Margaux F. Keller

Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease

We conducted a meta-analysis of Parkinsons disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as having genome-wide significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that four loci, including GBA, GAK-DGKQ, SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinsons disease across 24 loci. Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55–4.30; P = 2 × 10−16). We also show six risk loci associated with proximal gene expression or DNA methylation.

A Multicenter Study of Glucocerebrosidase Mutations in Dementia With Lewy Bodies

IMPORTANCE While mutations in glucocerebrosidase (GBA1) are associated with an increased risk for Parkinson disease (PD), it is important to establish whether such mutations are also a common risk factor for other Lewy body disorders. OBJECTIVE To establish whether GBA1 mutations are a risk factor for dementia with Lewy bodies (DLB). DESIGN We compared genotype data on patients and controls from 11 centers. Data concerning demographics, age at onset, disease duration, and clinical and pathological features were collected when available. We conducted pooled analyses using logistic regression to investigate GBA1 mutation carrier status as predicting DLB or PD with dementia status, using common control subjects as a reference group. Random-effects meta-analyses were conducted to account for additional heterogeneity. SETTING Eleven centers from sites around the world performing genotyping. PARTICIPANTS Seven hundred twenty-one cases met diagnostic criteria for DLB and 151 had PD with dementia. We compared these cases with 1962 controls from the same centers matched for age, sex, and ethnicity. MAIN OUTCOME MEASURES Frequency of GBA1 mutations in cases and controls. RESULTS We found a significant association between GBA1 mutation carrier status and DLB, with an odds ratio of 8.28 (95% CI, 4.78-14.88). The odds ratio for PD with dementia was 6.48 (95% CI, 2.53-15.37). The mean age at diagnosis of DLB was earlier in GBA1 mutation carriers than in noncarriers (63.5 vs 68.9 years; P < .001), with higher disease severity scores. CONCLUSIONS AND RELEVANCE Mutations in GBA1 are a significant risk factor for DLB. GBA1 mutations likely play an even larger role in the genetic etiology of DLB than in PD, providing insight into the role of glucocerebrosidase in Lewy body disease.

Using genome-wide complex trait analysis to quantify ‘missing heritability’ in Parkinson's disease

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinsons disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.

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 Parkinsons 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 Parkinsons 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 Parkinsons Progression Marker Initiative (PPMI) study for 367 patients with Parkinsons disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinsons 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 Parkinsons disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinsons Disease Biomarkers Program (PDBP), the Parkinsons Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinsons 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 Parkinsons 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 Parkinsons 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 Parkinsons disease converted to Parkinsons disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinsons disease underwent conversion (test of proportions, p=0·003). INTERPRETATION Our model provides a potential new approach to distinguish participants with Parkinsons disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinsons 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.

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

Large C9orf72 repeat expansions are not a common cause of Parkinson's disease

50-

Genome-wide association analysis of red blood cell traits in African Americans: the COGENT Network

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.

Gene-Centric Meta-Analysis of Lipid Traits in African, East Asian and Hispanic Populations

The concept of a pathological overlap between neurodegenerative disorders is gaining momentum. We sought to determine the contribution of C9orf72 repeat expansions, recently discovered as a cause of frontotemporal dementia and amyotrophic lateral sclerosis, in a large number of Parkinsons disease patients. No large expansions were identified in our cohort.

Genetic comorbidities in Parkinson's disease

Laboratory red blood cell (RBC) measurements are clinically important, heritable and differ among ethnic groups. To identify genetic variants that contribute to RBC phenotypes in African Americans (AAs), we conducted a genome-wide association study in up to ~16 500 AAs. The alpha-globin locus on chromosome 16pter [lead SNP rs13335629 in ITFG3 gene; P < 1E-13 for hemoglobin (Hgb), RBC count, mean corpuscular volume (MCV), MCH and MCHC] and the G6PD locus on Xq28 [lead SNP rs1050828; P < 1E - 13 for Hgb, hematocrit (Hct), MCV, RBC count and red cell distribution width (RDW)] were each associated with multiple RBC traits. At the alpha-globin region, both the common African 3.7 kb deletion and common single nucleotide polymorphisms (SNPs) appear to contribute independently to RBC phenotypes among AAs. In the 2p21 region, we identified a novel variant of PRKCE distinctly associated with Hct in AAs. In a genome-wide admixture mapping scan, local European ancestry at the 6p22 region containing HFE and LRRC16A was associated with higher Hgb. LRRC16A has been previously associated with the platelet count and mean platelet volume in AAs, but not with Hgb. Finally, we extended to AAs the findings of association of erythrocyte traits with several loci previously reported in Europeans and/or Asians, including CD164 and HBS1L-MYB. In summary, this large-scale genome-wide analysis in AAs has extended the importance of several RBC-associated genetic loci to AAs and identified allelic heterogeneity and pleiotropy at several previously known genetic loci associated with blood cell traits in AAs.

Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians

Meta-analyses of European populations has successfully identified genetic variants in over 100 loci associated with lipid levels, but our knowledge in other ethnicities remains limited. To address this, we performed dense genotyping of ∼2,000 candidate genes in 7,657 African Americans, 1,315 Hispanics and 841 East Asians, using the IBC array, a custom ∼50,000 SNP genotyping array. Meta-analyses confirmed 16 lipid loci previously established in European populations at genome-wide significance level, and found multiple independent association signals within these lipid loci. Initial discovery and in silico follow-up in 7,000 additional African American samples, confirmed two novel loci: rs5030359 within ICAM1 is associated with total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) (p = 8.8×10−7 and p = 1.5×10−6 respectively) and a nonsense mutation rs3211938 within CD36 is associated with high-density lipoprotein cholesterol (HDL-C) levels (p = 13.5×10−12). The rs3211938-G allele, which is nearly absent in European and Asian populations, has been previously found to be associated with CD36 deficiency and shows a signature of selection in Africans and African Americans. Finally, we have evaluated the effect of SNPs established in European populations on lipid levels in multi-ethnic populations and show that most known lipid association signals span across ethnicities. However, differences between populations, especially differences in allele frequency, can be leveraged to identify novel signals, as shown by the discovery of ICAM1 and CD36 in the current report.