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Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 2012 Apr 2;109(16):6006–6011. doi: 10.1073/pnas.1119506109

Evidence for a convergent slowdown in primate molecular rates and its implications for the timing of early primate evolution

Michael E Steiper a,b,c,d,1, Erik R Seiffert e
PMCID: PMC3341044  PMID: 22474376

Abstract

A long-standing problem in primate evolution is the discord between paleontological and molecular clock estimates for the time of crown primate origins: the earliest crown primate fossils are ∼56 million y (Ma) old, whereas molecular estimates for the haplorhine-strepsirrhine split are often deep in the Late Cretaceous. One explanation for this phenomenon is that crown primates existed in the Cretaceous but that their fossil remains have not yet been found. Here we provide strong evidence that this discordance is better-explained by a convergent molecular rate slowdown in early primate evolution. We show that molecular rates in primates are strongly and inversely related to three life-history correlates: body size (BS), absolute endocranial volume (EV), and relative endocranial volume (REV). Critically, these traits can be reconstructed from fossils, allowing molecular rates to be predicted for extinct primates. To this end, we modeled the evolutionary history of BS, EV, and REV using data from both extinct and extant primates. We show that the primate last common ancestor had a very small BS, EV, and REV. There has been a subsequent convergent increase in BS, EV, and REV, indicating that there has also been a convergent molecular rate slowdown over primate evolution. We generated a unique timescale for primates by predicting molecular rates from the reconstructed phenotypic values for a large phylogeny of living and extinct primates. This analysis suggests that crown primates originated close to the K–Pg boundary and possibly in the Paleocene, largely reconciling the molecular and fossil timescales of primate evolution.

When molecular rates are constant, divergence dates can be estimated by using fossils to calibrate “molecular clocks” (1). However, there is great variation in molecular rates among species (2–7), and this phenomenon can lead to inaccurate or biased molecular clock estimates. This problem has precipitated the development of methods that model rate variation across lineages to date phylogenies when rates vary (8–11). These “relaxed clock” methods use multiple calibrations and allow rates to vary according to the parameters of a model. Relaxed clock methods are especially appropriate for primates, because this clade exhibits large and systematic variation in molecular rates both within and among groups (e.g., the “hominoid slowdown”) (2, 4, 7, 12, 13, 14). Nevertheless, the application of these methods still results in large differences between paleontological and molecular estimates for many primate groups (Fig. 1 and Table S1). This discordance is particularly striking for the origin of the primate crown group, because recent molecular studies suggest a Late Cretaceous estimate (an average of ∼82 Ma) for this event and yet the oldest crown primate fossils are ∼56 Ma old (15)—a difference of ∼45%. Furthermore, studies that have statistically modeled sampling, speciation, and preservation rates over the course of primate evolution are similarly consistent with an ancient origin for crown primates (16, 17), showing that there are also wide gaps between these methods and “direct reading” approaches to the fossil record.

Fig. 1.

Fig. 1.

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Time-scaled phylogeny depicting divergence dates for the main groups of primates. Dates along the x axis are in Ma. Average molecular clock date estimates are from six recent studies (Table S1). The fossil femur cartoon indicates the earliest paleontological crown representatives of each taxon: crown Primates and crown Haplorhini, Teilhardina asiatica, 55.8 Ma, or, less securely, slightly older Altiatlasius koulchii (15, 57, 58); crown Strepsirrhini, Saharagalago, 37 Ma (59–61); crown Anthropoidea, Biretia, 37 Ma (59); crown Catarrhini, Morotopithecus, 20.6 Ma (62), crown Cebidae, long-lineage hypothesis (63), Branisella, 26–27 Ma (64); crown Cebidae, successive radiation hypothesis (65), Lagonimico (66) and others, 13.3 Ma (66, 67); crown Cercopithecoidea, Microcolobus, 9.9 Ma (68); crown Hominidae, Sivapithecus, 12.5 Ma (69). The colored circles indicate the average divergence estimates from both uncorrected and corrected methods (Table 3).

Here we investigate an alternative hypothesis for the discrepancy between molecular and fossil estimates of early primate divergences—that molecular rates were exceptionally rapid in the earliest primates, and that these rates have convergently slowed over the course of primate evolution. Indeed, a convergent rate slowdown has been suggested as an explanation for the large differences between the molecular and fossil evidence for the timing of placental mammalian evolution generally (18, 19). However, this hypothesis has not been directly tested within a particular mammalian group.

Here we test this “convergent rate slowdown” hypothesis in primates using a two-step analysis. First, using a comprehensive paleontological and neontological primate dataset (Table S2), we modeled the evolutionary history of three phenotypic traits: body size (BS), absolute endocranial volume (EV), and relative endocranial volume (REV). These traits were chosen because they are correlated with primate life history (20–22), which is in turn related to molecular evolutionary rates (23). Furthermore, BS, EV, and REV are much more easily estimable from fossils than are life-history variables themselves. These analyses yielded ancestral reconstructions for BS, EV, and REV from the entire primate radiation, enabling assessments of phenotypic and life-history changes over primate evolution. Second, we explicitly tested whether patterns of variation in molecular rates are correlated with patterns of variation in BS, EV, and REV. Molecular rates were estimated from four large DNA-sequence datasets totaling over 100 Mb, and these rates were correlated with BS, EV, and REV using phylogenetically corrected regression techniques.

Finally, we joined the results of these two analyses as a molecular clock technique. We predicted molecular rates for primates based on the BS, EV, and REV reconstruction from our analysis of fossil and extant primates and our regression formulae. In other words, we predict the molecular rates of long-extinct primates using our knowledge of their phenotypic attributes rooted in the fossil and extant data. This is a significant departure from the traditional method of generating molecular rates using fossils as calibrations. Because our method estimates molecular rates by using paleontological, phylogenetic, geological, and neontological sources, we feel that it has strong advantages over traditional calibration techniques.

Results

First, we tested whether BS, EV, and REV have changed directionally over primate evolution by using a Bayesian method to calculate the harmonic mean-likelihood values for a number of models (Table 1). For all three traits, the nondirectional Brownian-motion model was rejected in favor of a directional evolution model based on an analysis of the harmonic mean-likelihood values using Bayes factors (SI Text and Tables S3–S6). Subsequently, we used a Bayesian method to reconstruct the ancestral values for BS, EV, and REV at nodes throughout the primate phylogeny (24) (Fig. 2 and Tables S7 and S8). This analysis reconstructed the BS of the primate last common ancestor (LCA) as ∼55 g, with a small EV (2.3 cm3; cc) only slightly larger than the fossil plesiadapiform Ignacius (2.14 cc) (25). The REV of the primate LCA was reconstructed as having been lower than that of any known primate, living or extinct.

Table 1.

Final harmonic mean likelihoods

Model Scaling parameters BS EV REV
Brownian – −125.185 −32.841 24.900
δ −127.322 −33.503 23.837
κ −107.814 −26.165 30.016
λ −119.938 −33.413 26.009
δλ −119.861 −34.255 25.969
δκ −109.111 −24.485 30.815
κλ −109.295 −27.616 26.336
δλκ −108.973 −26.978 27.838
Directional – −120.583 −27.893 32.761
δ −125.564 −31.753 26.340
κ −103.524 −22.331 38.399
λ −113.443 −28.336 38.413
δλ −114.656 −29.661 35.745
δκ −107.025 −24.759 33.610
κλ −104.183 −24.570 40.275
δλκ −104.401 −25.350 37.554
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Fig. 2.

Fig. 2.

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Phenotypic reconstructions of ancestral BS, EV, and REV. Cladogram depicts ancestral trait reconstructions for BS, EV, and REV at key nodes in primate evolution. For BS, the area of the outside circle includes the area under the colored circle. Branches are not to scale.

We conclude that BS, EV, and REV have evolved directionally over primate evolution. Since the primate LCA, all major primate lineages have convergently evolved higher BS, EV, and REV. Because of the evidence that life-history correlates are inversely related to molecular rates in mammals (6, 23, 26), this result alone generally supports a slowdown in molecular rates in primate evolution.

Second, we tested for a specific relationship between BS, EV, and REV and molecular rates in four large DNA-sequence datasets. In 10 of the 12 phylogenetically corrected regressions there is a significant inverse relationship between molecular rates and our three phenotypic predictors: BS, EV, and REV (Fig. 3 and Table 2). These traits explain a large proportion of the variance in molecular rates. This result provides strong evidence that life-history correlates are related to molecular rates in primates, as has been found for primates and other mammals (6, 23, 26). Specifically noteworthy is the finding that molecular rates are very rapid in primates with small BS and EV and low REV. Critically, these analyses generated regression models that can be used to predict molecular rates from BS, EV, and REV data in primates, allowing us to predict the specific molecular rates associated with specific phenotypic values.

Fig. 3.

Fig. 3.

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Phenotypic data and molecular rate scatter plots. Scatter plots of molecular rate (per 108 y; y axis) and BS, EV, and REV. PGLS regression lines are shown. All regression statistics are found in Table 2. Red, Perelman et al. (7); green, the CYP7A1 region (16); blue, Jameson et al. (14); purple, the CFTR region of Prasad et al. (49).

Table 2.

Phylogenetic generalized least-square regression results

DNA dataset Scaling parameters Predictor R2 F df P value (F) β P value (β) α P value (α)
1 κλ BS 0.589 17.23 2, 12 0.0003 *** −0.0459 0.0014 ** 0.3174 0.0000 ***
2 κ 0.495 5.88 2, 6 0.0386 * −0.0401 0.0515 ⋅ 0.267 0.0014 ***
3 κ 0.111 7.40 2, 59 0.0014 ** −0.0194 0.0086 ** 0.1458 0.0000 ***
4 κ 0.415 9.92 2, 14 0.0021 ** −0.0316 0.0071 ** 0.2407 0.0001 ***
1 κ EV 0.345 6.32 2, 12 0.0133 * −0.0716 0.0272 * 0.2806 0.0001 ***
2 κ 0.500 5.99 2, 6 0.0372 * −0.0474 0.0500 ⋅ 0.2076 0.0004 ***
3 κ 0.162 10.66 2, 55 0.0001 *** −0.0358 0.0019 ** 0.1303 0.0000 ***
4 κ 0.407 9.60 2, 14 0.0024 ** −0.0373 0.0079 ** 0.1917 0.0001 ***
1 κλ REV 0.467 10.53 2, 12 0.0023 ** −0.1898 0.0070 ** 0.241 0.0000 ***
2 κλ 0.761 19.07 2, 6 0.0025 ** −0.1484 0.0047 ** 0.2025 0.0001 ***
3 κλ 0.009 0.48 2, 55 0.6196 n.s. −0.0214 0.4901 n.s. 0.0968 0.0000 ***
4 κλ 0.117 1.85 2, 14 0.1940 n.s. −0.0373 0.1955 n.s. 0.1559 0.0014 **
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Significance indicators: ⋅, P ∼ 0.05; *, 0.05 < P < 0.01; **, 0.01 < P < 0.001; ***, 0.001 < P; n.s., not significant. DNA dataset 1, CYP7A1 (16); 2, Jameson et al. (14); 3, Perelman et al. (7); 4, CFTR region of Prasad et al. (49).

We joined the ancestral reconstruction data to the formulae from the phylogenetically controlled regressions to predict the molecular rates for all of the lineages of the crown primate radiation. For each alignment, the regression formula was used to generate molecular rates for each lineage based on the ancestral reconstructions for each phenotypic trait. This resulted in primate phylogenies with “corrected” molecular rates that were scaled to absolute time and converted to ultrametric format (Fig. S1 and SI Appendix). These corrected molecular clock estimates are younger than those based on traditional calibration methods for most major divergences within primates (Fig. 1 and Table 3). The effect is strongest at the earliest primate nodes, where the discrepancy between molecular and fossil evidence for divergence times is most profound. The correction based on REV produced the youngest dates for the deepest primate divergences. Our estimates for the origin of crown primates were 70 Ma using the BS correction, 68 Ma using the EV correction, and 63 Ma using the REV correction. The uncorrected molecular clock estimate for this node was considerably older, both for these four datasets (76 Ma) and from other recent studies of primate molecular divergence dates (82 Ma).

Table 3.

Molecular clock results (all dates in Ma)

Uncorrected standard molecular clock method
 BS correction
 EV correction
 REV correction
Node Dataset Mean 95% CI low 95% CI high Estimate 95% low 95% high Estimate 95% low 95% high Estimate 95% low 95% high
Crown Primate 1 75.8 69.4 87.1 72.6 66.4 80.1 67.3 60.6 75.5 66.3 61.1 77.8
2 73.4 60.8 86.9 67.6 61.7 74.9 67.1 61.3 73.9 59.7 55.2 69.1
3 74.8 64.4 83.2 70.5 65.3 76.7 69.8 62.1 79.7 n.s. n.s. n.s.
4 79.1 70.2 89.8 69.2 64.1 75.2 68.7 63.6 74.6 n.s. n.s. n.s.
Average 75.8 66.2 86.8 70 64.4 76.7 68.2 61.9 75.9 63 58.1 73.5
Crown Haplorhini 1 72 64.7 84.4 67.4 61.5 74.5 62.4 56.3 69.9 62.5 54.3 73.9
2 68.8 56.3 77.7 64.2 58.3 71.4 63 57.7 69.4 56.8 49.9 66.2
3 70.2 60.3 78.3 66.4 61.5 72.4 66.4 59.2 75.7 n.s. n.s. n.s.
Average 70.3 60.4 80.1 66 60.4 72.7 63.9 57.7 71.7 59.7 52.1 70
Crown Strepsirrhini 1 50.7 40.3 61.1 59 54.2 64.7 54 48.9 60.2 53.9 52.1 61.6
2 56.3 42.2 67.3 52.9 48.8 57.8 53 48.8 57.9 48.2 46.4 54.6
3 52.9 42.3 62.9 54 50.1 58.6 52.4 46.9 59.5 n.s. n.s. n.s.
4 49.1 39.6 59.9 49.2 45.9 53 50.1 46.6 54.2 n.s. n.s. n.s.
Average 52.2 41.1 62.8 53.8 49.7 58.5 52.4 47.8 57.9 51.1 49.2 58.1
Crown Anthropoidea 1 35.9 30.3 42.1 36.3 33 40.5 34.7 31.2 39.1 36 30.6 44.2
2 34.4 27.3 44.3 35.5 31.9 40 34.7 31.6 38.5 32.3 27.6 39.3
3 41.4 36 47.4 35.4 32.7 38.6 37.5 33.4 43 n.s. n.s. n.s.
4 41.9 36.9 47.5 34.2 31.6 37.3 33.3 31.1 35.9 n.s. n.s. n.s.
Average 38.4 32.6 45.3 35.3 32.3 39.1 35.1 31.8 39.1 34.1 29.1 41.7
Crown Platyrrhini 3 19.5 16 22.4 19.8 18.6 21.1 21 19.1 23.2 n.s. n.s. n.s.
4 21 17.3 25 18.1 17.2 19.2 18.8 17.8 19.9 n.s. n.s. n.s.
Average 20.3 16.7 23.7 19 17.9 20.2 19.9 18.5 21.6 — — —
Crown Cebidae 1 16.2 13.1 19.9 18.7 17.6 19.9 17.6 16.5 18.9 22.5 19.8 26.4
3 15.9 12.8 18.5 16.7 15.8 17.8 17.1 15.8 18.6 n.s. n.s. n.s.
4 17.1 14.1 20.7 15.1 14.4 15.9 15.8 15 16.7 n.s. n.s. n.s.
Average 16.4 13.3 19.7 16.8 15.9 17.8 16.8 15.8 18 22.5 19.8 26.4
Crown Catarrhini 1 24.2 20.9 27.6 25.2 22.5 28.6 24.9 22.1 28.5 24.1 20.0 30.7
2 24 19.7 28.6 25.8 23 29.4 25.3 22.9 28.3 22.8 19.1 28.4
3 29.3 25.8 32.9 23.8 21.8 26.2 27.3 23.8 32 n.s. n.s. n.s.
4 28.3 25.2 31.9 23.1 21.2 25.9 22.1 20.6 23.9 n.s. n.s. n.s.
Average 26.4 22.9 30.3 24.5 22.1 27.6 24.9 22.3 28.2 23.5 19.6 29.5
Crown Cercopithecoidea 1 12.4 10.2 15.5 14.1 12.7 15.8 13.4 12.3 14.9 14.3 12.2 17.6
3 14.7 12.9 16.4 10.3 9.6 11.2 11.7 10.5 13.3 n.s. n.s. n.s.
4 14 12.7 16 11.1 10.2 12 11 10.3 11.9 n.s. n.s. n.s.
Average 13.7 11.9 16 11.8 10.8 13 12.1 11 13.3 14.3 12.2 17.6
Crown Hominidae (great ape) 1 15.7 13.2 19 16.6 14.8 18.8 17.2 15.2 19.7 15.9 13.1 20.7
2 13.5 10.4 18.3 16.3 14.5 18.6 15.5 14 17.3 13.7 11.4 17.5
3 16.3 14.2 18.7 15.4 14.1 17 22.1 18.7 26.8 n.s. n.s. n.s.
4 16.9 15.1 19.2 13.6 12.5 15 13.3 12.4 14.4 n.s. n.s. n.s.
Average 15.6 13.2 18.8 15.5 14 17.3 17 15.1 19.5 14.8 12.2 19.1
Human–chimpanzee 1 6.8 6.0 7.9 7.4 6.7 8.3 8.3 7.5 9.3 9.3 7.5 12.4
2 6.9 6.0 8.3 5.6 5.2 6.2 5.7 5.3 6.3 5.9 4.8 7.7
3 6.7 6.0 7.9 7.2 6.8 7.6 12 10.3 14.3 n.s n.s n.s
4 6.8 6.0 7.9 5.2 5 5.6 5.4 5.1 5.8 n.s n.s n.s
Average 6.8 6.0 8.0 6.4 5.9 6.9 7.9 7.1 8.9 7.6 6.2 10.1
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DNA dataset 1, CYP7A1 (16); 2, Jameson et al. (14); 3, Perelman et al. (7); 4, CFTR (49). CI, confidence interval; n.s., nonsignificant; —, not determined.

Discussion

Our study bears on three key issues in primate evolution: the phenotypes of early primates, the relationship between molecular clock rates and life-history correlates in primates, and the molecular and fossil timescale of primate evolution.

Our results support the hypothesis that the first crown primates were small (approximately the size of the smallest mouse lemurs) with relatively small brains (25, 27, 28), a phenotype that generally persisted along the haplorhine and anthropoid stem lineages (29, 30). This is at odds with alternative hypotheses that suggest that early primates were either smaller (31) or larger (32). Indeed, there has apparently been a convergent increase in BS (“Cope's rule”), EV, and REV in all of the major primate lineages. These results show that the early crown primates were not as “primate-like” as would be expected based on an analysis of exclusively living taxa, strongly supporting the idea that extinct primates must play a key role in models of early primate paleobiology.

Our results provide empirical support for an inverse relationship between three primate life-history correlates and molecular rates in nuclear genomes. This supports the long-standing idea that an organism's phenotype is correlated with its substitution rate, especially generation time (GT) (3, 6, 23, 26, 33–35) and body size (36–38). Our finding that both EV and REV are inversely correlated with molecular rates is key to testing these hypotheses, because the mutational mechanisms behind these two hypotheses are different. The GT hypothesis assumes that most germ-line mutations occur during DNA replication (3), whereas the BS hypothesis posits that smaller species have higher mass-specific metabolic rates and therefore a higher rate of DNA damage from oxygen radicals that are a by-product of metabolic processes (36). Because large brain sizes are linked to the extended life histories of primates (20), the inverse relationship between EV and REV and molecular rates is consistent with the GT hypothesis. Basal metabolic rate (BMR) is increased in primates with large brain sizes even when controlling for body size (39), however, so the BS hypothesis would predict that larger-brained primates should have higher mutation rates, which is inconsistent with our findings. Our results may support a role of body size in molecular rates, but only if the mechanism behind the mutation rate is not BMR-related. An alternative body-size hypothesis is that rates are related to selection for higher-fidelity DNA replication in large-bodied organisms because they have larger numbers of cells to maintain (19). Our results also support the intriguing idea that mutation rates are not primarily a by-product of other processes but are rather part of an overall life-history strategy (19, 40), a hypothesis that deserves further consideration.

Regardless of the mechanism, we clearly show that early primates had low BS, EV, and REV and, by inference, also had fast molecular rates. Furthermore, we show that rates slowed down over the course of primate evolution, strongly supporting the application of the convergent rate slowdown hypothesis (18, 19) to primates. Due to the strength of these relationships, we were able to predict the molecular rates of different primate lineages from ancestral reconstructions of BS, EV, and REV, side-stepping the numerous concerns surrounding the use of the fossil record to calibrate molecular rates (41, 42). Our corrected dates indicate that crown primates originated near the K–Pg boundary and perhaps as recently as the Paleocene. These estimates are younger than those provided by past molecular clock and modeling studies and are much closer in age to the earliest undoubted crown primate, Teilhardina asiatica (∼56 Ma) (15) and direct reading interpretations of the fossil record (43). Our findings suggest that the primate fossil record is not necessarily poorly sampled. Rather, convergent evolution of extended life histories within primates has led to artificially ancient molecular clock estimates in this group. Further work is required to assess whether a convergent rate slowdown hypothesis (18, 19) can resolve the molecular and fossil timescales of other placental mammalian clades.

Materials and Methods

Our study has three methodological steps. The first step is an analysis of BS, EV, and REV evolution across primates, including estimation of ancestral values. The second step is a test for a relationship between molecular clock rates and BS, EV, and REV. The third step links the results of the first two steps to generate corrected molecular clock rates that were applied to molecular branch lengths, thereby generating corrected molecular divergence date estimates.

Phenotypic Evolution.

Phenotypic data.

We modeled the evolution of three phenotypic traits: BS, EV, and REV (Table S2). Residuals from a phylogenetically corrected BS/EV regression were used as a measure of REV by taking the difference between the observed and the predicted EV values (28) (SI Text).

Models of trait evolution.

We used BayesTraits (44–46) to test different models of trait evolution for BS, EV, and REV. We tested whether a directional model of trait evolution was preferred to a nondirectional Brownian-motion model. These analyses also tested whether different phylogenetic scaling parameters (δ, λ, and κ) improved the model of evolution by modeling the tempo, model, and phylogenetic association of a trait's evolution (45). Our analysis closely follows Montgomery et al. (28).

Phylogenetic tree.

The analyses require trait data from extant and fossil taxa and a bifurcating, time-scaled tree. We used a supertree approach to obtain a time-scaled phylogeny of living and extinct primates, combining results from multiple studies into a taxon-rich phylogeny of 62 extant primate genera and 123 extinct primates (SI Text and Fig. S2).

Model testing.

Models were compared using Bayes factors (BFs). BFs operate in a manner similar to likelihood ratio testing. The test statistic is 2 × (log[harmonic mean(better model)] − log[harmonic mean(worse model)]), and values greater than 2 are evidence for the better model being preferred (47). The harmonic mean of the likelihoods of a long run of postburnin samples approximates the marginal likelihood of each model. For both the directional and nondirectional model, BFs were used to compare the inclusion of all permutations of the scaling parameters (none included, δ, κ, λ, δλ, δκ, κλ, and δκλ). (This was also used to choose the scaling factors for the regression analysis used for estimating REV data.) Once the model was chosen, BFs were used to test between the directional and nondirectional models. For each model, the Markov chain Monte Carlo (MCMC) run had 500,000 burn-in steps followed by at least 15 million steps sampled every 500th step. Acceptance rates were tuned to 15–40%. To assess convergence, each model's run was conducted twice. If the final harmonic mean likelihoods differed by >1, the number of iterations was increased until the differences were <1. Runs had up to 100 million iterations. The final harmonic mean likelihoods from these two runs were averaged for use in the BF analysis (SI Text and Tables S3–S6 and S9).

Ancestral trait reconstruction.

For each trait, we used MCMC data from the best model in conjunction with the phylogeny described above to estimate ancestral trait values in BayesTraits. For each reconstruction, model data were compiled from two MCMC analyses to calculate ancestral values in a phylogenetic context (24). Each MCMC run had 500,000 burnin and 2–10 million subsequent steps sampled every 500th step. Acceptance rates were tuned to 15–40% by changing the proposal parameter and by adding different sets of nodes to each run. Convergence was assessed with effective sample sizes (48) and multiple runs. The final estimate and 95% highest posterior densities (HPDs) for each trait value for each node were averages of two runs (Tables S7 and S8).

Analysis of Molecular Rates.

DNA-sequence datasets.

The four datasets analyzed were: (i) a realignment of data from the CFTR region from 16 primates from ref. 49, (ii) a taxonomically supplemented alignment of the CYP7A1 region (16) from 14 primates, (iii) a genome-wide sample of codon third positions from 1,078 transcripts from eight primates (14), and (iv) a taxonomically well-sampled, genome-wide set of regions (7). In total, these four alignments are over 6 Mb in length and total over 100 Mb of alignment data. These datasets allowed estimates of rate variation from datasets that are alternately very long, very genomically widespread, and very taxonomically well-sampled. Methodological details for the alignment of the CFTR and CYP7A1 data are in SI Text.

Molecular rate regressions.

We conducted weakly calibrated relaxed clock analyses (11) to generate rates across the primate phylogeny for the four datasets independently using a relaxed clock technique (10, 11, 50) (SI Text). Using a phylogenetically corrected least-squares method (PGLS) (51), we regressed these molecular rates from phylogenetic tips against the tip values for BS, EV, and REV for each of the four datasets. For the phenotypic variable, the average trait value over the entire tip lineage was used. For example, the BS estimate used for the human lineage was the average of the human BS, the reconstructed BS at the human/Australopithecus ancestor, and the reconstructed BS at the human/Pan ancestor. This was done because the rate estimates are estimated for an entire branch and therefore the same should hold for the phenotypic traits. We restricted the analysis to tips to allow the phenotypic estimates to derive more directly from observable data rather than reconstructions. Furthermore, it was sensible to exclude the deeper phylogenetic lineages from the regressions because it is within the earliest parts of primate phylogeny where the discord between molecular rates and phenotypes is predicted to be most profound. Regressions were conducted in caper (52).

In two cases the regression was significant, but the P values for β were only near significance [Jameson et al. (14) dataset; BS, β P = 0.05154 and EV, β P = 0.05001]. Because of the overall significance of the regression and the closeness of the probability values to the standard α value (0.05), these regression coefficients were used in subsequent analyses.

Correcting Molecular Clock Date Estimates.

Predicting corrected molecular rates.

To predict the molecular clock rates for each lineage, we used the reconstructed trait values from the first step with the regression formula for that trait for each DNA-sequence dataset from the second step. The average trait value for each lineage was used, as in the molecular rate regressions. For example, for the CFTR alignment, the BS regression formula is molecular rate (per 108 y) = −0.0316 × BS + 0.2407. For the Pongo lineage, with a BS value of 4.558, the formula predicts that the molecular rate is 0.0967 changes per 108 y. Corrected rategrams for each trait for each sequence dataset are in SI Appendix.

Estimating corrected molecular dates.

Using these corrected molecular rates, molecular clock estimates (branch lengths in absolute time) were generated for each lineage, for each phenotypic trait. To do this, the maximum-likelihood branch lengths for each DNA-sequence dataset were rescaled by the corrected molecular rates. For each dataset, the maximum-likelihood branch lengths were calculated using baseml (50) under the HKY+Γ5 model (53, 54) (SI Appendix). In the CFTR alignment, for example, the Pongo branch length was 0.0131. Dividing this by the BS corrected molecular rate for Pongo (0.0967 changes per 108 y) yields a branch length of 13.6 Ma. The uncorrected estimate for the Pongo lineage for this dataset was longer (16.9 Ma).

This method was applied to the four DNA-sequence datasets for each trait for which there was a significant PGLS regression, resulting in 10 trees with each branch length scaled to absolute time. Corrected nonultrametric molecular clock trees for each trait for each of the DNA-sequence datasets are in SI Appendix. Because there is variation in the time-scaled branch lengths, the trees are not ultrametric. To generate ultrametric trees, we used the mean path length (MPL) method (55) implemented in APE (56). We generated interval estimates for these divergence dates by conducting the same molecular date correction technique using the upper and lower limits from the 95% Bayesian HPDs of the BS, EV, and REV reconstructions. Corrected ultrametric molecular clock trees for each trait for each of the DNA-sequence datasets are found in Fig. S3 and SI Appendix.

Because the MPL method generates ultrametric trees, when one particular branch has a large difference relative to close neighboring branches, negative branch lengths are sometimes computed to allow all of the tips to “line up.” In our results, some short negative branch lengths are present within the New World monkeys and also within some groups of closely related taxa in the taxon-rich datasets (e.g., in the lemuriform clade). Within closely related taxa, small differences in branch lengths can lead to negative branch lengths because of short internodal branches. In these cases, there is also sometimes phylogenetic uncertainty that may cause short negative branch lengths. It may also be the case that some of the variance in molecular rate variation is not adequately modeled by the present method, another potential cause of the negative branch lengths. Additional fossil and phylogenetic evidence would help determine whether these differences are stochastic, have a deterministic cause related to the present method (such as undocumented changes in body size), or another cause.

Supplementary Material

Supporting Information
supp_109_16_6006__index.html (1.1KB, html)

Acknowledgments

We thank E. Douzery, J. Fleagle, A. Meade, D. Orme, H. Pontzer, and the reviewers for comments on the manuscript and assistance with methods. This research was supported, in part, by National Science Foundation Grants CNS-0958379, CNS-0855217, and BCS-0819186 and the City University of New York High Performance Computing Center. The infrastructure of the Anthropological Genetics Laboratory at Hunter College was supported, in part, by Grant RR003037 from the National Center for Research Resources, a component of the National Institutes of Health.

Footnotes

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1119506109/-/DCSupplemental.

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