REVIEWS

The influence of evolutionary history on human health and disease

Mary Lauren Benton 1,2, Abin Abraham3,4, Abigail L. LaBella 5, Patrick Abbot5, Antonis Rokas 1,3,5 and John A. Capra 1,5,6 ✉ Abstract | Nearly all genetic variants that influence disease risk have human-specific​ origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual’s DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.

Genetic disease is a necessary product of evolution These studies are radically changing our understanding (Box 1). Fundamental biological systems, such as DNA of the genetic architecture of disease8. It is also now possi- replication, transcription and translation, evolved very ble to extract and sequence ancient DNA from remains early in the history of life. Although these ancient evo- of organisms that are thousands of years old, enabling 1Department of Biomedical lutionary innovations gave rise to cellular life, they also scientists to reconstruct the history of recent human Informatics, Vanderbilt created the potential for disease. Subsequent innovations adaptation with unprecedented resolution9,10. These University School of Medicine, along life’s long evolutionary history have similarly ena- breakthroughs have revealed the recent, often compli- Nashville, TN, USA. bled both adaptation and the potential for dysfunction. cated, history of our species and how it influences the 2Department of Computer Against this ancient background, young genetic vari- genetic architecture of disease8,11. With the expansion Science, Baylor University, Waco, TX, USA. ants specific to the human lineage interact with modern of clinical whole-​genome sequencing and personal- environments to produce human disease phenotypes. ized medicine, the influence of our evolutionary past 3Vanderbilt Genetics Institute, Vanderbilt University, Consequently, the substrates for genetic disease in mod- and its implications for understanding human disease Nashville, TN, USA. ern humans are often far older than the human lineage can no longer remain overlooked by medical practice; 12,13 4Vanderbilt University itself, but the genetic variants that cause them are usually evolutionary perspectives must inform medicine . Medical Center, Vanderbilt unique to humans. Much like a family’s medical history over genera- University, Nashville, The advent of high-throughput​ genomic technologies tions, the genome is fundamentally a historical record. TN, USA. has enabled the sequencing of the genomes of diverse Decoding the evolution of the human genome provides 5 Department of Biological species from across the tree of life1. Analysis of these valuable context for interpreting and modelling disease. Sciences, Vanderbilt University, Nashville, genomes has, in turn, revealed the striking conservation This context is not limited to recent human evolution TN, USA. of many of the molecular pathways that underlie the but also includes more ancient events that span life’s his- 6Bakar Computational Health function of biological systems that are essential for cel- tory. In this Review, we trace the 4 billion-year​ interplay Sciences Institute and lular life2. The same technologies have also spearheaded between evolution and disease by illustrating how inno- Department of Epidemiology a revolution in human genomics3; currently, more than vations during the course of life’s history have established and Biostatistics, University 120,000 individual whole human genome sequences the potential for, and inevitability of, disease. Beginning of California, San Francisco, CA, USA. are publicly available, and genome-​scale data from with events in the very deep past, where most genes ✉e-mail:​ tony.capra@ hundreds of thousands more have been generated by and pathways involved in human disease originate, we 4 ucsf.edu consumer genomics companies . Huge nationwide bio- explain how ancient biological systems, recent genetic https://doi.org/10.1038/ banks are also characterizing the genotypes and pheno- variants and dynamic environments interact to produce s41576-020-00305-9 types of millions of people from around the world5–7. both adaptation and disease risk in human populations.

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Box 1 | The evolutionary necessity of disease Myriad age-​related diseases in humans, and many other multicellular organisms, are a manifestation The definition of disease of this first evolutionary trade-off​ . varies across biological, A The evolution of multicellularity, which has occurred medical and evolutionary B many times across the tree of life, illustrates the inter- perspectives. Viewing C 18 disease through the lens of play between evolutionary innovation and disease . evolution provides a flexible D The origin of multicellularity enabled complex body

and powerful framework for Disease risk plans with trillions of cells, involving innovations defining and classifying associated with the ability of cells to regulate their cell disease12. As illustrated in cycles, modulate their growth and form intricate net- the reaction norms plotted Environment works of communication. But multicellularity also estab- in the figure, disease risk lished the foundation for cancer19,20. Genes that regulate (y axis) is a function of both genotype (coloured lines) and environment (x axis). cell cycle control are often divided into two groups: care- Some genotypes lead to disease in all environments (line A); high-penetrance​ Mendelian takers and gatekeepers21,22. The caretakers are involved in disorders fall into this group. At the other extreme, disease risk may only occur in the case of basic control of the cell cycle and DNA repair, and muta- a very specific pairing of environment and genotype (line D). Phenylketonuria (PKU), which manifests only in the presence of mutations that render both copies of the phenylalanine tions in these genes often lead to increased mutation rates hydroxylase enzyme non-functional​ and a diet that includes phenylalanine, illustrates this or genomic instability, both of which increase cancer risk. case. Most diseases fall between these extremes (lines B and C). Disease often arises from Caretaker genes are enriched for functions with origins fundamental evolutionary ‘mismatches’ between genotype and environment. For example, dating back to the first cells23. The gatekeepers appeared the high risk for obesity, a chronic disease with substantial heritability (30–40%)177, in many later, at the genesis of metazoan multicellularity23. modern populations is due (at least in part) to rapid and recent changes in human lifestyle178, The gatekeepers are directly linked to tumorigenesis such as eating higher-calorie​ foods, maintaining more sedentary lifestyles and sleeping through their roles in regulating cell growth, death and fewer hours. Here, obesity manifests due to a ‘mismatch’ between the genotype and a communication. The progression of individual tumours rapidly changing environment. Genotypes often have opposing effects on different in a given patient is likewise informed by an evolutionary traits. This evolutionary pattern, called antagonistic pleiotropy, often leads to disease179. perspective. Designing treatments that account for A canonical example is balancing selection to maintain variation at the haemoglobin subunit-​ (HBB) locus that protects against malarial disease but recessively leads to sickle the evolution of drug resistance and heterogeneity in β 24–29 cell anaemia. Antagonistic pleiotropy has also been detected in complex genetic traits, such tumours is a tenet of modern cancer therapy . as heart disease where alleles that increase lifetime reproductive success also increase the Like multicellularity, the evolution of immune risk for heart disease180. As these examples illustrate, many modern human diseases exist systems also set the stage for dysregulation and dis- because populations have not adapted to changing environments or previous adaptations ease. Mammalian innate and adaptive immune systems lead to trade-​offs between health and fitness. However, disease is not just a product of are both ancient. Components of the innate immune the modern world. As long as there is phenotypic variation, disease is inevitable; some system are present across metazoans and even some individuals will be better suited to some environments (and thus healthier) than others. plants30,31, whereas the adaptive immune system is pres- ent across jawed vertebrates32. These systems provided Given this scope, we cannot provide a comprehensive molecular mechanisms for self-/non-​self-​recognition account of all evolutionary events relevant to human dis- and response to pathogens, but they evolved in a piece-

Genetic architecture ease. Instead, our goal is to illustrate through examples meal fashion, using many different, pre-​existing genes All aspects of the genetic the relevance of both deep and recent evolution to the and processes. For example, co-​option of endogenous variants that influence variation study and treatment of genetic disease. Many of these retroviruses provided novel regulatory elements for of a trait in a population. key insights stem from recent discoveries, which have yet interferon response33. As well, it is clear that the human Commonly studied attributes to be integrated into the broader canvas of evolutionary immune system has co-​evolved with parasites, such of genetic architecture include (Box 2) the number of genetic loci that biomedicine . as helminths, over millions of years. Helminth infec- influence a trait, the frequency tion both induces and modulates an immune response of these variants, the Macroevolutionary imprints on human disease in humans34. magnitudes of their effects and Systems involved in disease have ancient origins. Many Evolutionary analyses of development have revealed how they interact with one another and the environment. of cellular life’s essential biological systems and pro- that new anatomical structures often arise by co-​opting The genetic architectures of cesses, such as DNA replication, transcription and existing structures and molecular pathways that were traits vary along these axes; translation, represent ancient evolutionary innovations established earlier in the history of life. For example, for example, some traits are shared by all living organisms. Although essential, each animal eyes, limb structure in tetrapods and pregnancy influenced by many common of these ancient innovations generated the conditions for in mammals (Box 3) each evolved by adapting and inte- variants of small effect, (Fig. 1) whereas others are driven by a modern disease . In this section, we provide exam- grating ancient genes and regulatory circuits in new 35–38 few rare variants of large effect. ples of how several ancient innovations have created sub- ways . This integration of novel traits into the existing strates for dysfunction and disease, and how considering network of biological systems gives rise to links between Reaction norms these histories contributes to understanding the biology diverse traits via the shared genes that underlie their Representations of how the 36 expressed phenotype for a of disease and extrapolating results from model systems development and function . As a result, many genes are genotype varies in response to humans. pleiotropic — they have effects on multiple, seemingly to a range of environments. As a foundational (if obvious) example, the origin of unrelated, traits. We do not have space here to cover the Reaction norms can be used to self-replicating​ molecules 4 billion years ago formed the full evolutionary scope of these innovations and their illustrate many of the concepts basis of life, but also the root of genetic diseases12,14,15. legacies, but just as in each of the cases described above, described in this Review, including evolutionary Similarly, asymmetric cell division may have evolved as an innovations and adaptations spanning from the origin mismatches and antagonistic efficient way to handle cellular damage, but it also estab- of metazoans to modern human populations shape the pleiotropy. lished the basis for ageing in multicellular organisms16,17. substrate upon which disease appears.

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Evolutionary trade-off​ Medical implications. Although ancient macroevolu- challenging to extrapolate results about the regulation Adaptations that are tionary innovations may seem far removed from modern of birth timing from model organisms, such as mouse, advantageous for one human phenotypes, their imprint remains on the human to humans (Box 3). More broadly, differences in genetic phenotype have costs for body and genome. Understanding the constraints they networks that underlie the development of homologous others. Evolutionary trade-offs​ often result when genes impose can provide insight into mechanisms of disease. traits across mammals explain why the majority of suc- influence multiple phenotypes Mapping the origins and evolution of traits and cessful animal trials fail to translate to human clinical (pleiotropy) or when there is a identifying the genetic networks that underlie them are trials41,42. Molecular mechanisms of ancient systems, limited resource that must be critical to the accurate selection of model systems and such as DNA replication, can be studied using phyloge- apportioned to different extrapolation to human populations. Failure to consider netically distant species; however, ‘humanizing’ these functions. Because of trade-offs,​ there is not an the evolutionary history of homologous systems, their models to research human-​specific aspects of traits may optimal genotype across all phylogenetic relationships and their functional contexts not be possible and comparative studies of closely related environments. in different organisms can lead to inaccurate generaliza- species may be required40. tion. Instead, when considering a model system, key evo- Although evolutionary divergence in homologous Pleiotropic Pertaining to pleiotropy, which lutionary questions about both the organism and the trait traits is an impediment to the direct translation of find- is when a genetic locus (for of interest can indicate how translatable the research will ings from a model system to humans, understanding example, a gene or regulatory be to humans39,40. For example, is the similarity between how these evolutionary differences came about can element) has effects on the trait in humans and the trait in the model system due also yield insights into disease mechanisms. For exam- multiple unrelated phenotypes. to shared ancestry, that is, homology? The presence of ple, intuition would suggest that large animals (many Antagonistic pleiotropy results when a locus has a beneficial homology in a human gene or system of study suggests cells and cell divisions) with long lifespans (many age- effect on one trait and a potential as a model system; however, homology alone ing cells), such as elephants and whales, would be at detrimental effect on another. is not sufficient justification. Environmental and life his- increased risk for developing cancer. However, size and tory factors shape traits, and divergence between species lifespan are not significantly correlated with cancer risk complicates the simple assumption that homology pro- across species; despite their large size, elephants and vides genetic or mechanistic similarity. Thus, homology whales do not have a higher risk of developing cancer43,44. must be supplemented by understanding of whether Why is this so? Recent studies of the evolution of genes the evolutionary divergence between humans and the involved in the DNA damage response in elephants have proposed model led to functional divergence. For exam- revealed mechanisms that may contribute to cancer ple, the rapid evolution of the placenta and variation in resistance. An ancient leukaemia inhibiting factor pseu- reproductive strategy across mammals have made it dogene (LIF6) regained its function in the ancestor of modern elephants. This gene works in conjunction with Box 2 | Evolutionary medicine the tumour suppressor gene TP53, which has increased Evolutionary medicine is the study of how evolutionary processes have produced in copy number in elephants, to reduce elephants’ risk 45,46 human traits/disease and how evolutionary principles can be applied in medicine. for cancer despite their large body size . This illus- This Review focuses on recent advances in evolutionary genomics as they relate to our trates a basic life history trade-​off: selection has created understanding of the origins and genetic basis for disease. Evolutionary medicine is a mechanisms for cancer suppression and somatic main- larger field that has been extensively reviewed elsewhere12,26,181. For context, we introduce tenance in large vertebrates that are not needed in small major principles of evolutionary medicine here. Evolutionary perspectives on medicine short-​lived vertebrates. Studying such seeming para- are predicated on the idea that human diseases emerge out of the constraints, trade-offs,​ doxes, especially those with clear contrasts to human mismatches and conflicts inherent to complex biological systems interacting (via natural disease risk, will shed light on broader disease mecha­ selection) with diverse and shifting environments (Box 1). nisms and suggest targets for functional interventions Evolutionary medicine has identified several categories of explanation for complex with translation potential. genetic diseases. The first category of evolutionary explanation is that natural selection does not result in perfect bodies but operates on relative reproductive fitness constrained by the laws of physics and the role, availability and interactions of pre-existing​ biological Human-specific​ evolution variation that shapes or constrains the subsequent course of evolution182,183. A second Human adaptation, trade-​offs and disease. The mac- explanation is mismatch between our biological legacy and our modern environments184. roevolutionary events described above created the Mismatch between our biological adaptations to ancestral environments and modern foundation of genetic disease, but considering the more lifestyles contributes to many common diseases, such as obesity, diabetes and heart recent changes that occurred during the evolutionary disease, that are promoted by sedentary lifestyles and poor nutrition185,186. For example, history of the human lineage is necessary to illuminate past exposure to calorie-poor​ conditions may promote metabolically efficient ‘thrifty’ the full context of human disease. Comparisons between gene variants that may contribute to increased obesity in calorie-rich​ environments. humans and their closest living primate relatives, such as A third explanation is that of trade-​offs, the idea that there are combinations of traits 50,51 chimpanzees, have revealed diseases that either do not that cannot be simultaneously optimized by natural selection . The trade-​off concept 47 is related to evolutionary constraint, but encompasses a large set of phenomena that appear in other species or take very different courses . shape trait evolution. For example, many fitness-​related traits draw on common energetic We are beginning to understand the genetic differences reserves, and investment in one comes at the expense of another52. Likewise, pleiotropic underlying some of these human-​specific conditions, genetic variants that influence multiple systems create potential for trade-offs.​ with particular insights into infectious diseases. Furthermore, symptoms that are interpreted as disease may actually represent The last common ancestors of humans and chim- conditionally adaptive responses. Finally, evolutionary conflicts provide a fourth panzees underwent a complex speciation event that is possible explanation. All multicellular organisms are aggregates of genes and genomes likely to have involved multiple rounds of gene flow with different evolutionary histories and with diverse strategic interests. This means between ~12 and 6 million years ago (mya)48. Over the that all traits expressed by complex metazoans are a balanced compromise between millions of years after this divergence, climatic, demo- different genetic elements and bodily systems187. Pathology can emerge out of conflict graphic and social pressures drove the evolution of many when conditions perturb these compromises. physical and behavioural traits unique to the human

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Deep evolutionary past Recent human evolution

• Self-replicating molecules Immune system Divergence from Anatomically • Agriculture • Asymmetric cell division development chimpanzee modern humans • Urbanization

Population expansion and • Bipedalism Neanderthal migration; • Increased brain size Out-of-Africa and Denisovan modern Multicellularity Placentation • Gene duplications bottleneck introgression environments

~4 bya ~100 mya … ~7 mya ~100 kya ~10 kya

• Genetic disease Cancer Pre-eclampsia • Schizophrenia Decreased genetic • Chronic kidney • Ageing • Autism spectrum disorder diversity in non-Africans disease • Epithelial cancers • Obesity • Pathogen response • Type 2 diabetes • Autoimmune diseases • HIV-1/AIDS • Sickle cell disease • Allergies • Ulcerative colitis • Asthma • Neuropsychiatric disorders • Coeliac disease

Fig. 1 | Evolutionary events in both the deep evolutionary past and recent human evolution shape the potential for disease. A timeline of evolutionary events (top) in the deep evolutionary past and on the human lineage that are relevant to patterns of human disease risk (bottom). The ancient innovations on this timeline (left) formed biological systems that are essential, but are also foundations for disease. During recent human evolution (right), the development of new traits and recent rapid demographic and environmental changes have created the potential for mismatches between genotypes and modern environments that can cause disease. The timeline is schematic and not shown to scale. bya, billion years ago; kya, thousand years ago; mya, million years ago.

lineage, including bipedalism (~7 mya), lack of body a variant has contrasting effects on multi­ple bodily sys- hair (~2–3 mya) and larger brain volume relative to tems. In extreme cases, some diseases that manifest well body size (~2 mya)12,47. These traits evolved in a diverse after reproductive age, for example, Alzheimer disease, array of hominin groups, mainly in Africa, although have been less visible to selection and, thus, potentially some of these species, such as Homo erectus, ventured more susceptible to trade-​offs. Cancer and neuro­ into Europe and Asia. degenerative disorders also exhibit this diametric These human adaptations developed on the sub- pattern, where cancer risk is inversely associated with strate of tightly integrated systems shaped by billions of Alzheimer disease, Parkinson disease and Huntington years of evolution, and thus beneficial adaptations with disease. This association is hypothesized to be mediated respect to one system often incurred trade-​offs in the by differences in the neuronal energy use and trade-offs​ form of costs on other linked systems49. The trade-​off in cell proliferation and apoptosis pathways49. Similarly, concept derives from a branch of evolutionary biology osteoarthritis (breakdown of cartilage in joints often known as life history theory. It is based on the obser- accompanied by high bone mineral density) and osteo­ vation that organisms contain combinations of traits porosis (low bone mineral density) rarely co-​occur. that cannot be simultaneously optimized by natural Their diametric pattern reflects, at least in part, differ- selection50,51. For example, many fitness-​related traits ent probabilities across individuals of mesenchymal stem draw on common energetic reserves, and investment cells within bone marrow to develop into osteoblasts Homology 52 49,55 Similarity in traits, bodily in one comes at the expense of another . Large body versus non-​bone cells such as adipocytes . In another structures or genomic size may improve survival in certain environments, but example, a history of selection for a robust immune sequences due to shared it comes at the expense of longer development and lower response can now lead to an increased risk for auto­ ancestry between two species. numerical investment in reproduction. immune and inflammatory diseases, especially when Homology is considered when The trade-off​ concept is clinically relevant because it coupled with new environmental mismatches49,54. Other selecting model systems to study a particular phenotype; dispenses with the notion of a single ‘optimal’ pheno­type examples of trade-​offs are found throughout the human 49,53,54 however, it does not guarantee or fitness state for an individual . Given the inter- body, manifesting in risk for diverse diseases, including underlying functional or connected deep evolution of the human body, many psychiatric and rheumatoid disorders49,56. mechanistic similarity. diseases are tightly linked, in the sense that decreasing Just as adaptations in deep evolutionary time cre-

Diametric diseases the risk for one increases the risk for the other. Such ated new substrates for disease, evolutionary pressures Linked diseases for which diametric diseases and the trade-​offs that produce them exerted on the human lineage established the founda- decreasing the risk for one are the starkest when there is competition within the tion for complex cognitive capabilities, but they also increases the risk for the other, body for limited resources; for example, energy used for established the potential for many neuropsychiatric or such as protection from reproduction cannot be used for growth, immune func- neurodevelopmental diseases. For example, genomic infectious disease increasing 54 risk for autoimmune disease. tion or other energy-​consuming survival processes . structural variants enabled functional innovation in the 57–60 Diametric disorders result from The molecular basis for diametric diseases often results brain through the emergence of novel genes . Many evolutionary trade-offs.​ from antagonistic pleiotropy at the genetic level — when human-specific​ segmental duplications influence genes

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Viviparous that are essential to the development of the human arrays in neuroblastoma breakpoint family (NBPF) An animal that gives birth to brain, such as SRGAP2C and ARHGAP11B. Both of genes, and have been associated with both increased live young, rather than laying these genes function in cortical development and may brain size and neuropsychiatric diseases, including eggs. be involved in the expansion of human brain size61–63. autism and schizophrenia65. These examples suggest

Human accelerated regions The human-​specific NOTCH2NL is also hypothesized that the genomic organization of these human-specific​ (HARs). Genomic loci to have evolved from a partial duplication event, and duplications may have enabled human-specific​ changes conserved across mammalian is implicated in increased output during human corti- in brain development while also increasing the likeli- species that experienced an cogenesis, another potential key contributor to human hood of detrimental rearrangements that cause human increase in substitution rate brain size59,60. Although these structural variants were disease59,64. Furthermore, genomic regions associ- specific to the human lineage. 58 Genetic changes in HARs are probably adaptive , they may have also predisposed ated with neuropsychiatric diseases have experienced responsible for some attributes humans to neuropsychiatric diseases and develop- human-specific​ accelerated evolution and recent positive of human-specific​ biology. mental disorders. Copy number variation in the region selection, providing additional evidence for the role of flanking ARHGAP11B, specifically a microdeletion at recent evolutionary pressures on human disease risk66,67. 15q13.3, is associated with risk for intellectual disabil- Schizophrenia-associated​ loci, for example, are enriched ity, autism spectrum disorder (ASD), schizophrenia and near human accelerated regions (HARs) that are con- epilepsy58,64. Duplications and deletions of NOTCH2NL served in non-human​ primates68. Variation in HARs has and surrounding regions are implicated in macro- also been associated with risk for ASD, possibly through cephaly and ASD or microcephaly and schizophrenia, perturbations of gene regulatory architecture69. respectively59. These trade-offs​ also play out at the pro- Human immune systems have adapted in response tein domain level. For example, the Olduvai domain to changes in environment and lifestyles over the past (previously known as DUF1220) is a 1.4-​kb sequence few million years; however, the rapid evolution of the that appears in ~300 copies in the human genome; this immune system may have left humans vulnerable to domain has experienced a large human-specific​ increase certain diseases, such as HIV-1 infection. A similar in copy number. These domains appear in tandem virus, simian immunodeficiency virus (SIV), is found in

Box 3 | Pregnancy as a case study in evolutionary medicine Mammalian pregnancy illustrates how consideration of a trait’s history progesterone receptor197. The human-specific​ changes in the PGR across evolutionary time can inform our understanding of disease. influence its transcription and probably its phosphorylation198,199. Similarly, Every human who ever lived experienced pregnancy, but its complexity loci associated with human preterm birth have experienced diverse is remarkable — it involves coordination between multiple genomes evolutionary forces, including balancing selection, positive selection and and physiological integration between individuals, and is administered population differentiation200. The rapid and diverse types of evolutionary by a transient organ, the placenta188. Furthermore, by ensuring the change observed in the PGR and some of the loci associated with preterm generational transmission of genetic information, it provides the substrate birth make it challenging to extrapolate analyses of their molecular for all evolution and renewal of life itself189. functions in animal models, such as mice. In addition, humans and mice differ in reproductive strategies, morphology of the uterus, placentation, Macroevolutionary hormone production and the drivers of uterine activation201. For example, Pregnancy in placental mammals, which appeared ~170 million years ago, progesterone is produced maternally in mice throughout pregnancy, involves physiological integration of fetal and maternal tissues via the whereas in humans its production shifts to the placenta after the early placenta, a transient fetal-derived,​ extra-embryonic​ organ. Live birth and stages of pregnancy. Given the unique evolutionary history of human placentation open the door to interplay between mother and fetus over pregnancy, many molecular aspects of pregnancy may be better studied resource provisioning, with the potential for the mother to provide less in other model organisms or human cell-​based systems. than fetal demands because of other energetic needs, such as caring for other offspring. In some mammals, including humans, placentation is Human population level highly invasive, setting up a physiological tug of war between mother A central enigma of mammalian pregnancy is that the maternal immune and fetus over provisioning. When this precarious balance is disrupted, system does not reject the foreign fetus; rather, it has not only evolved 202,203 diseases of pregnancy can occur. Poor maternal arterial remodelling to accept the fetus but is also critical in the process of placentation . during placentation limits placental invasion, which invokes a The centrality of the maternal immune system in pregnancy has important compensatory response by the distressed fetus. This imbalance results medical implications. The modulation of the maternal immune system in inflammation, hypertension, kidney damage and proteinuria in the during pregnancy results in a lowered ability to clear certain 204,205 mother, and an increase in oxidative stress and spontaneous preterm infections . Uterine natural killer (uNK) cells and their killer cell birth in the fetus190. Pregnancy-associated​ maternal hypertension inhibitory receptors (KIRs) cooperate with fetal trophoblasts to regulate with proteinuria is clinically defined as pre-​eclampsia with vascular the maternal immune response. In addition, uNK cells are also involved in aetiologies, with a poor prognosis for both mother and fetus if untreated. immune response to pathogens, and this dual role provides the substrate Understanding pre-eclampsia​ as the result of an evolutionary tug of war for evolutionary trade-​offs. For example, the human-​specific KIR AA between mother and the fetus has medical implications191–194. haplotype is associated with lower birthweight and pre-eclampsia​ as well as with a more effective defence against Ebola virus and hepatitis206,207 Human-​specific (Fig. 4a). Modern human populations have variation in the diversity and Timing of birth is key to a successful, healthy pregnancy, but little is known identity of KIR haplotypes, probably due to selection on both placentation about the mechanisms governing the initiation of parturition. The steroid and host defence208. Infectious disease outbreaks, therefore, place a hormone progesterone and its receptors are involved in parturition in all unique selective pressure on pregnancy. Severe outbreaks of infectious viviparous species; however, how progesterone regulates parturition is diseases, such as malaria, often produce significant shifts in likely to be species-specific.​ For example, the human progesterone population-level​ allele frequencies in pregnancy-​related genes, such as receptor (PGR) exhibited rapid evolution after divergence from the last FLT1 in malaria-​endemic populations of Tanzania209. The varying pressures common ancestor with chimpanzees195,196. There are functional from infectious disease are likely to contribute to variation in risk of differences between the human and Neanderthal versions of the pregnancy-​related diseases between modern populations.

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chimpanzees and other primates, and studies in the early Trade-offs​ at the cellular level also have medical implica- 2000s found evidence of AIDS-like​ symptoms (primarily tions. For example, cellular senescence is a necessary and a reduction in CD4+ T cells) in chimpanzees infected beneficial part of many basic bodily responses, but the with SIV. Although the effects of SIV in chimpanzees accumulation of senescent cells underlies many ageing-​ mirror some of the effects of HIV in humans70, cap- related disorders. Thus, individuals with different solu- tive chimpanzees infected with HIV-1 do not typically tions to this trade-off​ may have very different ‘molecular’ develop AIDS and have better clinical outcomes. The versus ‘chronological’ ages85. differences in outcome are influenced by human-specific​ Identifying such trade-​offs by studying disease and immune evolution. For example, humans have lost treatment response is of great interest, but is challeng- expression of several Siglecs, cell surface proteins that ing for several reasons: the number of possible combi- binds sialic acids, in T lymphocytes compared with great nations of traits to consider is large; many humans must apes71. In support of this hypothesis, human T cells with have experienced the negative effects; and data must be high Siglec-5 expression survive longer after HIV-1 available on both traits in the same individuals. Here, infection72. Moreover, there is a possible role for the evolution paired with massive electronic health record rapidly evolving Siglecs in other diseases, such as epi- (EHR)-linked​ biobanks5,86,87 provides a possible solution. thelial cancers, that differentially affect humans relative By considering the evolutionary context and potential to closely related primates73,74. linkages between traits, the search space of possible Another human-​specific immune change is the trade-​offs can be constrained. Then, diametric traits deletion of an exon of CMP-​N-​acetylneuraminic can be tested for among individuals in the EHRs by per- acid hydroxylase (CMAH) leading to a difference in forming phenome-​wide association studies (PheWAS) human cell surface sialoglycans compared with other either on traits or genetic loci of interest and looking for great apes75–77. The change in human sialic acid to inverse relationships88. The mechanisms underlying the an N-​acetylneuraminic acid (Neu5Ac) termination, observed associations could then be evaluated in model rather than N-​glycolylneuraminic acid (Neu5Gc), may systems and, if validated, anticipated in future human have been driven by pressure to escape infection by treatments. Plasmodium reichenowi, a parasite that binds Neu5Gc In addition to trade-​offs, evolutionary analyses can and causes malaria in chimpanzees. Conversely, the help us identify therapeutic targets for uniquely human prevalence of Neu5Ac probably made humans more sus- diseases. A small subset of humans infected with HIV ceptible to infection by the malaria parasite Plasmodium never progress to AIDS — a resistance phenotype that falciparum, which binds to Neu5Ac78,79, and another has been generally attributed to host genomics89–91. human-​specific pathology: typhoid fever80. Typhoid Identifying and understanding the genes that contribute toxin binds specifically and is cytotoxic to cells expressing to non-​progression is of great interest in the develop- Neu5Ac glycans. Thus, the deletion of CMAH was likely ment of vaccines and treatments for HIV infection. to have been selected for by pressure from pathogens, Genome-​wide association studies (GWAS) and func- but has in turn enabled other human-​specific diseases tional studies have supported the role of the MHC class I such as malaria and typhoid fever81. The rapid evolu- region, specifically the HLA-​B*27/B*57 molecules, tion of the human immune system creates the potential in HIV non-​progression92–94. Comparative genom- for human-​specific disease. As a result, human-​specific ics with chimpanzees identified a chimpanzee MHC varia­tion in many other human immune genes influences class I molecule functionally analogous to that of the human-specific​ disease risk82,83. non-​progressors that contains amino acid substitutions that change binding affinity for conserved areas of the Medical implications. These examples from recent HIV-1 and SIV viruses. Evolutionary analy­sis of this human evolution highlight the ongoing interplay of region suggests that these substitutions are the result of genetic variation, adaptation and disease. Understanding an ancient selective sweep in chimpanzee genomes that the evolutionary history of traits along with the aetiol- did not occur in humans95. This analysis not only helps ogy of related diseases can help identify and evaluate us understand how humans are uniquely susceptible to risks for unintended consequences of treatments due HIV progression but also highlights functional varia- to trade-​offs. For example, ovarian steroids have pleio­ tion in the MHC that are potential targets of medical tropic effects stimulating both bone growth and mito- intervention. sis in breast tissues to mobilize calcium stores during Bottlenecks 54 Rapid decreases in the size of lactation . However, later in life this link gives rise to a Recent human demographic history populations that lead to a clinical trade-off.​ Hormone replacement therapy in post- Most genetic variants are young, but have diverse histories. decrease in genetic diversity. menopausal women reduces the risk for osteoporosis and The complex demographic history of modern humans Genetic bottlenecks can be ovarian cancer, but also, as a result of its effects on breast in the past 200,000 years has created differences in the caused by environmental factors (such as famine or tissue, increases the risk for breast cancer. Given the genetic architecture of and risk for specific diseases among disease) or demographic commonality of the trade-off​ between maintenance and human populations. With genomic sequences of thou- factors (such as migration). proliferation, this is just one of many examples of cancer sands of humans from diverse locations, we can compare The ancestors of most modern risk emerging as a result of trade-offs​ in immune, repro- genetic information over time and geography to better non-African​ populations ductive and metabolic systems56,84. Pregnancy is also rife understand the origins and evolution of both individual experienced a bottleneck as 96–98 they left Africa, which is often with clinically relevant trade-​offs given the interaction genetic variants and human populations . The vast referred to as the out-​of-Africa​ between multiple individuals and genomes (mother, majority of human genetic variants are not shared with bottleneck. father and fetus) with different objectives (Box 3). other species99. Demographic events such as bottlenecks,

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introgression Introgression and population expansion shaped the genetic 15,000 years ago. The details and uncertainties sur- The flow of genetic material composition of human populations, whereas rapid intro- rounding these origin and migration events are more between two species through duction of humans into new environments and the sub- extensively reviewed elsewhere98. interbreeding followed by sequent adaptations created potential for evolutionary Populations that experience bottlenecks and backcrossing. Analyses of (Figs 2,3) founder effects mutation load ancient DNA have revealed mismatches . have a higher than popu- that introgression was Approximately 200,000 years ago, ‘anatomically modern lations that do not, largely due to their lower effective common in human history over humans’ (AMHs) first appeared in Africa. This group population sizes reducing the efficacy of selection100 the past several hundred had the key physical characteristics of modern human (Fig. 3a). During this dispersal, the migrant human thousand years. groups and exhibited unique behavioural and cogni- populations harboured less genetic variation than was Anatomically modern tive abilities that enabled rapid improvements in tool present in Africa. The reduction in diversity caused humans development, art and material culture. Approximately by the out-of-​ Africa​ and subsequent bottlenecks shaped (AMHs). Individuals, both 100,000 years ago, AMH groups began to migrate out the genetic landscape of all populations outside Africa. modern and ancient, with the of Africa. The populations ancestral to all modern AMHs did not live in isolation after migration physical characteristics of humans (Homo sapiens) Eurasians are likely to have left Africa tens of thou- out of Africa. Instead, there is evidence of multiple 98 living today. sands of years later , but quickly spread across Eurasia. admixture events with other archaic hominin groups, Expansions into the Americas and further bottlenecks namely Neanderthals and Denisovans101,102. Modern are thought to have occurred between 35,000 and non-​African populations derive approximately 2% of

TRPM8 TLR1–TLR6–TLR10, HLA, STAT2, OAS Pressure: Cold temperature Pressure: Pathogens Trait: Thermoregulation Trait: Modulation of immune system sensitivity Disease: Migraine Diseases: Autoimmune and inflammatory FADS1, FADS2 disorders, COVID-19 risk Pressure: High/low-fat diet Trait: Lipid metabolism SLC22A4 Disease: Cardio-metabolic disease Pressure: Low ergothioneine levels Trait: Ergothioneine absorption CPT1A Diseases: Coeliac disease and Crohn’s Pressure: High-fat diet, cold temperatures disease due to genetic hitch-hiking Traits: Lipid metabolism, energy homeostasis Diseases: Hypoketotic hypoglycaemia, infant mortality SLC24A5, SLC45A2, MC1R, BNC2 Pressure: UV radiation Trait: Skin pigmentation Diseases: Dermatological disorders (keratosis, sunburn, cancer)

SLC16A11, SLC16A13 Pressure: Diet Trait: Lipid metabolism Disease: Type 2 diabetes

APOL1 Pressure: Trypanosome infection Traits: Trypanosome resistance, cytotoxicity Disease: Chronic kidney disease

HBB, G6PD, GYPA, GYPB, GYPC Pressure: Malaria infection EGLN1, EPAS1 Trait: Malaria resistance via various Pressure: High altitude CREBRF mechanisms Trait: Response to hypoxia Pressure: Variable diet Diseases: Sickle cell anaemia, Diseases: Erythrocytosis, Traits: Fat storage, energy use thalassaemia, G6PD deficiency pulmonary hypertension Diseases: Type 2 diabetes, obesity

Fig. 2 | Recent adaptation has produced evolutionary trade-offs that influenced and the associated disease(s). The approximate regions lead to disease in some environments. Representative genes that have where the adaptations occurred are indicated by blue circles. Arrows experienced local adaptive evolution over the past 100,000 years as humans represent the expansion of human populations, and purple shading moved across the globe. We focus on adaptations that also produced the represents introgression events with archaic hominins. Supplementary potential for disease due to trade-​offs or mismatches with modern Table S1 presents more details and references. COVID-19, coronavirus environments. For each, we list the evolutionary pressure, the trait(s) disease 2019; G6PD, glucose-6-phosphate​ dehydrogenase; UV, ultraviolet.

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Founder effects their ancestry from Neanderthals, with some Asian low-​frequency genetic variants. Although the impact Reduced genetic diversity as populations having an even higher proportion of archaic of rare alleles is not completely understood, they often a result of a small number of hominin ancestry (Fig. 3b). African populations have only have a deleterious role in variation in traits in modern individuals establishing a new a small amount of Neanderthal and Denisovan ancestry, populations109. Although there is still debate about the population from a larger original population. Founder largely from back migration from European populations combined effects of these recent demographic differ- 103 effects can lead to genetic with archaic ancestry . However, there is evidence of ences, a consensus is emerging that they are likely to conditions that were rare in the admixture with other, as yet unknown, archaic hominins have only minor effects on the efficacy of selection and original population becoming in the genomes of modern African populations104–106. the mutation load between human populations100,110–114. common in the new population. Following their expansion around the globe, Nonetheless, there are substantial differences in allele Serial founder effects occurred as anatomically modern humans have experienced explosive growth over the frequency between populations that are relevant to 115 humans spread out of Africa past 10,000 years, in particular in modern Eurasian disease risk . and colonized the world. populations107,108 (Fig. 3c). Growth in population size The exposure of humans to new environments and modifies the genetic architecture of traits by increas- major lifestyle shifts, such as agriculture and urban- ing the efficacy of selection and generating many more ization, created the opportunity for adaptation96,116.

Time a

a

Disease-associated variant

b b Neanderthal Neanderthal Neanderthal variant …

d Ancient Eurasian

c c African European Asian Population size Population d t1 t2 Admixed individuals Time African

…

European

Disease-associated variant Common variant Rare variant

Fig. 3 | Effects of recent demographic events in human history on genetic introgression events (illustrated in grey) contained Neanderthal-​derived mechanisms underlying disease. Ancient human migrations, introgression variants (denoted by red circles) associated with increased disease risk in events with other archaic hominins and recent population expansions have modern populations. c | In the last 10,000 years, the burden of rare all contributed to the introduction of variants associated with human disease. disease-​associated variants (denoted by yellow circles) has increased due to Schematic of human evolutionary history, where the branches represent rapid population expansion. d | Modern human individuals with admixture different human populations and the branch widths represent population size in their recent ancestry, such as African Americans, can have differences in (top left). Letter labels refer to the processes illustrated in parts a–d. a | Human genetic risk for disease, because of each individual’s unique mix of genomic populations migrating out of Africa maintained only a subset of genetic regions with African and European evolutionary ancestry. For example, each diversity present in African populations. The resulting out-of-​ Africa​ bottleneck of the three admixed individuals depicted have the same proportions of is likely to have increased the fraction of deleterious, disease-​associated African and European ancestry, but do not all carry the disease-​associated variants in non-​African populations. Coloured circles represent different variant found at higher frequency in European populations (illustrated by genetic variants. Circles marked with X denote deleterious, disease-​associated yellow circles). Summarizing clinical risk for a patient requires a higher variants. b | When anatomically modern humans left Africa, they encountered resolution view of evolutionary ancestry along the genome and improved other archaic hominin populations. Haplotypes introduced by archaic representation of genetic variation from diverse human populations.

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Mutation load Ancient DNA sequencing efforts coupled with recent the increase in inflammatory and autoimmune diseases 34 The component of the genetic statistical advances are beginning to enable the linking seen in modern humans . For example, loci associated load contributed by recent of human adaptations to specific environmental shifts with ten different inflammatory diseases, including deleterious variants; other in the recent past96,117,118. However, these rapid environ- Crohn’s disease and multiple sclerosis, show evidence factors that contribute to the hygiene hypothesis126 overall genetic load include mental changes also created new patterns of complex of selection consistent with the . the amount of heterozygote disease. Mismatch between our biological suitability Furthermore, recent positive selection on variants in advantage and inbreeding. for ancestral environments and modern environments the type 2 immune response pathway favoured alleles accounts for the prevalence of many common dis- associated with susceptibility to asthma127. This sug- Admixture eases, such as obesity, diabetes or heart disease that gests that recent evolutionary processes may have led The creation of novel genotypes from interbreeding derive from sedentary lifestyles and poor nutrition. to elevated or altered immune responses at the expense between two genetically The ancestral susceptibility model proposes that ancestral of increased susceptibility to inflammatory and auto­ differentiated populations. alleles that were adapted to ancient environments can, in immune diseases. This insight has broad clinical impli- modern populations, increase the risk for disease119,120. cations, including the potential targeted use of helminths Archaic hominin Ancient individuals on the Supporting this hypothesis, both ancestral and derived and natural products for immune modulation in patients 121,122 128,129 human lineage, such as alleles increase disease risk in modern humans . with chronic inflammatory disease . Neanderthals and Denisovans, However, underscoring the importance of recent demo- Archaic introgression is relevant to modern medicine that diverged before the origin graphic history, patterns of risk for ancestral and derived because alleles introduced by these evolutionary events of anatomically modern alleles differ in African and European populations, with continue to have an impact on modern populations even humans. Use of this terminology is established in ancestral risk alleles at higher frequencies in African though the archaic hominin lineages are now extinct 115 human evolutionary genetics, populations . (Fig. 3b). Archaic hominins had considerably lower effec- but is not consistent across tive population sizes than AMHs, and thus they probably fields due to historical Medical implications. The different evolutionary his- carried a larger fraction of weakly deleterious mutations differences in the use of 101 taxonomic terms and the tories of modern human individuals and populations than AMHs . As a result, Neanderthal introgression is fluidity of the species concept described in the previous section influence disease sus- predicted to have substantially increased the genetic load in the presence of substantial ceptibilities and outcomes. Perhaps most striking are of non-​African AMHs130,131. Large-​scale sequencing introgression. the mismatches and trade-​offs resulting from recent efforts, in combination with analysis of clinical biobanks immune system adaptations. Classic examples include and improved computational methods, have revealed Ancestral susceptibility model genetic variants conferring resistance to malaria also the potential impacts of introgressed DNA on modern 96,123 A model proposing that causing sickle cell-related​ diseases in homozygotes , human genomes. Several recent studies link regions of ancestral alleles adapted to or the predominantly African G1 and G2 variants in archaic admixture in modern populations with a range ancient environments can APOL1 protecting against trypanosomes and ‘sleeping of diseases, including immunological, neuro­psychiatric increase disease risk in modern 102,132–139 environments due to sickness’ but leading to chronic kidney disease in indi- and dermatological phenotypes . This demon- 96 evolutionary mismatches. viduals with these genotypes . Similarly, a variant in strates the functional impact of introgressed sequence Many human populations are CREBRF that is thought to have improved survival for on disease risk in non-African​ humans today. However, likely to be subject to such people in times of starvation is now linked to obesity and some of these associations may be influenced by linked mismatches due to rapidly type 2 diabetes124. In a study of ancient European popu- non-​Neanderthal alleles140. For example, in addition to changing environments. lations, a variant in SLC22A4, the ergothioneine trans- alleles of Neanderthal origin, introgression also reintro- Hygiene hypothesis porter, that may have been selected for to protect against duced ancestral alleles that were lost in modern Eurasian A hypothesis proposing that deficiency of ergothioneine (an antioxidant) is also asso- populations prior to interbreeding (for example, in immune systems adapted for ciated with gastrointestinal problems such as coeliac dis- the out-​of-​Africa bottleneck)141. Some introgressed environments with a high 118 pathogen load are now ease, ulcerative colitis and irritable bowel syndrome . alleles may have initially lessened adverse effects from mismatched to current The variant responsible did not reach high frequency in migration to northern climates, dietary changes and environments with low European populations until relatively recently, and cur- introduction to novel pathogens117,142,143. For example, pathogen load. This mismatch rent disease associations are likely to be new, perhaps as Neanderthal alleles contribute to variation in innate is further hypothesized to a result of mismatches with the current environment118. immune response across populations125,132,134,144 and contribute to the higher incidence of autoimmune and The possibility of mismatch is further supported by the probably helped AMHs adapt to new viruses, in particu- 145 inflammatory diseases. varying prevalence of coeliac disease between human lar RNA viruses in Europe . However, due to recent populations related to population-​specific selection for demographic and environmental changes, some previ- Genetic load several risk alleles82. Indeed, recent studies suggest that ously adaptive Neanderthal alleles may no longer pro- The decrease in population 146 fitness caused by the presence there is a relationship between ancestry and immune vide the same benefits . For example, there is evidence of non-optimal​ alleles compared response, with individuals of African ancestry demon- that an introgressed Neanderthal haplotype increases with the most fit genotype: strating stronger responses. This could be the result of risk for SARS-CoV-2​ (ref.147). (W – W ) / W , where W max mean max max selective processes in response to new environments for Physicians regularly rely on proxies for our more is the maximum possible fitness European populations, or a larger pathogen burden in recent evolutionary history in the form of self-​reported and Wmean is the average fitness over all observed genotypes. Africa now leading to a higher instance of inflammatory ancestry in their clinical practice; however, these and autoimmune disorders. This is still an open area of measures fail to capture the complex evolutionary research, and more evidence is needed before strong ancestry of each individual patient. For example, two conclusions can be drawn125. individuals who identify as African Americans may In modern human environments, there is also a mis- both have 15% European ancestry, but this ancestry match between the current low parasite infection levels will be at different genomic loci and from different and the immune system that evolved under higher para- ancestral European and African populations (Fig. 3d). site load. This mismatch is hypothesized to contribute to Thus, one may carry a disease-​increasing European

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149–151 Box 4 | Evolutionary medicine in clinical practice European-​ancestry populations (Fig. 4). In 2016, 81% of GWAS data were from studies conducted on Evolutionary perspectives have yet to be integrated into most areas of clinical practice. European populations149. Although this is an improve- Notable exceptions involve diseases in which evolutionary processes act over short ment from 96% in 2009, most non-​European popula- timescales to drive the progression of disease. For example, knowledge of the intense tions still lack appropriate representation. The problem selective pressures underlying the evolution of drug resistance of microorganisms and the growth of tumours now guides the application of precise therapies and drug delivery is more extreme for many phenotypes or traits of inter- strategies210–213. These examples illustrate how an evolutionary perspective can improve est. For example, only 1.2% of the studies in a survey patient outcomes. However, they differ from the main focus of this article — the influence of 569 GWAS on neurological phenotypes included of human evolution on common genetic disease — where the relevant evolutionary individuals of African ancestry150,152. processes have acted over thousands or millions of years. Ancestry biases in genomic databases and GWAS Nonetheless, accounting for the innovations, adaptations and trade-offs​ that propagate through other strategies that are designed have shaped human populations should be considered in the clinical application to translate population genetic insights to the clinic, of precision medicine to complex disease. For example, polygenic risk scores (PRSs) such as polygenic risk scores (PRSs)153,154 (Fig. 4b). PRSs are a burgeoning technology with great clinical potential to stratify individuals by risk hold the promise of predicting medical outcomes and enable preventative care154,214, but they have a fundamental dependence on from genomic data alone. However, the evolutionary underlying evolutionary processes. Individuals have different genetic backgrounds based on their ancestry, and these different histories alter the relationships between perspective suggests that the genetic architecture of genotypes, environmental factors and risk of disease (Fig. 4). From this evolutionary diseases should differ between populations due to the perspective, PRSs should not be expected to generalize across populations and effects of the demographic and environmental differ- environments given the varied demographic histories of human populations that shape ences discussed above. Indeed, many PRSs generalize genetic variation155,156,215. Indeed, failure to account for this diversity in the application poorly across populations and are subject to biases155,156. of PRSs and other genetics-​based prediction methods can cause substantial harm and Prioritization of Mendelian disease genes is also chal- contribute to health disparities by producing misdiagnosis, improper drug dosing lenging in under-​represented populations. Generally, 149–151,158,160–164 and inaccurate risk predictions . An evolutionary approach is integral to African-​ancestry individuals have significantly more solving this problem. PRSs must be developed and critically evaluated across the full variants, yet we know less about the pathogenicity of var- range of human diversity to determine when genetic factors can provide an accurate iants that are absent from or less frequent in European risk profile for individuals. This is crucial in individuals with recent admixture in their 157 ancestry, as risk profiles can vary based on the unique patterns of ancestry in each populations . Patients of African and Asian ancestry individual (Fig. 3). If genetic information is to inform personalized predictions about are currently more likely than those of European ances- disease risk, explicitly considering evolution by quantifying genetic ancestry must be try to receive ambiguous genetic test results after exome a critical component of this process. sequencing or be told that they have variants of uncer- The development of PRSs provides a timely and illustrative case study of how tain significance (VUS)158. Indeed, disease-causing​ vari- evolutionary perspectives can move from research contexts to inform clinical ants of African origin are under-represented​ in common application. It also highlights the pitfalls of ignoring the implications of human databases159. This under-representation​ covers a range of evolutionary history when generalizing findings across populations. The establishment phenotypic traits and outcomes, including interpreting of a new technology (genome sequencing) enabled the measurement of a signal that is the effects of CYP2D6 variants on drug response160,161, informative about disease risk (genetic variation) but is also influenced by evolutionary risk identification and classification for breast cancer history. The knowledge gained from 100 years of basic research in population genetics 162 about how human populations have evolved provides the context for these new across populations , and disparate effects of GWAS technologies and the path towards ensuring that new treatments are not biased against associations for traits including body mass index (BMI) 163 specific populations. and type 2 diabetes in non-European​ populations . In a Beyond providing context for existing analyses and treatments, new approaches are study on hypertrophic cardiomyopathy, benign variants needed to translate our understanding of the history of human evolution from basic in African Americans were incorrectly classified as path- research to clinical relevance. In the main text, we highlight examples of how trade-offs,​ ogenic on the basis of GWAS results from a European caused by competition for resources or antagonistic pleiotropy, may produce contrasting ancestry cohort. Inclusion of individuals of African effects on disease risk within an individual. Similarly, new environmental conditions, descent in the initial GWAS could have prevented these such as a new pathogen, may rapidly create genetic mismatches in some populations. errors164. We propose that evolution-guided​ analysis of large-scale​ phenotype databases, such as those in electronic health record (EHR)-linked​ biobanks, are a promising approach for Conclusions and future perspectives identifying novel patterns of diametric disease or mismatches in patient populations. For example, if a gene with pleiotropic functions is targeted by a treatment, such as a All diseases have evolutionary histories, and the sig- drug, knowledge of the gene’s evolution and functions can suggest specific phenotypes natures of those histories are archived in our genomes. to test for diametric occurrence in the biobank. Given the overlapping evolutionary Recent advances in genomics are enabling us to read histories of molecular pathways involved in most traits, we anticipate that many clinically these histories with high accuracy, resolution and depth. relevant trade-offs​ are waiting to be discovered. Insights from evolutionary genomics reveal that there is not one answer to the question of why we get sick. Rather, diseases affect patchworks of ancient biological ancestry allele whereas the other does not. Mapping systems that evolved over millennia, and although the Polygenic risk scores (PRSs). Results of a mathe­ fine-​scale genetic ancestry across patients’ genomes can systems involved are ancient, the variation that is rele- matical model to estimate the improve our ability to summarize clinically relevant vant for human disease is recent. Furthermore, evolu- genetic risk of a disease for an risk148, but such approaches require broad sampling tionary genomics approaches have the power to identify individual based on the sum of across populations and awareness of human diversity potential mechanisms, pathways and networks and to the effects of all their genetic (Box 4). The profound need to increase the sam- suggest clinical targets. In this context, we argue that an variants as estimated in a genome-wide​ association study. pling of diverse groups is demonstrated by the lack evolutionary perspective can aid the implementation of The clinical utility of PRSs is a of diversity in genomic studies, and the potential for precision medicine in the era of genome sequencing and topic of current debate (Box 4). health disparities caused by the over-​representation of editing165 (Box 4).

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Mendelian disease Combining knowledge of evolutionary events along an evolutionary explanation of genetic hitch-​hiking 166 A disease caused by a variant in the human lineage with results from recent genomic for the lower incidence in non-​African populations . a single gene, such as sickle cell studies provides an explanatory framework beyond Haplotypes with protective effects against prostate can- anaemia, cystic fibrosis and descriptions of disease risk or association. For exam- cer may have risen to higher frequency in non-​African phenylketonuria. Mendelian (also known as monogenic) ple, a recent analysis of the higher incidence of prostate populations because of selection on the nearby variants disorders are usually rare and cancer among men of African ancestry not only discov- associated with skin pigmentation (Fig. 4c). Thus, evolu- follow simple dominant or ered a set of genetic variants associated with increased tionary perspectives not only help answer the question recessive inheritance patterns. risk, but also used measures of selection to propose of how we get sick but also why we get sick. As the genetic information available from diverse a KIR AA KIR BB populations increases, we can specifically map the genet- Pre-eclampsia Pre-eclampsia ics of traits in different populations and more precisely risk risk define disease risk on an individual basis167,168. However, Resistance to Resistance to we emphasize that environmental and social factors are viral infection viral infection major determinants of disease risk that often contrib-

KIR BB frequency ute more than genetics, and thus must be prioritized. KIR AA frequency KIR AA Birthweight Birthweight AFR EUR AFR EUR Studying diverse human populations will provide addi- tional power to discover trait-associated​ loci and under- stand genetic architecture across different environmental b exposures and evolutionary histories150,169. For example, a GWAS with small sample size in a Greenlandic Inuit population found a variant in a fatty-​acid enzyme that affects height in both this population and European 170 AFR EUR populations . Previous GWAS probably missed this variant due to its low frequency in European populations (0.017 compared with 0.98 in the Inuit); nevertheless, it has a much greater effect on height than other variants

| Variants missing Fig. 4 Illustrations of the need to consider diverse PRS (EUR-trained) from PRS human populations in the genetic analysis of disease. a | Interactions between the maternal killer cell inhibitory Prisk = w1 + w2 + w3 EUR Variants with receptor (KIR) genotype and the fetal trophoblasts illustrate AFR different effects evolutionary trade-offs​ in pregnancy. Birthweight is under stabilizing selection in human populations. The interaction between maternal KIR genotypes (a diversity of which are maintained in the population) and the fetal trophoblasts influence birthweight. African (AFR) populations, relative to European (EUR) populations, maintain larger proportions of the KIR AA haplotype176, which is associated with improved Predicted risk Predicted Predicted risk Predicted maternal immune response to some viral challenges; Risk Risk however, it is also associated with low birthweight. Alternatively, the KIR BB haplotype is associated with higher c Positive selection on birthweight but increased risk of pre-eclampsia.​ b | Current haplotype B for lighter strategies for predicting genetic risk are confounded by skin pigmentation EUR Increased a lack of inclusion of diverse human populations. Thus, they frequency of are more likely to fail in genetic risk prediction in populations haplotype B that are under-​represented in genetic databases. relative to haplotype A For example, polygenic risk score (PRS) models trained on European populations often perform poorly when applied Prostate cancer risk to African populations. This poor performance stems from the fact that the genetic diversity of African populations, differences in effect sizes between populations and AFR differential evolutionary pressures are not taken into account. Ancestral haplotypes The weights for each variant (blue circles) in the PRS derived Decreased frequency of from genome-wide​ association studies are signified by w1, w2 and w3. c | Population-specific​ adaptation and genetic Haplotype A haplotype B relative to hitch-hiking​ can produce different disease risk between Allele associated with darker haplotype A populations. Haplotypes with protective effects against skin pigmentation disease may rise to high frequency in specific populations Prostate cancer risk allele Prostate cancer risk through genetic hitch-​hiking with nearby alleles under selection for a different trait. For example, selection for Haplotype B lighter skin pigmentation caused a haplotype that carried a variant associated with lighter skin (blue circle) to increase in Allele associated with lighter frequency in European populations compared with African skin pigmentation populations. This haplotype also carried a variant protective Prostate cancer protective allele against prostate cancer (blue triangle).

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previously identified through GWAS170. Similarly, a Although evolutionary assumptions are tacit in med- recent study of height in 3,000 Peruvians identified ical practice, until recently self-​reported family history another variant with an even greater influence on remained the best representation of our evolutionary height171. The growth of large DNA biobanks in which ancestry’s imprint on our disease risk. However, a fam- hundreds of thousands of patients’ EHRs are linked to ily history cannot fully capture the complex evolutionary DNA samples represents a substantial untapped resource and demographic history of each individual. New tech- for evolutionary medicine5,86,87. These data enable test- nologies now enable the collection and interpretation of ing of the functional effects of genetic variants on an individual’s family history in a much longer and com- diverse traits at minimal additional cost. Shifting from plementary form — their genome. New data and meth- single-​ancestry GWAS to trans-​ethnic or multi-​ethnic ods are substantially increasing the resolution and depth GWAS will capitalize on the benefits of both a larger with which these histories can be quantified, providing sample size and the inherent diversity of human popula- opportunities for evolution to inform medical practice. tions for replication of established signals and discovery of new ones172–175. Published online 6 January 2021

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FLT1 alleles and their association with malaria 214. Li, R., Chen, Y., Ritchie, M. D. & Moore, J. H. Electronic Author contributions resistance in utero. Proc. Natl Acad. Sci. USA 105, health records and polygenic risk scores for predicting The authors contributed to all aspects of the article. 14488–14491 (2008). disease risk. Nat. Rev. Genet. 21, 493–502 (2020). 210. Andersson, D. I. et al. Antibiotic resistance: turning 215. Duncan, L. et al. Analysis of polygenic risk score usage Competing interests evolutionary principles into clinical reality. FEMS and performance in diverse human populations. Nat. The authors declare no competing interests. Microbiol. Rev. 44, 171–188 (2020). Commun. 10, 3328 (2019). 211. Enriquez-Navas,​ P. M. et al. Exploiting evolutionary principles to prolong tumor control in preclinical Acknowledgements Publisher’s note models of breast cancer. Sci. Transl. Med. 8, 327ra24 The authors thank L. Muglia and members of the Capra, Springer Nature remains neutral with regard to jurisdictional (2016). Rokas and Abbot laboratories for helpful discussions. They claims in published maps and institutional affiliations. 212. Gatenby, R. A. & Brown, J. S. Integrating evolutionary also thank the National Institutes of Health (NIH) dynamics into cancer therapy. Nat. Rev. Clin. Oncol. (T32LM012412 to M.L.B. and R35GM127087 to J.A.C.), Supplementary information 17, 675–686 (2020). the Burroughs Wellcome Fund Preterm Birth Initiative (A.R. Supplementary information is available for this paper at 213. Fairlamb, A. H., Gow, N. A. R., Matthews, K. R. and J.A.C.), the March of Dimes Prematurity Research Center https://doi.org/10.1038/s41576-020-00305-9. & Waters, A. P. Drug resistance in eukaryotic Ohio Collaborative (P.A., A.R. and J.A.C.) and the American microorganisms. Nat. Microbiol. 1, 16092 (2016). Heart Association (A.A.) for support. © Springer Nature Limited 2021

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