A disease-associated gene desert directs macrophage inflammation through ETS2.

Miller, F. W. The increasing prevalence of autoimmunity and autoimmune diseases: an urgent call to action for improved understanding, diagnosis, treatment, and prevention. Curr. Opin. Immunol. 80, 102266 (2023).

pubmed: 36446151 doi: 10.1016/j.coi.2022.102266

Dowden, H. & Munro, J. Trends in clinical success rates and therapeutic focus. Nat. Rev. Drug Discov. 18, 495–496 (2019).

pubmed: 31267067 doi: 10.1038/d41573-019-00074-z

de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).

pubmed: 28067908 pmcid: 5289481 doi: 10.1038/ng.3760

International Genetics of Ankylosing Spondylitis Consortium et al. Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nat. Genet. 45, 730–738 (2013).

doi: 10.1038/ng.2667

Ji, S. G. et al. Genome-wide association study of primary sclerosing cholangitis identifies new risk loci and quantifies the genetic relationship with inflammatory bowel disease. Nat. Genet. 49, 269–273 (2017).

pubmed: 27992413 doi: 10.1038/ng.3745

Ortiz-Fernandez, L. et al. Identification of susceptibility loci for Takayasu arteritis through a large multi-ancestral genome-wide association study. Am. J. Hum. Genet. 108, 84–99 (2021).

pubmed: 33308445 doi: 10.1016/j.ajhg.2020.11.014

Stathias, V. et al. LINCS Data Portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res. 48, D431–D439 (2020).

pubmed: 31701147 doi: 10.1093/nar/gkz1023

Harrison, R. K. Phase II and phase III failures: 2013–2015. Nat. Rev. Drug Discov. 15, 817–818 (2016).

pubmed: 27811931 doi: 10.1038/nrd.2016.184

Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).

pubmed: 31915397 pmcid: 7405896 doi: 10.1038/s41586-019-1879-7

King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).

pubmed: 31830040 pmcid: 6907751 doi: 10.1371/journal.pgen.1008489

Cader, M. Z. et al. C13orf31 (FAMIN) is a central regulator of immunometabolic function. Nat. Immunol. 17, 1046–1056 (2016).

pubmed: 27478939 pmcid: 6581540 doi: 10.1038/ni.3532

Murthy, A. et al. A Crohn’s disease variant in Atg16l1 enhances its degradation by caspase 3. Nature 506, 456–462 (2014).

pubmed: 24553140 doi: 10.1038/nature13044

Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

pubmed: 24119843 doi: 10.1016/j.cell.2013.09.053

Park, M. D., Silvin, A., Ginhoux, F. & Merad, M. Macrophages in health and disease. Cell 185, 4259–4279 (2022).

pubmed: 36368305 pmcid: 9908006 doi: 10.1016/j.cell.2022.10.007

Kugathasan, S. et al. Loci on 20q13 and 21q22 are associated with pediatric-onset inflammatory bowel disease. Nat. Genet. 40, 1211–1215 (2008).

pubmed: 18758464 pmcid: 2770437 doi: 10.1038/ng.203

Xue, J. et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 40, 274–288 (2014).

pubmed: 24530056 pmcid: 3991396 doi: 10.1016/j.immuni.2014.01.006

Kuo, D. et al. HBEGF

pubmed: 31068444 pmcid: 6726376 doi: 10.1126/scitranslmed.aau8587

Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

pubmed: 22371084 pmcid: 3297981 doi: 10.1038/nbt.2137

Nerlov, C. & Graf, T. PU.1 induces myeloid lineage commitment in multipotent hematopoietic progenitors. Genes Dev. 12, 2403–2412 (1998).

pubmed: 9694804 pmcid: 317050 doi: 10.1101/gad.12.15.2403

Minderjahn, J. et al. Mechanisms governing the pioneering and redistribution capabilities of the non-classical pioneer PU.1. Nat. Commun. 11, 402 (2020).

pubmed: 31964861 pmcid: 6972792 doi: 10.1038/s41467-019-13960-2

Martinez, L. A. Mutant p53 and ETS2, a tale of reciprocity. Front. Oncol. 6, 35 (2016).

pubmed: 26925389 pmcid: 4757666 doi: 10.3389/fonc.2016.00035

Wei, G. et al. Activated Ets2 is required for persistent inflammatory responses in the motheaten viable model. J. Immunol. 173, 1374–1379 (2004).

pubmed: 15240733 doi: 10.4049/jimmunol.173.2.1374

Zhao, J., Huang, K., Peng, H. Z. & Feng, J. F. Protein C-ets-2 epigenetically suppresses TLRs-induced interleukin 6 production in macrophages. Biochem. Biophys. Res. Commun. 522, 960–964 (2020).

pubmed: 31810605 doi: 10.1016/j.bbrc.2019.11.123

Chung, S. W., Chen, Y. H. & Perrella, M. A. Role of Ets-2 in the regulation of heme oxygenase-1 by endotoxin. J. Biol. Chem. 280, 4578–4584 (2005).

pubmed: 15590657 doi: 10.1074/jbc.M409125200

Quinn, S. R. et al. The role of Ets2 transcription factor in the induction of microRNA-155 (miR-155) by lipopolysaccharide and its targeting by interleukin-10. J. Biol. Chem. 289, 4316–4325 (2014).

pubmed: 24362029 doi: 10.1074/jbc.M113.522730

Ma, X. et al. Ets2 suppresses inflammatory cytokines through MAPK/NF-κB signaling and directly binds to the IL-6 promoter in macrophages. Aging 11, 10610–10625 (2019).

pubmed: 31785145 pmcid: 6914388 doi: 10.18632/aging.102480

Aperlo, C., Pognonec, P., Stanley, E. R. & Boulukos, K. E. Constitutive c-ets2 expression in M1D

pubmed: 8943340 pmcid: 231688 doi: 10.1128/MCB.16.12.6851

Henkel, G. W. et al. PU.1 but not ets-2 is essential for macrophage development from embryonic stem cells. Blood 88, 2917–2926 (1996).

pubmed: 8874188 doi: 10.1182/blood.V88.8.2917.bloodjournal8882917

Mittal, M., Siddiqui, M. R., Tran, K., Reddy, S. P. & Malik, A. B. Reactive oxygen species in inflammation and tissue injury. Antioxid. Redox Signal. 20, 1126–1167 (2014).

pubmed: 23991888 pmcid: 3929010 doi: 10.1089/ars.2012.5149

Kelly, B. & O’Neill, L. A. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. 25, 771–784 (2015).

pubmed: 26045163 pmcid: 4493277 doi: 10.1038/cr.2015.68

Martin, J. C. et al. Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy. Cell 178, 1493–1508 (2019).

pubmed: 31474370 pmcid: 7060942 doi: 10.1016/j.cell.2019.08.008

Peloquin, J. M. et al. Characterization of candidate genes in inflammatory bowel disease-associated risk loci. JCI Insight 1, e87899 (2016).

pubmed: 27668286 pmcid: 5033062 doi: 10.1172/jci.insight.87899

Sazonovs, A. et al. Large-scale sequencing identifies multiple genes and rare variants associated with Crohn’s disease susceptibility. Nat. Genet. 54, 1275–1283 (2022).

pubmed: 36038634 pmcid: 9700438 doi: 10.1038/s41588-022-01156-2

Slowikowski, K., Hu, X. & Raychaudhuri, S. SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30, 2496–2497 (2014).

pubmed: 24813542 pmcid: 4147889 doi: 10.1093/bioinformatics/btu326

Cramer, T. et al. HIF-1α is essential for myeloid cell-mediated inflammation. Cell 112, 645–657 (2003).

pubmed: 12628185 pmcid: 4480774 doi: 10.1016/S0092-8674(03)00154-5

Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).

pubmed: 23535595 pmcid: 4031686 doi: 10.1038/nature11986

Shiratori, R. et al. Glycolytic suppression dramatically changes the intracellular metabolic profile of multiple cancer cell lines in a mitochondrial metabolism-dependent manner. Sci. Rep. 9, 18699 (2019).

pubmed: 31822748 pmcid: 6904735 doi: 10.1038/s41598-019-55296-3

Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

pubmed: 22955991 pmcid: 3431496 doi: 10.1101/gr.136184.111

Basuyaux, J. P., Ferreira, E., Stehelin, D. & Buttice, G. The Ets transcription factors interact with each other and with the c-Fos/c-Jun complex via distinct protein domains in a DNA-dependent and -independent manner. J. Biol. Chem. 272, 26188–26195 (1997).

pubmed: 9334186 doi: 10.1074/jbc.272.42.26188

Sevilla, L. et al. Bcl-XL expression correlates with primary macrophage differentiation, activation of functional competence, and survival and results from synergistic transcriptional activation by Ets2 and PU.1. J. Biol. Chem. 276, 17800–17807 (2001).

pubmed: 11278399 doi: 10.1074/jbc.M008270200

Kong, L. et al. The landscape of immune dysregulation in Crohn’s disease revealed through single-cell transcriptomic profiling in the ileum and colon. Immunity 56, 444–458 (2023).

pubmed: 36720220 pmcid: 9957882 doi: 10.1016/j.immuni.2023.01.002

Chapuy, L. et al. Two distinct colonic CD14

pubmed: 30670762 doi: 10.1038/s41385-018-0126-0

Newman, J. A., Cooper, C. D., Aitkenhead, H. & Gileadi, O. Structural insights into the autoregulation and cooperativity of the human transcription factor Ets-2. J. Biol. Chem. 290, 8539–8549 (2015).

pubmed: 25670864 pmcid: 4375503 doi: 10.1074/jbc.M114.619270

Liu, H. et al. ERK differentially regulates Th17- and T

pubmed: 23620016 doi: 10.1002/eji.201242889

Koboziev, I., Karlsson, F., Zhang, S. & Grisham, M. B. Pharmacological intervention studies using mouse models of the inflammatory bowel diseases: translating preclinical data into new drug therapies. Inflamm. Bowel Dis. 17, 1229–1245 (2011).

pubmed: 21312318 doi: 10.1002/ibd.21557

Argmann, C. et al. Biopsy and blood-based molecular biomarker of inflammation in IBD. Gut 72, 1271–1287 (2023).

pubmed: 36109152 doi: 10.1136/gutjnl-2021-326451

Malle, L. et al. Autoimmunity in Down’s syndrome via cytokines, CD4 T cells and CD11c

pubmed: 36813963 pmcid: 9945839 doi: 10.1038/s41586-023-05736-y

Feagan, B. G. et al. Guselkumab plus golimumab combination therapy versus guselkumab or golimumab monotherapy in patients with ulcerative colitis (VEGA): a randomised, double-blind, controlled, phase 2, proof-of-concept trial. Lancet Gastroenterol. Hepatol. 8, 307–320 (2023).

pubmed: 36738762 doi: 10.1016/S2468-1253(22)00427-7

Friedrich, M. et al. IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies. Nat. Med. 27, 1970–1981 (2021).

pubmed: 34675383 pmcid: 8604730 doi: 10.1038/s41591-021-01520-5

Klesse, L. J. et al. The use of MEK inhibitors in neurofibromatosis type 1-associated tumors and management of toxicities. Oncologist 25, e1109–e1116 (2020).

pubmed: 32272491 pmcid: 7356675 doi: 10.1634/theoncologist.2020-0069

Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. Fine-mapping from summary data with the “sum of single effects” model. PLoS Genet. 18, e1010299 (2022).

pubmed: 35853082 pmcid: 9337707 doi: 10.1371/journal.pgen.1010299

1000 Genomes Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

doi: 10.1038/nature09534

Kerimov, N. et al. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet. 53, 1290–1299 (2021).

Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

pubmed: 24604202 pmcid: 4064786 doi: 10.1126/science.1246949

Quach, H. et al. Genetic adaptation and Neandertal admixture shaped the immune system of human populations. Cell 167, 643–656 (2016).

pubmed: 27768888 pmcid: 5075285 doi: 10.1016/j.cell.2016.09.024

Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016).

pubmed: 27863251 pmcid: 5119954 doi: 10.1016/j.cell.2016.10.026

Nedelec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).

pubmed: 27768889 doi: 10.1016/j.cell.2016.09.025

Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).

pubmed: 29379200 pmcid: 6548559 doi: 10.1038/s41588-018-0046-7

Wallace, C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet. 17, e1009440 (2021).

pubmed: 34587156 pmcid: 8504726 doi: 10.1371/journal.pgen.1009440

Bourges, C. et al. Resolving mechanisms of immune-mediated disease in primary CD4 T cells. EMBO Mol. Med. 12, e12112 (2020).

pubmed: 32239644 pmcid: 7207160 doi: 10.15252/emmm.202012112

Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).

pubmed: 27863249 pmcid: 5123897 doi: 10.1016/j.cell.2016.09.037

Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14, 7 (2013).

doi: 10.1186/1471-2105-14-7

Peters, J. E. et al. Insight into genotype-phenotype associations through eQTL mapping in multiple cell types in health and immune-mediated disease. PLoS Genet. 12, e1005908 (2016).

pubmed: 27015630 pmcid: 4807835 doi: 10.1371/journal.pgen.1005908

Conant, D. et al. Inference of CRISPR edits from Sanger trace data. CRISPR J. 5, 123–130 (2022).

pubmed: 35119294 doi: 10.1089/crispr.2021.0113

Kalita, C. A. et al. QuASAR-MPRA: accurate allele-specific analysis for massively parallel reporter assays. Bioinformatics 34, 787–794 (2018).

pubmed: 29028988 doi: 10.1093/bioinformatics/btx598

de Santiago, I. et al. BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes. Genome Biol. 18, 39 (2017).

pubmed: 28235418 pmcid: 5326502 doi: 10.1186/s13059-017-1165-7

Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

pubmed: 28846090 pmcid: 5623106 doi: 10.1038/nmeth.4396

Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51, 1494–1505 (2019).

pubmed: 31570894 pmcid: 6858557 doi: 10.1038/s41588-019-0505-9

Reske, J. J., Wilson, M. R. & Chandler, R. L. ATAC-seq normalization method can significantly affect differential accessibility analysis and interpretation. Epigenet. Chromatin 13, 22 (2020).

doi: 10.1186/s13072-020-00342-y

Brown, A. C. et al. Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis. Cell Genom. 3, 100306 (2023).

pubmed: 37388915 pmcid: 10300554 doi: 10.1016/j.xgen.2023.100306

Lienhard, M., Grimm, C., Morkel, M., Herwig, R. & Chavez, L. MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics 30, 284–286 (2014).

pubmed: 24227674 doi: 10.1093/bioinformatics/btt650

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

pubmed: 19910308 doi: 10.1093/bioinformatics/btp616

Randzavola, L. O. et al. EROS is a selective chaperone regulating the phagocyte NADPH oxidase and purinergic signalling. eLife 11, e76387 (2022).

pubmed: 36421765 pmcid: 9767466 doi: 10.7554/eLife.76387

Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

pubmed: 31375807 pmcid: 7605509 doi: 10.1038/s41587-019-0201-4

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

pubmed: 19505943 pmcid: 2723002 doi: 10.1093/bioinformatics/btp352

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).

pubmed: 24227677 doi: 10.1093/bioinformatics/btt656

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

pubmed: 25605792 pmcid: 4402510 doi: 10.1093/nar/gkv007

Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).

Qing, G. et al. Single-cell RNA sequencing revealed CD14

pubmed: 34671607 pmcid: 8521054 doi: 10.3389/fcell.2021.761300

Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730 (2019).

pubmed: 31348891 pmcid: 6662628 doi: 10.1016/j.cell.2019.06.029

Gao, K. M. et al. Human nasal wash RNA-Seq reveals distinct cell-specific innate immune responses in influenza versus SARS-CoV-2. JCI Insight 6, e152288 (2021).

pubmed: 34618691 pmcid: 8663782 doi: 10.1172/jci.insight.152288

Yang, Q. et al. The interaction of macrophages and CD8 T cells in bronchoalveolar lavage fluid is associated with latent tuberculosis infection. Emerg. Microbes Infect. 12, 2239940 (2023).

pubmed: 37470432 pmcid: 10399483 doi: 10.1080/22221751.2023.2239940

Reyes, M. et al. An immune-cell signature of bacterial sepsis. Nat. Med. 26, 333–340 (2020).

pubmed: 32066974 pmcid: 7235950 doi: 10.1038/s41591-020-0752-4

Mulder, K. et al. Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. Immunity 54, 1883–1900 (2021).

pubmed: 34331874 doi: 10.1016/j.immuni.2021.07.007

Cassetta, L. et al. Human tumor-associated macrophage and monocyte transcriptional landscapes reveal cancer-specific reprogramming, biomarkers, and therapeutic targets. Cancer Cell 35, 588–602 (2019).

pubmed: 30930117 pmcid: 6472943 doi: 10.1016/j.ccell.2019.02.009

Zernecke, A. et al. Integrated single-cell analysis-based classification of vascular mononuclear phagocytes in mouse and human atherosclerosis. Cardiovasc. Res. 119, 1676–1689 (2023).

pubmed: 36190844 doi: 10.1093/cvr/cvac161

Ellinghaus, D. et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat. Genet. 48, 510–518 (2016).

pubmed: 26974007 pmcid: 4848113 doi: 10.1038/ng.3528

Renauer, P. A. et al. Identification of susceptibility loci in IL6, RPS9/LILRB3, and an intergenic locus on chromosome 21q22 in Takayasu arteritis in a genome-wide association study. Arthritis Rheumatol. 67, 1361–1368 (2015).

pubmed: 25604533 pmcid: 4414813 doi: 10.1002/art.39035

Terao, C. et al. Genetic determinants and an epistasis of LILRA3 and HLA-B*52 in Takayasu arteritis. Proc. Natl Acad. Sci. USA 115, 13045–13050 (2018).

pubmed: 30498034 pmcid: 6304955 doi: 10.1073/pnas.1808850115

Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

pubmed: 35396580 pmcid: 9392466 doi: 10.1038/s41586-022-04434-5

Bussi, C. et al. Lysosomal damage drives mitochondrial proteome remodelling and reprograms macrophage immunometabolism. Nat. Commun. 13, 7338 (2022).

pubmed: 36443305 pmcid: 9705561 doi: 10.1038/s41467-022-34632-8

Behrends, V., Tredwell, G. D. & Bundy, J. G. A software complement to AMDIS for processing GC-MS metabolomic data. Anal. Biochem. 415, 206–208 (2011).

pubmed: 21575589 doi: 10.1016/j.ab.2011.04.009

Meers, M. P., Bryson, T. D., Henikoff, J. G. & Henikoff, S. Improved CUT&RUN chromatin profiling tools. eLife 8, e46314 (2019).

pubmed: 31232687 pmcid: 6598765 doi: 10.7554/eLife.46314

Leporcq, C. et al. TFmotifView: a webserver for the visualization of transcription factor motifs in genomic regions. Nucleic Acids Res. 48, W208–W217 (2020).

pubmed: 32324215 pmcid: 7319436 doi: 10.1093/nar/gkaa252

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

pubmed: 25215497 pmcid: 4163041 doi: 10.1016/j.cell.2014.08.009

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

pubmed: 34062119 pmcid: 8238499 doi: 10.1016/j.cell.2021.04.048

He, S. et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat. Biotechnol. 40, 1794–1806 (2022).

pubmed: 36203011 doi: 10.1038/s41587-022-01483-z

Danaher, P. et al. InSituType: likelihood-based cell typing for single cell spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2022.10.19.512902 (2022).

Vadstrup, K. et al. Validation and optimization of an ex vivo assay of intestinal mucosal biopsies in Crohn’s disease: reflects inflammation and drug effects. PLoS ONE 11, e0155335 (2016).

pubmed: 27171179 pmcid: 4865152 doi: 10.1371/journal.pone.0155335

Prufer, K. et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505, 43–49 (2014).

pubmed: 24352235 doi: 10.1038/nature12886

Prufer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).

pubmed: 28982794 pmcid: 6185897 doi: 10.1126/science.aao1887

Hajdinjak, M. et al. Reconstructing the genetic history of late Neanderthals. Nature 555, 652–656 (2018).

pubmed: 29562232 pmcid: 6485383 doi: 10.1038/nature26151

Mafessoni, F. et al. A high-coverage Neandertal genome from Chagyrskaya Cave. Proc. Natl Acad. Sci. USA 117, 15132–15136 (2020).

pubmed: 32546518 pmcid: 7334501 doi: 10.1073/pnas.2004944117

Meyer, M. et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222–226 (2012).

pubmed: 22936568 pmcid: 3617501 doi: 10.1126/science.1224344

Slon, V. et al. The genome of the offspring of a Neanderthal mother and a Denisovan father. Nature 561, 113–116 (2018).

pubmed: 30135579 pmcid: 6130845 doi: 10.1038/s41586-018-0455-x

Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016).

pubmed: 27654912 pmcid: 5161557 doi: 10.1038/nature18964

Speidel, L., Forest, M., Shi, S. & Myers, S. R. A method for genome-wide genealogy estimation for thousands of samples. Nat. Genet. 51, 1321–1329 (2019).

pubmed: 31477933 pmcid: 7610517 doi: 10.1038/s41588-019-0484-x

Speidel, L. et al. Inferring population histories for ancient genomes using genome-wide genealogies. Mol. Biol. Evol. 38, 3497–3511 (2021).

pubmed: 34129037 pmcid: 8383901 doi: 10.1093/molbev/msab174

Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

pubmed: 23950696 pmcid: 3738458 doi: 10.1371/journal.pcbi.1003118

Blighe, K. et al. EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling. Bioconductor https://doi.org/10.18129/B9.bioc.EnhancedVolcano (2023).

Hadley, W. Ggplot2 (Springer, 2016).

Warnes, G. et al. gplots: various R programming tools for plotting data. CRAN https://CRAN.R-project.org/package=gplots (2022).

Gel, B. & Serra, E. karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics 33, 3088–3090 (2017).

pubmed: 28575171 pmcid: 5870550 doi: 10.1093/bioinformatics/btx346

Stankey, C. T. et al. Data for ‘A disease-associated gene desert directs macrophage inflammation through ETS2’. Zenodo https://doi.org/10.5281/zenodo.10707942 (2024).

A disease-associated gene desert directs macrophage inflammation through ETS2.

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