The genomes of ancient humans, Neandertals, and Denisovans contain many alleles that influence disease risks. Using genotypes at 3,180 disease-associated loci, we estimated the disease burden of 147 ancient genomes. After correcting for missing data, genetic risk scores (GRS) were generated for nine disease categories and the set of all combined diseases. We used these genetic risk scores to examine the effects of different types of subsistence, geography, and sample age on the number of risk alleles in each ancient genome. On a broad scale, hereditary disease risks are similar for ancient hominins and modern-day humans, and the GRS percentiles of ancient individuals span the full range of what is observed in present-day individuals. In addition, there is evidence that ancient pastoralists may have had healthier genomes than hunter-gatherers and agriculturalists. We also observed a temporal trend whereby genomes from the recent past are more likely to be healthier than genomes from the deep past. This calls into question the idea that modern lifestyles have caused genetic load to increase over time. Focusing on individual genomes, we found that the overall genomic health of the Altai Neandertal is worse than 97% of present-day humans and that Ötzi, the Tyrolean Iceman, had a genetic predisposition for gastrointestinal and cardiovascular diseases. As demonstrated by this work, ancient genomes afford us new opportunities to diagnose past human health, which has previously been limited by the quality and completeness of remains.

Methods that leverage the information about population history contained within the increasingly abundant genetic sequences of extant and extinct hominid populations are diverse in form and versatile in application. Here, we review key methods recently developed to detect and quantify admixture and ancestry in modern human populations. We begin with an overview of the f- and D-statistics, covering their conceptual principles and important applications, as well as any extensions developed for them. We then cover a combination of more recent and more complex methods for admixture and ancestry inference, discussing tests for direct ancestry between two populations, quantification of admixture in large data sets, and determination of admixture dates. These methods have revolutionized our understanding of human population history and highlight its complexity. Therefore, we emphasize that current methods may not capture this population history in its entirety but nonetheless provide a reasonable picture that is supported by data from multiple methods and from the historical record.

The human immune system is under great pathogen-mediated selective pressure. Divergent infectious disease pathogenesis across human populations combined with the overrepresentation of “immune genes” in genomic regions with signatures of positive selection suggests that pathogens have significantly altered the human genome. However, important features of the human immune system can confound searches for and interpretations of signatures of pathogen-mediated evolution. Immune system redundancy, immune gene pleiotropy, host ability to acquire immunity and alter the immune repertoire of offspring through “priming,” and host microbiome complicate evolutionary interpretations of host-pathogen interactions. The overall promiscuity and sensitivity of the immune system to local environments can also muddy assumptions about the origins of a selective pressure on a given set of genes. This review addresses (a) how features of the immune system, the primary buffer between a pathogen and the human genome, affect evolutionary signal and (b) the considerations that must be made when assessing how pathogens have contributed to human diversification.

Human populations have a complex history of introgression and of changing population size. Human genetic variation has been affected by both these processes, so inference of past population size depends upon the pattern of gene flow and introgression among past populations. One remarkable aspect of human population history as inferred from genetics is a consistent “wave” of larger effective population sizes, found in both African and non-African populations, that appears to reflect events prior to the last 100,000 years. I carried out a series of simulations to investigate how introgression and gene flow from genetically divergent ancestral populations affect the inference of ancestral effective population size. Both introgression and gene flow from an extinct, genetically divergent population consistently produce a wave in the history of inferred effective population size. The time and amplitude of the wave reflect the time of origin of the genetically divergent ancestral populations and the strength of introgression or gene flow. These results demonstrate that even small fractions of introgression or gene flow from ancient populations may have visible effects on the inference of effective population size.

Access to a geographically diverse set of modern human samples from the present time and from ancient remains, combined with archaic hominin samples, provides an unprecedented level of resolution to study both human history and adaptation. The amount and quality of ancient human data continue to improve and enable tracking the trajectory of genetic variation over time. These data have the potential to help us redefine or generate new hypotheses of how human evolution occurred and to revise previous conjectures. In this article, we argue that leveraging all these data will help us better detail adaptive histories in humans. As a case in point, we focus on one of the most celebrated examples of human adaptation: the evolution of lactase persistence. We briefly review this dietary adaptation and argue that, effectively, the evolutionary history of lactase persistence is still not fully resolved. We propose that, by leveraging data from multiple populations across time and space, we will find evidence of a more nuanced history than just a simple selective sweep. We support our hypotheses with simulation results and make some cautionary notes regarding the use of haplotype-based summary statistics to estimate evolutionary parameters.