April 26, 2024
An atlas of genetic scores to predict multi-omic traits – Nature

An atlas of genetic scores to predict multi-omic traits – Nature

  • Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Moore, C. et al. The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials 15, 363 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ritchie, S. C. et al. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat. Metab. 3, 1476–1483 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lambert, S. A. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53, 420–425 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Adeyemo, A. et al. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat. Med. 27, 1876–1884 (2021).

    Article 

    Google Scholar
     

  • Xu, Y. et al. Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease. Cell Genomics 2, 100086 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mosley, J. D. et al. Probing the virtual proteome to identify novel disease biomarkers. Circulation 138, 2469–2481 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hutcheon, J. A., Chiolero, A. & Hanley, J. A. Random measurement error and regression dilution bias. Br. Med. J. 340, 1402–1406 (2010).

    Article 

    Google Scholar
     

  • Pividori, M., Schoettler, N., Nicolae, D. L., Ober, C. & Im, H. K. Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies. Lancet Respir. Med. 7, 509–522 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lannelongue, L., Grealey, J., Bateman, A. & Inouye, M. Ten simple rules to make your computing more environmentally sustainable. PLoS Comput. Biol. 17, e1009324 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Igl, W., Johansson, A. & Gyllensten, U. The Northern Swedish Population Health Study (NSPHS)—a paradigmatic study in a rural population combining community health and basic research. Rural Remote Health 10, 1363 (2010).

    PubMed 

    Google Scholar
     

  • McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kerr, S. M. et al. An actionable KCNH2 Long QT Syndrome variant detected by sequence and haplotype analysis in a population research cohort. Sci. Rep. 9, 10964 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tan, K. H. X. et al. Cohort profile: the Singapore Multi-Ethnic Cohort (MEC) study. Int. J. Epidemiol. 47, 699–699j (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Katz, D. H. et al. Whole genome sequence analysis of the plasma proteome in black adults provides novel insights into cardiovascular disease. Circulation 145, 357–370 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fabregat, A. et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Patrick, et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inf. 7, e14325 (2019).

    Article 

    Google Scholar
     

  • Sarwar, N. et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet 379, 1205–1213 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Haiman, C. A. et al. Levels of β-microseminoprotein in blood and risk of prostate cancer in multiple populations. J. Natl Cancer Inst. 105, 237–243 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ding, E. L. et al. Sex hormone-binding globulin and risk of type 2 diabetes in women and men. N. Engl. J. Med. 361, 1152–1163 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Saini, V. Molecular mechanisms of insulin resistance in type 2 diabetes mellitus. World J. Diabetes 1, 68 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Qi, L. et al. Genetic variants in ABO blood group region, plasma soluble E-selectin levels and risk of type 2 diabetes. Hum. Mol. Genet. 19, 1856–1862 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Peters, M. C. et al. Plasma interleukin-6 concentrations, metabolic dysfunction, and asthma severity: a cross-sectional analysis of two cohorts. Lancet Respir. Med. 4, 574–584 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Banaganapalli, B. et al. Exploring celiac disease candidate pathways by global gene expression profiling and gene network cluster analysis. Sci. Rep. 10, 16290 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gagliano Taliun, S. A. et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat. Genet. 52, 550–552 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, H. I. et al. Fine mapping and functional analysis reveal a role of SLC22A1 in acylcarnitine transport. Am. J. Hum. Genet. 101, 489 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tamai, I. Pharmacological and pathophysiological roles of carnitine/organic cation transporters (OCTNs: SLC22A4, SLC22A5 and Slc22a21). Biopharm. Drug Dispos. 34, 29–44 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chang, H. B., Gao, X., Nepomuceno, R., Hu, S. & Sun, D. Na+/H+ exchanger in the regulation of platelet activation and paradoxical effects of cariporide. Exp. Neurol. 272, 11–16 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • de Vries, P. S. et al. Whole-genome sequencing study of serum peptide levels: the Atherosclerosis Risk in Communities study. Hum. Mol. Genet. 26, 3442–3450 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Babaev, V. R. et al. Loss of 2 Akt (protein kinase B) isoforms in hematopoietic cells diminished monocyte and macrophage survival and reduces atherosclerosis in Ldl receptor-null mice. Arterioscler. Thromb. Vasc. Biol. 39, 156–169 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Miteva, K. et al. Cardiotrophin-1 deficiency abrogates atherosclerosis progression. Sci. Rep. 10, 5791 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Agrawal, S. et al. Signal transducer and activator of transcription 1 is required for optimal foam cell formation and atherosclerotic lesion development. Circulation 115, 2939–2947 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Peltola, K. J. et al. Pim-1 kinase inhibits STAT5-dependent transcription via its interactions with SOCS1 and SOCS3. Blood 103, 3744–3750 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Khor, C. C. et al. CISH and susceptibility to infectious diseases. N. Engl. J. Med. 362, 2092–2101 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baldini, C., Moriconi, F. R., Galimberti, S., Libby, P. & De Caterina, R. The JAK–STAT pathway: an emerging target for cardiovascular disease in rheumatoid arthritis and myeloproliferative neoplasms. Eur. Heart J. 42, 4389–4400 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Skah, S., Uchuya-Castillo, J., Sirakov, M. & Plateroti, M. The thyroid hormone nuclear receptors and the Wnt/β-catenin pathway: an intriguing liaison. Dev. Biol. 422, 71–82 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chen, G. et al. Regulation of GSK-3β in the proliferation and apoptosis of human thyrocytes investigated using a GSK-3β-targeting RNAi adenovirus expression vector: involvement the Wnt/β-catenin pathway. Mol. Biol. Rep. 37, 2773–2779 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Ely, K. A., Bischoff, L. A. & Weiss, V. L. Wnt signaling in thyroid homeostasis and carcinogenesis. Genes 9, 204 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Haerlingen, B. et al. Small-molecule screening in zebrafish embryos identifies signaling pathways regulating early thyroid development. Thyroid 29, 1683–1703 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Narumi, S. et al. GWAS of thyroid dysgenesis identifies a risk locus at 2q33.3 linked to regulation of Wnt signaling. Hum. Mol. Genet. 31, 3967–3974 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xu, D. et al. USP25 regulates Wnt signaling by controlling the stability of tankyrases. Genes Dev. 31, 1024–1035 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lin, D. et al. Induction of USP25 by viral infection promotes innate antiviral responses by mediating the stabilization of TRAF3 and TRAF6. Proc. Natl Acad. Sci. USA 112, 11324–11329 (2015).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nelson, J. K. et al. USP25 promotes pathological HIF-1-driven metabolic reprogramming and is a potential therapeutic target in pancreatic cancer. Nat. Commun. 13, 2070 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Blount, J. R., Burr, A. A., Denuc, A., Marfany, G. & Todi, S. V. Ubiquitin-specific protease 25 functions in endoplasmic reticulum-associated degradation. PLoS One 7, e36542 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article 

    Google Scholar
     

  • Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lundberg, M., Eriksson, A., Tran, B., Assarsson, E. & Fredriksson, S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 39, e102 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Folkersen, L. et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat. Metab. 2, 1135–1148 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Surendran, P. et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat. Med. 28, 2321–2332 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Karjalainen, M. K. et al. Genome-wide characterization of circulating metabolic biomarkers reveals substantial pleiotropy and novel disease pathways. Preprint at medRxiv https://doi.org/10.1101/2022.10.20.22281089 (2022).

  • Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fort, A. et al. MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets. Bioinformatics 33, 1895–1897 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Taylor-Weiner, A. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol. 20, 228 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stacklies, W., Redestig, H., Scholz, M., Walther, D. & Selbig, J. pcaMethods—a bioconductor package providing PCA methods for incomplete data. Bioinformatics 23, 1164–1167 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat. Commun. 11, 6397 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bretherick, A. D. et al. Linking protein to phenotype with Mendelian randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet. 16, e1008785 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kierczak, M. et al. Contribution of rare whole-genome sequencing variants to plasma protein levels and the missing heritability. Nat. Commun. 13, 2532 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ritchie, S. C. et al. Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants. Sci. Data 10, 64 (2023).

  • Wong, E. et al. The Singapore National Precision Medicine strategy. Nat. Genet. 55, 178–186 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, F. et al. Ancestry-agnostic estimation of DNA sample contamination from sequence reads. Genome Res. 30, 185–194 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Taylor, H. A. J. et al. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study. Ethn. Dis. 15, S6-4-17 (2005).

    PubMed 

    Google Scholar
     

  • Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ngo, D. et al. Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease. Circulation 134, 270–285 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chatterjee, N., Shi, J. & Garcia-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Okser, S. et al. Regularized machine learning in the genetic prediction of complex traits. PLoS Genet. 10, e1004754 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).

  • Tipping, M. E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001).

    MathSciNet 
    MATH 

    Google Scholar
     

  • Privé, F., Arbel, J. & Vilhjálmsson, B. J. LDpred2: better, faster, stronger. Bioinformatics 36, 5424–5431 (2021).

    Article 

    Google Scholar
     

  • Pietzner, M. et al. Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat. Commun. 12, 6822 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Davidson-Pilon, C. lifelines: survival analysis in Python. J. Open Source Softw. 4, 1317 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Lannelongue, L., Grealey, J. & Inouye, M. Green algorithms: quantifying the carbon footprint of computation. Adv. Sci. 8, 2100707 (2021).

    Article 

    Google Scholar
     

  • Di Angelantonio, E. et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. Lancet 390, 2360–2371 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

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