Lynch, M. et al. Genetic drift, selection and the evolution of the mutation rate. Nat. Rev. Genet. 17, 704–714 (2016).
Google Scholar
Bergeron, L. A. et al. The mutationathon highlights the importance of reaching standardization in estimates of pedigree-based germline mutation rates. eLife 11, e73577 (2022).
Google Scholar
Lynch, M. Evolution of the mutation rate. Trends Genet. 26, 345–352 (2010).
Google Scholar
Acuna-Hidalgo, R., Veltman, J. A. & Hoischen, A. New insights into the generation and role of de novo mutations in health and disease. Genome Biol. 17, 241 (2016).
Google Scholar
Sturtevant, A. H. Essays on evolution. I. On the effects of selection on mutation rate. Q. Rev. Biol. 12, 464–467 (1937).
Google Scholar
Zhang, G. The mutation rate as an evolving trait. Nat. Rev. Genet. 24, 3 (2022).
Google Scholar
Mugal, C. F., Arndt, P. F., Holm, L. & Ellegren, H. Evolutionary consequences of DNA methylation on the GC content in vertebrate genomes. G3 5, 441–447 (2015).
Google Scholar
Baer, C. F., Miyamoto, M. M. & Denver, D. R. Mutation rate variation in multicellular eukaryotes: causes and consequences. Nat. Rev. Genet. 8, 619–631 (2007).
Google Scholar
Wright, S. D., Ross, H. A., Jeanette Keeling, D., McBride, P. & Gillman, L. N. Thermal energy and the rate of genetic evolution in marine fishes. Evol. Ecol. 25, 525–530 (2011).
Google Scholar
Ohta, T. An examination of the generation-time effect on molecular evolution. Proc. Natl Acad. Sci. USA 90, 10676–10680 (1993).
Google Scholar
Martin, A. P. & Palumbi, S. R. Body size, metabolic rate, generation time, and the molecular clock. Proc. Natl Acad. Sci. USA 90, 4087–4091 (1993).
Google Scholar
Bergeron, L. A. et al. The germline mutational process in rhesus macaque and its implications for phylogenetic dating. Gigascience 10, giab029 (2021).
Google Scholar
Wu, F. L. et al. A comparison of humans and baboons suggests germline mutation rates do not track cell divisions. PLoS Biol. 18, e3000838 (2020).
Google Scholar
Wang, R. J. et al. Paternal age in rhesus macaques is positively associated with germline mutation accumulation but not with measures of offspring sociability. Genome Res. 30, 826–834 (2020).
Google Scholar
Campbell, C. R. et al. Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur. Heredity 127, 233–244 (2021).
Google Scholar
Besenbacher, S., Hvilsom, C., Marques-Bonet, T., Mailund, T. & Schierup, M. H. Direct estimation of mutations in great apes reconciles phylogenetic dating. Nat. Ecol. Evol. 3, 286–292 (2019).
Google Scholar
Thomas, G. W. C. et al. Reproductive longevity predicts mutation rates in primates. Curr. Biol. 28, 3193–3197.e5 (2018).
Google Scholar
Cagan, A. et al. Somatic mutation rates scale with lifespan across mammals. Nature 604, 517–524 (2022).
Google Scholar
Chintalapati, M. & Moorjani, P. Evolution of the mutation rate across primates. Curr. Opin. Genet. Dev. 62, 58–64 (2020).
Google Scholar
Wang, R. J. et al. De novo mutations in domestic cat are consistent with an effect of reproductive longevity on both the rate and spectrum of mutations. Mol. Biol. Evol. 39, msac147 (2022).
Google Scholar
Venn, O. et al. Strong male bias drives germline mutation in chimpanzees. Science 344, 1272–1275 (2014).
Google Scholar
Jónsson, H. et al. Parental influence on human germline de novo mutations in 1,548 trios from Iceland. Nature 549, 519–522 (2017).
Google Scholar
Tatsumoto, S. et al. Direct estimation of de novo mutation rates in a chimpanzee parent-offspring trio by ultra-deep whole genome sequencing. Sci. Rep. 7, 13561 (2017).
Google Scholar
Yuen, R. K. C. et al. Genome-wide characteristics of de novo mutations in autism. npj Genomic Med. 1, 160271–1602710 (2016).
Google Scholar
Wang, H. & Zhu, X. De novo mutations discovered in 8 Mexican American families through whole genome sequencing. BMC Proc. 8, S24 (2014).
Google Scholar
Li, W.-H., Yi, S. & Makova, K. Male-driven evolution. Curr. Opin. Genet. Dev. 12, 650–656 (2002).
Google Scholar
Miyata, T., Hayashida, H., Kuma, K., Mitsuyasu, K. & Yasunaga, T. Male-driven molecular evolution: a model and nucleotide sequence analysis. Cold Spring Harb. Symp. Quant. Biol. 52, 863–867 (1987).
Google Scholar
Wilson Sayres, M. A. & Makova, K. D. Genome analyses substantiate male mutation bias in many species. BioEssays 33, 938–945 (2011).
Google Scholar
Ellegren, H. & Fridolfsson, A.-K. Male-driven evolution of DNA sequences in birds. Nat. Genet. 17, 182–184 (1997).
Google Scholar
Sayres, M. A. W., Venditti, C., Pagel, M. & Makova, K. D. Do variations in substitution rates and male mutations bias correlate with life-history traits? A study of 32 mammalian genomes. Evolution 65, 2800–2815 (2011).
Google Scholar
de Manuel, M., Wu, F. L. & Przeworski, M. A paternal bias in germline mutation is widespread in amniotes and can arise independently of cell division numbers. eLife 11, e80008 (2022).
Google Scholar
Francioli, L. C. et al. Genome-wide patterns and properties of de novo mutations in humans. Nat. Genet. 47, 822–826 (2015).
Google Scholar
Gao, Z. et al. Overlooked roles of DNA damage and maternal age in generating human germline mutations. Proc. Natl Acad. Sci. USA 116, 9491–9500 (2019).
Google Scholar
Lindsay, S. J., Rahbari, R., Kaplanis, J., Keane, T. & Hurles, M. E. Similarities and differences in patterns of germline mutation between mice and humans. Nat. Commun. 10, 4053 (2019).
Google Scholar
Gibbs, R. A. et al. Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428, 493–520 (2004).
Google Scholar
Blumenstiel, J. P. Sperm competition can drive a male-biased mutation rate. J. Theor. Biol. 249, 624–632 (2007).
Google Scholar
Birkhead, T. R., Briskie, J. V. & Møller, A. P. Male sperm reserves and copulation frequency in birds. Behav. Ecol. Sociobiol. 32, 85–93 (1993).
Google Scholar
Moller, A. P. Sperm competition, sperm depletion, paternal care, and relative testis size in birds. Am. Nat. 137, 882–906 (1991).
Google Scholar
Birkhead, T. R. & Montgomerie, R. Three decades of sperm competition in birds. Phil. Trans. R. Soc. B 375, 20200208 (2020).
Google Scholar
Brouwer, L. & Griffith, S. C. Extra-pair paternity in birds. Mol. Ecol. 28, 4864–4882 (2019).
Google Scholar
Hunter, F. M., Harcourt, R., Wright, M. & Davis, L. S. Strategic allocation of ejaculates by male Adélie penguins. Proc. R. Soc. Lond. B 267, 1541–1545 (2000).
Google Scholar
Hamamah, S. & Gatti, J. L. Role of the ionic environment and internal pH on sperm activity. Hum. Reprod. 13, 20–30 (1998).
Google Scholar
Gribbins, K. Reptilian spermatogenesis. Spermatogenesis 1, 250–269 (2011).
Google Scholar
Gribbins, K. M., Gist, D. H. & Congdon, J. D. Cytological evaluation of spermatogenesis and organization of the germinal epithelium in the male slider turtle, Trachemys scripta. J. Morphol. 255, 337–346 (2003).
Google Scholar
Schulz, R. W. et al. Spermatogenesis in fish. Gen. Comp. Endocrinol. 165, 390–411 (2010).
Google Scholar
Lubzens, E., Young, G., Bobe, J. & Cerdà, J. Oogenesis in teleosts: how fish eggs are formed. Gen. Comp. Endocrinol. 165, 367–389 (2010).
Google Scholar
Jalabert, B. Particularities of reproduction and oogenesis in teleost fish compared to mammals. Reprod. Nutr. Dev. 45, 261–279 (2005).
Google Scholar
Jónsson, H. et al. Multiple transmissions of de novo mutations in families. Nat. Genet. 50, 1674–1680 (2018).
Google Scholar
Martin, H. C. et al. Insights into platypus population structure and history from whole-genome sequencing. Mol. Biol. Evol. 35, 1238–1252 (2018).
Google Scholar
Smeds, L., Qvarnström, A. & Ellegren, H. Direct estimate of the rate of germline mutation in a bird. Genome Res. 26, 1211–1218 (2016).
Google Scholar
Feng, C. et al. Moderate nucleotide diversity in the Atlantic herring is associated with a low mutation rate. eLife 6, e23907 (2017).
Google Scholar
Gao, Z., Wyman, M. J., Sella, G. & Przeworski, M. Interpreting the dependence of mutation rates on age and time. PLoS Biol. 14, e1002355 (2016).
Google Scholar
Goodman, M. Rates of molecular evolution: the hominoid slowdown. BioEssays 3, 9–14 (1985).
Google Scholar
Moorjani, P., Amorim, C. E. G., Arndt, P. F. & Przeworski, M. Variation in the molecular clock of primates. Proc. Natl Acad. Sci. USA 113, 10607–10612 (2016).
Google Scholar
Scally, A. & Durbin, R. Revising the human mutation rate: implications for understanding human evolution. Nat. Rev. Genet. 13, 745–753 (2012).
Google Scholar
Soojin, V. Y. Morris Goodman’s hominoid rate slowdown: the importance of being neutral. Mol. Phylogenet. Evol. 66, 569–574 (2013).
Google Scholar
Faircloth, B. C. et al. Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales. Syst. Biol. 61, 717–726 (2012).
Google Scholar
Garcia, J. A. & Lohmueller, K. E. Negative linkage disequilibrium between amino acid changing variants reveals interference among deleterious mutations in the human genome. PLoS Genet. 17, e1009676 (2021).
Google Scholar
Hedrick, P. W. & Garcia-Dorado, A. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31, 940–952 (2016).
Google Scholar
Bonnet, T. et al. Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals. Science 376, 1012–1016 (2022).
Google Scholar
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
Google Scholar
Chen, Y. et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience 7, 1–6 (2017).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).
Kong, A. et al. Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488, 471–475 (2012).
Google Scholar
Besenbacher, S. et al. Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios. Nat. Commun. 6, 5969 (2015).
Google Scholar
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).
Google Scholar
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
Google Scholar
Alfaro, M. E. et al. Explosive diversification of marine fishes at the Cretaceous–Palaeogene boundary. Nat. Ecol. Evol. 2, 688–696 (2018).
Google Scholar
Faircloth, B. C. PHYLUCE is a software package for the analysis of conserved genomic loci. Bioinformatics 32, 786–788 (2016).
Google Scholar
Katoh, K., Misawa, K., Kuma, K. I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
Google Scholar
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).
Google Scholar
Sanderson, M. J. Estimating absolute rates of molecular evolution and divergence times: a penalized likelihood approach. Mol. Biol. Evol. 19, 101–109 (2002).
Google Scholar
Kim, J. & Sanderson, M. J. Penalized likelihood phylogenetic inference: bridging the parsimony-likelihood gap. Syst. Biol. 57, 665–674 (2008).
Google Scholar
Meredith, R. W. et al. Impacts of the cretaceous terrestrial revolution and KPg extinction on mammal diversification. Science 334, 521–524 (2011).
Google Scholar
Hughes, L. C. et al. Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data. Proc. Natl Acad. Sci. USA 115, 6249–6254 (2018).
Google Scholar
Benton, M. J. & Donoghue, P. C. J. Paleontological evidence to date the tree of life. Mol. Biol. Evol. 24, 26–53 (2007).
Google Scholar
Green, R. E. et al. Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs. Science 346, 1254449 (2014).
Google Scholar
Sues, H. D. & Olsen, P. E. Triassic vertebrates of Gondwanan aspect from the Richmond basin of Virginia. Science 249, 1020–1023 (1990).
Google Scholar
Bauer, A. M., Böhme, W. & Weitschat, W. An Early Eocene gecko from Baltic amber and its implications for the evolution of gecko adhesion. J. Zool. 265, 327–332 (2005).
Google Scholar
Gelabert, P. et al. Evolutionary history, genomic adaptation to toxic diet, and extinction of the Carolina parakeet. Curr. Biol. 30, 108–114.e5 (2020).
Google Scholar
Maretty, L. et al. Sequencing and de novo assembly of 150 genomes from Denmark as a population reference. Nature 548, 87–91 (2017).
Google Scholar
Orme, D. et al. The caper package: comparative analysis of phylogenetics and evolution in R. R version 1.0.1 https://cran.r-project.org/package=caper (2018).
Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).
Google Scholar
Schmitz, J. et al. Genome sequence of the basal haplorrhine primate Tarsius syrichta reveals unusual insertions. Nat. Commun. 7, 12997 (2016).
Google Scholar
Vijay, N. et al. Population genomic analysis reveals contrasting demographic changes of two closely related dolphin species in the last glacial. Mol. Biol. Evol. 35, 2026–2033 (2018).
Google Scholar
Liu, Y. C. et al. Genome-wide evolutionary analysis of natural history and adaptation in the world’s tigers. Curr. Biol. 28, 3840–3849.e6 (2018).
Google Scholar
Xu, S., Zhao, L., Xiao, S. & Gao, T. Whole genome resequencing data for three rockfish species of Sebastes. Sci. Data 6, 97 (2019).
Google Scholar
Yuan, Z. et al. Historical demography of common carp estimated from individuals collected from various parts of the world using the pairwise sequentially markovian coalescent approach. Genetica 146, 235–241 (2018).
Google Scholar
Fitak, R. R. & Johnsen, S. Green sea turtle (Chelonia mydas) population history indicates important demographic changes near the mid-Pleistocene transition. Mar. Biol. 165, 110 (2018).
Google Scholar
Nadachowska-Brzyska, K., Li, C., Smeds, L., Zhang, G. & Ellegren, H. Temporal dynamics of avian populations during pleistocene revealed by whole-genome sequences. Curr. Biol. 25, 1375–1380 (2015).
Google Scholar
Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 356 (2014).
Google Scholar
Milholland, B. et al. Differences between germline and somatic mutation rates in humans and mice. Nat. Commun. 8, 15183 (2017).
Google Scholar
The 1000 Genomes Project. Variation in genome-wide mutation rates within and between human families. Nat. Genet. 43, 712–714 (2011).
Rahbari, R. et al. Timing rates and spectra of human germline mutation. Nat. Genet. 48, 126–133 (2015).
Google Scholar
Wong, W. S. W. et al. New observations on maternal age effect on germline de novo mutations. Nat. Commun. 7, 10486 (2016).
Google Scholar
Turner, T. N. et al. Genomic patterns of de novo mutation in simplex autism. Cell 171, 710–722.e12 (2017).
Google Scholar
Sasani, T. A. et al. Large three-generation human families reveal post-zygotic mosaicism and variability in germline mutation accumulation. eLife 8, e46922 (2019).
Google Scholar
Kessler, M. D. et al. De novo mutations across 1465 diverse genomes reveal mutational insights and reductions in the Amish founder population. Proc. Natl Acad. Sci. USA 117, 2560–2569 (2020).
Google Scholar
Malinsky, M. et al. Whole-genome sequences of Malawi cichlids reveal multiple radiations interconnected by gene flow. Nat. Ecol. Evol. 2, 1940–1955 (2018).
Google Scholar
Koch, E. M. et al. De novo mutation rate estimation in wolves of known pedigree. Mol. Biol. Evol. 36, 2536–2547 (2019).
Google Scholar
Harland, C. et al. Frequency of mosaicism points towards mutation-prone early cleavage cell divisions in cattle. Preprint at bioRxiv https://doi.org/10.1101/079863 (2017).
Pfeifer, S. P. Direct estimate of the spontaneous germ line mutation rate in African green monkeys. Evolution 71, 2858–2870 (2017).
Google Scholar