Parkinson's disease polygenic risk score is not associated with impulse control disorders: A longitudinal study

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

Genetic variation and pesticide exposure influence blood DNA methylation signatures in females with early-stage Parkinson's disease

Although sex, genetics, and exposures can individually influence risk for sporadic Parkinson's disease (PD), the joint contributions of these factors to the epigenetic etiology of PD have not been comprehensively assessed. Here, we profiled sex-stratified genome-wide blood DNAm patterns, SNP genotype, and pesticide exposure in agricultural workers (71 early-stage PD cases, 147 controls) and explored replication in three independent samples of varying demographics (n = 218, 222, and 872). Using a region-based approach, we found more associations of blood DNAm with PD in females (69 regions) than in males (2 regions, Δβadj| ≥0.03, padj ≤ 0.05). For 48 regions in females, models including genotype or genotype and pesticide exposure substantially improved in explaining interindividual variation in DNAm (padj ≤ 0.05), and accounting for these variables decreased the estimated effect of PD on DNAm. The results suggested that genotype, and to a lesser degree, genotype-exposure interactions contributed to variation in PD-associated DNAm. Our findings should be further explored in larger study populations and in experimental systems, preferably with precise measures of exposure.

Genotype-phenotype correlation in PRKN-associated Parkinson's disease

Bi-allelic pathogenic variants in PRKN are the most common cause of autosomal recessive Parkinson's disease (PD). 647 patients with PRKN-PD were included in this international study. The pathogenic variants present were characterised and investigated for their effect on phenotype. Clinical features and progression of PRKN-PD was also assessed. Among 133 variants in index cases (n = 582), there were 58 (43.6%) structural variants, 34 (25.6%) missense, 20 (15%) frameshift, 10 splice site (7.5%%), 9 (6.8%) nonsense and 2 (1.5%) indels. The most frequent variant overall was an exon 3 deletion (n = 145, 12.3%), followed by the p.R275W substitution (n = 117, 10%). Exon3, RING0 protein domain and the ubiquitin-like protein domain were mutational hotspots with 31%, 35.4% and 31.7% of index cases presenting mutations in these regions respectively. The presence of a frameshift or structural variant was associated with a 3.4 ± 1.6 years or a 4.7 ± 1.6 years earlier age at onset of PRKN-PD respectively (p < 0.05). Furthermore, variants located in the N-terminus of the protein, a region enriched with frameshift variants, were associated with an earlier age at onset. The phenotype of PRKN-PD was characterised by slow motor progression, preserved cognition, an excellent motor response to levodopa therapy and later development of motor complications compared to early-onset PD. Non-motor symptoms were however common in PRKN-PD. Our findings on the relationship between the type of variant in PRKN and the phenotype of the disease may have implications for both genetic counselling and the design of precision clinical trials.

Performance on the balloon analogue risk task and anticipatory response inhibition task is associated with severity of impulse control behaviours in people with Parkinson's disease

Dopamine agonist medication is one of the largest risk factors for development of problematic impulse control behaviours (ICBs) in people with Parkinson's disease. The present study investigated the potential of dopamine gene profiling and individual performance on impulse control tasks to explain ICB severity. Clinical, genetic and task performance data were entered into a mixed-effects linear regression model for people with Parkinson's disease taking (n = 50) or not taking (n = 25) dopamine agonist medication. Severity of ICBs was captured via the Questionnaire for Impulsive-compulsive disorders in Parkinson's disease Rating Scale. A cumulative dopamine genetic risk score (DGRS) was calculated for each participant from variance in five dopamine-regulating genes. Objective measures of impulsive action and impulsive choice were measured on the Anticipatory Response Inhibition Task and Balloon Analogue Risk Task, respectively. For participants on dopamine agonist medication, task performance reflecting greater impulsive choice (p = 0.014), and to a trend level greater impulsive action (p = 0.056), as well as a longer history of DA medication (p < 0.001) all predicted increased ICB severity. DGRS however, did not predict ICB severity (p = 0.708). No variables could explain ICB severity in the non-agonist group. Our task-derived measures of impulse control have the potential to predict ICB severity in people with Parkinson's and warrant further investigation to determine whether they can be used to monitor ICB changes over time. The DGRS appears better suited to predicting the incidence, rather than severity, of ICBs on agonist medication.

DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome

Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.

Age at onset as stratifier in idiopathic Parkinson’s disease – effect of ageing and polygenic risk score on clinical phenotypes

Several phenotypic differences observed in Parkinson’s disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients.

The role of neurotrophin genes involved in the vulnerability to gambling disorder

Evidence about the involvement of genetic factors in the development of gambling disorder (GD) has been assessed. Among studies assessing heritability and biological vulnerability for GD, neurotrophin (NTF) genes have emerged as promising targets, since a growing literature showed a possible link between NTF and addiction-related disorders. Thus, we aimed to explore the role of NTF genes and GD with the hypothesis that some NTF gene polymorphisms could constitute biological risk factors. The sample included 166 patients with GD and 191 healthy controls. 36 single nucleotide polymorphisms (SNPs) from NTFs (NGF, NGFR, NTRK1, BDNF, NTRK2, NTF3, NTRK3, NTF4, CNTF and CNTFR) were selected and genotyped. Linkage disequilibrium (LD) and haplotype constructions were analyzed, in relationship with the presence of GD. Finally, regulatory elements overlapping the identified SNPs variants associated with GD were searched. The between groups comparisons of allele frequencies indicated that 6 SNPs were potentially associated with GD. Single and multiple-marker analyses showed a strong association between both NTF3 and NTRK2 genes, and GD. The present study supports the involvement of the NTF family in the aetiopathogenesis of GD. An altered cross-regulation of different NTF members signalling pathways might be considered as a biological vulnerability factor for GD.

Genetics of cognitive dysfunction in Parkinson's disease

Presentation and progression of cognitive symptoms in Parkinson's disease are highly variable. PD is a genetically complex disorder with multiple genetic risk factors and understanding the role that genes play in cognitive outcomes is important for patient counseling and treatment. Currently, there are seven well-described genes that increase the risk for PD, with variable levels of penetrance: SNCA, LRRK2, VPS35, PRKN, PINK1, DJ1 and GBA. In addition, large, genome-wide association studies have identified multiple loci in our DNA which increase PD risk. In this chapter, we summarize what is currently known about each of the seven strongly-associated PD genes and select PD risk variants, including PITX3, TMEM106B, SNCA Rep1, APOɛ4, COMT and MAPT H1/H1, along with their respective relationships to cognition.

Polygenic Risk Scores Contribute to Personalized Medicine of Parkinson's Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder characterized by the loss of dopaminergic neurons. The vast majority of PD patients develop the disease sporadically and it is assumed that the cause lies in polygenic and environmental components. The overall polygenic risk is the result of a large number of common low-risk variants discovered by large genome-wide association studies (GWAS). Polygenic risk scores (PRS), generated by compiling genome-wide significant variants, are a useful prognostic tool that quantifies the cumulative effect of genetic risk in a patient and in this way helps to identify high-risk patients. Although there are limitations to the construction and application of PRS, such as considerations of limited genetic underpinning of diseases explained by SNPs and generalizability of PRS to other populations, this personalized risk prediction could make a promising contribution to stratified medicine and tailored therapeutic interventions in the future.

Impulse control disorders and related behaviors in Parkinson's disease: risk factors, clinical and genetic aspects, and management

PURPOSE OF REVIEW

To review recent findings and research directions on impulse control disorders and related behaviors (ICDRBs) in Parkinson's disease (PD).

RECENT FINDINGS

Longitudinal studies found that prevalence increases during PD progression, incident ICDRBs being around 10% per year in patients treated with dopaminergic therapies. Screening tools and severity scales already developed have been validated and are available in several countries and languages. The main clinical risk factors include young age, male gender, type, doses and duration of dopaminergic therapy, PD motor severity and dyskinesia, depression, anxiety, apathy, sleep disorders, and impulsivity traits. Genetic factors are suspected by a high estimated heritability, but individual genes and variants remain to be replicated. Management of ICDRBs is centered on dopamine agonist decrease, with the risk to develop withdrawal symptoms. Cognitive behavioral therapy and subthalamic nucleus deep brain stimulation also improve ICDRBs. In the perspective of precision medicine, new individual prediction models of these disorders have been proposed, but they need further independent replication.

SUMMARY

Regular monitoring of ICDRB during the course of PD is needed, particularly in the subject at high risk of developing these complications. Precision medicine will require the appropriate use of machine learning to be reached in the clinical setting.

Exploratory analysis of the genetics of impulse control disorders in Parkinson's disease using genetic risk scores

OBJECTIVE

To study the association between impulse control disorders (ICDs) in Parkinson's disease (PD) and genetic risk scores (GRS) for 40 known or putative risk factors (e.g. depression, personality traits).

BACKGROUND

In absence of published genome-wide association studies (GWAS), little is known about the genetics of ICDs in PD. GRS of related phenotypes, for which large GWAS are available, may help shed light on the genetic contributors of ICDs in PD.

METHODS

We searched for GWAS on European ancestry populations with summary statistics publicly available for a broad range of phenotypes, including other psychiatric disorders, personality traits, and simple phenotypes. We separately tested their predictive ability in two of the largest PD cohorts with clinical and genetic available: the Parkinson's Progression Markers Initiative database (N = 368, 33% female, age range = [33-84]) and the Drug Interaction With Genes in Parkinson's Disease study (N = 373, 40% female, age range = [29-85]).

RESULTS

We considered 40 known or putative risk factors for ICDs in PD for which large GWAS had been published. After Bonferroni correction for multiple comparisons, no GRS or the combination of the 40 GRS were significantly associated with ICDs from the analyses in each cohort separately and from the meta-analysis.

CONCLUSION

Albeit unsuccessful, our approach will gain power in the coming years with increasing availability of genotypes in clinical cohorts of PD, but also from future increase in GWAS sample sizes of the phenotypes we considered. Our approach may be applied to other complex disorders, for which GWAS are not available or limited.