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).
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).
Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987 (2014).
Google Scholar
Jarman, S. N. et al. Molecular biomarkers for chronological age in animal ecology. Mol. Ecol. 24, 4826–4847 (2015).
Google Scholar
Thompson, M. J., vonHoldt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging 9, 1055–1068 (2017).
Google Scholar
De Paoli-Iseppi, R. et al. Measuring animal age with DNA methylation: from humans to wild animals. Front. Genet. 8, 106 (2017).
Google Scholar
Bell, C. G. et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 20, 249 (2019).
Google Scholar
Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).
Google Scholar
Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).
Google Scholar
Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 e956 (2017).
Google Scholar
Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, 68 (2017).
Google Scholar
Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction, and rapamycin treatment. Genome Biol. 18, 57 (2017).
Google Scholar
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).
Google Scholar
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).
Google Scholar
Voisin, S. et al. An epigenetic clock for human skeletal muscle. J. Cachexia Sarcopenia Muscle https://doi.org/10.1002/jcsm.12556 (2020).
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).
Google Scholar
Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8, 9998 (2018).
Google Scholar
Chen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 8, 1844–1865 (2016).
Google Scholar
Christiansen, L. et al. DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell 15, 149–154 (2016).
Google Scholar
Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi‐ supercentenarians and their offspring. Aging 7, 1159–1170 (2018).
Marioni, R. E. et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 16, 25 (2015).
Google Scholar
Perna, L. et al. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenetics 8, 64 (2016).
Google Scholar
Mitchell, C., Schneper, L. M. & Notterman, D. A. DNA methylation, early life environment, and health outcomes. Pediatr. Res. 79, 212–219 (2016).
Google Scholar
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).
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).
Google Scholar
Lee, R. S. et al. Chronic corticosterone exposure increases expression and decreases deoxyribonucleic acid methylation of Fkbp5 in mice. Endocrinology 151, 4332–4343 (2010).
Google Scholar
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).
Google Scholar
Biemont, C. Inbreeding effects in the epigenetic era. Nat. Rev. Genet. 11, 234 (2010).
Google Scholar
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).
Google Scholar
Vergeer, P., Wagemaker, N. C. & Ouborg, N. J. Evidence for an epigenetic role in inbreeding depression. Biol. Lett. 8, 798–801 (2012).
Google Scholar
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).
Google Scholar
Thompson, M. J. et al. A multi-tissue full lifespan epigenetic clock for mice. Aging 10, 2832–2854 (2018).
Google Scholar
Zhang, Q. et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 11, 54 (2019).
Google Scholar
Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics 14, 912–926 (2019).
Google Scholar
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).
Moehlman, P. D. Equids: Zebras, Asses, and Horses Status Survey and Conservation Action Plan Vol. 37, 190 pp (IUCN/SSC Equid Specialist Group, 2002).
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).
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).
Google Scholar
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).
Google Scholar
Curik, I., Ferenčaković, M. & Sölkner, J. Inbreeding and runs of homozygosity: a possible solution to an old problem. Livest. Sci. 166, 26–34 (2014).
Anderson, J. A. et al. The costs of competition: high social status males experience accelerated epigenetic aging in wild baboons. eLife 10, e66128 (2020).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. https://doi.org/10.1038/nbt.1630 (2010).
Gronniger, E. et al. Aging and chronic sun exposure cause distinct epigenetic changes in human skin. PLoS Genet. 6, e1000971 (2010).
Google Scholar
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).
Jonsson, H. et al. Speciation with gene flow in equids despite extensive chromosomal plasticity. Proc. Natl Acad. Sci. USA 111, 18655–18660 (2014).
Google Scholar
Vilstrup, J. T. et al. Mitochondrial phylogenomics of modern and ancient equids. PLoS One 8, e55950 (2013).
Google Scholar
Jensen-Seaman, M. I. & Hooper-Boyd, K. A. in Encyclopedia of Life Sciences (ELS) (John Wiley & Sons, Ltd., 2008).
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).
Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. Epigenomics 10, 695–706 (2018).
Google Scholar
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).
Google Scholar
Fox, C. W. Inbreeding depression increases with maternal age. Evolut. Ecol. Res. 12, 961–972 (2010).
Benton, C. H. et al. Inbreeding intensifies sex- and age-dependent disease in a wild mammal. J. Anim. Ecol. 87, 1500–1511 (2018).
Google Scholar
Mayne, B., Berry, O., Davies, C., Farley, J. & Jarman, S. A genomic predictor of lifespan in vertebrates. Sci. Rep. 9, 17866 (2019).
Google Scholar
McClain, A. T. & Faulk, C. The evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging 10, 561–572 (2018).
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).
Google Scholar
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).
Google Scholar
Li, Z. et al. PBX3 is an important cofactor of HOXA9 in leukemogenesis. Blood 121, 1422–1431 (2013).
Google Scholar
Malecki, M. T. et al. Mutations in NEUROD1 are associated with the development of type 2 diabetes mellitus. Nat. Genet. 23, 323–328 (1999).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Giampietro, C. et al. The alternative splicing factor Nova2 regulates vascular development and lumen formation. Nat. Commun. 6, 8479 (2015).
Google Scholar
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).
Google Scholar
Deneen, B. et al. The transcription factor NFIA controls the onset of gliogenesis in the developing spinal cord. Neuron 52, 953–968 (2006).
Google Scholar
Hiraike, Y. et al. NFIA co-localizes with PPARgamma and transcriptionally controls the brown fat gene program. Nat. Cell Biol. 19, 1081–1092 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Mittelstaedt, T., Alvarez-Baron, E. & Schoch, S. RIM proteins and their role in synapse function. Biol. Chem. 391, 599–606 (2010).
Google Scholar
Schoch, S. et al. RIM1α forms a protein scaffold for regulating neurotransmitter release at the active zone. Nature 415, 321–326 (2002).
Google Scholar
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).
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Ambeskovic, M. et al. Ancestral stress programs sex-specific biological aging trajectories and non-communicable disease risk. Aging 12, 3828–3847 (2020).
Google Scholar
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).
Google Scholar
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).
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).
Google Scholar
Dube, J. B. et al. Genetic determinants of “cognitive impairment, no dementia”. J. Alzheimers Dis. 33, 831–840 (2013).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Gao, Z. et al. Neurod1 is essential for the survival and maturation of adult-born neurons. Nat. Neurosci. 12, 1090–1092 (2009).
Google Scholar
Badawi, Y. & Nishimune, H. Presynaptic active zones of mammalian neuromuscular junctions: Nanoarchitecture and selective impairments in aging. Neurosci. Res. 127, 78–88 (2018).
Google Scholar
Tollervey, J. R. et al. Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res. 21, 1572–1582 (2011).
Google Scholar
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).
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).
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).
Google Scholar
Quach, A. et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging 9, 419–446 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Karesh, W. B. in Zoo and Wild Animal Medicine: Current Therapy (eds Fowler Murray, E. & Eric Miller, R.) 298−308 (Saunders Elsevier, 2008).
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).
Google Scholar
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).
Snyder-Mackler, N. et al. Efficient genome-wide sequencing and low-coverage pedigree analysis from noninvasively collected samples. Genetics 203, 699–714 (2016).
Google Scholar
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).
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).
Kalbfleisch, T. S. et al. Improved reference genome for the domestic horse increases assembly contiguity and composition. Commun. Biol. 1, 197 (2018).
Google Scholar
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).
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).
Google Scholar
Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011).
Google Scholar
Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).
Google Scholar
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).
Larison, B. et al. Population structure, inbreeding and stripe pattern abnormalities in plains zebras. Mol. Ecol. 30, 379–390 (2021).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows−Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).
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).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Google Scholar
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).
Google Scholar
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).
Google Scholar
McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359–372 (2008).
Google Scholar
Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Zeileis, A. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 11, 1–17 (2004).
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).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).
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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