The influence of evolutionary history on human health and disease

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.

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.

Fig. 2. Recent adaptation has produced evolutionary trade-offs that lead to disease in some environments.

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

Fig. 3. Effects of recent demographic events in human history on genetic mechanisms underlying disease.

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

Fig. 4. Illustrations of the need to consider diverse human populations in the genetic analysis of disease.

a | Interactions between the maternal killer cell inhibitory receptor (KIR) genotype and the fetal trophoblasts illustrate 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 haplotype, which is associated with improved maternal immune response to some viral challenges; however, it is also associated with low birthweight. Alternatively, the KIR BB haplotype is associated with higher birthweight but increased risk of pre-eclampsia. b | Current strategies for predicting genetic risk are confounded by a lack of inclusion of diverse human populations. Thus, they are more likely to fail in genetic risk prediction in populations that are under-represented in genetic databases. For example, polygenic risk score (PRS) models trained on European populations often perform poorly when applied to African populations. This poor performance stems from the fact that the genetic diversity of African populations, differences in effect sizes between populations and differential evolutionary pressures are not taken into account. The weights for each variant (blue circles) in the PRS derived from genome-wide association studies are signified by w1, w2 and w3. c | Population-specific adaptation and genetic hitch-hiking can produce different disease risk between populations. Haplotypes with protective effects against disease may rise to high frequency in specific populations through genetic hitch-hiking with nearby alleles under selection for a different trait. For example, selection for lighter skin pigmentation caused a haplotype that carried a variant associated with lighter skin (blue circle) to increase in frequency in European populations compared with African populations. This haplotype also carried a variant protective against prostate cancer (blue triangle).