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Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids

  • 1.

    Beissinger, S. R. & Westphal, M. I. On the use of demographic models of population viability in endangered species management. J. Wildl. Manag. 62, 821–841 (1998).

    Google Scholar 

  • 2.

    Campana, S. Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. J. Fish. Biol. 59, 197–242 (2001).

    Google Scholar 

  • 3.

    Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 4.

    Jarman, S. N. et al. Molecular biomarkers for chronological age in animal ecology. Mol. Ecol. 24, 4826–4847 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • 5.

    Thompson, M. J., vonHoldt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging 9, 1055–1068 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 6.

    De Paoli-Iseppi, R. et al. Measuring animal age with DNA methylation: from humans to wild animals. Front. Genet. 8, 106 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 7.

    Bell, C. G. et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 20, 249 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 8.

    Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 9.

    Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 10.

    Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 e956 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 11.

    Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, 68 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 12.

    Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction, and rapamycin treatment. Genome Biol. 18, 57 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 13.

    Nussey, D. H., Froy, H., Lemaitre, J. F., Gaillard, J. M. & Austad, S. N. Senescence in natural populations of animals: widespread evidence and its implications for bio-gerontology. Ageing Res. Rev. 12, 214–225 (2013).

    PubMed 

    Google Scholar 

  • 14.

    Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 15.

    Voisin, S. et al. An epigenetic clock for human skeletal muscle. J. Cachexia Sarcopenia Muscle https://doi.org/10.1002/jcsm.12556 (2020).

  • 16.

    De Paoli-Iseppi, R. et al. Age estimation in a long-lived seabird (Ardenna tenuirostris) using DNA methylation-based biomarkers. Mol. Ecol. Resour. 19, 411–425 (2019).

    PubMed 

    Google Scholar 

  • 17.

    Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8, 9998 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 18.

    Chen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 8, 1844–1865 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 19.

    Christiansen, L. et al. DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell 15, 149–154 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • 20.

    Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi‐ supercentenarians and their offspring. Aging 7, 1159–1170 (2018).

    Google Scholar 

  • 21.

    Marioni, R. E. et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 16, 25 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 22.

    Perna, L. et al. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenetics 8, 64 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 23.

    Mitchell, C., Schneper, L. M. & Notterman, D. A. DNA methylation, early life environment, and health outcomes. Pediatr. Res. 79, 212–219 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • 24.

    Pérez, R. F., Santamarina, P., Fernández, A. F., & Fraga, M. F. Epigenetics and Lifestyle: The Impact of Stress, Diet, and Social Habits on Tissue Homeostasis. In Epigenetics and Regeneration (ed. Palacios, D.) pp. 461–489 (Academic Press, 2019).

  • 25.

    Szyf, M., Tang, Y. Y., Hill, K. G. & Musci, R. The dynamic epigenome and its implications for behavioral interventions: a role for epigenetics to inform disorder prevention and health promotion. Transl. Behav. Med. 6, 55–62 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 26.

    Lee, R. S. et al. Chronic corticosterone exposure increases expression and decreases deoxyribonucleic acid methylation of Fkbp5 in mice. Endocrinology 151, 4332–4343 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 27.

    Zannas, A. S. et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 16, 266 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 28.

    Biemont, C. Inbreeding effects in the epigenetic era. Nat. Rev. Genet. 11, 234 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • 29.

    Venney, C. J., Johansson, M. L. & Heath, D. D. Inbreeding effects on gene-specific DNA methylation among tissues of Chinook salmon. Mol. Ecol. 25, 4521–4533 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • 30.

    Vergeer, P., Wagemaker, N. C. & Ouborg, N. J. Evidence for an epigenetic role in inbreeding depression. Biol. Lett. 8, 798–801 (2012).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 31.

    Han, W. et al. Genome-wide analysis of the role of DNA methylation in inbreeding depression of reproduction in Langshan chicken. Genomics 112, 2677–2687 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • 32.

    Thompson, M. J. et al. A multi-tissue full lifespan epigenetic clock for mice. Aging 10, 2832–2854 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 33.

    Zhang, Q. et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 11, 54 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 34.

    Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics 14, 912–926 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 35.

    Pinho, G. M. et al. Hibernation slows epigenetic aging in yellow-bellied marmots. Preprint at bioRxiv https://doi.org/10.1101/2021.03.07.434299 (2021).

  • 36.

    Moehlman, P. D. Equids: Zebras, Asses, and Horses Status Survey and Conservation Action Plan Vol. 37, 190 pp (IUCN/SSC Equid Specialist Group, 2002).

  • 37.

    Moehlman, P. D. & King, S. R. B. IUCN SSC Equid Specialist Group 2020 Report. https://www.iucn.org/commissions/ssc-groups/mammals/mammals-a-e/equid (2020).

  • 38.

    Rubinacci, S., Ribeiro, D. M., Hofmeister, R. & Delaneau, O. Efficient phasing and imputation of low-coverage sequencing data using large reference panels. Nat. Genet. 53, 120–126 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • 39.

    Ceballos, F. C., Hazelhurst, S. & Ramsay, M. Runs of homozygosity in sub-Saharan African populations provide insights into complex demographic histories. Hum. Genet. 138, 1123–1142 (2019).

    CAS 
    PubMed 

    Google Scholar 

  • 40.

    Curik, I., Ferenčaković, M. & Sölkner, J. Inbreeding and runs of homozygosity: a possible solution to an old problem. Livest. Sci. 166, 26–34 (2014).

    Google Scholar 

  • 41.

    Anderson, J. A. et al. The costs of competition: high social status males experience accelerated epigenetic aging in wild baboons. eLife 10, e66128 (2020).

    Google Scholar 

  • 42.

    McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. https://doi.org/10.1038/nbt.1630 (2010).

  • 43.

    Gronniger, E. et al. Aging and chronic sun exposure cause distinct epigenetic changes in human skin. PLoS Genet. 6, e1000971 (2010).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 44.

    Robeck, T. R. et al. Multi-species and multi-tissue methylation clocks for age estimation in toothed whales and dolphins. Commun. Biol. https://doi.org/10.1038/s42003-021-02179-x (2021).

  • 45.

    Jonsson, H. et al. Speciation with gene flow in equids despite extensive chromosomal plasticity. Proc. Natl Acad. Sci. USA 111, 18655–18660 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 46.

    Vilstrup, J. T. et al. Mitochondrial phylogenomics of modern and ancient equids. PLoS One 8, e55950 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 47.

    Jensen-Seaman, M. I. & Hooper-Boyd, K. A. in Encyclopedia of Life Sciences (ELS) (John Wiley & Sons, Ltd., 2008).

  • 48.

    Farrell, C., Snir, S. & Pellegrini, M. The epigenetic pacemaker—modeling epigenetic states under an evolutionary framework. Bioinformatics https://doi.org/10.1093/bioinformatics/btaa585 (2020).

  • 49.

    Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. Epigenomics 10, 695–706 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 50.

    Charlesworth, B. & Hughes, K. A. Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence. Proc. Natl Acad. Sci. USA 93, 6140–6145 (1996).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 51.

    Fox, C. W. Inbreeding depression increases with maternal age. Evolut. Ecol. Res. 12, 961–972 (2010).

    Google Scholar 

  • 52.

    Benton, C. H. et al. Inbreeding intensifies sex- and age-dependent disease in a wild mammal. J. Anim. Ecol. 87, 1500–1511 (2018).

    PubMed 

    Google Scholar 

  • 53.

    Mayne, B., Berry, O., Davies, C., Farley, J. & Jarman, S. A genomic predictor of lifespan in vertebrates. Sci. Rep. 9, 17866 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 54.

    McClain, A. T. & Faulk, C. The evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging 10, 561–572 (2018).

    Google Scholar 

  • 55.

    Alpi, A. F., Pace, P. E., Babu, M. M. & Patel, K. J. Mechanistic insight into site-restricted monoubiquitination of FANCD2 by Ube2t, FANCL, and FANCI. Mol. Cell 32, 767–777 (2008).

    CAS 
    PubMed 

    Google Scholar 

  • 56.

    Kannan, M. B., Solovieva, V. & Blank, V. The small MAF transcription factors MAFF, MAFG, and MAFK: current knowledge and perspectives. Biochim. Biophys. Acta 1823, 1841–1846 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • 57.

    Li, Z. et al. PBX3 is an important cofactor of HOXA9 in leukemogenesis. Blood 121, 1422–1431 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 58.

    Malecki, M. T. et al. Mutations in NEUROD1 are associated with the development of type 2 diabetes mellitus. Nat. Genet. 23, 323–328 (1999).

    CAS 
    PubMed 

    Google Scholar 

  • 59.

    Ding, Q., Joshi, P. S., Xie, Z. H., Xiang, M. & Gan, L. BARHL2 transcription factor regulates the ipsilateral/contralateral subtype divergence in postmitotic dI1 neurons of the developing spinal cord. Proc. Natl Acad. Sci. USA 109, 1566–1571 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 60.

    Mo, Z., Li, S., Yang, X. & Xiang, M. Role of the Barhl2 homeobox gene in the specification of glycinergic amacrine cells. Development 131, 1607–1618 (2004).

    CAS 
    PubMed 

    Google Scholar 

  • 61.

    Giampietro, C. et al. The alternative splicing factor Nova2 regulates vascular development and lumen formation. Nat. Commun. 6, 8479 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • 62.

    Yano, M., Hayakawa-Yano, Y., Mele, A. & Darnell, R. B. Nova2 regulates neuronal migration through an RNA switch in disabled-1 signaling. Neuron 66, 848–858 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 63.

    Deneen, B. et al. The transcription factor NFIA controls the onset of gliogenesis in the developing spinal cord. Neuron 52, 953–968 (2006).

    CAS 
    PubMed 

    Google Scholar 

  • 64.

    Hiraike, Y. et al. NFIA co-localizes with PPARgamma and transcriptionally controls the brown fat gene program. Nat. Cell Biol. 19, 1081–1092 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 65.

    Caricasole, A., Sala, C., Roncarati, R., Formenti, E. & Terstappen, G. C. Cloning and characterization of the human phosphoinositide-specific phospholipase C-beta 1 (PLCβ1). Biochim. Biophys. Acta 1517, 63–72 (2000).

    CAS 
    PubMed 

    Google Scholar 

  • 66.

    McOmish, C. E., Burrows, E. L., Howard, M. & Hannan, A. J. PLC-beta1 knockout mice as a model of disrupted cortical development and plasticity: behavioral endophenotypes and dysregulation of RGS4 gene expression. Hippocampus 18, 824–834 (2008).

    CAS 
    PubMed 

    Google Scholar 

  • 67.

    Mittelstaedt, T., Alvarez-Baron, E. & Schoch, S. RIM proteins and their role in synapse function. Biol. Chem. 391, 599–606 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • 68.

    Schoch, S. et al. RIM1α forms a protein scaffold for regulating neurotransmitter release at the active zone. Nature 415, 321–326 (2002).

    CAS 
    PubMed 

    Google Scholar 

  • 69.

    Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.01.18.426733 (2021).

  • 70.

    Nishikawa, K. et al. Maf promotes osteoblast differentiation in mice by mediating the age-related switch in mesenchymal cell differentiation. J. Clin. Invest. 120, 3455–3465 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 71.

    Saidak, Z., Hay, E., Marty, C., Barbara, A. & Marie, P. J. Strontium ranelate rebalances bone marrow adipogenesis and osteoblastogenesis in senescent osteopenic mice through NFATc/Maf and Wnt signaling. Aging Cell 11, 467–474 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • 72.

    McClay, J. L. et al. A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects. Hum. Mol. Genet. 23, 1175–1185 (2014).

    CAS 
    PubMed 

    Google Scholar 

  • 73.

    Ambeskovic, M. et al. Ancestral stress programs sex-specific biological aging trajectories and non-communicable disease risk. Aging 12, 3828–3847 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 74.

    Burger, C., Lopez, M. C., Baker, H. V., Mandel, R. J. & Muzyczka, N. Genome-wide analysis of aging and learning-related genes in the hippocampal dentate gyrus. Neurobiol. Learn Mem. 89, 379–396 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 75.

    Horvath, S. et al. DNA methylation clocks show slower progression of aging in naked mole-rat queens. Preprint at bioRxiv https://doi.org/10.1101/2021.03.15.435536 (2021).

  • 76.

    Rapoport, S. I., Primiani, C. T., Chen, C. T., Ahn, K. & Ryan, V. H. Coordinated expression of phosphoinositide metabolic genes during development and aging of human dorsolateral prefrontal cortex. PLoS One 10, e0132675 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 77.

    Dube, J. B. et al. Genetic determinants of “cognitive impairment, no dementia”. J. Alzheimers Dis. 33, 831–840 (2013).

    CAS 
    PubMed 

    Google Scholar 

  • 78.

    Hinney, A. et al. Genetic variation at the CELF1 (CUGBP, elav-like family member 1 gene) locus is genome-wide associated with Alzheimer’s disease and obesity. Am. J. Med. Genet. B Neuropsychiatr. Genet. 165B, 283–293 (2014).

    PubMed 

    Google Scholar 

  • 79.

    Ntalla, I. et al. Replication of established common genetic variants for adult BMI and childhood obesity in Greek adolescents: the TEENAGE study. Ann. Hum. Genet. 77, 268–274 (2013).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 80.

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 81.

    Gao, Z. et al. Neurod1 is essential for the survival and maturation of adult-born neurons. Nat. Neurosci. 12, 1090–1092 (2009).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 82.

    Badawi, Y. & Nishimune, H. Presynaptic active zones of mammalian neuromuscular junctions: Nanoarchitecture and selective impairments in aging. Neurosci. Res. 127, 78–88 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 83.

    Tollervey, J. R. et al. Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res. 21, 1572–1582 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 84.

    Kim, B. H., Nho, K. & Lee, J. M., Alzheimer’s Disease Neuroimaging, I. Genome-wide association study identifies susceptibility loci of brain atrophy to NFIA and ST18 in Alzheimer’s disease. Neurobiol. Aging 102, 200 e201–200 e211 (2021).

    Google Scholar 

  • 85.

    Horvath, S. et al. DNA methylation aging and transcriptomic studies in horses. Preprint at bioRxiv https://doi.org/10.1101/2021.03.11.435032 (2021).

  • 86.

    Benayoun, B. A., Pollina, E. A. & Brunet, A. Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat. Rev. Mol. Cell Biol. 16, 593–610 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 87.

    Quach, A. et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging 9, 419–446 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 88.

    Crary-Dooley, F. K. et al. A comparison of existing global DNA methylation assays to low-coverage whole-genome bisulfite sequencing for epidemiological studies. Epigenetics 12, 206–214 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 89.

    Reed, K., Poulin, M. L., Yan, L. & Parissenti, A. M. Comparison of bisulfite sequencing PCR with pyrosequencing for measuring differences in DNA methylation. Anal. Biochem. 397, 96–106 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • 90.

    Tost, J., Dunker, J. & Gut, I. G. Analysis and quantification of multiple methylation variable positions in CpG islands by Pyrosequencing. Biotechniques 35, 152–156 (2003).

    CAS 
    PubMed 

    Google Scholar 

  • 91.

    Karesh, W. B. in Zoo and Wild Animal Medicine: Current Therapy (eds Fowler Murray, E. & Eric Miller, R.) 298−308 (Saunders Elsevier, 2008).

  • 92.

    Chiou, K. L. & Bergey, C. M. Methylation-based enrichment facilitates low-cost, noninvasive genomic scale sequencing of populations from feces. Sci. Rep. 8, 1975 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 93.

    Orkin, J. D. et al. The genomics of ecological flexibility, large brains, and long lives in capuchin monkeys revealed with fecalFACS. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2010632118 (2021).

  • 94.

    Snyder-Mackler, N. et al. Efficient genome-wide sequencing and low-coverage pedigree analysis from noninvasively collected samples. Genetics 203, 699–714 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 95.

    Harley, E. H., Knight, M. H., Lardner, C., Wooding, B. & Gregor, M. The Quagga project: progress over 20 years of selective breeding. South African J. Wildlife Res. https://doi.org/10.3957/056.039.0206 (2009).

  • 96.

    Arneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences Preprint at bioRxiv https://doi.org/10.1101/2021.01.07.425637 (2021).

  • 97.

    Kalbfleisch, T. S. et al. Improved reference genome for the domestic horse increases assembly contiguity and composition. Commun. Biol. 1, 197 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 98.

    Wade, C. M. et al. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science https://doi.org/10.1126/science.1178158 (2009).

  • 99.

    Zhou, W., Triche, T. J. Jr., Laird, P. W. & Shen, H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 100.

    Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 101.

    Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 102.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 103.

    R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).

  • 104.

    Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).

  • 105.

    Larison, B. et al. Population structure, inbreeding and stripe pattern abnormalities in plains zebras. Mol. Ecol. 30, 379–390 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • 106.

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

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 107.

    Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).

  • 108.

    Freed, D., Aldana, R., Weber, J. A. & Edwards, J. S. The Sentieon Genomics Tools—A fast and accurate solution to variant calling from next-generation sequence data. Preprint at bioRxiv https://doi.org/10.1101/115717 (2017).

  • 109.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 110.

    Meyermans, R., Gorssen, W., Buys, N. & Janssens, S. How to study runs of homozygosity using PLINK? A guide for analyzing medium density SNP data in livestock and pet species. BMC Genomics 21, 94 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 111.

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 112.

    McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359–372 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 113.

    Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).

    Google Scholar 

  • 114.

    Zeileis, A. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 11, 1–17 (2004).

    Google Scholar 

  • 115.

    Zeileis, A., Köll, S. & Graham, N. Various versatile variances: an object-oriented implementation of clustered covariances in R. J. Stat. Softw. 95, 1–36 (2020).

    Google Scholar 

  • 116.

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

    Google Scholar 

  • 117.

    Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A. & Williams, R. M. J. Adjustment During Army Life (Princeton University Press, 1949).


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