A comprehensive analysis of SNCA-related genetic risk in sporadic parkinson disease

Abstract

Objective: The goal of this study was to refine our understanding of disease risk attributable to common genetic variation in SNCA, a major locus in Parkinson disease, with potential implications for clinical trials targeting α-synuclein. We aimed to dissect the multiple independent association signals, stratify individuals by SNCA-specific risk profiles, and explore expression quantitative trait loci.

Methods: We analyzed participant-level data from 12,503 patients and 12,502 controls, optimizing a risk model and assessing SNCA-specific risk scores and haplotypes as predictors of individual risk. We also explored hypotheses about functional mechanisms and correlated risk variants to gene expression in human brain and protein levels in cerebrospinal fluid.

Results: We report and replicate a novel, third independent association signal at genome-wide significance level downstream of SNCA (rs2870004, p = 3.0*10^-8 , odds ratio [OR] = 0.88, 95% confidence interval [CI] = 0.84-0.92). SNCA risk score stratification showed a 2-fold difference in disease susceptibility between top and bottom quintiles (OR = 1.99, 95% CI = 1.78-2.23). Contrary to previous reports, we provide evidence supporting top variant rs356182 as functional in itself and associated with a specific SNCA 5' untranslated region transcript isoform in frontal cortex.

Interpretation: The SNCA locus harbors a minimum of 3 independent association signals for Parkinson disease. We demonstrate a fine-grained stratification of α-synuclein-related genetic burden in individual patients of potential future clinical relevance. Further efforts to pinpoint the functional mechanisms are warranted, including studies of the likely causal top variant rs356182 and its role in regulating levels of specific SNCA mRNA transcript variants. Ann Neurol 2018;83:117-129.

FIGURE 1:

The SNCA locus with association results and linkage disequilibrium. The top and bottom left panels show regional Manhattan plots for 3 rounds of stepwise conditional analysis in the discovery data and degree of linkage disequilibrium (LD) with the lead single nucleotide polymorphism (SNP). Note the lack of SNPs in high LD with rs356182 and the different scale of the y-axis across plots. At the bottom right, the pattern of LD between these SNPs as well as the 2 proposed functional variants rs356165 and rs356168 is illustrated as an LD plot. The numbers represent r² values, and darker shade represents higher D′. The figure was made using LocusZoom (locuszoom.org) and Haploview (www.broadinstitute.org/haploview). [Color figure can be viewed at www.annalsofneurology.org]

FIGURE 2:

Transcript variants at the 5′ end of SNCA and genomic context of rs356182. (A) Figure zooming in on the 5′ end of the SNCA gene, showing variable 5′ untranslated region (UTR) corresponding to the 3 individual RefSeq transcript variants investigated in expression quantitative trait locus analysis. Translation starts at the ATG codon 26 base pairs into exon 2. (B) Illustration showing the chromosomal location of rs356182 downstream of SNCA. Note the 3′ end of the SNCA gene at top right. The variant is located within a putative brain-specific regulatory element identified by H3K27Ac chromatin immunoprecipitation sequencing in human brain, showing no similar signal in 7 cell lines (Layered H3K27Ac) from ENCODE. Furthermore, rs356182 falls within an open chromatin region differentially accessible across neuronal and nonneuronal cells, and overlaps a conserved predicted transcription factor binding site (TFBS) from the Transfac database. [Color figure can be viewed at www.annalsofneurology.org]

FIGURE 3:

Increasing susceptibility across SNCA risk score quintiles. The figure shows odds ratios of risk quintiles incorporating the independent effect of 3 significant association signals at the SNCA locus in a combined score, assessed by logistic regression. Odds ratios are shown relative to the first quintile. Error bars represent 95% confidence intervals. Note that score performance was assessed in the replication dataset, which is independent from the discovery data that were used to generate the allele weights for the scoring algorithm.

FIGURE 4:

Expression of NM_001146054 by number of rs356182 risk alleles. Note the increased expression of NM_001146054 for each rs356182 risk allele across all 3 datasets. For comparison of effect sizes across gene expression analysis methods, mRNA expression levels have been converted to z scores. CAGE = cap analysis gene expression; PCR = polymerase chain reaction. [Color figure can be viewed at www.annalsofneurology.org]