The centromere is the unique chromosomal locus that forms the kinetochore, which interacts with spindle microtubules and directs segregation of replicated chromosomes to daughter cells (McKinley and Cheeseman, 2016). Human centromeres assemble on a subset of large blocks (many Mbps) of highly repeated (171 bp) α-satellite arrays found on all chromosomes. These repetitive arrays and the flanking heterochromatin (together the Centromere Proximal Regions or CPRs) play critical roles in the integrity of mitotic and meiotic inheritance (Janssen et al., 2018). In somatic tissues, chromosome instability, including loss and gain of chromosomes, plays large and complex roles in aging, cancer (Naylor and van Deursen, 2016), and human embryonic survival (McCoy, 2017). Sequence variation in CPRs can affect meiotic pairing (Dernburg et al., 1996; Karpen et al., 1996), kinetochore formation (Rosin and Mellone, 2017) and nonrandom segregation (Karpen et al., 1996). A large component of genetic disease stems from aneuploidies arising during meiosis (Nagaoka et al., 2012). Further, the unique asymmetry of transmission in female meiosis, where only one parental chromosome is transmitted, presents an opportunity for the evolution of strong deviations from mendelian segregation ratios (meiotic drive) (Pardo-Manuel de Villena and Sapienza, 2001; Chmátal et al., 2014). Recurrent meiotic drive is a potential cause of the evolutionarily rapid divergence of satellite DNAs and centromeric chromatin proteins (Malik and Henikoff, 2001; Talbert et al., 2004; Rudd et al., 2006; Malik and Henikoff, 2009), as well as the observed high levels of meiotic aneuploidy arising from the tradeoff between fidelity and drive (Zwick et al., 1999). However, the challenges inherent to assessing genomic variation in these repetitive and dynamic regions remain a significant barrier to incisive functional and evolutionary investigations.

Recognizing the potential research value of well-genotyped diversity across human CPRs, we hypothesized that the low rates of meiotic exchange in these regions (Nambiar and Smith, 2016) might result in large haplotypes in populations, perhaps even spanning the α-satellite arrays. To test this, we examined the Single Nucleotide Polymorphism (SNP) linkage disequilibrium (LD) and haplotype variation surrounding the centromeres among the diverse collection of genotyped individuals in Phase 3 of the 1000 Genomes Project (Auton et al., 2015). Figure 1a depicts the predicted patterns of strong LD (red) and associated unbroken haplotypic structures surrounding the gap of unassembled satellite DNA of a metacentric chromosome. Unweighted Pair Group Method with Arithmetic Mean (UMPGA) clustering on 800 SNPs immediately flanking the chrX centromeric gap in males (Figure 1c) reveals a clear haplotypic structure that spans the gap and extends, as predicted, to a much larger region (≈7 Mbp, Figure 1b). Similar clustering of the imputed genotypes of females also falls into the same distinct high-level haplotypes (Figure 1—figure supplement 1). This discovery of the predicted haplotypes spanning CPRs (hereafter referred to as cenhaps) on chrX and most metacentric chromosomes (Figure 2) opens a new window into their evolutionary history and functional associations.

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Figure 1—source data 1

The 21 chrX coding genes in the CPR (8 left and 13 right of the centromere gap) used in the UPGMA clustering and estimation of TMRCAs.

Gene models and alignments from Ensembl release 92 (April 2018). Numbers of sites divergent (human-chimp): div_sites. Numbers of sites polymorphic: polym_sites. Average nonsynomymous divergence: nonsyn_div. Average synonymous divergence: syn_div. Average nonsynonymous diversity: nonsyn_π. Average synonymous diversity: syn_π.

https://doi.org/10.7554/eLife.42989.007

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Figure 2

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Figure 2—source data 1

Centromere-Proximal Regions examined.

The hg19 coordinates (p_begin to p_end and q_begin to q_end) of the CPRs in which SNPs in the 1000 Genomes (Phase 3) were investigated, panel b in Figure 2. Imputed haplotypes were UMPGA clustered based on filtered SNPs in a symmetrical central region immediately flanking the centromeric gap in the assembly (p_c to p_end and q_begin to p_c).

https://doi.org/10.7554/eLife.42989.010

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The pattern of geographic differentiation across the inferred chrX CPR (Figure 1b,c) exhibits higher diversity in African samples, as observed throughout the genome (Auton et al., 2015). Despite being fairly common among Africans today, a distinctly diverged chrX cenhap (cenhap 1, highlighted in purple, Figure 1b,c) is rare outside of Africa. Examination of the haplotypic clustering and estimated synonymous divergence in the coding regions of 21 genes included in the chrX cenhap region (see Figure 1—source data 1) yields a parallel relationship among the three major cenhaps and an estimated Time of the Most Recent Common Ancestor (TMRCA) of ≈600 KYA (Figure 1d) for this most diverged example. While ancient segments have been inferred in African genomes (Hammer et al., 2011; Hsieh et al., 2016), this cenhap stands out as genomically (if not genetically) large. The presence of such polymorphic ancient cenhaps is inconsistent with the predicted hitchhiking effect of sequential fixation of new meiotically driven centromeres (Malik and Henikoff, 2001). Further, the detection of near-ancient segments spanning the centromere contrasts with the observation of substantially more recent ancestry across the remainder of chrX and with the expectation of reduced archaic sequences on chrX (Dutheil et al., 2015). The large block on the right in Figure 1b is comprised of SNPs in exceptionally high frequency in Africans. The synonymous divergence in coding genes in this block indicates it too is quite old (data not shown) and may share ancestry with the ancient African cenhap. Putative distal recombinants of this block are observed outside of Africa and may contribute to associations of SNPs in this region with a diverse set of phenotypes, including male pattern hair loss (Hagenaars et al., 2017) and prostate cancer (Al Olama et al., 2014).

This deep history of the chrX CPR raises the possibility of even more ancient lineages on other chromosomes, either derived by admixture with archaic hominins or maintained by balancing forces. Although putatively introgressed archaic segments in African genomes have been inferred from genome-wide demographic modeling (Hammer et al., 2011; Hsieh et al., 2016; Durvasula and Sankararaman, 2019; Speidel et al., 2019), ancient cenhaps could also persist within the ancestral population due to natural selection. The relatively recent origin of AMHs outside of Africa and the availability of Neanderthal and Denisovan genomes derived from fossil DNAs support more direct methods for detection of enrichment of archaic segments outside of Africa (Green et al., 2010). Such studies firmly establish genome-wide evidence of recent introgression into Eurasian populations of AMHs (Green et al., 2010; Patterson et al., 2012; Sankararaman et al., 2012; Prüfer et al., 2014; Sankararaman et al., 2014; Prüfer et al., 2017).

To identify likely Neanderthal or Denisovan introgressed cenhaps, we looked for highly diverged examples in non-African populations (see Figure 2) that shared a strong excess of derived alleles with those archaic hominids and not with African genomes, using chimpanzee as the outgroup (Green et al., 2010; Patterson et al., 2012; Prüfer et al., 2014; Prüfer et al., 2017). Applying this approach to the CPR of chr11 revealed a compelling example of Neanderthal introgression, which is illustrated in Figure 3a in the context of the seven most common chr11 cenhaps. The most diverged lineage contains a small basal group of primarily out-of-Africa genomes (cenhap 1, highlighted in green). This cenhap carries a large proportion of the derived alleles assigned to the Neanderthal lineage, DM/(DM+DN)=0.98, where DM is the cenhap mean number of shared Neanderthal Derived Matches and DN is the cenhap mean number of Neanderthal Derived Non-matches (Figure 3a, at left). The ratio DM/(DM+AN)=0.91, where AN is the number of Neanderthal-cenhap Non-matches that are Ancestral in the Neanderthal, suggests that this large cenhap lineage, including the centromere and satellite sequences, shared most of its evolutionary history with Neanderthals. This diverged cenhap is limited to populations outside of Africa, supporting the conclusion that it is an introgressed archaic centromere. Figure 3b shows these mean counts for each SNP class by cenhap group, confirming that the affinity to Neanderthals is slightly stronger than to Denisovans. A second basal lineage found principally in Africa (cenhap 2, highlighted in purple, Figure 3a) separates shortly after the inferred Neanderthal. It is unclear if this cenhap represents an introgression from a distinct archaic hominin in Africa or a surviving ancient lineage within the population that gave rise to AMHs.

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The 37 chr11 coding genes in the CPR (2 left and 35 right of the centromere gap) used in the UPGMA clustering and estimation of TMRCAs.

Gene models and alignments from Ensembl release 92 (April 2018). Numbers of nonsynonymous differences in the two basal cenhaps (1, 2 and both, 1_&_2; see Figure 3) from the other cenhaps of the 5008 imputed chr11 CPR haplotypes (see Materials and methods). Numbers of sites divergent (human-chimp): div_sites. Numbers of sites polymorphic: polym_sites. Average nonsynomymous divergence: nonsyn_div. Average synonymous divergence: syn_div. Average nonsynonymous diversity: nonsyn_π. Average synonymous diversity: syn_π.

https://doi.org/10.7554/eLife.42989.019

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Figure 3—source data 2

The eight chr8 coding genes in the CPR (8 left and 0 right of the centromere gap) used in the UPGMA clustering and estimation of TMRCAs.

Gene models and alignments from Ensembl release 92 (April 2018). Numbers of sites divergent (human-chimp): div_sites. Numbers of sites polymorphic: polym_sites. Average nonsynomymous divergence: nonsyn_div. Average synonymous divergence: syn_div. Average nonsynonymous diversity: nonsyn_π. Average synonymous diversity: syn_π.

https://doi.org/10.7554/eLife.42989.020

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The relatively large expanses of these chr11 cenhaps and unexpectedly sparse evidence of recombination could be explained by either relatively recent introgressions or cenhap-specific suppression of crossing over with other AMH genomes in this CPR (e.g., an inversion). As with chrX above, the clustering of cenhaps based on coding synonymous SNPs in an extended window containing 37 genes (Figure 3d, Figure 3—figure supplement 1) yields a congruent topology and estimates of TMRCAs for the two basal cenhaps of ~1 and ~0.5 MYA, consistent with relatively ancient origins. Among the 37 genes in the chr11 CPR are 34 of the ~300 known (Malnic et al., 2004) odorant receptors (ORs). Nonsynonymous SNPs in these chr11 ORs are associated with variation in human olfactory perception of particular volatile chemicals (Trimmer et al., 2019). 73 amino acid replacements polymorphisms are observed in 25 of these ORs (Figure 3—source data 1). The vast majority (63) are on the lineages to the two putative archaic cenhaps. Indeed, 60 are on the lineage to the Neanderthal haplotype suggesting this cenhap encodes Neanderthal-specific determinants of smell and taste. Similarly, in the second putative archaic African cenhap, seven of these ORs harbor ten amino acid replacements, of which only one is shared with cenhap 1 (see Figure 3—source data 1). The frequencies of the Neanderthal cenhap in Europe, South Asia and the Americas (0.061, 0.032 and 0.033 respectively), and of second ancient cenhap in Africa (0.036), are sufficiently high that together they contribute ≈ 18% of the amino acid replacement diversity in these 34 ORs among the 1000 Genomes. Thus, a substantial and rather unique part of the variation in chemical perception among AMH may be contributed by these two ancient cenhaps.

The most diverged, basal clade in the chr12 CPR (Figure 3c, indicated in brown) is common in Africa, but, like the most diverged chrX cenhap, is not represented among the descendants of the out-of-Africa migrations (Bae et al., 2017). The great depth of the lineage of this cenhap is further supported by comparison to homologous archaic sequences (Green et al., 2010; Prüfer et al., 2014; Prüfer et al., 2017). Consistent with the hypothesis that this branch split off before that of Neanderthals/Denisovans, members of this cenhap share fewer matches with derived SNPs on the Neanderthal and Denisovan lineages (DM) and exhibit strikingly more ancestral non-matches (AN) than other chr12 cenhaps (see Figure 3b). This putatively archaic chr12 cenhap represents a large and obvious example of the potentially introgressed sequences within African populations inferred from model-based analyses of the distributions of sequence divergence (Hammer et al., 2011; Hsieh et al., 2016; Durvasula and Sankararaman, 2019). The small out-of-Africa cenhap nested within a mostly African subclade (indicated in blue in Figure 3c) appears to be a typical Eurasian archaic introgression with higher affinity to Neanderthals (DM/(DN + DM)=0.91 and DM/(DM +AN)=0.90) than to Denisovans (Figure 3b). This bolsters the conclusion that the basal African cenhap represents a distinctly older archaic lineage. Unfortunately, there are too few coding bases in this region to support confident estimation of the TMRCAs of these ancient chr12 cenhaps. Based on the numbers of SNPs underlying the cenhaps, this basal cenhap is twice as diverged as the apparent introgressed Neanderthal cenhap, placing the TMRCA at ~1.1 MYA, assuming the Neanderthal TMRCA was 575KYA (Prüfer et al., 2017). While there is no direct evidence of recent introgression, the large genomic scale of the most diverged chr12 cenhap (relative to apparent exchanges in other cenhaps) is consistent with recent admixture with an extinct archaic in Africa; yet, again, selective maintenance of ancient cenhaps with associated suppression of crossing over is an alternative explanation.

Chromosomes X, 11 and 12 harbor a diversity of large cenhaps, including those representing archaic lineages. Notably, the CPRs of other chromosomes include diverged, basal lineages that are likely to be relatively old, if not archaic (Figure 2). Two examples are chromosome 8, containing an ancient cenhap limited to Africa with an estimated TMRCA of ~730 KYA (Figure 3—figure supplement 2), and chr10 that appears to harbor another clear Neanderthal cenhap introgression (Figure 3—figure supplement 3). Obtaining genomic sequence from African archaics would shed light on the evolutionary origins of the ancient cenhaps not associated with Neanderthal and Denisovan introgression. It should be noted that the very large genomic sizes of these ancient cenhaps could allow identification of archaic homology even with modest genomic sequence coverages from archaic fossils.

These SNP-based cenhaps portray a rich, highly structured view of the diversity in the unique segments flanking repetitive regions. While the divergence of satellites may be dynamic on a shorter time scale (Smith, 1976), we reasoned that the paucity of exchange in these regions would create cenhap associations with satellite divergence in both sequence and array size. Miga et al. (2014) generated chromosome-specific graphical models of the α-satellite arrays and reported a bimodal distribution in estimated chrX-specific α-satellite array (DXZ1) sizes (Willard et al., 1983) for a subset of the 1000 Genomes males. Figure 1b extends this observation to the entire data. The cumulative distributions of estimated array sizes of the three common chrX cenhaps designated in Figure 1c show substantial differences (Figure 4a). α-satellite array sizes in cenhap-homozygous females are parallel to males, and imputed cenhap heterozygotes are intermediate, as expected. Similarly, Figure 4b shows an even more striking example of variation in array size between cenhap homozygotes on chr11, and Figure 4c demonstrates that heterozygotes of the two most common cenhaps are reliably intermediate in size. While we confirmed that reference bias does not explain the observed cenhaps with large array size on chrX and chr11 (see Materials and methods, Figure 1b, Figure 4b and Figure 4—figure supplement 1), it is a potential explanation for particular instances of cenhaps with small estimated array sizes, for example the relatively low chrX-specific α-satellite content in the highly diverged African cenhap (see Figure 1b,c and Figure 4a, cenhap 1, highlighted in purple). Importantly, our results demonstrate that cenhaps do robustly tag a substantial component of the genetic variation in array size.

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The potential impact of sequence variation in CPRs and their associated satellites on centromere and heterochromatin functions has been long recognized but difficult to study (Pardo-Manuel de Villena and Sapienza, 2001). Both binding of the centromere-specific histone, CENPA (Sullivan et al., 2011), and kinetochore size (Iwata-Otsubo et al., 2017) are known to scale with the size of arrays and to fluctuate with sequence variation in satellite DNAs (Aldrup-MacDonald et al., 2016). Through these interactions with kinetochore function and other roles for heterochromatin in chromosome segregation (Dernburg et al., 1996; Karpen et al., 1996; Peng and Karpen, 2008), α-satellite array variations can affect mitotic stability in human cells (Sullivan et al., 2017), as well as meiotic drive systems in the mouse (Chmátal et al., 2014). Meiotic drive has been proposed as the likely explanation for the saltatory divergence of satellite sequences and the excess of nonsynonymous divergence of several centromere proteins, some of which interact directly with the DNA (Malik and Henikoff, 2001; Talbert et al., 2004; Malik and Henikoff, 2009). However, the high levels of haplotypic diversity and deep cenhap lineages observed (Figure 2) conflict with the predictions of a naïve turnover model based on recurrent strong directional selection yielding sequential fixation of new centromeric haplotypes. Indeed, the levels of synonymous diversity, π_s, in the few coding genes in the CPR of chrX, 0.00062 (0.00043–0.00128) and chr11, 0.00128 (0.00088–0.00217), are not different from levels of diversity in non-CPR regions (Dutheil et al., 2015). The genes in the CPR of chr8 show a considerably lower mean π_s, 0.00010 (0.00007–0.00019); but we note there are only eight genes and their mean divergence from Pan orthologs is also low (Figure 3—source data 2). The inherent frequency-dependence of meiotic drive (Charlesworth and Hartl, 1978), associative overdominance (Ohta, 1971), a likely tradeoff between meiotic transmission bias and the fidelity of segregation of driven centromeres (Zwick et al., 1999), and the expected impact of unlinked suppressors (Charlesworth and Hartl, 1978) are plausible forces that would mitigate the impact of hitchhiking and background selection on the levels of standing polymorphism in CPRs.

The identification of human cenhaps raises new questions about the evolution of these unique genomic regions, but also provides the resolution and framework necessary to quantitatively address them. Our results transform large, previously obscure and shunned genomic regions into genetically rich and tractable resources, revealing unexpected diversity, including immense ancient CPRs, several of which are apparent Neanderthal introgressions. Most importantly, cenhaps can now be investigated for associations with variation in evolutionarily important chromosome functions, such as meiotic drive (Meyer et al., 2012) and recombination (Nambiar and Smith, 2016), as well as disease-related functions, such as aneuploidy in the germline (Nagaoka et al., 2012) and in development (McCoy, 2017), cancer and aging (Naylor and van Deursen, 2016).

All data needed to evaluate the conclusions in the paper are present in the paper or the supplementary materials. The human populations genomic variation analyzed for linkage disequilibria and haplotypic structure in the Centromere-Proximal Regions of each chromosome was accessed from the 1000 Genomes Project (Phase 3) (ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/). The inference of Neanderthal and Denisovan ancestry in the Centromere Proximal Regions was based on data available at https://bioinf.eva.mpg.de/jbrowse described in Prüfer et al. 2017. The inference of haplotypes of variation in the Centromere Proximal Regions of the HuRef reference genome used to create the CEN regions in hg38 was based on the genotyping available at http://bioinform.github.io/huref-gs/ and described in Mu, et al. 2015.

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Author details

1. Sasha A Langley

   1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
   2. Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, United States

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5138-7394

2. Karen H Miga

   UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3670-4507

3. Gary H Karpen

   1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
   2. Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, United States

   Contribution

   Conceptualization, Supervision, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1534-0385

4. Charles H Langley

   Department of Evolution and Ecology, University of California, Davis, Davis, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   chlangley@ucdavis.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6160-5503

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