Cited by 193Genetic Associations and EpidemiologyGenomics and Rare DiseasesBioinformatics and Genomic Networks

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Rare variant associations with plasma protein levels in the UK Biobank

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Dhindsa, Ryan S., Burren, Oliver S., Sun, Benjamin B., Prins, Bram P., Matelska, Dorota, Wheeler, Eleanor, Mitchell, Jonathan, Oerton, Erin, Hristova, Ventzislava A., Smith, Katherine R., Carss, Keren, Wasilewski, Sebastian, Harper, Andrew R., Paul, Dirk S., Fabre, Margarete A., Runz, Heiko, Viollet, Coralie, Challis, Benjamin, Platt, Adam, AstraZeneca Genomics Initiative, Ågren, Rasmus, Anderson-Dring, Lauren, Atanur, Santosh, Baker, David, Barrett, Carl, Belvisi, Maria, Bohlooly-Y, Mohammad, Buvall, Lisa, Camacho, Niedzica, Cazares, Lisa, Cameron-Christie, Sophia, Chen, Morris, Cohen, Suzanne, Danielson, Regina F., Das, Shikta, Davis, Andrew, Deevi, Sri Vishnu Vardhan, Ding, Wei, Dougherty, Brian, Fairhurst-Hunter, Zammy, Garg, Manik, Georgi, Benjamin, Rangel, Carmen Guerrero, Haefliger, Carolina, Hammar, Mårten, Hanna, Richard N., Hansen, Pernille B. L., Harrow, Jennifer, Henry, Ian, Hess, Sonja, Hollis, Ben, Hu, Fengyuan, Jiang, Xiao, Kundu, Kousik, Lai, Zhongwu, Lal, Mark, Lassi, Glenda, Liang, Yupu, Lopes, Margarida, Lythgow, Kieren, MacArthur, Stewart, Maisuria-Armer, Meeta, March, Ruth, Martins, Carla, Megy, Karine, Menzies, Rob, Michaëlsson, Erik, Middleton, Fiona, Mowrey, Bill, Muthas, Daniel, Nag, Abhishek, O’Dell, Sean, Ohne, Yoichiro, Olsson, Henric, O’Neill, Amanda, Ostridge, Kristoffer, Pullman, Benjamin, Rae, William, Raies, Arwa, Reznichenko, Anna, Ros, Xavier Romero, Ryaboshapkina, Maria, Sanganee, Hitesh, Sidders, Ben, Snowden, Mike, Stankovic, Stasa, Stevens, Helen, Tachmazidou, Ioanna, Taiy, Haeyam, Tian, Lifeng, Underwood, Christina, Walentinsson, Anna, Wang, Qing-Dong, Zehir, Ahmet, Zou, Zoe, Vitsios, Dimitrios, Ashley, Euan A., Whelan, Christopher D., Pangalos, Menelas N., Wang, Quanli, Petrovski, Slavé·2023·Nature

Abstract

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets 1–4 . Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype–protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort 5 . We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene–protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2 , encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4 , identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2- CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Rare protein-coding variant association testing in UK Biobank plasma proteomics identified thousands of variant- and gene-level protein associations, most of which were not detected by common-variant GWAS. The resource links rare variation to plasma protein levels and demonstrates applications in allelic series, biomarker discovery, clonal haematopoiesis, and pQTL-augmented PheWAS.

Key findings

  • In 49,736 UK Biobank individuals with 2,923 plasma protein abundances, variant-level ExWAS found 151,491 significant genotype–protein associations, including 5,433 rare-variant associations (MAF ≤ 0.1%); 81% of rare associations were undetected in a previous GWAS of the same cohort. Gene-level collapsing analysis found 1,962 unique gene–protein associations, and of 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels.
  • Rare cis-CDS pQTLs had larger effects than common cis-CDS pQTLs (median |β| = 1.85 versus 0.3; Wilcoxon P < 10−300), and rare trans-CDS pQTLs had larger effects than common trans-CDS pQTLs (median |β| = 1.26 versus 0.08; Wilcoxon P < 10−300). Among 538 rare cis-CDS PTV pQTLs, 518 (96%) decreased protein abundance.
  • STAB1 and STAB2 emerged as pleiotropic loci with 77 and 41 protein associations, respectively; ASGR1 had 186 and GNPTAB had 37. GNPTAB trans-CDS pQTLs included 35 of 37 (95%) lysosomal proteins, all increased in PTV carriers.
  • Clonal haematopoiesis analyses found 36 trans associations with JAK2-CH, 15 with TET2-CH, 8 with ASXL1-CH, and 4 each with SF3B1-CH and SRSF2-CH. TET2-CH was associated with increased FLT3 (P = 3.4 × 10−16; β = 0.50) and decreased FLT3LG (P = 6.4 × 10−64; β = −0.97).

Methods

  • UKB-PPP plasma proteomics used the Olink Explore 3072 platform, measuring 2,923 unique proteins across 54,219 plasma samples; exome sequencing was available for 52,217 participants, and after quality control 49,736 multi-ancestry samples were analyzed, including 46,327 of European ancestry.
  • Variant-level ExWAS tested 617,073 variants against 2,923 plasma proteins using linear regression with covariates; gene-level collapsing analyses aggregated rare qualifying variants across 18,885 genes using nine non-synonymous models plus a synonymous negative-control model, with study-wide significance P ≤ 1 × 10−8.
  • Additional analyses included pan-ancestry collapsing, somatic CH variant calling with GATK Mutect2 in 15 recurrently mutated genes, and pQTL-augmented PheWAS models (`ptvolink`, `ptvolink2pcnt`) incorporating cis-CDS missense pQTLs with negative β.

Limitations

  • The paper states that potential epitope effects could confound cis-CDS pQTLs; one possible epitope effect was observed in GOT1, and antibody binding-site data were unavailable on request. It also states that putatively novel associations should be validated with orthogonal experiments and that the pQTL-informed collapsing approach is constrained by the UKB-PPP sample size used for pQTL discovery.
  • Evident from the text: the main analyses are predominantly European-ancestry (46,327 of 49,736; 93%), limiting generalizability, although a pan-ancestry analysis was also performed. The Olink platform measures 2,923 proteins rather than the full plasma proteome, and the UK Biobank cohort is middle-aged to older adults.

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Crossref · 10.1038/s41586-023-06547-x

Cites

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Figures

Fig. 1 ExWAS. a, Summary of significant (P ≤ 1 × 10−8) cis- and trans-pQTLs across the exome, limited to variants with MAF ≤ 0.1%. P values were generated via linear regression. If multiple variants in a gene were associated with the same protein, we displayed the most significant association for ease of visualization. The P values were not corrected for multiple testing; the study-wide significance threshold is P ≤ 1 × 10−8. b, Percentage of significant rare (MAF ≤ 0.1%) and common (MAF > 0.1%) ExWAS genotype–protein associations that were also significant in the UKB-PPP GWAS. c, The proportion of significant cis-CDS pQTLs per variant class across three MAF bins. ‘All tested variants’ refers to the total number of variants occurring in the genes corresponding to the proteins measured via the Olink platform that were included in the ExWAS. d, Effect sizes of significant rare pQTLs in each variant class. For all plots, if the same genotype–protein association was detected in multiple ExWAS models, we retained the association with the smallest P value.
Fig. 1 ExWAS. a, Summary of significant (P ≤ 1 × 10−8) cis- and trans-pQTLs across the exome, limited to variants with MAF ≤ 0.1%. P values were generated via linear regression. If multiple variants in a gene were associated with the same protein, we displayed the most significant association for ease of visualization. The P values were not corrected for multiple testing; the study-wide significance threshold is P ≤ 1 × 10−8. b, Percentage of significant rare (MAF ≤ 0.1%) and common (MAF > 0.1%) ExWAS genotype–protein associations that were also significant in the UKB-PPP GWAS. c, The proportion of significant cis-CDS pQTLs per variant class across three MAF bins. ‘All tested variants’ refers to the total number of variants occurring in the genes corresponding to the proteins measured via the Olink platform that were included in the ExWAS. d, Effect sizes of significant rare pQTLs in each variant class. For all plots, if the same genotype–protein association was detected in multiple ExWAS models, we retained the association with the smallest P value.CC BY · PubMed Central
Fig. 2 Gene-level collapsing analysis. a, Miami plot of 1,962 unique gene–protein abundance associations across nine collapsing models. We excluded the empirical null synonymous model. The y axis is capped at 60. If the same gene–protein association was detected in multiple QV models, we retained the association with the smallest P value. The four labelled loci indicate trans-CDS pQTL hotspots. P values were generated via linear regression and were not corrected for multiple testing; the study-wide significance threshold is P ≤ 1 × 10−8. b, The number of unique significant (P ≤ 1 × 10−8) protein abundance associations per gene across the collapsing models. c, The effect sizes of significant gene–protein associations in each collapsing model are stratified by cis versus trans effects.
Fig. 2 Gene-level collapsing analysis. a, Miami plot of 1,962 unique gene–protein abundance associations across nine collapsing models. We excluded the empirical null synonymous model. The y axis is capped at 60. If the same gene–protein association was detected in multiple QV models, we retained the association with the smallest P value. The four labelled loci indicate trans-CDS pQTL hotspots. P values were generated via linear regression and were not corrected for multiple testing; the study-wide significance threshold is P ≤ 1 × 10−8. b, The number of unique significant (P ≤ 1 × 10−8) protein abundance associations per gene across the collapsing models. c, The effect sizes of significant gene–protein associations in each collapsing model are stratified by cis versus trans effects.CC BY · PubMed Central
Fig. 3 CH trans-CDS pQTL associations. a–e, Significant (P ≤ 1 × 10−8) trans-CDS pQTLs associated with somatic mutations in JAK2 (a), TET2 (b), ASXL1 (c), SF3B1 (d) and SRSF2 (e). Red lines indicate positive betas and black lines indicate negative betas. Line width is proportional to the absolute beta. We plotted significant associations for each gene in any of the four CH collapsing models.
Fig. 3 CH trans-CDS pQTL associations. a–e, Significant (P ≤ 1 × 10−8) trans-CDS pQTLs associated with somatic mutations in JAK2 (a), TET2 (b), ASXL1 (c), SF3B1 (d) and SRSF2 (e). Red lines indicate positive betas and black lines indicate negative betas. Line width is proportional to the absolute beta. We plotted significant associations for each gene in any of the four CH collapsing models.CC BY · PubMed Central
Fig. 4 pQTL-informed collapsing analyses. a, Schematic representing the pQTL-informed collapsing framework. The purple diamonds represent missense pQTLs that would be included as QVs in the ptvolink model and ptvolink2pcnt model. PTVs, illustrated as X’s, are included in both models. b, The P values of gene-level associations for binary traits in which the P values improved in the ptvolink model compared with the ptv model. For comparison, we also include P values for the flexdmg model, which includes PTVs and rare (MAF < 0.1%) missense variants predicted to be damaging via REVEL (REVEL > 0.25), and the flexnonsyn model, which includes PTVs and missense variants without a REVEL cut-off. Only three genes that reached significance in the flexdmg model were not among the 25 genes significant across both ptvolink models. Of these, two were already captured by the standard ptv model. P values were generated via a two-tailed Fisher’s exact test and were not corrected for multiple testing. The dashed line indicates the study-wide significance threshold of P ≤ 1 × 10−8.
Fig. 4 pQTL-informed collapsing analyses. a, Schematic representing the pQTL-informed collapsing framework. The purple diamonds represent missense pQTLs that would be included as QVs in the ptvolink model and ptvolink2pcnt model. PTVs, illustrated as X’s, are included in both models. b, The P values of gene-level associations for binary traits in which the P values improved in the ptvolink model compared with the ptv model. For comparison, we also include P values for the flexdmg model, which includes PTVs and rare (MAF < 0.1%) missense variants predicted to be damaging via REVEL (REVEL > 0.25), and the flexnonsyn model, which includes PTVs and missense variants without a REVEL cut-off. Only three genes that reached significance in the flexdmg model were not among the 25 genes significant across both ptvolink models. Of these, two were already captured by the standard ptv model. P values were generated via a two-tailed Fisher’s exact test and were not corrected for multiple testing. The dashed line indicates the study-wide significance threshold of P ≤ 1 × 10−8.CC BY · PubMed Central
Extended Data Fig. 1 Study design. (a) Schematic depicting the overall study design and sample sizes for the variant-level ExWAS and the gene-level collapsing analyses. The number of significant gene-level pQTLs corresponds to the number of unique genes associated with at least one protein abundance. (b) Depiction of cis-, trans-, and cis-position trans-CDS pQTLs.
Extended Data Fig. 1 Study design. (a) Schematic depicting the overall study design and sample sizes for the variant-level ExWAS and the gene-level collapsing analyses. The number of significant gene-level pQTLs corresponds to the number of unique genes associated with at least one protein abundance. (b) Depiction of cis-, trans-, and cis-position trans-CDS pQTLs.CC BY · PubMed Central
Extended Data Fig. 2 ExWAS pQTL effect sizes. (a) Effect size distributions of cis- versus trans-CDS pQTLs stratified by allele frequency. (b) Effect sizes of rare (MAF ≤ 0.1%) pQTLs.
Extended Data Fig. 2 ExWAS pQTL effect sizes. (a) Effect size distributions of cis- versus trans-CDS pQTLs stratified by allele frequency. (b) Effect sizes of rare (MAF ≤ 0.1%) pQTLs.CC BY · PubMed Central
Extended Data Fig. 3 Missense cis-CDS pQTLs. (a) Enrichment of ClinVar pathogenic and likely pathogenic (P/LP) variants among missense cis-CDS pQTLs. P-values calculated via two-tailed binomial test and are uncorrected. (b) REVEL scores of cis-CDS missense pQTLs. P-values were calculated with the Mann-Whitney U test (two-sided) and are not corrected for multiple testing. The appropriate Bonferroni-adjusted p-value threshold is p < 0.017. The boxplots show the median (centre line) and interquartile ranges (IQR) (box limits).
Extended Data Fig. 3 Missense cis-CDS pQTLs. (a) Enrichment of ClinVar pathogenic and likely pathogenic (P/LP) variants among missense cis-CDS pQTLs. P-values calculated via two-tailed binomial test and are uncorrected. (b) REVEL scores of cis-CDS missense pQTLs. P-values were calculated with the Mann-Whitney U test (two-sided) and are not corrected for multiple testing. The appropriate Bonferroni-adjusted p-value threshold is p < 0.017. The boxplots show the median (centre line) and interquartile ranges (IQR) (box limits).CC BY · PubMed Central
Extended Data Fig. 4 Overlap between pQTLs detected in the ExWAS and collapsing analysis. (a) Number of unique gene-phenotype associations among non-synonymous pQTLs in the ExWAS versus the collapsing analysis. (b) Number of unique gene-phenotype associations among rare (MAF ≤ 0.1%) PTV-driven pQTLs in the ExWAS and ptv collapsing model.
Extended Data Fig. 4 Overlap between pQTLs detected in the ExWAS and collapsing analysis. (a) Number of unique gene-phenotype associations among non-synonymous pQTLs in the ExWAS versus the collapsing analysis. (b) Number of unique gene-phenotype associations among rare (MAF ≤ 0.1%) PTV-driven pQTLs in the ExWAS and ptv collapsing model.CC BY · PubMed Central
Extended Data Fig. 5 pQTL atlas and interactive browser. (a) Illustration of potential applications of this trans-CDS pQTL atlas to drug development. The chord diagram represents trans-CDS pQTLs detected in the collapsing analysis (p ≤ 1 × 10−8). Created using biorender.com (b) The AstraZeneca pQTL browser, highlighting LDLR as an example query. Users can browse pQTLs from both the ExWAS and gene-based collapsing analyses using an intuitive range of parameters and thresholds.
Extended Data Fig. 5 pQTL atlas and interactive browser. (a) Illustration of potential applications of this trans-CDS pQTL atlas to drug development. The chord diagram represents trans-CDS pQTLs detected in the collapsing analysis (p ≤ 1 × 10−8). Created using biorender.com (b) The AstraZeneca pQTL browser, highlighting LDLR as an example query. Users can browse pQTLs from both the ExWAS and gene-based collapsing analyses using an intuitive range of parameters and thresholds.CC BY · PubMed Central
Extended Data Fig. 6 pQTL-informed collapsing analyses. The p-values of gene-level associations for quantitative traits in which the p-values improved in the ptvolink model compared to the ptv model. For comparison, we also include p-values for the flexdmg model, which includes PTVs and rare (MAF < 0.1%) missense variants predicted to be damaging via REVEL (REVEL > 0.25), and the flexnonsyn model, which includes PTVs and missense variants without a REVEL cutoff. An additional 17 genes were not among the 87 significantly associated genes in the ptvolink models, and only 9 of these were not already captured by the ptv model. P-values were generated via linear regression and were not corrected for multiple testing. The dashed line indicates the study-wide significance threshold of p ≤ 1 × 10-8.
Extended Data Fig. 6 pQTL-informed collapsing analyses. The p-values of gene-level associations for quantitative traits in which the p-values improved in the ptvolink model compared to the ptv model. For comparison, we also include p-values for the flexdmg model, which includes PTVs and rare (MAF < 0.1%) missense variants predicted to be damaging via REVEL (REVEL > 0.25), and the flexnonsyn model, which includes PTVs and missense variants without a REVEL cutoff. An additional 17 genes were not among the 87 significantly associated genes in the ptvolink models, and only 9 of these were not already captured by the ptv model. P-values were generated via linear regression and were not corrected for multiple testing. The dashed line indicates the study-wide significance threshold of p ≤ 1 × 10-8.CC BY · PubMed Central

Verification

Claims the paper makes

84 Own result · 21 Own method · 16 Partly supported · 15 Supported · 15 Own interpretation · 6 No basis given · 5 Couldn’t confirm · 5 No full text of the sources · 3 Supported so far · accuracy not yet measured

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  • Probably not a substantial claim. 43
  • It may state two separate claims as one. 36
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  • The paper relies on this without support.

    The Olink Explore 3072 antibody-based measurements accurately reflect plasma protein abundances.

    “Proteomic profiling on blood plasma was performed with the Olink Explore 3072 platform, which measures 2,941 protein analytes across 2,923 unique proteins (Supplementary Table 1).”
  • The paper relies on this without support.

    Exome sequencing and variant calling accurately identify rare protein-coding variants.

    “Exome sequencing data were available for 52,217 (96%) of these participants, which we processed through our previously published cloud-based pipeline11 (Extended Data Fig. 1a).”
  • The paper relies on this without support.

    In collapsing analysis, antibody-binding disruption events represent only a modest fraction of a gene's complete allelic series.

    “In the setting of collapsing analysis, in which we aggregate many variant effects in a gene, we expect these events to represent only a modest fraction of a gene’s complete allelic series.”
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Metadata

Authors
Dhindsa, R. S., Burren, O. S., Sun, B. B., Prins, B. P., Matelska, D., Wheeler, E., Mitchell, J., Oerton, E., Hristova, V. A., Smith, K. R., Carss, K., Wasilewski, S., Harper, A. R., Paul, D. S., Fabre, M. A., Runz, H., Viollet, C., Challis, B., Platt, A., Initiative, A. G., Ågren, R., Anderson-Dring, L., Atanur, S., Baker, D., Barrett, C., Belvisi, M., Bohlooly-Y, M., Buvall, L., Camacho, N., Cazares, L., Cameron-Christie, S., Chen, M., Cohen, S., Danielson, R. F., Das, S., Davis, A., Deevi, S. V. V., Ding, W., Dougherty, B., Fairhurst-Hunter, Z., Garg, M., Georgi, B., Rangel, C. G., Haefliger, C., Hammar, M., Hanna, R. N., Hansen, P. B. L., Harrow, J., Henry, I., Hess, S., Hollis, B., Hu, F., Jiang, X., Kundu, K., Lai, Z., Lal, M., Lassi, G., Liang, Y., Lopes, M., Lythgow, K., MacArthur, S., Maisuria-Armer, M., March, R., Martins, C., Megy, K., Menzies, R., Michaëlsson, E., Middleton, F., Mowrey, B., Muthas, D., Nag, A., O’Dell, S., Ohne, Y., Olsson, H., O’Neill, A., Ostridge, K., Pullman, B., Rae, W., Raies, A., Reznichenko, A., Ros, X. R., Ryaboshapkina, M., Sanganee, H., Sidders, B., Snowden, M., Stankovic, S., Stevens, H., Tachmazidou, I., Taiy, H., Tian, L., Underwood, C., Walentinsson, A., Wang, Q.-D., Zehir, A., Zou, Z., Vitsios, D., Ashley, E. A., Whelan, C. D., Pangalos, M. N., Wang, Q., & Petrovski, S.
Affiliations
  • AstraZeneca (United States) (US)
  • AstraZeneca (United Kingdom) (GB)
  • Biogen (United States) (US)
  • AstraZeneca (Sweden) (SE)
  • Alexion Pharmaceuticals (United States) (US)
  • The University of Melbourne (AU)
Corresponding author
  • Dhindsa, Ryan S.
  • Petrovski, Slavé
Venue
Nature
Published
2023-10-04
Volume
622
Issue
7982
Pages
339-347
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W4387354365
Cited by
193
References
79
Citations by year
2026: 68 · 2025: 72 · 2024: 47 · 2023: 6
FWCI
32.04
Type
journal article
Language
en
Open access
Locations
  • Nature (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • Amgen
  • Bristol-Myers Squibb
  • Pfizer
  • AstraZeneca
  • Biogen
  • Alnylam Pharmaceuticals
  • Regeneron Pharmaceuticals
  • Novo Nordisk
  • Genentech
  • Janssen Pharmaceuticals
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
rare variants, plasma protein levels, proteogenomics, truncating mutations, stabilin-1, stabilin-2, scavenger receptors, protein clearance, NLRC4, HSD17B13, hepatic steatosis, TET2, clonal hematopoiesis, FLT3, protein quantitative trait loci, biomarkers, therapeutic target
MeSH terms
Alleles, Blood Proteins, Calcium-Binding Proteins, DNA-Binding Proteins, United Kingdom, Hematopoiesis, Humans, Mutation, Plasma, 17-Hydroxysteroid Dehydrogenases, Biomarkers, Cell Adhesion Molecules, Neuronal, Receptors, Lymphocyte Homing, Databases, Factual, Biological Specimen Banks, Genomics, Proteomics, Dioxygenases, fms-Like Tyrosine Kinase 3, CARD Signaling Adaptor Proteins, Genetic Association Studies, Exome

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