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The genetic structure in Europe was mostly shaped by admixture between the Western Hunter-Gatherers, Early European Farmers and Steppe Bronze Age ancestral components. Such structure is regarded as a confounder in GWAS and follow-up studies, and gold-standard methods exist to correct for it. However, it is still poorly understood to which extent these ancestral components contribute to complex trait variation in present-day Europe. In this work we harness the UK Biobank to address this question. By extensive demographic simulations, exploiting data on siblings and incorporating previous results we obtained from the Estonian Biobank, we carefully evaluate the significance and scope of our findings. Heart rate, platelet count, bone mineral density and many other traits show stratification similar to height and pigmentation traits, likely targets of selection and divergence across ancestral groups. We show that the reported ancestry-trait associations are not driven by environmental confounders by confirming our results when using between-sibling differences in ancestry. The consistency of our results across biobanks further supports this and indicates that these genetic predispositions that derive from post-Neolithic admixture events act as a source of variability and as potential confounders in Europe as a whole., (© 2024. The Author(s).)

Individuals infected with the SARS-CoV-2 virus present with a wide variety of symptoms ranging from asymptomatic to severe and even lethal outcomes. Past research has revealed a genetic haplotype on chromosome 3 that entered the human population via introgression from Neanderthals as the strongest genetic risk factor for the severe response to COVID-19. However, the specific variants along this introgressed haplotype that contribute to this risk and the biological mechanisms that are involved remain unclear. Here, we assess the variants present on the risk haplotype for their likelihood of driving the genetic predisposition to severe COVID-19 outcomes. We do this by first exploring their impact on the regulation of genes involved in COVID-19 infection using a variety of population genetics and functional genomics tools. We then perform a locus-specific massively parallel reporter assay to individually assess the regulatory potential of each allele on the haplotype in a multipotent immune-related cell line. We ultimately reduce the set of over 600 linked genetic variants to identify four introgressed alleles that are strong functional candidates for driving the association between this locus and severe COVID-19. Using reporter assays in the presence/absence of SARS-CoV-2 , we find evidence that these variants respond to viral infection. These variants likely drive the locus' impact on severity by modulating the regulation of two critical chemokine receptor genes: CCR1 and CCR5 . These alleles are ideal targets for future functional investigations into the interaction between host genomics and COVID-19 outcomes., Competing Interests: EJ, DM, GS, VG, KB, FM, DR, DF, EL, CC, AB, LP, TC No competing interests declared, (© 2023, Jagoda, Marnetto et al.)

One important confounder in genome-wide association studies (GWASs) is population genetic structure, which may generate spurious associations if not properly accounted for. This may ultimately result in a biased polygenic risk score (PRS) prediction, especially when applied to another population. To explore this matter, we focused on principal component analysis (PCA) and asked whether a population genetics informed strategy focused on PCs derived from an external reference population helps in mitigating this PRS transferability issue. Throughout the study, we used two complex model traits, height and body mass index, and samples from UK and Estonian Biobanks. We aimed to investigate 1) whether using a reference population (1000G) for computation of the PCs adjusted for in the discovery cohort improves the resulting PRS performance in a target set from another population and 2) whether adjusting the validation model for PCs is required at all. Our results showed that any other set of PCs performed worse than the one computed on samples from the same population as the discovery dataset. Furthermore, we show that PC correction in GWAS cannot prevent residual population structure information in the PRS, also for non-structured traits. Therefore, we confirm the utility of PC correction in the validation model when the investigated trait shows an actual correlation with population genetic structure, to account for the residual confounding effect when evaluating the predictive value of PRS., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pärna, Nolte, Snieder, Fischer, Marnetto and Pagani.)

The contemporary European genetic makeup formed in the last 8,000 years when local Western Hunter-Gatherers (WHGs) mixed with incoming Anatolian Neolithic farmers and Pontic Steppe pastoralists. 1-3 This encounter combined genetic variants with distinct evolutionary histories and, together with new environmental challenges faced by the post-Neolithic Europeans, unlocked novel adaptations. 4 Previous studies inferred phenotypes in these source populations, using either a few single loci 5-7 or polygenic scores based on genome-wide association studies, 8-10 and investigated the strength and timing of natural selection on lactase persistence or height, among others. 6 , 11 , 12 However, how ancient populations contributed to present-day phenotypic variation is poorly understood. Here, we investigate how the unique tiling of genetic variants inherited from different ancestral components drives the complex traits landscape of contemporary Europeans and quantify selection patterns associated with these components. Using matching individual-level genotype and phenotype data for 27 traits in the Estonian biobank 13 and genotype data directly from the ancient source populations, we quantify the contributions from each ancestry to present-day phenotypic variation in each complex trait. We find substantial differences in ancestry for eye and hair color, body mass index, waist/hip circumferences, and their ratio, height, cholesterol levels, caffeine intake, heart rate, and age at menarche. Furthermore, we find evidence for recent positive selection linked to four of these traits and, in addition, sleep patterns and blood pressure. Our results show that these ancient components were differentiated enough to contribute ancestry-specific signatures to the complex trait variability displayed by contemporary Europeans., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

American populations are one of the most interesting examples of recently admixed groups, where ancestral components from three major continental human groups (Africans, Eurasians and Native Americans) have admixed within the last 15 generations. Recently, several genetic surveys focusing on thousands of individuals shed light on the geography, chronology and relevance of these events. However, even though gene flow could drive adaptive evolution, it is unclear whether and how natural selection acted on the resulting genetic variation in the Americas. In this study, we analysed the patterns of local ancestry of genomic fragments in genome-wide data for ~ 6000 admixed individuals from 10 American countries. In doing so, we identified regions characterized by a divergent ancestry profile (DAP), in which a significant over or under ancestral representation is evident. Our results highlighted a series of genomic regions with DAPs associated with immune system response and relevant medical traits, with the longest DAP region encompassing the human leukocyte antigen locus. Furthermore, we found that DAP regions are enriched in genes linked to cancer-related traits and autoimmune diseases. Then, analysing the biological impact of these regions, we showed that natural selection could have acted preferentially towards variants located in coding and non-coding transcripts and characterized by a high deleteriousness score. Taken together, our analyses suggest that shared patterns of post admixture adaptation occurred at a continental scale in the Americas, affecting more often functional and impactful genomic variants., (© The Author(s) 2021. Published by Oxford University Press.)

A general imbalance in the proportion of disembarked males and females in the Americas has been documented during the Trans-Atlantic Slave Trade and the Colonial Era and, although less prominent, more recently. This imbalance may have left a signature on the genomes of modern-day populations characterised by high levels of admixture. The analysis of the uniparental systems and the evaluation of continental proportion ratio of autosomal and X chromosomes revealed a general sex imbalance towards males for European and females for African and Indigenous American ancestries. However, the consistency and degree of this imbalance are variable, suggesting that other factors, such as cultural and social practices, may have played a role in shaping it. Moreover, very few investigations have evaluated the sex imbalance using haplotype data, containing more critical information than genotypes. Here, we analysed genome-wide data for more than 5000 admixed American individuals to assess the presence, direction and magnitude of sex-biased admixture in the Americas. For this purpose, we applied two haplotype-based approaches, ELAI and NNLS, and we compared them with a genotype-based method, ADMIXTURE. In doing so, besides a general agreement between methods, we unravelled that the post-colonial admixture dynamics show higher complexity than previously described.

Detecting natural selection signals in admixed populations can be problematic since the source of the signal typically dates back prior to the admixture event. On one hand, it is now possible to study various source populations before a particular admixture thanks to the developments in ancient DNA (aDNA) in the last decade. However, aDNA availability is limited to certain geographical regions and the sample sizes and quality of the data might not be sufficient for selection analysis in many cases. In this study, we explore possible ways to improve detection of pre-admixture signals in admixed populations using a local ancestry inference approach. We used masked haplotypes for population branch statistic (PBS) and full haplotypes constructed following our approach from Yelmen et al. (2019) for cross-population extended haplotype homozygosity (XP-EHH), utilizing forward simulations to test the power of our analysis. The PBS results on simulated data showed that using masked haplotypes obtained from ancestry deconvolution instead of the admixed population might improve detection quality. On the other hand, XP-EHH results using the admixed population were better compared with the local ancestry method. We additionally report correlation for XP-EHH scores between source and admixed populations, suggesting that haplotype-based approaches must be used cautiously for recently admixed populations. Additionally, we performed PBS on real South Asian populations masked with local ancestry deconvolution and report here the first possible selection signals on the autochthonous South Asian component of contemporary South Asian populations., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

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., Competing Interests: The authors have declared that no competing interests exist.

Several recent studies detected fine-scale genetic structure in human populations. Hence, groups conventionally treated as single populations harbour significant variation in terms of allele frequencies and patterns of haplotype sharing. It has been shown that these findings should be considered when performing studies of genetic associations and natural selection, especially when dealing with polygenic phenotypes. However, there is little understanding of the practical effects of such genetic structure on demography reconstructions and selection scans when focusing on recent population history. Here we tested the impact of population structure on such inferences using high-coverage (~30×) genome sequences of 2305 Estonians. We show that different regions of Estonia differ in both effective population size dynamics and signatures of natural selection. By analyzing identity-by-descent segments we also reveal that some Estonian regions exhibit evidence of a bottleneck 10-15 generations ago reflecting sequential episodes of wars, plague and famine, although this signal is virtually undetected when treating Estonia as a single population. Besides that, we provide a framework for relating effective population size estimated from genetic data to actual census size and validate it on the Estonian population. This approach may be widely used both to cross-check estimates based on historical sources as well as to get insight into times and/or regions with no other information available. Our results suggest that the history of human populations within the last few millennia can be highly region specific and cannot be properly studied without taking local genetic structure into account.

Polygenic Scores (PSs) describe the genetic component of an individual's quantitative phenotype or their susceptibility to diseases with a genetic basis. Currently, PSs rely on population-dependent contributions of many associated alleles, with limited applicability to understudied populations and recently admixed individuals. Here we introduce a combination of local ancestry deconvolution and partial PS computation to account for the population-specific nature of the association signals in individuals with admixed ancestry. We demonstrate partial PS to be a proxy for the total PS and that a portion of the genome is enough to improve susceptibility predictions for the traits we test. By combining partial PSs from different populations, we are able to improve trait predictability in admixed individuals with some European ancestry. These results may extend the applicability of PSs to subjects with a complex history of admixture, where current methods cannot be applied.

The presence of genomic signatures of Eurasian origin in contemporary Ethiopians has been reported by several authors and estimated to have arrived in the area from 3000 years ago. Several studies reported plausible source populations for such a signature, using haplotype based methods on modern data or single-site methods on modern or ancient data. These studies did not reach a consensus and suggested an Anatolian or Sardinia-like proxy, broadly Levantine or Neolithic Levantine as possible sources. We demonstrate, however, that the deeply divergent, autochthonous African component which accounts for ~50% of most contemporary Ethiopian genomes, affects the overall allele frequency spectrum to an extent that makes it hard to control for it and, at once, to discern between subtly different, yet important, Eurasian sources (such as Anatolian or Levant Neolithic ones). Here we re-assess pattern of allele sharing between the Eurasian component of Ethiopians (here called "NAF" for Non African) and ancient and modern proxies. Our results unveil a genomic legacy that may connect the Eurasian genetic component of contemporary Ethiopians with Sea People and with population movements that affected the Mediterranean area and the Levant after the fall of the Minoan civilization.

The human genetic diversity of the Americas has been affected by several events of gene flow that have continued since the colonial era and the Atlantic slave trade. Moreover, multiple waves of migration followed by local admixture occurred in the last two centuries, the impact of which has been largely unexplored. Here, we compiled a genome-wide dataset of ∼12,000 individuals from twelve American countries and ∼6,000 individuals from worldwide populations and applied haplotype-based methods to investigate how historical movements from outside the New World affected (1) the genetic structure, (2) the admixture profile, (3) the demographic history, and (4) sex-biased gene-flow dynamics of the Americas. We revealed a high degree of complexity underlying the genetic contribution of European and African populations in North and South America, from both geographic and temporal perspectives, identifying previously unreported sources related to Italy, the Middle East, and to specific regions of Africa., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Genetic variation in contemporary South Asian populations follows a northwest to southeast decreasing cline of shared West Eurasian ancestry. A growing body of ancient DNA evidence is being used to build increasingly more realistic models of demographic changes in the last few thousand years. Through high-quality modern genomes, these models can be tested for gene and genome level deviations. Using local ancestry deconvolution and masking, we reconstructed population-specific surrogates of the two main ancestral components for more than 500 samples from 25 South Asian populations and showed our approach to be robust via coalescent simulations. Our f3 and f4 statistics-based estimates reveal that the reconstructed haplotypes are good proxies for the source populations that admixed in the area and point to complex interpopulation relationships within the West Eurasian component, compatible with multiple waves of arrival, as opposed to a simpler one wave scenario. Our approach also provides reliable local haplotypes for future downstream analyses. As one such example, the local ancestry deconvolution in South Asians reveals opposite selective pressures on two pigmentation genes (SLC45A2 and SLC24A5) that are common or fixed in West Eurasians, suggesting post-admixture purifying and positive selection signals, respectively., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Genome expansion is believed to be an important driver of the evolution of gene regulation. To investigate the role of a newly arising sequence in rewiring regulatory networks, we estimated the age of each region of the human genome by applying maximum parsimony to genome-wide alignments with 100 vertebrates. We then studied the age distribution of several types of functional regions, with a focus on regulatory elements. The age distribution of regulatory elements reveals the extensive use of newly formed genomic sequence in the evolution of regulatory interactions. Many transcription factors have expanded their repertoire of targets through waves of genomic expansions that can be traced to specific evolutionary times. Repeated elements contributed a major part of such expansion: many classes of such elements are enriched in binding sites of one or a few specific transcription factors, whose binding sites are localized in specific portions of the element and characterized by distinctive motif words. These features suggest that the binding sites were available as soon as the new sequence entered the genome, rather than being created later by accumulation of point mutations. By comparing the age of regulatory regions to the evolutionary shift in expression of nearby genes, we show that rewiring through genome expansion played an important role in shaping human regulatory networks., (Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

FADS genes encode fatty acid desaturases that are important for the conversion of short chain polyunsaturated fatty acids (PUFAs) to long chain fatty acids. Prior studies indicate that the FADS genes have been subjected to strong positive selection in Africa, South Asia, Greenland, and Europe. By comparing FADS sequencing data from present-day and Bronze Age (5-3k years ago) Europeans, we identify possible targets of selection in the European population, which suggest that selection has targeted different alleles in the FADS genes in Europe than it has in South Asia or Greenland. The alleles showing the strongest changes in allele frequency since the Bronze Age show associations with expression changes and multiple lipid-related phenotypes. Furthermore, the selected alleles are associated with a decrease in linoleic acid and an increase in arachidonic and eicosapentaenoic acids among Europeans; this is an opposite effect of that observed for selected alleles in Inuit from Greenland. We show that multiple SNPs in the region affect expression levels and PUFA synthesis. Additionally, we find evidence for a gene-environment interaction influencing low-density lipoprotein (LDL) levels between alleles affecting PUFA synthesis and PUFA dietary intake: carriers of the derived allele display lower LDL cholesterol levels with a higher intake of PUFAs. We hypothesize that the selective patterns observed in Europeans were driven by a change in dietary composition of fatty acids following the transition to agriculture, resulting in a lower intake of arachidonic acid and eicosapentaenoic acid, but a higher intake of linoleic acid and α-linolenic acid., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Comparisons of DNA from archaic and modern humans show that these groups interbred, and in some cases received an evolutionary advantage from doing so. This process-adaptive introgression-may lead to a faster rate of adaptation than is predicted from models with mutation and selection alone. Within the last couple of years, a series of studies have identified regions of the genome that are likely examples of adaptive introgression. In many cases, once a region was ascertained as being introgressed, commonly used statistics based on both haplotype as well as allele frequency information were employed to test for positive selection. Introgression by itself, however, changes both the haplotype structure and the distribution of allele frequencies, thus confounding traditional tests for detecting positive selection. Therefore, patterns generated by introgression alone may lead to false inferences of positive selection. Here we explore models involving both introgression and positive selection to investigate the behavior of various statistics under adaptive introgression. In particular, we find that the number and allelic frequencies of sites that are uniquely shared between archaic humans and specific present-day populations are particularly useful for detecting adaptive introgression. We then examine the 1000 Genomes dataset to characterize the landscape of uniquely shared archaic alleles in human populations. Finally, we identify regions that were likely subject to adaptive introgression and discuss some of the most promising candidate genes located in these regions., (© The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Access to a geographically diverse set of modern human samples from the present time and from ancient remains, combined with archaic hominin samples, provides an unprecedented level of resolution to study both human history and adaptation. The amount and quality of ancient human data continue to improve and enable tracking the trajectory of genetic variation over time. These data have the potential to help us redefine or generate new hypotheses of how human evolution occurred and to revise previous conjectures. In this article, we argue that leveraging all these data will help us better detail adaptive histories in humans. As a case in point, we focus on one of the most celebrated examples of human adaptation: the evolution of lactase persistence. We briefly review this dietary adaptation and argue that, effectively, the evolutionary history of lactase persistence is still not fully resolved. We propose that, by leveraging data from multiple populations across time and space, we will find evidence of a more nuanced history than just a simple selective sweep. We support our hypotheses with simulation results and make some cautionary notes regarding the use of haplotype-based summary statistics to estimate evolutionary parameters.

Changes in gene regulatory networks are believed to have played an important role in the development of human-specific anatomy and behavior. We identified the human genome regions that show the typical chromatin marks of regulatory regions but cannot be aligned to other mammalian genomes. Most of these regions have become fixed in the human genome. Their regulatory targets are enriched in genes involved in neural processes, CNS development, and diseases such as autism, depression, and schizophrenia. Specific transposable elements contributing to the rewiring of the human regulatory network can be identified by the creation of human-specific regulatory regions. Our results confirm the relevance of regulatory evolution in the emergence of human traits and cognitive abilities and the importance of newly acquired genomic elements for such evolution., (Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)