Influence of Coding Variability in APP-Aβ Metabolism Genes in Sporadic Alzheimer's Disease

The cerebral deposition of Aβ42, a neurotoxic proteolytic derivate of amyloid precursor protein (APP), is a central event in Alzheimer's disease (AD)(Amyloid hypothesis). Given the key role of APP-Aβ metabolism in AD pathogenesis, we selected 29 genes involved in APP processing, Aβ degradation and clearance. We then used exome and genome sequencing to investigate the single independent (single-variant association test) and cumulative (gene-based association test) effect of coding variants in these genes as potential susceptibility factors for AD, in a cohort composed of 332 sporadic and mainly late-onset AD cases and 676 elderly controls from North America and the UK. Our study shows that common coding variability in these genes does not play a major role for the disease development. In the single-variant association analysis, the main hits, none of which statistically significant after multiple testing correction (1.9e-4 Collapse

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MESH Headings

• Alzheimer Disease/genetics
• Alzheimer Disease/metabolism
• Amyloid beta-Peptides/genetics
• Amyloid beta-Peptides/metabolism
• Amyloid beta-Protein Precursor/genetics
• Amyloid beta-Protein Precursor/metabolism
• Computational Biology
• Exome
• Humans

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Grants

• U24 AG021886 NIA NIH HHS
• P50 AG016574 NIA NIH HHS
• U01 AG052411 NIA NIH HHS
• MR/L016397/1 Medical Research Council
• P01 AG026276 NIA NIH HHS
• Z01 AG000950 Intramural NIH HHS
• G1100695 Medical Research Council
• RF1 AG044546 NIA NIH HHS
• Wellcome Trust
• R01 AG018023 NIA NIH HHS
• U01 AG006786 NIA NIH HHS
• P01 AG003991 NIA NIH HHS
• P50 AG005681 NIA NIH HHS
• Alzheimer’s Research UK
• Wellcome Trust/MRC Joint Call in Neurodegeneration Award
• National Institute for Health Research Biomedical Research Unit in Dementia at University College London Hospitals
• the Big Lottery
• Alzheimer's Research UK
• Intramural Research Programs of the National Institute on Aging and the National Institute of Neurological Disease and Stroke

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Genetic and phenotypic characterization of complex hereditary spastic paraplegia

The hereditary spastic paraplegias are a heterogeneous group of degenerative disorders that are clinically classified as either pure with predominant lower limb spasticity, or complex where spastic paraplegia is complicated with additional neurological features, and are inherited in autosomal dominant, autosomal recessive or X-linked patterns. Genetic defects have been identified in over 40 different genes, with more than 70 loci in total. Complex recessive spastic paraplegias have in the past been frequently associated with mutations in SPG11 (spatacsin), ZFYVE26/SPG15 , SPG7 (paraplegin) and a handful of other rare genes, but many cases remain genetically undefined. The overlap with other neurodegenerative disorders has been implied in a small number of reports, but not in larger disease series. This deficiency has been largely due to the lack of suitable high throughput techniques to investigate the genetic basis of disease, but the recent availability of next generation sequencing can facilitate the identification of disease-causing mutations even in extremely heterogeneous disorders. We investigated a series of 97 index cases with complex spastic paraplegia referred to a tertiary referral neurology centre in London for diagnosis or management. The mean age of onset was 16 years (range 3 to 39). The SPG11 gene was first analysed, revealing homozygous or compound heterozygous mutations in 30/97 (30.9%) of probands, the largest SPG11 series reported to date, and by far the most common cause of complex spastic paraplegia in the UK, with severe and progressive clinical features and other neurological manifestations, linked with magnetic resonance imaging defects. Given the high frequency of SPG11 mutations, we studied the autophagic response to starvation in eight affected SPG11 cases and control fibroblast cell lines, but in our restricted study we did not observe correlations between disease status and autophagic or lysosomal markers. In the remaining cases, next generation sequencing was carried out revealing variants in a number of other known complex spastic paraplegia genes, including five in SPG7 (5/97), four in FA2H (also known as SPG35 ) (4/97) and two in ZFYVE26 / SPG15 . Variants were identified in genes usually associated with pure spastic paraplegia and also in the Parkinson’s disease-associated gene ATP13A2 , neuronal ceroid lipofuscinosis gene TPP1 and the hereditary motor and sensory neuropathy DNMT1 gene, highlighting the genetic heterogeneity of spastic paraplegia. No plausible genetic cause was identified in 51% of probands, likely indicating the existence of as yet unidentified genes.

ABCA7 p.G215S as potential protective factor for Alzheimer's disease

Genome-wide association studies (GWASs) have been effective approaches to dissect common genetic variability underlying complex diseases in a systematic and unbiased way. Recently, GWASs have led to the discovery of over 20 susceptibility loci for Alzheimer's disease (AD). Despite the evidence showing the contribution of these loci to AD pathogenesis, their genetic architecture has not been extensively investigated, leaving the possibility that low frequency and rare coding variants may also occur and contribute to the risk of disease. We have used exome and genome sequencing data to analyze the single independent and joint effect of rare and low-frequency protein coding variants in 9 AD GWAS loci with the strongest effect sizes after APOE (BIN1, CLU, CR1, PICALM, MS4A6A, ABCA7, EPHA1, CD33, and CD2AP) in a cohort of 332 sporadic AD cases and 676 elderly controls of British and North-American ancestry. We identified coding variability in ABCA7 as contributing to AD risk. This locus harbors a low-frequency coding variant (p.G215S, rs72973581, minor allele frequency = 4.3%) conferring a modest but statistically significant protection against AD (p-value = 0.024, odds ratio = 0.57, 95% confidence interval = 0.41–0.80). Notably, our results are not driven by an enrichment of loss of function variants in ABCA7, recently reported as main pathogenic factor underlying AD risk at this locus. In summary, our study confirms the role of ABCA7 in AD and provides new insights that should address functional studies.

Genetics in Parkinson disease: Mendelian versus non-Mendelian inheritance

Parkinson's disease is a common, progressive neurodegenerative disorder, affecting 3% of those older than 75 years of age. Clinically, Parkinson's disease (PD) is associated with resting tremor, postural instability, rigidity, bradykinesia, and a good response to levodopa therapy. Over the last 15 years, numerous studies have confirmed that genetic factors contribute to the complex pathogenesis of PD. Highly penetrant mutations producing rare, monogenic forms of the disease have been discovered in singular genes such as SNCA, Parkin, DJ-1, PINK 1, LRRK2, and VPS35. Unique variants with incomplete penetrance in LRRK2 and GBA have been shown to be strong risk factors for PD in certain populations. Additionally, over 20 common variants with small effect sizes are now recognized to modulate the risk for PD. Investigating Mendelian forms of PD has provided precious insight into the pathophysiology that underlies the more common idiopathic form of disease; however, no treatment methodologies have developed. Furthermore, for identified common risk alleles, the functional basis underlying risk principally remains unknown. The challenge over the next decade will be to strengthen the findings delivered through genetic discovery by assessing the direct, biological consequences of risk variants in tandem with additional high-content, integrated datasets. This review discusses monogenic risk factors and mechanisms of Mendelian inheritance of Parkinson disease. Highly penetrant mutations in SNCA, Parkin, DJ-1, PINK 1, LRRK2 and VPS35 produce rare, monogenic forms of the disease, while unique variants within LRRK2 and GBA show incomplete penetrance and are strong risk factors for PD. Additionally, over 20 common variants with small effect sizes modulate disease risk. The challenge over the next decade is to strengthen genetic findings by assessing direct, biological consequences of risk variants in tandem with high-content, integrated datasets. This article is part of a special issue on Parkinson disease.

Genome-wide estimate of the heritability of Multiple System Atrophy

INTRODUCTION

Multiple System Atrophy (MSA) is a neurodegenerative disease which presents heterogeneously with symptoms and signs of parkinsonism, ataxia and autonomic dysfunction. Although MSA typically occurs sporadically, rare pathology-proven MSA families following either autosomal recessive or autosomal dominant patterns have been described, indicating a heritable contribution to the pathogenesis.

METHODS

We used Genome-Wide Complex Trait Analysis (GCTA) to estimate the heritable component of MSA due to common coding variability in imputed genotype data of 907 MSA cases and 3866 population-matched controls. GCTA only assesses the effect of putative causal variants in linkage disequilibrium (LD) with all common SNPs on the genotyping platform.

RESULTS

We estimate the heritability among common variants of MSA in pooled cases at 2.09-6.65%, with a wider range of values in geographic and diagnostic subgroups. Meta-analysis of our geographic cohorts reveals high between-group heterogeneity. Contributions of single chromosomes are generally negligible. We suggest that all calculated MSA heritability among common variants could be explained by the presence of misdiagnosed cases in the clinical subgroup based on a Bayesian estimate using literature-derived rates of misdiagnosis.

DISCUSSION

MSA is a challenging disease to study due to high rates of misdiagnosis and low prevalence. Given our low estimates of heritability, common genetic variation appears to play a less prominent role in risk for MSA than in other complex neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and Amyotrophic Lateral Sclerosis. The success of future gene discovery efforts rests on large pathologically-confirmed case series and an interrogation of both common and rare genetic variants.

Genetic overlap between Alzheimer's disease and Parkinson's disease at the MAPT locus

We investigated the genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD). Using summary statistics (P-values) from large recent genome-wide association studies (GWAS) (total n=89 904 individuals), we sought to identify single nucleotide polymorphisms (SNPs) associating with both AD and PD. We found and replicated association of both AD and PD with the A allele of rs393152 within the extended MAPT region on chromosome 17 (meta analysis P-value across five independent AD cohorts=1.65 × 10(-7)). In independent datasets, we found a dose-dependent effect of the A allele of rs393152 on intra-cerebral MAPT transcript levels and volume loss within the entorhinal cortex and hippocampus. Our findings identify the tau-associated MAPT locus as a site of genetic overlap between AD and PD, and extending prior work, we show that the MAPT region increases risk of Alzheimer's neurodegeneration.

Plasma 24-metabolite Panel Predicts Preclinical Transition to Clinical Stages of Alzheimer's Disease

We recently documented plasma lipid dysregulation in preclinical late-onset Alzheimer's disease (LOAD). A 10 plasma lipid panel, predicted phenoconversion and provided 90% sensitivity and 85% specificity in differentiating an at-risk group from those that would remain cognitively intact. Despite these encouraging results, low positive predictive values limit the clinical usefulness of this panel as a screening tool in subjects aged 70-80 years or younger. In this report, we re-examine our metabolomic data, analyzing baseline plasma specimens from our group of phenoconverters (n = 28) and a matched set of cognitively normal subjects (n = 73), and discover and internally validate a panel of 24 plasma metabolites. The new panel provides a classifier with receiver operating characteristic area under the curve for the discovery and internal validation cohort of 1.0 and 0.995 (95% confidence intervals of 1.0-1.0, and 0.981-1.0), respectively. Twenty-two of the 24 metabolites were significantly dysregulated lipids. While positive and negative predictive values were improved compared to our 10-lipid panel, low positive predictive values provide a reality check on the utility of such biomarkers in this age group (or younger). Through inclusion of additional significantly dysregulated analyte species, our new biomarker panel provides greater accuracy in our cohort but remains limited by predictive power. Unfortunately, the novel metabolite panel alone may not provide improvement in counseling and management of at-risk individuals but may further improve selection of subjects for LOAD secondary prevention trials. We expect that external validation will remain challenging due to our stringent study design, especially compared with more diverse subject cohorts. We do anticipate, however, external validation of reduced plasma lipid species as a predictor of phenoconversion to either prodromal or manifest LOAD.

Genome-wide Meta-analysis on the Sense of Smell Among US Older Adults

Olfactory dysfunction is common among older adults and affects their safety, nutrition, quality of life, and mortality. More importantly, the decreased sense of smell is an early symptom of neurodegenerative diseases such as Parkinson disease (PD) and Alzheimer disease. However, the genetic determinants for the sense of smell have been poorly investigated. We here performed the first genome-wide meta-analysis on the sense of smell among 6252 US older adults of European descent from the Atherosclerosis Risk in Communities (ARIC) study, the Health, Aging, and Body Composition (Health ABC) study, and the Religious Orders Study and the Rush Memory and Aging Project (ROS/MAP). Genome-wide association study analysis was performed first by individual cohorts and then meta-analyzed using fixed-effect models with inverse variance weights. Although no SNPs reached genome-wide statistical significance, we identified 13 loci with suggestive evidence for an association with the sense of smell (Pmeta < 1 × 10). Of these, 2 SNPs at chromosome 17q21.31 (rs199443 in NSF, P = 3.02 × 10; and rs2732614 in KIAA1267-LRRC37A, P = 6.65 × 10) exhibited cis effects on the expression of microtubule-associated protein tau (MAPT, 17q21.31) in 447 frontal-cortex samples obtained postmortem and profiled by RNA-seq (P < 1 × 10). Gene-based and pathway-enrichment analyses further implicated MAPT in regulating the sense of smell in older adults. Similar results were obtained after excluding participants who reported a physician-diagnosed PD or use of PD medications. In conclusion, we provide preliminary evidence that the MAPT locus may play a role in regulating the sense of smell in older adults and therefore offer a potential genetic link between poor sense of smell and major neurodegenerative diseases.

Human Obesity Associated with an Intronic SNP in the Brain-Derived Neurotrophic Factor Locus

Brain-derived neurotrophic factor (BDNF) plays a key role in energy balance. In population studies, SNPs of the BDNF locus have been linked to obesity, but the mechanism by which these variants cause weight gain is unknown. Here, we examined human hypothalamic BDNF expression in association with 44 BDNF SNPs. We observed that the minor C allele of rs12291063 is associated with lower human ventromedial hypothalamic BDNF expression (p < 0.001) and greater adiposity in both adult and pediatric cohorts (p values < 0.05). We further demonstrated that the major T allele for rs12291063 possesses a binding capacity for the transcriptional regulator, heterogeneous nuclear ribonucleoprotein D0B, knockdown of which disrupts transactivation by the T allele. Binding and transactivation functions are both disrupted by substituting C for T. These findings provide a rationale for BDNF augmentation as a targeted treatment for obesity in individuals who have the rs12291063 CC genotype.

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

BACKGROUND

Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts.

METHODS

We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD).

FINDINGS

In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003).

INTERPRETATION

Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions.

FUNDING

National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Change in Epigenome-Wide DNA Methylation Over 9 Years and Subsequent Mortality: Results From the InCHIANTI Study

Patterns of DNA methylation (DNAm) that track with aging have been identified. However, the relevance of these patterns for aging outcomes remains unclear. Longitudinal epigenome-wide DNAm information was obtained from the InCHIANTI study, a large representative European population. DNAm was evaluated using the Illumina HumanMethylation450 array on blood samples collected at baseline and 9-year follow-up: observations from 499 participants with paired longitudinal blood sample and information on differential blood count were included in analyses. A total of 56,579 markers were significantly associated with age in cross-sectional analysis of DNAm at year 9, 31,252 markers were changed significantly over the 9-year follow-up, and 16,987 markers were both cross-sectionally associated with age and significantly changed over time. Rates of change at 76 markers and year 9 level of DNAm at 88 markers were identified as strongly associated with mortality in Cox proportional hazard models adjusted for age and relevant covariates (mean follow-up time 4.4 years). Less than 0.05% of markers associated with age or that changed over time were also associated with mortality after adjusting for chronological age. Although the influence of DNAm on health and longevity remains unclear, these findings confirm that aging is associated cross-sectionally and longitudinally with robust and consistent patterns of methylation change.

Genome-wide association study of neocortical Lewy-related pathology

Objective

Dementia with Lewy bodies is an α-synucleinopathy characterized by neocortical Lewy-related pathology (LRP). We carried out a genome-wide association study (GWAS) on neocortical LRP in a population-based sample of subjects aged 85 or over.

Methods

LRP was analyzed in 304 subjects in the Vantaa 85+ sample from Southern Finland. The GWAS included 41 cases with midbrain, hippocampal, and neocortical LRP and 177 controls without midbrain and hippocampal LRP. The Medical Research Council Cognitive Function and Ageing Study (CFAS) material was used for replication (51 cases and 131 controls).

Results

By analyzing 327,010 markers the top signal was obtained at the HLA-DPA1/DPB1 locus (P = 1.29 × 10⁻⁷); five other loci on chromosomes 15q14, 2p21, 2q31, 18p11, and 5q23 were associated with neocortical LRP at P < 10⁻⁵. Two loci were marked by multiple markers, 2p21 (P = 3.9 × 10⁻⁶, upstream of the SPTBN1 gene), and HLA-DPA1/DPB1; these were tested in the CFAS material. Single marker (P = 0.0035) and haplotype (P = 0.04) associations on 2p21 were replicated in CFAS, whereas HLA-DPA1/DPB1 association was not. Bioinformatic analyses suggest functional effects for the HLA-DPA1/DPB1 markers as well as the 15q14 marker rs8037309.

Interpretation

We identified suggestive novel risk factors for neocortical LRP. SPTBN1 is the candidate on 2p21, it encodes beta-spectrin, an α-synuclein binding protein and a component of Lewy bodies. The HLA-DPA1/DPB1 association suggests a role for antigen presentation or alternatively, cis-regulatory effects, one of the regulated neighboring genes identified here (vacuolar protein sorting 52) plays a role in vesicular trafficking and has been shown to interact with α-synuclein in a yeast model.

Baseline genetic associations in the Parkinson's Progression Markers Initiative (PPMI)

BACKGROUND

The Parkinson's Progression Marker Initiative is an international multicenter study whose main goal is investigating markers for Parkinson's disease (PD) progression as part of a path to a treatment for the disease. This manuscript describes the baseline genetic architecture of this study, providing not only a catalog of disease-linked variants and mutations, but also quantitative measures with which to adjust for population structure.

METHODS

Three hundred eighty-three newly diagnosed typical PD cases, 65 atypical PD and 178 healthy controls, from the Parkinson's Progression Marker Initiative study have been genotyped on the NeuroX or Immunochip arrays. These data are freely available to all researchers interested in pursuing PD research within the Parkinson's Progression Marker Initiative.

RESULTS

The Parkinson's Progression Marker Initiative represents a study population with low genetic heterogeneity. We recapitulate known PD associations from large-scale genome-wide association studies and refine genetic risk score models for PD predictability (area under the curve, ∼0.74). We show the presence of six LRRK2 p.G2019S and nine GBA p.N370S mutation carriers.

CONCLUSIONS

The Parkinson's Progression Marker Initiative study and its genetic data are useful in studies of PD biomarkers. The genetic architecture described here will be useful in the analysis of myriad biological and clinical traits within this study.

Rare and Coding Region Genetic Variants Associated With Risk of Ischemic Stroke: The NHLBI Exome Sequence Project

IMPORTANCE

Stroke is the second leading cause of death and the third leading cause of years of life lost. Genetic factors contribute to stroke prevalence, and candidate gene and genome-wide association studies (GWAS) have identified variants associated with ischemic stroke risk. These variants often have small effects without obvious biological significance. Exome sequencing may discover predicted protein-altering variants with a potentially large effect on ischemic stroke risk.

OBJECTIVE

To investigate the contribution of rare and common genetic variants to ischemic stroke risk by targeting the protein-coding regions of the human genome.

DESIGN, SETTING, AND PARTICIPANTS

The National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP) analyzed approximately 6000 participants from numerous cohorts of European and African ancestry. For discovery, 365 cases of ischemic stroke (small-vessel and large-vessel subtypes) and 809 European ancestry controls were sequenced; for replication, 47 affected sibpairs concordant for stroke subtype and an African American case-control series were sequenced, with 1672 cases and 4509 European ancestry controls genotyped. The ESP's exome sequencing and genotyping started on January 1, 2010, and continued through June 30, 2012. Analyses were conducted on the full data set between July 12, 2012, and July 13, 2013.

MAIN OUTCOMES AND MEASURES

Discovery of new variants or genes contributing to ischemic stroke risk and subtype (primary analysis) and determination of support for protein-coding variants contributing to risk in previously published candidate genes (secondary analysis).

RESULTS

We identified 2 novel genes associated with an increased risk of ischemic stroke: a protein-coding variant in PDE4DIP (rs1778155; odds ratio, 2.15; P = 2.63 × 10(-8)) with an intracellular signal transduction mechanism and in ACOT4 (rs35724886; odds ratio, 2.04; P = 1.24 × 10(-7)) with a fatty acid metabolism; confirmation of PDE4DIP was observed in affected sibpair families with large-vessel stroke subtype and in African Americans. Replication of protein-coding variants in candidate genes was observed for 2 previously reported GWAS associations: ZFHX3 (cardioembolic stroke) and ABCA1 (large-vessel stroke).

CONCLUSIONS AND RELEVANCE

Exome sequencing discovered 2 novel genes and mechanisms, PDE4DIP and ACOT4, associated with increased risk for ischemic stroke. In addition, ZFHX3 and ABCA1 were discovered to have protein-coding variants associated with ischemic stroke. These results suggest that genetic variation in novel pathways contributes to ischemic stroke risk and serves as a target for prediction, prevention, and therapy.

EIF4G1 mutations do not cause Parkinson's disease

EIF4G1 mutations were previously reported as a cause of Parkinson's disease (PD). As a result of this finding, considerable work has been performed to test this idea and to examine the functional role of eukaryotic translation initiation factor 4-gamma in the pathogenic process underlying PD. Here, we show that the originally described mutation is likely a rare benign variant. We tested this variant in a very large series of subjects and show that it is more frequent in controls than cases. We argue here that this infers that EIF4G1 mutations are not related to PD.

Genetic risk and age in Parkinson's disease: Continuum not stratum

BACKGROUND

Recent genomewide association study meta-analyses have identified 28 loci associated with risk of Parkinson's disease (PD). We sought to investigate whether these genetic risk factors are associated with PD age at onset.

METHODS

Genetic risk scores from these loci were calculated for 6,249 cases. Linear regression tested associations between cumulative genetic risk and PD age at onset.

RESULTS

Increasing genetic risk scores were associated with earlier age at onset (beta = -0.10, P = 2.92 × 10(-8) , adjusted r(2)  = 0.27). Single standard deviation increase in genetic risk score is associated with 37.44 d earlier age at onset. Highest genetic risk was found at 31 to 60 y, onset slightly below average age at onset (AAO).

CONCLUSIONS

Common genetic risk factors have a small but consistent association with AAO in PD.

Polygenic risk of Parkinson disease is correlated with disease age at onset

Objective

We have investigated the polygenic architecture of Parkinson disease (PD) and have also explored the potential relationship between an individual's polygenic risk score and their disease age at onset.

Methods

This study used genotypic data from 4,294 cases and 10,340 controls obtained from the meta‐analysis of PD genome‐wide association studies. Polygenic score analysis was performed as previously described by the International Schizophrenia Consortium, testing whether the polygenic score alleles identified in 1 association study were significantly enriched in the cases relative to the controls of 3 independent studies. Linear regression was used to investigate the relationship between an individual's polygenic score for PD risk alleles and disease age at onset.

Results

Our polygenic score analysis has identified significant evidence for a polygenic component enriched in the cases of each of 3 independent PD genome‐wide association cohorts (minimum p = 3.76 × 10⁻⁶). Further analysis identified compelling evidence that the average polygenic score in patients with an early disease age at onset was significantly higher than in those with a late age at onset (p = 0.00014).

Interpretation

This provides strong support for a large polygenic contribution to the overall heritable risk of PD and also suggests that early onset forms of the illness are not exclusively caused by highly penetrant Mendelian mutations, but can also be contributed to by an accumulation of common polygenic alleles with relatively low effect sizes. Ann Neurol 2015;77:582–591

NeuroX, a fast and efficient genotyping platform for investigation of neurodegenerative diseases

Our objective was to design a genotyping platform that would allow rapid genetic characterization of samples in the context of genetic mutations and risk factors associated with common neurodegenerative diseases. The platform needed to be relatively affordable, rapid to deploy, and use a common and accessible technology. Central to this project, we wanted to make the content of the platform open to any investigator without restriction. In designing this array we prioritized a number of types of genetic variability for inclusion, such as known risk alleles, disease-causing mutations, putative risk alleles, and other functionally important variants. The array was primarily designed to allow rapid screening of samples for disease-causing mutations and large population studies of risk factors. Notably, an explicit aim was to make this array widely available to facilitate data sharing across and within diseases. The resulting array, NeuroX, is a remarkably cost and time effective solution for high-quality genotyping. NeuroX comprises a backbone of standard Illumina exome content of approximately 240,000 variants, and over 24,000 custom content variants focusing on neurologic diseases. Data are generated at approximately $50-$60 per sample using a 12-sample format chip and regular Infinium infrastructure; thus, genotyping is rapid and accessible to many investigators. Here, we describe the design of NeuroX, discuss the utility of NeuroX in the analyses of rare and common risk variants, and present quality control metrics and a brief primer for the analysis of NeuroX derived data.

DNA methylation of lipid-related genes affects blood lipid levels

BACKGROUND

Epigenetic mechanisms might be involved in the regulation of interindividual lipid level variability and thus may contribute to the cardiovascular risk profile. The aim of this study was to investigate the association between genome-wide DNA methylation and blood lipid levels high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol. Observed DNA methylation changes were also further analyzed to examine their relationship with previous hospitalized myocardial infarction.

METHODS AND RESULTS

Genome-wide DNA methylation patterns were determined in whole blood samples of 1776 subjects of the Cooperative Health Research in the Region of Augsburg F4 cohort using the Infinium HumanMethylation450 BeadChip (Illumina). Ten novel lipid-related CpG sites annotated to various genes including ABCG1, MIR33B/SREBF1, and TNIP1 were identified. CpG cg06500161, located in ABCG1, was associated in opposite directions with both high-density lipoprotein cholesterol (β coefficient=-0.049; P=8.26E-17) and triglyceride levels (β=0.070; P=1.21E-27). Eight associations were confirmed by replication in the Cooperative Health Research in the Region of Augsburg F3 study (n=499) and in the Invecchiare in Chianti, Aging in the Chianti Area study (n=472). Associations between triglyceride levels and SREBF1 and ABCG1 were also found in adipose tissue of the Multiple Tissue Human Expression Resource cohort (n=634). Expression analysis revealed an association between ABCG1 methylation and lipid levels that might be partly mediated by ABCG1 expression. DNA methylation of ABCG1 might also play a role in previous hospitalized myocardial infarction (odds ratio, 1.15; 95% confidence interval=1.06-1.25).

CONCLUSIONS

Epigenetic modifications of the newly identified loci might regulate disturbed blood lipid levels and thus contribute to the development of complex lipid-related diseases.

Head injury, potential interaction with genes, and risk for Parkinson's disease

INTRODUCTION

To evaluate the association between head injury and Parkinson's disease (PD), focusing on the timing of head injury, and to explore potential interactions between head injury and genetic factors in PD etiology.

METHODS

The analysis included 507 PD cases and 1330 controls, all non-Hispanic Whites. Head injury was retrospectively asked, and genotyping was performed mainly as part of a previous GWAS.

RESULTS

We found a positive association between head injury and PD risk. Compared with no previous head injury, the odds ratio (OR) was 1.39 (95% confidence interval [CI]: 1.00, 1.94) for one and 2.33 (95% CI: 1.25, 4.35) for two or more head injuries (P for trend = 0.0016). We further found that the higher risk was largely attributed to head injuries before age 30. Compared with no previous head injury, the OR was 2.04 (95% CI: 1.33, 3.14) for head injury that occurred before age 18, 1.39 (95% CI: 0.81, 2.36) for head injury between ages 18-<30, and 1.04 (95% CI: 0.58, 1.87) for head injury that occurred at age 30 or older (P for trend = 0.001). Exploratory interaction analyses showed a significant interaction between head injury and a SNP at the RBMS3 locus (rs10510622, uncorrected P = 0.0001). No interaction was found with GWAS tag SNPs at or near the MAPT, SNCA, LRRK2, and HLA loci.

CONCLUSION

Our study suggests that head injury early in life may be an important risk factor for PD. The potential interaction with RBMS3 needs confirmation.

Genetic diversity is a predictor of mortality in humans

BACKGROUND

It has been well-established, both by population genetics theory and direct observation in many organisms, that increased genetic diversity provides a survival advantage. However, given the limitations of both sample size and genome-wide metrics, this hypothesis has not been comprehensively tested in human populations. Moreover, the presence of numerous segregating small effect alleles that influence traits that directly impact health directly raises the question as to whether global measures of genomic variation are themselves associated with human health and disease.

RESULTS

We performed a meta-analysis of 17 cohorts followed prospectively, with a combined sample size of 46,716 individuals, including a total of 15,234 deaths. We find a significant association between increased heterozygosity and survival (P = 0.03). We estimate that within a single population, every standard deviation of heterozygosity an individual has over the mean decreases that person's risk of death by 1.57%.

CONCLUSIONS

This effect was consistent between European and African ancestry cohorts, men and women, and major causes of death (cancer and cardiovascular disease), demonstrating the broad positive impact of genomic diversity on human survival.

A 7.5-Mb duplication at chromosome 11q21-11q22.3 is associated with a novel spastic ataxia syndrome

BACKGROUND

The autosomal dominant spinocerebellar ataxias are most commonly caused by nucleotide repeat expansions followed by base-pair changes in functionally important genes. Structural variation has recently been shown to underlie spinocerebellar ataxia types 15 and 20.

METHODS

We applied single-nucleotide polymorphism (SNP) genotyping to determine whether structural variation causes spinocerebellar ataxia in a family from France.

RESULTS

We identified an approximately 7.5-megabasepair duplication on chromosome 11q21-11q22.3 that segregates with disease. This duplication contains an estimated 44 genes. Duplications at this locus were not found in control individuals.

CONCLUSIONS

We have identified a new spastic ataxia syndrome caused by a genomic duplication, which we have denoted as spinocerebellar ataxia type 39. Finding additional families with this phenotype will be important to identify the genetic lesion underlying disease.