Periodic Reporting for period 1 - NAMU (Demography and social structure of Polynesian outliers: genomic investigations of over 100 individuals buried in Namu, Taumako island)
Reporting period: 2022-08-15 to 2024-12-14
Oceanians are descendants of two main migration waves: the early migration of modern humans out-of-Africa more than 50,000 years ago (ya), which ended in the Solomon Islands (Near Oceania); and the Austronesian expansion, that started around 5,000 ya in Taiwan and peopled the remaining unexplored territories of Oceania (Remote Oceania) up to Polynesia from 3,000 ya. Subsequent migrations within the region created a complex genetic landscape. Near Oceanian populations moved into Remote Oceania at least 2,500 ya. Afterwards, Polynesian-speaking groups expanded westward, outside the Polynesian triangle, to other Oceanian islands, giving rise to the so-called Polynesian Outlier communities. This complex population history created a highly differentiated sociocultural background among Oceanians, whose interactions remain poorly understood. We will leverage genetic data for 114 ancient individuals from the ancient Polynesian Outlier site of Namu, a burial in the Taumako island, occupied from the 13th to the 18th centuries AD. We will use cutting-edge paleogenomic methods to study: the settlement processes and the demographic history of Taumako, and the social organization and socioeconomic stratification of the Namu site, according to genetic ancestry, sex, kin relations and age. The ultimate objective is to understand the structure of Oceanian societies and its relation with their migratory history.
To achieve the main aims we structured the project in different working packages (WP) as detailed below.
WP1 Genomic data generation and processing.
We have analysed the genomic data of 114 individuals from the Namu site. The sequenced libraries were processed through EAGER pipeline including AdapterRemoval for clipping adapter sequences. Then, sequences were mapped against the human reference genome hg19 using BWA. Pseudo-haploid genotypes were generated for each individual using pileupCaller, which randomly calls alleles of the 1,240K-targeted SNPs at least once on high-quality bases. Subsequently, we performed a quality control on the sequence data. We assessed the ancient DNA damage with mapDamage2.0. We also estimated heterozygosity in polymorphic sites in the X chromosome of males with ANGSD. Finally, schmutzi was used to determine the mitochondrial human contamination in both males and females.
WP2 Demographic inferences from the Namu site.
Task1 Genetic sex inference. We calculated the normalized X-ratio (coverage on X chromosome SNPs/coverage on autosomal SNPs) and Y-ratio (coverage on Y chromosome SNPs/coverage on autosomal SNPs) per individual and infered genetic sex.
Task2. Populations genetic analyses. The 114 samples generated here were merged with previous sequenced and genotyped data from populations across the Pacific: Polynesian Outlier groups, including modern and ancient samples.
a) Uniparental markers. mtDNA haplogroups were identified per individual with Haplogrep and Y chromosome haplogroups using Yleaf.
b) Ancestry and population affinities. A general perspective on the genetic background of Taumako and their affinity with close populations was obtained by Principal Component Analyses and ADMIXTURE, projecting the ancient DNA samples to overcome the problems of missing data generated by DNA degradation. We used qpADM to estimate the genetic ancestry of the Namu and other Oceanian populations, from the different source populations. We compared the autosomal and X chromosome ancestry proportions to infer sex-biased during admixture.
Task3. Relatedness estimations. We combined different methods to infer the genetic relatedness between individuals. We ran KIN, READ and lcmlKIN, and compared the results of the three methods, which were globally consistent. Moreover, we used ancIBD to refine the genetic relatedness and infer up to 6th degree relatedness.
Task4. Parental relatedness and effective population size (Ne). We used hapROH method to infer the runs of homozygosity (ROH) of the Namu individuals. Then, we used this information to estimate the effective population size (Ne). Furthermore, we infered parental relatedness based on ROH.
WP3 Integration of data and analyses.
We integrated the different variables inferred, together with archaeological data in a series of linear regression models.
WP4 Interpretation of the results on the Pacific context within an interdisciplinary framework.
We interpreted our results relying on a network of experts of different disciplines. We applied a gender dimension perspective to our analyses, considering how gender contributed to the social structure of the studied population.
WP5 Dissemination in the scientific community and general public.
We have disseminated the project in the scientific community in different conferences and communications with collaborators. This is an ongoing process that will continue in the next years.
WP6 Career development and skills training.
At the beginning of the project, we developed the Career Development Program.
WP7 Project management.
WP1 Genomic data generation and processing.
We have analysed the genomic data of 114 individuals from the Namu site. The sequenced libraries were processed through EAGER pipeline including AdapterRemoval for clipping adapter sequences. Then, sequences were mapped against the human reference genome hg19 using BWA. Pseudo-haploid genotypes were generated for each individual using pileupCaller, which randomly calls alleles of the 1,240K-targeted SNPs at least once on high-quality bases. Subsequently, we performed a quality control on the sequence data. We assessed the ancient DNA damage with mapDamage2.0. We also estimated heterozygosity in polymorphic sites in the X chromosome of males with ANGSD. Finally, schmutzi was used to determine the mitochondrial human contamination in both males and females.
WP2 Demographic inferences from the Namu site.
Task1 Genetic sex inference. We calculated the normalized X-ratio (coverage on X chromosome SNPs/coverage on autosomal SNPs) and Y-ratio (coverage on Y chromosome SNPs/coverage on autosomal SNPs) per individual and infered genetic sex.
Task2. Populations genetic analyses. The 114 samples generated here were merged with previous sequenced and genotyped data from populations across the Pacific: Polynesian Outlier groups, including modern and ancient samples.
a) Uniparental markers. mtDNA haplogroups were identified per individual with Haplogrep and Y chromosome haplogroups using Yleaf.
b) Ancestry and population affinities. A general perspective on the genetic background of Taumako and their affinity with close populations was obtained by Principal Component Analyses and ADMIXTURE, projecting the ancient DNA samples to overcome the problems of missing data generated by DNA degradation. We used qpADM to estimate the genetic ancestry of the Namu and other Oceanian populations, from the different source populations. We compared the autosomal and X chromosome ancestry proportions to infer sex-biased during admixture.
Task3. Relatedness estimations. We combined different methods to infer the genetic relatedness between individuals. We ran KIN, READ and lcmlKIN, and compared the results of the three methods, which were globally consistent. Moreover, we used ancIBD to refine the genetic relatedness and infer up to 6th degree relatedness.
Task4. Parental relatedness and effective population size (Ne). We used hapROH method to infer the runs of homozygosity (ROH) of the Namu individuals. Then, we used this information to estimate the effective population size (Ne). Furthermore, we infered parental relatedness based on ROH.
WP3 Integration of data and analyses.
We integrated the different variables inferred, together with archaeological data in a series of linear regression models.
WP4 Interpretation of the results on the Pacific context within an interdisciplinary framework.
We interpreted our results relying on a network of experts of different disciplines. We applied a gender dimension perspective to our analyses, considering how gender contributed to the social structure of the studied population.
WP5 Dissemination in the scientific community and general public.
We have disseminated the project in the scientific community in different conferences and communications with collaborators. This is an ongoing process that will continue in the next years.
WP6 Career development and skills training.
At the beginning of the project, we developed the Career Development Program.
WP7 Project management.
The Namu project has provided scientific impact in different fields. We have applied updated methods in human population genetics and overcome some of the challenges of aDNA. Then, this project shows the state of the art of archaeogenetics and the possibilities of the field to solve questions related to the social structure in ancient human societies. Our work continues to enlarge the knowledge on human diversity, both cultural and genetic, and contributes to a more unbiased understanding of human past.
The results of the project will serve to scientists working on different disciplines with a focus on Oceania. But it will also be useful to researchers focused on other geographical regions interested in applying similar methods to their research questions.
The Namu project has already left a valuable impact on my career. I have participated in different trainings on leadership and career development. Moreover, I have participated on conferences and collaborated with researchers from different disciplines, which has enabled me to create a network of collaborations that will enhance my next research steps.
The results of the project will serve to scientists working on different disciplines with a focus on Oceania. But it will also be useful to researchers focused on other geographical regions interested in applying similar methods to their research questions.
The Namu project has already left a valuable impact on my career. I have participated in different trainings on leadership and career development. Moreover, I have participated on conferences and collaborated with researchers from different disciplines, which has enabled me to create a network of collaborations that will enhance my next research steps.