The genomic origins of the world's first farmers.

The precise genetic origins of the first Neolithic farming populations in Europe and Southwest Asia, as well as the processes and the timing of their differentiation, remain largely unknown. Demogenomic modeling of high-quality ancient genomes reveals that the early farmers of Anatolia and Europe emerged from a multiphase mixing of a Southwest Asian population with a strongly bottlenecked western hunter-gatherer population after the last glacial maximum. Moreover, the ancestors of the first farmers of Europe and Anatolia went through a period of extreme genetic drift during their westward range expansion, contributing highly to their genetic distinctiveness. This modeling elucidates the demographic processes at the root of the Neolithic transition and leads to a spatial interpretation of the population history of Southwest Asia and Europe during the late Pleistocene and early Holocene.

Genome-wide data from medieval German Jews show that the Ashkenazi founder event pre-dated the 14th century.

We report genome-wide data from 33 Ashkenazi Jews (AJ), dated to the 14th century, obtained following a salvage excavation at the medieval Jewish cemetery of Erfurt, Germany. The Erfurt individuals are genetically similar to modern AJ, but they show more variability in Eastern European-related ancestry than modern AJ. A third of the Erfurt individuals carried a mitochondrial lineage common in modern AJ and eight carried pathogenic variants known to affect AJ today. These observations, together with high levels of runs of homozygosity, suggest that the Erfurt community had already experienced the major reduction in size that affected modern AJ. The Erfurt bottleneck was more severe, implying substructure in medieval AJ. Overall, our results suggest that the AJ founder event and the acquisition of the main sources of ancestry pre-dated the 14th century and highlight late medieval genetic heterogeneity no longer present in modern AJ.

Human population history at the crossroads of East and Southeast Asia since 11,000 years ago.

Past human genetic diversity and migration between southern China and Southeast Asia have not been well characterized, in part due to poor preservation of ancient DNA in hot and humid regions. We sequenced 31 ancient genomes from southern China (Guangxi and Fujian), including two ∼12,000- to 10,000-year-old individuals representing the oldest humans sequenced from southern China. We discovered a deeply diverged East Asian ancestry in the Guangxi region that persisted until at least 6,000 years ago. We found that ∼9,000- to 6,000-year-old Guangxi populations were a mixture of local ancestry, southern ancestry previously sampled in Fujian, and deep Asian ancestry related to Southeast Asian Hòabìnhian hunter-gatherers, showing broad admixture in the region predating the appearance of farming. Historical Guangxi populations dating to ∼1,500 to 500 years ago are closely related to Tai-Kadai and Hmong-Mien speakers. Our results show heavy interactions among three distinct ancestries at the crossroads of East and Southeast Asia.

Identifying and Interpreting Apparent Neanderthal Ancestry in African Individuals.

Admixture has played a prominent role in shaping patterns of human genomic variation, including gene flow with now-extinct hominins like Neanderthals and Denisovans. Here, we describe a novel probabilistic method called IBDmix to identify introgressed hominin sequences, which, unlike existing approaches, does not use a modern reference population. We applied IBDmix to 2,504 individuals from geographically diverse populations to identify and analyze Neanderthal sequences segregating in modern humans. Strikingly, we find that African individuals carry a stronger signal of Neanderthal ancestry than previously thought. We show that this can be explained by genuine Neanderthal ancestry due to migrations back to Africa, predominately from ancestral Europeans, and gene flow into Neanderthals from an early dispersing group of humans out of Africa. Our results refine our understanding of Neanderthal ancestry in African and non-African populations and demonstrate that remnants of Neanderthal genomes survive in every modern human population studied to date.

Genomic History of Neolithic to Bronze Age Anatolia, Northern Levant, and Southern Caucasus.

Here, we report genome-wide data analyses from 110 ancient Near Eastern individuals spanning the Late Neolithic to Late Bronze Age, a period characterized by intense interregional interactions for the Near East. We find that 6th millennium BCE populations of North/Central Anatolia and the Southern Caucasus shared mixed ancestry on a genetic cline that formed during the Neolithic between Western Anatolia and regions in today's Southern Caucasus/Zagros. During the Late Chalcolithic and/or the Early Bronze Age, more than half of the Northern Levantine gene pool was replaced, while in the rest of Anatolia and the Southern Caucasus, we document genetic continuity with only transient gene flow. Additionally, we reveal a genetically distinct individual within the Late Bronze Age Northern Levant. Overall, our study uncovers multiple scales of population dynamics through time, from extensive admixture during the Neolithic period to long-distance mobility within the globalized societies of the Late Bronze Age. VIDEO ABSTRACT.

Large-Scale Whole-Genome Sequencing of Three Diverse Asian Populations in Singapore.

Underrepresentation of Asian genomes has hindered population and medical genetics research on Asians, leading to population disparities in precision medicine. By whole-genome sequencing of 4,810 Singapore Chinese, Malays, and Indians, we found 98.3 million SNPs and small insertions or deletions, over half of which are novel. Population structure analysis demonstrated great representation of Asian genetic diversity by three ethnicities in Singapore and revealed a Malay-related novel ancestry component. Furthermore, demographic inference suggested that Malays split from Chinese ∼24,800 years ago and experienced significant admixture with East Asians ∼1,700 years ago, coinciding with the Austronesian expansion. Additionally, we identified 20 candidate loci for natural selection, 14 of which harbored robust associations with complex traits and diseases. Finally, we show that our data can substantially improve genotype imputation in diverse Asian and Oceanian populations. These results highlight the value of our data as a resource to empower human genetics discovery across broad geographic regions.

Analysis of Human Sequence Data Reveals Two Pulses of Archaic Denisovan Admixture.

Anatomically modern humans interbred with Neanderthals and with a related archaic population known as Denisovans. Genomes of several Neanderthals and one Denisovan have been sequenced, and these reference genomes have been used to detect introgressed genetic material in present-day human genomes. Segments of introgression also can be detected without use of reference genomes, and doing so can be advantageous for finding introgressed segments that are less closely related to the sequenced archaic genomes. We apply a new reference-free method for detecting archaic introgression to 5,639 whole-genome sequences from Eurasia and Oceania. We find Denisovan ancestry in populations from East and South Asia and Papuans. Denisovan ancestry comprises two components with differing similarity to the sequenced Altai Denisovan individual. This indicates that at least two distinct instances of Denisovan admixture into modern humans occurred, involving Denisovan populations that had different levels of relatedness to the sequenced Altai Denisovan. VIDEO ABSTRACT.

Genetic Adaptation and Neandertal Admixture Shaped the Immune System of Human Populations.

Humans differ in the outcome that follows exposure to life-threatening pathogens, yet the extent of population differences in immune responses and their genetic and evolutionary determinants remain undefined. Here, we characterized, using RNA sequencing, the transcriptional response of primary monocytes from Africans and Europeans to bacterial and viral stimuli-ligands activating Toll-like receptor pathways (TLR1/2, TLR4, and TLR7/8) and influenza virus-and mapped expression quantitative trait loci (eQTLs). We identify numerous cis-eQTLs that contribute to the marked differences in immune responses detected within and between populations and a strong trans-eQTL hotspot at TLR1 that decreases expression of pro-inflammatory genes in Europeans only. We find that immune-responsive regulatory variants are enriched in population-specific signals of natural selection and show that admixture with Neandertals introduced regulatory variants into European genomes, affecting preferentially responses to viral challenges. Together, our study uncovers evolutionarily important determinants of differences in host immune responsiveness between human populations.

KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies.

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.

Heritability within groups is uninformative about differences among groups: Cases from behavioral, evolutionary, and statistical genetics.

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give-directly or indirectly-some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the "between-group heritability" sometimes invoked in behavior genetics, the statistic [Formula: see text] used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

Denisovan admixture facilitated environmental adaptation in Papua New Guinean populations.

Neandertals and Denisovans, having inhabited distinct regions in Eurasia and possibly Oceania for over 200,000 y, experienced ample time to adapt to diverse environmental challenges these regions presented. Among present-day human populations, Papua New Guineans (PNG) stand out as one of the few carrying substantial amounts of both Neandertal and Denisovan DNA, a result of past admixture events with these archaic human groups. This study investigates the distribution of introgressed Denisovan and Neandertal DNA within two distinct PNG populations, residing in the highlands of Mt Wilhelm and the lowlands of Daru Island. These locations exhibit unique environmental features, some of which may parallel the challenges that archaic humans once confronted and adapted to. Our results show that PNG highlanders carry higher levels of Denisovan DNA compared to PNG lowlanders. Among the Denisovan-like haplotypes with higher frequencies in highlander populations, those exhibiting the greatest frequency difference compared to lowlander populations also demonstrate more pronounced differences in population frequencies than frequency-matched nonarchaic variants. Two of the five most highly differentiated of those haplotypes reside in genomic areas linked to brain development genes. Conversely, Denisovan-like haplotypes more frequent in lowlanders overlap with genes associated with immune response processes. Our findings suggest that Denisovan DNA has provided genetic variation associated with brain biology and immune response to PNG genomes, some of which might have facilitated adaptive processes to environmental challenges.

A general approach for inferring the ancestry of recent ancestors of an admixed individual.

The genome of an individual from an admixed population consists of segments originated from different ancestral populations. Most existing ancestry inference approaches focus on calling these segments for the extant individual. In this paper, we present a general ancestry inference approach for inferring recent ancestors from an extant genome. Given the genome of an individual from a recently admixed population, our method can estimate the proportions of the genomes of the recent ancestors of this individual that originated from some ancestral populations. The key step of our method is the inference of ancestors (called founders) right after the formation of an admixed population. The inferred founders can then be used to infer the ancestry of recent ancestors of an extant individual. Our method is implemented in a computer program called PedMix2. To the best of our knowledge, there is no existing method that can practically infer ancestors beyond grandparents from an extant individual's genome. Results on both simulated and real data show that PedMix2 performs well in ancestry inference.

The genetic admixture and assimilation of Ahom: a historic migrant from Thailand to India.

The Northeastern region of India is considered a gateway for modern humans' dispersal throughout Asia. This region is a mixture of various ethnic and indigenous populations amalgamating multiple ancestries. One reason for such amalgamation is that, South Asia experienced multiple historic migrations from various parts of the world. A few examples explored genetically are Jews, Parsis and Siddis. Ahom is a dynasty that historically migrated to India during the 12th century. However, this putative migration has not been studied genetically at high resolution. Therefore, to validate this historical evidence, we genotyped autosomal data of the Modern Ahom population residing in seven sister states of India. Principal Component and Admixture analyses haave suggested a substantial admixture of the Ahom population with the local Tibeto-Burman populations. Moreover, the haplotype-based analysis has linked these Ahom individuals mainly with the Kusunda (a language isolated from Nepal) and Khasi (an Austroasiatic population of Meghalaya). Such unexpected presence of widespread population affinities suggests that Ahom mixed and assimilated a wide variety of Trans-Himalayan populations inhabiting this region after the migration. In summary, we observed a significant deviation of Ahom from their ancestral homeland (Thailand) and extensive admixture and assimilation with the local South Asian populations.

Methods for Assessing Population Relationships and History Using Genomic Data.

Genetic data contain a record of our evolutionary history. The availability of large-scale datasets of human populations from various geographic areas and timescales, coupled with advances in the computational methods to analyze these data, has transformed our ability to use genetic data to learn about our evolutionary past. Here, we review some of the widely used statistical methods to explore and characterize population relationships and history using genomic data. We describe the intuition behind commonly used approaches, their interpretation, and important limitations. For illustration, we apply some of these techniques to genome-wide autosomal data from 929 individuals representing 53 worldwide populations that are part of the Human Genome Diversity Project. Finally, we discuss the new frontiers in genomic methods to learn about population history. In sum, this review highlights the power (and limitations) of DNA to infer features of human evolutionary history, complementing the knowledge gleaned from other disciplines, such as archaeology, anthropology, and linguistics.

Fast, accurate local ancestry inference with FLARE.

Local ancestry is the source ancestry at each point in the genome of an admixed individual. Inferred local ancestry is used for admixture mapping and population genetic analyses. We present FLARE (fast local ancestry estimation), a method for local ancestry inference. FLARE achieves high accuracy through the use of an extended Li and Stephens model, and it achieves exceptional computational performance through incorporation of computational techniques developed for genotype imputation. Memory requirements are reduced through on-the-fly compression of reference haplotypes and stored checkpoints. Computation time is reduced through the use of composite reference haplotypes. These techniques allow FLARE to scale to datasets with hundreds of thousands of sequenced individuals and to provide superior accuracy on large-scale data. FLARE is open source and available at https://github.com/browning-lab/flare.

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics.

The heritability explained by local ancestry markers in an admixed population (hγ2) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of hγ2 can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA hγ2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe hˆγ2 in the 20 phenotypes range from 0.0025 to 0.033 (mean hˆγ2 = 0.012 ± 9.2 × 10-4), which translates to hˆ2 ranging from 0.062 to 0.85 (mean hˆ2 = 0.30 ± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 ± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.

Challenges of accurately estimating sex-biased admixture from X chromosomal and autosomal ancestry proportions.

Sex-biased admixture can be inferred from ancestry-specific proportions of X chromosome and autosomes. In a paper published in the American Journal of Human Genetics, Micheletti et al.1 used this approach to quantify male and female contributions following the transatlantic slave trade. Using a large dataset from 23andMe, they concluded that African and European contributions to gene pools in the Americas were much more sex biased than previously thought. We show that the reported extreme sex-specific contributions can be attributed to unassigned genetic ancestry as well as the limitations of simple models of sex-biased admixture. Unassigned ancestry proportions in the study by Micheletti et al. ranged from ∼1% to 21%, depending on the type of chromosome and geographic region. A sensitivity analysis illustrates how this unassigned ancestry can create false patterns of sex bias and that mathematical models are highly sensitive to slight sampling errors when inferring mean ancestry proportions, making confidence intervals necessary. Thus, unassigned ancestry and the sensitivity of the models effectively prohibit the interpretation of estimated sex biases for many geographic regions in Micheletti et al. Furthermore, Micheletti et al. assumed models of a single admixture event. Using simulations, we find that violations of demographic assumptions, such as subsequent gene flow and/or sex-specific assortative mating, may have confounded the analyses of Micheletti et al., but unassigned ancestry was likely the more important confounding factor. Our findings underscore the importance of using complete ancestry information, sufficiently large sample sizes, and appropriate models when inferring sex-biased patterns of demography. This Matters Arising paper is in response to Micheletti et al.,1 published in American Journal of Human Genetics. See also the response by Micheletti et al.,2 published in this issue.

Demographic modeling of admixed Latin American populations from whole genomes.

Demographic models of Latin American populations often fail to fully capture their complex evolutionary history, which has been shaped by both recent admixture and deeper-in-time demographic events. To address this gap, we used high-coverage whole-genome data from Indigenous American ancestries in present-day Mexico and existing genomes from across Latin America to infer multiple demographic models that capture the impact of different timescales on genetic diversity. Our approach, which combines analyses of allele frequencies and ancestry tract length distributions, represents a significant improvement over current models in predicting patterns of genetic variation in admixed Latin American populations. We jointly modeled the contribution of European, African, East Asian, and Indigenous American ancestries into present-day Latin American populations. We infer that the ancestors of Indigenous Americans and East Asians diverged ∼30 thousand years ago, and we characterize genetic contributions of recent migrations from East and Southeast Asia to Peru and Mexico. Our inferred demographic histories are consistent across different genomic regions and annotations, suggesting that our inferences are robust to the potential effects of linked selection. In conjunction with published distributions of fitness effects for new nonsynonymous mutations in humans, we show in large-scale simulations that our models recover important features of both neutral and deleterious variation. By providing a more realistic framework for understanding the evolutionary history of Latin American populations, our models can help address the historical under-representation of admixed groups in genomics research and can be a valuable resource for future studies of populations with complex admixture and demographic histories.

Impact of cross-ancestry genetic architecture on GWASs in admixed populations.

Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.

Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.

Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 105 to 106 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.