Direct estimation of mutations in great apes reconciles phylogenetic dating

Besenbacher, Søren; Hvilsom, Christina; Marquès‐Bonet, Tomàs; Mailund, Thomas; Schierup, Mikkel H.

doi:10.1038/s41559-018-0778-x

Cited by 147 publications

(226 citation statements)

References 23 publications

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“…Generation interval can be difficult to define precisely, even for well-studied taxa such as modern humans (Scally & Durbin, 2012). Reliable estimates of mutation rates are not easily obtained, because phylogenetic estimates of long-term evolutionary rates are not necessarily applicable at the population level (Ho, Duchêne, Molak, & Shapiro, 2015); rates of spontaneous mutation have been inferred for a limited number of eukaryote species (Besenbacher et al, 2019;Smeds, Qvarnström, & Ellegren, 2016). If estimates of the mutation rate are unavailable for the target species, common practice has been to employ the phylogenetically closest estimate (see Section 5).…”

Section: Scaling the Graphsmentioning

confidence: 99%

“…The use of demographic plots for this purpose is subject to a host of confounding factors, including those that affect the standard use of PSMC to infer population-size history. Other more generally accepted methods for estimating mutation rates, particularly those based on genome sequences from parent-offspring sets (e.g., Besenbacher, Hvilsom, Marques-Bonet, Mailund, & Schierup, 2019;Roach et al, 2010) or mutation-accumulation lines (e.g., Ossowski et al, 2010), are likely to be substantially more accurate.…”

Section: Estimating Mutation Ratementioning

confidence: 99%

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A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

Mather

Traves

2019

Ecology and Evolution

105

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A common goal of population genomics and molecular ecology is to reconstruct the demographic history of a species of interest. A pair of powerful tools based on the sequentially Markovian coalescent have been developed to infer past population sizes using genome sequences. These methods are most useful when sequences are available for only a limited number of genomes and when the aim is to study ancient demographic events. The results of these analyses can be difficult to interpret accurately, because doing so requires some understanding of their theoretical basis and of their sensitivity to confounding factors. In this practical review, we explain some of the key concepts underpinning the pairwise and multiple sequentially Markovian coalescent methods (PSMC and MSMC, respectively). We relate these concepts to the use and interpretation of these methods, and we explain how the choice of different parameter values by the user can affect the accuracy and precision of the inferences. Based on our survey of 100 PSMC studies and 30 MSMC studies, we describe how the two methods are used in practice. Readers of this article will become familiar with the principles, practice, and interpretation of the sequentially Markovian coalescent for inferring demographic history.

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Section: Scaling the Graphsmentioning

confidence: 99%

Section: Estimating Mutation Ratementioning

confidence: 99%

A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

Mather

Traves

2019

Ecology and Evolution

105

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“…As re-deposition from lower, older layers seems unlikely (10,12), this might indicate that the genetic dates, based on the current mutation rate in humans, is incorrect. Possible explanations could be that Neandertals had a lower mutation rate than modern humans or that that the modern human mutation rate decreased recently (15). Additional high-quality genomes determined from well-dated Neandertal remains are needed to address these possibilities.…”

Section: Genome Sequencing and Age Estimatesmentioning

confidence: 99%

A high-coverage Neandertal genome from Chagyrskaya Cave

Mafessoni

Grote

Filippo

et al. 2020

Preprint

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We sequenced the genome of a Neandertal from Chagyrskaya Cave in the Altai Mountains, Russia, to 27-fold genomic coverage. We estimate that this individual lived ~80,000 years ago and was more closely related to Neandertals in western Eurasia (1,2) than to Neandertals who lived earlier in Denisova Cave (3), which is located about 100 km away. About 12.9% of the Chagyrskaya genome is spanned by homozygous regions that are between 2.5 and 10 centiMorgans (cM) long. This is consistent with that Siberian Neandertals lived in relatively isolated populations of less than 60 individuals. In contrast, a Neandertal from Europe, a Denisovan from the Altai Mountains and ancient modern humans seem to have lived in populations of larger sizes. The availability of three Neandertal genomes of high quality allows a first view of genetic features that were unique to Neandertals and that are likely to have been at high frequency among them. We find that genes highly expressed in the striatum in the basal ganglia of the brain carry more amino acid-changing substitutions than genes expressed elsewhere in the brain, suggesting that the striatum may have evolved unique functions in Neandertals.

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“…OWMs is approximately half that of great apes, in which direct estimates range from 251 1.13×10 −8 per bp in gorillas to 1.68×10 −8 per bp in orangutans (23). Existing point 252 estimates for OWM species are generally lower than those of apes, varying from a point 253 estimate of 0.37×10 −8 per bp in rhesus macaques to 0.94×10 −8 per bp in green monkeys 254 (24,38).…”

Section: Estimating Sex-specific Germline Mutation Rates and Age Effementioning

confidence: 99%

A comparison of humans and baboons suggests germline mutation rates do not track cell divisions

Strand

Ober

et al. 2019

Preprint

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In humans, most germline mutations are inherited from the father. This observation is widely interpreted as resulting from the replication errors that accrue during spermatogenesis. If so, the male bias in mutation should be substantially lower in a closely related species with similar rates of spermatogonial stem cell divisions but a shorter mean age of reproduction. To test this hypothesis, we resequenced two 3–4 generation nuclear families (totaling 29 individuals) of olive baboons (Papio anubis), who reproduce at ~10 years of age on average. We inferred sex-specific mutation rates by analyzing the data in parallel with three three-generation human pedigrees (26 individuals). The mutation rate per generation in baboons is 0.55×10−8 per base pair, approximately half that of humans. Strikingly, however, the degree of male mutation bias is approximately 3:1, similar to that of humans; in fact, a similar male bias is seen across mammals that reproduce months, years or decades after birth. These results echo findings in humans that the male bias is stable with parental ages and cast further doubt on the assumption that germline mutations track cell divisions. Our mutation rate estimates for baboons raise a further puzzle in suggesting a divergence time between apes and Old World Monkeys of 67 My, too old to be consistent with the fossil record; reconciling them now requires not only a slowdown of the mutation rate per generation in humans but also in baboons.

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Direct estimation of mutations in great apes reconciles phylogenetic dating

Cited by 147 publications

References 23 publications

A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

A high-coverage Neandertal genome from Chagyrskaya Cave

A comparison of humans and baboons suggests germline mutation rates do not track cell divisions

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