Genetic architecture of plasma Alzheimer disease biomarkers

Abstract

Genome-wide association studies (GWAS) of cerebrospinal fluid (CSF) Alzheimer's Disease (AD) biomarker levels have identified novel genes implicated in disease risk, onset and progression. However, lumbar punctures have limited availability and may be perceived as invasive. Blood collection is readily available and well accepted, but it is not clear whether plasma biomarkers will be informative for genetic studies. Here we perform genetic analyses on concentrations of plasma amyloid-β peptides Aβ40 (n = 1,467) and Aβ42 (n = 1,484), Aβ42/40 (n = 1467) total tau (n = 504), tau phosphorylated (p-tau181; n = 1079) and neurofilament light (NfL; n = 2,058). GWAS and gene-based analysis was used to identify single variant and genes associated with plasma levels. Finally, polygenic risk score and summary statistics were used to investigate overlapping genetic architecture between plasma biomarkers, CSF biomarkers and AD risk. We found a total of six genome-wide significant signals. APOE was associated with plasma Aβ42, Aβ42/40, tau, p-tau181 and NfL. We proposed 10 candidate functional genes on the basis of 12 single nucleotide polymorphism-biomarker pairs and brain differential gene expression analysis. We found a significant genetic overlap between CSF and plasma biomarkers. We also demonstrate that it is possible to improve the specificity and sensitivity of these biomarkers, when genetic variants regulating protein levels are included in the model. This current study using plasma biomarker levels as quantitative traits can be critical to identification of novel genes that impact AD and more accurate interpretation of plasma biomarker levels.

Figure 1

Study Design. The project follows the figure above top-to-bottom, left-to-right order. We normalize phenotype by log10, z-score transformation, subset baseline records and harmonize the three cohort tables. Next, we perform subject-level QC by removing individuals with no plasma or GWAS data, are related/duplicates, and are not of European ancestry. We perform single variant analysis using Plink v2.0. Finally, we perform post-GWAS analyses including functional annotation, gene-set analysis, modified prediction models and overlap with other phenotypes.

Figure 2

Manhattan plots from SVA. Manhattan plots of SVA results for Aβ40 (A), Aβ42 (B), tau (C), p-tau181 (D) and NfL (E). The red line represents the genome-wide significance threshold of 5 × 10⁻⁰⁸ and the blue line represents the suggestive threshold of 1 × 10⁻⁰⁵.

Figure 3

ROC plots from SNP-adjusted prediction models. ROC plots for Aβ40 (A), Aβ42 (B), tau (C), p-tau181 (D) and NfL (E). The default model in each plot is indicated by a solid black line. Each SNP adjusted model is represented by a dashed line and color, shown in the legend to the right of each plot. AUCs and P-values are summarized in Table 3.