The Genetic Architecture of Parkinson Disease in Spain: Characterizing Population‐Specific Risk, Differential Haplotype Structures, and Providing Etiologic Insight

Bandrés‐Ciga, Sara; Ahmed, Sarah; Sabir, Marya S.; Blauwendraat, Cornelis; Adarmes‐Gómez, Astrid; Bernal‐Bernal, Inmaculada; Bonilla‐Toribio, Marta; Buiza‐Rueda, Dolores; Carrillo, Fátima; Carrión‐Claro, Mario; Gómez‐Garre, Pilar; Jesús, Silvia; Labrador‐Espinosa, Miguel A.; Macías, Daniel; Barrio, C Méndez Del; Periñán, María Teresa; Tejera‐Parrado, Cristina; Vargas‐González, Laura; Díez-Fairén, Mónica; Álvarez, Victoria; Tartari, Juan Pablo; Buongiorno, Maríateresa; Aguilar, Miquel; Gorostidi, Ana; Bergareche, J; Mondragón, Elisabet; Vinagre-Aragón, Ana; Croitoru, Ioana; Ruíz‐Martínez, Javier; Dols‐Icardo, Oriol; Kulisevsky, Jaime; Marín‐Lahoz, Juan; Pagonabarraga, Javier; Pascual-Sedano, Berta; Ezquerra, Mario; Cámara, Ana; Compta, Yaroslau; Fernández, Manel; Fernández‐Santiago, Rubén; Muñoz, Esteban; Tolosa, Eduard; Valldeoriola, Francesc; Aramburu, Isabel González; Rodríguez, A; Sierra, María; Menéndez-González, Manuel; Blázquez, Marta; García, Ciara; Martin, Esther Suarez-San; Ruiz, Pedro; Martínez-Castrillo, Juan Carlos; Vela-Desojo, Lydia; Ruz, Clara; Barrero, Francisco Javier; Escamilla-Sevilla, Francisco; Minguez-Castellanos, Adolfo; Cerdan, Debora; Tabernero, C; Heredia, Maria Jose Gómez; Errázquin, Francisco; Romero-Acebal, Manolo; Feliz, Cici; López‐Sendón, José; Mata, Marina; Martínez‐Torres, Irene; Kim, Jonggeol Jeffrey; Dalgard, Clifton L.; Brooks, Janet; Sáez-Atiénzar, Sara; Gibbs, J. Raphael; Jorda, Rafael; Botía, Juan A.; Bonet-Ponce, Luis; Morrison, Karen E.; Clarke, Carl E; Tan, Manuela; Morris, Huw; Edsall, Connor; Hernandez, Dena G.; Simón‐Sánchez, Javier; Nalls, Mike A.; Scholz, Sonja W.; Jiménez‐Escrig, Adriano; Duarte, Jacinto; Vives, Francisco; Durán, Raquel; Hoenicka, Janet; Álvarez, Victoria; Infante, Jon; Martı́, Marı́a José; Clarimón, Jordi; Munáin, Adolfo López de; Pástor, Pau; Mir, Pablo; Singleton, Andrew B.

doi:10.1002/mds.27864

Cited by 53 publications

(21 citation statements)

References 48 publications

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“…Quality-Control measured included limiting to samples with call rates of >95%, excluding samples with excess heterogeneity (F statistic > ± 0.25), and excluding samples whose genotyped sex did not match the sample demographics. CNVs were identified by manual inspection of the B Allele Frequency and Log R Ratio for the PRKN gene region (Chr6: 161770811 -163140694, hg19), using the gglot2 visualization package for R (https://www.r-project.org/), as described previously 25 .…”

Section: Genotyping Nih-pd Cohortmentioning

confidence: 99%

Heterozygous PRKN mutations are common but do not increase the risk of Parkinson’s disease

Zhu

Huang

Yoon

et al. 2021

Preprint

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PRKN mutations are the most common recessive cause of Parkinson′s disease (PD) and are a promising target for gene and cell replacement therapies. Identification of biallelic PRKN patients (PRKN-PD) at the population scale, however, remains a challenge, as roughly half are copy number variants (CNVs) and many single nucleotide polymorphisms (SNPs) are of unclear significance. Additionally, the true prevalence and disease risk associated with heterozygous PRKN mutations is unclear, as a comprehensive assessment of PRKN SNPs and CNVs has not been performed at a population scale. To address these challenges, we evaluated PRKN mutations in 2 cohorts analyzed with both a genotyping array and exome or genome sequencing: the NIH PD cohort, a deeply phenotyped cohort of PD patients, and the UK Biobank, a population scale cohort with nearly half a million participants. Genotyping array identified the majority of PRKN mutations and at least 1 mutation in most biallelic PRKN mutation carriers in both cohorts. Additionally, in the NIH PD cohort, functional assays of patient fibroblasts resolved variants of unclear significance in biallelic carriers and ruled out cryptic loss of function variants in monoallelic carriers. In the UK Biobank, we identified 2,692 PRKN CNVs from genotyping array data from nearly half a million participants (the largest collection to date). Deletions or duplications involving exons 2 accounted for roughly half of all CNVs and the vast majority (88%) involved exons 2, 3, or 4. Combining estimates from whole exome sequencing (from ~200,000 participants) and genotyping array data, we found a pathogenic PRKN mutation in 1.8% of participants and 2 mutations in ~1/7,800 participants. Those with 1 PRKN pathogenic variant were as likely as non-carriers to have PD (OR = 0.91, CI= 0.58 – 1.38, p-value = 0.76) or a parent with PD (OR = 1.12, CI = 0.94 – 1.31, p-value = 0.19). Together our results demonstrate that heterozygous pathogenic PRKN mutations are common in the population but do not increase the risk of PD. Additionally, they suggest a cost-effective framework to screen for biallelic PRKN patients at the population scale for targeted studies.

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Section: Genotyping Nih-pd Cohortmentioning

confidence: 99%

Heterozygous PRKN mutations are common but do not increase the risk of Parkinson’s disease

Zhu

Huang

Yoon

et al. 2021

Preprint

Self Cite

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“…The interaction analysis using the GenEpi machine learning approach identified eight SNP pairs having joint genetic effects associated with PD, including a strong genome-wide significant interaction association signal at chromosome 17q21.31 in CRHR1 , although producing no significant association signals of those two SNPs individually for PD risk. Given that SNPs in CRHR1 have been previously reported to be associated with PD [ 5 , 32 , 36 , 37 ] and Alzheimer’s disease [ 38 ] and SNP–SNP interactions are identified in SNCA , GAK and MAPT , a well-known risk gene for PD, this suggests that these joint effects are true findings and nicely demonstrate the utility of our approach to identify joint genetic effects associated with complex diseases like PD. However, in GenEpi two criteria were adopted before modelling the genotype features: first, exclude features with genotype frequency (proportion of a genotype among the total samples in the dataset) ≤ 5%; and second, exclude features with weak association (χ 2 test p ≥ 0.01) with the disease.…”

Section: Discussionmentioning

confidence: 64%

“…In addition, our results show strong evidence for multiple association signals: one at chromosome 17p13.2 in HASPIN substantiating the importance of this gene in PD risk, and one at chromosome 4q22.1 in SNCA. SNPs at chromosome 4p22.1 are well known for their association with PD [ 15 , 32 , 33 , 34 ] and several other diseases including dementia with Lewy bodies [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson’s Disease Risk

Rodrigo

Nyholt

2021

Genes

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Given that improved imputation software and high-coverage whole genome sequence (WGS)-based haplotype reference panels now enable inexpensive approximation of WGS genotype data, we hypothesised that WGS-based imputation and analysis of existing ExomeChip-based genome-wide association (GWA) data will identify novel intronic and intergenic single nucleotide polymorphism (SNP) effects associated with complex disease risk. In this study, we reanalysed a Parkinson’s disease (PD) dataset comprising 5540 cases and 5862 controls genotyped using the ExomeChip-based NeuroX array. After genotype imputation and extensive quality control, GWA analysis was performed using PLINK and a recently developed machine learning approach (GenEpi), to identify novel, conditional and joint genetic effects associated with PD. In addition to improved validation of previously reported loci, we identified five novel genome-wide significant loci associated with PD: three (rs137887044, rs78837976 and rs117672332) with 0.01 < MAF < 0.05, and two (rs187989831 and rs12100172) with MAF < 0.01. Conditional analysis within genome-wide significant loci revealed four loci (p < 1 × 10−5) with multiple independent risk variants, while GenEpi analysis identified SNP–SNP interactions in seven genes. In addition to identifying novel risk loci for PD, these results demonstrate that WGS-based imputation and analysis of existing exome genotype data can identify novel intronic and intergenic SNP effects associated with complex disease risk.

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“…6,38 The AD-associated variant in this region ( rs9275152 ) is also a risk variant for Parkinson’s disease. 39 Finally, the genomic region surrounding the SPI1 gene (in which variant rs3740688 maps) has been previously associated with cognitive traits (intelligence, depression) 40(p300) and, with lower evidence, with kidney disease and cancer. 41,42 The remaining variants rs56402156, rs7920721 , and rs4351014 (in/near EPHA1, ECHDC3 , and HS3ST1 ) have not been directly associated with other traits, although their associated genes were implicated in systemic lupus erythematosus ( HS3ST1 ) and cancer ( EPHA1, ECHDC3 ).…”

Section: Discussionmentioning

confidence: 99%

The effect of Alzheimer’s disease-associated genetic variants on longevity

Tesi

Hulsman

et al. 2021

Preprint

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The genetics underlying human longevity is influenced by the genetic risk to develop -or escape- age-related diseases. As Alzheimer's disease (AD) represents one of the most common conditions at old age, an interplay between genetic factors for AD and longevity is expected. We explored this interplay by studying the prevalence of 38 AD-associated single-nucleotide-polymorphisms (SNPs) identified in AD-GWAS, in self-reported cognitively healthy centenarians, and we replicated findings in the largest GWAS on parental-longevity. We found that 28/38 SNPs identified to associate with increased AD-risk also associated with decreased odds of longevity. For each SNP, we express the imbalance between AD- and longevity-risk as an effect-size distribution. When grouping the SNPs based on these distributions, we found three groups: 17 variants increased AD-risk more than they decreased the risk of longevity (AD-group): these variants were functionally enriched for β-amyloid metabolism and immune signaling, and they were enriched in microglia. 11 variants reported a larger effect on longevity as compared to their AD-effect (Longevity-group): these variants were enriched for endocytosis/immune signaling, and at the cell-type level were enriched in microglia and endothelial cells. Next to AD, these variants were previously associated with other aging-related diseases, including cardiovascular and autoimmune diseases, and cancer. Unexpectedly, 10 variants associated with an increased risk of both AD and longevity (Unex-group). The effect of the SNPs in AD- and Longevity-groups replicated in the largest GWAS on parental-longevity, while the effects on longevity of the SNPs in the Unexpected-group could not be replicated, suggesting that these effects may not be robust across different studies. Our study shows that some AD-associated variants negatively affect longevity primarily by their increased risk of AD, while other variants negatively affect longevity through an increased risk of multiple age-related diseases, including AD.

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The Genetic Architecture of Parkinson Disease in Spain: Characterizing Population‐Specific Risk, Differential Haplotype Structures, and Providing Etiologic Insight

Cited by 53 publications

References 48 publications

Heterozygous PRKN mutations are common but do not increase the risk of Parkinson’s disease

Heterozygous PRKN mutations are common but do not increase the risk of Parkinson’s disease

Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson’s Disease Risk

The effect of Alzheimer’s disease-associated genetic variants on longevity

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