MBE Advance Access originally published online on August 21, 2008
Molecular Biology and Evolution 2008 25(11):2247-2250; doi:10.1093/molbev/msn184
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Letters |
Positive Selection in ASPM Is Correlated with Cerebral Cortex Evolution across Primates but Not with Whole-Brain Size
* Department of Psychology, National University of Singapore, Singapore
Department of Biological Sciences and University Scholars Programme, National University of Singapore, Singapore
E-mail: dbsmr{at}nus.edu.sg.
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Abstract |
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The rapid increase of brain size is a key event in human evolution. Abnormal spindle-like microcephaly associated (ASPM) is discussed as a major candidate gene for explaining the exceptionally large brain in humans but ASPM’s role remains controversial. Here we use codon-specific models and a comparative approach to test this candidate gene that was initially identified in Homo–chimp comparisons. We demonstrate that accelerated evolution of ASPM (
= 4.7) at 16 amino acid sites occurred in 9 primate lineages with major changes in relative cerebral cortex size. However, ASPM’s evolution is not correlated with major changes in relative whole-brain or cerebellum sizes. Our results suggest that a single candidate gene such as ASPM can influence a specific component of the brain across large clades through changes in a few amino acid sites. We furthermore illustrate the power of using continuous phenotypic variability across primates to rigorously test candidate genes that have been implicated in the evolution of key human traits.
Key Words: ASPM • cerebral cortex evolution • positive selection • primates • PAML • maximum likelihood
Mutations in abnormal spindle-like microcephaly associated (ASPM) are responsible for a severely reduced brain size with no other significant abnormality (primary microcephaly) in a clinical sample of humans (Bond et al. 2002
). Comparative study of sequence evolution limited mostly to humans and other apes revealed that this gene has an accelerated rate of evolution in the Homo lineage (Zhang 2003; Evans et al. 2004), with ASPM possibly affecting brain size through controlling the spindle assembly during neural cell division (Fish et al. 2006). However, ASPM’s role as a candidate gene for brain size has recently been challenged based on gene expression studies (Kouprina et al. 2005), strong homology to genes not associated with the brain (Ponting 2006), and a lack of correlation between ASPM haplotypes and normal human brain size variability (Rushton et al. 2006; Woods et al. 2006; Dobson-Stone et al. 2007; Thimpson et al. 2007). One avenue for addressing such controversies surrounding candidate genes is through employing the comparative method (Goodman et al. 2005) by testing whether sequence evolution of candidate genes is correlated with quantitative phenotypic changes across a large clade. Such tests can now rely on recently developed techniques in evolutionary genetics that allow for detecting positive selection in specific codons as opposed to whole genes (Yang and Nielsen 2002; Zhang et al. 2005). Here we use these approaches to test whether changes in brain size found across primates are correlated with molecular evolution of ASPM.
We sequenced the two large exons of ASPM (exons 3 and 18; 70% of the transcribed ASPM protein) for 23 primate species to complement existing ASPM data for 11 species from GenBank (supplementary table S1, Supplementary Material online). We chose these exons because they contain most of the mutations that cause human primary microcephaly (Bond et al. 2002), have elevated rates of gene average in humans (Zhang 2003; Evans et al. 2004), and encompass the main functional sites of the protein (Ponting and Jackson 2005). We then identified those primate lineages that had major changes in relative whole-brain, cerebral cortex, and cerebellum size. Due to the lack of phenotypic data for many primate species, only a subset of species could be analyzed in these analyses (whole brain: 28 spp.; cerebral cortex: 15 spp.; and cerebellum: 15 spp.). We find that 9 primate lineages have major changes in relative cerebral cortex size, the main phenotype of interest (fig. 1). These clades deviated by 1 or more standard deviations (SDs) from the mean of change as revealed by squared change parsimony (Maddison 1991). This cutoff is biologically meaningful because it can translate up to 300% change in absolute or >10% change in relative cerebral cortex volumes. We used these lineages as "foreground branches" in the branch-site Model A (Yang and Nielsen 2002; Zhang et al. 2005), whereas all other branches were treated as background branches. Branch-site model A uses the maximum likelihood approach of model fitting to detect codon-specific positive selection by allowing (the ratio of nonsynonymous changes per nonsynonymous sites [dN] to synonymous changes per synonymous sites [dS]) to vary across codon positions as well as across foreground and background lineages. We find that a model of positive selection using foreground branches with major changes in relative cerebral cortex size explained the data significantly better than the null model of no positive selection (2
l = 14.67, P < 0.001 as tested by a chi-square test with degree of freedom [df] = 1; table 1). The branch-site model detected a high level of positive selection for 16 codons ( = 4.70). Specifically, 77% of 27 changes in positively selected sites occurred in the foreground branches although these branches contributed only 29% of all branches and only 42% of all codon changes across the tree (two-tailed exact binomial tests using expected proportions of 29% and 42%, both Ps < 0.0001).
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We used several approaches to rigorously test the robustness of our results. First, in order to rule out an undue influence of the high
previously found in the Homo lineage, we excluded Homo sapiens from the foreground but our results remained highly significant (2l = 13.78, P < 0.001). We similarly carried out 8 additional analyses where we excluded each of the remaining 8 foreground branches. All 2l remained significant, indicating that no single branch was driving the results (supplementary table S2, Supplementary Material online). Second, we randomly selected 9 branches among the background branches in figure 1 (Model B). The model did not explain the data significantly better than the null model of no positive selection (Ps > 0.05). Third, we tested for the specificity of the evolutionary correlate of ASPM by correlating the gene's evolution with relative whole-brain size as well as the size of the cerebellum, a major subcortical brain component not known to have ASPM expression. We again examined a model of positive selection in ASPM whereby foreground branches had major changes in either of these structures (1 or more SDs). These models did not explain the data significantly better than the null model (all Ps > 0.05; table 1); that is, positive selection in ASPM is only significantly correlated with cerebral cortex size but not with relative whole-brain or cerebellum sizes.
Our result provides strong evidence that the single-gene ASPM is associated with major changes in relative cerebral cortex size across primates. It thus questions the validity of recent reviews that implicated ASPM in the brain size expansion of humans only (Ponting and Jackson 2005; Woods et al. 2005). Particularly, striking is the result that only major changes of cerebral cortex size and not major changes in whole-brain or cerebellum size are associated with positive selection in ASPM. This is consistent with an expression report indicating that ASPM’s expression is limited to the cerebral cortex of the brain (Bond et al. 2002). Our findings stand in contrast to recent null findings correlating ASPM genotypes with human brain size variation. Those studies used the relatively imprecise phenotypic trait of whole brain instead of cerebral cortex size (Rushton et al. 2006; Woods et al. 2006; Thimpson et al. 2007). Although previous studies have shown that parts of the brain scale strongly with one another and especially with whole brain (e.g., Finlay and Darlington 1995), evidence here suggests that different brain parts still have their own evolutionary and functional differentiation with unique genetic bases (Barton 1999). Our current study thus addresses the nature of brain evolution, and we recommend that future neuroimaging and genetic studies should be more specific by examining associations between ASPM variants and cerebral cortex size.
Previous large-scale analyses have extended our understanding of brain and cognition genes at the genomic level, showing that most brain and cognition genes are under strong negative selection (Shi et al. 2006; Yu et al. 2006; Wang et al. 2007). These studies, however, used a different technique for detecting positive selection (gene average ). The same technique applied to our ASPM data identifies only 4 primate branches with gene average > 1.0 and only 2 clades with major changes in cerebral cortex size have > 1.0 (supplementary fig. S1, Supplementary Material online). Overall, these results suggest that future research should use codon-specific models to investigate positive selection in additional genes associated with brain and cognition phenotypes. These models are more biologically relevant given that active sites in proteins comprise only a few codons. Finally, our study documents the power of utilizing the phenotypic variability across primates for testing candidate genes that are initially identified in clinical H. sapiens samples and/or in Homo–Pan comparisons. Such an approach has been recently applied to various taxa including primates (Kelly and Swanson 2008) but has yet to be expanded to brain and cognition genes for which the relevant phenotypes may be continuous in nature.
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Methods |
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We obtained primate tissues and DNA from the Singapore Zoological Gardens and the Coriell Institute, USA. Whole genomic DNA was extracted using the phenol–chloroform method. We used standard polymerase chain reaction recipes and newly designed primers to target exons 3 and 18 of the gene ASPM. New ASPM data for 23 primate species were generated in this study by sequencing the amplified fragments in both directions. Eleven published primate sequences from GenBank were added (supplementary table S1, Supplementary Material online) to yield a total alignment of about 7 kb for 34 species. The data set is over 80% complete in terms of sequence data for all sites. To correlate brain size evolution with changes in ASPM, we used literature data on body mass, whole-brain, cerebral cortex, and cerebellum sizes (Stephan et al. 1981
; Harvey and Clutton-Brock 1985; Zilles and Rehkamper 1988; Smith and Jungers 1997). Whole brain was corrected for body size using the encephalization quotient (Jerison 1961). As for cerebral cortex and cerebellum volumes, we followed previous authors by taking the ratio of the respective brain part to the rest of the brain and log transforming the data (Kudo and Dunbar 2001). The primate tree was reconstructed using a supermatrix comprising 71 genes (15 mitochondrial and 56 nuclear genes). The sequence data were either newly sequenced or obtained from GenBank, yielding a data set of 98,036 aligned base pairs. Bayesian analyses (Ronquist and Huelsenbeck 2003) utilized the GTR + 
+ invariant sites model as determined by Modeltest (Nylander 2004). The analysis yielded a phylogeny with all clades having a posterior probability of 1.00. The phenotypic measures were then mapped onto a rooted tree using squared change parsimony (Maddison 1991).
We applied the maximum likelihood branch-site Model A to detect correlations between positive selection in ASPM and phenotypic evolution (Yang and Nielsen 2002; Zhang et al. 2005). In Model A, the branches on the tree are classified into foreground and background branches. We classified as foreground branches those that experienced major changes in the evolution of relative cerebral cortex, whole-brain, and cerebellum sizes as inferred by the squared change parsimony method. Phenotypic changes of 1 or more SDs away from the mean of change were used as the criterion for determining branches with major changes in relative cerebral cortex, cerebellum, and whole-brain sizes. The branch-site models were tested against the recommended null hypothesis of no positive selection in any of the foreground or background branches. The likelihood ratio test was used whereby 2 times the change in log-likelihood scores (2l) of the more complex model versus the null hypothesis is computed and compared against a chi-distribution (df = 1) (Zhang et al. 2005). A statistical control, Model B, was also used to establish whether the observed results are different if random branches were used as foreground branches. Specifically, 9 of the background branches for relative cerebral cortex data set were randomly selected and set as foreground branches. Model B was applied 5 times in order to obtain an average 2l across different sets of random branches. In order to avoid type 1 errors, we followed recommendations (Anisimova and Yang 2007) to use a correction for multiple testing by setting a more conservative of 
= 0.01 in all models. For molecular evolution modeling, we used the software HyPhy (Pond et al. 2005) that utilizes the codon models of Codeml (Yang 1997). All models except Model B were applied twice to ensure convergence of log-likelihood scores and in order to avoid results based on local optima.
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Supplementary Material |
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Supplementary figure S1 and tables S1 and S2 are available at Molecular Biology and Evolution online (http://www.mbe.oxfordjournals.org/).
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Acknowledgements |
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We thank the Singapore Zoological Gardens for contributing primate tissues and T. B. Penney, T. J. Devoogd, two anonymous reviewers, and the Associate Editor for comments on an earlier draft. This work was partially funded by Sigma Xi (F.A.).
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Footnotes |
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Anne Stone, Associate Editor
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References |
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