Genetic risk factors in Finnish patients with Parkinson's disease

Introduction Variation contributing to the risk of Parkinson's disease (PD) has been identified in several genes and at several loci including GBA, SMPD1, LRRK2, POLG1, CHCHD10 and MAPT, but the frequencies of risk variants seem to vary according to ethnic background. Our aim was to analyze how variation in these genes contributes to PD in the Finnish population. Methods The subjects consisted of 527 Finnish patients with early-onset PD, 325 patients with late-onset PD and 403 population controls. We screened for known genetic risk variants in GBA, SMPD1, LRRK2, POLG1, CHCHD10 and MAPT. In addition, DNA from 225 patients with early-onset Parkinson's disease was subjected to whole exome sequencing (WES). Results We detected a significant difference in the length variation of the CAG repeat in POLG1 between patients with early-onset PD compared to controls. The p.N370S and p.L444P variants in GBA contributed to a relative risk of 3.8 in early-onset PD and 2.5 in late-onset PD. WES revealed five variants in LRRK2 and SMPD1 that were found in the patients but not in the Finnish ExAC sequences. These are possible risk variants that require further confirmation. The p.G2019S variant in LRRK2, common in North African Arabs and Ashkenazi Jews, was not detected in any of the 849 PD patients. Conclusions The POLG1 CAG repeat length variation and the GBA p.L444P variant are associated with PD in the Finnish population.Peer reviewe

Genetic Variability in CLU and Its Association with Alzheimer's Disease

Background: Recently, two large genome wide association studies in Alzheimer disease (AD) have identified variants in three different genes (CLU, PICALM and CR1) as being associated with the risk of developing AD. The strongest association was reported for an intronic single nucleotide polymorphism (SNP) in CLU.Methodology/Principal Findings: To further characterize this association we have sequenced the coding region of this gene in a total of 495 AD cases and 330 healthy controls. A total of twenty-four variants were found in both cases and controls. For the changes found in more than one individual, the genotypic frequencies were compared between cases and controls. Coding variants were found in both groups (including a nonsense mutation in a healthy subject), indicating that the pathogenicity of variants found in this gene must be carefully evaluated. We found no common coding variant associated with disease. In order to determine if common variants at the CLU locus effect expression of nearby (cis) mRNA transcripts, an expression quantitative loci (eQTL) analysis was performed. No significant eQTL associations were observed for the SNPs previously associated with AD.Conclusions/Significance: We conclude that common coding variability at this locus does not explain the association, and that there is no large effect of common genetic variability on expression in brain tissue. We surmise that the most likely mechanism underpinning the association is either small effects of genetic variability on resting gene expression, or effects on damage induced expression of the protein

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect

Cooperative Genome-Wide Analysis Shows Increased Homozygosity in Early Onset Parkinson's Disease

Parkinson's disease (PD) occurs in both familial and sporadic forms, and both monogenic and complex genetic factors have been identified. Early onset PD (EOPD) is particularly associated with autosomal recessive (AR) mutations, and three genes, PARK2, PARK7 and PINK1, have been found to carry mutations leading to AR disease. Since mutations in these genes account for less than 10% of EOPD patients, we hypothesized that further recessive genetic factors are involved in this disorder, which may appear in extended runs of homozygosity

Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain

Our knowledge of the transcriptome has become much more complex since the days of the central dogma of molecular biology. We now know that splicing takes place to create potentially thousands of isoforms from a single gene, and we know that RNA does not always faithfully recapitulate DNA if RNA editing occurs. Collectively, these observations show that the transcriptome is amazingly rich with intricate regulatory mechanisms for overall gene expression, splicing, and RNA editing. Genetic variability can play a role in controlling gene expression, which can be identified by examining expression quantitative trait loci (eQTLs). eQTLs are genomic regions where genetic variants, including single nucleotide polymorphisms (SNPs) show a statistical association with expression of mRNA transcripts. In humans, many SNPs are also associated with disease, and have been identified using genome wide association studies (GWAS) but the biological effects of those SNPs are usually not known. If SNPs found in GWAS are also found in eQTLs, then one could hypothesize that expression levels may contribute to disease risk. Performing eQTL analysis with GWAS SNPs in both blood and brain, specifically the frontal cortex and the cerebellum, we found both shared and tissue unique eQTLS. The identification of tissue-unique eQTLs supports the argument that choice of tissue type is important in eQTL studies (Paper I). Aging is a complex process with the mechanisms underlying aging still being poorly defined. There is evidence that the transcriptome changes with age, and hence we used the brain dataset from our first paper as a discovery set, with an additional replication dataset, to investigate any aging-gene expression associations. We found evidence that many genes were associated with aging. We further found that there were more statically significant expression changes in the frontal cortex versus the cerebellum, indicating that brain regions may age at different rates. As the brain is a heterogeneous tissue including both neurons and non-neuronal cells, we used LCM to capture Purkinje cells as a representative neuronal type and repeated the age analysis. Looking at the discovery, replication and Purkinje cell datasets we found five genes with strong, replicated evidence of age-expression associations (Paper II). Being able to capture and quantify the depth of the transcriptome has been a lengthy process starting with methods that could only measure a single gene to genome-wide techniques such as microarray. A recently developed technology, RNA-Seq, shows promise in its ability to capture expression, splicing, and editing and with its broad dynamic range quantification is accurate and reliable. RNA-Seq is, however, data intensive and a great deal of computational expertise is required to fully utilize the strengths of this method. We aimed to create a small, well-controlled, experiment in order to test the performance of this relatively new technology in the brain. We chose embryonic versus adult cerebral cortex, as mice are genetically homogenous and there are many known differences in gene expression related to brain development that we could use as benchmarks for analysis testing. We found a large number of differences in total gene expression between embryonic and adult brain. Rigorous technical and biological validation illustrated the accuracy and dynamic range of RNA-Seq. We were also able to interrogate differences in exon usage in the same dataset. Finally we were able to identify and quantify both well-known and novel A-to-I edit sites. Overall this project helped us develop the tools needed to build usable pipelines for RNA-Seq data processing (Paper III). Our studies in the developing brain (Paper III) illustrated that RNA-Seq was a useful unbiased method for investigating RNA editing. To extend this further, we utilized a genetically modified mouse model to study the transcriptomic role of the RNA editing enzyme ADAR2. We found that ADAR2 was important for editing of the coding region of mRNA as a large proportion of RNA editing sites in coding regions had a statistically significant decrease in editing percentages in Adar2 -/-Gria2 R/R mice versus controls. However, despite indications in the literature that ADAR2 may also be involved in splicing and expression regulatory machinery we found no changes in gene expression or exon utilization in Adar2 -/-Gria2 R/R mice as compared to their littermate controls (Paper IV). In our final study, based on the methods developed in Papers III and IV, we revisited the idea of age related gene expression associations from Paper II. We used a subset of human frontal cortices for RNA sequencing. Interestingly we found more gene expression changes with aging compared to the previous data using microarrays in Paper II. When the significant gene lists were analysed for gene ontology enrichment, we found that there was a large number of downregulated genes involved in synaptic function while those that were upregulated had enrichment in immune function. This dataset illustrates that the aging brain may be predisposed to the processes found in neurodegenerative diseases (Paper V)

Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study.

Background: Amyotrophic lateral sclerosis (ALS) is known to represent a collection of overlapping syndromes. Various classification systems based on empirical observations have been proposed, but it is unclear to what extent they reflect ALS population substructures. We aimed to use machine-learning techniques to identify the number and nature of ALS subtypes to obtain a better understanding of this heterogeneity, enhance our understanding of the disease, and improve clinical care. Methods: In this retrospective study, we applied unsupervised Uniform Manifold Approximation and Projection [UMAP]) modelling, semi-supervised (neural network UMAP) modelling, and supervised (ensemble learning based on LightGBM) modelling to a population-based discovery cohort of patients who were diagnosed with ALS while living in the Piedmont and Valle d'Aosta regions of Italy, for whom detailed clinical data, such as age at symptom onset, were available. We excluded patients with missing Revised ALS Functional Rating Scale (ALSFRS-R) feature values from the unsupervised and semi-supervised steps. We replicated our findings in an independent population-based cohort of patients who were diagnosed with ALS while living in the Emilia Romagna region of Italy. Findings: Between Jan 1, 1995, and Dec 31, 2015, 2858 patients were entered in the discovery cohort. After excluding 497 (17%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 2361 (83%) patients were available for the unsupervised and semi-supervised analysis. We found that semi-supervised machine learning produced the optimum clustering of the patients with ALS. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (ie, bulbar, respiratory, flail arm, classical, pyramidal, and flail leg ALS). Between Jan 1, 2009, and March 1, 2018, 1097 patients were entered in the replication cohort. After excluding 108 (10%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 989 patients were available for the unsupervised and semi-supervised analysis. All 1097 patients were included in the supervised analysis. The same clusters were identified in the replication cohort. By contrast, other ALS classification schemes, such as the El Escorial categories, Milano-Torino clinical staging, and King's clinical stages, did not adequately label the clusters. Supervised learning identified 11 clinical parameters that predicted ALS clinical subtypes with high accuracy (area under the curve 0·982 [95% CI 0·980-0·983]). Interpretation: Our data-driven study provides insight into the ALS population substructure and confirms that the Chiò classification system successfully identifies ALS subtypes. Additional validation is required to determine the accuracy and clinical use of these algorithms in assigning clinical subtypes. Nevertheless, our algorithms offer a broad insight into the clinical heterogeneity of ALS and help to determine the actual subtypes of disease that exist within this fatal neurodegenerative syndrome. The systematic identification of ALS subtypes will improve clinical care and clinical trial design. Funding: US National Institute on Aging, US National Institutes of Health, Italian Ministry of Health, European Commission, University of Torino Rita Levi Montalcini Department of Neurosciences, Emilia Romagna Regional Health Authority, and Italian Ministry of Education, University, and Research. Translations: For the Italian and German translations of the abstract see Supplementary Materials section

Using Epigenetic Networks for the Analysis of Movement Associated with Levodopa Therapy for Parkinson's Disease

© 2016 The Author(s) Levodopa is a drug that is commonly used to treat movement disorders associated with Parkinson's disease. Its dosage requires careful monitoring, since the required amount changes over time, and excess dosage can lead to muscle spasms known as levodopa-induced dyskinesia. In this work, we investigate the potential for using epiNet, a novel artificial gene regulatory network, as a classifier for monitoring accelerometry time series data collected from patients undergoing levodopa therapy. We also consider how dynamical analysis of epiNet classifiers and their transitions between different states can highlight clinically useful information which is not available through more conventional data mining techniques. The results show that epiNet is capable of discriminating between different movement patterns which are indicative of either insufficient or excessive levodopa

Identity-by-descent filtering as a tool for the identification of disease alleles in exome sequence data from distant relatives

Large-scale, deep resequencing may be the next logical step in the genetic investigation of common complex diseases. Because each individual is likely to carry many thousands of variants, the identification of causal alleles requires an efficient strategy to reduce the number of candidate variants. Under many genetic models, causal alleles can be expected to reside within identity-by-descent (IBD) regions shared by affected relatives. In distant relatives, IBD regions constitute a small portion of the genome and can thus greatly reduce the search space for causal alleles. However, the effectiveness of this strategy is unknown. We test the simulated mini-exome data set in extended pedigrees provided by Genetic Analysis Workshop 17. At the fourth- and fifth-degree level of relatedness, case-case pairs shared between 1% and 9% of the genome identical by descent. As expected, no genes were shared identical by descent by all case subjects, but 43 genes were shared by many case subjects across at least 50 replicates. We filtered variants in these genes based on population frequency, function, informativeness, and evidence of association using the family-based association test. This analysis highlighted five genes previously implicated in triglyceride, lipid, and cholesterol metabolism. Comparison with the list of true risk alleles revealed that strict IBD filtering followed by association testing of the rarest alleles was the most sensitive strategy. IBD filtering may be a useful strategy for narrowing down the list of candidate variants in exome data, but the optimal degree of relatedness of affected pairs will depend on the genetic architecture of the disease under study

Genome-Wide Association Studies of Cognitive and Motor Progression in Parkinson's Disease.

BACKGROUND: There are currently no treatments that stop or slow the progression of Parkinson's disease (PD). Case-control genome-wide association studies have identified variants associated with disease risk, but not progression. The objective of the current study was to identify genetic variants associated with PD progression. METHODS: We analyzed 3 large longitudinal cohorts: Tracking Parkinson's, Oxford Discovery, and the Parkinson's Progression Markers Initiative. We included clinical data for 3364 patients with 12,144 observations (mean follow-up 4.2 years). We used a new method in PD, following a similar approach in Huntington's disease, in which we combined multiple assessments using a principal components analysis to derive scores for composite, motor, and cognitive progression. These scores were analyzed in linear regression in genome-wide association studies. We also performed a targeted analysis of the 90 PD risk loci from the latest case-control meta-analysis. RESULTS: There was no overlap between variants associated with PD risk, from case-control studies, and PD age at onset versus PD progression. The APOE ε4 tagging variant, rs429358, was significantly associated with composite and cognitive progression in PD. Conditional analysis revealed several independent signals in the APOE locus for cognitive progression. No single variants were associated with motor progression. However, in gene-based analysis, ATP8B2, a phospholipid transporter related to vesicle formation, was nominally associated with motor progression (P = 5.3 × 10-6 ). CONCLUSIONS: We provide early evidence that this new method in PD improves measurement of symptom progression. We show that the APOE ε4 allele drives progressive cognitive impairment in PD. Replication of this method and results in independent cohorts are needed. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.Funding sources: Parkinson’s U