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Genome-wide association studies identify genetic determinants of synucleinopathy biomarkers.

This genome-wide association study identifies both established and novel genetic variants that significantly modulate cerebrospinal fluid, plasma, and urine biomarkers in α-synucleinopathies, highlighting the critical need to account for genetic background when interpreting these biomarkers for diagnosis and therapeutic development.

Original authors: Emma N. Somerville, Lang Liu, Michael Ta, Hirotaka Iwaki, Konstantin Senkevich, Roy N. Alcalay, Ziv Gan-Or

Published 2026-07-08

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CC BY 4.0

Original authors: Emma N. Somerville, Lang Liu, Michael Ta, Hirotaka Iwaki, Konstantin Senkevich, Roy N. Alcalay, Ziv Gan-Or

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine your body is a massive, bustling city. In this city, there are specific "messengers" (biomarkers) that float through the streets (your blood, urine, and spinal fluid) carrying news about what's happening inside the buildings (your cells). For diseases like Parkinson's, doctors have been trying to find the right messengers to tell them when the city is starting to break down, so they can fix it early.

However, there's a problem: the messages these messengers carry aren't always the same for everyone. Sometimes, the message is loud; sometimes, it's quiet. This paper is like a detective story that tries to figure out why these messages vary so much. The detectives (the researchers) discovered that the "genetic blueprint" you were born with acts like a volume knob, turning these messengers up or down, regardless of whether you are sick or healthy.

Here is the breakdown of their findings using simple analogies:

1. The "Volume Knobs" of the City

The researchers looked at 63 different messengers in 581 people. They wanted to see if specific genetic switches (DNA variants) were controlling the volume of these messengers. They found that for many messengers, your genes are the main reason they are high or low.

2. The "LRRK2" and "GBA1" Switches (The Urine Messengers)

Think of BMPs (a type of messenger found in urine) as a specific type of street sign.

The LRRK2 Switch: The researchers found that if you have a specific genetic variation called LRRK2 p.G2019S, it acts like a volume knob turned up. It makes these urine signs much louder (higher levels).
The GBA1 Switch: Conversely, if you have a variation called GBA1 p.N370S, it acts like a volume knob turned down, making the signs quieter (lower levels).
The Twist: The study found that while your genes control how loud these signs are, the signs themselves don't necessarily tell you if you have Parkinson's. A person with the "Loud" gene might be healthy, and a person with the "Quiet" gene might be sick. This means these specific urine signs are better at telling you about your genetic family tree than they are at diagnosing the disease itself.

3. The "APOE" Switch (The Brain's Cleanup Crew)

The researchers looked at Amyloid Beta (Aβ), a messenger in the spinal fluid that is famous for being linked to Alzheimer's.

They found the APOE gene (specifically the ε4 version) is the master volume knob for this messenger.
If you have this version, the "Amyloid" signal in your spinal fluid gets turned down. The researchers explain this is likely because the Amyloid is getting stuck in clumps (plaques) in the brain, so there is less of it floating freely in the fluid. This confirms what we already knew: APOE is the boss of Amyloid regulation.

4. New Discoveries: The "Mystery Switches"

The paper also found some new, previously unknown switches that control other messengers:

The Ceramide Switch: They found that genes called MCF2L2 and GMNN control levels of "ceramides" (a type of fat messenger) in the spinal fluid. Think of these as new, unexplored traffic lights in the city that regulate how fats move around.
The Tau Switch: They found a gene called TP63 that controls "Tau" (a protein involved in cell structure). This is a new discovery; nobody knew this gene was a volume knob for Tau before. It's like finding a new remote control for a TV you didn't know existed.

5. Why This Matters (The "Noise" Problem)

The main lesson of this paper is about noise.
Imagine you are trying to listen to a faint radio signal (the disease) in a room full of loud music (genetic variation). If you don't know that the music is being played by a specific DJ (your genes), you might think the music is the signal, or you might think the signal is gone when it's just being drowned out.

The authors conclude that when scientists or doctors look at these biomarkers to diagnose disease or test new drugs, they must account for these genetic volume knobs. If they don't, they might get confused by the genetic "noise" and miss the real story of the disease.

In short: Your DNA sets the baseline volume for many biological messengers. To accurately hear the "disease signal," we first need to understand and adjust for the "genetic volume" that each person is born with.

Technical Summary: Genome-wide association studies identify genetic determinants of synucleinopathy biomarkers

Problem Statement
$\alpha$ -Synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), present significant diagnostic and therapeutic challenges due to clinical and biological heterogeneity. While biomarkers such as $\alpha$ -synuclein seed amplification assays (SAAs), amyloid- $\beta$ (A $\beta$ ), tau, and lysosomal lipids are critical for early diagnosis, patient stratification, and clinical trial enrichment, their utility is complicated by genetic modifiers. Genetic variation is known to influence biomarker levels, potentially biasing interpretation in research and clinical settings. For instance, specific genetic carriers (e.g., PRKN or LRRK2) may exhibit altered biomarker profiles independent of disease state. There is a need to systematically identify common genetic variants that modulate these biomarker levels to ensure accurate interpretation and improve translational relevance.

Methodology
The study utilized data from the Parkinson's Progression Markers Initiative (PPMI) cohort, comprising 581 individuals of European ancestry, including sporadic PD cases, healthy controls, SWEDD (scans without evidence of dopaminergic deficit) individuals, and prodromal individuals.

Biomarker Analysis: 63 candidate biomarkers were analyzed across cerebrospinal fluid (CSF), plasma, and urine. These included $\alpha$ -synuclein, A $\beta$ , tau isoforms, neurofilament light chain (NfL), and various lipid metabolites (bis(monoacylglycerol)phosphate [BMP] isoforms, ceramides, lactosylceramides, and sphingomyelins).
Genetic Analysis: Genome-wide association studies (GWAS) were performed for 59 biomarkers after quality control and exclusion of markers with inflated lambda statistics. The analysis utilized two genotyping datasets (NeuroX and OmniExpress Exome+) which were imputed using the TOPMed reference panel.
Statistical Approach: Linear regression was employed to test associations between common variants and biomarker levels, adjusting for age, sex, disease status, and the top 5 principal components. Meta-analyses were conducted for biomarkers measured across both genotyping arrays. Conditional and joint analyses (GCTA-COJO) were used to identify independent signals. Additionally, logistic regressions and Area Under the Curve (AUC) analyses were performed to assess the predictive power of biomarkers for PD status.
Replication: Selected findings were validated in an external cohort from Columbia University.

Key Contributions and Results
The study identified both established and novel genetic modulators of synucleinopathy biomarkers:

Urine BMP Isoforms and LRRK2/GBA1:
- Strong associations were confirmed between urine BMP isoforms (specifically 2,2'-di-22:6, total di-18:1, and total di-22:6) and the LRRK2 p.G2019S variant (positive association) and the GBA1 p.N370S variant (negative association).
- These associations were replicated in an external cohort.
- Crucially, while BMP levels are driven by these genotypes, the study found no significant difference in BMP levels between PD cases and controls within specific genetic carrier groups. This suggests BMPs function better as indicators of the LRRK2 pathway rather than as standalone diagnostic biomarkers for PD.
CSF A $\beta$ and APOE:
- A strong negative association was identified between CSF A $\beta$ levels and the APOE $\epsilon$ 4 allele (rs429358), reinforcing the central role of APOE in amyloid regulation. This signal drove associations observed in A $\beta$ /tau ratios as well.
CSF Ceramides and Novel Loci:
- Novel associations were detected between CSF ceramide isoforms (C22, C23, C24) and the MCF2L2 locus. Notably, MCF2L2 is part of a known PD-risk locus (MCCC1;LAMP3;MCF2L2), though the lead variant in this study was not in strong linkage disequilibrium with the primary PD GWAS signal.
- The GMNN locus was also associated with CSF ceramide levels, representing a previously unreported link to $\alpha$ -synucleinopathy biomarkers.
CSF Tau and TP63:
- A significant negative association was found between CSF total tau (t-tau) levels and an intergenic locus near TP63. A similar trend was observed for phosphorylated tau (p-tau). This region had not been previously implicated in tau biology.
PD and DLB Risk Loci and Lysosomal Lipids:
- Multiple PD risk loci were associated with lysosomal lipid biomarkers, indicating pathway-level convergence:
  - MAPT locus: Associated with increased plasma lactosylceramide (GL2) levels.
  - SIPA1L2: Associated with increased plasma GL2 C23.
  - MCCC1/LAMP3: Associated with plasma sphingomyelin (SM) levels.
  - RAB29: Associated with plasma ceramide levels.
- In DLB analysis, the APOE variant was associated with decreased CSF A $\beta$ and A $\beta$ /tau ratios.

Significance and Claims
The paper concludes that genetic background significantly shapes the levels of potential $\alpha$ -synucleinopathy biomarkers. The authors assert that these genetic influences must be accounted for in biomarker-based research, clinical trials, and therapeutic development to prevent misinterpretation of data and to improve patient stratification.

Specifically, the study highlights that:

Biomarkers like urine BMPs are robustly driven by LRRK2 and GBA1 genotypes but may not reflect disease state differences, necessitating a shift in how they are utilized (as pathway indicators rather than diagnostic tools).
Novel loci such as TP63 and GMNN may represent previously unrecognized biological pathways relevant to neurodegeneration.
The integration of genetic context is essential for developing robust, reliable, and actionable biomarkers, particularly as precision medicine approaches for PD and DLB advance.

The authors acknowledge limitations, including the restriction to individuals of European ancestry, the cross-sectional nature of the data (precluding causal inference), and the limited sample size for replicating rare variant associations. They emphasize the need for larger, diverse cohorts with comprehensive genetic and biomarker data to validate and refine these findings.