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The settlement of Sahul, the lost continent of Oceania, remains one of the most ancient and debated human migrations. Modern New Guineans inherited a unique genetic diversity tracing back 50,000 years, and yet there is currently no model reconstructing their past population dynamics. We generated 58 new whole-genome sequences from Papua New Guinea, filling geographical gaps in previous sampling, specifically to address alternative scenarios of the initial migration to Sahul and the settlement of New Guinea. Here, we present the first genomic models for the settlement of northeast Sahul considering one or two migrations from Wallacea. Both models fit our data set, reinforcing the idea that ancestral groups to New Guinean and Indigenous Australians split early, potentially during their migration in Wallacea where the northern route could have been favored. The earliest period of human presence in Sahul was an era of interactions and gene flow between related but already differentiated groups, from whom all modern New Guineans, Bismarck islanders, and Indigenous Australians descend. The settlement of New Guinea was probably initiated from its southeast region, where the oldest archaeological sites have been found. This was followed by two migrations into the south and north lowlands that ultimately reached the west and east highlands. We also identify ancient gene flows between populations in New Guinea, Australia, East Indonesia, and the Bismarck Archipelago, emphasizing the fact that the anthropological landscape during the early period of Sahul settlement was highly dynamic rather than the traditional view of extensive isolation. [ABSTRACT FROM AUTHOR]

Contemporary individuals are the combination of genetic fragments inherited from ancestors belonging to multiple populations, as the result of migration and admixture. Isolating and characterizing these layers are crucial to the understanding of the genetic history of a given population. Ancestry deconvolution approaches make use of a large amount of source individuals, therefore constraining the performance of Local Ancestry Inferences when only few genomes are available from a given population. Here we present WINC, a local ancestry framework derived from the combination of ChromoPainter and NNLS approaches, as a method to retrieve local genetic assignments when only a few reference individuals are available. The framework is aided by a score assignment based on source differentiation to maximize the amount of sequences retrieved and is capable of retrieving accurate ancestry assignments when only two individuals for source populations are used. [ABSTRACT FROM AUTHOR]

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases. Author summary: Generative neural networks have been effectively used in many different domains in the last decade, including machine dreamt photo-realistic imagery. In our work, we apply a similar concept to genetic data to automatically learn its structure and, for the first time, produce high quality realistic genomes. These novel genomes are distinct from the original ones used for training the generative networks. We show that artificial genomes, as we name them, retain many complex characteristics of real genomes and the heterogeneous relationships between individuals. They can be used in intricate analyses such as imputation of missing data as we demonstrated. We believe they have a high potential to become alternatives for many genome databases which are not publicly available or require long application procedures or collaborations and remove an important accessibility barrier in genomic research in particular for underrepresented populations. [ABSTRACT FROM AUTHOR]

Following the dispersal out of Africa, where hominins evolved in warm environments for millions of years, our species has colonised different climate zones of the world, including high latitudes and cold environments. The extent to which human habitation in (sub-)Arctic regions has been enabled by cultural buffering, short-term acclimatization and genetic adaptations is not clearly understood. Present day indigenous populations of Siberia show a number of phenotypic features, such as increased basal metabolic rate, low serum lipid levels and increased blood pressure that have been attributed to adaptation to the extreme cold climate. In this study we introduce a dataset of 200 individuals from ten indigenous Siberian populations that were genotyped for 730,525 SNPs across the genome to identify genes and non-coding regions that have undergone unusually rapid allele frequency and long-range haplotype homozygosity change in the recent past. At least three distinct population clusters could be identified among the Siberians, each of which showed a number of unique signals of selection. A region on chromosome 11 (chr11:66–69 Mb) contained the largest amount of clustering of significant signals and also the strongest signals in all the different selection tests performed. We present a list of candidate cold adaption genes that showed significant signals of positive selection with our strongest signals associated with genes involved in energy regulation and metabolism (CPT1A, LRP5, THADA) and vascular smooth muscle contraction (PRKG1). By employing a new method that paints phased chromosome chunks by their ancestry we distinguish local Siberian-specific long-range haplotype signals from those introduced by admixture. [ABSTRACT FROM AUTHOR]

The Genographic Project is an international effort aimed at charting human migratory history. The project is nonprofit and nonmedical, and, through its Legacy Fund, supports locally led efforts to preserve indigenous and traditional cultures. Although the first phase of the project was focused on uniparentally inherited markers on the Y-chromosome and mitochondrial DNA (mtDNA), the current phase focuses on markers from across the entire genome to obtain a more complete understanding of human genetic variation. Although many commercial arrays exist for genome-wide single-nucleotide polymorphism (SNP) genotyping, they were designed for medical genetic studies and contain medically related markers that are inappropriate for global population genetic studies. GenoChip, the Genographic Project’s new genotyping array, was designed to resolve these issues and enable higher resolution research into outstanding questions in genetic anthropology. The GenoChip includes ancestry informative markers obtained for over 450 human populations, an ancient human (Saqqaq), and two archaic hominins (Neanderthal and Denisovan) and was designed to identify all known Y-chromosome and mtDNA haplogroups. The chip was carefully vetted to avoid inclusion of medically relevant markers. To demonstrate its capabilities, we compared the FST distributions of GenoChip SNPs to those of two commercial arrays. Although all arrays yielded similarly shaped (inverse J) FST distributions, the GenoChip autosomal and X-chromosomal distributions had the highest mean FST, attesting to its ability to discern subpopulations. The chip performances are illustrated in a principal component analysis for 14 worldwide populations. In summary, the GenoChip is a dedicated genotyping platform for genetic anthropology. With an unprecedented number of approximately 12,000 Y-chromosomal and approximately 3,300 mtDNA SNPs and over 130,000 autosomal and X-chromosomal SNPs without any known health, medical, or phenotypic relevance, the GenoChip is a useful tool for genetic anthropology and population genetics. [ABSTRACT FROM PUBLISHER]

Arsenic is a carcinogen associated with skin lesions and cardiovascular diseases. The Colla population from the Puna region in Northwest Argentinean is exposed to levels of arsenic in drinking water exceeding the recommended maximum by a factor of 20. Yet, they thrive in this challenging environment since thousands of years and therefore we hypothesize strong selection signatures in genes involved in arsenic metabolism. We analyzed genome-wide genotype data for 730,000 loci in 25 Collas, considering 24 individuals of the neighbouring Calchaquíes and 24 Wichí from the Gran Chaco region in the Argentine province of Salta as control groups. We identified a strong signal of positive selection in the main arsenic methyltransferase AS3MT gene, which has been previously associated with lower concentrations of the most toxic product of arsenic metabolism monomethylarsonic acid. This study confirms recent studies reporting selection signals in the AS3MT gene albeit using different samples, tests and control populations. [ABSTRACT FROM AUTHOR]

Arctic populations live in an environment characterized by extreme cold and the absence of plant foods for much of the year and are likely to have undergone genetic adaptations to these environmental conditions in the time they have been living there. Genome-wide selection scans based on genotype data from native Siberians have previously highlighted a 3 Mb chromosome 11 region containing 79 protein-coding genes as the strongest candidates for positive selection in Northeast Siberians. However, it was not possible to determine which of the genes might be driving the selection signal. Here, using whole-genome high-coverage sequence data, we identified the most likely causative variant as a nonsynonymous G>A transition (rs80356779; c.1436C>T [p.Pro479Leu] on the reverse strand) in CPT1A , a key regulator of mitochondrial long-chain fatty-acid oxidation. Remarkably, the derived allele is associated with hypoketotic hypoglycemia and high infant mortality yet occurs at high frequency in Canadian and Greenland Inuits and was also found at 68% frequency in our Northeast Siberian sample. We provide evidence of one of the strongest selective sweeps reported in humans; this sweep has driven this variant to high frequency in circum-Arctic populations within the last 6–23 ka despite associated deleterious consequences, possibly as a result of the selective advantage it originally provided to either a high-fat diet or a cold environment. [ABSTRACT FROM AUTHOR]