Long-read sequencing enables accurate analysis of complex and repetitive genomic regions but has distinct error profiles compared to short-read sequencing. Consequently, the tools for calling small variants — the predominant source of human genetic diversity — have shifted towards deep learning-based methods. This transition has reshaped variant calling across biological contexts, including germline, de novo, somatic and mosaic variants, and sequencing modalities for capturing both DNA and RNA at different resolutions. These deep learning frameworks are being developed in combination with alignment-based, assembly-based and pangenome-based strategies for small variant detection. In this Review, we chart the trajectory towards complete telomere-to-telomere profiling of small variants and consider the computational innovations needed to benchmark newly accessible repetitive regions and complex variants. Long-read sequencing is expanding the scope of small-variant detection, enabling analysis of genomic regions that were previously difficult to interrogate. In this Review, the authors examine approaches for long-read small-variant calling across sequencing platforms and applications, highlighting advances in deep learning, assembly-based and pangenome-based methods, benchmarking strategies and future challenges.
Population epigenomic studies remain dominated by participants of European genetic ancestries, mirroring well-documented disparities in genomics more broadly. This imbalance has implications for the discovery, prediction and translation of findings relating to complex traits, and a more inclusive and globally relevant epigenomics research agenda is needed. Population epigenomic studies remain heavily skewed towards participants of European genetic ancestries, limiting the discovery and translation of DNA methylation associations across contexts. The Diverse Epigenetic Epidemiology Partnership outlines priorities for building a more equitable global epigenomics research agenda and improving the relevance of epigenetic insights for global health.
Pregnancy is a distinct physiological context in which the mother hosts and nurtures the development of a genetically distinct fetus. Healthy pregnancy outcomes rely on successful placental development, during which specialized placental cells with fetal genotype, known as trophoblasts, drive the establishment of the maternal–fetal interface, a balanced tissue ecosystem that supports fetal development while safeguarding maternal well-being. Advances in single-cell, single-nuclei and spatial genomics technologies, along with data science, are now providing an unprecedented view of trophoblast regulation, which drives their differentiation into diverse cell subtypes that dynamically transform the maternal niche. More specifically, these cells modulate the immune system and remodel the vasculature to facilitate communication with maternal cells, leading to the successful formation of the maternal–fetal interface. These advances, when combined with in vitro modelling, hold promise for inferring causality in mechanisms that govern genome regulation, cellular behaviour and ultimately pregnancy homeostasis and maladaptive responses. Human placentation establishes a dynamic maternal–fetal interface in which fetal trophoblast cells coordinate tissue remodelling, immune adaptation and resource exchange to support pregnancy. In this Review, the authors discuss how single-cell, spatial and functional omics are uncovering gene regulatory mechanisms underlying trophoblast differentiation and maternal interactions and how these insights are advancing understanding of healthy placentation and pregnancy disorders.
For over a century, scientists have debated the extent to which genetic and phenotypic variation among present-day humans is the result of natural selection — in which heritable traits influence survival or reproduction — versus neutral processes such as genetic drift or population history. The initial sequencing of the human genome and subsequent population resequencing studies enabled genome-scale searches for signatures of selection in present-day genomes. This first generation of genome-wide selection scans identified many targets but left open questions about the timing and nature of selection, making it challenging to identify environmental and biological drivers. Recent methodological advances based on reconstructing ancestral recombination graphs have increased the potential power and resolution of selection scans based on present-day genomes, while the availability of new data on ancient DNA has facilitated the direct reconstruction of genetic change through time. However, there is little consensus on how to use these data to detect and interpret signatures of selection, while avoiding confounders. Here, we review the current state of knowledge about the impact of selection on human genomic diversity and highlight conceptual advances in our understanding of human evolution over the past 10,000 years. Advances in genome-wide selection scans and ancient DNA have greatly expanded our ability to study human evolution. This Review discusses the conceptual and statistical challenges in identifying and interpreting selection signals, and how recent approaches are reshaping our understanding of human evolution over the past 10,000 years.
Closely related species, such as danionin fishes of the Danio, Danionella and Devario genera, often differ in their biology despite their shared evolutionary history, providing a platform for defining the molecular basis for the divergence of phenotypic traits. Such an approach requires the availability of large-scale genomic data, which have been provided by recent reports detailing the genomes of several danionins. Facilitated by the large number of genetic tools that are available for manipulation of the most studied member of this subgroup - the zebrafish, Danio rerio - the danionins have emerged as a useful comparative model system. Here we review their phylogeny and outline the phenotypic traits that are distinct to individual species or genera. We highlight how functional genetic tools such as interspecies hybridization, mutagenesis and transgenesis, as well as the recently reported genome assemblies, have enabled new avenues for hypothesis-driven and technology-driven exploration that collectively establish danionins as important genetic models for understanding a wide range of evolutionary innovations.
