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The Finnish population is a unique example of a genetic isolate affected by a recent founder event. Previous studies have suggested that the ancestors of Finnic-speaking Finns and Estonians reached the circum-Baltic region by the 1(st) millennium BC. However, high linguistic similarity points to a more recent split of their languages. To study genetic connectedness between Finns and Estonians directly, we first assessed the efficacy of imputation of low-coverage ancient genomes by sequencing a medieval Estonian genome to high depth (23×) and evaluated the performance of its down-sampled replicas. We find that ancient genomes imputed from >0.1× coverage can be reliably used in principal-component analyses without projection. By searching for long shared allele intervals (LSAIs; similar to identity-by-descent segments) in unphased data for >143,000 present-day Estonians, 99 Finns, and 14 imputed ancient genomes from Estonia, we find unexpectedly high levels of individual connectedness between Estonians and Finns for the last eight centuries in contrast to their clear differentiation by allele frequencies. High levels of sharing of these segments between Estonians and Finns predate the demographic expansion and late settlement process of Finland. One plausible source of this extensive sharing is the 8(th)–10(th) centuries AD migration event from North Estonia to Finland that has been proposed to explain uniquely shared linguistic features between the Finnish language and the northern dialect of Estonian and shared Christianity-related loanwords from Slavic. These results suggest that LSAI detection provides a computationally tractable way to detect fine-scale structure in large cohorts.

Pathogens imposed selective pressure on humans and shaped genetic variation in immunity genes. This can also be true for a fraction of causal variants implicated in chronic inflammatory disorders. Hence, locating adaptive mutations among candidate variants for these disorders can be a promising way to prioritize and decipher their functional response to microbial stimuli and contribution to pathogenesis. This idea has been discussed for decades, but challenges in locating adaptive SNPs hindered its application in practice. Our study addresses this issue and shows that a fraction of candidate variants for inflammatory conditions evolved under moderate and weak selection regimes (sweeps), and such variants are mappable. Using a novel powerful local-tree-based methodology, we show that 204 out of 593 risk loci for 21 autoimmune disorders contain at least one candidate SNP with strong evidence of selection. More importantly, in 28% of cases, these candidates for causal variants colocalize with SNPs under natural selection that we fine-mapped in this study. Causal SNPs under selection represent promising targets for functional experiments. Such experiments will help decipher molecular events triggered by infectious agents, a likely early event in autoimmunity. Finally, we show that a large fraction (60%) of candidate variants are either hitchhikers or linked with the selected mutation. Our findings, thus, support both hitchhiking and natural selection models, with the latter having important practical implications in medicine.

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 loci5–7 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 biobank13 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.

The contemporary European genetic makeup formed in the last 8000 years as the combination of three main genetic components: the local Western Hunter-Gatherers, the incoming Neolithic Farmers from Anatolia and the Bronze Age component from the Pontic Steppes. When meeting into the post-Neolithic European environment, the genetic variants accumulated during their three distinct evolutionary histories mixed and came into contact with new environmental challenges.Here we investigate how this genetic legacy reflects on the complex trait landscape of contemporary European populations, using the Estonian Biobank as a case study.For the first time we directly connect the phenotypic information available from biobank samples with the genetic similarity to these ancestral groups, both at a genome-wide level and focusing on genomic regions associated with each of the 27 complex traits we investigated. We also found SNPs connected to pigmentation, cholesterol, sleep, diastolic blood pressure, and body mass index (BMI) to show signals of selection following the post Neolithic admixture events. We recapitulate existing knowledge about pigmentation traits, corroborate the connection between Steppe ancestry and height and highlight novel associations. Among others, we report the contribution of Hunter Gatherer ancestry towards high BMI and low blood cholesterol levels.Our results show that the ancient components that form the contemporary European genome were differentiated enough to contribute ancestry-specific signatures to the phenotypic variability displayed by contemporary individuals in at least 11 out of 27 of the complex traits investigated here.

Anatomically modern humans evolved around 300 thousand years ago in Africa1. Modern humans started to appear in the fossil record outside of Africa about 100 thousand years ago though other hominins existed throughout Eurasia much earlier2–4. Recently, several researchers argued in favour of a single out of Africa event for modern humans based on whole-genome sequences analyses5–7. However, the single out of Africa model is in contrast with some of the findings from fossil records, which supports two out of Africa8,9, and uniparental data, which proposes back to Africa movement10,11. Here, we used a novel deep learning approach coupled with Approximate Bayesian Computation and Sequential Monte Carlo to revisit these hypotheses from the whole genome sequence perspective. Our results support the back to Africa model over other alternatives. We estimated that there are two successive splits between Africa and out of African populations happening around 60-80 thousand years ago and separated by 12-13 thousand years. One of the populations resulting from the more recent split has to a large extent replaced the older West African population while the other one has founded the out of Africa populations.

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.

Medieval era encounters of nomadic groups of the Eurasian Steppe and largely sedentary East Europeans had a variety of demographic and cultural consequences. Amongst these outcomes was the emergence of the Lipka Tatars—a Slavic-speaking Sunni-Muslim minority residing in modern Belarus, Lithuania and Poland, whose ancestors arrived in these territories via several migration waves, mainly from the Golden Horde. Our results show that Belarusian Lipka Tatars share a substantial part of their gene pool with Europeans as indicated by their Y-chromosomal, mitochondrial and autosomal DNA variation. Nevertheless, Belarusian Lipkas still retain a strong genetic signal of their nomadic ancestry, witnessed by the presence of common Y-chromosomal and mitochondrial DNA variants as well as autosomal segments identical by descent between Lipkas and East Eurasians from temperate and northern regions. Hence, we document Lipka Tatars as a unique example of former Medieval migrants into Central Europe, who became sedentary, changed language to Slavic, yet preserved their faith and retained, both uni- and bi-parentally, a clear genetic echo of a complex population interplay throughout the Eurasian Steppe Belt, extending from Central Europe to northern China.

Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, several studies argued in favor of a single out of Africa event for modern humans on the basis of whole-genome sequence analyses. However, the single out of Africa model is in contrast with some of the findings from fossil records, which support two out of Africa events, and uniparental data, which propose a back to Africa movement. Here, we used a deep-learning approach coupled with approximate Bayesian computation and sequential Monte Carlo to revisit these hypotheses from the whole-genome sequence perspective. Our results support the back to Africa model over other alternatives. We estimated that there are two sequential separations between Africa and out of African populations happening around 60-90 thousand years ago and separated by 13-15 thousand years. One of the populations resulting from the more recent split has replaced the older West African population to a large extent, while the other one has founded the out of Africa populations.

Highlanders and lowlanders of Papua New Guinea (PNG) have faced distinct environmental conditions. These environmental differences lead to specific stress on PNG highlanders and lowlanders, such as hypoxia and environment-specific pathogen exposure, respectively. We hypothesise that these constraints induced specific selective pressures that shaped the genomes of both populations. In this study, we explored signatures of selection in newly sequenced whole genomes of 54 PNG highlanders and 74 PNG lowlanders. Based on multiple methods to detect selection, we investigated the 21 and 23 genomic top candidate regions for positive selection in PNG highlanders and PNG lowlanders, respectively. To identify the most likely candidate SNP driving selection in each of these regions, we computationally reconstructed allele frequency trajectories of variants in each of these regions and chose the SNP with the highest likelihood of being under selection with CLUES. We show that regions with signatures of positive selection in PNG highlanders genomes encompass genes associated with the hypoxia-inducible factors pathway, brain development, blood composition, and immunity, while selected genomic regions in PNG lowlanders contain genes related to immunity and blood composition. We found that several candidate driver SNPs are associated with haematological phenotypes in the UK biobank. Moreover, using phenotypes measured from the sequenced Papuans, we found that two candidate SNPs are significantly associated with altered heart rates in PNG highlanders and lowlanders. Furthermore, we found that 16 of the 44 selection candidate regions harboured archaic introgression. In four of these regions, the selection signal might be driven by the introgressed archaic haplotypes, suggesting a significant role of archaic admixture in local adaptation in PNG populations.