The HXB/BXH family of recombinant inbred rat strains is a unique genetic resource that has been extensively phenotyped over 25 years, resulting in a vast dataset of quantitative molecular and physiological phenotypes. We built a pangenome graph from 10x Genomics Linked-Read data for 31 recombinant inbred rats to study genetic variation and association mapping. The pangenome includes 0.2Gb of sequence that is not present the reference mRatBN7.2, confirming the capture of substantial additional variation. We validated variants in challenging regions, including complex structural variants resolving into multiple haplotypes. Phenome-wide association analysis of validated SNPs uncovered variants associated with glucose/insulin levels and hippocampal gene expression. We propose an interaction between Pirl1l1, chromogranin expression, TNF-α levels, and insulin regulation. This study demonstrates the utility of linked-read pangenomes for comprehensive variant detection and mapping phenotypic diversity in a widely used rat genetic reference panel.

Department of Genetics Genomics and Informatics University of Tennessee Health Science Center Memphis TN USA

Department of Pharmacology Addiction Science and Toxicology University of Tennessee Health Science Center

Institute of Genetics and Biophysics National Research Council Naples 80111 Italy

Institute of Physiology Czech Academy of Sciences 14200 Prague Czech Republic

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iScience. 2025 Jan 23;28(2):111835. doi: 10.1016/j.isci.2025.111835. PubMed

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Gibbs R.A., Weinstock G.M., Metzker M.L., Muzny D.M., Sodergren E.J., Scherer S., Scott G., Steffen D., Worley K.C., Burch P.E., et al. (2004). Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428, 493–521. 10.1038/nature02426. PubMed DOI

Printz M.P., Jirout M., Jaworski R., Alemayehu A., and Kren V. (2003). Invited Review: HXB/BXH rat recombinant inbred strain platform: a newly enhanced tool for cardiovascular, behavioral, and developmental genetics and genomics. J. Appl. Physiol. 94, 2510–2522. 10.1152/japplphysiol.00064.2003. PubMed DOI

Pravenec M., Churchill P.C., Churchill M.C., Viklicky O., Kazdova L., Aitman T.J., Petretto E., Hubner N., Wallace C.A., Zimdahl H., et al. (2008). Identification of renal Cd36 as a determinant of blood pressure and risk for hypertension. Nat. Genet. 40, 952–954. 10.1038/ng.164. PubMed DOI

Adriaens M.E., Lodder E.M., Moreno-Moral A., Šilhavý J., Heinig M., Glinge C., Belterman C., Wolswinkel R., Petretto E., Pravenec M., et al. (2018). Systems Genetics Approaches in Rat Identify Novel Genes and Gene Networks Associated With Cardiac Conduction. J. Am. Heart Assoc. 7, e009243. 10.1161/JAHA.118.009243. PubMed DOI PMC

Bielavská E., Kr V., and Pravenec M. Genome Scanning of the HXB/BXH Sets of Recombinant Inbred Strains of the Rat for Quantitative Trait Loci Associated with Conditioned Taste Aversion. PubMed

Lusk R., Saba L.M., Vanderlinden L.A., Zidek V., Silhavy J., Pravenec M., Hoffman P.L., and Tabakoff B. (2018). Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism. Alcohol. Clin. Exp. Res. 42, 1177–1191. 10.1111/acer.13763. PubMed DOI PMC

Eizenga J.M., Novak A.M., Sibbesen J.A., Heumos S., Ghaffaari A., Hickey G., Chang X., Seaman J.D., Rounthwaite R., Ebler J., et al. (2020). Pangenome Graphs. Annu. Rev. Genomics Hum. Genet. 21, 139–162. 10.1146/annurev-genom-120219-080406. PubMed DOI PMC

Liao W.-W., Asri M., Ebler J., Doerr D., Haukness M., Hickey G., Lu S., Lucas J.K., Monlong J., Abel H.J., et al. (2023). A draft human pangenome reference. Nature 617, 312–324. 10.1038/s41586-023-05896-x. PubMed DOI PMC

Guarracino A., Buonaiuto S., de Lima L.G., Potapova T., Rhie A., Koren S., Rubinstein B., Fischer C., Human Pangenome Reference Consortium, Abel H.J., et al. (2023). Recombination between heterologous human acrocentric chromosomes. Nature 617, 335–343. 10.1038/s41586-023-05976-y. PubMed DOI PMC

de Jong T.V., Pan Y., Rastas P., Munro D., Tutaj M., Akil H., Benner C., Chitre A.S., Chow W., Colonna V., et al. (2023). A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats (Genomics) 10.1101/2023.04.13.536694. PubMed DOI PMC

Marks P., Garcia S., Barrio A.M., Belhocine K., Bernate J., Bharadwaj R., Bjornson K., Catalanotti C., Delaney J., Fehr A., et al. (2019). Resolving the full spectrum of human genome variation using Linked-Reads. Genome Res. 29, 635–645. 10.1101/gr.234443.118. PubMed DOI PMC

Weisenfeld N.I., Kumar V., Shah P., Church D.M., and Jaffe D.B. (2017). Direct determination of diploid genome sequences. Genome Res. 27, 757–767. 10.1101/gr.214874.116. PubMed DOI PMC

Huang N., and Li H. (2023). miniBUSCO: a faster and more accurate reimplementation of BUSCO (Genomics) 10.1101/2023.06.03.543588. PubMed DOI PMC

Yun T., Li H., Chang P.-C., Lin M.F., Carroll A., and McLean C.Y. (2021). Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics 36, 5582–5589. 10.1093/bioinformatics/btaa1081. PubMed DOI PMC

Garrison E., Sirén J., Novak A.M., Hickey G., Eizenga J.M., Dawson E.T., Jones W., Garg S., Markello C., Lin M.F., et al. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat. Biotechnol. 36, 875–879. 10.1038/nbt.4227. PubMed DOI PMC

Tarailo-Graovac M., and Chen N. (2009). Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinforma. Chapter 4, 4.10.1–4.10.14. 10.1002/0471250953.bi0410s25. PubMed DOI

Sirén J., Monlong J., Chang X., Novak A.M., Eizenga J.M., Markello C., Sibbesen J.A., Hickey G., Chang P.-C., Carroll A., et al. (2021). Pangenomics enables genotyping of known structural variants in 5202 diverse genomes. Science 374, abg8871. 10.1126/science.abg8871. PubMed DOI PMC

Mulligan M.K., Mozhui K., Prins P., and Williams R.W. (2017). GeneNetwork: A Toolbox for Systems Genetics. In Systems Genetics Methods in Molecular Biology., Schughart K. and Williams R. W., eds. (Springer; New York: ), pp. 75–120. 10.1007/978-1-4939-6427-7_4. PubMed DOI PMC

Marissal-Arvy N., Heliès J.-M., Tridon C., Moisan M.-P., and Mormède P. (2014). Quantitative Trait Loci Influencing Abdominal Fat Deposition and Functional Variability of the HPA Axis in the Rat. Horm. Metab. Res. 46, 635–643. 10.1055/s-0034-1383574. PubMed DOI

Schafer M.K.-H., Mahata S.K., Stroth N., Eiden L.E., and Weihe E. (2010). Cellular distribution of chromogranin A in excitatory, inhibitory, aminergic and peptidergic neurons of the rodent central nervous system. Regul. Pept. 165, 36–44. 10.1016/j.regpep.2009.11.021. PubMed DOI PMC

Ciesielski-Treska J., Ulrich G., Taupenot L., Chasserot-Golaz S., Corti A., Aunis D., and Bader M.-F. (1998). Chromogranin A Induces a Neurotoxic Phenotype in Brain Microglial Cells. J. Biol. Chem. 273, 14339–14346. 10.1074/jbc.273.23.14339. PubMed DOI

Jirout M.L., Friese R.S., Mahapatra N.R., Mahata M., Taupenot L., Mahata S.K., Křen V., Zídek V., Fischer J., Maatz H., et al. (2010). Genetic regulation of catecholamine synthesis, storage and secretion in the spontaneously hypertensive rat. Hum. Mol. Genet. 19, 2567–2580. 10.1093/hmg/ddq135. PubMed DOI PMC

Zhang K., Mir S.A., Hightower C.M., Miramontes-Gonzalez J.P., Maihofer A.X., Chen Y., Mahata S.K., Nievergelt C.M., Schork N.J., Freedman B.I., et al. (2015). Molecular Mechanism for Hypertensive Renal Disease: Differential Regulation of Chromogranin A Expression at 3′-Untranslated Region Polymorphism C+87T by MicroRNA-107. J. Am. Soc. Nephrol. 26, 1816–1825. 10.1681/ASN.2014060537. PubMed DOI PMC

Nagozir S., Shakouri Khomartash M., Parsania M., Vahidi M., and Ghorbani M. (2023). Association between genetic variants in the CD209 gene and susceptibility to COVID-19 in Iranian population. Hum. Gene 38, 201215. 10.1016/j.humgen.2023.201215. DOI

Morton C.O., Fliesser M., Dittrich M., Mueller T., Bauer R., Kneitz S., Hope W., Rogers T.R., Einsele H., and Loeffler J. (2014). Gene Expression Profiles of Human Dendritic Cells Interacting with Aspergillus fumigatus in a Bilayer Model of the Alveolar Epithelium/Endothelium Interface. PLoS ONE 9, e98279. 10.1371/journal.pone.0098279. PubMed DOI PMC

Sakuntabhai A., Turbpaiboon C., Casadémont I., Chuansumrit A., Lowhnoo T., Kajaste-Rudnitski A., Kalayanarooj S.M., Tangnararatchakit K., Tangthawornchaikul N., Vasanawathana S., et al. (2005). A variant in the CD209 promoter is associated with severity of dengue disease. Nat. Genet. 37, 507–513. 10.1038/ng1550. PubMed DOI PMC

Vannberg F.O., Chapman S.J., Khor C.C., Tosh K., Floyd S., Jackson-Sillah D., Crampin A., Sichali L., Bah B., Gustafson P., et al. (2008). CD209 Genetic Polymorphism and Tuberculosis Disease. PLoS ONE 3, e1388. 10.1371/journal.pone.0001388. PubMed DOI PMC

Ivancevic A., and Chuong E.B. (2020). Transposable elements teach T cells new tricks. Proc. Natl. Acad. Sci. 117, 9145–9147. 10.1073/pnas.2004493117. PubMed DOI PMC

Bosco A., McKenna K.L., Firth M.J., Sly P.D., and Holt P.G. (2009). A Network Modeling Approach to Analysis of the Th2 Memory Responses Underlying Human Atopic Disease. J. Immunol. 182, 6011–6021. 10.4049/jimmunol.0804125. PubMed DOI PMC

Herazo-Maya J.D., Noth I., Duncan S.R., Kim S., Ma S.-F., Tseng G.C., Feingold E., Juan-Guardela B.M., Richards T.J., Lussier Y., et al. (2013). Peripheral Blood Mononuclear Cell Gene Expression Profiles Predict Poor Outcome in Idiopathic Pulmonary Fibrosis. Sci. Transl. Med. 5. 10.1126/scitranslmed.3005964. PubMed DOI PMC

Zeng D., Wu J., Luo H., Li Y., Xiao J., Peng J., Ye Z., Zhou R., Yu Y., Wang G., et al. (2021). Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer. J. Immunother. Cancer 9, e002467. 10.1136/jitc-2021-002467. PubMed DOI PMC

Chen J., Xu X., and Zhang S. (2019). Silence of long noncoding RNA NEAT1 exerts suppressive effects on immunity during sepsis by promoting microRNA-125-dependent MCEMP1 downregulation. IUBMB Life 71, 956–968. 10.1002/iub.2033. PubMed DOI

Wood H. (2016). MCEMP1 — a new prognostic and diagnostic biomarker for stroke? Nat. Rev. Neurol. 12, 127–127. 10.1038/nrneurol.2016.17. PubMed DOI

Choi Y.J., Yoo J.-S., Jung K., Rice L., Kim D., Zlojutro V., Frimel M., Madden E., Choi U.Y., Foo S.-S., et al. (2023). Lung-specific MCEMP1 functions as an adaptor for KIT to promote SCF-mediated mast cell proliferation. Nat. Commun. 14, 2045. 10.1038/s41467-023-37873-3. PubMed DOI PMC

Huang N., and Li H. (2023). compleasm: a faster and more accurate reimplementation of BUSCO. Bioinformatics 39, btad595. 10.1093/bioinformatics/btad595. PubMed DOI PMC

Guarracino Garrison 2021. wfmash: a pangenome-scale aligner.

Garrison E., Guarracino A., Heumos S., Villani F., Bao Z., Tattini L., Hagmann J., Vorbrugg S., Marco-Sola S., Kubica C., et al. (2023). Building pangenome graphs (Bioinformatics) 10.1101/2023.04.05.535718. PubMed DOI

Danecek P., Bonfield J.K., Liddle J., Marshall J., Ohan V., Pollard M.O., Whitwham A., Keane T., McCarthy S.A., Davies R.M., et al. (2021). Twelve years of SAMtools and BCFtools. GigaScience 10, giab008. 10.1093/gigascience/giab008. PubMed DOI PMC

Cleary J.G., Braithwaite R., Gaastra K., Hilbush B.S., Inglis S., Irvine S.A., Jackson A., Littin R., Rathod M., Ware D., et al. (2015). Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines (Bioinformatics) 10.1101/023754. DOI

Martin F.J., Amode M.R., Aneja A., Austine-Orimoloye O., Azov A.G., Barnes I., Becker A., Bennett R., Berry A., Bhai J., et al. (2023). Ensembl 2023. Nucleic Acids Res. 51, D933–D941. 10.1093/nar/gkac958. PubMed DOI PMC

Wheeler T.J., Clements J., Eddy S.R., Hubley R., Jones T.A., Jurka J., Smit A.F.A., and Finn R.D. (2012). Dfam: a database of repetitive DNA based on profile hidden Markov models. Nucleic Acids Res. 41, D70–D82. 10.1093/nar/gks1265. PubMed DOI PMC

Guarracino A., Heumos S., Nahnsen S., Prins P., and Garrison E. (2022). ODGI: understanding pangenome graphs. Bioinforma. Oxf. Engl. 38, 3319–3326. 10.1093/bioinformatics/btac308. PubMed DOI PMC

Wick R.R., Schultz M.B., Zobel J., and Holt K.E. (2015). Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352. 10.1093/bioinformatics/btv383. PubMed DOI PMC

Sayers E.W., Bolton E.E., Brister J.R., Canese K., Chan J., Comeau D.C., Connor R., Funk K., Kelly C., Kim S., et al. (2022). Database resources of the national center for biotechnology information. Nucleic Acids Res. 50, D20–D26. 10.1093/nar/gkab1112. PubMed DOI PMC

Tan A., Abecasis G.R., and Kang H.M. (2015). Unified representation of genetic variants. Bioinformatics 31, 2202–2204. 10.1093/bioinformatics/btv112. PubMed DOI PMC

Garrison E., Kronenberg Z.N., Dawson E.T., Pedersen B.S., and Prins P. (2022). A spectrum of free software tools for processing the VCF variant call format: vcflib, bio-vcf, cyvcf2, hts-nim and slivar. PLOS Comput. Biol. 18, e1009123. 10.1371/journal.pcbi.1009123. PubMed DOI PMC

English A.C., Menon V.K., Gibbs R.A., Metcalf G.A., and Sedlazeck F.J. (2022). Truvari: refined structural variant comparison preserves allelic diversity. Genome Biol. 23, 271. 10.1186/s13059-022-02840-6. PubMed DOI PMC

Cingolani P., Platts A., Wang L.L., Coon M., Nguyen T., Wang L., Land S.J., Lu X., and Ruden D.M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w 1118; iso-2; iso-3. Fly (Austin) 6, 80–92. 10.4161/fly.19695. PubMed DOI PMC

Arends D (2017)) BXDtools.

Gel B., and Serra E. (2017). karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics 33, 3088–3090. 10.1093/bioinformatics/btx346. PubMed DOI PMC

Li H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100. 10.1093/bioinformatics/bty191. PubMed DOI PMC

Robinson J.T. (2011). Integrative genomics viewer. C O Rresp O N N Ce 29. PubMed PMC