Genome biogeography reveals the intraspecific spread of adaptive mutations for a complex trait

Abstract

Physiological novelties are often studied at macro‐evolutionary scales such that their micro‐evolutionary origins remain poorly understood. Here, we test the hypothesis that key components of a complex trait can evolve in isolation and later be combined by gene flow. We use C₄ photosynthesis as a study system, a derived physiology that increases plant productivity in warm, dry conditions. The grass Alloteropsis semialata includes C₄ and non‐C₄ genotypes, with some populations using laterally acquired C₄‐adaptive loci, providing an outstanding system to track the spread of novel adaptive mutations. Using genome data from C₄ and non‐C₄ A. semialata individuals spanning the species’ range, we infer and date past migrations of different parts of the genome. Our results show that photosynthetic types initially diverged in isolated populations, where key C₄ components were acquired. However, rare but recurrent subsequent gene flow allowed the spread of adaptive loci across genetic pools. Indeed, laterally acquired genes for key C₄ functions were rapidly passed between populations with otherwise distinct genomic backgrounds. Thus, our intraspecific study of C₄‐related genomic variation indicates that components of adaptive traits can evolve separately and later be combined through secondary gene flow, leading to the assembly and optimization of evolutionary innovations.

Introduction

Over evolutionary time, living organisms have been able to colonize almost every possible environment, often via the acquisition of novel adaptations. While impressive changes can be observed across phyla, adaptive evolution by natural selection occurs within populations (e.g. Geber & Griffen 2003; Morjan & Rieseberg 2004). For most complex adaptive novelties, the intraspecific dynamics that lead to their progressive emergence are poorly understood. Indeed, if novel complex traits gain their function only when multiple anatomical and/or biochemical components work together, the order of acquisition of such components raises intriguing questions (Meléndez‐Hevia et al. 1996; Lenski et al. 2003). One possibility is that the acquisition of one key component is sufficient to trigger a novel trait (e.g. Ourisson & Nakatani 1994), allowing the subsequent selection of novel mutations for the other components in the genetic pool that fixed the first component. The alternative would assume that components accumulate independently of each other in isolated populations and are later assembled by secondary gene flow and subsequent selection to form the complex trait (Morjan & Rieseberg 2004; Leinonen et al. 2006; Hufford et al. 2013; Ellstrand 2014; Miller et al. 2014). Differentiating these scenarios requires the inference of the order of mutations for a novel complex trait, as well as their past spread throughout the history of divergence, migration and secondary gene flow in one or several related species. Such investigations must rely on study systems in which variation in an adaptive complex trait, and its underlying genomic basis, can be traced back through time.

C₄ photosynthesis is a physiological state, present in ~3% of plant species (Sage 2016), which results from the co‐ordinated action of multiple enzymes and anatomical components (Hatch 1987; Christin & Osborne 2014). C₄ biochemistry relies on well‐characterized enzymes that also exist in non‐C₄ plants, but with altered abundance, cellular and subcellular localization, regulation and kinetics (Kanai & Edwards 1999). The main effect of C₄ photosynthesis is an increase in CO₂ concentration at the place of its fixation by the enzyme Rubisco in the Calvin–Benson cycle (von Caemmerer & Furbank 2003). This is advantageous in conditions that restrict CO₂ availability, especially in warm and arid environments under the low‐CO₂ atmosphere that has prevailed for the last 30 million years (Sage et al. 2012). C₄ plants consequently dominate most open biomes in tropical and subtropical regions, where they achieve high growth rates and large biomass (Griffith et al. 2015; Atkinson et al. 2016). Despite its apparent complexity, C₄ photosynthesis evolved more than 60 times independently over the ancestral C₃ type (Sage et al. 2011), and evolutionary transitions were facilitated by the existence of anatomical and genetic enablers in some groups of plants (Christin et al. 2013b, 2015). However, the micro‐evolutionary history of photosynthetic transitions is yet to be addressed.

Most C₄ lineages evolved this photosynthetic system millions of years ago, so that the initial changes linked to C₄ evolution remain obscured (Christin & Osborne 2014). In a couple of groups, closely related species present a spectrum of more or less complete C₄ traits, which is interpreted as the footprint of the gradual evolution of C₄ (e.g. McKown et al. 2005; Christin et al. 2011; Fisher et al. 2015). These groups provide powerful systems to reconstruct the order of changes during the transition to C₄ photosynthesis (e.g. McKown & Dengler 2007; Heckmann et al. 2013; Williams et al. 2013). However, the presumed lack of gene flow among these related species impedes testing hypotheses about the importance of secondary gene flow mixing mutations that were fixed in isolated populations. So far, the presence of genotypes with different photosynthetic types has been reported in only one taxon, the grass Alloteropsis semialata.

Alloteropsis semialata includes C₃ and C₄ individuals (Ellis 1974), and a recent study further described individuals with only some of the C₄ anatomical and biochemical components, which allow a weak C₄ cycle (i.e. C₃–C₄ intermediates; Lundgren et al. 2016). Other species in this genus, Alloteropsis angusta, Alloteropsis cimicina, Alloteropsis paniculata and Alloteropsis papillosa, are C₄, but perform the C₄ cycle using different enzymes and leaf tissues than A. semialata, which points to independent realizations of the C₄ phenotype (Christin et al. 2010). Analyses of genes for key C₄ enzymes in a handful of accessions have revealed that some populations of A. semialata carry C₄ genes that have been laterally acquired from distant C₄ relatives (Christin et al. 2012). The laterally acquired genes include one for phosphoenolpyruvate carboxykinase (pck) and three different copies for phosphoenolpyruvate carboxylase (ppc). These laterally acquired genes are integrated into the C₄ cycle of some extant accessions of Alloteropsis (Christin et al. 2012, 2013a), but genes for other C₄ enzymes have been transmitted following the species tree (vertically inherited), and gained their C₄ function via novel mutations (Christin et al. 2013a). Some C₄ Alloteropsis populations presumably still use the vertically inherited ppc and pck homologs for their C₄ cycles. However, the laterally acquired ppc and pck copies spent millions of years in other C₄ species, where they acquired adaptive mutations that likely increased their fit for the C₄ function before their transfer (Christin et al. 2012). The potential adaptive value of the laterally acquired genes, as well as their restriction to some C₄ populations, provides a tractable system to elucidate gene movements that led to the emergence and strengthening of the complex C₄ adaptive trait. However, the geographical distributions and frequencies of these laterally acquired genes are still poorly understood, and the genome history of A. semialata remains largely unexplored.

In this study, we obtain low‐coverage whole‐genome sequencing data from A. semialata individuals spread across the species’ geographical range and differing in photosynthetic type. We use the data to first infer the history of isolation and secondary contact, and then to track the acquisition and spread of the laterally acquired genes. This biogeographic framework allows us to test whether the C₄ complex trait was assembled via the sequential fixation of novel mutations within each isolated gene pool or via gene flow combining mutations that had been fixed in distinct gene pools (Fig. 1). In the first scenario, the history of C₄‐adaptive mutations, represented by the laterally acquired genes, would correspond to the sequence of migration and isolation of populations and largely match the history of the rest of the genome (Fig. 1A). In the second scenario, the history of C₄‐adaptive mutations would differ from that of the rest of the genome, their selection‐driven spread across genetic lineages resulting in more recent coalescence times and gene topologies that differ from the species topology (Fig. 1B). This first intraspecific spatial genomic analysis of key components of the C₄ complex trait opens new avenues to understand the micro‐evolutionary processes that led to macro‐evolutionary innovations.

Figure 1.

Competing scenarios for the assembly of a complex trait. (A) The trait is assembled by sequential fixation of mutations within each genetic pool. (B) Mutations that were fixed in isolation are later assembled via secondary gene flow. The species tree is outlined by thick grey branches, and coloured branches indicate novel mutations on individual genes. Individual gene trees are drawn on the right. In scenario A, the histories of adaptive mutations correspond to the history of the rest of the genome and all gene trees are concordant, while in scenario B, the histories of the adaptive mutations differ from that of the rest of the genome, with gene trees that do not match the species tree.

Material and methods

Sampling, sequencing and genome sizing

A low‐coverage whole‐genome sequencing approach (genome skimming) was used to reconstruct the genome history of Alloteropsis. This approach has become increasingly attractive for inferring population parameters (e.g. Buerkle & Gompert 2013; Fumagalli et al. 2013) and for studying complex traits (Li et al. 2011). It also allows de novo assembly of high copy number regions of the genome, such as organelle genomes (Besnard et al. 2014; Dodsworth 2015), and can be applied to samples of limited quality and quantity, such as herbarium or museum collections (Besnard et al. 2014). Genome‐skimming data for eleven Alloteropsis semialata individuals, and one of each of the congeneric Alloteropsis cimicina and Alloteropsis angusta, were retrieved from a previous study that used them to assemble chloroplast genomes (Table S1, Supporting information; Lundgren et al. 2015, 2016). The photosynthetic type of these samples has been determined previously, and they encompass non‐C₄ individuals with and without a weak C₄ cycle, as well as multiple C₄ accessions (Table S1, Supporting information; Lundgren et al. 2015, 2016). An additional eight Alloteropsis accessions were sampled here to increase the resolution of genome biogeography for the group (Table S1, Supporting information). These include one accession from each of the congeneric species Alloteropsis paniculata and A. angusta, and six additional A. semialata individuals. These samples were selected to increase the plastid and photosynthetic diversity, with a special focus on the Zambezian biogeographic region (spanning Tanzania, Zambia and the Democratic Republic of Congo – DRC; Linder et al. 2012; Table S1, Supporting information), where the majority of the chloroplast and photosynthetic diversities are found (Lundgren et al. 2015, 2016). Three of the newly sequenced A. semialata accessions (‘DRC3’, ‘TAN3’ and ‘KEN1’) were previously characterized with stable carbon isotopes (Lundgren et al. 2015), which can distinguish plants grown using mainly C₄ photosynthesis from those that acquired a significant portion of their carbon via the ancestral C₃ cycle, whether or not it is complemented by a weak C₄ cycle (Smith & Brown 1973; Cerling et al. 1997). One of these three accessions (‘TAN3’) is isotopically intermediate, indicating that a strong C₄ cycle occurs, but that some atmospheric carbon is still fixed directly by the C₃ cycle (Peisker 1986; Monson et al. 1988). For four of the new samples, carbon isotopes were measured on a leaf fragment as previously described (Lundgren et al. 2015), which revealed that all of them had carbon isotope values within the C₄ range (Table S1, Supporting information).

DNA was extracted, quality checked and sequenced as described in Lundgren et al. (2015), except that the DNA of these accessions was not sonicated prior to the library preparation due to the high degree of DNA degradation in these herbarium specimens. Each sample was individually barcoded and pooled with 23 other samples (from the same or unrelated projects) before paired‐end sequencing (100–150 bp) on one Illumina lane (HiSeq‐2500 or HiSeq‐3000) at the Genopole platform of Toulouse or at the Genoscope platform of Evry (only A. paniculata; Table S1, Supporting information). The final data set consisted of sequence data for a total of 21 individuals, sequenced in six different runs (Table S1, Supporting information).

The genome size was estimated for accessions for which live material was available by flow cytometry following the one‐step protocol of Doležel et al. (2007) with minor modifications as described in Clark et al. (2016). We selected Oryza sativa IR36 (2C = 1 pg; Bennett & Smith 1991) and the Ebihara buffer (Ebihara et al. 2005) as the most appropriate internal standard and nuclei isolation buffer for all but one accessions (Table S1, Supporting information). For the ‘RSA3’ accession, whose C‐value was estimated to be about three time larger than other accessions, we used the Pisum sativum ‘Ctirad’ standard (2C = 9.09 pg; Doležel et al. 1992) and the GPB buffer (Loureiro et al. 2007), supplemented with 3% of PVP.

Assembly and analyses of chloroplast genomes

Complete chloroplast genomes were de novo assembled for the newly sequenced individuals using the genome walking method described in Lundgren et al. (2015). The newly generated chloroplast genomes were manually aligned with those already available, and a time‐calibrated phylogenetic tree was inferred with beast v. 1.5.4 (Drummond & Rambaut 2007), as described in Lundgren et al. (2015). Monophyly of the outgroup (A. cimicina + A. paniculata) and the ingroup (A. angusta + A. semialata) was enforced to root the phylogeny, which is consistent with all previous analyses (Ibrahim et al. 2009; Christin et al. 2012; GPWGII 2012; Lundgren et al. 2015). The root of the tree was fixed to 11 Ma (as found by Lundgren et al. 2015), which was achieved with a normal distribution of mean of 11 and standard deviation of 0.0001. Two different analyses were run for 20 000 000 generations, sampling a tree every 1000 generations. After checking the convergence of the runs in tracer v. 1.5.0 (Drummond & Rambaut 2007), the burn‐in period was set to 2 000 000 generations, and the maximum credibility tree was identified from the trees sampled after the burn‐in period in both analyses, mapping median ages on nodes.

Genotyping across the nuclear genome

A reference genome for Alloteropsis is currently lacking. However, the grass Setaria italica (comment name: Foxtail millet) belongs to the same tribe as Alloteropsis (Paniceae) and has a well‐assembled reference genome (jgiv2.0.27; Bennetzen et al. 2012). Setaria and Alloteropsis diverged approximately 20 Ma (Christin et al. 2012), a time that is sufficient for a complete turnover of noncoding sequences (Ammiraju et al. 2008). However, reads corresponding to coding regions across the genome can still be reliably mapped (see Results).

Raw sequencing reads were quality filtered using the ngs qc toolkit v. 2.3.3 (Patel & Jain 2012). Reads with more than 20% of the bases having a quality score below Q20 and reads with ambiguous bases were removed. Furthermore, low‐quality bases (<Q20) were trimmed from the 3’ end of the remaining reads. The filtered reads were mapped to the Setaria reference genome, using bowtie2 v. 2.2.3 (Langmead et al. 2009). Raw alignment files were cleaned using samtools v.1.2 (Li et al. 2009) and picard tools v.1.92 (http://picard.sourceforge.net/). PCR duplicates were removed, and only uniquely aligned reads in proper pairs were kept. This will remove most of the reads mapped to repetitive sequences, such as transposable elements, while retaining reads mapping to sequences that have been duplicated after the split of Alloteropsis and Setaria. The cleaned alignments were used to call single nucleotide polymorphic variants (SNPs) with samtools v. 0.1.19 using the mpileup function followed by the vcfutil.pl script with default setting supplied with the program. The South African C₄ individual ‘RSA3’ was excluded during SNP calling to avoid any bias that might result from the presence of more than two alleles in this polyploid (see Lundgren et al. 2015 and Table S1, Supporting information). Genotypes of each accession, including ‘RSA3’, at all called SNP positions were extracted from the alignments using the mpileup function in samtools v.0.1.19, supplying the program with the positions of the called SNPs, and in‐house developed scripts for further processing (Appendix S1, Supporting information). The low‐coverage data caused genotype probabilities to be low, which precluded effective filtering based on these probabilities. Therefore, fixed genotype calls were used. To evaluate the proportion of SNPs corresponding to exon sequences, annotations were extracted for the 25 727 coding regions of the Setaria genome with homologs in maize and rice genomes (from now on referred to as SZR homologs). The positions of the raw SNPs were intersected with the SZR homolog annotations in bedtools v.2.19.1 using default settings (Quinlan & Hall 2010).

SNPs with coverage above 2.5 times the genomewide coverage (Table S2, Supporting information) were converted to unknown genotype calls. Furthermore, genotypes with more than two allele calls were also converted to missing data, and finally, positions with more than 50% missing data/unknown genotypes were discarded. The remaining 170 629 positions were used to infer a phylogenetic tree, using PhyML (Guindon et al. 2010) and a GTR substitution model (the best fit model as determined by hierarchical likelihood ratio tests), after coding heterozygous sites with IUPAC codes. Support was evaluated with 100‐bootstrap pseudoreplicates. The low‐coverage data likely cause some alleles to be missed, leading to an overestimate of homozygosity. However, no bias is expected in the missing allele, so that the low coverage is unlikely to lead to spurious groupings.

To test for a bias due to uneven coverage across samples (Table S2, Supporting information), we repeated the phylogenetic analysis on a resampled alignment, where all samples have the same number of bases mapped to the Setaria genome. Reads were randomly sampled without replacement from the filtered alignment files until the number of bases across the sampled reads equalled that of the sample with the lowest coverage (Appendix S2, Supporting information). These reanalyses were first conducted with all samples, which resulted in a low number of positions constrained to the samples with the lowest coverage. While analyses on the resampled data set were consistent with the whole‐data set analyses, the limited number of characters resulted in reduced support. We consequently repeated the resampling allowing for the full alignment of the two A. semialata samples with the lowest coverage and alignment success (‘AUS1’ and ‘RSA2’) to be retained at a slightly lower coverage than the rest of the samples. SNPs were called as outlined above, which allowed for the retention of 22 821 SNPs.

Genetic structure and test for secondary gene flow

Preliminary cluster analyses with a focus on A. semialata showed that a more stringent filtering of the SNPs improved convergence of the analyses. Only positions with <10% missing data (2607 SNPs) within this species were consequently kept for analyses of its population structure, using the structure software (Pritchard et al. 2000). Ten independent analyses were run for each number of population components (K) from one to ten, under the admixture model. The adequate run length and burn‐in periods were determined through preliminary analyses, which indicated that a burn‐in period of 300 000 generations followed by 200 000 iterations provided stable estimates for all K values. The optimal K values were determined using the method of Evanno et al. (2005), as implemented in structureharvester (Earl & vonHoldt 2012). The results of the ten runs for each K were summarized using clumpp v. 1.1.2 (Jakobsson & Rosenberg 2007) and graphically displayed using distruct v. 1.1 (Rosenberg 2004). These analyses were repeated without the polyploid individual ‘RSA3’, which led to the same cluster assignments, showing that differences in ploidy levels do not affect the conclusions. Finally, the cluster analyses were repeated on alignments based on the reads subsampled to similar coverage in all sampled, allowing for 25% missing data per site (retention of 681 SNPs).

Different relationships among fractions of the nuclear genome can result from reticulated evolution or incomplete lineage sorting (Green et al. 2010; Durand et al. 2011). To distinguish these two possibilities, the ABBA–BABA method, which relies on the D statistic to test for asymmetry in the frequencies of incongruent phylogenetic groupings (Green et al. 2010; Durand et al. 2011), was used to test for secondary gene flow on a genomewide level in cases suggested by phylogenetic and clustering analyses (see Results). The low coverage likely leads to an overestimate of homozygous sites, but no bias is expected towards ABBA or BABA sites, leaving estimations of distorted gene flow unaffected. For each test, a four‐taxon phylogeny was selected, consisting of an outgroup and three tips among which secondary gene flow is suspected. Reads mapping to the 170 629 SNPs were recovered from the filtered alignment files using bedtools v.2.19.1 by intersecting the alignment files with positional information of the SNPs using default settings. The recovered reads were evaluated using the ‐doAbbababa option in the angsd program version 0.911 (Korneliussen et al. 2014). A jackknifed estimate of the D statistic and the corresponding Z‐value were obtained by the jackknif.R script supplied with the angsd program.

Assembly and analyses of selected genes

Two different groups of closely related genes were selected for detailed analyses. The genes selected were two C₄‐related protein‐coding genes, phosphoenolpyruvate carboxylase (ppc genes) and phosphoenolpyruvate carboxykinase (pck genes), that include some copies acquired by Alloteropsis from distantly related species via lateral gene transfer, while other copies were vertically inherited following the species tree (Christin et al. 2012). Previous conclusions regarding the distribution of these genes among accessions of Alloteropsis were based on PCR and Sanger sequencing, which can be biased due to the possibility of primer binding mismatches. The presence/absence of the laterally acquired ppc and pck genes and their vertically inherited homologs across the accessions were therefore re‐evaluated here using the genome‐skimming data, as well as new PCR and Sanger sequencing with primer verified against the new genomic data. Using molecular dating, the divergence times of the laterally acquired genes were compared to those of vertically inherited homologs belonging to the same set of accessions.

Reads were first mapped on gene segments of the ppc and pck genes from different accessions of Alloteropsis (grass co‐orthogols ppc‐1P3 and pck‐1P1; Christin et al. 2012, 2015). These segments have been previously sequenced and analysed in a number of other C₃ and C₄ grasses (Christin et al. 2012). The availability of this rich reference data set allows mapping to closely related accessions of Alloteropsis, which improves the alignment success compared to the whole‐genome approach described above, and increases the confidence in the assignment. The gene segments cover exons 8–10 for ppc and exons 3–10 for pck, including introns, and represent approximately 46% (1492 bp) and 63% (1487 bp), respectively, of the full‐length coding sequences. In‐house Perl scripts (Appendix S3, Supporting information) were used to unambiguously assign reads to genes of these data sets, following the phylogenetic annotation method of Christin et al. (2015). In summary, this approach consists of: (i) building a reference data set of sequences with known identity for closely related gene lineages, (ii) using blast searches to identify all sequences homologous to any of these reference sequences in the query data set (the filtered reads in this case), (iii) independently aligning each homologous sequence to the reference data set and inferring a phylogenetic tree and (iv) establishing the identity of each of the query sequences based on the phylogenetic group in which it is nested. Assignment of reads to the gene lineages was verified by visual inspection of the phylogenetic trees and the alignments. Subsequently, all reads assigned to each of the vertically inherited or laterally acquired gene lineages were retrieved, and aligned to PCR‐isolated sequences (see Results) using geneious v. 6.8 (Kearse et al. 2012). The reads were then assembled into gene models, comprising introns and exons, for the studied segments. Multiple gene models were assembled for a single individual when the existence of distinct alleles was supported by at least two different polymorphic sites, each with at least two independent reads. Paired‐end reads were then merged into contigs if they shared the polymorphisms. Reads that did not overlap the polymorphic sites were merged with all alleles, replacing additional polymorphisms with IUPAC ambiguity codes.

To check whether partial pseudogenes that do not include the studied segments exist in some genomes, the presence of laterally acquired ppc genes was also tested using only coding sequences corresponding to exons 1–7, which were retrieved from a transcriptome study of A. semialata (Christin et al. 2012). This transcriptome was generated for a South African C₄ polyploid with two laterally acquired ppc genes, but the vertically inherited versions of ppc and pck were not available in this transcriptome, preventing phylogenetic analyses. Blastn searches were used to identify reads mapping to one of the two laterally acquired ppc genes on at least 50 bp with at least 99% of identity. Finally, the presence/absence of the different pck and ppc copies was further confirmed via PCR and Sanger sequencing using primers specific to the different gene copies (Table S3, Supporting information; Christin et al. 2012). PCR, purification and sequencing were conducted as described in Lundgren et al. (2015), except for changes of the annealing temperature and/or extension time (Table S3, Supporting information). These PCR were conducted only on samples for which good quality DNA was available. Indeed, DNA isolated from herbarium samples is highly degraded, precluding reliable PCR screening.

To verify the gene models assembled from genome skimming for ppc and pck, the PCR amplified and Sanger sequenced fragments of the vertically inherited and laterally acquired genes were added to the genes assembled from short‐read data. The data sets were aligned using muscle v3.8.31 (Edgar 2004) with default parameters, and the alignments were manually refined. Maximum‐likelihood phylogenetic trees were inferred using PhyML, under a GTR+G model, and with 100‐bootstrap pseudoreplicates. Molecular dating was performed on the same alignments using beast as described above for chloroplast markers, but with a coalescent prior. The Andropogoneae/Paspaleae group (represented by Sorghum, Paspalum and one of the laterally acquired ppc) was selected as the outgroup, and the root of the tree was calibrated with a normal distribution with a mean of 31 Ma, and a standard deviation of 0.0001, as previously estimated for this node (Christin et al. 2014).

Results

Read alignment and SNP calling

The number of filtered paired‐end reads varied across samples, for a genomewide coverage ranging from 0.70 to 4.52 (Table S2, Supporting information). The proportion of filtered paired‐end reads that aligned to the Setaria genome varied between 4.04% and 10.23%, and between 1.22% and 2.94% aligned to the coding regions (Table S2, Supporting information). While the mapping was performed across the whole genome of Setaria (excluding the organelle genomes), divergence of noncoding sequences means that high mapping success is expected to be concentrated mostly onto coding sequences. About 9% of the Setaria genome corresponds to exons (Bennetzen et al. 2012). Assuming that the total length of exons is similar in the two species, the larger genome of Alloteropsis means that this proportion should be about 4.5%, so that approximately half of the reads corresponding to nuclear exons were mapped. The rest of the reads that belong to exons probably correspond to gene sections that are too divergent between the two species to successfully map.

Only uniquely aligned reads were used to call SNPs, which inherently excludes common repetitive regions such as transposons. However, 1111 raw SNPs had a higher than expected coverage (>5×) across at least 50% of the samples. The positions of 91% (1007) of these high‐coverage SNPs fell outside of the SZR homolog regions, and the rest were concentrated to only 14 SZR homologs. We therefore hypothesize that these high‐coverage SNPs stem from genetic regions (mostly noncoding) that have been duplicated after the split between Alloteropsis and Setaria and they were subsequently removed from the analyses.

A total of 170 629 SNPs with <50% missing data across the 21 accessions were finally selected for downstream analyses. These sites are spread across all chromosomes (Fig. S1, Supporting information) and 96% of them fall within one of 9948 SZR homologs. The 2607 SNPs used for the cluster analysis (<90% missing data across the Alloteropsis semialata samples) were equally well spread across the genome (Fig. S1, Supporting information) and 97% fall within one of 848 SZR homologs. Most of the variation in missing data across samples (Table S2, Supporting information) is likely explained by differences in coverage, although the presence/absence of genes within each accession might also influence the individual mapping success.

Overall, our analyses show that our pipeline, despite a low overall coverage and low alignment success due to the large divergence time between Alloteropsis and Setaria, captures variation in almost 10 000 genes spread across the genomes of grasses.

Phylogenetic trees

The plastid phylogeny identified two C₄ individuals from DRC with haplotypes that form a new C₄ plastid lineage based on divergence times (i.e. lineage G, sister to lineage F; Figs 2 and S2, Supporting information). Relationships based on markers sampled across the nuclear genome confirm the monophyly of A. semialata and its sister‐group relationship to Alloteropsis angusta, but present multiple incongruences with the chloroplast tree within A. semialata (Figs 2 and S3, Supporting information). In this genomewide tree, the first divergence leads to a group composed of the non‐C₄ accessions of A. semialata from South Africa without any known C₄ cycle (Clade I; Fig 2 and S3, Supporting information), and the second divergence leads to a group comprising the non‐C₄ accessions from the Zambezian region that use a weak C₄ cycle (Clade II; Fig 2 and S3, Supporting information; C₃‐C₄ intermediates sensu Lundgren et al. 2016). The isotopically intermediate accession ‘TAN3’ is then sister to all C₄ accessions (Figs 2 and S3, Supporting information). The two C₄ accessions bearing the plastid lineage G form a paraphyletic clade, while the other C₄ accessions from the Zambezian region (‘TAN4’, ‘DRC3’ and ‘DRC4’) are grouped in a strongly supported clade (Clade III; Figs 2 and S3, Supporting information). The South African polyploid individual ‘RSA3’ is sister to the C₄ individuals sampled outside of the Zambezian region, and the rest of the C₄ accessions form the strongly supported clade IV, with two subclades corresponding to Africa plus Madagascar and Asia plus Australia (Figs 2 and S3, Supporting information). The nuclear phylogeny based on the resampled data set is mostly identical to the one based on the whole data set (Figs S3 and S4, Supporting information).

Figure 2.

Comparison of nuclear (on the left) and plastid (on the right) topologies (without branch lengths). The putative origin of individuals with mixed genetic back ground was added using dashed lines. Branches of the nuclear tree are coloured according to clustering analyses (Fig. 3). Photosynthetic types (PT) and presence of laterally acquired genes (LGT) are indicated by symbols at the tips; purple bar = presence of pck‐1P1_LGT:C, blue bar = ppc‐1P1_LGT:M, orange bar = ppc‐1P1_LGT:C, dark blue bar = ppc‐1P1_LGT:A. Geographic origin is indicated on the left. Secondary gene flow is numbered as in Fig. 6; (3) hybridization between non‐C₄ and C₄ populations within the Zambezian region, (4) allopolyploidy between C₄ populations in Africa (‘RSA3’), (5) pollen‐mediated gene flow from mainland Africa to Madagascar.

Genetic structure and secondary gene flow within Alloteropsis semialata

Based on the whole‐genome clustering analysis, four clusters explain the data set best, and adding groups does not improve the likelihood (Fig. 3B). However, the method of Evanno et al. (2005) indicates that the maximum fit improvement is at two clusters, with four clusters representing the second maximum fit improvement (Fig. 3C). With four clusters, the main clades from the genome wide phylogeny are recovered (Figs 2 and 3A). This genetic structure matches the photosynthetic types rather than the geographic origin, with the non‐C₄ clades I and II and the C₄ clades III and IV each forming distinct homogenous groups (Fig. 3A). The three Zambezian individuals that formed a paraphyletic clade in the nuclear phylogeny (‘TAN3’, ‘DRC1’ and ‘DRC2’) are partially assigned to two Zambezian groups, the non‐C₄ clade II and the C₄ clade III (Figs 2 and 3A). Finally, the polyploid individual from South Africa, ‘RSA3’, is partially assigned to the two C₄ clades III and IV (Fig. 3A). The cluster results based on the resampled data set are less stable due to a lower number of sites, but the assignments are similar (Figs 3 and S5, Supporting information).

Figure 3.

Assignment of Alloteropsis semialata individuals to genetic clusters. (A) Assignment of each individual to the different clusters (K 2–4). The photosynthetic type is indicated by symbols next to the names, as in Fig. 2. (B) Mean likelihood (±SD) over 10 runs for each K value (1–10), and (C) |L′′(K)|/SD (fit improvement) as calculated according to Evanno et al. (2005).

Heterozygosity was estimated for each sample based on the 22 821 SNPs from the resampled data set with similar coverage across samples. While these estimates are based only on sites variable within Alloteropsis and should consequently not be interpreted as genomewide heterozygosity, it is possible to compare the estimates between samples. The individuals assigned to multiple clusters had the highest percentage of heterozygous SNPs (Fig. S6, Supporting information), which confirms their genetic diversity.

Together, our intraspecific genetic analyses reveal the existence of distinct gene pools despite overlapping distributions (Figs 3A and 4), but also suggest that genetic exchanges have happened among groups. The incongruences between the phylogenetic structures of the chloroplast and nuclear genomes, together with the assignment of some individuals to multiple genetic clusters, suggest that the three Zambezian individuals ‘TAN3’, ‘DRC2’ and ‘DRC1’ have ancestors from distinct genetic groups, in this case the nuclear clades II and III. ABBA–BABA tests were therefore conducted to test this hypothesis, using A. angusta (individual Ang2) as the outgroup. The individual ‘TAN4’ was selected as the representative of clade III because it is geographically more distant and distinct on all genetic markers (Figs 4, S2 and S3, Supporting information). Significant indications (P < 0.05 after correction for multiple testing) of gene flow from the non‐C₄ clade II (‘TAN2’ and ‘TAN1’) into the populations assigned to multiple clusters (‘TAN3’, ‘DRC2’ and ‘DRC1’) were found (Table 1). In contrast, there is no evidence of a significant secondary contribution of clade II into individuals of clade III (‘DRC3’ or ‘DRC4’; Table 1). However, in one case, a slight excess of BABA sited was detected, which was not significant after correction for multiple testing (Table 1). This would suggest some genetic contribution from one non‐C₄ population of clade II (‘TAN1’) into the C₄ population represented by ‘TAN4’ (Table 1).

Figure 4.

Geographic distribution of Alloteropsis semialata genetic lineages. (A) World distribution, highlighting the Zambezian region with a rectangle and (B) details of the Zambezian region. For each point, the colour of the outline indicates the plastid lineage (blue = clade A; green = clade BC; yellow = clade FG; red = clade DE), while the colour of the background represents the nuclear lineage (blue = clade I; green = clade II; yellow = clade III; red = clade IV; black = mixed genetic background; grey = congeners). Finally, the shape of the point indicates the photosynthetic type, as determined by carbon isotopes (square = non‐C₄; circle = C₄; triangle = isotopically intermediate).

Table 1.

Results of ABBA–BABA tests

Outgroupa	P3a	P2a	P1a	# ABBA sites	# BABA sites	D b	Z	P‐valuec	Conclusion
Alloteropsis angusta	TAN2	TAN3	TAN4	2630	1805	0.186	8.279	<0.0001	TAN2 closer to TAN3 than to TAN4
A. angusta	TAN1	TAN3	TAN4	2630	1750	0.201	8.757	<0.0001	TAN1 closer to TAN3 than to TAN4
A. angusta	TAN2	DRC2	TAN4	2037	1546	0.137	5.939	<0.0001	TAN2 closer to DRC2 than to TAN4
A. angusta	TAN1	DRC2	TAN4	1960	1570	0.110	4.724	<0.0001	TAN1 closer to DRC2 than to TAN4
A. angusta	TAN2	DRC1	TAN4	2240	1752	0.122	5.463	<0.0001	TAN2 closer to DRC1 than to TAN4
A. angusta	TAN1	DRC1	TAN4	2194	1749	0.113	5.692	<0.0001	TAN1 closer to DRC1 than to TAN4
A. angusta	TAN2	DRC4	TAN4	1177	1164	0.006	0.223	0.824	TAN2 equally close to DRC4 and TAN4/correct phylogeny
A. angusta	TAN1	DRC4	TAN4	1075	1123	−0.022	−0.866	0.386	TAN1 equally close to DRC4 and TAN4/correct phylogeny
A. angusta	TAN2	DRC3	TAN4	1372	1451	−0.028	−1.080	0.280	TAN2 equally closer to DRC3 and TAN4/correct phylogeny
A. angusta	TAN1	DRC3	TAN4	1248	1431	−0.068	−2.885	0.004d	TAN1 might be closer to TAN4 than to DRC3
TAN4	TPE1	MAD1	BUR1	1314	1129	0.076	2.603	0.009d	TPE1 might be closer to MAD1 than to BUR1

^a(Outgroup,(P3,(P2,P1))).

^b D statistic: (ABBA‐BABA)/(ABBA+BABA).

^c P‐value for Z score as calculated by jackknife for whether D differs significantly from zero.

^dNonsignificant after Bonferroni correction for multiple testing.

Within clade IV, the C₄ individual from Madagascar (‘MAD1’) was grouped with Asian C₄ accessions on plastid genomes but grouped with the African C₄ accessions based on markers from across the nuclear genome (Figs S2 and S3, Supporting information). An ABBA–BABA test was conducted to test the hypothesis of secondary gene flow after the split of the African and Asian C₄ accessions. The accession ‘TAN4’ was used as the outgroup, being sister to all accessions from clade IV. The Taiwan accession (‘TPE1’) was selected as the Asian sample, while the Burkinabe accession (‘BUR1’) represented Africa. Overall, more ABBA than BABA sites were detected (Table 1), indicating that the Asian accession was closer to the accession from Madagascar (‘MAD1’) than to the accession from mainland Africa, but the D statistic for this test was not significant after correcting for multiple testing (Table 1). Plastid markers, which represent seed dispersal, group the Madagascan accessions with Asian individuals. Therefore, a possible scenario involves an initial seed dispersal from Africa to Madagascar and then from Madagascar to Asia, with subsequent pollen flow between Africa and Madagascar.

Assembly and analyses of selected genes

The presence/absence of ppc and pck genes was established by mapping reads directly onto reference sequences from Alloteropsis. The distribution of the genes was also confirmed by PCR followed by Sanger sequencing (Fig. S7, Supporting information).

Together, the results confirmed previous phylogenetic analyses (Christin et al. 2012), but with a significant increase of the sample size. Reads assigned to the pck gene copy laterally acquired from members of the Cenchrus genus (pck‐1P1_LGT:C) were detected in the two A. angusta accessions and all A. semialata accessions, except the two non‐C₄ accessions from South Africa (Table 2; Figs S7 and S8, Supporting information). The sequences assembled for the laterally acquired pck gene were highly similar between the different accessions, leading to a poorly resolved phylogeny (Fig. S8, Supporting information). By contrast, the sequences assembled for the vertically inherited pck gene were variable among accessions, and the nuclear clades I and II were recovered in their phylogeny, while clades III and IV were not differentiated (Fig. S8, Supporting information). Interestingly, one accession with mixed genetic backgrounds (‘DRC1’) has two divergent alleles, one of which groups with clade II and the other with clade III/IV (Fig. S8, Supporting information). Dating analyses indicate that the divergence of A. angusta and A. semialata is more recent for the laterally acquired pck than for the vertically inherited copy (Fig. S9, Supporting information). However, the divergence of C₄ accessions of A. semialata is estimated at a similar time based on the vertically inherited and laterally acquired pck (Fig. 5).

Table 2.

Number of read assigned to each of the laterally acquired pck and ppc genes

Species	Accession	Phylogenetic group (plastid; nuclear)	ppc‐1P3_LGT:A a	ppc‐1P3_LGT:M b	ppc‐1P3_LGT:C c	pck‐1P1_LGT:C c
Alloteropsis cimicina	Cim1	–	0	149d	0	0
Alloteropsis paniculata	Pan1	–	0	37d	0	0
Alloteropsis angusta	Ang2	–	0	0	0	78
	Ang1	–	0	0	0	49
Alloteropsis semialata	RSA1	A; I	0	0	0	0
	RSA2	A; I	0	0	0	0
	TAN1	C; II	0	0	0	57
	TAN2	B; II	0	0	0	73
	TAN3	B; mixed	0	54d	0e	216d
	DRC1	G; mixed	0	57d	56	183d
	DRC2	G; mixed	0	29	50d	95d
	DRC3	E; III	0	6	25	135d
	DRC4	E; III	0	10	12	88d
	TAN4	F; III	0	76	0	83
	RSA3	E; IV	0	55d	63	113
	KEN1	E; IV	0	36	0	85
	BUR1	E; IV	0	26	0	130
	MAD1	D; IV	0	46	0	101
	THA1	D; IV	0	0	0	123
	TPE1	D; IV	0	0	0	118
	AUS1	D; IV	55	0	0	110

^aLaterally acquired from Andropogoneae.

^bLaterally acquired from Melinidinae.

^cLaterally acquired from Cenchrinae.

^dAssembly of more than one allele.

^eNote that seven reads were retrieved for exons 1–7, which indicates that this gene is truncated in the genome of this accession.

Figure 5.

Divergence times of C₄ accessions of Alloteropsis semialata based on vertically inherited and laterally acquired genes. For five ppc and pck genes, the posterior distribution of times to the last common ancestor of the C₄ A. semialata is shown, in million years (Ma).

The vertically inherited ppc was recovered from all samples, and the assembled gene models were variable enough to partially resolve the phylogeny, with well‐supported clades corresponding to the different species (Fig. S10, Supporting information). Although support was limited within A. semialata, the non‐C₄ clades I and II (including sequences from individuals assigned to multiple clades) were sister to a clade composed of the C₄ accessions from clade IV nested within those of clade III (Fig. S10, Supporting information). The divergence of vertically inherited ppc from C₄ accessions (excluding those partially assigned to clusters other than III and IV) matches the divergence of the vertically inherited pck for the same accessions (Fig. 5).

The ppc gene laterally acquired from Andropogoneae (ppc‐1P3_LGT:A) was only detected in the Australian C₄ accession (‘AUS1’; Table 2, Figs S7 and S10, Supporting information). An almost complete sequence for the studied segment was assembled, which was identical to those isolated by PCR.

The ppc gene laterally acquired from the Setaria palmifolia complex (ppc‐1P3_LGT:C) was detected in the C₄ accessions from South Africa (‘RSA3’) and the DRC (Table 2, Figs S7 and S10, Supporting information). Although no reads matching exons 8–10 of ppc‐1P3_LGT:C were detected in the accession ‘TAN3’, a total of seven reads from this individual matched exons 1–7. This suggests that the gene is truncated and probably exists as a pseudogene in this individual. The ppc‐1P3_LGT:C sequences were largely conserved, although distinct alleles were assembled in one of the accessions with mixed genetic background (‘DRC2’; Fig. S10, Supporting information). The divergence of ppc‐1P3_LGT:C genes belonging to different C₄ accessions was more recent than for the vertically inherited ppc and pck of the same accessions (Fig. 5).

The ppc gene acquired from Melinidinae (ppc‐1P3_LGT:M) was identified in nine C₄ accessions of A. semialata, the isotopically intermediate A. semialata, and the two congeners Alloteropsis cimicina and Alloteropsis paniculata (Table 2, Figs S7 and S10, Supporting information). Highly divergent alleles of the ppc‐1P3_LGT:M gene were inferred for A. cimicina and A. paniculata (Fig. S10, Supporting information). However, the sequences of ppc‐1P3_LGT:M from A. semialata were very similar to each other, and nested within the alleles from A. cimicina/paniculata (Fig. S10, Supporting information). The split of A. semialata and A. cimicina is more recent for ppc‐1P3_LGT:M than for the vertically inherited ppc and pck (Fig. S9, Supporting information). In addition, the divergence of C₄ accessions of A. semialata based on this ppc‐1P3_LGT:M gene occurred more recently than the divergence based on the vertically inherited ppc and pck (Fig. 5).

Discussion

Divergence of photosynthetic types in isolation followed by secondary gene flow

Overall, our genomewide analyses reveal a strong genetic structure, which matches photosynthetic types better than geographic origins, although both play a role. All C₄ individuals form a monophyletic group based on genomewide markers, which is sister to a clade composed of non‐C₄ accessions from the Zambezian region with a weak C₄ cycle (clade II; Figs 2 and 4), and together, these two groups are sister to the non‐C₄ accessions from South Africa that lack a C₄ cycle (clade I; Figs 2 and 4). The C₄ clade contains two clearly distinct subgroups, one from the Zambezian region (clade III; Figs 2 and 4) and the other one encompassing all C₄ accessions sampled outside this region, from Western Africa to Australia (clade IV; Figs 2 and 4). The Zambezian region encompasses more genetic diversity than the rest of the species’ range, including a total of five plastid lineages, four of which are endemic (clades B, C, F and G; Figs 2 and S2, Supporting information). This finding further supports this region as the centre of origin for Alloteropsis semialata (Lundgren et al. 2015). Based on both plastid and nuclear genomes, the divergence of photosynthetic types likely also happened within this region (Fig. 6). Both C₄ and non‐C₄ populations in the Zambezian region are associated with Miombo woodlands. Periodic cycles of contraction and expansion of these wooded savannas during recent geological times might have isolated populations of A. semialata in this geologically and topographically complex region (Cohen et al. 2007; Beuning et al. 2011). The ancestral photosynthetic state is likely non‐C₄ and mutations altering the leaf anatomy and upregulation of enzymes already present in the non‐C₄, ancestors likely led to the emergence of a constitutive C₄ cycle in some isolated populations (Mallmann et al. 2014; Bräutigam & Gowik 2016). One of the lineages descending from the initial C₄ pool, corresponding to clade IV, later left the Miombo of the Zambezian region and rapidly spread across Africa and all the way to Asia and Australia (Figs 4 and 6). This biogeographical history therefore points to the initial emergence of the C₄ physiology in A. semialata within the Zambezian region, with subsequent isolation of the C₄ descendants (Fig. 6).

Figure 6.

Inferred history of divergence, secondary exchanges and spread of laterally acquired ppc genes in A. semialata. A summary phylogeny is shown for the C₄ and non‐C₄ accessions of A. semialata, excluding the non‐C₄ individuals from South Africa. The C₄ phenotype is represented with red outlines. (1) The divergence of photosynthetic types is inferred in the Zambezian region (dashed red line indicates C₄ emergence). (2) A C₄ lineage migrated outside of the Zambezian region. (3) Hybridization occurred between non‐C₄ and C₄ populations within the Zambezian region. (4) The C₄ polyploids of South Africa (RSA) resulted from segmental allopolyploidy. (5) Pollen‐mediated gene flow occurred from mainland Africa to Madagascar. The lateral acquisition of three ppc genes is indicated with dashed lines, and their subsequent spread is indicated with solid lines. Geographic regions are indicated at the bottom.

The lack of association between chloroplast and nuclear groups (Figs 2, S2 and S3, Supporting information) in the Zambezian region suggests ancient, but recurrent, secondary gene flow followed by homogenization of the local gene pools. In addition, the presence of three individuals with mixed nuclear backgrounds indicates relatively recent gene flow between previously isolated groups. The maximum expansion of the Miombo woodlands during interglacial periods, as presently occurs, would likely favour seed dispersal over a larger area, leading to secondary contacts (Vincens 1989; Cohen et al. 2007; Beuning et al. 2011), a process frequently reported in Europe (reviewed in, e.g. Hewitt 2000; Schmitt 2007). We propose that this expansion allowed genetic exchanges between previously isolated lineages, some of which had made the transition to a full C₄ physiology during the previous isolation. No evidence of gene flow between C₄ and non‐C₄ individuals was found outside of the Zambezian region, and crosses might be prevented in South Africa, the other region where C₄ and non‐C₄ individuals overlap, by differences in ploidy levels (Fig. 4; Liebenberg & Fossey 2001). However, our analyses suggest that allopolyploidy contributed to the mixing of nuclear groups III and IV in Southern Africa (Fig. 6). In addition, while the recent divergence decreases statistical confidence, we found suggestions for secondary gene flow between different subgroups of the C₄ clade IV in Madagascar (Fig. 6).

Repeated isolation followed by recurrent, but rare secondary gene flow has created a dynamic population structure whereby adaptive mutations, such as those for the C₄ trait, can appear and sweep to fixation in isolation and later come together through admixing in times of contact. While mutations for increasing the expression of the C₄‐related genes and altering the leaf anatomy are unknown, genes for two key C₄ enzymes were laterally acquired by A. semialata (Christin et al. 2012). These lateral gene transfers likely took place in A. semialata plants that already used C₄ photosynthesis, and once acquired, these genes presumably replaced the function of the vertically inherited gene copies that were overexpressed but not biochemically optimized (Christin et al. 2012). The biogeographic history inferred here for the nuclear genome allows us to estimate the region where these lateral gene transfers likely occurred and track the subsequent spread of these genes among different gene pools.

Spread of C₄‐adaptive mutations among gene pools

Our analyses detected the laterally acquired pck gene in all Alloteropsis angusta and A. semialata individuals apart from two non‐C₄ A. semialata South African accessions of A. semialata from South Africa, confirming previous PCR‐based approaches (Table 2; Christin et al. 2012). The divergence time is younger between the laterally acquired pck genes from A. angusta and A. semialata than between the vertically inherited genes of the same species (Figs 5 and S9, Supporting information). This suggests that the laterally acquired pck was passed between A. angusta and A. semialata through secondary gene flow.

The accessions from Taiwan and Thailand do not possess any laterally acquired ppc genes, yet carbon isotopes unambiguously indicate that they carry out C₄ photosynthesis (Table 2; Lundgren et al. 2015). It is therefore likely that they overexpress their vertically inherited ppc and other genes required to generate a working C₄ cycle in the absence of repeated rounds of fixation of adaptive amino acids, as observed in older C₄ lineages (Christin et al. 2007; Besnard et al. 2009; Huang et al. in press).

Out of the three different ppc genes acquired via lateral gene transfers from distant C₄ relatives (Table 2; Christin et al. 2012), only ppc‐1P3_LGT:A is restricted to one of the accessions sampled here (‘AUS1’). This gene was only found in Australia, and it is thus likely that it was recently acquired in this region (Fig. 6). The other two laterally acquired ppc genes are absent from some individuals, but spread across multiple populations of A. semialata that belong to different genomic clusters (Table 2). This pattern could result from the presence of the gene in the common ancestor and subsequent losses in some populations. However, this scenario is not supported by the lack of phylogenetic structure on the laterally acquired genes (Fig. S10, Supporting information) and the comparison of divergence times, which indicate that the divergence of variants of both ppc‐1P3_LGT:M and ppc‐1P3_LGT:C found in C₄ accessions is more recent than the divergence of vertically inherited genes in the same accessions (Fig. 5).

The laterally acquired ppc‐1P3_LGT:M gene was identified in the C₄ congeners Alloteropsis cimicina and Alloteropsis paniculata, as well as all C₄ accessions of A. semialata from Africa and Madagascar (whether from clade III or IV; Table 2). However, this gene was absent from the Asian/Australian C₄ accessions from clade IV and the African non‐C₄ (clades I and II; Table 2). The divergence time between ppc‐1P3_LGT:M genes belonging to A. cimicina and A. semialata is younger than the divergence times for the vertically inherited genes from the same species (Fig. S9, Supporting information). In addition, the higher allelic diversity in A. cimicina compared to A. semialata suggests that the ppc‐1P3_LGT:M gene was first acquired by A. cimicina and then transferred to A. semialata, potentially via hybridization. This gene has subsequently likely spread across distinct genetic groups of A. semialata in Africa and Madagascar via secondary pollen flow (Fig. 6). The fixation of the ppc‐1P3_LGT:M gene within different populations would have been favoured by its improvement of the C₄ cycle, a function for which it was already optimized after millions of years spent in another C₄ lineage. Once this adaptive gene copy was acquired in a population, the vertically inherited ppc copy probably underwent pseudogenization as a result of relaxed selection. Indeed, the vertically inherited ppc genes bear frameshift mutations causing loss of function in two accessions with the laterally acquired ppc‐1P3_LGT:M (‘TAN4’ and ‘Cim1’), supporting the hypothesis that their function was taken over by the newly acquired gene, making them obsolete.

The last of the laterally acquired ppc genes, ppc‐1P3_LGT:C, was found in the South African C₄ polyploid (‘RSA3’) as well as in four C₄ and one isotopically intermediate individuals from the Zambezian region, two from clade III and three with genetic contributions from clades II and III (Table 2). This gene was laterally acquired from a species of the Setaria palmifolia complex (Christin et al. 2012), which co‐occurs with A. semialata in Zambezian Africa, where they grow metres apart, but not in South Africa (Clayton 1979). The transfer therefore likely occurred in the Zambezian region and later spread among the C₄ populations in this region through secondary gene flow (Fig. 6). Once acquired the ppc‐1P3_LGT:C gene presumably took over the C₄ function, which might have been fulfilled by the previously acquired ppc‐1P3_LGT:M. Indeed, ppc‐1P3_LGT:M is still expressed in the transcriptome of the South African C₄ accession, but possesses internal stop codons that prevent proper translation (Christin et al. 2012). The newly acquired ppc‐1P3_LGT:C likely spread to the C₄ populations from South Africa, through the putative segmental allopolyploidy event, providing a mechanism to propagate adaptive loci across genetic pools (Fig. 6). However, the Melinidinae ppc‐1P3_LGT:M discussed above was spread among diploid individuals from clades III and IV, showing that adaptive loci can be transmitted despite limited gene flow, without the need for polyploidization.

The laterally acquired genes, which can easily be tracked using genome scans, show that the distinct genetic pools in A. semialata constitute reservoirs of genes for the adaptation of other populations within the same species complex. The history of these markers proves that genes for a complex trait can evolve independently in isolated populations and later be combined via natural selection following gene flow. When high‐quality genome data accumulate for multiple accessions of A. semialata, such a scenario can be tested for vertically inherited genes, potentially explaining how novel adaptations can evolve in fragmented species complexes.

Conclusions

In this study, we analysed genomic data from multiple accessions of the grass Alloteropsis semialata using low‐coverage whole‐genome sequencing. Using a biogeographic framework for different parts of the genome, we demonstrate that multiple genetic pools exist, which are generally associated with different photosynthetic types. These pools originated more than 2 million years ago in the Zambezian region and were kept relatively isolated, but with recurrent secondary gene flow, including between non‐C₄ and C₄ individuals. These genetic exchanges contributed to the spread of adaptive loci, as illustrated by key C₄ genes acquired laterally in the Zambezian region and then rapidly passed to other African C₄ accessions. This process likely gradually optimized the initial C₄ pathway of some A. semialata populations through the assembly of different components. These genetic elements evolved in different parts of the species range, where limited gene flow might have facilitated local adaptation, but their subsequent combination likely improved the efficiency of the photosynthetic pathway of some accessions.

Data accessibility

All raw reads are available in the short sequence archive under Accession no. SRP082653. In the NCBI nucleotide database, all newly assembled chloroplast genomes are available under Accession nos KX752083‐KX752090, ppc genes under Accession nos KX788072‐KX788087 and pck genes under Accession nos KX788088‐KX788109.

J.K.O, M.B., P.N., C.P.O. and P.A.C. designed the study. G.B., M.R.L., and M.S.V. provided samples. J.K.O., M.B., G.B., and H.H. generated the sequence data. M.R.L. generated the carbon isotope data. O.H. and I.J.L. generated the genome size data. J.K.O., M.B., L.T.D. and P.A.C. analyzed the data. J.K.O., M.B. and P.A.C. interpreted the results and wrote the paper, with the help of all co‐authors.

Supporting information

Figure S1 Distribution of the 171,908 called SNPs along the Setaria italica genome.

Figure S2 Phylogenetic relationships based on complete chloroplast genomes from Alloteropsis.

Figure S3 Phylogenetic relationships based on whole genome sequencing of Alloteropsis accessions.

Figure S4 Phylogenetic relationships based on a sub‐set of the whole genome sequencing ofAlloteropsis accessions.

Figure S5 Assignment of Alloteropsis semialata individuals to genetic clusters based on a sub‐set ofthe aligned reads from the whole genome sequencing.

Figure S6 Percentage of heterozygous sites for each accession.

Figure S7 Results of PCR amplification of ppc‐IP3 and pck‐IP1 in Alloteropsis.

Figure S8 Phylogeny of pck‐1P1 in Alloteropsis.

Figure S9 Divergence times for different nodes estimated from vertically‐inherited andlaterally‐acquired genes.

Figure S10 Phylogeny of ppc‐1P3 in Alloteropsis.

Table S1 Sample and sequencing information.

Table S2 Alignment statistics of the Alloteropsis genome‐skimming data to the Setaria reference genome.

Table S3 Primer pairs for amplification of genes copies of phosphoenolpyruvate carboxylase (ppc) and phosphoenolpyruvate carboxykinase (pck).

Appendix S1 Scripts used to genotype the samples and produce a phylip file and an input file for Structure.

Appendix S2 Scripts used to resample a subset of reads.

Appendix S3 Perl scripts used identify and retrieve reads corresponding to different pck and ppc gene lineages.