Phenotypic differences between highlanders and lowlanders in Papua New Guinea

Objectives Altitude is one of the most demanding environmental pressures for human populations. Highlanders from Asia, America and Africa have been shown to exhibit different biological adaptations, but Oceanian populations remain understudied [Woolcock et al., 1972; Cotes et al., 1974; Senn et al., 2010]. We tested the hypothesis that highlanders phenotypically differ from lowlanders in Papua New Guinea, as a result of inhabiting the highest mountains in Oceania for at least 20,000 years. Materials and methods We collected data for 13 different phenotypes related to altitude for 162 Papua New Guineans living at high altitude (Mont Wilhelm, 2,300-2,700 m above sea level (a.s.l.) and low altitude (Daru, <100m a.s.l.). Multilinear regressions were performed to detect differences between highlanders and lowlanders for phenotypic measurements related to body proportions, pulmonary function, and the circulatory system. Results Six phenotypes were significantly different between Papua New Guinean highlanders and lowlanders. Highlanders show shorter height (p-value = 0.001), smaller waist circumference (p-value = 0.002), larger Forced Vital Capacity (FVC) (p-value = 0.008), larger maximal (pvalue = 3.20e -4) and minimal chest depth (p-value = 2.37e -5) and higher haemoglobin concentration (p-value = 3.36e -4). Discussion Our study reports specific phenotypes in Papua New Guinean highlanders potentially related to altitude adaptation. Similar to other human groups adapted to high altitude, the evolutionary history of Papua New Guineans appears to have also followed an adaptive biological strategy for altitude

Imputed genomes and haplotype-based analyses of the Picts of early medieval Scotland reveal fine-scale relatedness between Iron Age, early medieval and the modern people of the UK.

There are longstanding questions about the origins and ancestry of the Picts of early medieval Scotland (ca. 300-900 CE), prompted in part by exotic medieval origin myths, their enigmatic symbols and inscriptions, and the meagre textual evidence. The Picts, first mentioned in the late 3rd century CE resisted the Romans and went on to form a powerful kingdom that ruled over a large territory in northern Britain. In the 9th and 10th centuries Gaelic language, culture and identity became dominant, transforming the Pictish realm into Alba, the precursor to the medieval kingdom of Scotland. To date, no comprehensive analysis of Pictish genomes has been published, and questions about their biological relationships to other cultural groups living in Britain remain unanswered. Here we present two high-quality Pictish genomes (2.4 and 16.5X coverage) from central and northern Scotland dated from the 5th-7th century which we impute and co-analyse with >8,300 previously published ancient and modern genomes. Using allele frequency and haplotype-based approaches, we can firmly place the genomes within the Iron Age gene pool in Britain and demonstrate regional biological affinity. We also demonstrate the presence of population structure within Pictish groups, with Orcadian Picts being genetically distinct from their mainland contemporaries. When investigating Identity-By-Descent (IBD) with present-day genomes, we observe broad affinities between the mainland Pictish genomes and the present-day people living in western Scotland, Wales, Northern Ireland and Northumbria, but less with the rest of England, the Orkney islands and eastern Scotland-where the political centres of Pictland were located. The pre-Viking Age Orcadian Picts evidence a high degree of IBD sharing across modern Scotland, Wales, Northern Ireland, and the Orkney islands, demonstrating substantial genetic continuity in Orkney for the last ~2,000 years. Analysis of mitochondrial DNA diversity at the Pictish cemetery of Lundin Links (n = 7) reveals absence of direct common female ancestors, with implications for broader social organisation. Overall, our study provides novel insights into the genetic affinities and population structure of the Picts and direct relationships between ancient and present-day groups of the UK

Reconstructing past human genetic variation with ancient DNA: case studies from ancient Egypt and medieval Europe

Recent advances in palaeogenomics have massively improved our knowledge of human history. An explosion in the number of sequenced ancient human genomes has allowed direct exploration of ancestry dispersion, and processes of assimilation of these ancestries (e.g., admixture, replacement). Despite this, some regions and periods remain under-studied. In this thesis, DNA is recovered from populations yet to be characterized in order to use genomics to answer archaeological and anthropological questions. Despite the rapidly growing availability of ancient genomes from western Eurasia, the Picts of early medieval Scotland (ca. 300-900 CE) are under-represented among these genomes and remain poorly characterised. Chapter 2 describes the genetic relationship of the Picts with Iron Age, early medieval and present-day populations throughout Eurasia. Genome-wide data (0.1 – 16.5X coverage) were retrieved from four individuals representing two Pictish sites (5th-7th centuries CE), Balintore (Easter Ross, northern Pictland) and Lundin Links (Fife, southern Pictland), and mitochondrial genomes from seven individuals from a cemetery at Lundin Links. Using allele frequency-based methods (PCA, ADMIXTURE, f-statistics, qpAdm), their affinities to a reference panel of ancient and modern genomes were assessed. Mitochondrial DNA (mtDNA) haplogroups were reconstructed to gain insight into social organisation at Lundin Links. Two Picts (BAL003 and LUN004) were genetically similar to north-western people dated from the Iron Age and early medieval period; the two remaining Picts (LUN001 and LUN003) showed admixture from a European source and a source related to present-day Native Americans, previously widely spread in eastern Eurasia, with uncertainties concerning the authenticity of this signal. At Lundin Links, the individuals did not share common maternal ancestors, consistent with high female exogamy at the site. Chapter 3 assess the fine-scale relationship of the Picts with western Europeans dated from the Iron Age onward. A new combined set of imputed ancient European genomes (n = 285, coverage 0.7-16X), including re-imputed diploid genomes dated to the Iron Age through medieval periods from Britain, Iceland, Scandinavia and central Europe, were used to analyse the Pictish genomes with several haplotype-based methods (identity-by-descent, ChromoPainter and FineSTRUCTURE). These high-resolution techniques allow for a refined assessment of the biological relatedness of the Picts to populations of north-western Europe. Overall, the Picts showed a greater relative genetic affinity with Iron Age populations from the British Isles than with ancient Europeans. In addition, the Picts are genetically similar to present-day Welsh, Northern Irish and Scottish populations. The data presented here highlight the advantages of using high-quality DNA sequences, coupled with new haplotype-based analytic tools, to disentangle complex and recent demographic history between ancient populations. Compared with Europe, palaeogenetics in Africa is poorly studied, in part because DNA degrades faster in tropical and dry environments. Chapter 4 aims to unveil population movements in Egypt and Sudan from the Neolithic onward. DNA was extracted from 94 samples from Armant (Egypt), Nuerat (Egypt) and Ghaba (Sudan) dated from the Early Neolithic to the historic period. Genome-wide data were successfully recovered from one sample from Nuerat sequenced to 0.22X coverage, dated to 2,868-2,492 cal BCE (95.4% probability) - consistent with the 3rd-4th Dynasties of the Old Kingdom. Allele frequency-based analyses (PCA, ADMIXTURE, f-statistics, qpAdm) show a strong genetic affinity of this sample to Levantine Natufians. Compared with genomes dated from the end of the Dynastic period (Third Intermediate Period) and present-day Egyptians, the Nuerat sample did not carry the Caucasus Hunter-Gatherer genetic component that started to spread across West Asia ~4,000 years ago and is widely spread in present-day populations. The presence of this component in Egypt is likely associated with admixture between local Egyptian populations and Bronze Age-related populations from West Asia. This admixture pattern might result from the dominance of Lower Egypt by Canaanite (Levantine) rulers during the Second Intermediate Period (ca. 1,650-1,550 BCE). Even though optimised wet-laboratory techniques improved retrieval of aDNA, it is still a challenging technique, with a higher proportion of failure than success, especially for samples from tropical and sub-tropical regions. Alternative methods are required. Tooth crown and root traits, like those in the Arizona State University Dental Anthropology System (ASUDAS), are seemingly useful as genetic proxies. Chapter 5 investigates whether such traits can approximate genetic relatedness in the absence of aDNA, among continental and global samples. For 12 African and 32 global populations, Mantel correlations were calculated between the mean measure of divergence (MMD) distances from up to 36 ASUDAS traits, and FST distances from >350,000 single nucleotide polymorphisms (SNPs) among matched dental and genetic samples. A close ASUDAS/SNP association, based on MMD and FST correlations, is evident, with rm-values between 0.84 in Africa and 0.72 globally. Partial MMD/FST correlations controlling for geographic distances are strong for Africa (0.78) and moderate globally (0.4). Relative to prior studies, MMD/FST correlations imply greater dental and genetic correspondence. The implication is that ASUDAS traits can be used instead of genomic markers when the latter are unavailable

Suivi téléphonique des patients testés positifs au SARS-CoV-2 au Département d’oncologie du CHUV [Telephone follow-up of SARS-CoV-2 positive patients at the Oncology Department of Lausanne University Hospital]

Compared with the general population, oncology patients face a higher morbidity and mortality caused by the COVID-19 pandemic. As a result, health systems had to quickly adapt cancer care in order to maintain the best quality and patient safety. From March to May and from October to December 2020, 254 patients diagnosed with cancer and tested positive for SARS-CoV-2 benefited from a tele-health monitoring at the Oncology Department at CHUV. This article describes the key points of the development, implementation and operation of this tele-health monitoring, enabled by an interdisciplinary and inter-professional collaboration between different units and healthcare professionals

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [694665]; French government, through the 3IA Cote DAzur Investments in the Future project [ANR-19-P3IA-0002]; EPSRCUK Research &amp; Innovation (UKRI)Engineering &amp; Physical Sciences Research Council (EPSRC) [EP/N018702/1, MR/T020296/1, ISLRA-2009]; European Space AgencyEuropean Space AgencyEuropean Commission; Belgian Science Policy Office-ProdexBelgian Federal Science Policy Office; Research Foundation Flanders (FWO Vlaanderen)FWO [12M3119N, G0D7216N]; Wellcome Trust Investigator AwardWellcome Trust [096646/Z/11/Z]; Wellcome Trust Strategic AwardWellcome Trust [104943/Z/14/Z]; Polish National Agency for Academic ExchangePolish National Agency for Academic Exchange (NAWA) [PN/BEK/2019/1/00421]; Ministry of Science and Higher Education (Poland)Ministry of Science and Higher Education, Poland [692/STYP/13/2018]; AGH Science and Technology, Poland [16.16.120.773]; Linkoping University (LiU) Center for Industrial Information Technology (CENIIT); LiU Cancer [VINNOVA/ITEA3 17021 IMPACT]; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RMX18-0056]; "la Caixa" FoundationLa Caixa Foundation [100010434]; European UnionEuropean Commission [847648, LCF/BQ/PI20/11760029]; Ministerio de Ciencia e Innovacion" of SpainSpanish Government [RTI2018-094569-B-I00]; National Institute for Biomedical Imaging [5R01EB027585-02]</p