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

This is one of the first collaborative endeavours between Karl Heilbron (23andMe) and I... and I hope one of many. I have to say Karl did the bulk of the leg work and I was grateful for the opportunity to help pull it all together...

These data from 23andMe offer unparalleled opportunity to explore reported associations with Parkinson's. Using a rigorous pipeline for selecting phenotypic traits (self reported health conditions, dietary preferences, non-motor symptoms etc) we were able to produce an incredibly detailed exploration of the Parkinson's phenome (the sum total of Parkinson's associated traits). One of the fascinating things about the results is that many of the traits are instantly recognisable to those of us that see patients with Parkinson's regularly... this in turn increases our confidence about the novel associations that we observed and which offer further avenue for further research.

Additionally, where possible each trait was tested to if it was associated with disease duration, thereby suggesting that it might be a manifestation of disease rather than a risk factor or prodromal feature. Of course cross-sectional data has limitations when it comes to understanding cause and effect, but for the clinical neurologist seeing patients with PD this report of co-existing symptoms, behaviours and related health conditions could have huge practical use. The plan is to make all analyses available in order to stimulate further research in this area...

- Alastair Noyce

Karl Heilbron, Alastair Noyce, Pierre Fontanillas, Babak Alipanahi, The 23andMe Research Team, Mike Nalls, Paul Cannon

doi: https://doi.org/10.1101/270934

https://www.biorxiv.org/content/early/2018/02/28/270934

Background
Observational studies have begun to characterize the wide spectrum of phenotypes associated with Parkinson's 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.

Methods
We 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.

Findings
PD 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.

Interpretation
We 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.

Funding
The Michael J. Fox Foundation, Parkinson's UK, Barts Charity, National Institute on Aging, and 23andMe, Inc.