Editorial: Insights in human and medical genomics 2024

The field of human and medical genomics is undergoing two major transformations (Figure 1). First, data density and dimensionality is increasing. Second, a combination of new and old analytical techniques-notably empowered by artificial intelligence (AI)-are enabling extraction of mechanistic insight and knowledge from these data. In this editorial we highlight a fraction of the many key papers recently published in this field, with a particular emphasis on those that were invited to our research topic with speculative insights based on literature review or research that reflected state-of-the-art methods, detailed recent developments, or highlighted pivotal accomplishments. A focus of this research topic was to showcase progress and pinpoint hurdles that need to be surmounted to push the boundaries of what is medically and technically feasible in genetics.Two years ago, at the launch of this research topic, we envisioned potential advances in the following categories: (1) novel genomic technologies and their clinical application; (2) genetic determinants of complex diseases; (3) ethical, legal, and social implications of genomic research; (4) personalized medicine and genomic tailoring; (5) advances in genomic sequencing techniques; and (6) integration of genomic data with AI and machine learning approaches. These fit into a broader arc of progress in our field. Human genomics fundamentally starts with the genome and ends with personalized medicine. Variant annotation remains the foundation of human genomics and must be built on careful observation of patients 1 reported on the scale of case reports 2 all the way up to population compilations. 3,4 AlphaMissense provides an example of advancing sophistication in variant annotation (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1487608/full). The insight is that variant annotation cannot end without consideration of complex genomic context. To advance mechanistic insights the field will increasingly incorporate epistasis, 5 synergies, population and ancestry context, 6 and whole genome interactions into this functional foundation. Viewing the genome as a web of interactions, and not as a linear construct is a necessary start. Pangenome representations (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1679660/full) illustrate an important step in this direction. Furthermore, The Peruvian Genome Project (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1614021/full) highlights the importance of worldwide pan-population data generation coupled to FAIR data governance (https://www.go-fair.org/fair-principles/). Once a foundation of data is generated, it must be analyzed. The insight is that this analysis must be increasingly mechanistic and holistic, focusing on Self-critique of the field must address major questions:Are current omics data collections big enough? Dense data drives twenty-first century research. 10 Even though modern data sets may comprise data on thousands of variables (e.g., a combination of plasma proteomics and metabolomics), these may not be enough to truly illuminate the dynamic state of the entire human systems and its environment. A combination of increasing technological efficiency, brute force of scale, and careful hypothesis driven selection of assayed biological targets will be necessary to drive complete insights (e.g., shifting from left to right in Figure 1). Are we examining enough dimensions? Many analyses focus on genomics coupled to blood proteomics, metabolomics, & epigenetics. The microbiome is gaining increasing attention. For better systems understanding, we will need to look at multiple tissues and cells (and their compartments) simultaneously, and to measure the entire system dynamically over time. Larger, less biased, recruitment must occur in clinical trials and studies. Continuous monitoring devices and solutions that enable remote trials with massive population participation will drive the future.As analyses get increasingly complex, it becomes more important to carefully design controls, and to red-team (systematically testing for flaws or biases) the computational workflows. Control analyses limit the ways our complex workflows can fool us into conclusions not supported by the data. The safeguards provided by controls become critically important given the widespread availability of AI-and ML-tools that automate data handling without necessarily exercising judgment or understanding. We must use systems thinking and rational epistemological approaches such as Hill's criteria to arrive at medically useful translatable knowledge. 7Are we creating papers and disseminating data that will be part of large meta-analyses? As research becomes increasingly collaborative and cross-discipline, we must write manuscripts that will be accessible and understood by lay people, students, experts, and AI agents. Datasets must be publicly available, easing any barriers to high-throughput automated access, following the FAIR