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# Histories of Admixture

Paul Verdu

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Paul Verdu. Histories of Admixture. Populations and Evolution [q-bio.PE]. Museum National d'Histoire Naturelle, 2022. tel-03817698

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# MUSEUM NATIONAL D'HISTOIRE NATURELLE

## Année 2022

## HABILITATION A DIRIGER DES RECHERCHES

du

MUSEUM NATIONAL D'HISTOIRE NATURELLE

Spécialité : Anthropologie et génétique des populations

Présentée et soutenue publiquement par

Paul Verdu

2022

## Histoires de Métissages

### Histories of Admixture

### JURY :

<table><tr><td>Mme. Anouk BARBEROUSSE</td><td>Professeure à Sorbonne Université</td><td>Examinatrice</td></tr><tr><td>Mr. Simon BOITARD</td><td>Chargé de Recherche à l'Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement</td><td>Examinateur</td></tr><tr><td>Mme. Catherine BOURGAIN</td><td>Directrice de recherche à l'Institut National de la Santé et de la Recherche Médicale</td><td>Rapportrice</td></tr><tr><td>Mr. Simon GRAVEL</td><td>Professeur à l'Université McGill</td><td>Rapporteur</td></tr><tr><td>Mme. Emmanuelle PORCHER</td><td>Professeure au Muséum National d'Histoire Naturelle</td><td>Examinatrice</td></tr><tr><td>Mr. Alexandre ROBERT</td><td>Professeur au Muséum National d'Histoire Naturelle</td><td>Rapporteur</td></tr></table>
A Marie-France Mifune,

Grâce à qui tout est possible.
Table of Contents

Introduction. Histories of Admixture .................................................. 1

Chapter 1. Who should I sample? .............................................................. 7

1.1. Sampling individuals in Central Africa to reconstruct the genetic origins and evolutionary history of "Pygmy" and neighboring "non-Pygmy" populations .................................................. 8

1.1.a. Who are the Central African Pygmies? .................................................................................. 9

1.1.b. Categorizing Central African populations into a binary category: Pygmies and non-Pygmyes .................................................................................................................................................. 12

1.1.c. Population genetics questions about Central Africans categorized as Pygmies and non-Pygmyes .................................................................................................................................................. 14

1.2. Sampling individuals in Cabo Verde to reconstruct the genetic and linguistic admixture histories of the archipelago .................................................................................................................. 18

1.2.a. Participants' inclusion in population genetics studies related to the TAST: who did they sample? .................................................................................................................................................. 21

1.2.b. Sampling design for population genetics purposes in Cabo Verde: who did I sample? ..... 24

1.2.c. Genesis of a multidisciplinary sampling-design for joint genetic-linguistics investigations. .................................................................................................................................................. 28

1.3. Concluding remarks to sections 1.1 and 1.2. .................................................................................. 33

1.3.a. A practical toolbox for anthropological categorization issues in human population genetics research. .................................................................................................................................................. 33

1.3.b. Miscellaneous conclusion. .................................................................................................................. 34

1.4. Ethics, deontology, laws, and scientific research administration: "what information can I share about whom I sampled?" .................................................................................................................. 36

1.4.a. Human individual genetic data is "identifiable sensitive data". .................................................. 37

1.4.b. Updating Informed Consents for novel population-genetics research projects .................... 39

1.4.c. When genetic data escape researchers for scientific necessities .................................................. 39

1.4.d. Returning individual genetic data to their biological owners........................................................ 40

1.4.e. Who gets to use the genetic data once I am gone?.................................................................................. 44

1.4.f. What about anthropological and linguistic data? .................................................................................. 46

1.5. Concluding remarks to section 1.4, and personal recommendations to researchers and students. .................................................................................................................................................. 51

1.6. Perspectives: Population genetics categorization issues in the paleo-genomics era and interdisciplinarity necessities between genetics and anthropologies. .................................................... 53

1.6.a. Archaeological cultures are not population-genetics' populations .................................................. 54

1.6.b. Is there really a paleo-genetics revolution, or at least its' possibility, in paleo-anthropology and archaeology? .................................................................................................................. 59
<table><tr><td>1.6.c. "The interdisciplinary requirement" for human population genetics and paleoanthropologists</td><td>61</td></tr></table>

## Chapter 2. Admixture in the demographic history and biological evolution of Central African “Pygmy” and neighboring “non-Pygmy” populations .................................................................................................................. 66

2.1. Genetic variation patterns among Central African populations with respect to different anthropological categories .................................................................................................................. 69

2.2. Reconstructing the origins of Central African Pygmy and neighboring non-Pygmy populations using Approximate Bayesian Computations .................................................................................................................. 74

2.2.a Briefly introducing Approximate Bayesian Computation statistical inference ........................ 74

2.2.b Approximate Bayesian Computation inferences in practice in Central Africa ........................ 76

2.3. Complex sex-biased admixture processes between Central African Pygmy and neighboring non-Pygmy populations inferred with Approximate Bayesian Computations .................................................. 82

2.4. A synthesis of anthropological genetics perspectives on the neutral demographic history of Central African peopling in the framework of the Pygmy/non-Pygmy complex categorization ...... 86

2.5. Admixture as a major force driving phenotypic diversity and evolutionary trajectories of Central African populations: what about height?.................................................................................................................. 91

2.6. Ongoing and future perspectives about the evolutionary history of Central African populations .................................................................................................................................................................................. 96

## Chapter 3. A general theoretical framework for investigating complex admixture histories .................................................................................................................. 100

3.1. Classical mechanistic models of admixture .................................................................................................................. 101

3.2. A general mechanistic model for admixture histories.................................................................................................................. 103

3.3. Distribution of admixture fractions across individuals in the admixed population........................ 107

3.4. A sex-specific version of the general mechanistic admixture model ........................................................ 113

3.5. Perspectives for theoretical developments of complex admixture histories models investigated with genetic data.................................................................................................................................................................................. 115

## Chapter 4. *MetHis*: a novel Approximate Bayesian Computation framework for reconstructing complex admixture histories from genetic data.................................................................................................................. 119

4.1. *MetHis* software for simulating data and calculating summary-statistics under the Verdu and Rosenberg 2011 general mechanistic model of complex historical admixture.................................................. 122

4.1.a. Simulating genetic data under the Verdu and Rosenberg 2011 model.................................................. 123

4.1.b. Calculating summary-statistics for ABC inferences .................................................................................................................. 125

4.2. *MetHis* coupled with machine-learning ABC in practice .................................................................................................................. 127

4.2.a. *MetHis-Random Forest ABC scenario-choice; MetHis-Neural Network ABC posterior parameters joint inferences*.................................................................................................................................................................................. 127

4.2.b. A case study for evaluating *MetHis-ABC* performances in practice .................................................................................................................. 129
<table>
  <tbody>
    <tr>
      <td>4.3. Ongoing developments and future perspectives for the inference of complex admixture histories from genetic data using Approximate Bayesian Computation</td>
      <td>136</td>
    </tr>
    <tr>
      <td>4.3.a. Novel summary-statistics for MetHis-ABC</td>
      <td>136</td>
    </tr>
    <tr>
      <td>4.3.b. Other types of genetic data and models simulated with MetHis</td>
      <td>138</td>
    </tr>
    <tr>
      <td>4.3.c. Using MetHis deterministically</td>
      <td>138</td>
    </tr>
    <tr>
      <td>4.3.d. Ongoing projects using MetHis-ABC for non-human species</td>
      <td>139</td>
    </tr>
    <tr>
      <td><strong>Chapter 5. The genetic and linguistic admixture histories of Cabo Verde</strong></td>
      <td><strong>142</strong></td>
    </tr>
    <tr>
      <td>5.1. The genetic admixture histories of Cabo Verde</td>
      <td>145</td>
    </tr>
    <tr>
      <td>5.1.a. European and African populations at the source of Cabo Verdean genetic diversity today.</td>
      <td>145</td>
    </tr>
    <tr>
      <td>5.1.b. Isolation-By-Distance genetic patterns at reduced geographical scale within the archipelago</td>
      <td>153</td>
    </tr>
    <tr>
      <td>5.1.c. Complex admixture histories for each Cabo Verdean islands reconstructed with MetHis-ABC</td>
      <td>155</td>
    </tr>
    <tr>
      <td>5.2 Ongoing work and perspectives for section 5.1: the genetic admixture histories of Cabo Verde.</td>
      <td>163</td>
    </tr>
    <tr>
      <td>5.3. Parallel trajectories of genetic and linguistic admixture histories of Cabo Verde</td>
      <td>164</td>
    </tr>
    <tr>
      <td>5.4. Ongoing perspectives: A novel "population linguistic" framework, and novel joint inferences of genetic and linguistic histories from observed data</td>
      <td>171</td>
    </tr>
    <tr>
      <td>5.4.a. ABC inference for reconstructing the linguistic history of populations</td>
      <td>171</td>
    </tr>
    <tr>
      <td>5.4.b. ABC inference for reconstructing jointly the genetic and linguistic history of groups of individuals</td>
      <td>175</td>
    </tr>
    <tr>
      <td><strong>Conclusion. Histories of Admixture</strong></td>
      <td><strong>183</strong></td>
    </tr>
    <tr>
      <td><strong>Remerciements</strong></td>
      <td><strong>187</strong></td>
    </tr>
    <tr>
      <td><strong>References</strong></td>
      <td><strong>191</strong></td>
    </tr>
    <tr>
      <td><strong>Summary</strong></td>
      <td><strong>202</strong></td>
    </tr>
  </tbody>
</table>
# Introduction

## Histories of Admixture
**Introduction. Histories of Admixture**

The object of study at the core of any population genetics investigation of biological evolution processes is genetic variation, observed, or imagined, among individuals within and between populations and species. In a classical synthetic theory of evolution framework, neutral and neo-Darwinian, illustrated in the schematic figure below, genetic variation is influenced by major evolutionary forces. Mutation is the only force generative, randomly, of genetic variation across individuals from one generation to the next. Demographic forces (or rather “demo-genetic” forces) then shape the distribution of this diversity across individuals and populations, randomly via genetic drift, or through reproductive isolation and, conversely, genetic admixture or migration. Finally, Darwinian natural selection influences indirectly genetic variation via the selection of those particular phenotypes related to reproductive fitness that have a genetic determination.

As I was implicitly taught classically during most of my studies before my PhD, genetic variation and natural selection pertained to the “natural”, “innate”, world, while mutation and demography were largely considered as formal forces often disjointed from any specific environmental considerations as they mechanistically influenced genetic variation “blindly”. The Neutral Theory of evolution (Kimura 1968, 1983), which ultimately lead to this synthetic theory of genetic evolution, made a tremendous impression on me personally: only a small proportion of genetic variation was determining directly phenotypic variation across individuals.

<figure><img src="image_3.png" /></figure>

**Figure Introduction**

Schematic representation of the influence of evolutionary forces (mutation, demography, selection) on genetic diversity and partially associated phenotypic diversity, in the classical neutral, neo-Darwinian, synthetic theory of evolution.

Culture, defined as all practices and behaviors transmitted from one generation to the next in a non-genetic way, indirectly influences genetic diversity patterns of all species that practice it. Admixture and migrations are mainly demographic forces known to influence genetic diversity patterns. Note that, the Neutral Theory of Evolution (Kimura 1968, 1983), showed that only a very limited part of genetic diversity determines directly phenotypic diversity.
Culture, which I define comfortably here as all practices and behaviors transmitted among individuals from one generation to the next in a non-genetic way, was recognized as a possible force influencing the distribution of genetic variation of any species which practiced it, including humans but not limited to them, by far. However, in humans, while cultural practices were known to influence reproductive practices and/or nutritional and pathogenic environments, thus indirectly shaping demographic and natural selection forces, evolutionary geneticists very often considered that this force was too versatile and changing to influence significantly the evolution of our species. From a cultural anthropology perspective, the raging “Nature-Culture” (or “Nature-Nurture”) debate often resulted in separating human biology from the realm of cultural behaviors, creating different, hermetically sealed, objects of study. On one hand, anthropological biologists would focus on the natural part of our evolution and the strict equivalence between genetic diversity and phenotypic diversity, hereby often completely missing the teachings of the Neutral Theory revolution. On the other hand, cultural anthropologists would focus on the cultural practices and representations disconnected from biological considerations, and relatively often disconnected even from natural environments in which humans lived.

In this context, genetic admixture and migrations represented essential demographic mechanisms of human biological evolution, as for any other species investigated by population geneticists. A large body of seminal theoretical approaches, built since the very beginning of population genetics at the turn of the 20th century, provided methodological statistical tools to identify admixture and migration signatures in the observed genetic data, but populations were first conceived as isolated and non-admixed before admixture and migration may influence their evolution. Furthermore, how admixture or migration had occurred in human genetic evolution was often not directly investigated until the end of the 20th century.

The object of this dissertation is to recapitulate advances in anthropological genetics and human population genetics since the early 2000’s having furthered our understanding on how admixture and migration influenced genetic evolution. In particular, I illustrate these developments with my personal research endeavors since 2005 and the beginning of my PhD. I hereby intend to show that socio-cultural processes massively determine admixture and migration across human populations and communities, in complex ways over time and space. This shows that Culture is far from being a weak force in human evolution, and further illustrates how the opposition of Nature and Culture is often not operational to understand human genetic variation and evolution. Altogether, I strongly plea for the necessity to embrace different disciplinary paradigms and methods to investigate the multi-faceted history of human evolution.

I believe that my research work stands at the crossroads of certain cultural anthropology disciplines, mainly ethnology and historical-, computational-, and socio-linguistics, with population genetics. In particular, most of my research aim at reconstructing the past demographic and evolutionary history of human populations from genetic variation patterns observed within and across populations today, and using population genetics approaches. I thus consider my work to be either anthropological genetics or human population genetics work, two disciplines sharing the same population genetics paradigms and most methods, and that I differentiate, somehow a bit arbitrarily and cosmetically I reckon, only based on their finality: anthropological genetics is mainly interested in the history of human populations and groups, while human population genetics is mainly interested in the genetic evolution of our species (Cavalli-Sforza and Feldman 2003).

In practice, I first elaborate anthropological questions about the peopling and evolutionary history of human populations and about the influence of socio-cultural behaviors having shaped the biological and cultural diversity of populations. I then design and conduct fieldwork in order to collect cultural anthropology, genetic, and linguistic data from extant populations, mainly in Central and Western Africa,
so far. I then generate the genetics data on the collected samples using a variety of molecular genetics
technics. In parallel, I elaborate certain theoretical, mathematized, population genetics frameworks for
analyzing population genetics data, when existing approaches do not allow me to directly investigate my
specific questions of interest. I also develop the methodological framework and associated statistical and
computational tools to use these new frameworks for the analysis of observed genetic data. I then apply
these methods to the newly generated data, as well as any other existing population genetics statistical
approaches relevant to address my anthropological questions of interest. More recently, my research
developed to jointly investigate genetic and linguistic diversity within populations and languages, which
required developing a novel framework, defining novel objects of study, and the accompanying population
genetics and linguistics methods for investigating them. Therefore, my research is both of an
anthropological and a population genetics nature.

In this dissertation, I aimed at recapitulating my past research, with a particular focus on explaining
how did I obtain results and, thus, on how my research was elaborated and conducted. Indeed, I am mostly
interested in how scientific results are obtained, often vastly more than in the obtained results themselves.
Therefore, I was not interested in producing here a summary of all my results, big or small, supposedly
influential or confidential, without trying to explain how I got to ask myself these questions and how I
decided to try to answer them. In particular, several of my former and current PhD students repeatedly
asked me how to elaborate a research project from scratch, and how did I proceed for my previous published
works. While I try to teach them exactly this by doing research, as any research supervisor I believe, I
thought it might be interesting to them and my future students to have a written document presenting
numerous conceptual and practical aspects of my previous research endeavors, concerning the elaboration
of anthropological questions of interest, fieldwork design, ethics and deontology, theories and
methodologies, molecular data generation, and statistical analyses.

Therefore, I tried to narrate as much as possible, sometimes with personal comments, how I conducted
my research, how I made mistakes and restarted, how I sometimes succeeded, and, most of all, how I did
not do things alone, far from it... Indeed, my work is essentially collaborative and pluri-disciplinary,
sometimes even interdisciplinary, and I would be incapable of pursuing scientific research without the help
and support of numerous researchers, including all the students I have had the chance and honor to supervise
over the years.

Hopefully, and sincerely, this dissertation will also be of some interest to other more senior researchers
in cultural anthropology, evolutionary biology, and population genetics, more generally and among others.

The **first Chapter** of this dissertation presents how scientific and non-scientific categories and categorization processes of human individuals and populations shape the possible questions of interest to different disciplines of anthropology and evolutionary biology, often naïvely and sometime prejudicially, and also shape all the downstream scientific dialectics deployed to address them. I introduce here the genesis of my principal research projects conducted over the years in Central Africa and Cabo Verde in West Africa, the building of the anthropological questions that I tried to address, and the design of fieldwork sampling strategies and methods. I provide a practical guide concerning categorization issues in anthropological genetics and human population genetics, a major subject in my daily research practice. I then discuss specific ethical and deontological issues in the practice of human population genetics research, in particular focusing on data privacy protection and data sharing in the Open Science era. I also provide a practical guide about these issues. Finally, I develop perspectives about categorization issues in the paleo-genomics era, that I believe to be at the root of difficult relationships between archaeologists and geneticists.
Note that the first two sections of this chapter also largely serve as detailed introductions to **Chapter 2** and **5**, respectively. Finally, this chapter is the only largely original production of this dissertation.

**Chapter 2** focuses on summarizing the dialectics, methodologies, and results we have generated for addressing multiple questions about the peopling and evolutionary histories of Central African populations, and on how they were profoundly shaped by complex socio-cultural behavior determining admixture processes among populations. Altogether, this work, conducted since 2005, highlighted, for me, how complex admixture processes could be a major mechanism of human biological evolution, which determined all my other research endeavors.

**Chapter 3** focuses specifically on the theoretical, mathematical, framework we have elaborated for investing highly complex admixture histories using genetic data.

**Chapter 4** presents the methodological approach we developed for reconstructing such complex admixture histories from observed genetic data, and the sets of statistical and computational tools we produced to achieve this goal.

**Chapter 5**, finally, summarizes our investigations of the genetic and linguistic diversity of Cabo Verde, and how complex admixture processes, determined by changing socio-historical contexts over the last 400 years, may have shaped them. Ultimately, I present here the novel joint population genetics and population linguistics framework that we are currently developing for investigating the diversity of human biological and cultural histories.

Note that, instead of listing perspectives of future research in the conclusion, each chapter presents specific “ongoing research” and “perspectives” sub-sections were I elaborate on some of my current endeavors and future prospects concerning each subject separately.

Finally, **I conclude** this dissertation with general perspectives echoing the first part of this introduction, and with future challenges of disciplinary and inter-disciplinary anthropology research. I finish with a personal recommendation to vastly increase the efforts for mediating and diffusing the Scientific Methods towards specialists and non-specialists alike, a major academic interdisciplinary necessity as well as a crucial challenge for scientific research in the society.
# Chapter 1

## Who should I sample?
<figure><img src="image_4.png" /><figcaption>Nditam at dawn.
Pays Tikar, Région Centre, Cameroun, 2016
©Paul Verdu</figcaption></figure>
**Chapter 1. Who should I sample?**

At the heart of every population genetics investigation lies the genetic variation that emerges from the comparison of germinal DNA sequences (or genotypes) among individuals. Contrarily to, for instance, numerous fungi, algae or vegetal multicellular organisms, *Homo sapiens* individuals are most often easily identified as spatially separated biological organisms. Investigating human genetic variation is thus a priori unequivocal as genetic variation itself stems directly from the comparison of DNA sequences unambiguously gathered from different individuals. Fundamental scientific categorization issues, with major ethical, deontological, methodological, and dialectical consequences for almost every aspect of anthropological genetics research, arise only a single small step ahead of this trivial and common-place consideration, when researchers group individuals’ DNA into groups and populations.

Anthropological geneticists and human population geneticists are interested in reconstructing the evolutionary history of predefined groups of individuals or *populations*. In population genetics, most fundamental theoretical expectations, statistical reasoning, hypothesis testing approaches, and methodologies in general rely on the allelic composition and frequencies estimated on sets of individuals grouped a priori into *populations*. Thus, they inherently depend on which individuals are grouped in which *populations*. In other words, if one groups individuals differently from another, estimates of allelic composition and frequencies will possibly be different; and all subsequent results of statistical inferences and their interpretations may also differ. Hence, explicitly providing the criteria used to group individuals into *populations* is essential to understand and interpret the outcome of population genetics statistical descriptions and historical inference reconstructions.

This liminal problem of each and every statistical-based scientific method becomes very practical for population geneticists involved in the primary sampling of individual DNA in the field, including human population geneticists. Indeed, “*Who should I sample?*” is the single question that determines a priori both the anthropological genetics questions that researchers may or may not address and which population genetics methods may or may not be deployed for addressing them.

In this chapter, I will present the categorization challenges and sampling methodologies we developed in order to reconstruct the peopling and evolutionary histories of Central African populations (**section 1.1**), and the genetic and linguistic admixture histories of Cabo Verde (**section 1.2**). *Note* that both sections will also largely serve as introductions to **Chapters 2 and 5** respectively.

In **section 1.3**, I conclude based on these experiences by providing practical guidelines for thoroughly informing and discussing categorization criteria of individuals and populations in human population genetics and anthropological genetics research.

In **section 1.4**, I elaborate on the ethical and deontological aspects of human population genetics research specifically related to these categorization issues and, more generally, on the sharing of genetic, anthropological, and linguistic data.

In **section 1.5**, I conclude this latter ethics and deontology section with practical recommendations to researchers and students.

Finally, in **section 1.6**, I develop on the perspectives of these issues in the recently emerged and massively developing field of human paleo-genetics.
**1.1. Sampling individuals in Central Africa to reconstruct the genetic origins and evolutionary history of “Pygmy” and neighboring “non-Pygmy” populations**

Back in 2005, the PhD project I started at the UMR7206 Eco-anthropology (UMR5145 back then) provided me with the rare and ambitious opportunity to elaborate an anthropological genetics project starting with the collection of DNA samples in the field. The project designed initially by Pr. Evelyne Heyer (my official PhD advisor) and Pr. Serge Bahuchet (director of the lab at the time and unofficial PhD advisor) aimed at gathering DNA samples from numerous Congo Basin so-called “*Pygmy*” populations in order to characterize their genetic diversity and descriptively compare genetic differentiation patterns with linguistic variation from the same populations. To do so, the population genetics side of the project could capitalize on the massive expertise of the diverse ethnologists from the laboratory historically specialized in the study of Central African Pygmy populations’ cultures and ways-of-life from Cameroon, Gabon, Central-African Republic (CAR), Congo, Democratic Republic of Congo (DRC), Uganda, Burundi, and Rwanda (Figure F1.1.a).

<figure><img src="image_5.png" /><figcaption>Figure F1.1.a.</figcaption></figure>

Geographical map of historical “Pygmy” populations in Central Africa, inferred from ethnological surveys by S. Bahuchet, A. Froment, B. Hewlett, S. Le Bomin, H. Pagézy, P. Verdu and colleagues. Figure originally published in Verdu P. *Current Biology* 2016
Therefore, as a nascent population geneticist willing to learn about every aspect of an anthropological
genetics study, from sampling in the field to population genetics data analyses via molecular genetics data
generation, I had the reassuring luxury to focus on historically emblematic populations from a largely
under-investigated region of the world in a scientific environment built since the 1960's by most of the
main worldwide specialists of this geo-cultural region. Theoretically, I thus just had to go to Central African
Pygmy villages long studied by the ethnologists of the team (together with them in a pluri-disciplinary
framework), gather DNA samples from volunteer participants, come-back to France, and do some
population genetics magic.

1.1.a. Who are the Central African Pygmies?

But who are the Pygmies? How am I to know who should I sample and who should I not sample on a field I had never been to myself? In the anthropological genetics' realm, Central African Pygmy populations had been the focus of famous studies initiated mainly by L. L. Cavalli-Sforza et al. in the 1980s (Cavalli-Sforza 1986), and further explored in seminal work by Destro-Bisol et al. (2004). Some DNA samples from mainly three populations, called by these authors the Biaka and Mbenzele from CAR, and the Mbuti from DRC, had been gathered and various aspects of their genetic diversity explored. They were presented as hunter-gatherer populations specialized in equatorial forest activities, living separated from other agriculturalist sedentary populations in the region, and, therefore, thought to have retained a nomadic hunter-gatherer way-of-life in the forest exemplifying most of human evolutionary history before the Neolithic agricultural revolutions. However, none of these population genetics studies detailed the inclusion criteria of individuals nor the categorization criteria of their grouping in each population and further grouping of populations into the "Pygmy" category. They were named and labelled as if the nature of these categorial groupings was obvious and innate. As a matter of fact, and most importantly, these different populations were *de facto* assumed to have a common biological origin differing from the origin of other populations in the region: they were the famous "Pygmies" and everyone was supposed to know who we were talking about...

In this context, the ethnologists involved in my PhD project had a very different perspective,
substantiated by numerous publications since the 1950's and summarized in, to my views, one of the most
important works on the subject: “L’Invention des Pygmées” by Serge Bahuchet (1993). With the leading
expertise of Pr. Bahuchet and colleagues, I rapidly learned that, in fact, “the Pygmies do not exist”. Indeed,
the word Pygmy itself is derived from ancient Greek and refers to a cubit, thus a measure of short size (or
the small volume of a clenched fist in alternative translations). It was first explicitly used by Homer in his
Iliad (Song 3, v. 1-8), in a single instance, where the Trojan warriors fighting against the Achaeans are
compared to a flock of migrating cranes pouncing on the “Pygmy” population with whom they are at war,
somewhere on the other side of the seas. And that’s it... Pygmies are thus, classically, anthropomorphic
individuals of short stature (the word itself), living somewhere far and unknown, and at war with big
migrating birds.

Why on earth did 20th century human population geneticists grouped numerous human populations
from the entire Congo Basin area under such blanket term? Well, since Homer, Western cultures have been
swarming with myths elaborating on Homer's few words, whether representing Homer's Pygmies on
ancient vases as short-stature individuals at war with birds (Figure F1.1.b), or medieval dissertations and
poems about the alleged particularities of the Pygmy people, and even cinematographic representations in
the very first Tarzan movie in the 20th century, among numerous other instances (Bahuchet 1993; Verdu
2009).
<figure><img src="image_6.png" /><figcaption>Figure F1.1.b.</figcaption></figure>

Two ancient vases figuring classical representations of “Pygmies” fighting cranes in reference to Homer’s Iliad.

Left vase: Attic red-figure chous (oinochoe, type 3), dated around 430–420 BCE; National Archaeological Museum (Madrid) -Wikimedia commons.

https://upload.wikimedia.org/wikipedia/commons/thumb/2/25/Fight_Pygmy_crane_MAN.jpg/640px-Fight_Pygmy_crane_MAN.jpg.

Right vase: Attic black-figure hydria, Etruria (?), dated around 530-520 BC;

Roma, Museo nazionale etrusco di Villa Giulia 50425 / 438 - Beazley Archive Pottery Database 43377 - Wikimedia commons.

https://upload.wikimedia.org/wikipedia/commons/thumb/3/36/Attic_black-figure_hydria_-_ABV_extra_-_Pygmies_and_cranes_-_orgiastic_komos_-_Roma_MNEVG_50425_-_04.jpg/640px-Attic_black-figure_hydria_-_ABV_extra_-_Pygmies_and_cranes_-_orgiastic_komos_-_Roma_MNEVG_50425_-_04.jpg.

Furthermore, Western explorers, including Marco Polo, travelling the world from the 14th century and on, often searched for Homer’s Pygmies, obviously not finding populations at war with birds. Far from questioning the reality of Homer’s depiction, they, instead, always moved the putative location of existence of the famous Pygmies to the other side of the edge of the known world, thus somewhere yet unexplored.

We thus had to wait, as Bahuchet said, more than 20 centuries after Homer, for two Western explorers independently travelling the Congo Basin during the mid-19th century (Paul Du Chaillu from West to East, and Georg Schweinfürth from East to West), who claimed to have finally found the famous Pygmies from Homer (Schweinfurth 1873; Du Chaillu 1892). Of course, they were not at war with birds... but they were apparently of relatively short stature, lived a hunting-gathering way of life in the Central African equatorial forest, and were known as different populations specialized in the forest realm by other Congo Basin agriculturalist populations with whom Westerners already used to interact since at least the 15th century. From then on, the Central African Pygmies were born, and more than 20 populations throughout the Congo Basin (Figure F1.1) met by various Western explorers and adventurers, including the famous journalist Stanley for instance, were categorized historically under the blanket-term “Pygmy” based on being apparently of relatively short stature (in fact, so-designated “Pygmies” were not often measured before the mid-20th century, Froment 1993), and on being recognized by outsiders as different populations specialized in forest activities.

Throughout the 20th century, ethnologists and cultural anthropologists extensively described and characterized cultures and ways-of-life in numerous (but not all) populations historically labelled as Pygmies, in turn substantiating the grouping of these populations into this category or, instead, revealing their massive cultural variation. Ultimately, they showed that numerous non-mutually exclusive cultural criteria could be informed and intersected to group populations into “Pygmies” in reference to the historical
category used by Westerners (Bahuchet 1992a, 1992b; Bahuchet and Guillaume 1992; Hewlett 1996; Fürniss 2011; Verdu and Destro-Bisol 2012; Hewlett 2014; Verdu 2014, 2016).

1) Pygmy populations would self-identify as different communities with different ethno-names than outsiders, and would symmetrically be recognized as different communities by outsiders.

2) Pygmy populations would be recognized by outsiders as specialists of hunting, fishing, and gathering subsistence activities, as well as specialists of medical and magical knowledge of forest environments.

3) They would live a mobile way-of-life in their environment, with semi-permanent habitat, as opposed to more sedentary neighboring agriculturalist populations.

4) They would practice specific musical and dancing activities, for ritual or entertainment purposes, recognized as specific and different by outsiders.

Importantly, and somewhat apparently contradictory, ethnologists and cultural anthropologists systematically described exceptions to at least one of the second, third, and fourth inclusion criteria in a given historically labelled “Pygmy” population or another throughout the Congo Basin<fnref n="1" />.

First, while Pygmy populations are indeed often specialized in foraging activities in the forest, and recognized as such by outsiders, they all know about, and practice, agriculture and horticulture, whether in their own fields or gardens, or in their neighbors’ fields and gardens; and the neighboring agriculturalists very often hunt, fish, and gather in the rain-forest themselves. Furthermore, some Pygmy populations, such as the Twa from Rwanda and Burundi (Figure F1.1.a), are recognized as skilled potters or weavers rather than specialized hunters and gatherers.

Second, some Pygmy populations, such as the Bongo from Gabon or the Twa from Rwanda (Figure F1.1.a), in fact live a very sedentary way-of-life in villages much resembling those of their agriculturalist’s neighbors, and only conduct their foraging activities for limited specific periods of time rather than frequently move across semi-permanent forest-camps such as some communities of Aka from CAR and Baka from Cameroon (Figure F1.1.a). Furthermore, it is not uncommon that Pygmy communities and their neighbors live in the very same villages, sometimes in separate neighborhoods but sometimes not.

Third, some Pygmy populations live in other environments than the equatorial rainforest, such as the Bezan from Cameroon who live in clear-forest environments, or the Twa from Western DRC who live in forest swamps (Figure F1.1.a).

Fourth, some Pygmy populations indeed have very specific musical practices, with different vocal techniques and instruments than their neighbors, such as the emblematic practice of polyphonies and yodel in Aka from CAR and Baka from Cameroon or flutes orchestras in the Efe from Eastern DRC, but numerous other historical Pygmy populations do not have specific musical practices fundamentally differing from those of their neighbors.

Finally, Pygmy populations do not have a specific “Pygmy” language; “unfortunately” as the linguistic difference criteria is often used by geneticists for grouping individuals into different and separate populations. Instead, Pygmy populations all practice a language most often practiced by some of the neighboring populations not categorized as Pygmies, sometimes even from different linguistic families (Bahuchet 2012; Verdu and Destro-Bisol 2012). Thus, languages spoken by the different Pygmy populations, even geographically close from one-another, are sometimes massively differing and not mutually intelligible without learning (Figure F1.1.a).

<footnote n="1">¹ The first criteria always held throughout the Congo Basin, but it could not be used on its own to group populations into the Pygmy category, as there is no equivalent to a “Pygmy” word shared by all populations in the Congo Basin and as this criterion is essential for identifying any community separate from any other everywhere around the world.</footnote>
1.1.b. Categorizing Central African populations into a binary category: Pygmies and non-
Pygmies

In this context, grouping populations under the blanket-term Pygmy could be achieved by intersecting the majority of the above criteria of categorization with another non-exclusive and complex criteria, the only one shared by all Congo Basin populations historically labelled “Pygmies”. Indeed, ethnologists and cultural anthropologists showed that all Central African Pygmy populations shared specific complex socio-economic interactions with at least one of the neighboring populations not labelled historically as Pygmies. These interactions often involved exchanges of goods, such as forest products and game, or medical and magical knowledge specific to the Pygmy groups, against money, agricultural produces, or iron products not produced by the Pygmy groups. Most importantly, these interactions are not limited to exchanges of products or knowledge, but also involve complex systems of Pygmy vassalage and infeudation to their dominant neighbors, including labor-force employment and socio-marital segregation and discrimination against Pygmies (Kazadi 1981; Bahuchet 1992a; Bahuchet and Guillaume 1992; Joiris 2003; Hewlett 2014; Verdu 2014).

As a result, it became obvious to me that I could not rely on the historical categorization of populations
into Pygmies without gathering the detailed information of each population I was to visit to inform these
criteria and, only then, label individuals into populations and “super-group” Pygmies. Furthermore, a
classical ethnographic categorization method, relying on a nucleus of cultural traits and practices shared by
members of a group excluding all other individuals, would also prove difficult in the Central African context
of complex socio-economic interactions and multilingualism described above. Therefore, I decided to rely
on the anthropological framework proposed by Frederik Barth which favors, instead, informing the dynamic
and shifting borders and frontiers among cultural features shared across different groups in socio-cultural
interactions locally (Barth 1969). Finally, ethnologists have had a long-standing debate about how to group,
or not, Central African populations that were not historically grouped into the Pygmy category. They have
been often designated as “Villagers”, albeit as mentioned above, some Pygmy populations are also living
in villages; “Agriculturalists”, albeit all Pygmy populations practice agriculture, sometimes to a large
extent; or even, in particular by geneticists, designated as “Bantus”, a vast linguistic family encompassing
more than 250 languages spoken from Central to Austral Africa, and, again, very problematic as numerous
Pygmy populations themselves practice Bantu languages largely undifferentiated from those of their
neighbors (Figure F1.1.a).

Facing these fundamental categorization issue, I decided to introduce a binary categorization system
that would at least allow me to maintain separate categories systematically based on the same numerous
and complex criteria described above and informed indiscriminately in all populations locally: the
Pygmy/non-Pygmy binary categories (Verdu et al. 2009). We thus designed interview questionnaires
heavily relying on ethnologists’ expertise at a local scale that would allow us to formally inform cultural
practices in all visited communities with respect to i) endogenous systems of self-identification and of
identification of other than self; ii) subsistence strategies and specializations; iii) life-history traits related
to spatial mobility; iv) musical practices; v) socio-economic and cultural relationships with immediate
neighboring populations including intermarriages and discriminations. Intersecting *a posteriori* these
cultural criteria informed in various communities locally would thus allow us to categorize samples into
populations with different ethno-names, categorize every such population into Pygmy or non-Pygmy
populations, and further provide key information about detailed ways-of-life for all individuals investigated
and sampled, whichever the population category. Indeed, possessing all this anthropological information
from each population for which we had collected DNA samples would further allow us *a posteriori* to group
the same sets of individuals into different categories, in order to formally evaluate how cultural differences
or similarities may, or may not, translate into genetic differentiation patterns.

**Important warning:** Throughout my work, I have explicitly decided to keep the ambiguous “Pygmy” label (and its counter-part “non-Pygmy”) for naming the groups of individuals based on the above detailed criteria of categorization. It is important to emphasize here that whether it is relevant to continue to use this label is still an ongoing debate in the cultural and biological anthropology communities; a debate to which I have myself participated (Hewlett 2014). A practical problem of using this somehow archaic term is due to the fact, that, in some parts of Central Africa, the word “pygmy” itself is used derogatorily by non-Pygmy neighbors. However, I also found it to be ambivalent in the field. Indeed, certain Pygmy groups or individuals choose to claim this name in front of outsiders, governmental or non-governmental outsiders, nationals or foreign, to emphasize their socio-cultural particularity and advocate, in their local power play, against the socio-economic discriminations they suffer from their neighbors. Finally, at least one tentative “re-naming” of the four historical Cameroonian “Pygmy” populations (the Bezan, the Baka, the Ba.Ghieli, and the Ba.Koya), into the “Quatre B” (the “four Bs”), by Cameroonian civil society in the 2000’s, also resulted in this new label being used derogatorily as well, at least as I last witnessed in 2016...

In this complex context, I decided to first define precisely in my publications the categorization criteria
used for grouping individuals into the binary category explained throughout this chapter, and decided to
keep the word “Pygmy” as a label, making almost always sure to mention the potential derogatory usage
of the term. I decided to use the original historical term for two reasons: as an exogenous term from ancient
Greek, I believe it is more prone to ask the naïve reader the question “who are we talking about?”, which I
think is essential for reflexive critical thinking about the results and study design. Second, I do it in reference
to the history of anthropology, and to the works of my giant predecessors.

However, as long as criteria of categorization are maintained, I really have no problems whatsoever to
use another “label”, as this will not change anything to the population genetics statistical descriptions and
inferences performed, nor their discussions based on said criteria rather than only on the label “Pygmy”.
We just haven’t reached a consensus on a name, yet. Human population geneticists often prefer to transform
the “Pygmy” and “non-Pygmy” categories into “Rainforest Hunter Gatherers” and “Agriculturalists”, as I
did myself in certain collaborative publications. I believe this is very far from ideal and, in fact, even more
problematic than the classical exogenous and literary “Pygmy” and “non-Pygmy”. Indeed, this particular
denomination for genetics research formally essentializes ways-of-life and economic practices and strictly
separates them albeit, as explained above, the Central African context is not as strictly dichotomic regarding
these cultural traits, far from it. Furthermore, this essentialization, as it is highly meaningful for
anthropological geneticists and human population geneticists, might be in fact misleading in that it
oversimplifies hypotheses and the methods used to test them into a “case-control” approach that may
inevitably fail to capture the known more complex reality of human adaptation to varying ways-of-life.

For all these reasons, and bearing all these warnings in mind, I will henceforth keep using the “Pygmy”
and “non-Pygmy” labels throughout this dissertation.
1.1.c. Population genetics questions about Central Africans categorized as Pygmies and non-Pygmies

Most importantly, investigating this classical anthropology categorization problem of Central African
populations, and further experimenting it during numerous pluri-disciplinary fieldwork in Gabon,
Cameroon and Uganda, highlighted two fundamental questions that remained, in fact, largely unexplored.

**First**, do Central African Pygmy populations share a common genetic origin more recently than they share a common genetic origin with neighboring non-Pygmy populations throughout the Congo Basin?

In other words, do populations categorized as Pygmies all share a common biological evolution distinct from that of neighboring non-Pygmy populations after their divergences from the common ancestor, or not?

Indeed, ethnological approaches are most often synchronic by nature and explore shared and distinct cultural features among populations at a given point in time, without formally testing whether these features stem originally from a shared origin, from converging but independent cultural constructions, or from ancient exchanges among otherwise distinct communities. Furthermore, with respect to ethnological endogenous representations of the origins of Pygmy populations, note that the various Pygmy populations very rarely know the existence of other communities also labelled “Pygmies” by Westerners, even when geographically close, and that they do not share, with other such communities, a common cosmogony nor myths of origin<fnref n="2" />. Therefore, ethnologists repeatedly discussed the nature of the observed similarities and profound differences among the various Pygmy populations and with neighboring non-Pygmy populations, leaving most often explicitly open the question of their ancient common or independent origins. Surprisingly, before the 2005’s, human population geneticists virtually never formally considered this question, probably due to the fact that the “Pygmy” category itself de facto assumed a common origin of all these populations (and see third point below). Nevertheless, the nature of population genetics questioning and statistical-based inference methods represented an ideal framework to formally test the common or independent biological origin of numerous populations categorized, based on cultural criteria only, into Pygmies and neighboring non-Pygmies, provided that these criteria had been systematically informed adequately in a first place.

**Second**, are Central African Pygmy populations reproductively isolated from their non-Pygmy neighbors, or do they form distinct cultural communities nevertheless commonly intermarrying? In other words, do Pygmy/non-Pygmy categories and associated discriminations against intermarriages indeed translated into populations' genetic isolation or, instead, were these cultural behaviors not prevalent and normative enough to avoid substantial admixture events or even random matting among communities?

Again, population geneticists interested in Central African Pygmy populations very rarely explicitly investigated population migrations and admixture at a local scale, considering a priori Pygmy populations as isolated and remote from contacts with other populations. This was consistent with common popular representations of remote and isolated Central African Pygmies in the Western cultures, despite extensive ethnologists' work describing a very different reality. This is true to the point that, most of the time, while some historical Pygmy populations were sampled for DNA in the Congo Basin, no non-Pygmy neighboring populations from the region were investigated, propagating implicitly in the human population genetics community the wrong idea that Pygmy populations were the sole inhabitants of the rain-forest...

<footnote n="2">² Note that several non-Pygmy neighboring populations do share common myths of origins involving “Pygmy” populations in highly similar ways, but these myths are not shared by the Pygmy populations themselves (Klieman 2003).</footnote>
**Finally,** what about height, the alleged morphological characteristics for which the word Pygmy was used in a first place? Most interestingly, the categorization system described above does not rely on morphological criteria. Nevertheless, intersecting these cultural categories with individual height, biological anthropologists identified during the 20th century that, indeed, Pygmy populations were among the shortest worldwide. However, in a seminal work (Froment 1993), biological anthropologist Alain Froment showed the massive variation of average non-pathological adult height across the various Pygmy populations throughout the Congo Basin, ranging from 143cm high in DRC Efe males to 161cm in Twa males from Western DRC. Moreover, and most importantly, Froment et al. further showed that numerous neighboring non-Pygmy populations were also of relatively short stature, such as the Ba.Konjo from Western Uganda measuring on average less than 158cm high. Therefore, using only a single threshold of non-pathologic adult height in the Congo Basin cannot be sufficient to categorize individuals into the Pygmy and non-Pygmy categories overlapping the other cultural criteria described above; quite the opposite in fact as it would result in reversing the cultural and historical categorization for numerous Pygmy as well as non-Pygmy populations. Nevertheless, before the 2010’s, numerous biological anthropologists and population geneticists alike considered this short stature criteria as a proof of common origin and shared evolutionary history, without ever formally testing it, and most often not correcting for inter-population variation of height. Also, importantly, note that in an almost ubiquitous way in the biological anthropology and genetics literature before the 2010’s, researchers most often tried to identify why Pygmies were shorter (Perry and Dominy 2009), but never explicitly focused on why non-Pygmy neighbors were taller, nor on the evolutionary relevance of height variation among groups.

In this context, we decided to conduct state-of-the-art morphological measurements of individual height in, hopefully, all individuals sampled for DNA and interviewed extensively as described above during our project. However, we never used height as a criterion of inclusion of individuals in our study or of categorization into “Pygmy” and “non-Pygmy”. Instead, we collected height in order to formally test whether individual Central African non-pathological adult height variation could be due to genetic factors or solely explained by environmental plasticity, a still vigorously debated question in the biological anthropologist community at the beginning of the 21st century. Finally, in this framework, note that during all of my yearly fieldwork interviews in Central Africa between 2006 and 2016, height differences were never invoked as an endogenous criterion for distinguishing communities and individuals. Pygmy groups never referred to height as a shared common feature of their community distinct from that of other, taller, non-Pygmy communities, and, vice versa, non-Pygmy neighbors never referred to height as identifying an individual as Pygmy or different than self.

Based on these premises, I conducted, between 2006 and 2016, 9 fieldworks (totaling 8 months residing in sampled communities) in Gabon, Cameroon, and Uganda, in order to collect more than 800 DNA samples from 14 communities categorized *a posteriori* in Pygmy and neighboring non-Pygmy based on the categorization criteria and sampling scheme presented above (Figure F1.1.c). Notably, I could not sample myself numerous other populations of interest to the peopling history of Central Africa, and relied for these on sampling performed by other researchers with whom I could interact during my project (Figure F1.1.c). I could thus obtain the various information needed to inform my categorization criteria directly from the samplers themselves, which allowed me to exclude from analyses samples for which all of my categorization criteria were not sufficiently informed.

This is often out of reach of population genetics studies conducted on data previously collected and accessible in genomics databases (see **section 1.4**). As exemplified above, this poses vast difficulties as detailed categorization criteria used for the inclusion of DNA samples in a population are often not readily
provided to other researchers. In turn, this lack of information often limits the spectrum of questions that
can be addressed using population genetics methods about the genetic diversity of human populations and
demographic and evolutionary processes involved at its origins. Furthermore, it is plausible that, in the lack
of sufficient categorization information, population geneticists would presume that sampling strategies and
inclusion criteria might be comparable across studies interested in similar questions and geo-cultural areas.
Based on this assumption they may spend substantial amount of time reconciling apparently discrepant
results and interpretations, or alternatively, overinterpreting similarities and homologies. However, it is
plausible that categories and inclusion criteria may differ in a first place, making results from different
studies hardly comparable without detailed formal discussions, themselves rendered impossible by the
initial lack of information about the samples investigated.

I further develop and exemplify these aspects about another research setting part of my long-term
research project in Cabo Verde, in the following section.
<figure><img src="image_7.png" /><figcaption>Figure F1.1.c.</figcaption></figure>

<table><thead><tr><th>Population Name</th><th>Code</th><th>Country</th><th>Linguistic Family (Guthrie 1971)</th><th>Sampling</th></tr></thead><tbody><tr><td>Bezan</td><td>BEZ</td><td>Cameroon</td><td>Bantoid Non-Bantu, Tikar</td><td>P. Verdut</td></tr><tr><td>Bezan</td><td>KOL</td><td>Cameroon</td><td>Bantu, A80</td><td>A. Froment</td></tr><tr><td>Central Baka</td><td>CBK</td><td>Cameroon</td><td>Adamawa-Ubangian, Gbanzili-Sere, Ngbaka</td><td>A. Froment, P. Verdut</td></tr><tr><td>Eastern Baka</td><td>EBK</td><td>Cameroon</td><td>Adamawa-Ubangian, Gbanzili-Sere, Ngbaka</td><td>A. Froment</td></tr><tr><td>Southern Baka</td><td>SBK</td><td>Cameroon</td><td>Adamawa-Ubangian, Gbanzili-Sere, Ngbaka</td><td>A. Froment, P. Verdut</td></tr><tr><td>Gabonese Baka</td><td>GBK</td><td>Gabon</td><td>Adamawa-Ubangian, Gbanzili-Sere, Ngbaka</td><td>J-M Hombert, L. Van Der Veen</td></tr><tr><td>Koya</td><td>KOY</td><td>Gabon</td><td>Bantu, B20</td><td>S. Le Bomin</td></tr><tr><td>Central Bongo</td><td>CBG</td><td>Gabon</td><td>Bantu, B30</td><td>P. Verdut, S. Le Bomin</td></tr><tr><td>Eastern Bongo</td><td>EBG</td><td>Gabon</td><td>Bantu, B70</td><td>P. Verdut, S. Le Bomin</td></tr><tr><td>Southern Bongo</td><td>SBG</td><td>Gabon</td><td>Bantu, B30-B50</td><td>P. Verdut, S. Le Bomin</td></tr><tr><td>Aka</td><td>AKA</td><td>CAR</td><td>Bantu, C10</td><td>A. Hewlett</td></tr><tr><td>Biaka</td><td>BIAC</td><td>CAR</td><td>Bantu, C10</td><td>A. Hewlett</td></tr><tr><td>NSU</td><td>NSU</td><td>Uganda</td><td>Sudanic, Mangbetu, Ete</td><td>L. Cavalli-Sforza, B. Hewlett</td></tr><tr><td>MBU</td><td>MBU</td><td>Uganda</td><td>DRC</td><td>L. Cavalli-Sforza, B. Hewlett</td></tr><tr><td>Twa</td><td>TWA</td><td>Uganda</td><td>nd</td><td>G.H. Perry, L. Barreiro</td></tr><tr><td>Tikar</td><td>TIK</td><td>Cameroon</td><td>Bantoid Non-Bantu, Tikar</td><td>P. Verdut</td></tr><tr><td>Nzime</td><td>NZI</td><td>Cameroon</td><td>Bantu, A80</td><td>A. Froment, P. Verdut</td></tr><tr><td>Banganda</td><td>BGD</td><td>Cameroon</td><td>Adamawa-Ubangian, Bangandu, Gbaya</td><td>A. Froment</td></tr><tr><td>Nuumbu</td><td>NGB</td><td>Cameroon</td><td>Bantu, A80</td><td>A. Froment</td></tr><tr><td>Fang</td><td>CFG</td><td>Cameroon</td><td>Bantu, A70</td><td>A. Froment</td></tr><tr><td>Fang</td><td>GFG</td><td>Gabon</td><td>Bantu, A70</td><td>J-M Hombert, L. Van Der Veen</td></tr><tr><td>Akele (Bongamp)</td><td>AKL</td><td>Gabon</td><td>Bantu, B20</td><td>S. Le Bomin</td></tr><tr><td>Leke</td><td>TEK</td><td>Gabon</td><td>Bantu, B70</td><td>J-M Hombert, L. Van Der Veen</td></tr><tr><td>Yolo</td><td>NZE</td><td>Gabon</td><td>Bantu, B50</td><td>J-M Hombert, L. Van Der Veen</td></tr><tr><td>Tawita</td><td>TSG</td><td>Gabon</td><td>Bantu, B30</td><td>J-M Hombert, L. Van Der Veen</td></tr><tr><td>Kongo</td><td>KJO</td><td>Uganda</td><td>Bantu, D30</td><td>P. Verdut, M.F. Milune</td></tr><tr><td>Kiga</td><td>KIG</td><td>Uganda</td><td>nd</td><td>G.H. Perry, L. Barreiro</td></tr></tbody></table>

Figure and table adapted from previous publication in Verdu P. in *Hunter-Gatherers from the Congo Basin*, B. Hewlett Eds 2014.

Color legends for peopling areas can be found in Figure F1.1.a above.
**1.2. Sampling individuals in Cabo Verde to reconstruct the genetic and linguistic admixture histories of the archipelago**

In 2009, at the beginning of my post-doctorate research on theoretical approaches to complex admixture
processes under the supervision of Pr. Noah A. Rosenberg at the University of Michigan, I had the chance
to be associated to initiate a new project about the peopling history of Cabo Verde, which eventually became
one of my long-term research endeavors (see **Chapter 5**). With Pr. Rosenberg, we were initially contacted
by Marlyse Baptista, linguistics' professor at the University of Michigan, who hoped to get insights from
genetics about the peopling history of the Cabo Verde archipelago.

Cabo Verde is a small Atlantic-ocean archipelago 500 km West of Senegal in Western Africa (Figure
F1.2.a), discovered in the 1460's presumably un-inhabited (based on largely consensual historical records)
by Antonio De Noli, an Italian navigator sailing the Atlantic coast of Africa on behalf of the Portuguese
crown (Albuquerque et al. 1991; Carreira 2000). Soon after, Portuguese migrants supported by the
Portuguese Crown settled its main island, Santiago, aiming to benefit from the ideal geo-strategic location
of Cabo Verde in the developing commercial routes between Europe and Africa. Equally rapidly, enslaved
Africans were forcibly removed from continental Africa and deported to the archipelago to participate in
the building of the colony, the first European peopling-colony in Sub-Saharan Africa during this era. By
the end of the 15th century, missionaries and adventurers reported the emergence of a novel language, the
Kriolu language, spoken by Cabo Verdeans and built from Portuguese and a variety of African languages,
de facto making it the first Atlantic Creole<fnref n="3" /> language from the era (Quint 2000; Baptista 2002, 2015; Lang
2009).

During the following 400 years, Cabo Verde served as a major slave-trade platform between Africa,
Europe and the Americas, in particular during the massive expansion of the Trans-Atlantic Slave Trade
induced by the expansion of the plantation economic system in the Caribbean and the Americas, between
the 1630's, and the abolition of the TAST and that of slavery in the mid-19th century (Eltis and Richardson
2015). During this Plantation Economy era, historical records unequivocally show that thousands of
enslaved Africans were forcibly deported to Cabo Verde from all over the Atlantic coast of the continent,
thus bringing together numerous populations and languages that contributed to shape Cabo Verdean society.
In parallel to these forced African migrations, diverse European populations also migrated to Cabo Verde,
attracted by novel economic opportunities (including blooming piracy during the 16th and 17th centuries),
exiled due to religious persecutions in Europe, or prisoners being deported to the archipelago (Albuquerque
et al. 1991). On top of this already complex historical peopling, the nine inhabited islands of the archipelago
were successively settled over the course of more than 350 years since the founding of Santiago colony,
due to the hot and dry Sahelian climate of the archipelago with few easily accessible water resources on
most islands, shifting political and commercial opportunities, including temporary shifts of dominion
among European colonial empires, intense piracy disrupting the fragile settlements, and recurring pandemic
outbreaks and famines (Albuquerque et al. 1991; Patterson 1988; Carreira 2000; Soares 2011)...

<footnote n="3">³ Notably, “Créole” is the French derivative of the Portuguese word “crioulo” (from which Kriolu stems), itself deriving from the latin “creāre” meaning in general “to create”, “to feed”, or “to raise”, and more specifically in our context here “servant fed at home”.</footnote>
<figure><img src="image_8.png" /><figcaption>Figure F1.2.a</figcaption></figure>

Schematic representation of the successive peopling of Cabo Verde islands in the TAST context since the 1460's until the abolition of the TAST and of Slavery in the 19th century, inferred from historical records only.
In this context, Pr. Baptista had been working extensively on the linguistic variation of Cabo Verdean Kriolu and was interested in better understanding the origins of the people having historically given birth to Kriolu speakers today. Genetics would thus possibly, in her views at the time, further inform which African and European languages may have been involved in the construction of Cabo Verdean Kriolu.

Indeed, while substantial historical records attested to the multiple origins of people forcibly deported or voluntarily immigrated to Cabo Verde, they very often lacked precision due to partial and biased geographical, linguistic, and anthropological knowledge of slave-traders, slave-owners, and administrators; or due to voluntary alterations and destructions of records during and after the TAST<fnref n="4" />. In this context, historical and comparative linguistics already had attested that European Portuguese, French, and English languages, and African Mandingo, Wolof, Temne, and Kimbundu languages, in particular, contributed to the lexical and syntactic diversity of Cabo Verdean Kriolu today (Quint 2000; Baptista 2002; Lang 2009). However, when, during the peopling history of Cabo Verde, did these different European and African languages contribute to Kriolu remained often unclear. Furthermore, whether these linguistic borrowings were accompanied by individual migrations and long-term residency (forced or voluntary) in the archipelago, or not, also remained largely unelucidated. Finally, it is important to stress that the origins of numerous linguistic traits, such as lexical roots or morphological and grammatical constructs of this contact language, remain unknown to linguists.

Therefore, any information allowing to determine which enslaved-African communities remained only temporarily in Cabo Verde before being re-deported elsewhere, and which remained for longer periods of time or even indefinitely, would be of major interest to historians and linguists, as well as every anthropologist interested in the TAST era (Albuquerque et al. 1991; Carreira 2000; Quint 2000; Baptista 2015). Similarly, which European populations remained in Cabo Verde or only transited there is also largely unknown, although it would massively benefit to our understanding of the socio-cultural and biological construction of this new population, the first to be born from the TAST (Soares 2011). Most importantly, note that these questions concern in fact all enslaved-African descendant populations on either side of the Atlantic, and studying Cabo Verde in this context would provide major information about the influence of the TAST on cultural and biological diversity of admixed populations since the very beginning of this era in the 15th century, before Columbus.

As a result, during the construction of the project with Pr. Baptista, we decided that we would try to answer the following questions in a pluri-disciplinary framework: which European and African populations contributed to the linguistic and genetic diversities observed today throughout the Cabo Verdean archipelago, and when did they do so?

In this context, the fundamental question of this chapter, “*who should I sample?*”, had, in all appearances, a much more trivial answer for this project than for the previous Central African context: we should sample genetic data from “Cabo Verdean individuals” that “speak Kriolu”. I thus had, *a priori*, no major categorization challenges to design and prepare field-work for this project… Nevertheless, against my somewhat candid optimism, two major partly-overlapping categorization and interdisciplinarity issues rapidly arose about: i) comparing our future genetics results with the rest of the human population genetics literature about other enslaved-African descendant admixed populations, and therefore determining in part who should I sample in Cabo Verde; and ii) recruiting participants on the field for the purpose of joint anthropological, genetic, and linguistic sampling.

<footnote n="4">⁴ These historical knowledge issues are similar for most if not all populations descending from enslaved-Africans forcibly removed from the continent during the TAST.</footnote>
1.2.a. Participants' inclusion in population genetics studies related to the TAST: who did they sample?

Since the beginning, in the early 20th century, of the mathematical formalization of Mendel's genetic inheritance laws at the root of the emergence of population genetics as a discipline, extensive research has been conducted to characterize and elucidate the genetic diversity and evolutionary histories of TAST enslaved-African descendant populations in the Americas. Indeed, since the first statistical formalization of expected genetic diversity patterns in the context of admixture among previously isolated human populations by Bernstein (1931), the genetic diversity of genetically admixed enslaved-African descendant communities in the USA, known as Afro-American or African American communities today (see below), has been continuously and massively investigated to understand the major influence of admixture processes on human recent biological evolution<fnref n="5" />.

Jumping forward in time to the beginning of the 21st century, with the major advances of DNA sequencing and genotyping technologies<fnref n="6" />, human population genetics investigation of USA African American communities continued to attract major attention in human genetics. Furthermore, they have been enriched by similar investigations conducted on other TAST-related genetically admixed populations in the Caribbean, North, Central, and South America, as well as Africa and Europe albeit massively less frequently there (e.g. Baharian et al. 2016; Mathias et al. 2016; Ongaro et al. 2019; Micheletti et al. 2020; Fortes-Lima and Verdu 2021; Chapter 5).

Despite massive amounts of datasets and results available today about enslaved-African descendant populations mainly in the Americas, as we illustrated in a recent review article with my former post-doctorate student Cesar Fortes-Lima, all these investigations did not necessarily sample individuals based on comparable categorization criteria (Fortes-Lima and Verdu 2021). While medical population genetics studies included participants based on explicit medical criteria, forensic, biological anthropology, and historical demography inference studies relied instead on data collected potentially with very different criteria of inclusion of participants. It is therefore often difficult to compare the results obtained on genetic admixture patterns and the inferred admixture histories across studies, as well as with the expected results for our own new Cabo Verde project.

For instance, numerous human population genetics studies use a criterion of inclusion of participants based on self-reported or self-identified African descendency, whether for medical genetics or other forensic or anthropological genetics questions of interest. However, it is often unclear in publications if individuals self-reported African origins based on known genealogical information or based on alleged origins stemming from community identity constructions and representations without explicit genealogical knowledge. In turn, this may result in “populations” grouping individuals with ancient admixture histories due to TAST migrations and slavery, together with individuals whose African ancestors are genealogically known in the last couple generations and thus not related to the historical TAST. This is the case in the USA for individuals not descending from enslaved-Africans deported to the USA during the TAST, but descending, for instance, from African migrants after the 1960’s when such recent migrations have brought more than 10 times more Africans to the USA in 30 years than the slave-trade did during the 17th and 18th century altogether. These individuals of recent continental African origins might be identified or might self-

<footnote n="5">⁵ To a point where several thousands of articles have been published on the subject, rendering, I believe (but perhaps I am wrong of course), the task of exhaustive academic bibliographic census-work improbable.</footnote>

<footnote n="6">⁶ Born, in particular, from the 1980’s invention of Polymerase Chain Reaction and from the completion of the first assemblage of the human genome in the 2000’s.</footnote>
identify as “African American” although they do not share a common demographic, migratory, and social-
segregation history with individuals also self-identifying in the same category but descending from
enslaved-Africans during the TAST.

This is often the case in studies targeting individuals from specific communities recognized in national demographic census protocols and historically linked to enslaved-African forced migrations. The historical and social construction and recognition of these national, or census-recognized, communities is complex and diverse across countries and across time within-countries, as we illustrated in Fortes-Lima and Verdu (2021) on page R83:

> "Among 23 continental American countries, the last census questionnaires record up to four questions about self-reported indigenous, ethnic, linguistic, or racial identity (only four countries use explicitly the word 'race' or its literal translation). Apart from French Guyana where no self-categorizations are recorded, the census questionnaires' categories are based on self-reported imprecise skin-colour criteria, genealogical ancestry and cultural affinities, linguistic practices, or mixtures of these criteria. 14 countries out of 22 propose a category based on self-reported African descent, seven of which propose the word 'Black' (or its translation) as an alternative naming for the same category, and three of 14 which distinguish the 'Afro-descendant' category from the 'Black' category. Additionally, four countries among the 22 consider only a 'Black' category without explicit reference to 'Africa'. Furthermore, eight countries out of the 22 propose a separate category for 'explicitly' admixed individuals of African descent ('Mulata/o', 'Creole' or 'Mixed'), two countries have a separate 'Brown' category, and three countries gather some or all these labels under the broader 'Afro-descendant' category. In this complex categorization patchwork, population geneticists showed that self-perception of ancestry and self-reported identities overlapping national census categories are often at odds with individuals' genomic ancestries and admixture patterns. This is due to the fact that self-constructed cultural identities emerge from multiple familial and societal experiences superimposing self-perceived phenotypic features. This process is thus much more complex than simple mendelian inheritance which only reflects a very specific (genetic) part of an individual's history, furthermore only very partially translated into complex phenotypic features such as skin-colour."

For an additional example, the USA's NIH defined<fnref n="7" /> “racial” categories (namely in 2022: “American Indian” or “Alaska Native”, “Asian”, “Black” or “African American”, “Native Hawaiian” or “Other Pacific Islander”, and “White”) juxtaposed with two mutually-exclusive “ethnic not-racial” categories ("Hispanic or Latino" and "Not Hispanic or Latino"). Conversely, USA census categories include often many more “boxes” (e.g. for the 2010 US Census: 15 explicit racial category boxes and 4 explicit categories for the “ethnic not racial Hispanic or Latino” categories), which change with every national census every ten years, sometimes massively from one census to the next. Interestingly, both NIH categories and Census Bureau categories are legally required to follow the same definitions and criteria of self-identification since 1997⁸,

<footnote n="7">⁷ See the official NIH 2015 grant web-page which provides very interesting definitions of these categories:
https://grants.nih.gov/grants/guide/notice-files/not-od-15-089.html</footnote>

<footnote n="8">⁸ Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity.
https://obamawhitehouse.archives.gov/omb/fedreg_1997standards</footnote>
but the number of possible categories and sub-categories available for individuals’ self-identification
nevertheless may differ albeit based on the same legal requirements. In this context, an individual identified
or self-identifying as African American at some point in time, might not necessarily self-identify in the
same category after the change of its official definition.

The problems here suggested do not stem from the diversity of inclusion criteria and recruitment and
sampling methods themselves. Ultimately, we expect different enslaved-African descendant admixed
populations to have experienced different histories of admixture in different, changing, socio-historical
contexts. In fact, this is what anthropological geneticists are generally interested in and what I am personally
very interested in for my research. The problems instead stem from the fact that inclusion criteria, sampling
methods, and questions asked by recruiters to potential participants are not often explicitly exposed and
discussed in the human population genetics literature (including some of the work I participated in, I
reckon). Similarly, when categorizing individuals into different communities or populations with a given
label, categorization processes are not always clarified by the researchers.

As a first consequence in anthropological genetics, when using previously published datasets or
discussing novel results in the light of previous work, it is often very difficult to know “who are we talking
about?”, and to derive expectations and reconcile results about observed admixture patterns or about the
admixture histories inferred from these patterns. As a second major and critical consequence, we may work
to test hypotheses on a given dataset that we would never have thought reasonable to test in a first place, if
we have had access to the detailed information about “who did they sample?”, instead of drawing wrong
assumptions of a population’s evolutionary history based on their sole “label” and sampling location
provided by the original authors.

For a last, frequent, example with major theoretical and applied issues, certain studies considered
individuals recruited in the general population for other purposes, such as medical genetics. After the initial
population genetics description of admixture patterns in these samples, authors decided to group sub-
samples of individuals based on arbitrary threshold levels of genetic admixture from a given source
population rather than other self-reported information prior to genetic testing. They therefore use a genetics
criterion for selecting individuals that they will then consider as a “population”, whose genetic evolutionary
history they will, then, infer using the very same genetic data they used to categorize them in a first place.

Beyond the risks of dialectical circularity and those, complex, of double use of the data for statistical
inferences that such approaches may critically face (Devezer et al. 2021), this raises the question of how to
define “a genetically admixed population” in a population genetics perspective. Indeed, I find surprisingly
unclear, in both the human and non-human population genetics literatures, whether the admixed or hybrid
population of interest is composed only of genetically admixed individuals<fnref n="9" />, or whether the admixed
population is a population where admixture is possible but not necessary (see **Chapter 3**). In other words,
should we consider as the admixed population of interest the group of individuals possibly reproducing
with one another (the classical definition of a population in population genetics) before knowing whether
they are actually genetically admixed or not? Or should we consider instead as the admixed population the
sub-sample of individuals within this group that are indeed genetically admixed and only them? In the
former, we expect proportions of admixture from each source population to be between 0 and 1, included,
while in the latter, such admixture proportions will be bounded by the thresholds chosen by the authors. In
turn, allelic composition and frequencies, admixture fraction distributions, theoretically-derived
expectations (see **Chapter 3**), appropriate and adapted statistical methods (see **Chapter 4**), and population

<footnote n="9">⁹ Individuals who have at least two-gene-pools from different, previously isolated, populations of origin contributing to their genome</footnote>
genetics inferences' results will differ between the two definitions although the same larger group of
individuals was initially sampled and investigated.

1.2.b. *Sampling design for population genetics purposes in Cabo Verde: who did I sample?*

In this context, relatively extensive historical information about Cabo Verde peopling showed that these
population-genetics and anthropological categorization issues could be adequately circumvented (but never
fully overcome nonetheless) by a sampling scheme anchored in classical anthropological family
questionnaires about mobility behaviors.

First, as opposed to continental Africa, the Caribbean, and the Americas, it is highly likely that Cabo Verde was not permanently settled at the time of its colonization by the Portuguese crown. Indeed, despite few recent archeological traces of possible temporary and limited peopling likely due to shipwreck survivors, no historical records attest to pre-established colonies in the archipelago at the time of its exploration by De Noli; although this question nevertheless remains investigated by historians and archaeologists (Albuquerque et al. 1991).

Second, after a long and complex history of socio-economic and socio-marital segregation between
enslaved and non-enslaved communities during the colonial era, as well as between newcomers and pre-
established communities, Cabo Verde does not recognize, among its national citizens, communities based
on self-identified European or African or other descent<fnref n="10" />, since its independence in 1975. It is clear that
Cabo Verdean society allows the formation of communities and civil associations among individuals self-
identifying to a given descent, but democratic representation and governmental redistribution, segregation,
and discriminations of any form for individuals or groups of individuals based on these criteria are
constitutionally prohibited<fnref n="11" />.

Finally, historical records clearly show that admixture events between Portuguese settlers and free or
enslaved Africans occurred since the very beginning of Santiago settlement in the 1470's (Albuquerque et
al. 1991; Carreira 2000). Indeed, there are numerous commercial, marital and notarial records mentioning
the genetically admixed origin of individuals in Cabo Verde (using Portuguese words such as “criolo”,
“mulato”, or “mestiço” among others), as well as enslaved Africans freed by their owners willing to legally
transmit to them their financial and real estate possessions. From my interpretation of historical records and
investigations, one could imagine that the Cabo Verdean founding peopling had been particularly difficult
due to limited access to water resources in the dry Sahelian climate of the archipelago and distance to the
European homeland. Therefore, the Portuguese crown rapidly and repeatedly released laws and very liberal
authorizations of commerce to incite Portuguese settlers to colonize Santiago for its geo-strategic position
in the developing commercial routes between Europe and Africa. The initial settlers were thus, apparently
in historical investigations, largely young Portuguese males seeking fortune or even common law prisoners
deported to the archipelago, and very few Portuguese females seemed to have voluntarily migrated
permanently during the first 50 years of the colony. In this context, freeing enslaved African females or
genetically admixed offspring provided opportunities for single males to legally transmit their inheritance
to their descendance. Conversely during the very beginning of the colony at least, males married to
Portuguese females most often seemed to have sought to ultimately return to Portugal after having socially
and economically succeeded in the colony, whether their wives actually came to Cabo Verde or remained

<footnote n="10">¹⁰ file:///C:/Users/Paul/AppData/Local/Temp/relatorio_metodologico_rgph-2010.pdf - p76-79.</footnote>

<footnote n="11">¹¹ https://www.governo.cv/governo/constituicao/ - Article 1.2</footnote>
in Portugal. Finally, at the other end of colonial history, since the end of the TAST and of Slavery in the
19th century and up until today, numerous continental European or African populations migrated to Cabo
Verde whether individually seeking economic opportunities or via work-force migrations organized across
countries throughout the former Lusophone empire or beyond; an economic migratory process still active
today.

It was therefore clear to me that genetic admixture *might have been at least possible* between
populations of continental European and African origins voluntarily migrated or forcibly deported to the
archipelago throughout the entire history of Cabo Verde, since its very founding settlement in the second
half of the 15th century up until today. I was thus extremely interested to unravel when and how did
admixture occurred in Cabo Verde giving birth to the observed genetic diversity patterns today, and to
possibly link the inferred events with the variable socio-historical and demographic peopling contexts for
each island in the archipelago. The numerous peopling questions I sought to answer at the time, after the
deciphering of the classical questions of continental European and African origins of the Cabo Verde gene-
pool of initial interest to Pr. Baptista at the root of the project, were:

<figure><img src="image_9.png" /><figcaption>**Figure F1.2.b**
Schematic representation of founding and migration movements of populations among Cabo Verde islands between founding in the 1460’s and the end of the 19th century inferred from historical records</figcaption></figure>
i) What are the genetic diversity and genetic admixture patterns within Cabo Verde at reduced geographical scale? Are their islands or group of islands relatively more or less reproductively isolated than others due to serial founding events and isolation triggered by political changes over the 400 years of the peopling of the archipelago? Or, instead, are genetic diversity and admixture patterns evenly distributed throughout the archipelago as suggested by numerous population movements across islands throughout history? (Figure F1.2.b)

ii) When and where did the admixture events that built genetic diversity and admixture patterns today occur? Were there more admixture events identifiable during the massive population movements triggered by the expansion of Plantation Economy between the mid-17th and the early 19th century during the TAST? Did the abolition of the TAST reduce the opportunity for admixture events to shape extant genetic patterns? Did the abolition of slavery and the major sociological changes that followed increased the opportunity for admixture events to occur? Did recent 20th century work-related migrations result in novel, recent, admixture events? (Figure F1.2.a)

Altogether, I was thus interested in Cabo Verdean admixture history in general, and should thus recruit participants without distinction of self-reported descent, and whether admixture in their genealogical histories, if any at all, was recent or not, and I was not interested in foreigners having very recently migrated to Cabo Verde. I therefore decided to consider a single recruitment criterion: people should be Cabo Verdean citizens (and healthy adults only, as usual). I would then conduct detailed familial anthropology interviews with each volunteer to collect detailed information about her/his mobility history and the same information self-reported concerning her/his family. I thus collected geographical locations, main residence locations (at the time of the interview), birth-places, and the same information, when known, concerning all biological parents and grand-parents. I also collected information about the volunteer's history of mobility and the context and duration of these mobility events (for professional, familial, leisure, military, religious reasons for some frequent instances), first within Cabo Verde across islands, second outside of Cabo Verde. From previous experiences in Central Africa, I expected these interviews to require between 1 and 2 hours with each participant (depending on how much they would be talkative about their life history), compared to the 2 minutes usually required for providing a saliva sample for DNA... However, this extensive time would allow me to better intuit the reality of sampled individual's mobility behavior throughout Cabo Verde nowadays, and, most importantly, potentially qualitatively and quantitatively measure it systematically for all the sampled individuals. In turn, this would, a posteriori, allow me to formally compare genetics patterns in particular with interview, residence, and birth place locations and those self-reported for biological parents and grand-parents.

Finally, I had also prepared a classical interview section about individuals' mobility in marriages (residence prior and after marriage, prior and after the birth of each offspring) to be able to work on extant philopatry practices. Although I conducted systematically these parts of the interview for everyone interviewed during the project, it soon became clear that this part of my field-work design was profoundly un-adapted to Cabo Verdean society. I indeed rapidly found out that official marriages were in fact rare in Cabo Verdeans born after the 1950's (the vast majority of my participants), which I could have stumbled upon if I had conducted my bibliographical research more thoroughly, as I later discovered at the end of the extended three volumes of the General History of Cabo Verde (Albuquerque et al. 1991)... It also became clear on the field that social organization with respect to marital not-married life was complex and
that high levels of intimacy with participants would be required to access this information as politely and respectfully as required. Indeed, ethnographic semi-directed interviews on these matters require numerous questions to be asked, each of a highly personal nature, and thus much more intrusive compared to official publicly recorded marriage and divorce statements. Facing this setback, I decided that a novel sampling scheme would be required to investigate in detail philopatry and marital choices, together with a novel protocol and accompanying ethical authorizations and informed consents procedures, and decided to accept that these questions of major interest were out of direct reach of this project, and would need to be explored separately (see Chapter 5).

Importantly, as detailed above (section 1.2.a), the unique inclusion criteria on Cabo Verdean citizenship here chosen, put me at odds with numerous previous studies investigating the admixture history of enslaved-African descendants in the Caribbean and the Americas, as samples were often gathered with different criteria in different historical and social contexts, and for sometimes different population genetics aims. However, I would at least have different criteria of categorization explicitly and thoroughly informed for each participant that I could use at will to analyze and re-analyze the patterns of Cabo Verdean genetic diversity. I would thus be able to provide to the population genetics community extensive and detailed categorization information and let them decide whether it could be of any use to them, as well as have the material for informed discussion on the comparability of previous results with my own, in turn highlighting the complex processes and caveats described above.
1.2.c. Genesis of a multidisciplinary sampling-design for joint genetic-linguistics
investigations.

Numerous human population geneticists have been working with computational linguists (e.g. Cavalli-Sforza et al. 1992; Cavalli-Sforza 1997, 2001; Creanza et al. 2015), and compared the history of populations' genetic divergences and migrations (reconstructed from observed genetic diversity patterns), with the history of languages' divergences and migrations (reconstructed from observed linguistic variation patterns). Indeed, computational and historical linguistics are interested in part in the origins of linguistic diversity and the history of linguistic changes over time that produced observed languages; questions that strongly echo fundamental questions of human evolutionary biologists since Darwin himself in The Descent of Man (1871), and therefore triggered the mutual interest for intersecting results obtained separately.

However, numerous linguistic disciplines are interested in “Languages” as symbolic and performative
meta-constructions, while only certain socio-linguistic, cognitive, and natural-languages disciplines are
interested in the diversity of individual realizations of said languages in different contexts and at different
times. As a result, computational and historical linguists, *lato sensu*, reconstruct the phylo-linguistic
relationships among extant (or extinct) languages most often based on lexical or syntactic diversities across
languages; languages which are considered as such meta-constructions and which do not explicitly and
systematically consider inter-individual or within-individual linguistic variation. Here lies one of the major
paradigmatic discrepancies between genetics and linguistics in pluri-disciplinary approaches trying to
contrast human history of origins and migrations from either approach: the former stems from observed
individual genetic variation while the latter stems from variation across languages, languages here being
already complex objects built by linguistic expertise rather than inter-individual linguistic variation (a.k.a.
“no one speaks the dictionary, but every one speak parts of versions of it”).

Therefore, as I also witnessed personally in the field in Central Africa, linguists gather extensive data
from written material and/or specific linguistic interviews conducted with a minimal number of focal
informants chosen by the linguists based on specific criteria. For instance, after random discussions and a
series of preliminary interviews, linguists may identify individuals as particularly expert in certain aspects
of the language they speak, and privilege detailed interviews with them in particular. In all cases, they very
rarely perform systematic interviews with a vast number of individuals, recruited a priori based on other,
non-linguistic, criteria. They may, for a schematic instance, conduct word-list interviews with several
individuals of roughly the same age and born in a given location, but they rarely conduct the exact same
interview with more than 20 such individuals. They then transcribe and synthesize the information obtained
using extensive linguistic expertise and methods in order to build a single word-list for a given “language”.
As a result, linguistic word-lists, often used by computational and comparative linguists to reconstruct the
historical phylo-linguistic relationships among languages, are unique for a given language (e.g. Gray and
Jordan 2000; Gray and Atkinson 2003; Pagel 2009). Data about linguistic variation across individuals, even
not systematically recorded, form parts of the linguist’s corpus, but are rarely provided to the community
nor the direct object of their investigations. These concepts and this fundamental difference in classical
historical inferences from genetic and linguistic data are schematized in **Figure F1.2.c**, below. Note,
importantly, that I matured this schematic synthesis in collaboration with Valentin Thouzeau during his
PhD about joint genetic and linguistic inferences, that I co-supervised (with Dr. Frédéric Austerlitz)
between 2015 and 2018, thus several years after the beginning of our Cabo Verde project (see **Chapter 5**).
<figure><img src="image_10.png" /><figcaption>Figure F1.2.c</figcaption></figure>

Schematic representation of paradigmatic and methodological differences between population genetics and computational and historical linguistics inferences aiming at reconstructing populations and languages demographic histories of origins and migrations or borrowings.
In this context, Pr. Baptista initially came to ask us about our population genetics expertise for inferring
which populations of continental African and European origins contributed to the genetic landscape of Cabo
Verde today. She would then use the results of these investigations to discuss, independently, her linguistics
objects of study within the methodological framework of her linguistic discipline paradigms. Therefore, the
project was not ignited from a multidisciplinary perspective, let alone from a fully integrated
interdisciplinary approach. Nevertheless, Pr. Baptista taught Pr Rosenberg and me about the linguistic
diversity of Cabo Verdean Kriolu, and we discovered, as population geneticists, that lexical, phonetic, and
syntactic diversity, “substantially”<fnref n="12" /> differentiated the manners of performing the Cabo Verdean Kriolu
language across certain islands of the archipelago (Quint 2000; Baptista 2002, 2015; Lang 2009). Based on
my previous experiences in Central Africa (see **section 1.1** above and **Chapter 2**), I became very interested
in deploying classical genetic-linguistic comparisons as described above, but at the within language-scale,
across Cabo Verdean islands, rather than across languages as most often conducted in previous studies.
Following previous joint genetics-linguistics work, I thus hoped to be able not only to reconstruct the
genetic peopling history of Cabo Verde, but also to be able to compare it with the linguistic history of this
archipelago reconstructed separately with computational linguistics methods. This endeavor had never been
conducted, to my knowledge at the time, at the within-language scale and considering a contact language
such as Kriolu (see **Chapter 5**).

To do so, we needed to sample individuals' DNA across islands as explained above (**section 1.2.b**),
and obtain, separately, linguistic data, such as word-lists, already compiled for the same islands. However,
such linguistic information was not readily available nor homogeneously and systematically reported by
linguists for the different Cabo Verdean Kriolu variants they had identified based on numerous other criteria
than "just" lexical. Well, we could do it ourselves, couldn't we? And even more! Indeed, I then asked Pr.
Baptista whether we could consider individual linguistic sampling: we would collect familial anthropology
and saliva samples as planned above, and for the same participants, all of them, also collect linguistic data.
This would allow us to conduct at least descriptive quantitative analyses of the various manners of speaking
Kriolu across individuals at the same scale and among the same individuals for which we would investigate
familial anthropology and genetic data.

At that point, the choice of which linguistic data and which protocol became critical. Indeed, I had in
mind conducting “simple” word-lists, such as the famous Swadesh list (Swadesh 1971), for every
participant, similar to what I had participated to in Central Africa but for all participants rather than for
some specific individuals selected by linguistic expertise. Among the numerous classical and well-known
methodological difficulties of the Swadesh protocol (see **Chapter 5**), one was outstanding for the purpose
of capturing within language variation in a Kriolu language. Swadesh word-list was initially built mainly
to obtain lexical roots and phonetical transcriptions of highly stable words within languages (mainly of
European origins); such as words describing body parts (hands, fingers, nose...), numerals (1, 2, 3, 10,
100...), or basic action verbs (to see, to eat, to drink...). Indeed, Swadesh designed a list of lexical items
recognized by linguists as being very likely shared by individuals within a language, and as little polysemic
and/or ambiguous as possible. This was not a priori an ideal list for recording possible differences in
manners of speaking Kriolu, as we did not expect major diversity in ways to say, for instance, the number
“4” across individuals and islands... We were wrong here, as we indirectly found out during our first field-
works and as Valentin Thouzeau brilliantly further demonstrated during his PhD (see **Chapter 5**)...
Nevertheless, we opted at first for another strategy.

<footnote n="12">¹² Qualitatively from a linguistic perspective and expertise, not quantitatively measured.</footnote>
Pr. Baptista suggested instead to shunt another classical semi-directed interview protocol sometimes used in cognitive linguistic studies: *The Pear Story* movie (Chafe 1980). We decided to show volunteer participants this 6-minutes speech-less (but not silent) movie, *The Pear Story*, which roughly showed the story of the adventures of a kid on a bicycle stealing (or at least surreptitiously borrowing...) a basket of pears from a farmer<fnref n="13" />. We would then only ask individuals to narrate the story they just saw, in their own way of speaking Kriolu, the Kriolu they would “speak every day at home”. We would then record their entire speech without interruption and fully transcribe it for linguistic diversity analyses across participants. Note that we were interested in whichever semi-spontaneous speech hereby pronounced by participants, whether specifically narrating the movie itself or talking about something completely different (which did happen sometimes in real life, albeit relatively rarely...). Indeed, based on previous results from socio-linguistics and N-gram analyses about idiolectal diversity of manners of realizing a language in a given context (Labov 1972), we thought that any sufficient amount of speech would allow linguists to identify specific individual signatures of their manners of speaking Kriolu in the specific context of our interviews (that of researchers showing them a movie and recording what they had to say and how they would say it, with us as their only audience), whichever the subject of their speech.

Therefore, we set out to collect a very specific type of linguistic variation among individual realizations within a language, often referred to as “utterances” in linguistics (Croft 1991, p 107): “*a particular instance of actually-occurring language as it is pronounced, grammatically structured, and semantically interpreted in its context*”.

Obviously, while I could show the movie and record the speech, I could neither transcribe the speech, nor conduct linguistic analyses downstream, as this required linguistic expertise that I did not have as a biological anthropologist. Therefore, we needed to conduct the fieldwork together, Pr. Baptista and myself, at least, for the entire project and for collecting all the anthropological, genetic, and linguistic data for our entire corpus and for all Cabo Verdean islands. Despite the apparently difficult organizational and logistics challenges that this choice posed, it is important to say that, in fact, it increased our enthusiasm for the project: it would allow us to discover, in deep and first-hand, the practices and methods deployed in both disciplines on the field and beyond, and therefore better understand our respective challenges, intuitions, technics, and most importantly, what could be and what could not be analyzed and tested by either discipline based on the data we would collect jointly.

Therefore, we set out to collect such anthropological, genetics, and linguistic data from volunteer participants of Cabo Verdean nationality (section 1.2.b), and that self-reported speaking Kriolu. Indeed, in the context described above, we were interested in idiolectal variation, variation of manners of speaking Kriolu, in the given context of our project setting, whether individuals were deemed “Kriolu-specialists” or “barely speaking Kriolu” by expert linguists. While Pr. Baptista was at first not overly enthusiastic about this choice at odds with linguistic traditional approaches, she also recognized that it could not be detrimental to her work, as it would not prevent her from identifying key informants for future work among our participants. On the other hand, it was crucial for the genetics part of the project, in order to avoid spurious unexpected sampling biases, as illustrated above in the previous sections 1.2.a and b, that might have resulted in extended investigations of the genetic admixture history of Kriolu specialists only, rather than that of Cabo Verdeans more generally.

Nevertheless, from a socio-linguistics perspective, this choice would in turn require us to record at least some basic additional information about the participants’ birth-places, residences, and mobility

<footnote n="13">¹³ This movie was initially produced and used by linguists in a semi-directed protocol to inform language and narration construction by participants after they have seen it in a controlled environment.</footnote>
behaviors, which we already were interested in (see **section 1.2.b**), self-reported age and gender, main
languages spoken or practiced regularly and their contexts of usage, as well as that of the parents and grand-
parents, education duration (number of cumulated years of schooling, of higher education, and of temporary
professional training), as well as employment situation (Labov 1972)... Indeed, all these factors were
known by linguists to possibly influence manners of speaking a language. Note that, despite the additional
interview time needed to collect this information in a controlled way (another 1 hour per individual), our
goal was never to conduct a full-blown socio-linguistic investigation here. Instead, we simply aimed to
inform these aspects which would help us to contrast observed idiolectal variation patterns with possible
known covariables, similarly to how life-history mobility questionnaires were acquired mainly to contrast
possible genetic diversity patterns.

Ultimately, we conducted these interviews for 261 Cabo Verdean participants to our study (Chapter 5), originating from all nine islands of Cabo Verde, and interviewed in more than 60 different locations across 7 islands (Figure F1.2.d). Time constraints for Pr. Baptista due to other academic and personal commitments strongly restrained the duration of each one of our fieldworks to roughly 20 days per year. The first two fieldwork we conducted together, in 2010 and 2011, empirically showed that we would need backup, in particular for advertising our project and goals, and reaching out to possible participants interested in hearing us out and, hopefully, participating. Therefore, I first started to rigorously optimize field-work organization and logistics as much as possible which is, in fact, contradictory with the very nature of field work where, usually, a lot of room has to be made for uncertainty and the unexpected. Second, we had the chance to meet and hire for four fieldworks between 2014 and 2018, Sergio Costa, one of Pr. Baptista’s former student long before our project and a permanent translator (Portuguese – Kriolu – Portuguese) at the National Parliament of Cabo Verde. Sergio Costa was not only a skilled linguist, but also deeply passionate about Kriolu language and soon legitimately became a key scientific collaborator to our project, for successfully and efficiently conducting field work and in particular recruiting participants by word of mouth, for systematically and rigorously transcribing Kriolu in ALUPEC (the morpho-phonetic orthographic norm for Cabo Verdean Kriolu), as well as for discussing sampling strategies, downstream analyses and results, and participating to scientific publications as a co-author.

<figure><img src="image_11.png" /><figcaption>Figure F1.2.d</figcaption></figure>

Sampling and interview locations for Cabo Verde participants to our project between 2010 and 2018, as well as participants’ self-reported birth-places in Cabo Verde. GPS coordinates for each visited or reported location were recorded on site by myself, and complemented by geographical maps and Google Earth™ for a (minority) number of locations I did not have the chance to visit, in particular in the islands of Sal and Maio which we did not visit during our six fieldworks.
**1.3. Concluding remarks to sections 1.1 and 1.2.**

For concluding remarks concerning the above **sections 1.1 and 1.2**, note that the sampling schemes and categorization procedures we chose for our Central African and Cabo Verdean projects are never “ideal” nor should they be considered as “definitive references” for these projects or even beyond them; they are at the very best *ad hoc*, and I intended to demonstrate here that such issues should always be intended in an *ad hoc* manner for the questions of interest to a given project. Indeed, the main point I intended to make here, besides the scientific introduction to our research work and results synthesized in the following chapters of this work, is to show how these sampling and categorization questions are at the root of every downstream human population genetics and anthropological genetics research. In this context, the overall lack of detailed information about inclusion criteria, sampling methodologies, and categorization choices in the literature (including in some of the works I was implicated in, I reckon), from my perspective, strongly diminishes the range of other questions and discussions that the community may further ask about these datasets, when made available, and about contrasting novel results with previous publications. I intended to show here that this has not only consequences on general discussions and perspectives, but most importantly may affect the applicability of statistical testing and inference methods, similarly to whether a dataset fits the prerequisite or not of a parametric statistical test, and therefore whether it can be used or not. Indeed, equations can very often be mechanically applied to a dataset, they are just pluses and minuses after all. However, can the results obtained be interpreted as theoretically expected and intended for these equations? This may very well not be the case.

**1.3.a. A practical toolbox for anthropological categorization issues in human population genetics research.**

For a practical toolbox for human population geneticists about anthropological categorization issues, I suggest the following protocol aimed at informing “who are we talking about?” when collecting anew DNA samples in human groups or when using previously published genetic data collected by others, specifically for questions of evolutionary anthropology and population genetics analyses and inferences. These recommendations stem from my personal experiences described in part in the previous sections, backed up by theoretical and empirical classical cultural anthropology approaches about categorization issues.

1. *Population names and labels:* investigate the etymology of the name used or self-reported to designate a group of individuals and corresponding DNA samples or sequences. Is it self-reported or used by outsiders of the group? Is it an endogenous ethno-name or another type of classifier? If explicitly meaningful endogenously, what type of lexical category does this name refer to? Since when is it used to designate this group of individuals? Did the designated group of individuals change over time, incorporating or excluding individuals based on which criteria?

2. *Criteria for categorization in “self” and “others”:* what are the criteria by which individuals self-identify to a given group, different from another group geographically close? Of which nature are these criteria (e.g. linguistic, geographical, theological/religious, genealogical, ways-of-life, socio-economic, etc.)? Are these self-identification criteria shared uniformly by everyone in the group as well as recognized by outsiders? Do outsiders use other criteria for designating the target group than the criteria used endogenously? If yes, do they intersect or are they completely separated?
3. **Relationships between “self” and “other”, in particular regarding possible reproductive events:** what are the marital relationships and rules between geographically-close groups? Are there social rules or recommendations for mate choices within and across groups (e.g. marital segregation or favored marriages)? Are they prevalent and normative, and what are the rules for dealing with the transgressions of these norms? This point is in fact crucial for population geneticists: it is not the social rule and endogenous representation *per se* that may influence reproduction patterns, and thus genetic diversity patterns, but their prevalence in the society and over time (see **Chapter 2 and 5**). What is the mobility of spouses in marriages and divorces (philopatry)? What is the mobility of children in their parents’ marriages and divorces? Since when these rules apply?

4. **Systematic information related to individual samples:** do we have actual information about individuals’ language, birth-place<fnref n="14" />, residence location, as well as interview or sampling location? This information was self-reported by individuals or inferred by a third party? If the latter, who did it, and based on which criteria? If the former, how questions were asked to participants?

Those are, to my views, the fundamental questions that human population geneticists and
anthropological geneticists should at least consider before using previously published data, as well as when
collecting novel samples. It is clear that answering those questions can require a life-time of detailed
ethnological or familial anthropology work. If this work has been conducted before, it is for the best for
population geneticists, but it is likely not going to be the case in most cases for the specific group of DNA
samples under investigation. Nevertheless, trying to identify which information is available and which is
not will allow the researcher to reframe research questions to be more adequate to the investigated samples,
and also tune statistical inference methods used, as well as reframe discussion points. Ultimately, it shall
help evaluating the cost/benefit ratio of conducting tedious dataset merging and population genetics
analyses. Finally, for human population geneticists conducting DNA sampling on the field, it is crucial to
try to systematically record this information for each individual, which requires to design *ad hoc*
questionnaires. Note that, such questionnaires of an ethnological nature will always benefit (I am willing
to say “require”) from inputs from ethnologists and cultural anthropologists. Indeed, these disciplines have
extensive relevant expertise in how to ask a question, which is often not as trivial as biologists may sincerely
think. Finally, making this information available to the community is crucial as I advocated in the previous
sections. It nevertheless faces major ethical and deontological questions further discussed in the following
**section 1.4**.

1.3.b. *Miscellaneous conclusion.*

In my personal experience, cultural anthropologists are very well familiarized and often conscious about
anthropological categorization issues, as these often constrain and explain the cultural diversity they are
interested in in a first place. In mirror, I often find human population geneticists and human geneticists
surprisingly candid about the importance of these categorization issues on their own paradigms,
methodological tools and approaches, as well as results' interpretations. Intuitively to my views, this may

<footnote n="14">¹⁴ Note that population geneticists sometimes rebut birth-places information with comments like: “people are now always born in *the nearby hospital*. Therefore, birth-places do not mean much in terms of genetics.” First, I think instead that it means exactly what it means about reproductive behaviors and is, as such, of compelling importance to understand the genetic patterns observed. Birth-places are evidently directly related to reproduction, while sampling locations are most of the time conjectural to the research project... Second, birth place is an easy and “clean” categorization criterion: one is only born once and people often know where exactly. It can thus be recorded in a relatively unambiguous manner for all individuals. I think that this is perhaps one of the least ambiguous question of such anthropological questionnaire.</footnote>
possibly stem from the very different epistemological backgrounds experienced by the two communities
during their academic training. Indeed, human population geneticists most often come from medicine,
biology, ecology, mathematics, informatics and/or statistics backgrounds, rather than from cultural
anthropology and ethnology. As a consequence, it seems to me that, for certain human population
geneticists, knowledge can directly stem from DNA samples or sequences only, even when very limited
anthropological context is available. While I likewise think that numerous fundamental and essential
knowledge can indeed be acquired from these DNA samples or sequences only, I also think that knowledge
of an evolutionary anthropology nature cannot be rigorously acquired if detailed sampling and
categorization issues are not systematically discussed.

This is, to my views, highly similar to classical naturalist categorization issues, between taxonomic
and phylogenetic classifications for instance, or to ecology, demography, and conservation biology
questions. In these disciplines, “who did we sample?” and “how did we sample them?” are Material and
Methods questions that, despite also facing major methodological difficulties, are very often extensively
explained in the literature. I see no dialectical reasons why this should not be equally the case in human
population genetics and anthropological genetics studies.

Of major importance, I am very well aware that there are, in the human population genetics literature,
numerous glaring exceptions to the above critics. They will keep on inspiring me for the necessary changes
I need to bring to my research practices in the future.
1.4. Ethics, deontology, laws, and scientific research administration: “what
information can I share about whom I sampled?”

Disclaimer:

Ethics and deontology have represented and still represent a significant part of my work-time. However, they are not, per se, my objects of research nor my scientific discipline. My experience in these questions stems mainly from field sampling and data generating and sharing, and are almost exclusively empirical from my repeated interactions with various National Ethics Committees and academic Institutional Review Board, and from fieldwork experience with donors over the years in Central Africa and Cabo Verde. As a result, starting in 2014, I have been extensively working with CNRS and MNHN legal practitioners and Ethics committees to create (and validate) a novel package of CNIL (the French national commission for informatics and personal data protection) National Methodologies<fnref n="15" /> suited for anthropological genetics data sampling and usage, conducted in my research specifically but also tuned for almost all other anthropological genetics projects at the UMR7206 Eco-Anthropology. These methodologies and recommendations are now applicable for numerous other human sciences and biological anthropology projects conducted by academic researchers in France. Based on these previous empirical experiences, I aimed, in this section, to share with fellow researchers and students, some of my thoughts about major empirical ethical and deontological issues for anthropological genetics and human population genetics projects in the genome-wide era. This section thus strongly lacks philosophy and judicial academic rigor, in fact both disciplines outside my field of scientific expertise.

In the previous sections, I have been trying to answer the crucial question “who should I sample?” that I faced in my previous projects concerning anthropological, genetic, and linguistic data sampling on the field. I have strongly advocated for the need, in scientific publications and communications, to present and discuss anthropological categories for sampled individuals, as well as details about the sampling methodologies themselves. However, this raises another difficult ethical and deontological question, especially, but not only, for researchers conducting the fieldwork itself: “what information can I share about whom I sampled?”.

The Declaration of Helsinki, initiated in 1964 and of which the latest of 7 revised versions dated in 2013, provides “ethical principles for medical research involving human subjects”<fnref n="16" />. In fact, I never conducted “medical research” in the strict sense of this declaration. Nevertheless, the ethical and deontological principles here presented readily applied to “human genetics research” in general. Furthermore, a substantial number of items in the declaration, including the most essential core points research scientific finality and appropriate dimensionality, justice to access the study and focus groups, identifiability, personal privacy and vulnerable subjects, informed consents, risks, burdens, and benefits, and data sharing, could easily be translated for the anthropological genetic purposes described in the previous sections<fnref n="17" />. Indeed, simply replacing the words “medical research” and “physician”, by the words “anthropological research” and “anthropologist” in both the extended and synthetic versions of the declaration, provides a very useful framework and applicable guidelines for Informed Consent procurement during anthropological genetics field sampling.

<footnote n="15">15 https://www.legifrance.gouv.fr/jorf/id/JORFTEXT000037187386
https://www.cnil.fr/fr/declaration/mr-001-recherches-dans-le-domaine-de-la-sante-avec-recueil-du-consentement
https://www.cnil.fr/fr/declaration/mr-003-recherches-dans-le-domaine-de-la-sante-sans-recueil-du-consentement</footnote>

<footnote n="16">¹⁶ https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/</footnote>

<footnote n="17">¹⁷ In fact, virtually all essential aspects of the declaration beyond “clinical testing”, “health assistance”, and “invasive procedures” (this later point being easily avoided for me as I considered only non-invasive procedures for DNA sampling with saliva spits provided by donors, rather than blood samples or even buccal swabs).</footnote>
In this context, over the years and probably as everyone else in the community conducting sampling
in the field, I modeled my “Informed Consent” procedures to encompass all items suggested in the
Declaration of Helsinki and applicable to my work. I then proposed these to my colleagues and Ethics
Committees and Institutional Review Boards from the country where my labs were at the time, as well as
those of the countries where the sampling was taking place; received (sometimes laboriously after
substantial amount of time) comments; then corrected and resubmitted in order to eventually become
approved to conduct the proposed research.

I will not dwell here on all the aspects of these ethical and deontological guidelines. I previously
published a chapter in a book edited by Bonnie Lynn Hewlett, (Hewlett 2019), aiming at educating cultural
and biological anthropology students about fieldwork and “what we never tell to our students about
fieldwork, but we all know that we really really should”. Briefly, my chapter herein, entitled “*Do you
consent to participate in the research study?*” (Verdu 2019), approached the, I believe, massive scientific
and methodological benefits of preparing anthropological genetics fieldwork in the light of the Declaration
of Helsinki’s Informed Consent requirements. I also extensively exemplify important pitfalls, and their
possible consequences, due to the discrepancy between ethically approved Informed Consent procedures
and the reality I have faced in some of my Central African fieldwork experiences. Specifically, I developed
this chapter on pitfall examples concerning the *format of Informed Consent* procurement on the field, the
complexity of establishing “*voluntariness*” of volunteers, and troubled *benefits* and *compensations*.

Here, I would like to elaborate on the specific issues of data-sharing that arise between scientific
reproducibility and Open Access, on one side, and the respect of participants’ privacy and personal data,
on the other.

As developed in the previous sections, I think that it is critical for human population geneticists and
anthropological geneticists to provide detailed categorization criteria and associated anthropological
information gathered on the field, as well as inclusion and sampling processes, aiming at improving
scientific reproducibility and discussions. While numerous aspects of fieldwork processes and data can
readily be provided to the community in publications, some information are harder to share due, first, to
their format unfit for articles (genetic data, individual anthropological information tables...), and, second,
to the fact that they can represent identifiable privacy-protected information about participants, or both.
Furthermore, some of the data may be part of intellectual property and ownership policies of funding
agencies and institutions. In this context, note that such ethical and legal constraints may vary between the
countries where sampling is conducted (the first and foremost law that applies in our case), and countries
hosting academic research laboratories where data treatment and analyses are conducted, hence further
complexifying the matter.

1.4.a. *Human individual genetic data is “identifiable sensitive data”.*

Human genetic data is uniformly considered as “identifiable sensitive data” in all countries where my
previous research projects have been conducted, whether for sampling or for downstream analyses. Indeed,
a human DNA sample is very largely uniquely identifying his/her provider and may further carry
information that could be exploited for forensic and judicial purposes, for fundamental or medical and
pharmaceutical research purposes, or for commercial recreational purposes more recently. This is why there
are, to my knowledge, no country worldwide that has not already codified and legislated about the
conditions of sampling and usage of human genetic data in these three contexts (judicial, scientific research,
commercial). This is also why “data usage and sharing” are an important section of Informed Consent
procurement recommended by the Declaration of Helsinki, and, from my experience, one of the most
discussed sections of this procedure with Ethical committees, IRBs, and donors alike.

While some jurisdictions may allow that individual donors may specifically agree and allow by contract that researchers can release his/her personal genetic data freely and openly (nevertheless without providing their name associated with the study and a specific DNA sample or sequence), this is never the case in the French bio-ethics and research conduct and deontology laws under which I operate (see Note 15 above). I can thus never openly release human genetic data such as, for example, the 1000 Genome Project where data can be downloaded freely by anyone with a serious internet connection and a suitable hard drive. Furthermore, France also still completely prohibits commercial or recreational DNA sampling and testing, both for consumers and private firms, aiming at protecting the ill-usage of sensitive personal data for commercial or a variety of judiciary purposes (see below).

However, for ensuring scientific reproducibility of my work, and, separately, for sharing this genetic data (acquired within a public academic research framework and funding) with the scientific community in order to fuel novel projects and novel knowledge beyond my specific projects, other laws and provisional procedures apply. In all cases, I can only legally share genetic data with legally responsible and identified scientific research institutions and, namely, identified researchers, and if the proposed research, its means, and its proposed Data Management Plan fit what was agreed by donors upon procurement of the Informed Consent on the field. In agreement with the Declaration of Helsinki, no financial or commercial usage and patents can ever be conducted using the samples and data I collected in my research projects. Thus, other re-usage of the data for other research finalities, means, and DMPs can only be considered by re-obtaining a revised Informed Consent from the donors, including for my projects.

Based on these premises, often shared by academic researchers worldwide in my field, data is often
"made available to interested researchers in agreement with Informed Consent and Ethical requirements,
upon request to the corresponding author", as also extensively explained in Informed Consent procedures
to potential donors. More recently, the European Union created and maintains a data repository accessible
online, the European Genome-Phenome Archive (eGA<fnref n="18" />) which complies to the above French laws and
allows me to deposit data and control their access by other interested researchers on a case-by-case basis,
while providing me with secured storage and (to a small but significant extent) the administrative support
needed for this crucial task.

This apparent classical (in the population genetics community) solution to a fundamental problem due
to the nature of human genetic data nevertheless becomes, to my views, more challenging every day. This
is due to massive technical and methodological advances in the field over the past 20 years, to the increasing
diversity of complex tasks combined in any project which requires more researchers and labs involved (and
therefore more shared data), and to recent societal changes introduced in part by commercial/recreational<fnref n="19" />
broad-audience DNA testing in certain countries. Finally, the massive increase in different types and

<footnote n="18">¹⁸ https://ega-archive.org/</footnote>

<footnote n="19">¹⁹ “Recreational” DNA-testing was the term sometimes used even by the international private corporations proposing them to customers, at least when their businesses started roughly 10 years ago, for technologies providing data and analyses at the genome-wide scale as by 23andMe™, ancestry.com™, MyHeritage™, or ScotlandDNA™ for instances. “Commercial” or “Customer” DNA testing has since then been preferred by these actors and by consumers and genealogical companies lobbying for the legalization of such practices in countries where it was, or still is, forbidden (such as France). This re-branding was deemed necessary by these actors, as they considered that the word “recreational” could be used derogatorily by opponents, as well as conveyed a sense of frivolity that ill fitted the needs and interests of certain categories of consumers resorting to these private DNA testing (namely people in deep, sometimes medically pathological, search of genealogical roots or identity such as, among others, individuals with unknown biological parenthood or individuals with a complex uninformed genealogical history due to the trauma of the Trans-Atlantic Slave Trade).</footnote>
volumes of genetic data generated on DNA samples in the past 20 years, complexifies the already difficult question of legal transmission of responsibility of the data when the researcher who originally collected and/or generated them retires, quits academic research, or dies.

1.4.b. *Updating Informed Consents for novel population-genetics research projects*

Indeed, technical and methodological advances in population genetics since the first “complete” human DNA sequences have largely increased, and continue to increase every day, the ranges of possible applications beyond those explicitly planned for the initial research project at the root of the sampling and initial data generation. It is therefore harder and harder to evaluate whether the newly proposed research still fits the finality and scientific objectives on which relied the original Informed Consent procurement, in turn justifying to provide the data to the requester, or not. As a logical result, ethics committees and IRBs are legitimately more and more attentive to the finality of the proposed research project: “studying the genetic evolution of human populations” is no longer a plausible general finality accepted by these institutions; one has to be more precise and, therefore, constrain the possible research topics and methods used to achieve them, in turn de facto constraining the field of possible public academic scientific research that could be conducted without re-procuring Informed Consents.

While 10 years ago, I had little requests from other teams to access the data and I thus mostly re-
procured Informed Consent for new applications largely outside the scope of the initial study for my projects
only, it is now frequent that I have to refuse sharing the data for reasons of incompatibility with initial
Informed Consents. Even for my own projects, re-contacting previous volunteer participants is often
impossible (e.g. Central African populations that I worked with still rarely have electricity, let alone private
phones nor internet nor email access) without organizing a novel fieldwork; and, even then, some previous
participants may often be hard to find again several years after their first participation, as they might have
moved or changed names since. In turn, these legitimate and legal ethical constraints have also been
accompanied by an increasing amount of bureaucratic and administrative hurdles, very often at complete
odds with the reality of fieldwork sampling (Verdu 2019). Altogether, these developments may impair the
opportunities and enthusiasm of researchers willing to initiate novel fieldwork or procure updated Informed
Consents from previous donors. These tasks have very often become simply too big, too complex, and with
too little legal and administrative support for considering launching new projects, in particular for small-
scale projects requiring relatively modest means for limited amounts of time.

1.4.c. *When genetic data escape researchers for scientific necessities*

The increase in the number of researchers (lato sensu, including students, engineers, technicians, and faculty) from different institutions involved in a given project highly complexified the traceability of data and that of scientific productions (publications, conferences, thesis and memoires) generated using them. The increase itself in the number of interesting scientific methods to try on the data is perfectly expected and is simply due to the amount of work and seminal discoveries of our giant predecessors in the field, combined with the increasing number of students they trained in societies where access to advanced academic training has been strongly democratized in the last 70 years roughly<fnref n="20" />.

<footnote n="20">²⁰ This exponential phenomenon is by no means unique to human population genetics. It is happening in all active scientific fields of research.</footnote>
As a result, specialization has increased for complex tasks that require expertise that can desirably be
brought to the project by novel collaborations. In turn, the need for data sharing is greater than it used to
be. The number of researchers putting their hands on sensitive genetic and anthropological data within a
project increases, the number of students, destined to move elsewhere possibly with said data stored
somewhere on their portable hard drives, increases, and the number of other labs in which data are stored
and used, increases. It has therefore become virtually impossible for a single researcher responsible of
Informed Consents from donors, and thus downstream data usage, to keep track of all possible unlawful or
ill-advised data sharing and usage that may happen within only five years of the original Informed Consent
procurement, let alone on the longer term.

While laws, research institutions, and institutional databases (such as eGA), significantly support and protect individual researchers from deliberate abuses or simple inadvertences, the consequences of data escaping researchers remains ethically and even psychologically extremely heavy to bear for any researcher involved in primary field-sampling. Indeed, field sampling relies almost exclusively on the trust, respect, and honesty given by donors to researchers. I, as others researchers who shared their thoughts on the matter with me, deeply feel the need to honor these. Witnessing even the sheer possibility that data may escape us and be ill-used with respect to what was consented by donors is thus, literally, heart-breaking.

1.4.d. *Returning individual genetic data to their biological owners*

The ethical and deontological necessity to make the genetic data available to their donors is far from new
in the medical-genetics community, for medical-ethics reasons, transparency, and respect of the patient. It
is technically relatively easy to provide patients with the results of their genetic testing for a small number
of short targeted genetic regions or mutations: such result fit on one or two sheets of paper, and patients are
usually in regular contact with their physician hence facilitating communication. These two technical
aspects are, in fact, crucial for feasible “genetic data return to their biological owner”, and are often
impossible in the case of anthropological genetics fundamental research projects<fnref n="21" />, in particular in the
genomic era.

Indeed, while I could provide sheets of paper with an individual's genotyping results for 50 microsatellites 20 years ago, it is today impossible to provide individuals with a paper version of their entire genome. After a rapid computer-based simulation using Microsoft Office Word™, I calculated that a single almost-complete 6 billion bases (A, T, G, or C) diploid genome written in Times New Roman font size 10 on default-margins A4 format would span 1,139,817 one-sided pages, or an A4 paper-roll long of 338,5km, either format requiring roughly 2,590 trees each 12m high and 20cm wide for producing the paper<fnref n="22" />... This is not possible logistically and ecologically ill-advised. Of course, I could provide individuals with a portable hard-drive or high-capacity flash drive with said sequence in a compressed format, also disregarding the ecological cost of doing that for several hundreds of individuals often involved in a single project. However, I would then also ethically need to provide individuals with the computer means to actually access the data, train them to use these devices, and would further need to regularly provide them

<footnote n="21">²¹ Note that finding the individuals to give them back their genetic results is also far from trivial in numerous communities with little to no regular access to medical services and facilities, whether in developing countries or in developed countries.</footnote>

<footnote n="22">²² Despite what is here an attempt at humor, in my experience, broad audience public as well as legal practitioners involved in ethic committees rarely grasp the size of a genome and of genomic data in general, hence producing fundamentally unachievable procedures despite legitimate ethical concerns and requests. In practice, I found that this empirical exemplification helps them intuit what is cognitively really not that trivial to understand, even for specialists.</footnote>
with updated versions of the hardware and software against the rapid obsolescence of such material. Ultimately, and to be thorough, as I worked essentially in Central and Western African locations with more than difficult access to electricity, I would somehow also need to ensure access to stable sources of electricity for donors receiving such numerical version of their genome.

Fortunately, beyond these practical impossibilities, in my experience, participants were never interested in their raw genetic data. They were mostly interested in copies of the anthropological interviews I conducted with them to “keep a material archive for their offspring”, and in the knowledge that I would, with my (to them) magical scientific method, extract from their genetic data itself. Therefore, and in agreement with ethical committees providing research authorizations, I simply photocopied the raw anthropological interviews conducted on the field to give them back to participants, and prepared synthetic non-specialized versions of my published scientific findings that I presented to the communities in person during the following visit (see **Figure F1.4**), or for specific “return of the results” missions, and via postal mail on a field I visited only once in Uganda<fnref n="23" />. Also note that obtaining the funding for such return to participants on the field, as it requires similar logistics and associated costs as the original field sampling in the case of my low intensity fieldwork methodologies (**sections 1.1** and **1.2**), has massively improved, albeit still not being trivial, over my more than 15 years practice. Indeed, while funding agencies accepted but rarely required return to participants work-packages in research-proposals, and even sometimes proposed reducing the overall budget by cutting on these specific costs, they now strongly require that such return to participants plan be readily and thoroughly prepared and budgeted in the original proposals.

However, in my most recent fieldworks, things have changed at least for some people in the general public I try to recruit for my research projects, namely the younger and/or those with frequent personal access to internet. Due to the recent strong development of commercial DNA testing aimed at the general public in the last five years, and massive advertisement campaigns for these products on internet and TV, it is now not uncommon that some participants to my research studies ask me for their complete DNA data. They explained to me that they thus hoped to obtain their personal DNA testing results similar to what these commercial products propose.

In such cases, I would clarify an important point, in fact a complex population-genetics methodological point, already explicated during Informed Consent procurement before sampling, but apparently not sufficiently understood by these individuals (and I can't and do not blame them...). The aims of my projects are to reconstruct past histories of genetic migrations and origins for groups of individuals rather than reconstructing individually-centered specific genealogies. I thus work with population genetics paradigms and methods that apply to groups of individual genetic data rather than establish individual diagnostics similar to what is provided by medical genetics or these commercial tests (those at least who perform a decent job, which is very often not the case...). Therefore, the analyses I am conducting are fundamentally (mathematically) not the same than those conducted for medical genetics or these commercial tests, and the type of genetic data that I generate may also differ. In other, statistical, words, I use inference statistical approaches rather than diagnostic and/or predictive statistical approaches.

<footnote n="23">²³ Note that the Covid19 pandemia impaired me to conduct the final return mission to Cabo Verde, initially planned in the summer 2020 and which I will organize, hopefully in 2023. In parallel, we also think about conducting an online return event, which in all cases will not be sufficient as perhaps 80% of participants to this study do not have computer or internet access.</footnote>
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>Figure F1.4</figcaption></figure>

Photographs (courtesy of Evelyne Heyer) of Paul Verdu during a return-to-participants fieldwork in Bezan communities of Ngombe in the Tikar Country in Central Cameroon, in 2011. Individuals provided their consent for the non-commercial diffusion of their image for educational and pedagogical purposes only.

In my last fieldwork in 2018, when this happened a couple of times, I proposed to participants to provide them access to their genetic data upon request to me and via secured internet servers (and reminding them that they would need sufficient secured disk space and internet bandwidth to download and store said personal data, disclaiming responsibility for data loss after transfer), but cannot provide them with the results these companies claim they can provide as I do not do this. The answer has yet always been that they understood well the difference, that participating in my project was very interesting nonetheless, and that they retracted their request without a problem as, really, said raw data was of no interest to them: participating for the pleasure of participating and eventually hearing about the outcome of my research was completely sufficient for them to accept to participate.

The specialized informed reader may see how my answer to them is becoming more and more ambiguous: while it is still true that numerous population genetics inference methods rely on allelic and haplotypic frequencies only, thus not directly using individually inferred genealogies nor using individual-centered diagnostic or predictive approaches, several very interesting new methods now operate directly on individual genetic landscape and patterns rather than just population frequencies. Indeed, for one glaring example, certain local-ancestry approaches map genomic segments within individuals, and the genetic history of a population is, in fact in these specific methods, the marginal product of all the individual genetic
histories reconstructed for each individual separately; as opposed, again, to more classic inferences where
the specific genetic history of a given individual is not inferred and could only be assessed as a statistical
possible realization of the population-wide inferred history.

In this context, there are increasing societal and political needs for improving the diffusion of the
scientific method and culture to non-specialized audiences as well as reshaping scientific projects and
initiatives to be more intricated socially. As a result, ethics committees and commissions protecting
personal sensitive data now expect Data Management Plans to explicitly facilitate access to personal genetic
data for their original biological owners. They still hear that in most of my cases, this is not logistically and
practically feasible as explained above, including in terms of the lack of the truly massive informatic
investments and associated human resources that it would require from public academic institutions and
labs for such ambitious task. But for how long ethics institutions and volunteer participants will keep on
accepting our still legitimate derogations? I expect not long, which in turn readily clearly poses a major
problem that most fundamental academic research laboratories, equivalent to the one I work in now, are
not even close to be able to overcome.

And of course, the problem of traceability and protection of sensitive data for researchers exposed
above would then literally explode. I have been very frequently solicited in the past five years by TV, radio,
internet, and paper journalists, as well as by various civil-society associations for the defense of minorities
or of migrant communities, of adopted children, or genealogical societies, as well as by learned societies,
requiring my expertise about various aspects of genetic commercial testing (²⁴; see curriculum vitae for an
extended list). I systematically took these opportunities to try to educate the audience about what is genetic
data, what is the population genetics methods and dialectics deployed for reaching results, and what it can
or cannot tell. In all cases, I hereby tried to explain why personal genetic data are universally considered as
sensitive data that are always protected by specific laws (more or less restrictive in reality, but still); and
that every person should at least be careful and understand why sharing openly one's genetic data, or giving
them out to a private foreign corporation, is never trivial nor free of possible major backlash, individually
or societally<fnref n="25" />. This, in turn, also explains why, contrarily to these commercial enterprise, academic research
ethically approved protocols for collecting and using genetic data have to deploy numerous procedures and
heavy protocols meant in part to ensure privacy and protect donors from these risks.

This last comment has another perhaps imaginary but nevertheless possibly dreadful consequence: how can public research, aimed in general at improving the “common good”, hope to justify public-money expenditures for increasingly complex and time consuming (and thus expensive) procurement of ethical research authorizations and Informed Consent and high quality genetic data from volunteer participants, when certain private corporations obtain such data much more easily and cheaply (as a matter of fact, people pay them for this) with much lighter ethical and legal constraints?

<footnote n="24">²⁴ https://www.francetvinfo.fr/sante/biologie-genetique/tests-genetiques/c-est-important-il-me-manque-une-partie-de-moi-interdits-et-pourtant-largement-pratiques-faut-il-legaliser-les-tests-adn-genealogiques-en-france_3986789.html</footnote>

<footnote n="25">²⁵ Note that the dystopian anticipation movie *Gattaca*, by Andrew Nichols and released in 1998, was not only extremely accurate on numerous human genetics methodological aspects, but also profoundly accurately illustrated how a eugenic society could expand on the availability of massive genetic data. As a consequence, I recommend to use it for general public debates and for population genetics university classes. My experiences are that it works great both to explain the scientific method involved in human genetics and ethical and deontological issues in the field and, beyond that, society in general.</footnote>
1.4.e. Who gets to use the genetic data once I am gone?
***

Finally concerning specifically human genetic data in this section, we are now the first generation of
population geneticists for whom the archival and continuity for future re-usage of massive human genomic
data becomes a crucial question. Indeed, what happens to such sensitive personal data and the evaluation
of their future sharing and re-usage when the researcher who received the original Informed Consent from
donors retires, quits, or dies? To my knowledge, and after having interviewed legal practitioners at the
CNRS and the MNHN on this matter, this is at best a grey zone in French legal regulations, and completely
left out of the Declaration of Helsinki guidelines. While there are numerous laws and regulations concerning
the transmission and re-usage of personal medical data, and while there are provisions about the
transmission and archival of research data including biological samples in general within public academic
research institutions such as the CNRS and the MNHN, they do not explicitly indicate how human genetic
sensitive and personal data should be handled, and most importantly whether they can be re-used in the
absence of the researcher responsible for evaluating that data-access requests appropriately suit the original
Informed Consents.

I, unfortunately, recently experienced first-hand this issue; an unexpected example of what will
eventually always occur and may further illustrate how unprepared I and our institutions are. Together with
a fellow researcher, Dr. Trevor Pemberton, launching his lab in Manitoba State University (Canada), I
started in 2014 an ambitious project that required extensive anthropometrical and DNA sampling in 400
individuals from 4 “Pygmy” and neighboring “non-Pygmy” populations (see **section 1.1**) from Central and
South-East Cameroon. After two years of obtaining an NSRC Canadian grant, building the research
protocol and overcoming the ethical, deontological, and administrative hurdles, we were approved by
Canadian IRBs and ethic committees, as well as by the IRD (French public research Institute for Research
and Development) in Cameroon who, at the time, had a research agreement with Cameroonian government
to authorize research involving the human subject that applied to our specific project. We therefore set out
in the spring of 2016 to conduct two months of intense fieldwork in villages I had conducted numerous
researches in the past, together with our CNRS colleague Dr. Fernando Ramirez-Rossi, our IRD colleague
Dr. Alain Froment, and Canadian PhD student Dr. Alexandra Blant. Of major importance, I was never to
keep, even temporarily, any original DNA sample and anthropological data in my lab in France: all material
was to be stored and not shared, at least before the original publication, in Canada, and was directly sent
from Cameroon to Canada, without ever entering in my lab in France.

Everything went well, and DNA samples for this unique database (by far the first of its kind for Central
African populations) were extracted in the Canadian lab and ready to be genotyped and whole-genome
sequenced. At that point in mid-2018, Dr. Pemberton unexpectedly and brutally completely retired from
academic research for reasons I will not explain here. The department head of his lab at the time, Pr. Barbara
Triggs-Raine, was left, from one day to the next, with an inextricable situation regarding, in particular,
samples and data.

After a very long administrative and legal procedure, in 2019, the newly appointed dean of the
Canadian university requested that we destroy the samples, a decision that apparently was validated by the
university's ethics committee. Fortunately, Pr. Triggs-Raine opposed this decision and contacted me in an
emergency: did we have legal provisions in the original IRBs that would prevent such thing from happening
and was I able to legally be given the care of these samples? Fortunately, following the Declaration of
Helsinki guidelines and downstream discussion in various national bio-ethics committees, we included in
our Informed Consent and ethical applications the general provision that human DNA samples are sensitive
human biological samples that cannot be destroyed without the explicit consent or request of their original donors, unless they pose a biohazard imminent threat (which was not the case for us). Such provision was explicated to participants and agreed upon in my Informed Consent procedures under specific sections about “rights and procedures for retracting from the project”.

Furthermore, the Canadian IRBs had asked us to include a non-standard section about what we should do if the Canadian PI “died” (sic), which I had in all honesty totally forgotten about at that point. Indeed, reparsing the documents in my possession, we had simply made a provision that all material and responsibilities should be transferred to me and my lab, which had been accepted by ethics committees.

Together with Pr. Triggs-Raine, we thus launched an official appeal to the university’s ethics committee, pointing towards these two provisions that they had themselves endorsed initially. They then requested proof that I was involved first-hand in the original Informed Consents procurements to donors, even if my name and all contact information were explicitly included in Informed Consent forms provided as a paper archive to each participant in Cameroon. I have thus had to justify that I was in fact the one who procured Informed Consents from all participants during a fieldwork I had largely organized and conducted, with documents and photographs and video recordings<fnref n="26" />. Fortunately, our appeal was accepted, and we could launch the Material Transfer Agreement for permanent transfer of all material left in the freezers and drawers of my collaborator. This took another year and half, after extensive legal discussions between the Canadian university and the CNRS, mediated by Pr. Triggs-Raine and myself. Finally, during the fall of 2020, I received officially the material and right to store it as agreed upon between the Canadian university and the CNRS.

That would sound like the end of a long and tedious story, but no. As far as I have the right to keep and care for all this material, I do not have the right to exploit them. Indeed, the Cameroonian research authorization provided to my colleague could not be transferred to me in his absence. As of today, I am still trying to obtain this transfer from Cameroonian authorities. In fact, after a moving of the archives of the ethics committee, they seem to have lost our original file, and thus cannot legally amend it. I am now in the process of re-applying, which is not trivial as I need retro-activity for 2016, when the sampling happened. This is far from standard for ethics approvals to conduct research in general, and obviously not a priority for Cameroonian research ethics committees in particular...

This was an unexpected and accidental experience, but what will happen to, for instance, my eGA deposits and the Data Access Committees I pilot, if I quit or die, or when I retire? I do not have the answer to this question... yet!

<footnote n="26">²⁶ In fact, I had established a video-recorded protocol for Informed Consent procurement during my PhD, aiming at legally overcoming the multifactorial problem of having illiterate participants sign a lengthy classical Informed Consent form (Verdu 2019). During my post doctorate, after extensive discussion and audits, I got this protocol to be included in IRBs standard operating procedures of the University of Michigan and, later, I did the same with the CNIL in France. Video-recorded Informed Consents were now acceptable in my research cases following a precise protocol, and could only be used for legal and ethics committees’ procedures and requirements. I could not use them myself for any other purposes, even research (visual anthropology for instance, as I have had to reject requests of data access from researchers from this field in several occasions), although I have to ensure their storage and availability.</footnote>
1.4.f. What about anthropological and linguistic data?

The previous sections concerned specifically archival and sharing of human genetic data, as they are
intrinsically identifying data of a highly sensitive nature due to their vast judicial, scientific research, and
commercial potential. But, as I introduced in **sections 1.1 to 1.3**, what about the individual anthropological
(cultural or biological) and linguistic data I also collected during my fieldwork? Indeed, I strongly
advocated for releasing to the scientific community numerous information about individual's self-
identification, residence and birth locations, marital rules, and numerous other such data useful for
individual categorization in groups and populations. Furthermore, in my Cabo Verde project in particular,
I also investigated individual speech patterns to evaluate jointly the genetic and linguistic history having
given birth to the observed biological and cultural patterns of diversity throughout Cabo Verde (see **section
1.2**); and obviously advocated for releasing such data together with my publications in a similar way to
genetic data, for deontological purposes regarding reproducibility of scientific results as well as for
promoting future research endeavors.

i) *Linguistic recordings.*

After discussion with legal practitioners, we agreed to recognize that they could be used in existing
legal forensics procedures similar to existing cases when voice recordings are used as evidence in a trial.
My public research linguistic recordings were thus *de facto* identifying sensitive data... Furthermore, I had
legal practitioners realize that such recordings could also be of interest, beyond judicial and police
institutions, to scientific research laboratories and private companies alike working with natural languages
and automated voice recognition, a very active public and private Research and Development field today.
Altogether, we agreed with legal practitioners that it would be easier to build a methodology about this data
using as a template what we already had for human genetic samples and data; despite the lower risk-
potential of such data as they had, to our views, no obvious medical and pharmaceutical nor direct
individual-targeted commercial interests that we could imagine, yet... I thus included them under the exact
same Informed Consent procedures as the genetic samples and data, nevertheless precisely describing that
the nature and risks associated with either type of data strongly differed. This process has thus given birth
to a specific CNIL methodology usable for any research protocol archiving and analyzing individual
linguistic recordings, and my Cabo Verde protocol is since cited as a case-example of applicability for this
methodology by the CNRS ethics committee for Data Management Plans in particular towards interested
researchers in Human Social Sciences<fnref n="27" />.

ii) *Anthropological interviews, self-reported data.*

The French law, I reckon, is very specific and restrictive on personal data sharing as compared to the
Anglo-Saxon laws I have been in contact with both as a post doctorate fellow and, later, for sharing data
with USA fellow collaborators for my projects. In the French law and CNIL recommendations, any
information or batch of information allowing to identify an individual is subject, by default, to the right to
be protected, even if not "sensitive" as defined by them similarly as for genetic and linguistic data. In this
context, while individual birth-places cannot on their own allow to identify a given participant to my study,
the vector of age, sex, residence place, birth-place, and parental and grand parental birth and residence-

<footnote n="27">27 https://www.inshs.cnrs.fr/sites/institut_inshs/files/pdf/guide-rgpd_2.pdf ; which was regularly enthusiastically received by some members of this vast community, but not always, in particular, by those who thought, until then, that their research topics and methods were absolutely not concerned by such ethical and deontological matters...</footnote>
places can be used to do so in principle. Furthermore, in particular in Central Africa but not only, while the risk associated with such hypothetical identification is probably low, it is not reasonable to completely exclude that the political powers in place locally now or in the future will not use this information to act one way or another specifically towards individuals that participated to my research and/or the communities they belong to; physical retaliation being one of the worst-case scenarios.

Therefore, with the legal practitioners that assisted me in these matters, we decided to create a specific category for this part of my data in general, and to model it upon the existing methodologies concerning sensitive personal data protection about religious and political opinions, and marital and sexual orientations and opinions. As a matter of fact, I also had to explain to them why human population genetics research projects often gathered religious and marital data, even if only indirectly via their familial anthropology interviews: these are of major interest to certain human population geneticists (including me) as they are often key factors determining who can reproduce with who locally, and further may be part of the social frameworks determining marriage rules and norms and mate-choice recommendations, hence of obvious possible influence on genetic diversity distribution within and among communities. As a consequence, I cannot, in fact, freely and openly share such individual data as I advocated for previously in favor of Open Science and reproducibility, due, here again, to personal data protection and respect of privacy.

### iii) Transmitting population inclusion and categorization criteria to future generations

This poses also the same problem of transmission of this personal information about categorization criteria than for the DNA sample and genetic data itself as discussed above in **section 1.4.e**. As a last example of this issue, and to further exemplify here deontological problems of transmission of information about the categorization issues discussed in the previous **sections 1.1 to 1.3**, let us consider in some details the Human Genome Diversity Panel – Centre d'Etude du Polymorphisme Humain (HGDP-CEPH) DNA panel<fnref n="28" />. This panel has been used over the past 20 years to massively increase our understanding of human genetic diversity and evolutionary history. Indeed, more than 200 scientific publications directly investigated this panel, and, furthermore, samples from the panel were largely provided to other international human genetics initiatives of fundamental importance such as the HapMap Project or the 1000 Genomes Project among many others.

The HGDP-CEPH panel itself, provides 1048 individual DNA samples (and associated genotyped markers using various technologies and whole-genome sequences) from 58 worldwide populations (**Table T1.4**). Most importantly, very little information about the sampling and recording of the categorization-criteria for individuals are readily provided to researchers willing to use this panel, and, in fact, very little information about the recruitment of DNA donors and sampling methodologies on the field are made available or explicitly referenced. Nevertheless, the HGDP-CEPH panel readily provides population-information tables for which the population labels leave very little doubts about the fact that panel-inclusion criteria of DNA donors and population categorization-criteria were highly variable from one population to the other, some possibly involving self-report “populations” but some others very evidently not so.

Indeed (**Table T1.4**, first column “Population”), six population labels indicate UN-recognized states’ nationalities (“Japanese”, “French”, “Cambodian”, “Colombian”, “Palestinian”<fnref n="29" />, “Russian”). One population label indicates a vague geographic region within a state not evidently linked to any other ethno-geographical or ethno-linguistic group: the “North Italian” population, differentiated from the “Tuscan”

<footnote n="28">²⁸ https://cephb.fr/hgdp_panels.php</footnote>

<footnote n="29">²⁹ Note that Palestine was not yet recognized as a state by the UN when the HGDP-CEPH panel was made available to the scientific community.</footnote>
and “Sardinian” which are other Italian *populations* labeled with a name referring to official administrative regions of Italy, and possibly also referring to ethnic and ethnolinguistic regional groups. Seven populations’ labels start with the word “Bantu”, which is a linguistic family containing more than 250 different languages from all over Sub-Saharan Africa (Guthrie 1958), associated with three different geographical codes (“North East”, “South East” and “South West”), and six ethnic or ethnolinguistic collated labels (“Pedi”, “Sotho”, “Herero”, “Tswana”, “Ovambo”, “Zulu”) for the South African samples only, while the “Banthu North East” from Kenya is not further named in more details. Of particular interest to me (see **section 1.1**), note that the “Biaka Pygmy” population are also Bantu speakers but are not labeled as such. Indeed, only two African populations are named according to an ethno-name associated with the word “Pygmy” (“Biaka” and “Mbuti”), which, let me remind here, is an exogenous (European) extremely complex historical category with an ancient Greek etymology.

Finally, all other populations are labeled with a name indicating what can only be assumed to be
regional (geography), ethnic, ethno-geographic, and/or ethnolinguistic groups, albeit no definition of the
“ethnicity” criteria are provided here, with associated nationality in the sole case of the “French Basque”.
Note that, rather surprisingly, the “Population table” is associated with another “Individual population
information table” from the same HGDP-CEPH reference. The latter indicates that “Colombian” population
is in fact composed by “Piapocco and Curripacco” individuals, two ethnic and ethnolinguistic groups of
Colombia, a class of categorization naming that is only specified in this individual information table, but
not in the population table...

Furthermore, the same “individual information” table for HGDP-CEPH individuals readily provides a
grouping of individuals into so-called “Geographic origin” (Table T1.4, column 2), another grouping of
individuals into so-called “Regions” (Table T1.4, column 3), and a third grouping of individuals into so-
called “Pop7Groups” (Table T1.4, column 4), without providing any form of references or explanations
for the choices hereby made. Nevertheless, I strongly suspect that this Pop7Groups comes from the Analysis
of Molecular Variance (AMOVA) results published in the very famous and indeed seminal Rosenberg et
al. Science 2002 population genetics article on genome-wide microsatellite genotyping and analysis of this
panel; but it is not reference as such in this table provided directly on the HGDP-CEPH panel resources.
Thus, some of the HGDP populations are apparently categorized by the panelists based on alleged or pre-
assumed geographic origins, possibly using the results of genetic clustering analyses, and other inexplicit
historical and genealogical data...
<table><caption>Table T1.4: HGDP-CEPH population table (https://cephb.fr/hgdp_panels.php)</caption><thead><tr><th>HGDP-CEPH Population</th><th>Geographic origin</th><th>Region</th><th>Pop7Groups</th></tr></thead><tbody><tr><td>Surui</td><td>Brazil</td><td>America</td><td>America</td></tr><tr><td>Karitiana</td><td>Brazil</td><td>America</td><td>America</td></tr><tr><td>Colombian</td><td>Colombia</td><td>America</td><td>America</td></tr><tr><td>Maya</td><td>Mexico</td><td>America</td><td>America</td></tr><tr><td>Pima</td><td>Mexico</td><td>America</td><td>America</td></tr><tr><td>Cambodian</td><td>Cambodia</td><td>Asia</td><td>East Asia</td></tr><tr><td>Han</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Tujia</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Yizu</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Miaozu</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Miaozu</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Oroqen</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Daur</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Mongola</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Hezhen</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Xibo</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Uygur</td><td>China</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Dai</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Lahu</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>She</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Naxi</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Tu</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Japanese</td><td>China</td><td>Asia</td><td>East Asia</td></tr><tr><td>Brahui</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Balochi</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Hazara</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Makrani</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Sindhi</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Pathan</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Kalash</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Burusho</td><td>Pakistan</td><td>Asia</td><td>Central South Asia</td></tr><tr><td>Yakut</td><td>Siberia</td><td>Asia</td><td>East Asia</td></tr><tr><td>French</td><td>France</td><td>Europe</td><td>Europe</td></tr><tr><td>French Basque</td><td>France</td><td>Europe</td><td>Europe</td></tr><tr><td>Sardinian</td><td>Italy</td><td>Europe</td><td>Europe</td></tr><tr><td>Tuscan</td><td>Italy</td><td>Europe</td><td>Europe</td></tr><tr><td>North Italian</td><td>Italy (Bergamo)</td><td>Europe</td><td>Europe</td></tr><tr><td>Orcadian</td><td>Orkney Islands</td><td>Europe</td><td>Europe</td></tr><tr><td>Russian</td><td>Russia</td><td>Europe</td><td>Europe</td></tr><tr><td>Adygei</td><td>Russia Caucasus</td><td>Europe</td><td>Europe</td></tr><tr><td>Druze</td><td>Israel (Carmel)</td><td>Middle East</td><td>Middle East</td></tr><tr><td>Palestinian</td><td>Israel (Central)</td><td>Middle East</td><td>Middle East</td></tr><tr><td>Bedouin</td><td>Israel (Negev)</td><td>Middle East</td><td>Middle East</td></tr><tr><td>Mozabite</td><td>Algeria (Mzab)</td><td>North Africa</td><td>Middle East</td></tr><tr><td>NAN Melanesian</td><td>Bougainville</td><td>Oceania</td><td>Oceania</td></tr><tr><td>Papuan</td><td>New Guinea</td><td>Oceania</td><td>Oceania</td></tr><tr><td>Biaka Pygmy</td><td>Central African Republic</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Mbuti Pygmy</td><td>Democratic Republic of Congo</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Bantu N.E.</td><td>Kenya</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>San</td><td>Namibia</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Yoruba</td><td>Nigeria</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Mandenka</td><td>Senegal</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Bantu S.E. Pedi</td><td>South Africa</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Bantu S.E. S.Sotho</td><td>South Africa</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Bantu S.W. Herero</td><td>South Africa</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Bantu S.E. Tswana</td><td>South Africa</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Bantu S.W. Ovambo</td><td>South Africa</td><td>Subsaharan Africa</td><td>Africa</td></tr><tr><td>Bantu S.E. Zulu</td><td>South Africa</td><td>Subsaharan Africa</td><td>Africa</td></tr></tbody></table>
Finally, and of importance in the literature, the HapMap panel, also vastly used in human population genetics and genomics and further part of the 1000 Genomes Project, considers the “CEU” population simply described as “Utah residents with Northern and Western European ancestry from the HGDP-CEPH collection”. However, the HGDP-CEPH collection itself refers to said families as:

> « - 10 familles Françaises qui avaient participé aux travaux de recherche du Professeur Jean Dausset sur le complexe majeur d'histocompatibilité
> - 48 familles de Mormons d'Utah, collectées par le Professeur Ray White
> - 1 famille d'Amish de Pennsylvanie, collectée par le Professeur Ken Kidd
> - 2 familles du Venezuela, collectées par le Professeur Nancy Wexler »<fnref n="30" />

We may thus assume that the HapMap CEU who are said to have been sampled in Utah (USA), comprised the self-reported Mormon individuals from the HGDP-CEPH. They are often categorized in human population genetics studies, without discussion whatsoever, as “European” populations, and even sometimes mapped somewhere in Germany rather than in Utah, exactly as if these samples had been in fact sampled in non-Mormon communities from “Central Europe”. This categorization “approximation” neglects the recent history of European migrations to the USA and accompanying demographic founder events, as well as the specific socio-cultural marital behavior of Mormon populations allowing and even sometimes favoring polygyny and religious endogamy<fnref n="31" />, which very possibly influenced their resulting genetic diversity compared to extant Central European populations (at least one should test it formally before assuming).

Altogether for the HGDP-CEPH, note that since the recent passing of Luca Cavalli-Sforza and Howard Cann, leaders of the HGDP-CEPH initiative and direct major witnesses of the DNA sample inclusion in the panel, the detailed information about individual sampling has significantly been lost forever, as we cannot even directly ask them to recall what was the genesis of this table that, to my views, exemplifies largely beyond my specific research projects, what is faced by the human population genetics and anthropological genetics community regarding deontological practices on anthropological categorization issues and reproductible research.

<footnote n="30">³⁰ https://cephb.fr/familles_CEPH.php</footnote>

<footnote n="31">³¹ Polygamy was officially legally banned in Utah by the Morrill Act Federal law in 1862 (https://www.uen.org/utah_history_encyclopedia/p/POLYGAMY.shtml), but more recently, the ban was weakened successively in 2013 and recently in 2020 (https://www.nytimes.com/2020/05/13/us/utah-bigamy-law.html) as enacted in 2022 (https://le.utah.gov/xcode/title76/chapter7/76-7-s101.html)</footnote>
**1.5. Concluding remarks to section 1.4, and personal recommendations to researchers
and students.**

Altogether, for data sharing ethical and deontological issues discussed above in **section 1.4** concerning the reproducibility and associated advanced discussion of human population genetics and anthropological genetics investigation relying on complex categorization issues, as discussed in **sections 1.1 to 1.3**, and concerning self-reported anthropological data collected from interviews with participants,

***I recommend to:***

1. readily provide as many elements as possible in scientific publications and communications, within the ethical and deontological constraints stated above regarding individual privacy and personal data protection.

2. provide data at the level of “populations” or “communities”, sufficiently wide to avoid personal identification within these larger social or geographical groups of individuals, for types of individual-level data that could allow indirect identification of participants.

3. retain identifying and sensitive anthropological data at the individual scale, and share them exclusively within the frame of close collaborations or at least backed up by appropriate legally binding Material Transfer Agreement, or equivalent. For other research projects conducted outside the involvement of the researcher who originally procured Informed Consents, bind data sharing by MTA, or the equivalent, based only on explicit compliance to Informed Consents procured by donors.

Finally, for sharing *genetic samples and data*, and for *linguistic recordings and systematic transcripts*,

***I recommend to*** consider them as systematically identifying highly sensitive data. As such, I recommend to retain them and only share them based on judicial or medical research requests from authorized parties, and share them with the scientific community based only on explicit compliance to Informed Consents procured from donors, backed by legally-binding MTA, or equivalent.

Altogether, the issues discussed in the above **section 1.4** emerge from the fundamental contradiction between protecting sensitive private data, such as human genetic data, and using such data for the advance of science, technology, knowledge, and society. They are exacerbated by the recent possibilities of generating rapidly massive amounts of genetic data, and of sharing them ubiquitously virtually instantaneously.

The solutions however are not to be expected from ethical changes alone, I think and from my personal experiences. Indeed, I strongly believe that ethically justified deontological and legal constraints on the practice of anthropological genetics sampling and data sharing can improve research practices and, as exemplified in this chapter, strongly improve reproducibility and productive discussion for future work in the field. However, they highlight major gordian knots, sometimes impossible to break, between data protection and Open Science, which in turn may strongly limit academic freedom and the advances of research projects and endeavors.

The solutions to this conundrum are, I think, mainly socio-political, both nationally and internationally. To overcome, in practice, the ethical and deontological challenges I described here, researchers worldwide need more administrative and legal support from their institutions; countries need more administrative and
legal support devoted to appropriately evaluate the increasing number and complexity of requests submitted by researchers; and we all need to harmonize administrative requirements and procedures as well as conduct frequent ethical debates at an international scale, even broader than that of the Declaration of Helsinki.

This will only be achieved by a renewed effort and mobilization from the scientific community to:

i) engage and participate in the public debate concerning bio-ethics and deontological research practices, including concerning a reflexive critic of their own past practices;

ii) massively increase the efforts of diffusion of the scientific method itself towards civil non-specialized audiences in general, including specifically towards legal practitioners and policy-makers.

Indeed, I believe there is a crucial need for diffusing methods, dialectics, and, in general, the scientific production of evidence, rather than communication and advertisement of shiny results obtained without ever explaining how they were obtained. I believe that we lack massive understanding in the society today about how science operates in principle and in practice, which strongly impairs, to my views, the impact of the scientific community on societal decisions and policies (including that directly concerning scientific research). This is, I believe, the true “*Science in the Society*” challenge we face as a community in the proximal future.
**1.6. Perspectives: Population genetics categorization issues in the paleo-genomics era and interdisciplinarity necessities between genetics and anthropologies.**

*Disclaimer:*

I have not personally conducted paleo-genomics research projects in the past. However, my personal experience about this topic and the points of view exposed in this section rely on the building from scratch of the paleo-genomics molecular platform P2GM at the MNHN on the site of the Musée de l'Homme which started in 2011 (there were literally no walls in the lab section of the Musée de l'Homme when I started this project in 2011 during the complete makeover of the building). Since its opening in 2015, I have been co-directing the platform with Dr. Céline Bon (a real paleo-geneticists), coordinating the work of researchers and engineers specialized in this discipline for their projects conducted on P2GM. I have thus been in operational contact with more than 40 paleo-genomics projects conducted since then on the platform, concerning numerous scientific problematics and interested in numerous types of archaeological materials and species, including hominids. As a result, I have been largely involved, since 2019, in a human paleo-genomics project piloted by Dr. Marie-Claude Marsolier-Kergoat and Dr. Céline Bon on reconstructing the paleo-genetics history of the Paris Basin since the Mesolithic until now. My scientific participation in the project has gone beyond the operational co-coordination of molecular genetics experiments on P2GM since 2021 (an already substantial task in itself...), as I am now carrying a work-package of the project newly funded by the French ANR (Project ParisAncientDNA) about developing ABC inference methods suited for investigating specifically complex admixture processes using paleo-genomics data (see **Chapter 4.3.b**).

Finally, and as a result, I have been invited to participate in 2020 to a national workshop on the regulation of invasive and destructive scientific procedures conducted on human remains from archaeological material excavated in France and under the supervision of the Ministry of Culture. This workshop, initiated by the Minister of Culture overseeing all archaeological excavations and preservation in France, has put together the extensive national community of archaeology practitioners (academic researchers, INRAP, private preventive-archaeology enterprises...) and human remains collections curators (national, regional, institutional, academic researchers...), together with scientific representatives of the four main paleo-genomics public-research platforms, active in France at the time of this workshop. Based on the work of this taskforce, comprising more than 50 individuals, the Minister of Culture produced and released in June 2022 novel norms, regulations, and procedures for access to human remains archaeological material for conducting invasive research (paleo-genetics and isotopic research mainly), with the main goals to ensure deontological good-practices across involved parties, the integrity of research-conduct, and the maximal preservation of the resource for future generations.

Therefore, while not practicing it with my own hands, beyond paleo-genetics literature reading as any human population genetics, the thoughts presented below result from numerous extended interactions, and sometimes mediation, between the archaeologist and paleo-anthropological communities and that of human population genetics, as well as my interdisciplinary practice within the MNHN and Musée de l'Homme over the years.

However, the thoughts below are just food-for-thoughts, and will need to be formally elaborated and extensively substantiated to reach the academically acceptable level that they lack at that point.

For the past 20 years, and particularly since 2010, human paleo-genetics molecular techniques aiming at extracting and sequencing DNA from ancient human skeletal remains have massively improved. They fueled the adaptation and deployment of novel population genetics methods for the study of ancient genetic diversity patterns, which have profoundly re-drawn our knowledge of human evolutionary history, and which continue to do so almost on a daily basis. Today, paleo-genetics studies can ambition (technically and financially) to investigate “populations”, by which I mean here a statistically substantial number (>20) of individuals from the same geographic region and which have died relatively at the same time, rather than seldom individuals dispersed over gigantic geographic and temporal spaces.
These novel technological and methodological developments, together with those of environmental, archaeo-zoological, and archaeo-botanical paleo-genetics, provide population geneticists with what is almost a time-machine for travelling in the real history of genetic evolution. In practice, they bring novel almost direct information into genetic patterns of populations in the past and therefore allow researchers to formally test genetic continuity, or the absence of genetic continuity, across populations over time and until today. Furthermore, and importantly, they also allow to empirically test the power and accuracy of virtually all population genetics inference models and methods developed since the birth of population-genetics at the turn of the 20th century and, exclusively until now, aimed at reconstructing and inferring the past from the observed present. This allowed recently to revisit numerous previous results about human genetic evolution (Schlebusch and Jakobsson 2018; Bergström et al. 2021).

To be honest, we are not completely there yet, mainly due to the nature of ancient and degraded genetic data which differs strongly from that of “fresh” genetic data, and therefore requires very specific statistical treatments, in turn requiring adapting existing or developing new models and expectations, and therefore not always strictly comparable to “all previous population genetics methods”. Nevertheless, this emerging novel paradigm rapidly builds a novel scientific discipline which has roots and strong conceptual and methodological ties with “classical” population genetics. In return and as a consequence, it has already deeply impacted human population genetics and anthropological genetics to the point that it is impossible today for these practitioners to work, or be trained, without considering and discussing paleo-genetics methods and results. This is thus, I believe, a revolution for these latter disciplines.

In this context, I will next address, as a specific perspective, categorization issues for population genetics as raised in a novel manner due to paleo-genetics. Second, I will try to evaluate whether paleo-genetics is also a revolution for paleo-anthropologists and archaeologists, as very often claimed by geneticists and the journalists echoing them in the non-specialized audience. Ultimately, I will conclude these perspectives about the broader issues of the practice of interdisciplinary research between paleo-genetics and archaeology, echoing very similar issues discussed in the previous sections about interdisciplinary practices between cultural anthropology, human population genetics, and linguistics.

### 1.6.a. Archaeological cultures are not population-genetics’ populations

Paleo-geneticists require skeletal material as their primary object of investigation. The discipline itself is already strongly specialized in two, heavily intertwined, sub-fields of specialization: molecular genetics and population genetics. Molecular paleo-geneticists are primarily focusing on extracting and sequencing ancient and degraded DNA from skeletal remains, a major molecular-chemistry challenge in itself that only needs specific inputs from archeological knowledge about the remains themselves and the context from which they were unearthed. Namely, while molecular paleo-geneticists can work blindly (“*give me a bone, and I’ll extract and sequence it’s DNA*”), they often much prefer having additional information about the material itself, as it can crucially orientate their approaches and methods. Indeed, DNA preservation, and therefore the extraction and sequencing-preparation molecular techniques to be deployed, strongly depends on the age and the type and osteological structure of the remain, as well as on the environmental (temperature, pluviometry, humidity, type of vegetation coverage...) and geo-chemical (pH, soil chemical composition) context from which remains were unearthed by archaeologists. However, note that if this information is not fully available, this does not prevent molecular paleo-geneticists to exploit the material itself...