Complete blood count data can increase the accuracy of the diagnosis of severe malaria in children in high transmission settings.

A novel statistical framework and approach using sibling phenotype pairs allows inference of genetic architecture in the tails of complex traits to be made without genetic data.

Advanced statistical and computational methods are reviewed and compared for integrating microbiome, host genomics, and metabolomics data, together with future research directions.

Lifestyle interventions and statins may target different components of the lipid profile, suggesting that they are not redundant strategies but could be combined for better benefits.

Copy number alteration heterogeneity exists in many shapes and forms in breast cancer genomes and single-cell genomics is a powerful tool to further our understanding of its nature and significance.

Genetically predicted high insulin-like growth factor levels may decrease the risk of COVID-19 susceptibility and hospitalization.

Neural networks allow fast and accurate prediction of individual spatial locations from genotypes or sequencing data.

Compiling public datasets into a single, centralised repository and linking directly to analytical software completely transforms the scale and scope of causal inference across the phenome.

A health-system embedding method for genomic discovery and clinical characterization of disease highlights the importance of documenting a wider spectrum of genetic disorders in diverse populations.

Epigenetic regulations in the smoking- and blood pressure-related pathways to the future risk of coronary heart disease may reveal novel pathways or therapeutic targets.