Common and rare variants in the LRRK2 locus are associated with Parkinsons disease (PD) risk, but the downstream effects of these variants on protein levels remains unknown. We performed comprehensive proteogenomic analyses using the largest aptamer-based CSF proteomics study to date (7,006 aptamers (6,138 unique proteins) in 3,107 individuals). We identified eleven independent SNPs in the LRRK2 locus associated with the levels of 26 proteins as well as PD risk. Of these, only eleven proteins have been previously associated with PD risk (e.g., GRN or GPNMB). Proteome-wide association study (PWAS) analyses suggested that the levels of ten of those proteins were genetically correlated with PD risk and seven were validated in the PPMI cohort. Mendelian randomization analyses identified five proteins (GPNMB, GRN, HLA-DQA2, LCT, and CD68) causal for PD and nominate one more (ITGB2). These 26 proteins were enriched for microglia-specific proteins and trafficking pathways (both lysosome and intracellular). This study not only demonstrates that protein phenome-wide association studies (PheWAS) and trans-protein quantitative trail loci (pQTL) analyses are powerful for identifying novel protein interactions in an unbiased manner, but also that LRRK2 is linked with the regulation of PD-associated proteins that are enriched in microglial cells and specific lysosomal pathways.
INTRODUCTION In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta, Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers which leads to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. METHODS We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS result using this method with currently accepted methods. We also used a generalized mixture modelling to calculate the threshold for biomarker-positivity. RESULTS Z-scores method performed as well as meta-analysis and did not lead to any spurious results. Cutoffs calculated with this approach were found to be very similar to those reported previously. DISCUSSION This approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.
Background: Most existing protein quantitative trait locus (pQTL) studies focus on a single tissue and use the reference Genome Reference Consortium Human Build 37 (GRCh37/hg19) published in 2009. GRCh38/hg38 was updated in 2013 with correcting thousands of sequencing artifacts that cause false SNPs and updating non-nuclear genomic sequence. Many papers strongly recommend switching to GRCh38/hg38. The goal of this study is to use multiple tissues (brain, CSF and plasma) to explore the genetic architecture of protein levels in neurologically relevant tissues and to update the human reference genome as hg38 to compare the findings. Method:In this study, 805 CSF samples and 869 proteins, 529 plasma samples and 953 proteins and 378 brain samples and 1,300 proteins passed quality control process.We performed genome-wide association analyses of over 8 million autosomal variants (MAF ≥ 0.01) imputed using the most updated hg38 TOPMed imputation panel to identify cis/trans pQTLs. We performed conditional analysis to identify independent pQTLs. After removing pleiotropic loci and including Alzheimer's disease (AD) variants, Mendelian randomization (MR) was applied to detect the casual associations between proteins and AD risk. To decrease unmeasured pleiotropy effect, we used colocalization analysis to get more supporting information among multiple traits. Finally, we replicated our findings in much larger studies. Result:In brain tissue, we identified 4269 pQTLs in hg38 compared to 2418 pQTLs in hg19 with a threshold of 5 × 10 −8 for cis-pQTLs and 5 × 10 −8 /(number of independent proteins) for trans-pQTLs. We found the independent SNPs and highlight the complexity of regions with multiple independent local pQTLs. Analysis for CSF and plasma is underway. Conclusion:This study is to apply the hg38 as the human reference genome to detect much more pQTLs and correct possible false findings in the previous hg19 study. With the following analysis, we could get more valuable and confirmed information about the additional GWAS loci and identify the function of certain proteins on the disease risk. The multiple tissue samples and multiple traits could help us detect the complex genetic architecture of protein levels in neurologically relevant tissues.
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