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The wealth of DNA methylation data continues to grow rapidly, including from epigenome-wide association studies (EWAS). However, extracting meaningful biological and clinical information requires diverse computational approaches for data analysis. This Review discusses the range of statistical tools available, including for cell-type deconvolution, identification of important methylation data features, causation and system-level integration with other types of omic data.
Recent genome-wide association studies have catapulted the search for genes underlying human intelligence into a new era. Genome-wide polygenic scores promise to transform research on individual differences in intelligence, but not without societal and ethical implications, as the authors discuss in this Review.
Genomic technologies are providing a clearer picture of how nuclear receptors (NRs) regulate complex transcriptional networks and contribute to the development and progression of cancer. This deeper understanding of NRs will hopefully lead to improved prognostic tools and new therapeutic targets.
Including diverse populations in genomic studies has the potential to improve the use of genomic data in the clinic. Here, members of the National Human Genome Research Institute review the benefits of increasing diversity, the challenges to overcome and key recommendations for how to achieve this goal.
