Limited Evidence for Selection at the FADS Locus in Native American Populations

Signatures of natural selection may indicate a genetic basis for the beneficial effects of oily fish intake in indigenous people from coastal Ecuador

Atahualpa is a rural village located in coastal Ecuador, a region that has been inhabited by people as early as 10,000 years ago. The traditional diet of their indigenous inhabitants is rich in oily fish and they have, therefore, served as a model for investigating the beneficial effects of such a diet. However, the genetic background of this population has not been studied. In this study, we sequenced the genomes of Atahualpa residents to look for variants under natural selection, which could mediate the effects of oily fish intake. DNA was extracted from 50 blood samples from randomly selected individuals recruited in the Atahualpa Project Cohort. After applying various filters, we calculated genome-wide genotype likelihoods from 33 samples, and combined data from those samples with data from other populations to investigate how the Atahualpa population is genetically related to these populations. Using selection scans, we identified signals of natural selection that may explain the above-mentioned dietary effects. The genetic ancestry of Atahualpa residents is 94.1% of Indigenous American origin, but is substantially diverged from other indigenous populations in neighboring countries. Significant signatures of natural selection were found in the Atahualpa population, including a broad selection signal around the SUFU gene, which is a repressor of Hedgehog pathway signaling and associated with lipid metabolism, and another signal in the upstream region of LRP1B which encodes low-density lipoprotein (LDL) receptor-related protein 1B. Our selection study reveals genes under selection in the Atahualpa population, which could mediate the beneficial effects of oily fish intake in this population.

An ancient ecospecies of Helicobacter pylori

Helicobacter pylori disturbs the stomach lining during long-term colonization of its human host, with sequelae including ulcers and gastric cancer1,2. Numerous H. pylori virulence factors have been identified, showing extensive geographic variation1. Here we identify a 'Hardy' ecospecies of H. pylori that shares the ancestry of 'Ubiquitous' H. pylori from the same region in most of the genome but has nearly fixed single-nucleotide polymorphism differences in 100 genes, many of which encode outer membrane proteins and host interaction factors. Most Hardy strains have a second urease, which uses iron as a cofactor rather than nickel3, and two additional copies of the vacuolating cytotoxin VacA. Hardy strains currently have a limited distribution, including in Indigenous populations in Siberia and the Americas and in lineages that have jumped from humans to other mammals. Analysis of polymorphism data implies that Hardy and Ubiquitous coexisted in the stomachs of modern humans since before we left Africa and that both were dispersed around the world by our migrations. Our results also show that highly distinct adaptive strategies can arise and be maintained stably within bacterial populations, even in the presence of continuous genetic exchange between strains.

Deep estimation of the intensity and timing of natural selection from ancient genomes

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.

Genetic Features of Lipid and Carbohydrate Metabolism in Arctic Peoples

Prolonged adaptation of ancestors of indigenous peoples of the Far North of Asia and America to extreme natural and climatic conditions of the Arctic has resulted in changes in genes controlling various metabolic processes. However, most genetic variability observed in the Eskimo and Paleoasians (the Chukchi and Koryaks) is related to adaptation to the traditional Arctic diet, which is rich in lipids and proteins but extremely poor in plant carbohydrates. The results of population genetic studies have demonstrated that specific polymorphic variants in genes related to lipid metabolism (CPT1A, FADS1, FADS2, and CYB5R2) and carbohydrate metabolism (AMY1, AMY2A, and SI) are prevalent in the Eskimo and Paleoasian peoples. When individuals deviate from their traditional dietary patterns, the aforementioned variants of genetic polymorphism can lead to the development of metabolic disorders. American Eskimo-specific variants in genes related to glucose metabolism (TBC1D and ADCY) significantly increase the risk of developing type 2 diabetes. These circumstances indicate the necessity for a large-scale genetic testing of indigenous population of the Far North and the need to study the biochemical and physiological consequences of genetically determined changes in the activity of enzymes of lipid and carbohydrate metabolism.

Predicting Functional Consequences of Recent Natural Selection in Britain

Ancient DNA can directly reveal the contribution of natural selection to human genomic variation. However, while the analysis of ancient DNA has been successful at identifying genomic signals of selection, inferring the phenotypic consequences of that selection has been more difficult. Most trait-associated variants are noncoding, so we expect that a large proportion of the phenotypic effects of selection will also act through noncoding variation. Since we cannot measure gene expression directly in ancient individuals, we used an approach (Joint-Tissue Imputation [JTI]) developed to predict gene expression from genotype data. We tested for changes in the predicted expression of 17,384 protein coding genes over a time transect of 4,500 years using 91 present-day and 616 ancient individuals from Britain. We identified 28 genes at seven genomic loci with significant (false discovery rate [FDR] < 0.05) changes in predicted expression levels in this time period. We compared the results from our transcriptome-wide scan to a genome-wide scan based on estimating per-single nucleotide polymorphism (SNP) selection coefficients from time series data. At five previously identified loci, our approach allowed us to highlight small numbers of genes with evidence for significant shifts in expression from peaks that in some cases span tens of genes. At two novel loci (SLC44A5 and NUP85), we identify selection on gene expression not captured by scans based on genomic signatures of selection. Finally, we show how classical selection statistics (iHS and SDS) can be combined with JTI models to incorporate functional information into scans that use present-day data alone. These results demonstrate the potential of this type of information to explore both the causes and consequences of natural selection.

Human adaptations to diet: Biological and cultural coevolution

Modern humans evolved in Africa some 200,000 years ago, and since then, human populations have expanded and diversified to occupy a broad range of habitats and use different subsistence modes. This has resulted in different adaptations, such as differential responses to diseases and different abilities to digest or tolerate certain foods. The shift from a subsistence strategy based on hunting and gathering during the Palaeolithic to a lifestyle based on the consumption of domesticated animals and plants in the Neolithic can be considered one of the most important dietary transitions of Homo sapiens. In this text, we review four examples of gene-culture coevolution: (i) the persistence of the enzyme lactase after weaning, which allows the digestion of milk in adulthood, related to the emergence of dairy farming during the Neolithic; (ii) the population differences in alcohol susceptibility, in particular the ethanol intolerance of Asian populations due to the increased accumulation of the toxic acetaldehyde, related to the spread of rice domestication; (iii) the maintenance of gluten intolerance (celiac disease) with the subsequent reduced fitness of its sufferers, related to the emergence of agriculture and (iv) the considerable variation in the biosynthetic pathway of long-chain polyunsaturated fatty acids in native populations with extreme diets.

Unveiling recent and ongoing adaptive selection in human populations

Genome-wide scans for signals of selection have become a routine part of the analysis of population genomic variation datasets and have resulted in compelling evidence of selection during recent human evolution. This Essay spotlights methodological innovations that have enabled the detection of selection over very recent timescales, even in contemporary human populations. By harnessing large-scale genomic and phenotypic datasets, these new methods use different strategies to uncover connections between genotype, phenotype, and fitness. This Essay outlines the rationale and key findings of each strategy, discusses challenges in interpretation, and describes opportunities to improve detection and understanding of ongoing selection in human populations.

Predicting functional consequences of recent natural selection in Britain

Ancient DNA can directly reveal the contribution of natural selection to human genomic variation. However, while the analysis of ancient DNA has been successful at identifying genomic signals of selection, inferring the phenotypic consequences of that selection has been more difficult. Most trait-associated variants are non-coding, so we expect that a large proportion of the phenotypic effects of selection will also act through non-coding variation. Since we cannot measure gene expression directly in ancient individuals, we used an approach (Joint-Tissue Imputation; JTI) developed to predict gene expression from genotype data. We tested for changes in the predicted expression of 17,384 protein coding genes over a time transect of 4500 years using 91 present-day and 616 ancient individuals from Britain. We identified 28 genes at seven genomic loci with significant (FDR < 0.05) changes in predicted expression levels in this time period. We compared the results from our transcriptome-wide scan to a genome-wide scan based on estimating per-SNP selection coefficients from time series data. At five previously identified loci, our approach allowed us to highlight small numbers of genes with evidence for significant shifts in expression from peaks that in some cases span tens of genes. At two novel loci (SLC44A5 and NUP85), we identify selection on gene expression not captured by scans based on genomic signatures of selection. Finally we show how classical selection statistics (iHS and SDS) can be combined with JTI models to incorporate functional information into scans that use present-day data alone. These results demonstrate the potential of this type of information to explore both the causes and consequences of natural selection.

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

The role of Beringia in human adaptation to Arctic conditions based on results of genomic studies of modern and ancient populations

The results of studies in Quaternary geology, archeology, paleoanthropology and human genetics demonstrate that the ancestors of Native Americans arrived in mid-latitude North America mainly along the Pacific Northwest Coast, but had previously inhabited the Arctic and during the last glacial maximum were in a refugium in Beringia, a land bridge connecting Eurasia and North America. The gene pool of Native Americans is represented by unique haplogroups of mitochondrial DNA and the Y chromosome, the evolutionary age of which ranges from 13 to 22 thousand years. The results of a paleogenomic analysis also show that during the last glacial maximum Beringia was populated by human groups that had arisen as a result of interaction between the most ancient Upper Paleolithic populations of Northern Eurasia and newcomer groups from East Asia. Approximately 20 thousand years ago the Beringian populations began to form, and the duration of their existence in relative isolation is estimated at about 5 thousand years. Thus, the adaptation of the Beringians to the Arctic conditions could have taken several millennia. The adaptation of Amerindian ancestors to high latitudes and cold climates is supported by genomic data showing that adaptive genetic variants in Native Americans are associated with various metabolic pathways: melanin production processes in the skin, hair and eyes, the functioning of the cardiovascular system, energy metabolism and immune response characteristics. Meanwhile, the analysis of the existing hypotheses about the selection of some genetic variants in the Beringian ancestors of the Amerindians in connection with adaptation to the Arctic conditions (for example, in the FADS, ACTN3, EDAR genes) shows the ambiguity of the testing results, which may be due to the loss of some traces of the "Beringian" adaptation in the gene pools of modern Native Americans. The most optimal strategy for further research seems to be the search for adaptive variant.

Genetic adaptation to pathogens and increased risk of inflammatory disorders in post-Neolithic Europe

Ancient genomics can directly detect human genetic adaptation to environmental cues. However, it remains unclear how pathogens have exerted selective pressures on human genome diversity across different epochs and affected present-day inflammatory disease risk. Here, we use an ancestry-aware approximate Bayesian computation framework to estimate the nature, strength, and time of onset of selection acting on 2,879 ancient and modern European genomes from the last 10,000 years. We found that the bulk of genetic adaptation occurred after the start of the Bronze Age, <4,500 years ago, and was enriched in genes relating to host-pathogen interactions. Furthermore, we detected directional selection acting on specific leukocytic lineages and experimentally demonstrated that the strongest negatively selected candidate variant in immunity genes, lipopolysaccharide-binding protein (LBP) D283G, is hypomorphic. Finally, our analyses suggest that the risk of inflammatory disorders has increased in post-Neolithic Europeans, possibly because of antagonistic pleiotropy following genetic adaptation to pathogens.

Beringia and the peopling of the Western Hemisphere

Did Beringian environments represent an ecological barrier to humans until less than 15 000 years ago or was access to the Americas controlled by the spatial-temporal distribution of North American ice sheets? Beringian environments varied with respect to climate and biota, especially in the two major areas of exposed continental shelf. The East Siberian Arctic Shelf ('Great Arctic Plain' (GAP)) supported a dry steppe-tundra biome inhabited by a diverse large-mammal community, while the southern Bering-Chukchi Platform ('Bering Land Bridge' (BLB)) supported mesic tundra and probably a lower large-mammal biomass. A human population with west Eurasian roots occupied the GAP before the Last Glacial Maximum (LGM) and may have accessed mid-latitude North America via an interior ice-free corridor. Re-opening of the corridor less than 14 000 years ago indicates that the primary ancestors of living First Peoples, who already had spread widely in the Americas at this time, probably dispersed from the NW Pacific coast. A genetic 'arctic signal' in non-arctic First Peoples suggests that their parent population inhabited the GAP during the LGM, before their split from the former. We infer a shift from GAP terrestrial to a subarctic maritime economy on the southern BLB coast before dispersal in the Americas from the NW Pacific coast.

Evolutionary adaptation highlights the interconnection of fatty acids, sunlight, inflammation and epithelial adhesion

Gene variants that influence human biology today reflect thousands of years of evolution. Genetic effects on infant health are a major point of selective pressure, given that childhood survival is essential to evolutionary success. Knowledge of this evolutionary history can have implications for paediatric research. CONCLUSION: An episode of human adaptation to the extremely low ultraviolet radiation environment of the Arctic 20,000 years ago implicates the Ectodysplasin A Receptor (EDAR) and the Fatty Acid Desaturases (FADS) in human lactation and epithelial inflammation.

Population Genetics and Signatures of Selection in Early Neolithic European Farmers

Human expansion in the course of the Neolithic transition in western Eurasia has been one of the major topics in ancient DNA research in the last 10 years. Multiple studies have shown that the spread of agriculture and animal husbandry from the Near East across Europe was accompanied by large-scale human expansions. Moreover, changes in subsistence and migration associated with the Neolithic transition have been hypothesized to involve genetic adaptation. Here, we present high quality genome-wide data from the Linear Pottery Culture site Derenburg-Meerenstieg II (DER) (N = 32 individuals) in Central Germany. Population genetic analyses show that the DER individuals carried predominantly Anatolian Neolithic-like ancestry and a very limited degree of local hunter-gatherer admixture, similar to other early European farmers. Increasing the Linear Pottery culture cohort size to ∼100 individuals allowed us to perform various frequency- and haplotype-based analyses to investigate signatures of selection associated with changes following the adoption of the Neolithic lifestyle. In addition, we developed a new method called Admixture-informed Maximum-likelihood Estimation for Selection Scans that allowed us test for selection signatures in an admixture-aware fashion. Focusing on the intersection of results from these selection scans, we identified various loci associated with immune function (JAK1, HLA-DQB1) and metabolism (LMF1, LEPR, SORBS1), as well as skin color (SLC24A5, CD82) and folate synthesis (MTHFR, NBPF3). Our findings shed light on the evolutionary pressures, such as infectious disease and changing diet, that were faced by the early farmers of Western Eurasia.

Disentangling Signatures of Selection Before and After European Colonization in Latin Americans

Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. Although classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations, we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ∼4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico, and Peru. Our approach replicates previous reports of selection in the human leukocyte antigen region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.

Interpreting Clinical Trials With Omega-3 Supplements in the Context of Ancestry and FADS Genetic Variation

Human diets in developed countries such as the US have changed dramatically over the past 75 years, leading to increased obesity, inflammation, and cardiometabolic dysfunction. Evidence over the past decade indicates that the interaction of genetic variation with changes in the intake of 18-carbon essential dietary omega-6 (n-6) and omega-3 (n-3) polyunsaturated fatty acids (PUFA), linoleic acid (LA) and α-linolenic acid (ALA), respectively, has impacted numerous molecular and clinical phenotypes. Interactions are particularly relevant with the FADS1 and FADS2 genes, which encode key fatty acid desaturases in the pathway that converts LA and ALA to their long chain (≥20 carbons), highly unsaturated fatty acid (HUFA) counterparts. These gene by nutrient interactions affect the levels and balance of n-6 and n-3 HUFA that in turn are converted to a wide array of lipids with signaling roles, including eicosanoids, docosanoids, other oxylipins and endocannabinoids. With few exceptions, n-6 HUFA are precursors of pro-inflammatory/pro-thrombotic signaling lipids, and n-3 HUFA are generally anti-inflammatory/anti-thrombotic. We and others have demonstrated that African ancestry populations have much higher frequencies (vs. European-, Asian- or indigenous Americas-ancestry populations) of a FADS "derived" haplotype that is associated with the efficient conversion of high levels of dietary n-6 PUFA to pro-inflammatory n-6 HUFA. By contrast, an "ancestral" haplotype, carrying alleles associated with a limited capacity to synthesize HUFA, which can lead to n-3 HUFA deficiency, is found at high frequency in certain Hispanic populations and is nearly fixed in several indigenous populations from the Americas. Based on these observations, a focused secondary subgroup analysis of the VITAL n-3 HUFA supplementation trial stratifying the data based on self-reported ancestry revealed that African Americans may benefit from n-3 HUFA supplementation, and both ancestry and FADS variability should be factored into future clinical trials design.

Recent advances in understanding the adaptive evolution of metabolic genes and traits

PURPOSE OF REVIEW

This review summarizes the recent advances in understanding the adaptive evolution of metabolic genes and traits, providing insights into gene-diet interactions in human evolution and health.

RECENT FINDINGS

The rapid accumulation of ancient DNA across time and geography illuminates unprecedented details of some well-established examples of genetic adaptation to diet, such as the LCT and FADS genes. Novel cases of thrifty genes were identified, especially a microRNA at the LCT locus that controls energy expenditure and glucose homeostasis, connecting the historical adaptation to present-day metabolic disorders. A new example of gene-diet-microbiota interactions was established among the AMY1 copy number, starchy diets, and resistant-starch-digesting Ruminococcus. The explosion of genome-wide association studies in large cohorts unravels the present-day health implications of historically adaptive genetic variants. It also enables studies into the polygenic adaptation of metabolic traits, revealing intriguing adaptive signals for increased bone mineral density, blood pressure, and risk of type 2 diabetes, but decreased body mass index and HbA1c.

SUMMARY

The rapid accumulation of ancient and modern DNA has fueled the characterization of novel and existing cases of genetic adaptation. However, transferring these evolutionary insights into genome-informed precision nutrition requires extensive mechanistic studies and genotype-aware clinical trials.

Human adaptation over the past 40,000 years

Over the past few years several methodological and data-driven advances have greatly improved our ability to robustly detect genomic signatures of selection in humans. New methods applied to large samples of present-day genomes provide increased power, while ancient DNA allows precise estimation of timing and tempo. However, despite these advances, we are still limited in our ability to translate these signatures into understanding about which traits were actually under selection, and why. Combining information from different populations and timescales may allow interpretation of selective sweeps. Other modes of selection have proved more difficult to detect. In particular, despite strong evidence of the polygenicity of most human traits, evidence for polygenic selection is weak, and its importance in recent human evolution remains unclear. Balancing selection and archaic introgression seem important for the maintenance of potentially adaptive immune diversity, but perhaps less so for other traits.

Inference of natural selection from ancient DNA

Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.