Genetic risk converges on regulatory networks mediating early type 2 diabetes

by ARKANSAS DIGITAL NEWS

[ad_1]

  • Kahn, S. E., Hull, R. L. & Utzschneider, K. M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840–846 (2006).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Halban, P. A. et al. β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment. Diabetes Care 37, 1751–1758 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rai, V. et al. Single-cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol. Metab. 32, 109–121 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chiou, J. et al. Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk. Nat. Genet. 53, 455–466 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ahlqvist, E., Prasad, R. B. & Groop, L. Subtypes of type 2 diabetes determined from clinical parameters. Diabetes 69, 2086–2093 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Redondo, M. J. et al. The clinical consequences of heterogeneity within and between different diabetes types. Diabetologia 63, 2040–2048 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Weitz, J., Menegaz, D. & Caicedo, A. Deciphering the complex communication networks that orchestrate pancreatic islet function. Diabetes 70, 17–26 (2020).

    Article 
    PubMed Central 

    Google Scholar
     

  • Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–43 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Walker, J. T., Saunders, D. C., Brissova, M. & Powers, A. C. The human islet: mini-organ with mega-impact. Endocr. Rev. 42, bnab010 (2021).

    Article 

    Google Scholar
     

  • Brissova, M. et al. Assessment of human pancreatic islet architecture and composition by laser scanning confocal microscopy. J. Histochem. Cytochem. 53, 1087–1097 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Dai, C. et al. Stress-impaired transcription factor expression and insulin secretion in transplanted human islets. J. Clin. Invest. 126, 1857–1870 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wigger, L. et al. Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes. Nat. Metab. 3, 1017–1031 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Camunas-Soler, J. et al. Patch-seq links single-cell transcriptomes to human islet dysfunction in diabetes. Cell Metab. 31, 1017–1031.e4 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shapira, S. N., Naji, A., Atkinson, M. A., Powers, A. C. & Kaestner, K. H. Understanding islet dysfunction in type 2 diabetes through multidimensional pancreatic phenotyping: The Human Pancreas Analysis Program. Cell Metab. 34, 1906–1913 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Albrechtsen, N. J. W. et al. The liver–α-cell axis and type 2 diabetes. Endocr. Rev. 40, 1353–1366 (2019).

    Article 

    Google Scholar
     

  • Wu, M. et al. Single-cell analysis of the human pancreas in type 2 diabetes using multi-spectral imaging mass cytometry. Cell Rep. 37, 109919 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dam, T. J. Pvan et al. CiliaCarta: an integrated and validated compendium of ciliary genes. PLoS ONE 14, e0216705 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Smith, S. B. et al. Rfx6 directs islet formation and insulin production in mice and humans. Nature 463, 775–780 (2010).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Patel, K. A. et al. Heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance. Nat. Commun. 8, 888 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl Acad. Sci. USA 114, 2301–2306 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Walker, J. T. et al. Integrated human pseudoislet system and microfluidic platform demonstrates differences in G-protein-coupled-receptor signaling in islet cells. JCI Insight 5, e137017 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Viñuela, A. et al. Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D. Nat. Commun. 11, 4912 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kahn, S. E., Zraika, S., Utzschneider, K. M. & Hull, R. L. The beta cell lesion in type 2 diabetes: there has to be a primary functional abnormality. Diabetologia 52, 1003–1012 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meier, J. J. & Bonadonna, R. C. Role of reduced β-cell mass versus impaired β-cell function in the pathogenesis of type 2 diabetes. Diabetes Care 36, S113–S119 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cohrs, C. M. et al. Dysfunction of persisting β cells is a key feature of early type 2 diabetes pathogenesis. Cell Rep. 31, 107469 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • McCarthy, M. I. Painting a new picture of personalised medicine for diabetes. Diabetologia 60, 793–799 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chandra, V. et al. RFX6 regulates insulin secretion by modulating Ca2+ homeostasis in human β cells. Cell Rep. 9, 2206–2218 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Piccand, J. et al. Rfx6 maintains the functional identity of adult pancreatic β cells. Cell Rep. 9, 2219–2232 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Choksi, S. P., Lauter, G., Swoboda, P. & Roy, S. Switching on cilia: transcriptional networks regulating ciliogenesis. Development 141, 1427–1441 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Piasecki, B. P., Burghoorn, J. & Swoboda, P. Regulatory factor X (RFX)-mediated transcriptional rewiring of ciliary genes in animals. Proc. Natl Acad. Sci. USA 107, 12969–12974 (2010).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gloyn, A. L. et al. Every islet matters: improving the impact of human islet research. Nat. Metab. 4, 970–977 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Balamurugan, A. N., Chang, Y., Fung, J. J., Trucco, M. & Bottino, R. Flexible management of enzymatic digestion improves human islet isolation outcome from sub‐optimal donor pancreata. Am. J. Transplant. 3, 1135–1142 (2003).

    Article 
    PubMed 

    Google Scholar
     

  • Dai, C. et al. Age-dependent human β cell proliferation induced by glucagon-like peptide 1 and calcineurin signaling. J. Clin. Invest. 127, 3835–3844 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brissova, M. et al. α cell function and gene expression are compromised in type 1 diabetes. Cell Rep. 22, 2667–2676 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brissova, M. et al. Islet microenvironment, modulated by vascular endothelial growth factor-A signaling, promotes β cell regeneration. Cell Metab. 19, 498–511 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brissova, M. et al. The Integrated Islet Distribution Program answers the call for improved human islet phenotyping and reporting of human islet characteristics in research articles. Diabetologia 62, 1312–1314 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kayton, N. S. et al. Human islet preparations distributed for research exhibit a variety of insulin-secretory profiles. Am. J. Physiol. Endocrinol. Metab. 308, E592–E602 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fitzmaurice, G. M., Laird, N. M. & Ware, J. H. Applied Longitudinal Analysis (Wiley, 2011).

  • Shultz, L. D. et al. Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells. J. Immunol. 174, 6477–6489 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Dai, C. et al. Tacrolimus- and sirolimus-induced human β cell dysfunction is reversible and preventable. JCI Insight 5, e130770 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dorrell, C. et al. Transcriptomes of the major human pancreatic cell types. Diabetologia 54, 2832 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Saunders, D. C. et al. Ectonucleoside triphosphate diphosphohydrolase-3 antibody targets adult human pancreatic β cells for in vitro and in vivo analysis. Cell Metab. 29, 745–754.e4 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Dorrell, C. et al. Human islets contain four distinct subtypes of β cells. Nat. Commun. 7, 11756 (2016).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Haliyur, R. et al. Human islets expressing HNF1A variant have defective β cell transcriptional regulatory networks. J. Clin. Invest. 129, 246–251 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marzban, L., Park, K. & Verchere, C. B. Islet amyloid polypeptide and type 2 diabetes. Exp. Gerontol. 38, 347–351 (2003).

    Article 
    PubMed 

    Google Scholar
     

  • Westermark, P., Andersson, A. & Westermark, G. T. Islet amyloid polypeptide, islet amyloid, and diabetes mellitus. Physiol. Rev. 91, 795–826 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Hart, N. J. et al. Cystic fibrosis–related diabetes is caused by islet loss and inflammation. JCI Insight 3, e98240 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Noguchi, G. M. & Huising, M. O. Integrating the inputs that shape pancreatic islet hormone release. Nat. Metab. 1, 1189–1201 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).

    Article 
    MATH 

    Google Scholar
     

  • Luhn, H. P. The automatic creation of literature abstracts. IBM J. Res. Dev. 2, 159–165 (1958).

    Article 
    MathSciNet 

    Google Scholar
     

  • Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    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 
    PubMed 

    Google Scholar
     

  • Hartley, S. W. & Mullikin, J. C. QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments. BMC Bioinformatics 16, 224 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, L. et al. Measure transcript integrity using RNA-seq data. BMC Bioinformatics 17, 58 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, C., Patil, S. & Sartor, M. A. RNA-Enrich: a cut-off free functional enrichment testing method for RNA-seq with improved detection power. Bioinformatics 32, 1100–1102 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6, e21800 (2011).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Saeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract. 157, 107843 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteomics 11, M111.014647 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Breuer, K. et al. InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic Acids Res. 41, D1228–D1233 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. & Peterson, H. gprofiler2–an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000research 9, ELIXIR–709 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, J. et al. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. 53, 840–860 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bailey, T. L. et al. MEME Suite: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Orchard, P., Kyono, Y., Hensley, J., Kitzman, J. O. & Parker, S. C. J. Quantification, dynamic visualization, and validation of bias in ATAC-seq data with ataqv. Cell Syst. 10, 298–306.e4 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Yang, S. et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 21, 57 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).

  • Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thibodeau, A. et al. AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data. Genome Biol. 22, 252 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Speir, M. L. et al. UCSC Cell Browser: visualize your single-cell data. Bioinformatics 37, 4578–4580 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sande, B. Vde et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Quinlan, A. R. BEDTools: the Swiss‐army tool for genome feature analysis. Curr. Protoc. Bioinform. 47, 11.12.1–11.12.34 (2014).

    Article 

    Google Scholar
     

  • Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137–R137 (2008).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kheradpour, P. & Kellis, M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Chinwalla, A. T. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Bailey, T. L. & Elkan, C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Mol. Biol. 2, 28–36 (1994).

    PubMed 

    Google Scholar
     

  • Bailey, T. L. DREME: motif discovery in transcription factor ChIP–seq data. Bioinformatics 27, 1653–1659 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bowden, J., Smith, G. D. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bowden, J., Smith, G. D., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ye, T., Shao, J. & Kang, H. Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization. Ann. Stat. 49, 2079–2100 (2021).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Loh, P.-R. et al. Efficient Bayesian mixed model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bonner-Weir, S. & O’Brien, T. D. Islets in type 2 diabetes: in honor of Dr. Robert C. Turner. Diabetes 57, 2899–2904 (2008).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sakuraba, H. et al. Reduced beta-cell mass and expression of oxidative stress-related DNA damage in the islet of Japanese type II diabetic patients. Diabetologia 45, 85–96 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Butler, A. E. et al. β-cell deficit and increased β-cell apoptosis in humans with type 2 diabetes. Diabetes 52, 102–110 (2003).

    Article 
    PubMed 

    Google Scholar
     

  • Rahier, J., Guiot, Y., Goebbels, R. M., Sempoux, C. & Henquin, J. C. Pancreatic β‐cell mass in European subjects with type 2 diabetes. Diabetes Obes. Metab. 10, 32–42 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Talchai, C., Xuan, S., Lin, H. V., Sussel, L. & Accili, D. Pancreatic β cell dedifferentiation as a mechanism of diabetic β cell failure. Cell 150, 1223–1234 (2012).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Masters, S. L. et al. Activation of the NLRP3 inflammasome by islet amyloid polypeptide provides a mechanism for enhanced IL-1β in type 2 diabetes. Nat. Immunol. 11, 897–904 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Westwell-Roper, C. Y., Ehses, J. A. & Verchere, C. B. Resident macrophages mediate islet amyloid polypeptide–induced islet IL-1β production and β-cell dysfunction. Diabetes 63, 1698–1711 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Nair, G. & Hebrok, M. Islet formation in mice and men: lessons for the generation of functional insulin-producing β-cells from human pluripotent stem cells. Curr. Opin. Genet. Dev. 32, 171–180 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Arrojo e Drigo, R. et al. New insights into the architecture of the islet of Langerhans: a focused cross-species assessment. Diabetologia 58, 2218–2228 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Unger, R. H. & Cherrington, A. D. Glucagonocentric restructuring of diabetes: a pathophysiologic and therapeutic makeover. J. Clin. Invest. 122, 4–12 (2012).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • [ad_2]

    Source link

    Related Posts