which have not yet been identified. The causative variants underlying GWAS test associations are likely to be regulatory rather than coding. Therefore, experiments should be conducted that simultaneously assay global gene expression and genome-wide variation in a large number of individuals to map genetic factors underlying differences in expression levels. These datasets may be valuable tools for identifying the causative variants and biological bases for many loci associated with a complex trait through GWAS.
Unless a particular functional variant has been identified unambiguously, testing a tag SNP that is associated with a disease or trait in one population for risk assessment in an individual from another population can be problematic. This problem stems both from allele frequency differences between populations and from the fact that the LD pattern across loci that mark or co-segregate with a putative causally associated genetic variant may differ from population to population.
We need to consider several issues to conduct GWAS properly. Genotyping error, genotype proportions (Hardy-Weinberg equilibrium), multiple comparisons, replication, population stratification, genetic risk prediction, and the manipulation and interpretation of information should be addressed adequately. Publication bias (negative results tend to be not published) is another big problem.
Although the discovery of GWAS signals is exciting, the amount of work required to achieve and confirm causal variants should not be underestimated. However, we predict that GWAS will identify common generic risk variants for PD and other common complex diseases. Future genomic technologies, including whole genome sequencing and genome-wide measures of epigenetic variability and somatic variation, are likely to change the treatment strategy of PD and alter our perception of the genetic determination of the disease. Therefore, clinicians will need to have solid knowledge of genetic principles and of the interpretation of complex genetic information.

The author has no financial conflicts of interest.

Table 1
Common variants in PARK loci and related genes significantly associated with PD susceptibility (n = 27) in order of statistical significance
Chromosome SNP Positiona Gene Type of variant Alleleb Minor allele frequenciesc (cases/controls) Trend mode OR (95%CI)d Trend test p valuee
17 rs2435200 41427688 MAPT Intronic SNP A/G 0.372/0.422 0.74 (0.64–0.86) <0.0001
4 rs2736990 90897564 SNCA Intronic SNP C/T 0.490/0.470 1.27 (1.09–1.47) 0.0017
17 rs17652121 41429810 MAPT Synonymous C/T 0.164/0.196 0.76 (0.63–0.91) 0.0035
17 rs4792891 41329294 MAPT 5′ UTR SNP G/T 0.284/0.320 0.79 (0.68–0.93) 0.0036
17 rs17691610 41326456 MAPT Intronic SNP G/T 0.164/0.196 0.76 (0.64–0.92) 0.004
17 rs1052587 41458449 MAPT 3′ UTR SNP C/T 0.165/0.196 0.77 (0.64–0.92) 0.0041
17 rs17574361 41464049 MAPT Conserved A/G 0.164/0.197 0.77 (0.64–0.92) 0.0041
17 rs17651549 41417115 MAPT Conserved C/T 0.163/0.194 0.76 (0.63–0.92) 0.0041
17 H1/H2 MAPT Intragenic VNTR 0.77 (0.64–0.92) 0.0042
17 rs17770343 41325948 MAPT Intronic SNP C/T 0.164/0.196 0.77 (0.64–0.92) 0.0046
17 rs1052551 41424761 MAPT Synonymous A/G 0.165/0.196 0.77 (0.64–0.92) 0.0047
17 rs12150242 41371645 MAPT Intronic SNP A/G 0.165/0.197 0.77 (0.64–0.92) 0.0048
17 rs17574604 41467460 MAPT Conserved A/G 0.164/0.196 0.77 (0.64–0.92) 0.0048
17 rs17574228 41460355 MAPT 3′ UTR SNP C/T 0.165/0.196 0.77 (0.64–0.92) 0.0049
17 rs9468 41457408 MAPT 3′ UTR SNP C/T 0.164/0.196 0.77 (0.64–0.92) 0.005
17 rs17650901 41395527 MAPT 5′ UTR SNP A/G 0.164/0.196 0.77 (0.64–0.93) 0.0053
4 rs1372520 90976528 SNCA Intronic SNP C/T 0.171/0.198 0.77 (0.64–0.93) 0.0056
17 rs16940806 41459672 MAPT 3′ UTR SNP A/G 0.165/0.196 0.77 (0.64–0.93) 0.0059
17 rs1052553 41429726 MAPT Synonymous A/G 0.164/0.195 0.78 (0.65–0.93) 0.0072
4 rs2572324 90897821 SNCA Intronic SNP C/T 0.338/0.307 1.24 (1.05–1.45) 0.009
4 rs3775423 90876514 SNCA Intronic SNP C/T 0.099/0.081 1.41 (1.09–1.82) 0.009
4 REP1 91124217 SNCA 5′ UTR VNTR 1.18 (1.04–1.34) 0.0118
4 rs356186 90924387 SNCA Intronic SNP A/G 0.158/0.180 0.78 (0.64–0.95) 0.0119
12 rs17484286 38984953 LRRK2 Intronic SNP A/G 0.083/0.102 0.73 (0.57–0.93) 0.0128
4 rs10517002 40959306 UCHL1 Intronic SNP A/C 0.406/0.381 1.19 (1.02–1.39) 0.0228
4 rs356218 90856033 SNCA Conserved A/G 0.367/0.342 1.17 (1.01–1.37) 0.0419
6 rs12174410 162259158 PARKIN Conserved C/T 0.052/0.040 1.43 (1.01–2.04) 0.0435

a NCBI build 36 of the human genome,

b the REP1 variant is a variable-number tandem repeat; common allele lengths are 259, 261, and 263 bp,

c note that REP1 has three common alleles: the frequency of the 259-, 261-, and 263-bp allele was 0.24, 0.68, and 0.08, respectively. The frequency of the MAPT H1 and H2 haplotypes was 0.82 and 0.18, respectively,

d the OR for REP1 was coded using the score test method,

e only the MAPT gene variant rs2435200 remained significant after Bonferroni or permutation correction for multiple comparisons.

SNP: single nucleotide polymorphism, UTR: untranslated region, VNTR: variable-number tandem repeat, OR: odds ratio, CI: confidence interval

Table 2
Genome-wide association studies in Parkinson’s disease
Authors Journal (year) Exploratory sample (cases/controls) Replication sample (cases/controls) Ethnicity Chromosome Genes OR p-value Platform (SNPs)
Maraganore et al. Am J Hum Genet (2005) 443/443 332/332 North American White 5p15.2 SEMA5A 1.7 7.62 × 10−6 Perlegen (198,345)
Fung et al. Lancet Neurol (2006) 267/270 None North American White 10q11.21 Intergenic 2.5 2 × 10−6 Illumina (408,803)
4q13.2 BRDG1 2.0 2 × 10−6
11q14 DLG2 5.0 7 × 10−6
Pankratz et al. Human Genet (2009) 857/867 262/260 North American White 4p16.3 GAK/DGKQ 1.7 7 × 10−7 Illumina (328,189)
Satake et al. Nat Genet (2009) 988/2,521 933/15,753 Asian (Japanese) 4q22.1 SNCA 1.37 7 × 10−17 Illumina (453,470)
1q32.1 PARK16 1.3 2 × 10−12
4p15.32 BST1 1.24 3 × 10−9
12q12 LRRK2 1.39 3 × 10−8
Simón-Sánchez et al. Nat Genet (2009) 1,713/3,978 3,361/4,573 European White 17q21.31 MAPT 1.3 2 × 10−16 Illumina (463,185)
4q22.1 SNCA 1.23 2 × 10−16
1q32.1 PARK16 1.52 7 × 10−8
Edwards et al. Ann Hum Genet (2010) 1,752/1,745 None European White 4q22.1 SNCA 1.29 6.7 × 10−8 Illumina (495,715) (imputed)
17q21.31 MAPT 0.70 5.6 × 10−8
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