Biology subjects

Heilbron, K.

Publications and source records attributed to Heilbron, K..

9 recordsLinked to original sources

bioRxiv2026

Predicted Effector Gene Aggregation, Standards and Unified Schema (PEGASUS): A Community Framework for Effector Gene Reporting

Genome-wide association studies (GWAS) increasingly report predicted effector genes (PEGs) - genes hypothesised to mediate the biological effects of associated variants. These function as key outputs for advancing variant-to-function research, mechanistic understanding, and therapeutic discovery. However, the rapid growth of PEG lists has not been matched by standards for organising, annotating, and reporting these predictions. As shown by recent landscape analyses, PEG lists vary widely in methodology, evidence definition, nomenclature, provenance tracking, and data structure, limiting interoperability, benchmarking, reuse, and adherence to FAIR principles. To address this gap, we convened an international multi-stakeholder community comprising method developers, data generators, resource maintainers, curators, funders, journal editors, and downstream users. Through a 2024 workshop and a 2025 working group series, we developed the Predicted Effector Gene Aggregation, Standards and Unified Schema (PEGASUS) framework. PEGASUS specifies (i) a metadata standard to capture provenance, trait and GWAS descriptors, evidence sources, and integration methods; (ii) a structured evidence matrix reporting all genes and all evidence underpinning prioritisation at each locus; and (iii) a concise PEG list that summarises author-prioritised genes linked transparently to underlying evidence. The framework balances transparency, machine readability, burden on submitters, and alignment with existing community standards. PEGASUS provides the first community-developed schema for reporting predicted effector genes and their supporting evidence. Adoption of this framework by authors will improve the comparability, reproducibility, and reusability of PEG outputs across studies, facilitating more robust biological inference, enabling cross-resource comparison of gene-prioritisation methods to support community benchmarking, and integration into downstream resources and analytical pipelines. PEGASUS-compliant data can be shared via the PEG Data Registry platform (https://kpndataregistry.org/peg), promoting re-use and establishing the basis for future integration with publicly shared GWAS data.

genomics↗

bioRxiv2020

Parkinsons disease determinants, prediction and gene-environment interactions in the UK Biobank

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinsons disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate existing risk prediction algorithms and the impact of including addition genetic risk on the performance of prediction. MethodsWe identified individuals with incident PD diagnoses (n=1276) and unmatched controls (n=500,406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. A polygenic risk score (PRS) was constructed and used to examine gene-environment interactions. The PRS was also incorporated into a previously-developed prediction algorithm for finding incident cases. ResultsStrong evidence of association (Pcorr<0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, and daytime somnolence, and novel associations with epilepsy and earlier menarche. Individuals with the highest 10% of PRS scores had increased risk of PD (OR=3.30, 95% CI 2.57-4.24) compared to the lowest risk decile. Higher PRS scores were associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm improved model performance (Nagelkerke pseudo-R2 0.0053, p=6.87x10-14). We found evidence of interaction between the PRS and diabetes. InterpretationHere we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity and predictive power of a polygenic risk score, and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on prior genetic risk for PD.

genetics↗

bioRxiv2019

Fine-mapping of SNCA in REM sleep behavior disorder and overt synucleinopathies

ObjectiveREM-sleep behavior disorder (RBD) is a prodromal synucleinopathy, as >80% will eventually convert to overt synucleinopathy. We performed an in-depth analysis of the SNCA locus to identify RBD-specific risk variants.\n\nMethodsFull sequencing and genotyping of SNCA was performed in isolated/idiopathic RBD (iRBD, n=1,076), Parkinsons disease (PD, n=1,013), and dementia with Lewy bodies (DLB, n=415), and in control subjects (n=6,155). A replication cohort from 23andMe of PD patients with probable RBD (pRBD) was also analyzed (cases n=1,782, controls n=131,250). Adjusted logistic regression models and meta-analyses were performed. Effects on conversion rate were analyzed in 432 RBD patients with available data using Kaplan-Meier survival analysis.\n\nResultsA 5-region SNCA variant (rs10005233) was associated with iRBD (OR=1.43, p=1.1E-08), which was replicated in pRBD. This variant is in linkage disequilibrium (LD) with other 5 risk variants across the different synucleinopathies. An independent iRBD-specific suggestive association (rs11732740) was detected at the 3 of SNCA (OR=1.32, p=4.7E-04, not statistically significant after Bonferroni correction). Homozygous carriers of both iRBD-specific SNPs were at highly increased risk for iRBD (OR=5.74, p=2E-06). The known top PD-associated variant (3 variant rs356182) had an opposite direction of effect in iRBD compared to PD.\n\nInterpretationThere is a distinct pattern of association at the SNCA locus in RBD as compared to PD, with an opposite direction of effect at the 3 of SNCA. Several 5 SNCA variants are associated with iRBD and with pRBD in overt synucleinopathies, and may suggest a cognitive component to this region.

genetics↗

bioRxiv2019

Genetic modifiers of risk and age at onset in GBA associated Parkinson disease and Lewy body dementia

Parkinsons disease (PD) is a genetically complex disorder. Multiple genes have been shown to contribute to the risk of PD, and currently 90 independent risk variants have been identified by genome-wide association studies. Thus far, a number of genes (including SNCA, LRRK2, and GBA) have been shown to contain variability across a spectrum of frequency and effect, from rare, highly penetrant variants to common risk alleles with small effect sizes. Variants in GBA, encoding the enzyme glucocerebrosidase, are associated with Lewy body diseases such as PD and Lewy body dementia (LBD). These variants, which reduce or abolish enzymatic activity, confer a spectrum of disease risk, from 1.4- to >10-fold. An outstanding question in the field is what other genetic factors that influence GBA-associated risk for disease, and whether these overlap with known PD risk variants.\n\nUsing multiple, large case-control datasets, totalling 217,165 individuals (22,757 PD cases, 13,431 PD proxy cases, 622 LBD cases and 180,355 controls), we identified 1,772 PD cases, 711 proxy cases and 7,624 controls with a GBA variant (p.E326K, p.T369M or p.N370S). We performed a genome-wide association study and analysed the most recent PD-associated genetic risk score to detect genetic influences on GBA risk and age at onset. We attempted to replicate our findings in two independent datasets, including the personal genetics company 23andMe, Inc. and whole-genome sequencing data. Our analysis showed that the overall PD genetic risk score modifies risk for disease and decreases age at onset in carriers of GBA variants. Notably, this effect was consistent across all tested GBA risk variants. Dissecting this signal demonstrated that variants in close proximity to SNCA and CTSB (encoding cathepsin B) are the most significant contributors. Risk variants in the CTSB locus were identified to decrease mRNA expression of CTSB. Additional analyses suggest a possible genetic interaction between GBA and CTSB and GBA p.N370S neurons were shown to have decreased Cathepsin B expression compared to controls. These data provide a genetic basis for modification of GBA-associated PD risk and age at onset and demonstrate that variability at genes implicated in lysosomal function exerts the largest effect on GBA associated risk for disease. Further, these results have important implications for selection of GBA carriers for therapeutic interventions.

genetics↗

bioRxiv2019

The Parkinsons Disease Mendelian Randomization Research Portal

BackgroundMendelian randomization (MR) is a method for exploring observational associations to find evidence of causality.\n\nObjectiveTo apply MR between multiple risk factors/phenotypic traits (exposures) and Parkinsons disease (PD) in a large, unbiased manner, and to create a public resource for research.\n\nMethodsWe used two-sample MR in which the summary statistics relating to SNPs from genome wide association studies (GWASes) of 5,839 exposures curated on MR Base were used to assess causal relationships with PD. We selected the highest quality exposure GWASes for this report (n=401). For the disease outcome, summary statistics from the largest published PD GWAS were used. For each exposure, the causal effect on PD was assessed using the inverse variance weighted (IVW) method, followed by a range of sensitivity analyses. We used a false discovery rate (FDR) corrected p-value of <0.05 from the IVW analysis to prioritize traits of interest.\n\nResultsWe observed evidence for causal associations between twelve exposures and risk of PD. Of these, nine were causal effects related to increasing adiposity and decreasing risk of PD. The remaining top exposures that affected PD risk were tea drinking, time spent watching television and forced vital capacity, but the latter two appeared to be biased by violations of underlying MR assumptions.\n\nDiscussionWe present a new platform which offers MR analyses for a total of 5,839 GWASes versus the largest PD GWASes available (https://pdgenetics.shinyapps.io/pdgenetics/). Alongside, we report further evidence to support a causal role for adiposity on lowering the risk of PD.

neuroscience↗

bioRxiv2018

Parkinson disease age of onset GWAS: defining heritability, genetic loci and a-synuclein mechanisms

Increasing evidence supports an extensive and complex genetic contribution to Parkinsons disease (PD). Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age of onset are largely unknown. Here we performed an age of onset GWAS based on 28,568 PD cases. We estimated that the heritability of PD age of onset due to common genetic variation was ~0.11, lower than the overall heritability of risk for PD (~0.27) likely in part because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni corrected significant effect at other known PD risk loci, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. In addition, we identified that GBA coding variant carriers had an earlier age of onset compared to non-carriers. Notably, SNCA, TMEM175, SCARB2, BAG3 and GBA have all been shown to either directly influence alpha-synuclein aggregation or are implicated in alpha-synuclein aggregation pathways. Remarkably, other well-established PD risk loci such as GCH1, MAPT and RAB7L1/NUCKS1 (PARK16) did not show a significant effect on age of onset of PD. While for some loci, this may be a measure of power, this is clearly not the case for the MAPT locus; thus genetic variability at this locus influences whether but not when an individual develops disease. We believe this is an important mechanistic and therapeutic distinction. Furthermore, these data support a model in which alpha-synuclein and lysosomal mechanisms impact not only PD risk but also age of disease onset and highlights that therapies that target alpha-synuclein aggregation are more likely to be disease-modifying than therapies targeting other pathways.

genetics↗

bioRxiv2018

Parkinson’s disease genetics: identifying novel risk loci, providing causal insights and improving estimates of heritable risk

We performed the largest genome-wide association study of PD to date, involving the analysis of 7.8M SNPs in 37.7K cases, 18.6K UK Biobank proxy-cases, and 1.4M controls. We identified 90 independent genome-wide significant signals across 78 loci, including 38 independent risk signals in 37 novel loci. These variants explained 26-36% of the heritable risk of PD. Tests of causality within a Mendelian randomization framework identified putatively causal genes for 70 risk signals. Tissue expression enrichment analysis suggested that signatures of PD loci were heavily brain-enriched, consistent with specific neuronal cell types being implicated from single cell expression data. We found significant genetic correlations with brain volumes, smoking status, and educational attainment. In sum, these data provide the most comprehensive understanding of the genetic architecture of PD to date by revealing many additional PD risk loci, providing a biological context for these risk factors, and demonstrating that a considerable genetic component of this disease remains unidentified.

genetics↗

bioRxiv2018

Tendency towards being a “Morning person” increases risk of Parkinson’s disease: evidence from Mendelian randomisation

BackgroundCircadian rhythm may play a role in neurodegenerative diseases such as Parkinsons disease (PD). Chronotype is the behavioural manifestation of circadian rhythm and Mendelian randomisation (MR) involves the use of genetic variants to explore causal effects of exposures on outcomes. This study aimed to explore a causal relationship between chronotype and coffee consumption on risk of PD.\n\nMethodsTwo-sample MR was undertaken using publicly available GWAS data. Associations between genetic instrumental variables (IV) and \"morning person\" (one extreme of chronotype) were obtained from the personal genetics company 23andMe, Inc., and UK Biobank, and consisted of the per-allele odds ratio of being a \"morning person\" for 15 independent variants. The per-allele difference in log-odds of PD for each variant was estimated from a recent meta-analysis. The inverse variance weight method was used to estimate an odds ratio (OR) for the effect of being a \"morning person\" on PD. Additional MR methods were used to check for bias in the IVW estimate, arising through violation of MR assumptions. The results were compared to analyses employing a genetic instrument of coffee consumption, because coffee consumption has been previously inversely linked to PD.\n\nFindingsBeing a \"morning person\" was causally linked with risk of PD (OR 1*27; 95% confidence interval 1*06-1*51; p=0*012). Sensitivity analyses did not suggest that invalid instruments were biasing the effect estimate and there was no evidence for a reverse causal relationship between liability for PD and chronotype. There was no robust evidence for a causal effect of high coffee consumption using IV analysis, but the effect was imprecisely estimated (OR 1*12; 95% CI 0*89-1*42; p=0*22).\n\nInterpretationWe observed causal evidence to support the notion that being a \"morning person\", a phenotype driven by the circadian clock, is associated with a higher risk of PD. Further work on the mechanisms is warranted and may lead to novel therapeutic targets.\n\nFundingNo specific funding source.

epidemiology↗

bioRxiv2018

The Parkinson’s Phenome: Traits Associated with Parkinson’s Disease in a Large and Deeply Phenotyped Cohort

BackgroundObservational studies have begun to characterize the wide spectrum of phenotypes associated with Parkinsons disease (PD), but recruiting large numbers of PD cases and assaying a diversity of phenotypes has often been difficult. Here, we set out to systematically describe the PD phenome using a cross-sectional case-control design in a large database.\n\nMethodsWe analyzed the association between PD and 840 phenotypes derived from online surveys. For each phenotype, we ran a logistic regression using an average of 5,141 PD cases and 65,459 age- and sex-matched controls. We selected uncorrelated phenotypes, determined statistical significance after correcting for multiple testing, and systematically assessed the novelty of each significant association. We tested whether significant phenotypes were also associated with disease duration in PD cases.\n\nFindingsPD diagnosis was associated with 149 independent phenotypes. We replicated 32 known associations and discovered 49 associations that have not previously been reported. We found that migraine, obsessive-compulsive disorder, seasonal allergies, and anemia were associated with PD, but were not significantly associated with PD duration, and tend to occur decades before the average age of diagnosis for PD. Further work is needed to determine whether these phenotypes are PD risk factors or whether they share common disease mechanisms.\n\nInterpretationWe used a systematic approach in a single large dataset to assess the spectrum of traits that were associated with PD. Some of these traits may be risk factors for PD, features of the pre-diagnostic phase of disease, or manifestations of PD pathology. The model outputs from all 840 logistic regressions are available to the research community and may be used to generate hypotheses regarding PD etiology.\n\nFundingThe Michael J. Fox Foundation, Parkinsons UK, Barts Charity, National Institute on Aging, and 23andMe, Inc.\n\nResearch in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe used PubMed to perform a MEDLINE database search for review articles published up to January 21st, 2018 that contained the keywords \"Parkinson\" and \"epidemiology\" in the title or abstract. We performed additional MEDLINE searches for each phenotype that was significantly associated with PD. Although dozens of phenotypes have been tested for an association with PD, only a few associations have been consistently repeatable (e.g. pesticide exposure, coffee consumption).\n\nAdded value of this studyWe systematically tested for an association between PD and 840 phenotypes using up to 13,546 cases and 1{middle dot}3 million controls, making this one of the largest PD epidemiology studies ever conducted. We discovered 49 novel associations that will need to be replicated or validated. We found 44 associations for phenotypes that have previously been studied in relation to PD, but for which an association has not been consistently demonstrated.\n\nImplications of all the available evidenceTaken together with results from previous studies, this series of case-control analyses adds evidence for associations between PD and many phenotypes that are not currently thought to be part of the canonical PD phenome. This work paves the way for future studies to assess whether any of these phenotypes represent PD risk factors and whether any of these risk factors are modifiable.

epidemiology↗