CLU and MS4A6A expression are associated with AD status in a replication dataset.

<p>CLU and MS4A6A expression are associated with AD status in a replication dataset.</p

Gene SNPs do not significantly influence AD brain pathology.

<p>Covariates included in the analysis are marked: *Age, °APOE, §PMI, ∧CDR.</p

Summary of brain samples.

*<p>Mean ± SD.</p

AD GWAS SNPs do not modify gene expression in the parietal lobe of human brains.

<p>Covariates included in analyses are reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050976#pone.0050976.s005" target="_blank">Table S2</a>.</p

Rs3764650 in <i>ABCA7</i> is associated with age at onset.

<p>Kaplan-Meier curve. AAO, age at onset in years. SNPs were analyzed using an additive model. G, minor allele. Blue line, TT (11). Red line, TG (12). Green line, GG (22).</p

Interaction between the BDNF Val66Met (rs6265) variant with age on whole brain volume.

<p>Interaction between the BDNF Val66Met (rs6265) variant with age on whole brain volume.</p

Additional file 1: of Parkinson disease polygenic risk score is associated with Parkinson disease status and age at onset but not with alpha-synuclein cerebrospinal fluid levels

Tables S1 – S6. (DOCX 38 kb

Table3.DOCX

<p>Background: The prevalence of dementia in Parkinson disease (PD) increases dramatically with advancing age, approaching 80% in patients who survive 20 years with the disease. Increasing evidence suggests clinical, pathological and genetic overlap between Alzheimer disease, dementia with Lewy bodies and frontotemporal dementia with PD. However, the contribution of the dementia-causing genes to PD risk, cognitive impairment and dementia in PD is not fully established.</p><p>Objective: To assess the contribution of coding variants in Mendelian dementia-causing genes on the risk of developing PD and the effect on cognitive performance of PD patients.</p><p>Methods: We analyzed the coding regions of the amyloid-beta precursor protein (APP), Presenilin 1 and 2 (PSEN1, PSEN2), and Granulin (GRN) genes from 1,374 PD cases and 973 controls using pooled-DNA targeted sequence, human exome-chip and whole-exome sequencing (WES) data by single variant and gene base (SKAT-O and burden tests) analyses. Global cognitive function was assessed using the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA). The effect of coding variants in dementia-causing genes on cognitive performance was tested by multiple regression analysis adjusting for gender, disease duration, age at dementia assessment, study site and APOE carrier status.</p><p>Results: Known AD pathogenic mutations in the PSEN1 (p.A79V) and PSEN2 (p.V148I) genes were found in 0.3% of all PD patients. There was a significant burden of rare, likely damaging variants in the GRN and PSEN1 genes in PD patients when compared with frequencies in the European population from the ExAC database. Multiple regression analysis revealed that PD patients carrying rare variants in the APP, PSEN1, PSEN2, and GRN genes exhibit lower cognitive tests scores than non-carrier PD patients (p = 2.0 × 10⁻⁴), independent of age at PD diagnosis, age at evaluation, APOE status or recruitment site.</p><p>Conclusions: Pathogenic mutations in the Alzheimer disease-causing genes (PSEN1 and PSEN2) are found in sporadic PD patients. PD patients with cognitive decline carry rare variants in dementia-causing genes. Variants in genes causing Mendelian neurodegenerative diseases exhibit pleiotropic effects.</p

Table4.DOCX

<p>Background: The prevalence of dementia in Parkinson disease (PD) increases dramatically with advancing age, approaching 80% in patients who survive 20 years with the disease. Increasing evidence suggests clinical, pathological and genetic overlap between Alzheimer disease, dementia with Lewy bodies and frontotemporal dementia with PD. However, the contribution of the dementia-causing genes to PD risk, cognitive impairment and dementia in PD is not fully established.</p><p>Objective: To assess the contribution of coding variants in Mendelian dementia-causing genes on the risk of developing PD and the effect on cognitive performance of PD patients.</p><p>Methods: We analyzed the coding regions of the amyloid-beta precursor protein (APP), Presenilin 1 and 2 (PSEN1, PSEN2), and Granulin (GRN) genes from 1,374 PD cases and 973 controls using pooled-DNA targeted sequence, human exome-chip and whole-exome sequencing (WES) data by single variant and gene base (SKAT-O and burden tests) analyses. Global cognitive function was assessed using the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA). The effect of coding variants in dementia-causing genes on cognitive performance was tested by multiple regression analysis adjusting for gender, disease duration, age at dementia assessment, study site and APOE carrier status.</p><p>Results: Known AD pathogenic mutations in the PSEN1 (p.A79V) and PSEN2 (p.V148I) genes were found in 0.3% of all PD patients. There was a significant burden of rare, likely damaging variants in the GRN and PSEN1 genes in PD patients when compared with frequencies in the European population from the ExAC database. Multiple regression analysis revealed that PD patients carrying rare variants in the APP, PSEN1, PSEN2, and GRN genes exhibit lower cognitive tests scores than non-carrier PD patients (p = 2.0 × 10⁻⁴), independent of age at PD diagnosis, age at evaluation, APOE status or recruitment site.</p><p>Conclusions: Pathogenic mutations in the Alzheimer disease-causing genes (PSEN1 and PSEN2) are found in sporadic PD patients. PD patients with cognitive decline carry rare variants in dementia-causing genes. Variants in genes causing Mendelian neurodegenerative diseases exhibit pleiotropic effects.</p

Table3.DOCX

<p>Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families (N = 1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, and MAS1L) as candidate genes for familial LOAD.</p