Web-Based Genome-Wide Association Study Identifies Two Novel Loci and a Substantial Genetic Component for Parkinson's Disease

Georgia Institute of Technology, United States of America
PLoS Genetics (Impact Factor: 7.53). 06/2011; 7(6):e1002141. DOI: 10.1371/journal.pgen.1002141
Source: PubMed


Although the causes of Parkinson's disease (PD) are thought to be primarily environmental, recent studies suggest that a number of genes influence susceptibility. Using targeted case recruitment and online survey instruments, we conducted the largest case-control genome-wide association study (GWAS) of PD based on a single collection of individuals to date (3,426 cases and 29,624 controls). We discovered two novel, genome-wide significant associations with PD-rs6812193 near SCARB2 (p = 7.6 × 10(-10), OR = 0.84) and rs11868035 near SREBF1/RAI1 (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent cohort. We also replicated 20 previously discovered genetic associations (including LRRK2, GBA, SNCA, MAPT, GAK, and the HLA region), providing support for our novel study design. Relying on a recently proposed method based on genome-wide sharing estimates between distantly related individuals, we estimated the heritability of PD to be at least 0.27. Finally, using sparse regression techniques, we constructed predictive models that account for 6%-7% of the total variance in liability and that suggest the presence of true associations just beyond genome-wide significance, as confirmed through both internal and external cross-validation. These results indicate a substantial, but by no means total, contribution of genetics underlying susceptibility to both early-onset and late-onset PD, suggesting that, despite the novel associations discovered here and elsewhere, the majority of the genetic component for Parkinson's disease remains to be discovered.

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Available from: Samuel M Goldman, Oct 08, 2015
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    • "This problem is especially important when analyzing traits with a complex mode of inheritance (summarized by Visscher et al. 2012). The variety of GWAS models applied to complex traits covers linear regression, penalized regression approaches with various shrinkage priors for SNP effects, like the least absolute shrinkage and selection operator regression (LASSO) introduced by Tibshirani (1996) (e.g., applied by Wu et al. 2009), the elastic net introduced by Zou and Hastie (2005) (e.g., applied by Do et al. 2011), ridge regression introduced by Hoerl and Kennard (1970) (e.g., applied and extended by Zhan and Xu 2012), normal exponential gamma distribution proposed by Hoggart et al. (2008), or by incorporating the "
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    ABSTRACT: The goal of this study was to compare significant SNP selection approaches in the context of complex traits based on SNP estimates obtained by models: a model fitting a single SNP (M1), a model fitting a single SNP and a random polygenic effect (M2), the nonparametric CAR score (M3), a SNP-BLUP model with random effects of all SNPs fitted simultaneously (M4). There were 46,267 SNPs tested in a population of 2601 Holstein Friesian bulls, four traits (milk and fat yields, somatic cell score, non-return rate for heifers) were considered. The numbers of SNPs selected as significant differed among models. M1 selected a very large number of SNPs, except for a NRH in which no SNPs were significant. M2 and M3 both selected similar and low number of SNPs for each trait. M4 selected more SNPs than M2 and M3. Considering linkage disequilibrium between SNPs, for MY M2 and M3 selected SNPs more highly correlated with each other than in the case of M4, while for FY M3 selection contained more correlated SNPs than M2 and M4. In conclusion, if the research interest is to identify SNPs not only with strong, but also with moderate effects on a complex trait a multiple-SNP model is recommended. Such models are capable of accounting for at least a part of linkage disequilibrium between SNPs through the design matrix of SNP effects. Functional annotation of SNPs significant in M4 reveals good correspondence between selected polymorphisms and functional information as well as with QTL mapping results.
    Journal of applied genetics 08/2015; DOI:10.1007/s13353-015-0305-6 · 1.48 Impact Factor
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    • "A lack of association between rs6812193, rs6825004 and rs4889603 and PD was identified. A web-based GWAS first identified that FAM47E rs6812193 is associated with PD in populations of European ancestry (Do et al. 2011). After that, the IPDGC study and research from Germany replicated the result that FAM47E rs6812193 decreases the risk for PD in Caucasian populations WTCCC2 2011; Hopfner et al. 2013); however , a study from Greece found no association between the rs6812193 polymorphism and PD (Kalinderi et al. 2013). "
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    ABSTRACT: Recently, a series of studies found that the single-nucleotide polymorphisms (SNPs) rs6812193 in the family with sequence similarity 47, member E (FAM47E), rs6825004 in the scavenger receptor class B member 2 (SCARB2) and rs4889603 in the Syntaxin1B (STX1B) genes increase the risk for Parkinson's disease (PD). However, the results of subsequent independent studies were inconsistent. To explore the associations between the three SNPs and PD in the Chinese population, a large cohort was analyzed in a case-control study. A total of 1994 subjects, including 1179 PD and 815 healthy controls (HCs), were investigated. All subjects were genotyped for rs6812193, rs6825004 and rs4889603 using the Sequenom iPLEX Assay. There was no significant difference in additive genetic model of rs6812193, rs6825004 and rs4889603 between PD and controls, even after being stratified by sex and age. In addition, no significant differences were found between other subgroups of PD patients with regard to clinical presentation. Our findings suggested that FAM47E rs6812193, SCARB2 rs6825004 and STX1B rs4889603 do not confer a significant risk for PD in Chinese population.
    Journal of Neural Transmission 07/2015; DOI:10.1007/s00702-015-1430-4 · 2.40 Impact Factor
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    • "Another region that emerged with consistent evidence from regional FST and XP-EHH was found on chromosome 17 between 41.3 Mb and 41.5 Mb (Additional file 1: Figure S1) and encompassed three genes, two of which (STH and KANSL1) have previously been implicated with variation in intracranial volume [21] and the microtubule-associated protein tau (MAPT) gene has been consistently reported to be associated with Parkinson’s disease in Europeans [22-24]. The evidence from XP-EHH suggests the presence of positive selection at this locus in INS and not in GIH. "
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    ABSTRACT: Background India is home to many ethnically and linguistically diverse populations. It is hypothesized that history of invasions by people from Persia and Central Asia, who are referred as Aryans in Hindu Holy Scriptures, had a defining role in shaping the Indian population canvas. A shift in spoken languages from Dravidian languages to Indo-European languages around 1500 B.C. is central to the Aryan Invasion Theory. Here we investigate the genetic differences between two sub-populations of India consisting of: (1) The Indo-European language speaking Gujarati Indians with genome-wide data from the International HapMap Project; and (2) the Dravidian language speaking Tamil Indians with genome-wide data from the Singapore Genome Variation Project. Results We implemented three population genetics measures to identify genomic regions that are significantly differentiated between the two Indian populations originating from the north and south of India. These measures singled out genomic regions with: (i) SNPs exhibiting significant variation in allele frequencies in the two Indian populations; and (ii) differential signals of positive natural selection as quantified by the integrated haplotype score (iHS) and cross-population extended haplotype homozygosity (XP-EHH). One of the regions that emerged spans the SLC24A5 gene that has been functionally shown to affect skin pigmentation, with a higher degree of genetic sharing between Gujarati Indians and Europeans. Conclusions Our finding points to a gene-flow from Europe to north India that provides an explanation for the lighter skin tones present in North Indians in comparison to South Indians.
    BMC Genetics 07/2014; 15(1):86. DOI:10.1186/1471-2156-15-86 · 2.40 Impact Factor
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