Reviews & Analysis

This article reviews the current state of implementation of polygenic risk scores in the clinical setting, highlights key challenges and outlines future directions for the use of such scores to improve disease risk prediction and to enable personalized prevention.

Recent advances in forensic genetics have improved the range, precision and reliability of forensic information obtainable from biological trace material. The author reviews how non-targeted and targeted omics approaches and methods are improving crime scene analyses being applied for the identification of perpetrators and their relatives or victims, the prediction of phenotypic traits, and the determination of trace characteristics.

Spatial omics has empowered the discovery of developmental and disease-associated molecular signatures, cell states and multicellular niches, as well as the evaluation of disease heterogeneity within and across organs. The authors review spatially resolved molecular changes across diseases and discuss the potential of spatial multi-omics for clinical applications, including the recent impact of artificial intelligence.

Haplotype phasing and genotype imputation improve genomic analyses by determining which variants occur together on a chromosome and inferring unobserved varants, respectively. In this Review, Sun and Li describe how tools for haplotype phasing and genotype imputation have evolved to accommodate increasingly larger genomic datasets and new sequencing technologies.

Genome-wide approaches have uncovered the vast microbial and viral diversity across ecosystems. This Review explores advances in metagenomics, single-cell sequencing and functional profiling to elucidate the evolution, biogeography and ecological dynamics of Earth’s microbiomes.

Extraordinary advances in genomic science have defined the twenty-first century, transforming our understanding of human biology in both health and disease. This timeline Perspective charts two decades of genomic innovation since the human genome reference sequence became available, highlighting the evolution of sequencing technologies and how they, combined with computational advances, paved the way for genomic medicine.

In vivo CRISPR screens generate high-throughput, unbiased genotype–phenotypes maps for complex biological processes that cannot be studied in vitro. This Review outlines key criteria for understanding, designing and implementing such screens and discusses their potential impact on basic and translational research.

Fine-mapping aims to distinguish between the causal and non-causal genetic variants identified in genome-wide association studies of complex traits. In this Review, Li and Zhou cover the recent methodological advances of fine-mapping, including the refined modelling assumptions, improved computational efficiency and incorporation of additional information to expand biological insights.

Multiplexed assays of variant effect (MAVEs) are highly scalable experimental approaches used to generate functional data for genetic variants. In this Review, McEwen et al. discuss the advances in MAVE technologies and guidance on how to use MAVE data in the clinic, which is helping to reveal variant pathogenicity, develop personalized drugs and inform targeted therapies.

In this Review, Roux and Coclet outline current viromics approaches and discuss how they have contributed to our growing understanding of viral genomic diversity, focusing on uncultivated viruses of microorganisms.

In this Review, Ascensao and Desai discuss how methodological advances in genotype and phenotype manipulation are transforming experimental evolution approaches and providing new insights into the underlying genetics and forces that shape phenotypic evolution.

In this Review, Coyle and King explore how genomic and functional data from diverse species are providing new insights into the types of mechanistic changes that accompanied the evolutionary origin of animals.

Disruption of the 3D genome caused by structural variation contributes to developmental disorders and cancer. The authors review the causes and molecular and clinical consequences of position effects arising from disruptions to the genome architecture.

In this Review, Rippe and Papantonis describe advances in understanding the role of transcription compartments in gene regulation, specifically by collating and contrasting historical work on transcription factories with more recent work on transcriptional condensates.

Arthropod-borne viruses have a substantial impact on global health, with climate change and urbanization exacerbating their emergence. Integrating genomic surveillance and phylogenetic models with ecological and epidemiological data enhances our understanding of virus transmission dynamics, aiding in effective detection and the response to disease outbreaks affecting humans and animals.

In this Review, Ferretti et al. discuss advances in our understanding of interactions between the human genome and the microbiome, including the effects of the microbiome on host gene regulation.

Synonymous mutations, once deemed neutral, have been shown to influence gene expression and organismal fitness by affecting transcription, mRNA processing, translation and protein folding. In this Perspective, the authors highlight evidence for fitness effects of synonymous mutations and discuss resulting implications for evolutionary and disease genetics.

New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging. The authors review how the choice of analytical methods used to process, analyse and interpret genomic data can influence genomic research, as well as existing methodological approaches to promote equity and fairness in genomics.

Cell-type deconvolution methods are often needed to analyse spatial transcriptomic data to recover cell-type distributions. In this Review, the authors describe the process of cell-type deconvolution, contrast the tools available and highlight important considerations for which tool to use.

Barbadilla-Martínez et al. review recent progress in deep-learning-based sequence-to-expression models, which predict gene expression levels solely from DNA sequence. These models are providing new insights into the complex combinatorial logic underlying cis-regulatory control of gene expression.