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Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders

Abstract

Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. In this study, we generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer’s disease. We identified 274, 127 and 32 protein quantitative trait loci (pQTLs) for cerebrospinal fluid, plasma and brain, respectively. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer’s disease, Parkinson’s disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases.

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Fig. 1: Study design and overview of the significant pQTLs within each tissue.
Fig. 2: Identification of conditionally independent local pQTLs.
Fig. 3: Overview of the replication of the pQTLs and identification of pleiotropic regions within each tissue.
Fig. 4: Summary of the tissue-specificity analyses and co-localization of pQTLs with other molecular QTLs.
Fig. 5: MR-identified proteins implicated on seven neurological traits.

Data availability

Both summary statistics and individual-level data have been uploaded to the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site repository at https://www.niagads.org/datasets/ng00102 for the three tissues from the Knight ADRC dataset for discovery. Summary statistics (pQTL) data are freely available; as the data exceeds 500 Gb, please email niagads@pennmedicine.upenn.edu to set up an FTP transfer of the data. Summary association results can also be explored through Online Neurodegenerative Trait Integrative Multi-Omics Explorer (ONTIME) (https://ontime.wustl.edu/), a PheWeb (v1.1.14)-based browser.

CSF-Sasayama2017 dataset for replication: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83711.

Plasma-AddNeuroMed dataset for replication: https://www.synapse.org/#!Synapse:syn4988768.

Drug targets were queried using DrugBank database collected via UniProtKB (as of 3 January 2020) at https://www.uniprot.org/database/DB-0019.

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Acknowledgements

We thank all the participants and their families as well as the many involved institutions and their staff. Funding: This work was supported by grants from the National Institutes of Health (NIH) (R01AG044546 (C.C.), P01AG003991 (C.C. and J.C.M.), RF1AG053303 (C.C.), RF1AG058501 (C.C.), U01AG058922 (C.C.), R01NS118146 (B.A.B.) and R01AG057777 (O.H.)) and the Alzheimer Association (NIRG-11-200110 (C.C.), BAND-14-338165 (C.C.), AARG-16-441560 (C.C.) and BFG-15-362540 (C.C.)). This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders and the Departments of Neurology and Psychiatry at Washington University School of Medicine. The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50AG05681 (J.C.M.), P01AG03991 (J.C.M.) and P01AG026276 (J.C.M.).

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Authors

Contributions

C.Y. performed the analyses, interpreted the results and wrote the manuscript. F.H.G.F., L.I., M.V.F., F.W., J.L.B., Z.L., U.D., Y.S., K.M. and J.P.B. contributed to data collection, data processing, quality control and cleaning. J.C.M., A.M.F. and R.J.P. contributed samples and/or data. B.S. wrote the manuscript. J.A.B., B.E. and O.H. developed the PheWeb browser. B.A.B. interpreted the results. H.R., O.H. and C.C. designed the study, collected the data, supervised the analyses, interpreted the results and wrote the manuscript. C.Y., A.S. and C.C. addressed the comments from peer review and updated the manuscript. All authors read and contributed to the final manuscript.

Corresponding author

Correspondence to Carlos Cruchaga.

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Competing interests

C.C. receives research support from Biogen, EISAI, Alector and Parabon. C.C. is a member of the advisory board of Vivid Genomics, Halia Therapeutics and ADx Healthcare. The remaining authors declare no competing financial interests.

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Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 QC pipeline.

QC on both proteins (a to c) and samples (d) were described as follows: a, Flowchart of CSF protein level QC, starting from 1305; after step-1, Limit Of Detection VS 2-StDeviation, 807 proteins were kept with a pass-rate > = 85%; after step-2, given Max Difference of Scale Factor < 0.5, 749 proteins were kept; after step-3, given Coefficient of Variation (of calibrator) < 0.15 & step-4, given IQR, sum(outliers) < 15%, 746 proteins were kept. After step-5, 713 proteins that shared by < 30 samples (shared by ~80% of the subject outliers) were kept. b, Flowchart of plasma protein level QC, starting from 1305; after step-1, 1301 proteins were kept with a pass-rate > = 85%; after step-2, 956 proteins were kept; after step-3 & step-4, 955 proteins were kept. After step-5, 931 proteins that shared by < 10 samples were kept. c, Flowchart of brain protein level QC, starting from 1305; after step-1, 1109 proteins were kept with a pass-rate > = 85%; after step-2, 1107 proteins were kept; after step-3 & step-4, given IQR, sum(outliers) < 15%, 1106 proteins were kept. After step-5, 1079 proteins that shared by < 21 samples were kept. d, Table of sample size after each step of QC in genotype and proteomics. Within each tissue (1st column), we profiled proteomics from 1300 CSF, 648 plasma and 459 samples (2nd column). From unique donors in proteomics data (3rd column), we first kept donors with genotyping array data (4th column). We next kept only the donors with a European ancestry after checking principal components (5th column). Moreover, we kept donors that were not close with each other (PI_HAT < 0.05) after checking identity by descent (6th column). Finally, the samples remained only passing both the genotype and protein data QC (7th column).

Extended Data Fig. 2 Reproducibility of proteomic data.

a, Table of total sample size for each tissue before and after QC, including the biological and technical replicates. b, Venn diagram on the designed donor overlap across tissues. c, Scatterplot of 321 subjects with both longitudinal and baseline samples from CSF indicates a Pearson correlation coefficient of 0.995 (95% confidence interval from 0.995 to 0.995). d, Scatterplot of 11 subjects with both fasted and nonfasted samples from plasma indicates a Pearson correlation coefficient of 0.907 (95% confidence interval from 0.904 to 0.911). e, Scatterplot of one subject with both longitudinal and baseline samples from plasma indicates a Pearson correlation coefficient of 0.938 (95% confidence interval from 0.930 to 0.945). f, Scatterplot of one subject with two technical replicates from brain indicates a Pearson correlation coefficient of 0.976 (95% confidence interval from 0.976 to 0.981). All statistical tests used were two-sided from (c) to (f).

Extended Data Fig. 3 Overview of the sample size and number of pQTLs from pQTL studies mentioned in this paper and the summary statistics from the meta-analyses.

a, Scatter plot of sample size (log10-scaled) and number of total pQTLs after clumping or unique proteins when no clumping was performed (log10-scaled). Dot color represents the tissue type; dot size represents total number of proteins profiled. b, Table of these nine datasets listed the exact numbers for drawing the scatter plot. c, Table of three different combinations of meta-analyses: 2) meta2_WUcsf_PPMI19_JP17: meta-analysis on all three CSF studies by Sasayama and colleagues published in 2017, by PPMI released in 2019, and by Washington University cohort (this study); 3) meta3_WUcsf_WUplasma_WUbrain: meta-analysis on all three-tissue findings from CSF, plasma and brain respectively by Washington University cohort (this study); 4) meta4_ WUcsf_WUplasma_WUbrain_ PPMI19_JP17: meta-analysis on both the CSF studies by Sasayama and colleagues published in 2017 and by PPMI released in 2019 plus all three-tissue findings from CSF, plasma and brain respectively by Washington University cohort (this study). The columns include number of proteins in common, number of protein-level GWAS hits after meta-analysis, number of protein-level GWAS hits before meta-analysis using only the common proteins within each tissue for each combination. d, Stacked Manhattan plots for all three different combinations of meta-analyses. The darkred line represents P = 5 × 10-8.

Extended Data Fig. 4 Disease stratified analysis on comparing pQTLs effect size.

To investigate of disease status effect on pQTLs, we performed linear regression on the same protein-loci pairs (before conditioning on top variants) identified from above default model using three additional models: a, joint analysis but with disease status as another covariate (CO vs non-CO). Pearson correlation coefficient was 0.999 (p-value < 2.2 × 10-16, 95%CI = 0.999 to 0.999), 0.999 (p-value = 4.3 × 10-202, 95%CI = 0.999 to 0.999), 0.999 (p-value = 9.5 × 10-52, 95%CI = 0.999 to 0.999) for CSF, plasma, and brain respectively. Sample size for this joint analysis was 835, 529, and 380 for CSF, plasma, and brain respectively. b, AD case (CA) only using the same covariates as default model. Pearson correlation coefficient of 0.991 (p-value = 3.9 × 10-160, 95%CI = 0.988 to 0.993), 0.989 (p-value = 1.8 × 10-83, 95%CI = 0.983 to 0.992), 0.998 (p-value = 2.4 × 10-29, 95%CI = 0.995 to 0.999) for CSF, plasma, and brain respectively. Sample size for this AD case (CA) only analysis was 217, 168, and 248 for CSF, plasma, and brain respectively. c, Cognitive unimpaired (CO) only using the same covariates as default model. Pearson correlation coefficient of 0.999 (p-value = 5.2 × 10-234, 95%CI = 0.998 to 0.999), 0.998 (p-value = 1.17 × 10-122, 95%CI = 0.997 to 0.999), 0.602 (p-value = 0.002, 95%CI = 0.262 to 0.809) for CSF, plasma, and brain respectively. Sample size for this cognitive unimpaired (CO) only analysis was 614, 357, and 24 for CSF, plasma, and brain respectively. The relatively low correlation in default model comparison with control only in brain samples was due to much smaller sample size as a control for brain samples. All statistical tests used were two-sided from (a) to (c).

Extended Data Fig. 5 Global view of pleiotropic regions in CSF.

In total, 59 Pleiotropic regions passing genome-wide significance threshold (5 × 10-8) in CSF (sample size = 835). Unique non-overlapping regions associated with a given SOMAmer were first defined as 1-Mb region upstream and downstream of each significant variant for that SOMAmer. Within the region (2 Mb) containing the variant with the smallest P value, any overlapping regions were then merged into the same locus. Next, an LD-based clumping approach was adapted to identify whether a region was associated with multiple SOMAmers. Variants were combined into a single region per LD (EUR) defined loci. Any loci associated with more than one protein were identified as pleiotropic regions. Genomic locations of pQTLs were visualized by a squared-Manhattan plot. Dark-green represents cis-pQTLs; gold represents trans-pQTLs. X-axis indicates the positions of the top variant; and Y-axes indicates the gene encoding the protein. All pleiotropic genomic regions are annotated at the top of each plot along the X-axis.

Extended Data Fig. 6 Global view of pleiotropic regions in plasma.

In total, 34 pleiotropic regions passing genome-wide significance threshold (5 × 10-8) in plasma (sample size = 529). Genomic locations of pQTLs were visualized by a squared-Manhattan plot, same as Extended Data Fig. 5.

Extended Data Fig. 7 Global view of pleiotropic regions in brain.

In total, 10 pleiotropic regions passing genome-wide significance threshold (5 × 10-8) in brain (sample size = 380). Genomic locations of pQTLs were visualized by a squared-Manhattan plot, same as Extended Data Fig. 5.

Extended Data Fig. 8 Tissue specificity exploration with permissive thresholds.

To determine whether our tissue-specificity results were biased by statistical power, we performed similar analyses with two more permissive p-values on the 411 proteins. a, Venn diagrams of all pQTLs across all three tissues by fixing genome-wide significance threshold (5 × 10-8) for all three tissues. b, Venn diagrams of all pQTLs across all three tissues by fixing genome-wide significance threshold for one tissue and 0.001 for the other two tissues. For example, when checking CSF pQTLs shared in plasma or brain, we chose 5 × 10-8 as threshold for CSF and 0.001 for plasma or brain. c, Venn diagrams of all pQTLs across all three tissues by fixing genome-wide significance threshold for one tissue and 0.05 for the other two tissues. For example, when checking CSF pQTLs shared in plasma or brain, we chose 5 × 10-8 as threshold for CSF and 0.05 for plasma or brain.

Extended Data Fig. 9 Tissue specificity exploration with plasma result from INTERVAL study.

To further demonstrate that tissue-specificity findings are not a product of different sample size, we performed similar comparisons by analyzing the plasma pQTLs from the INTERVAL study on 616 proteins that passed QC in our CSF, brain and plasma INTERVAL. a, Venn diagrams of proteins passing QC across all three tissues: CSF and brain results are from WashU cohort, plasma result is from INTERVAL study. b, Venn diagrams of all pQTLs across all three tissues by fixing genome-wide significance threshold (5 × 10-8) for all three tissues. c, Venn diagrams of all pQTLs across all three tissues by fixing genome-wide significance threshold for one tissue and 0.001 for the other two tissues. For example, when checking CSF pQTLs shared in plasma or brain, we chose 5 × 10-8 as threshold for CSF and 0.001 for plasma or brain. d, Venn diagrams of all pQTLs across all three tissues by fixing genome-wide significance threshold for one tissue and 0.05 for the other two tissues. For example, when checking CSF pQTLs shared in plasma or brain, we chose 5 × 10-8 as threshold for CSF and 0.05 for plasma or brain.

Extended Data Fig. 10 Properties of pQTLs.

a, Dot plots of -log10(P) from all significant associations (via linear regression) against the distance of sentinel SNPs from TSS within each tissue. b, Dot plots of absolute effect size associated with MAF within each tissue. c, Forest plot of enrichment on the predicted functional annotation classes of pQTLs versus null sets of variants from permutation within each tissue (Data are presented as mean values of Odds Ratio + /- 95% confidence interval from Fisher’s Exact Test) and Bar plots of the proportion of variants annotate in each class. (Note: Features on exonic_splicing/ncRNA_splicing/splicing/UTR5_UTR3 are not shown due to not all tissues have these features). d, Histograms of variance explained by conditionally independent variants within each tissue. For CSF, the mean = 0.141, standard deviation = 0.144, mode = 0.061; For plasma, the mean = 0.157, standard deviation = 0.125, mode = 0.188; For brain, the mean = 0.208, standard deviation = 0.151, mode = 0.092.

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Yang, C., Farias, F.H.G., Ibanez, L. et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00886-6

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