Fulani from Cameroon show ancestry similar to Afroasiatic speakers from East Africa


Open access African evolutionary history inferred from whole genome sequence data of 44 indigenous African populations, by Fan et al. Genome Biology (2019) 20:82.

Interesting excerpts (emphasis mine):


To extend our knowledge of patterns of genomic diversity in Africa, we generated high coverage (> 30×) genome sequencing data from 43 geographically diverse Africans originating from 22 ethnic groups, representing a broad array of ethnic, linguistic, cultural, and geographic diversity (Additional file 1: Table S1). These include a number of populations of anthropological interest that have never previously been characterized for high-coverage genome sequence diversity such as Afroasiatic-speaking El Molo fishermen and Nilo-Saharan-speaking Ogiek hunter-gatherers (Kenya); Afroasiatic-speaking Aari, Agaw, and Amhara agro-pastoralists (Ethiopia); Niger-Congo-speaking Fulani pastoralists (Cameroon); Nilo-Saharan-speaking Kaba (Central African Republic, CAR); and Laka and Bulala (Chad) among others. We integrated this data with 49 whole genome sequences generated as part of the Simons Genome Diversity Project (SGDP) [14] (…)

Locations of samples included in this study. Each dot is an individual and the color indicates the language classification

Results and discussion

We found that the CRHG populations from central Africa, including the Mbuti from the Demographic Republic of Congo (DRC), Biaka from the CAR, and Baka, Bakola, and Bedzan from Cameroon, also form a basal lineage in the phylogeny. The other two hunter-gatherer populations, Hadza and Sandawe, living in Tanzania, group with populations from eastern Africa (Fig. 2). The two Nilo-Saharan-speaking populations, the Mursi from southern Ethiopia and the Dinka from southern Sudan, group into a single cluster, which is consistent with archeological data indicating that the migration of Nilo-Saharan populations to eastern Africa originated from a source population in southern Sudan in the last 3000 years [4, 23, 24, 25].

Phylogenetic relationship of 44 African and 32 west Eurasian populations determined by a neighbor joining analysis assuming no admixture. Here, the dots of each node represent bootstrap values and the color of each branch indicates language usage of each population. Human_AA human ancestral alleles

The Fulani people are traditionally nomadic pastoralists living across a broad geographic range spanning Sudan, the Sahel, Central, and Western Africa. The Fulani in our study, sampled from Cameroon, clustered with the Afroasiatic-speaking populations in East Africa in the phylogenetic analysis, indicating a potential language replacement from Afroasiatic to Niger-Congo in this population (Fig. 2). Prior studies suggest a complex history of the Fulani; analyses of Y chromosome variation suggest a shared ancestry with Nilo-Saharan and Afroasiatic populations [24], whereas mtDNA indicates a West African origin [26]. An analysis based on autosomal markers found traces of West Eurasian-related ancestry in this population [4], which suggests a North African or East African origin (as North and East Africans also have such ancestry likely related to expansions of farmers and herders from the Near East) and is consistent with the presence at moderate frequency of the −13,910T variant associated with lactose tolerance in European populations [15, 16].

Phylogenetic reconstruction of the relationship of African individuals under a model allowing for migration using TREEMIX [27] largely recapitulates the NJ phylogeny with the exception of the Fulani who cluster near neighboring Niger-Congo-speaking populations with whom they have admixed (Additional file 2: Figure S1). Interestingly, TREEMIX analysis indicates evidence for gene flow between the Hadza and the ancestors of the Ju|‘hoan and Khomani San, supporting genetic, linguistic, and archeological evidence that Khoesan-speaking populations may have originated in Eastern Africa [28, 29, 30].

ADMIXTURE analysis of 92 African and 62 West Eurasian individuals. Each bar is an individual and colors represent the proportion of inferred ancestry from K ancestral populations. The bottom bar shows the language classification of each individual. With the increasing of K, the populations are largely grouped by their current language usage

About the Fulani, this is what the referenced study of Y‐chromosome variation among 15 Sudanese populations by Hassan et al. (2008), had to say:

  • Haplogroups A-M13 and B-M60 are present at high frequencies in Nilo-Saharan groups except Nubians, with low frequencies in Afro-Asiatic groups although notable frequencies of B-M60 were found in Hausa (15.6%) and Copts (15.2%).
  • Haplogroup E (four different haplotypes) accounts for the majority (34.4%) of the chromosome and is widespread in the Sudan. E-M78 represents 74.5% of haplogroup E, the highest frequencies observed in Masalit and Fur populations. E-M33 (5.2%) is largely confined to Fulani and Hausa, whereas E-M2 is restricted to Hausa. E-M215 was found to occur more in Nilo-Saharan rather than Afro-Asiatic speaking groups.
  • In contrast, haplogroups F-M89, I-M170, J-12f2, and JM172 were found to be more frequent in the Afro-Asiatic speaking groups. J-12f2 and J-M172 represents 94% and 6%, respectively, of haplogroup J with high frequencies among Nubians, Copts, and Arabs.
  • Haplogroup K-M9 is restricted to Hausa and Gaalien with low frequencies and is absent in Nilo-Saharan and Niger-Congo.
  • Haplogroup R-M173 appears to be the most frequent haplogroup in Fulani, and haplogroup R-P25 has the highest frequency in Hausa and Copts and is present at lower frequencies in north, east, and western Sudan.
  • Haplogroups A-M51, A-M23, D-M174, H-M52, L-M11, OM175, and P-M74 were completely absent from the populations analyzed.
Image modified from “Fulfulde Language Family Report” Author: Annette Harrison; Cartographer: Irene Tucker; SIL International 2003.

This is what David Reich will talk about in the seminar Insights into language expansions from ancient DNA:

In this talk, I will describe how the new science of genome-wide ancient DNA can provide insights into past spreads of language and culture. I will discuss five examples: (1) the spread of Indo-European languages to Europe and South Asia in association with Steppe pastoralist ancestry, (2) the spread of Austronesian languages to the open Pacific islands in association with Taiwanese aboriginal-associated ancestry, (3) the spread of Austroasiatic languages through southeast Asia in association with the characteristic ancestry type that is also represented in western Indonesia suggesting that these languages were once widespread there, (4) the spread of Afroasiastic languages through in East Africa as part of the Pastoral Neolithic farming expansion, and (5) the spread of Na-Dene languages in North America in association with Proto-Paleoeskimo ancestry. I will highlight the ways that ancient DNA can meaningfully contribute to our understanding of language expansions—increasing the plausibility of some scenarios while decreasing the plausibility of others—while emphasizing that with genetic data by itself we can never definitively determine what languages ancient people spoke.

EDIT (3 MAY 2019): Apparently, there was not much to take from the talk:

Pastoralist Neolithic in Africa, through a pale-green Sahelo-Sudanian steppe corridor. See full map.

This seminar (and maybe some new paper on the Neolithic expansion in Africa) could shed light on population movements that may be related to the spread of Afroasiatic dialects. Until now, it seems that Bantu peoples have been more interesting for linguistics and archaeology, and South and East Africans for anthropology.

Archaeology in Africa appears to be in its infancy, as is population genomics. From the latest publication by Carina Schlebusch, Population migration and adaptation during the African Holocene: A genetic perspective, a chapter from Modern Human Origins and Dispersal (2019):

The process behind the introduction and development of farming in Africa is still unclear. It is not known how many independent invention events there were in the continent and to which extent the various first instances of farming in northern Africa are linked. Based on the archeological record, it was proposed that at least three regions in Africa may have developed agriculture independently: the Sahara/Sahel (around 7 ka), the Ethiopian highlands (7-4 ka), and western Africa (5-3 ka). In addition to these developments, the Nile River Valley is thought to have adopted agriculture (around 7.2 ka), from the Neolithic Revolution in the Middle East (Chapter 12 – Jobling et al. 2014; Chapter 35, 37 – Mitchell and Lane 2013). From these diverse centers of origin, farmers or farming practices spread to the rest of Africa, with domesticate animals reaching the southern tip of Africa ~2 ka and crop farming ~1,8 ka (Mitchell 2002; Huffman 2007)

Schematic representation of possible migration routes related to the expansion of herders and crop farmers during Holocene times. Arrow color indicate source populations; Brown-Eurasian, Green-western African, Blue-eastern African.

Similar to the case in Europe and the 1990s-2000s wrong haplogroup history based on the modern distribution of R1b, R1a, N, or I2, it is possible that neither of the most often mentioned haplogroups linked to the Afroasiatic expansion, E and J, were responsible for its early spread within Africa, despite their widespread distribution in certain modern Afroasiatic-speaking areas. The fact that such assessments include implausible glottochronological dates spanning up to 20,000 years for the parent language, combined with regional language continuities despite archaeological changes, makes them even more suspicious.

Similar to the case with Indo-Europeans and the “steppe ancestry” concept of the 2010s, it may be that the often-looked-for West Eurasian ancestry among Africans is the effect of recent migrations, unrelated to the Afroasiatic expansion. The results of this paper could be offering another sign of how this ancestry may have expanded only quite recently westwards from East Africa through the Sahel, after the Semitic expansion to the south:

1. From approximately 1000 BC, accompanying Nilo-Saharan peoples.

2. From approximately AD 1500, with the different population movements related to the nomadic Fulani:

Image from Sahel in West African History – Oxford Research Encyclopedia of African History.
  • Arguably, since the Fulani caste system wasn’t as elaborate in northern Nigeria, eastern Niger, and Cameroon, these specific groups would be a good example of the admixture with eastern populations, based on the (proportionally) huge amount of slaves they dealt with.
  • Similarly, it could be argued that the castes-based social stratification in most other territories (including Sudan) would have helped them keep a genetic make-up similar to their region of origin in terms of ancient lineages, hence similar to Chadic populations from west to east.

Reich’s assertion of the association of the language expansion with the spread of Pastoral Neolithic is still too vague, but – based on previous publications of ancient DNA in Africa and the Levant – I don’t have high hopes for a revolutionary paper in the near future. Without many samples and proper temporal transects, we are stuck with speculations based on modern distributions and scarce historical data.

A distribution map of Fula people. Dark green: a major ethnic group; Medium: significant; Light: minor. Modified from image by Sarah Welch at Wikipedia.

About the potential genetic make-up of Cameroon before the arrival of the Neolithic, from the recent SAA 84th Annual Meeting (Abstracts in PDF):

Lipson, Mark (Harvard Medical School), Mary Prendergast (Harvard University), Isabelle Ribot (Université de Montréal), Carles Lalueza-Fox (Institute of Evolutionary Biology CSIC-UPF) and David Reich (Harvard Medical School)

[253] Ancient Human DNA from Shum Laka (Cameroon) in the Context of African Population History We generated genome-wide DNA data from four people buried at the site of Shum Laka in Cameroon between 8000–3000 years ago. One individual carried the deeply divergent Y chromosome haplogroup A00 found at low frequencies among some present-day Niger-Congo speakers, but the genome-wide ancestry profiles for all four individuals are very different from the majority of West Africans today and instead are more similar to West-Central African hunter-gatherers. Thus, despite the geographic proximity of Shum Laka to the hypothesized birthplace of Bantu languages and the temporal range of our samples bookending the initial Bantu expansion, these individuals are not representative of a Bantu source population. We present a phylogenetic model including Shum Laka that features three major radiations within Africa: one phase early in the history of modern humans, one close to the time of the migration giving rise to non-Africans, and one in the past several thousand years. Present-day West Africans and some East Africans, in addition to Central and Southern African hunter-gatherers, retain ancestry from the first phase, which is therefore still represented throughout the majority of human diversity in Africa today.


Population size potentially affecting rates of language change


Open access Population Size and the Rate of Language Evolution: A Test Across Indo-European, Austronesian, and Bantu Languages, by Greenhill et al. Front. Psychol (2018) 9:576.

Summary (emphasis mine):

What role does speaker population size play in shaping rates of language evolution? There has been little consensus on the expected relationship between rates and patterns of language change and speaker population size, with some predicting faster rates of change in smaller populations, and others expecting greater change in larger populations. The growth of comparative databases has allowed population size effects to be investigated across a wide range of language groups, with mixed results. One recent study of a group of Polynesian languages revealed greater rates of word gain in larger populations and greater rates of word loss in smaller populations. However, that test was restricted to 20 closely related languages from small Oceanic islands. Here, we test if this pattern is a general feature of language evolution across a larger and more diverse sample of languages from both continental and island populations. We analyzed comparative language data for 153 pairs of closely-related sister languages from three of the world’s largest language families: Austronesian, Indo-European, and Niger-Congo. We find some evidence that rates of word loss are significantly greater in smaller languages for the Indo-European comparisons, but we find no significant patterns in the other two language families. These results suggest either that the influence of population size on rates and patterns of language evolution is not universal, or that it is sufficiently weak that it may be overwhelmed by other influences in some cases. Further investigation, for a greater number of language comparisons and a wider range of language features, may determine which of these explanations holds true.

Interesting excerpts:

Our analysis suggests that, as for Polynesian languages, smaller Indo-European languages have greater rates of word loss from basic vocabulary. This result is consistent with the claim that smaller populations are at greater risk of loss of language elements, and other aspects of culture, due to effects of incomplete sampling of variants over generations. However, we note that the relatively small sample size for this dataset complicates the interpretation of this result. Least squares regression after Welch & Waxman test has the same false positive rate but has much less power than Poisson regression when sample size is small (~ten or fewer pairs, Hua et al., 2015). This makes it difficult to interpret the inconsistent results of these two analyses, as they may be due to their difference in the statistical power. Hence, the negative relationship between rates of loss and population size for Indo-European languages would benefit from additional investigation. We do not find evidence for a negative relationship between population size and word loss rates in the Austronesian and Bantu groups. This finding suggests that either these datasets contain too few language variants to have sufficient power to detect rate differences, or that the increased loss rate in small populations is not a universal phenomenon, or that it is a relatively weak force in some language groups and thus may be overwhelmed by other social, linguistic or demographic factors.

Regarding potential drawbacks of the study:

[M]easuring speech community size is notoriously difficult. How exactly does one delimit a speech community (Crystal, 2008) and what degree of proficiency in a language is sufficient to be part of the community (Bloomfield, 1933)? This task is made harder as there are few national censuses that collect detailed speaker statistics. Further, speaker population size can change rapidly with many modern world languages (especially the Indo-European languages) experiencing rapid growth over the last few hundred years (Crystal, 2008), while others have experienced catastrophic declines (Bowern, 2010). For the same reasons, the difficulty of obtaining accurate population estimates is also a problem in biology. Furthermore, the relevant parameter for genetic change—the effective population size—is difficult to estimate directly, even when accurate census information is available (Wang et al., 2016). Likewise, there may be an important role played by population and network density—tight-knit networks may inhibit change, while loosely integrated speech communities (regardless of their size), may facilitate change (Granovetter, 1973; Milroy and Milroy, 1992). One way forward here is perhaps to simulate rates of change over a range of population sizes and network topologies (c.f. Reali et al., 2018).

As conclusions:

Firstly, we provide some evidence that rates of language change can be affected by demographic factors. Even if the effect is not universal, the finding of significant associations between population size and patterns of linguistic change in some languages urges caution for any analysis of language evolution that makes an assumption of uniform rates of change. These results also potentially provide a window on processes of language change in these lineages, providing further impetus to investigate the effect of number of speakers on patterns of language transmission and loss. A more detailed study of language change for a larger number of comparisons might clarify the relationship between population size and word loss rates, particularly within the Indo-European language family.

Secondly, we have shown that the significant patterns of language change identified in a previous study are not a universal phenomenon. Unlike the study of Polynesian languages, we did not find any significant relationships between word gain rate and population size, and the association between loss rates and population size was not evident for all language families analyzed. The lack of universal relationships suggests that it may be difficult to draw general conclusions about the influence of demographic factors on patterns and rates of language change. Many other factors have been proposed to influence rates of language change (Greenhill, 2014) including population density, social structure (Nettle, 1999; Labov, 2007; Ke et al., 2008; Trudgill, 2011), degree of contact, and connectedness with other languages (Matras, 2009; Bowern, 2010), degree of language diffusion within a speech community (Wichmann et al., 2008), degree of bilingualism or multilingualism (Lupyan and Dale, 2010; Bentz and Winter, 2013), language group diversity (Atkinson et al., 2008) and environmental factors such as habitat heterogeneity and latitude (Bowern, 2010; Blust, 2013; Amano et al., 2014). These factors might mediate or overwhelm the effect of speaker population size.

We find no evidence to support the hypothesis that uptake of new words should be faster in small populations, which is based on the assumption that new words can diffuse more efficiently through a smaller speaker population than a larger one (Nettle, 1999). Nor do we find support for the suggestion that large, widespread languages have a tendency to lose linguistic features a greater rate (Lupyan and Dale, 2010). However, this latter hypothesis is predominantly expected to explain loss of complex linguistic morphology (such as case systems), which may be harder for non-native speakers to learn, rather than basic vocabulary studied here which may be comparatively easier for second language learners to acquire (but see Kempe and Brooks, 2018). Further, our results cannot be interpreted as confirmation of previous studies that suggest there is no effect of population size on rates (Wichmann and Holman, 2009). The detection of significant patterns in rates of lexical change with population size variation in the Polynesian and Indo-European languages, but the failure to identify similar patterns in the Bantu and Austronesian data, suggests that patterns of rates may need to be investigated on a case-by-case basis.


A history of male migration in and out of the Green Sahara

Open access research highlight A history of male migration in and out of the Green Sahara, by Yali Xue, Genome Biology (2018) 19:30, on the recent paper by D’Atanasio et al.

Insights from the Green Saharan Y-chromosomal findings (emphasis mine):

It is widely accepted that sub-Saharan Y chromosomes are dominated by E-M2 lineages carried by Bantu-speaking farmers as they expanded from West Africa starting < 5 kya, reaching South Africa within recent centuries [4]. The E-M2-Bantu lineages lie phylogenetically within the E-M2-Green Sahara lineage and show at least three explosive lineage expansions beginning 4.9–5.3 kya [5] (Fig. 1a). These events of E-M2-Bantu expansion are slightly later than the R-V88 expansion, and highlight the range of male demographic changes in the mid-Holocene. North of the Sahara, in addition to the four trans-Saharan haplogroups, haplogroup E-M81 (which diverged from E-M78 ~ 13 kya) became very common in present-day populations as a result of another massive expansion ~ 2 kya [6] (Fig. 1a).

Simplified Y-chromosomal phylogeny and inferred past or observed present-day distribution of relevant Y-chromosomal lineages. a Calibrated phylogenetic tree of Y-chromosomal lineages discussed in the text. Green shading represents the period when the present-day Sahara Desert was green and fertile. Lineages represented by filled pentagons have undergone very rapid expansions. b [featured image] The Green Sahara period 5–12 kya. Green shading indicates that the present-day Sahara Desert was green and fertile. The colors within the large oval represent the four Y-chromosomal haplogroups deduced to be present in the region at this time; specific locations are not implied. The arrows indicate the inferred origins of these haplogroups to the north or south, but specific origins and routes are not implied. c The present-day distributions of the four Green Saharan Y-chromosomal haplogroups. Yellow shading indicates the Sahara Desert. Each circle represents a sampled population, with the presence or absence of the four Green Saharan haplogroups shown by the colored sectors; other haplogroups may also be present in these populations, but are not shown. The small arrows indicate the inferred northwards and southwards movements of these haplogroups when the Sahara became uninhabitable.

Although Y chromosomes exist within populations and so share and reflect the general history of those populations, they can sometimes show some departures from other parts of the genome that result from differences in male and female behaviors. D’Atanasio et al. [1] highlight one such contrast in their study. Present-day North African populations show substantial sub-Saharan autosomal and mtDNA genetic components ascribed to the Roman and Arab slave trades 1–2 kya [7], but carry few sub-Saharan Y lineages from this source, probably reflecting the smaller numbers of male slaves and their reduced reproductive opportunities when compared to those of female slaves. The sub-Saharan Y chromosomes in these North African populations thus originate predominantly from the earlier Green Sahara period.

In this part of Africa, the indigenous languages that are spoken belong to three of the four African linguistic families (Afro-Asiatic, Nilo-Saharan and Niger-Congo). Interestingly, these languages show non-random associations with Y lineages. For example, Chadic languages within the Afro-Asiatic family are associated with haplogroup R-V88, whereas Nilo-Saharan languages are associated with specific sublineages within A3-M13 and E-M78, further illustrating the complex human history of the region.

The main question after D’Atanasio et al. (2018) is thus:

(…) what are the reasons for the very rapid R-V88 expansion 5–6 kya [1] and E-M81 expansion ~ 2 kya [6], and how do these expansions fit within general worldwide patterns of male-specific expansions, which in other cases have been linked to cultural and technological changes [5]?

I think that the only known haplogroup expansion that might fit today the spread and dialectalization of Afroasiatic, a proto-language probably contemporaneous or slighly older than Middle Proto-Indo-European, is that of R1b-V88 lineages. However, without ancient DNA samples to corroborate this, we cannot be sure.

See also: