Analysis of Five Gene Sets in Chimpanzees Suggests Decoupling between the Action of Selection on Protein-Coding and on Noncoding Elements

Abstract

We set out to investigate potential differences and similarities between the selective forces acting upon the coding and noncoding regions of five different sets of genes defined according to functional and evolutionary criteria: 1) two reference gene sets presenting accelerated and slow rates of protein evolution (the Complement and Actin pathways); 2) a set of genes with evidence of accelerated evolution in at least one of their introns; and 3) two gene sets related to neurological function (Parkinson’s and Alzheimer’s diseases). To that effect, we combine human–chimpanzee divergence patterns with polymorphism data obtained from target resequencing 20 central chimpanzees, our closest relatives with largest long-term effective population size. By using the distribution of fitness effect-alpha extension of the McDonald–Kreitman test, we reproduce inferences of rates of evolution previously based only on divergence data on both coding and intronic sequences and also obtain inferences for other classes of genomic elements (untranslated regions, promoters, and conserved noncoding sequences). Our results suggest that 1) the distribution of fitness effect-alpha method successfully helps distinguishing different scenarios of accelerated divergence (adaptation or relaxed selective constraints) and 2) the adaptive history of coding and noncoding sequences within the gene sets analyzed is decoupled.

Introduction

Adaptive evolution may result in an excess of substitutions between species at functional sites compared with what would be expected from the fixation of neutral (and mildly deleterious) mutations. Methods based on that idea have been applied at two new levels of analysis. First, at the level of gene networks (or pathways), revealing that sets of functionally related genes can be subject to modes of selection that are generally consistent within sets, while differing between them (Serra et al. 2011). Second, at the level of noncoding regions, with the signature of selection detected in the regulatory regions of certain genes (Gazave et al. 2007; Haygood et al. 2007). However, the detailed, joint study of different classes of noncoding elements together with their accompanying coding regions in the context of gene pathways or functional gene sets has not yet been addressed. This is the central goal of this work.

The McDonald–Kreitman test (MKT) (McDonald and Kreitman 1991) along with several derived methods (Fay et al. 2001; Bustamante et al. 2002, 2005; Bierne and Eyre-Walker 2004) have been devised to disentangle whether an excess of functionally fixed mutations is due to the relaxation of selective constraints, the action of positive selection, or changes in rates of mutation. All of these methods compare diversity within a species with divergence between species at two types of sites: Functional (usually nonsynonymous changes) and putatively neutral sites (usually synonymous sites). In absence of adaptation, the ratio of functional to neutral divergence between species is expected to be equal to the corresponding ratio of polymorphism within a species. The most frequent scenario, however, is that functional variants are mildly deleterious and, since purifying selection tends to avoid their fixation, the ratio of functional to neutral divergence between species is smaller than the analogous ratio for polymorphism. In contrast, an excess of functional divergence (i.e., a higher ratio of functional to neutral fixed mutations relative to the same ratio for polymorphisms) is usually interpreted as the action of positive selection in promoting the fixation of functional variants.

Building upon these basic premises, modifications of the MKT allow for the quantification of the proportion of functional fixed differences that are adaptive (α) and the rate at which these new adaptive mutations appear (ω_α) (Smith and Eyre-Walker 2002; Eyre-Walker 2006; Fay 2011). Alpha (α) can be directly estimated as 1 − (dS pN)/(dN pS) (Smith and Eyre-Walker 2002) where pN and pS are the average number of polymorphisms per nonsynonymous and synonymous site, respectively; and dN and dS the average number of divergent differences for the same kind of sites. The ratio of adaptive to neutral divergence, omega-alpha (ω_α), is computed as ω_α = α·(dN/dS).

Because it uses interdigitated sites, the classical MKT is robust to variation in mutation rates (over scales greater than a single base pair) and to differential genealogies at the various classes of sites under analysis. However, other confounding factors such as the presence of slightly deleterious mutations or particular demographic events that could significantly contribute to the amount of polymorphism are known to affect the results of the test (Fay 2011). Recently, a number of extensions of the MKT have been proposed to control for these confounding factors when trying to estimate the proportion of adaptive changes along a given branch of the phylogeny. The most popular extensions use the site frequency spectra (SFS) in functional and putatively neutral sites to estimate population size histories and the distribution of fitness effects (DFEs) at functional sites (Eyre-Walker and Keightley 2007; Keightley and Eyre-Walker 2007; Boyko et al. 2008). One striking conclusion of these latter studies is that noncoding elements tend to experience more adaptive events than protein-coding genes, at least in mice (Eyre-Walker and Keightley 2007; Halligan et al. 2013) and Drosophila (Kousathanas et al. 2011; Mackay et al. 2012). However, when applying MKT extensions to noncoding elements results may also be affected by the choice of the neutrally evolving reference against which levels of functional divergence and polymorphism are compared. For instance, although nonregulatory intronic sequences, synonymous sites and ancestral repeat sequences are known to be generally neutral, they, or at least some fragments within them, may be subject to some selection (Lawrie et al. 2013).

Over several decades, all these methods have yielded important insights about the role of adaptation in shaping molecular evolution in different lineages. In particular, rates of adaptive molecular evolution have been found to range from low values in yeast (Elyashiv et al. 2010) or humans (Bustamante et al. 2005; Boyko et al. 2008) to high values in flies (Welch 2006; Elyashiv et al. 2010), bacteria (Charlesworth and Eyre-Walker 2006), or mice (Halligan et al. 2013). Such enormous variability across species has been attributed to different effective population sizes, as the efficiency of selection and, thus, the proportion of adaptive substitutions are higher in larger populations (Fay 2011; Jensen and Bachtrog 2011; Lanfear et al. 2014). A few attempts have also been made to quantify adaptive evolution in regulatory elements using similar MK-type tests. Although in hominids no adaptive evolution was detected neither upstream nor downstream protein-coding genes (Keightley et al. 2005), in Drosophila, the proportion of substitutions fixed by positive selection was estimated to be around 60% in untranslated regions (UTRs) and 20% in introns (Andolfatto 2005).

To date, and to the best of our knowledge, neither the MKT nor its many extensions have been applied to sets of genes interacting in the same pathway. Such pathway-based strategy might help to shed more light on how different functions are subject to different selective pressures than the analysis of single genes. Moreover, none of the analyses of natural selection available so far on pathways have investigated selection acting on noncoding regulatory regions of the studied genes, so it is not yet known whether modes of selection are shared among the coding and noncoding elements of a given functional gene set.

In this study, we aimed to investigate differences in the selective forces acting in different gene sets and in their coding and noncoding regions. To that effect, we initially selected three different reference gene sets that, according to the literature (Serra et al. 2011), and to our own results, are examples of evolution under different adaptive pressures in the chimpanzee lineage. The first two reference sets, the Complement and the Actin pathways present accelerated and constrained rates of protein evolution, respectively, whereas the third is a set of genes with evidence of accelerated evolution in at least one of their introns. Then, we combined polymorphism data from 20 central chimpanzee individuals with divergence patterns from humans and used the DFE-alpha extension of the MKT (Keightley and Eyre-Walker 2007) to compare the action of selection across pathways and across different classes of genomic elements. In particular, we compared patterns of evolution in protein-coding DNA sequences (CDS), introns, conserved noncoding sequences (CNCs), promoters, and UTRs of these reference sets with that of the same genomic elements from two additional gene sets related to neurological function (Parkinson’s and Alzheimer’s diseases). These neurological pathways may have evolved under different adaptive pressures in humans and chimpanzees, and, importantly, their associated diseases have not been observed in the latter species. We focused on Pan troglodytes troglodytes because its effective population size is one of the largest among the great apes (24.4–48.7 × 10³) (Prado-Martinez et al. 2013), being around two times higher than that of humans (13.1–16.2 × 10³) (Prado-Martinez et al. 2013). Moreover, the ancestral homo-chimpanzee effective population size has been estimated to be 49.6–62.9 × 10³(Prado-Martinez et al. 2013). By using that chimpanzee subspecies, we expected to have greater power to detect significant differences in the fraction of adaptive substitutions and to better distinguish any different evolutionary trend among pathways and their functional elements than if a similar approach was carried in the human lineage.

Materials and Methods

Samples

Blood-derived DNA samples for 20 wild-born nonrelated chimpanzees (P. t. troglodytes) from Gabon and Equatorial Guinea were obtained from the Research and Conservation Copenhagen Zoo. Detailed information on these individuals is provided in supplementary table S1, Supplementary Material online. DNA concentration was quantified with an Invitrogen QBit fluorometer to ensure a minimum of 6 ng (at 50 ng/µl) in each sample.

Gene Sets and Regions Analyzed

The selection of gene sets presenting accelerated or decelerated rates of protein evolution was based on the Gene Set Selection Analysis (GSSA) method presented in Serra et al. (2011). These authors focused on sets of functionally related genes included in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways (Kanehisa et al. 2004) and in GO (gene ontology) terms and compared their ω (dN/dS) values for humans, chimpanzees, rats, and mice. In particular, they ranked ω values in each lineage for all genes and tested whether certain pathways were overrepresented in the tails of the ω distributions. We focused on ω measures in the chimpanzee lineage only, and we selected the following KEGG pathways: 1) the “Regulation of Actin Cytoskeleton” (KEGG: hsa04810) pathway, whose protein coding regions have evolved at a slow rate. That is, 63% of the genes (median ω = 0.08) are in the lower part of a genome-wide ω distribution (with median = 0.17) and 2) the “Complement and coagulation cascade” (KEGG: hsa04610) pathway, which, on the contrary, shows an enrichment of genes with high dN/dS ratios, with 62% of the genes (median ω = 0.39) in the corresponding high side of the ω distribution (Serra et al. 2011). For these two pathways, we studied 58 genes from the Actin module and 109 genes from the Complement module.

Additionally, we defined a set of genes presenting evidence of accelerated rates of divergence in their noncoding regions. To do so, we analyzed intron divergence patterns by means of a likelihood ratio test implemented in HYPHY (Pond et al. 2005) and similar to that from Haygood et al. (2007). Briefly, the method compares rates of evolution of some target sequences against a neutral reference by performing a branch-site test. The test contrasts the likelihood of a substitution model in which only neutral and selected sites are allowed against a model in which positively selected sites are also possible. Human–chimpanzee alignments were obtained for all introns and subsequently tested using neighboring ancestral repeats as a neutral reference. After this procedure, we obtained a set of 134 chimpanzee genes with evidence of accelerated evolution in one of their introns (see further details in Supplementary Note S1, Supplementary Material online).

Finally, we included in our analysis two gene pathways related to two human neurological diseases (Parkinson’s and Alzheimer’s disease). In particular, 65 genes from the “Alzheimer disease-amyloid secretase” pathway (PANTHER: P00003), 109 genes from the “Alzheimer disease-Presenilin” pathway (PANTHER: P00004), and 87 genes from the “Parkinson disease” pathway (PANTHER: P0049). The two gene sets related to Alzheimer’s disease were merged in one category, comprising 156 genes. We will refer to each of these sets of curated genes as the “Complement,” “Actin,” “Alzheimer,” and “Parkinson” pathways in the rest of the text.

In total, we studied 518 unique genes (∼8% of genes appeared in different gene sets; see complete list in supplementary table S2, Supplementary Material online). From the longest transcript of each particular gene included in these datasets, we selected all their exons and both 5’- and 3’-UTRs. Promoters and trailers were defined as 5 kb upstream and downstream from the start and end of the gene, respectively. As for introns, we included up to a maximum of 1 intronic kb flanking each intron–exon boundary except for the case of those introns with accelerated substitution rates where the complete intron length was studied. We will refer to this latter group as “Only Acc. Introns.”

Capture Design and Sequencing

The complete list of genomic target intervals of interest in the PanTro2 chimpanzee genome (March 2006, University of California-Santa Cruz Genome Browser) was uploaded to the eArray XD software (Agilent Technologies) for custom design of two Agilent SureSelect Target Enrichment kits. Bait Tile parameters were set to use Illumina as a Sequencing Technology, Pair-End Long Read as Sequencing Protocol, and avoidance of masked repeat elements. We retiled those exonic regions not covered by baits, due to avoiding masked repeating regions, with the masking OFF option. To ensure the specific retrieval of these genomic regions during enrichment, baits created with the masking OFF option were then mapped with BLAT and only included in the design when their second match scored below 60 (half the baits length).

All capturing and sequencing procedures were performed at the Genomics Unit of the Center for Genomic Regulation Core Facilities. Briefly, 6 µg of genomic DNA from each individual was fragmented to 150–200 bp with the use of the Adaptive Focused Acoustics (Covaris), end-repaired, adenylated, and ligated to specific PE-tagged genomic adapters following the standard protocol of the Illumina Paired-End Sample Preparation kit. Different pools of 2–3 indexed libraries were then hybridized in multiplex with the 120 bp biotinylated RNA baits of each custom Agilent SureSelect kits. After enrichment with each individual kit, captured fragments were purified, pooled in two groups (each containing the two sets of captured regions from each of 10 different samples), and sequenced on two lanes of an Illumina HiSeq 2000 System with the use of 96-bp paired-end reads. See detailed information on enrichment and sequencing scheme in supplementary table S3, Supplementary Material online.

Mapping, Single-Nucleotide Polymorphism Calling, and Callable Genome Construction

Burrows–Wheeler Aligner was used to align reads to the chimpanzee reference genome (PanTro3), using the default parameters. We used the Genome Analysis Toolkit (GATK) to call indels and to perform re-alignments of reads falling in their surrounding regions. We then carried out recalibration of reads’ bases quality scores, considering cycle effect (position in the read) and sequencing chemistry effect (the preceding and current nucleotide). UnifiedGenotype from GATK was used to call genotypes. Next, we filtered single-nucleotide polymorphisms (SNPs) by the variant quality score recalibration (VQSR) method in GATK. We provided a training set consisting of a high-quality SNP subset, that is, SNPs called with at least 10,000 of quality score and not falling in SNP clusters (three or more SNPs in windows of 10 bp) or in a region at 5 bp or less from an indel. After filtering the whole set of SNPs using VQSR, we also removed SNPs falling in clusters and close to indels the same way than in the high-quality subset.

The proportion of genome that is callable and that we finally used in all analyses was calculated using the UnifiedGenotype tool from GATK, by emitting genotypes for all confident reference and variant sites. We kept only those sites with a confidence genotype call of at least 30 of Phred score (the same minimum threshold applied to variable sites). We masked all indels with a padding of 5 bp. All sites with SNPs filtered by the VQSR method and SNP cluster criteria were also removed from the callable fraction of the genome. We finally kept only those positions in which all-20 chimpanzee individuals possess coverage of at least 5X. That left 8,599,335 bp of callable regions including 79,650 SNPs with a Ti/Tv ratio of 2.29. SNPs found in coding regions have a Ti/Tv ratio of 3.89, consistent with the elevated GC content of exons (see supplementary table S4, Supplementary Material online, for detailed sequencing statistics in each individual sample).

Nonsynonymous and Synonymous Sites

Zero-fold degenerate sites were treated as nonsynonymous and the 4-fold degenerate sites as synonymous. Exonic sequences and genomic annotation from all genes were downloaded from the Pan troglodytes genes (CHIMP2.1.4) dataset at Ensembl v66 (http://feb2012.archive.ensembl.org/index.html, last accessed May 22, 2015) and mapped to the chimpanzee genome (PanTro3 assembly) using the liftOver tool at the Galaxy website (http://main.g2.bx.psu.edu/, last accessed May 22, 2015).

Conserved Noncoding Elements

CNCs within promoters, introns, and trailers of all genes studied were identified in the Human genome (hg19) using PhastCons on a 12-way Vertebrate Multiz Alignment. PhastCons parameters were tuned to produce 5% conserved elements in the genome for the vertebrate conservation measurement (expected length = 45, target coverage = 0.3, rho = 0.31). CNC hg19 coordinates were then mapped to the chimpanzee genome (PanTro3) using the liftOver tool at the Galaxy website (http://main.g2.bx.psu.edu/). Conserved elements in noncoding sequences can be used as a proxy for functional regulatory motifs in flanking regions surrounding CDS. Unfortunately, both their overall length and the total amount of each specific noncoding region (i.e., promoters, introns, and trailers) harbors in any given pathway, does not render enough power to evaluate, and compare their evolutionary patterns separately (data not shown). Thus, we concatenated all CNC from each pathway and analyzed them as a single category.

Inference of Unfolded SFS

SNPs were oriented according to ancestral states in the node separating chimpanzee, human, and orangutan. Ancestral states were inferred by parsimony in the Galaxy website (http://main.g2.bx.psu.edu/) using the following strategy. Sites diverging between panTro3 and hg19 were obtained using the regional variation/fetch substitutions from the pairwise alignments tool. For each identified nucleotide substitution, pairwise alignments of panTro3/hg19 and panTro3/ponAbe2 were downloaded using the fetch alignments/fetch pairwise MAF blocks tool given our set of genomics intervals. Substitutions in panTro3 were considered as derived when the nucleotide in panTro3 was different from the nucleotide in hg19 and ponAbe2. We did not include substitutions with a multiallelic state or without information in ponAbe2. Derived substitutions identified in panTro3 were compared with data from ancestral states obtained for P. t. troglodytes in Prado-Martinez et al. (2013), obtaining a 99.1% of coincidences for monomorphic positions and 97% of coincidences with the derived allele when including polymorphic positions in the node separating the three species. To obtain the SFS of polymorphism, the derived alleles in each frequency category from 1 to n − 1 chromosomes were counted. We also counted the number of invariant sites and the derived substitutions. Gap positions were not taken into account for the SFS.

Measures of Divergence and Polymorphism

For all gene sets and genomic elements in a given set, we obtained descriptive statistics of polymorphism and divergence in putatively selected and nonselected sites. For coding regions, we considered only the 4-fold sites as nonselected (i.e., neutral) sites and the 0-fold sites as putatively selected sites. For all noncoding elements, we used all trailer sites (excluding CNC) of each specific pathway as the neutral reference (which we denote as nonselected sites or NSel) and all sites in the remaining noncoding regions were considered as putatively under selection (PUS). In particular, we first obtained the proportion of nonsynonymous or PUS SNPs (pN or pPUS), the proportion of synonymous or NSel SNPs (pS or pNSel), the proportion of nonsynonymous or PUS substitutions (dN or dPUS), as well as the proportion of synonymous or NSel substitutions (dS or dNSel). We then calculated the corresponding rates of putatively selected versus neutral SNPs and substitutions as pN/pS and dN/dS in coding regions and as pPUS/pNSel and dPUS/dNSel in noncoding regions, respectively.

To evaluate whether there are significant differences between gene sets in their average values of rates and ratios of polymorphism and divergence, we obtained a distribution of all the corresponding descriptive values by randomly resampling 1,000 times with replacement individual genes within each gene set. At each round of random gene resampling, we required a minimum number of sites analyzed equal to the number of sites analyzed in the real list of genes for each gene set. These distributions allowed us to calculate, for each observed value, the percentile it represents in the distribution of 1,000 randomizations of the compared gene set. We considered that gene set values are significantly different (two-tail test) when observed values fall reciprocally in the tails of the distribution, considering percentiles 2.5% and 97.5% as thresholds.

Biased AT to GC (weak to strong; W->S) nucleotide substitutions can lead to accelerated rates of nonsynonymous changes and generate significant results for usual tests of positive selection, including likelihood ratio tests for accelerated dN/dS and the MKT (Berglund et al. 2009). To explore whether the accelerated rates observed in any genomic element could result from biased fixation of W->S mutations, we analyzed the ratio of W->S to S->W substitutions in all selected versus nonselected sites within each dataset (supplementary table S5, Supplementary Material online). Only introns in the Actin and the Alzheimer gene sets showed statistically different ratios of W->S versus S->W substitution types when compared with their corresponding neutral references (supplementary table S5, Supplementary Material online).

We also explored the possible bias introduced by different proportions of hyper-mutable CpG sites in different pathways. We compared the proportion of substitutions from C or G nucleotides with changes coming from A or T nucleotides in each genomic element against the corresponding neutral reference by means of a Fisher’s exact test (supplementary table S5, Supplementary Material online). We only found significant differences in introns and UTRs for the Alzheimer and Acc. Introns gene sets, respectively (Fisher’s exact test, P < 0.05).

DFEs of new Mutations

DFEs of new mutations and demographic parameters were obtained using a maximum likelihood approach based on polymorphism data as described by Keightley and Eyre-Walker (2007). Briefly, the method assumes two classes of sites (one evolving neutrally and another under mutation-selection-drift balance) and contrasts the folded SFS at these two classes. Under this model, sites can be either neutral or damaging. In the latter case, deleterious effects are sampled from a gamma distribution. The model estimates the frequencies of four different categories of values of the population selection value (N_es, where N_e is the effective population size and s is the selection coefficient against novel mutations). These four categories are: 0–1, 1–10, 10–100, and > 100, considered as nearly neutral, mildly deleterious, deleterious, and very deleterious, respectively. Demography is modeled considering 2-epochs with one-step change from N₁ initial size equal to 100 to N₂ final size in generation t in the past, the ratio of change N₁/N₂ is estimated together with the proportion of unmutated sites (f_o). As neutral evolutionary rates at 4-fold sites are similar among the different datasets (supplementary table S6, Supplementary Material online), we considered the concatenation of all 4-fold sites (synonymous sites) as the neutral reference for all coding regions analyzed. For noncoding regions, we used the trailer sites of each gene set as neutral reference.

A simple extension of the method described above allows the calculation of the proportion of fixed adaptive substitutions (α) and the relative rate of adaptive substitution (ω_a) using the parameters calculated in the DFE together with divergence data (Eyre-Walker and Keightley 2009). This is an extension of the classic MKT that benefits of accounting for the possible effect of slightly deleterious mutations contributing to polymorphisms and for biases due to demographic changes.

Statistical Comparisons between Gene Sets and Elements

We calculated α and ω_a for our putative selected classes within each element in all gene sets analyzed and calculated their confidence intervals by bootstrapping genes using the same strategy than for the polymorphism and divergence measures. We analyzed independently each genomic element and resampled genes with replacement requiring that the number of base pairs analyzed at each resample gene list should be at least equal to the number of base pairs analyzed in the original gene set. Again, to evaluate whether proportions and rates of adaptive changes based on α and ω_a values were significantly different between gene sets, we calculated the percentile where each observed value corresponding to a given gene set falls in the distribution of bootstrapped values of the gene set against which it is compared. We considered the gene set’s observed value significantly different from the one it is compared with if it falls in the tails of the distribution considering percentiles 2.5% and 97.5% as thresholds. Within this framework, we refer to a comparison between gene sets being reciprocally significant when both observed values fall beyond the significant threshold in the corresponding bootstrap distributions.

Results

Gene sets and Functional Elements under Study

We set out to study the rates of molecular evolution in coding and different regulatory regions of five functional and evolutionary gene sets by combining polymorphism data from 20 chimpanzees with human–chimpanzee divergence. The two first gene sets were the Complement pathway and the regulation of Actin cytoskeleton pathway (“Complement” and “Actin”), which we included in our study because, according to the GSSA presented in Serra et al. (2011), they were significantly enriched in genes with accelerated and de-accelerated evolution in the chimpanzee lineage, respectively. These rates of evolution are in agreement with the known functions of these pathways. The Actin cytoskeleton is essential for diverse structural and housekeeping functions in the cell and various human diseases are known to result from cytoskeletal malfunctions (Lee and Dominguez 2010). In contrast, the Complement system relates to the innate immunity response and genes involved in such pathway have been under the recurrent influence of positive and balancing selection as a response to interaction with pathogens (Ferrer-Admetlla et al. 2008; Kosiol et al. 2008).

A third set of genes was selected because of the putative signature of positive selection acting upon noncoding sequences. In particular, we studied a set of genes with introns under accelerated evolution. To compile that set, we analyzed intron divergence patterns using a likelihood ratio test similar to that from Haygood et al. (2007) (see supplementary note S1, Supplementary Material online, for details about the method) and obtained a set of 134 genes with at least one of their introns showing significantly accelerated substitution rates (see supplementary table S2, Supplementary Material online). We refer to this set as “Acc. Intron” dataset.

Finally, the evolutionary patterns found in the coding and noncoding regions of the gene sets described above were compared with those from two additional gene sets related to common neurodegenerative disorders (Alzheimer and Parkinson gene sets, see below). Interestingly, although both neurological diseases seem to appear exclusively in humans, the main neuropathological hallmarks of AD (i.e., Aβ and hyper-phosphorylated tau deposition) have also been observed in the ageing brain of chimpanzees (Gearing et al. 1994; Rosen et al. 2008). Furthermore, crucial proteins in AD and PD pathogenesis (i.e., APP, precursor of Aβ, tau, and alpha-synuclein) are conserved between human and chimpanzees (Hamilton 2004; Holzer et al. 2004; Rosen et al. 2008). However, these proteins are found within well-studied neurological networks whose elements might have undergone different evolutionary histories. Even though 14% of Alzheimer’s genes and 11% of Parkinson’s have significantly decreased their corresponding dN/dS ratios since the common human–chimpanzee ancestor, neither the PD nor the AD pathways were enriched for low or high ω values when using the data from Serra et al. (2011). All these features made pathways related to AD and PD interesting test pathways to be included in our MKT analysis of coding versus noncoding regions.

To study differences between coding and noncoding genomic elements across the five gene sets under study, we sequenced different classes of sequence: Protein coding (CDSs), introns, promoters, untranslated regions [UTRs], and CNCs (see details in Materials and Methods).

Sequencing and Descriptive Statistics

A complete list of genes included in each of the five datasets under study is presented in supplementary table S2, Supplementary Material online. Their corresponding CDS, introns, and regulatory regions were specifically sequenced with an Illumina Hiseq2000 instrument after enrichment with two custom Agilent SureSelect kits in 20 central chimpanzee individuals (supplementary table S1, Supplementary Material online) to an average depth of 72X (see Materials and Methods and supplementary tables S3 and S4, Supplementary Material online). A total of 79,650 SNPs (with a Ti/Tv ratio = 2.29) were identified in a total callable length of 8,599,335 bp (see Materials and Methods). Next, we calculated measures of polymorphism and human–chimpanzee divergence for all five-gene datasets and five differentiated genomic regions (fig. 1 and supplementary table S6, Supplementary Material online): CDS, introns, promoters, CNC, and UTRs.

Fig. 1.—

Divergence and polymorphism values for all genomic elements and pathways.

After comparing many polymorphism and divergence measures (see supplementary tables S6 and S7, Supplementary Material online), divergence at synonymous sites (dS) was the only one that showed no statistical differences between any studied gene set. The average nonsynonymous to synonymous divergence rate (dN/dS) between humans and chimpanzees is around 0.17–0.25 (Kosiol et al. 2008; Hvilsom et al. 2011) reflecting the overall effect of purifying selection acting on most coding parts of the genome. Although CDS from the Actin and the two neurological pathways presented similar dN/dS values to that genome-wide average range, those from the Complement and the Acc. Intron dataset did not differ among each other but presented significantly higher dN/dS values than the Actin, Alzheimer, and Parkinson pathways (supplementary tables S6 and S7, Supplementary Material online). For noncoding regions (i.e., introns, CNC, UTRs, and promoters), we considered any site as putatively under selection (PUS) and measured their rate of substitution per site (dPUS, equivalent to dN). We then used the substitution rates in trailers (NSel) to obtain equivalent estimations of ω (in this case dPUS/dNSel, equivalent to the dN/dS rates in CDS). We proceeded in the same way for polymorphism data (pPUS/pNSel). As expected, dPUS/dNSel values in most noncoding genomic elements were higher than the corresponding dN/dS values in CDS (fig. 1 and supplementary table S6, Supplementary Material online), with the exception of CNC regions. This pattern is consistent with stronger purifying selection against amino acid substitutions and with the requisite of sequence conservation for CNC regions. Moreover, as the definition of selected sites in noncoding elements is less strict than in CDS, dPUS includes a higher proportion of neutrally evolving sites than dN, increasing comparatively the dPUS/dNSel ratio. Notably, with the exception of the promoter sequences (which showed similar dPUS/dNSel values among all five datasets), all the remaining noncoding regions displayed significant differences in their dPUS/dNSel values (supplementary table S7, Supplementary Material online). The dPUS/dNSel value for introns in the Acc. Introns dataset was significantly higher than in the two neurological pathways but it was not different from that of the Complement and Actin pathways. On the contrary, the subset of introns with evidence of accelerated evolution (i.e., the Only Acc. Intron dataset) presented dPUS/dNSel values clearly higher than those of any other gene set (supplementary table S7, Supplementary Material online). In summary, divergence rates are in agreement with our initial rationale for selecting the Actin and Complement pathways as references, respectively, for constrained and accelerated evolution on CDS. However, in the Acc. Intron set, this is only true when considering the subset of introns previously identified as accelerated.

Contrary to the homogeneity found among dS values, pS showed notable differences between gene sets, with Parkinson and Acc. Introns displaying the lowest and highest values of pS, respectively. Complement and Acc. Introns displayed the higher pN/pS values, even if not all-pairwise comparisons were reciprocally significant (fig. 1 and supplementary tables S6 and S7, Supplementary Material online). The equivalent ratio in noncoding regions (pPUS/pNSel) was clearly higher than that in CDS, a pattern that again is consistent with the softer evolutionary constraints of noncoding regions. Among noncoding elements, CNC showed again the lowest level of pPUS/pNSel. Moreover, in noncoding sequences, we observed several cases of significant differences in the pPUS/pNSel ratio between gene sets (supplementary table S7, Supplementary Material online). Contrary to what we observed for the dPUS/dNSel ratio in the Only Acc. Introns subset, the pPUS/pNSel ratio did not show significant differences to that of any other gene set.

Divergence measures showed that dN/dS, dN, and dS values in CDS of Alzheimer and Parkinson were not significantly different between each other or from those in the Actin pathway, whereas all of them were significantly lower than those from the Complement and Acc. Introns gene sets. On the contrary, polymorphism measures showed that although pN/pS ratios were also similar between the two neurological and the Actin pathways, both pN and pS measures were significantly lower in the Parkinson pathway.

Finally, we examined the relationship between the dN/dS and pN/pS ratios (or their equivalents in introns) and found significant correlations in the case of CDS as well as for introns (P values of 0.02 and 0.023, and adjusted R² of 0.84 and 0.81, respectively), but only when not considering the Only Acc. Introns subset (supplementary fig. S1, Supplementary Material online). The higher divergence rate of this latter subset was discordant with its observed pPUS/pNSel value, which was lower than what would be predicted by the regression model.

Quantifying Adaptive Evolution

High dN/dS (or dPUS/dNSel) values could reflect either the signature of past positive selection or relaxation of selective constraints. By contrasting the levels of polymorphism and divergence at putatively neutral and functional sites, it is possible to discriminate between these two different scenarios. Here, we have done so by applying an extension of the MKT, the DFE-alpha method (Keightley and Eyre-Walker 2007). Briefly, neutral sites (either 4-fold or trailer, depending on the genomic element analyzed) were used to infer demographic parameters of the chimpanzee population. We then used the inferred demography to assess the DFEs at functional sites and to estimate α and ω_α for each gene set and class of genomic element under analysis.

Both the choice of different neutral references and the number of putative neutral sites can affect the estimation of α and ω_α. For that, we evaluated in CDS the potential discrepancies between estimates of α and ω_α when using, as neutral reference, either 1) only the 4-fold sites of each corresponding pathway or 2) the concatenation of the 4-fold sites of all pathways. In the rest of the text, we focus on the results obtained with the latter strategy but make the appropriate comments when the use of the 4-fold sites of each pathway produces discordant patterns. As for noncoding elements, and as explained above, we used the trailer region of each pathway (removing conserved tracks) as neutral reference.

DFE of New Mutations

We first examined the DFEs distinguishing between nearly neutral (N_es < 1), mildly deleterious (1 < N_es < 10), deleterious (10 < N_es < 100), and very deleterious (N_es > 100) mutations (Keightley and Eyre-Walker 2007). As expected under the action of purifying selection, the coding sequences of the Actin pathway show an excess of very deleterious mutations (47.5%, see supplementary fig. S2 and table S8, Supplementary Material online). The CDS of the two neurological pathways displayed a proportion of deleterious to very deleterious mutations similar to that from the purifying selection reference set (supplementary fig. S2 and table S8, Supplementary Material online). In contrast, the CDS of the Complement and Acc. Introns gene sets displayed the highest proportions of nearly neutral mutations (42% and 52%, respectively). Except for the Actin pathway, the use of each-pathway 4-fold sites as neutral reference produced very slightly different results, mainly affecting the proportion of very deleterious mutations.

As for DFE in noncoding elements, a substantial proportion of new mutations were predicted to be deleterious in CNC and UTRs, particularly in the two neurological pathways. This suggests the presence of a proportion of functional sites in these regulatory regions and that the action of selection can, in principle, be detected. In contrast, polymorphic sites in promoters and introns were predicted to be almost exclusively neutral in all gene sets. As in CDS, both neurological pathways showed very similar DFE in all noncoding genomic elements.

Estimation of the Proportion (α) and the Rate (ω_α) of Adaptive Substitutions

For coding regions, most estimates of α and ω_α were above zero, with Actin presenting slightly negative estimates. Noncoding regions presented trends toward higher α and ω_α values than coding regions (supplementary tables S9 and S10, Supplementary Material online). Our inferences in the analysis described below are limited to the comparison of the values of these estimates across pathways and for different genomic elements in the chimpanzee lineage, and we do not heed the precise and absolute quantification of adaptive evolution.

Adaptive Evolution in Coding Regions

We first focused on coding regions of the Complement and the Actin pathway and found them to present significantly higher α and ω_α values in Complement (figs. 2 and 3, supplementary table S10, Supplementary Material online). The use of each pathway’s 4-fold sites as neutral reference rendered consistent results with the use of the concatenation of all 4-fold sites: The complement pathway always presents significantly higher values of α and ω_α, with the single exception of the comparison of ω_α between the Actin and Complement pathways, which is not significant. However, the confidence intervals for both estimates when using this neutral reference were in general wider, probably due to the lower number of sites. Therefore, adding to our previous biological knowledge on these pathways, the results of GSSA enrichment analysis and our estimates on adaptive evolution, in what follows we will consider the Actin and Complement pathways as reference sets for purifying and positive selection, using them to benchmark the CDS of other gene sets.

Fig. 2.—

Fraction of substitutions due to positive selection: alpha (α) values. Alpha (α) values per genomic element and pathway. Significance values are obtained as explained in the text. Values for the 2.5% and 97.5% thresholds are indicated.

Fig. 3.—

Ratio of adaptive to neutral divergence. Omega (ω_α) values per genomic element and gene set. Significance values are obtained as explained in the text. Values for the 2.5% and 97.5% thresholds are indicated.

To confirm that adaptive differences between the two reference sets are due to global differentiated patterns between them rather than to outlier genes, we grouped CDS in three different subsets according to the percentile that the CDS of each gene occupies in the distribution of dN/dS values of each pathway and compared genes within categories. In particular, we considered three quantiles (0–25, 25–75, and 75–100; fig. 4, supplementary table S11, Supplementary Material online) and performed DFE-α tests comparing the corresponding subgroups. Estimates of dN/dS per gene were obtained from Serra et al. (2011). These gene dN/dS estimates were averaged for each quantile and compared with the dN/dS values of the corresponding concatenated genes in our dataset. For the Complement pathway, we observed that, within all three quantiles, the higher the dN/dS of the CDS the greater were the α and ω_α values. This was not the case for the Actin quantiles, indicating either lack of power or lack of correlation in such a constrained pathway. Thus, we could only carry out the study on the potential contributions of outliers by comparing Complement quantiles against the Actin set taken as a whole. Both α and ω_α values were significantly higher than those of the Actin set in Complement’s quantiles 25–75 and 75–100 but not in the 0–25 quantile (P = 0.03; threshold at 0.025). The reciprocal comparison followed the same trend: α and ω_α values of the Actin pathway were significantly lower in the two most divergent Complement percentiles but not in the 0–25 quantile (supplementary table S12, Supplementary Material online). The use of 4-fold sites of each gene set as neutral reference did not substantially affect the results (adjusted R² = 0.995 and 0.92; P value = 0.03 and 0.132, for α and ω_α respectively).

Fig. 4.—

CDS alpha (α) and omega (ω_α) values comparison between the Actin and Complement pathways. The comparison is shown overall and between the Actin and the percentiles 25, 25–75, and 75 of the Complement dN/dS gene distribution values as calculated in Serra et al. (2011). The neutral SFS is the same used in the whole study from the concatenation of all 4-fold sites from all pathways (observed values depicted as gray circles). Observed values obtained using each pathway 4-fold sites as neutral reference are indicated as red circles.

All these observations suggest first, that the accelerated evolution in the CDS of the Complement was contributed by adaptive processes rather than being the result of relaxed constraints and, second, that adaptation acted globally in the pathway rather than only into outlier genes.

In coding regions, the combination of information coming from both, divergence and polymorphism data, confirms and refines the evolutionary trends previously inferred using only divergence data. The CDS of the Actin pathway, which was included in the study for its constrained divergence levels, do show significantly lower α and ω_α values than the CDS selected for accelerated divergence (Complement). However, our observations go beyond that distinction. For instance, it is interesting that, although dN/dS measures (supplementary table S6, Supplementary Material online) were high in the coding regions of the Complement and the Acc. Intron datasets (dN/dS of 0.60 and 0.55, respectively; supplementary table S6, Supplementary Material online), α and ω_α values were significantly increased only in the Complement CDS. The coding regions of the Acc. Intron dataset which also show the second lower proportion of very deleterious mutations just after the Complement (supplementary fig. S2, Supplementary Material online) did not show significantly different ω_α values to those from the Actin pathway. These results suggest that while the increased dN/dS ratio in the Complement pathway has been driven by adaptive evolution, such elevated ratio in the coding regions of the Acc. Intron dataset rather reflects the relaxation of purifying selection.

When comparing the α and ω_α estimates obtained for the coding regions of the two neurological pathways with those of the reference gene sets for positive and purifying selection, some patterns emerge (figs. 2 and 3 and supplementary tables S9 and S10, Supplementary Material online): α and ω_α values for Alzheimer’s CDS are not significantly different from those of the highly conserved Actin pathway but are significantly lower than those of Complement. On the contrary, α and ω_α estimates for Parkinson’s CDS are significantly higher than those from the Actin pathway, and only ω_α is also significantly lower to that of the Complement.

When using each-pathway 4-fold sites as neutral reference, Parkinson α and ω_α values are not significantly different from those from Actin, and only the latter is significantly lower than Alzheimer’s ω_α. Moreover, α and ω_α values for Complement CDS only remain suggestive of being differentially increased to those of Parkinson and Alzheimer. However, the wider confidence intervals obtained in some pathways when using this latter reference may decrease the power of particular statistical comparisons. In particular, Parkinson and Complement pathways showed the largest residuals in both α and ω_α correlations, implying that these two gene sets are probably the most affected by the choice of the neutral reference. Although ω_α estimates obtained with the two different neutral references for CDS show significant correlation (adjusted R² = 0.81; P value = 0.024) that is not the case for α estimates (adjusted R² = 0.27; P value = 0.21). Thus, the most robust observation is that CDS in both Alzheimer and Parkinson gene sets have adaptive rates that are lower than those of the positively selected Complement pathway and overall similar to those of the remaining gene sets.

Adaptive Evolution in Noncoding Regions

We next examined patterns of evolution in the Acc. Introns dataset, where at least one intron showed significant divergence patterns of acceleration (see Materials and Methods). When considering either all introns or only the accelerated introns in this set and comparing them with the introns from the other gene sets, we did not find significantly different α and ω_α estimates (figs. 2 and 3; supplementary table S10, Supplementary Material online). Thus, introns in the Acc. Introns gene set did not display any particular adaptive signal neither as a whole or when considering only the accelerated introns. In this case, and contrary to what occurs in Complement’s CDS, the accelerated divergence found in the introns of the Only Acc. Introns set is not accompanied by evidence for any particularly higher adaptive process compared with the introns of other gene sets.

We then focused on the estimated values of α and ω_α in the remaining noncoding genomic elements and found no major differences among pathways for any of the noncoding elements. All noncoding regions in Actin and Complement gene set displayed similar values of α and ω_α, contrary to what we observed in their coding regions. Interestingly, CNC in the Acc. Introns gene set showed a significantly higher value of α and a significantly lower value of ω_α. This suggests that the few changes occurred in the CNC of this gene set (low ω_α) may have been the result of adaptive evolution in a particularly higher proportion (higher α). Another relevant observation is that all noncoding elements in the Parkinson pathway displayed significantly lower values of α and ω_α against most gene sets.

Comparing Adaptive Evolution in Coding and Noncoding Regions

Next, we focused on the question of whether evolutionary trends detected for the CDS in a given pathway are correlated with the noncoding regions in these pathways and vice versa, whether observations in noncoding elements are also observed in CDS.

First, it is clear that the differences between gene sets detected for a certain genomic element do not necessarily extend to the others. For instance, α and ω_α in Complement CDSs are significantly higher than those of the remaining CDS sets, but these higher proportion and rates of adaptive changes was not observed in any of its corresponding noncoding elements. That is, the α and ω_α values from noncoding elements of the Complement pathway did not display any particular trend when compared against the noncoding elements from the other gene sets. The same decoupling between coding and noncoding elements was found for the Acc. Intron dataset: α and ω_α values in CNC tends to be significantly higher and lower, respectively, than those of the noncoding elements in the rest of gene sets, but these differences are not detected in the CDS of the Acc. Introns gene set (supplementary table S10, Supplementary Material online, and figs. 2 and 3). These results suggest that natural selection is acting differently in coding and noncoding regions within the same functional or evolutionary gene set. This general decoupling is also observed in the case of Parkinson. Although noncoding elements in Parkinson present significantly lower α and ω_α values than those of the remaining gene sets, α and ω_α values in Parkinson CDS are not significantly decreased compared with CDS in any other gene set.

Discussion

We have applied an extension of the MK test to compare rates and proportions of adaptive evolution acting on different coding and noncoding genomic elements from different gene sets. This method has proven useful to discriminate between different scenarios resulting in accelerated divergence. Three gene sets were chosen for their enrichment in genes with accelerated or constrained dN/dS or high intron evolutionary rates. As we obviously did not select random gene sets, the finding of accelerated and decelerated rates of adaptive evolution on their corresponding genomic elements is expected. However, confirmation with polymorphism data was needed to use them as reference gene sets with comparative value. Indeed, although coding regions in the Complement pathway showed highest values of adaptive evolution, those of the Acc. Introns dataset, with similar divergence rates, did not show any particularly high proportion or rate of adaptive evolution. Similarly, highly accelerated divergence rates in introns are not accompanied by any particularly high estimate of adaptive evolution in the Only Acc. Introns subset. Thus, only in CDS, we can reinforce the idea that accelerated rates of divergence in these specific preselected elements are due to positive selection and not just to relaxation of purifying selection. Moreover, we have determined that at least for the Complement pathway, the enhanced adaptive process affected globally all genes within the pathway, rather than resulting from the contribution of few highly diverged genes.

Neurological Gene Sets

The coding regions of the Alzheimer and Parkinson pathways showed levels of polymorphism and divergence, as well as DFEs estimates, that are suggestive of the prevalent action of purifying selection on these two neurological gene sets in the chimpanzee lineage. Parkinson, in particular, presented a pattern of constrained diversity (lower pN and pS values with a left-skewed SFS), which is highly suggestive of the recent action of purifying selection in chimpanzees. The action of purifying selection in the chimpanzee lineage upon the Alzheimer and Parkinson pathways, even if presenting different intensities in the two pathways, is consistent with the low rates of protein evolution reported for these functional gene sets in humans (Hamilton 2004; Holzer et al. 2004; Rosen et al. 2008; Serra et al. 2011). Disease-related genes are known to present higher degrees of conservation (Arbiza et al. 2006). However, humans are the only animals known to be susceptible to Alzheimer and Parkinson, even though the main hallmarks of Alzheimer (i.e., Aβ and hyper-phosphorylated tau deposition) have also been reported in the great apes (Gearing et al. 1997; Kimura et al. 2001; Rosen et al. 2008). In a recent study analyzing aging-associated changes in brains from humans (up to 88-year old) and chimpanzees (up to 51-year old), the authors found significantly higher neuropathological effects in human brains and concluded that these resulted from an extended lifespan in our species (Sherwood et al. 2011). However, the neurobiology of ageing in the great apes is largely unknown due to the scarcity of available brain samples from older individuals, since even under continuous medical care the maximum lifespan of captive chimpanzees rarely reaches 60 years (Erwin et al. 2002). Thus, the absence of severe neurodegenerative processes in chimpanzees can hardly be considered a dogma and rather than the often claimed trade-off between human–specific neurological adaptations, or the even more unlikely human-specific relaxation of selective pressures in neurological function, neurodegeneration might have been reported only in humans due to our extended lifespan.

Analysis of Potential Caveats

Three important limitations when quantifying adaptive evolution are the selection of a proper neutral reference, the power rendered by the number of sites used to obtain the SFS, and the small proportion of sites that might actually be under selection in particularly large genomic elements such as introns or promoters.

A reasonable and well-established neutral reference for coding regions is the use of interdigitated sites, normally provided by the 4-fold degenerated positions. However, such strategy has the disadvantage of reducing considerably the data available to obtain a neutral SFS for correcting α and ω_α estimates with the information of the DFE and inferred linkage/demographic changes. We tried to overcome this limitation by using, as neutral SFS, the concatenation of all 4-fold sites in the whole dataset. This has the advantage of increasing the number of polymorphisms to construct the SFS but may produce a neutral SFS significantly different from the one of each specific gene set analyzed. In all analyses, we compared the results produced by using both strategies to emphasize only robust observations.

For noncoding elements, the use of 4-fold degenerated sites might not be as advisable since these are generally subjected to stronger constraints than most sites in noncoding regions (Lawrie et al. 2013). In our case, we used the information provided by the trailer regions, concatenating all 5-kb regions downstream the end of each gene within each given gene set and excluding all fragments that were candidates to being CNCs. This strategy has the additional advantage of providing enough sites in each individual gene set to be used as neutral reference, without the need of merging sites from different pathways.

As both α and ω_α estimates depend on quantifying an excess of fixed functional substitutions, there may exist a degree of correlation between dN/dS (or dPUS/dNSel in noncoding regions) and α and ω_α values. This might raise some concerns on circularity between the criteria for choosing genes and the measures obtained on them. Indeed, such correlation is expected but only when all polymorphisms are predicted to be neutral. This correlation can be broken when there are some deleterious mutations skewing the DFE and, therefore, it is important to correct for their contribution to divergence when estimating α and ω_α. In those elements where deleterious effects for new mutations are estimated at different proportions among gene sets (as in CDS), divergence measures will be corrected differently, producing ω_α estimates not-necessarily correlated with dN/dS. In contrast, in cases where all DFE are identical among gene sets (as in introns), such correlation is expected to be higher. As depicted in supplementary figure S3, Supplementary Material online, our observations match these expectations.

When all or most new mutations are predicted to be neutral, divergence rates perfectly correlate with α and ω_α adaptive estimates. This is the pattern we observed in introns and promoters. However, the absence of predicted nonneutral new mutations does not necessarily exclude the action of natural selection. Both strongly advantageous and very deleterious mutations will very rarely be observed as polymorphisms as they are either swept through or removed from the population very quickly. Therefore, an excess of substitutions in selected sites could still be interpreted as adaptive evolution coming from strongly selected advantageous mutations.

Decoupling Action of Selection on Genomic Elements

The adaptive history of coding and noncoding sequences seems to be decoupled, at least for the gene sets analyzed. For instance, when evidence for positive selection is inferred from protein divergence and polymorphism, it does not imply that noncoding regions of the same genes will present similar selective pressures. When making this observation, we do not imply that selection acts always differently in coding and noncoding elements, we rather state that the former is not a requisite or a general law. Moreover, we also observe instances of coupled action of selection among different noncoding genomic elements of particular pathways, with most Parkinson regulatory elements, for instance, evolving at consistently lower levels. In short, our results, although based on a limited number of gene sets, render some support to the idea that regulatory and protein-coding sequences can present different rates of substitution and adaptive changes responding to their own evolutionary dynamics.

Supplementary Material

Supplementary note S1, figures S1–S4, and tables S1–S12 are available at Genome Biology and Evolution online (http://www.gbe.oxfordjournals.org/).