![]() It also requires new technology for collecting functional data, phenotypes, and environmental measurements at scale, including epigenomic assays ( 32), remote sensing, thermal and fluorescence imaging ( 34), passive environmental sampling ( 35), geographic information system mapping ( 36), and participatory science ( 37). This requires transcriptomic and epigenomic data that vary by cell type and over time, samples from many individuals per species, and many samples per individual ( 31). While a genome sequence may be essential, it is not sufficient to elucidate the complex processes underlying development, growth, differentiation, host defense, environmental responses, and countless other facets of biology. If genomes are the source code of life, then the interpretation is an interaction between that code, the cellular machinery that reads it, and the environment in which it is manifested. In humans, cellular models are well developed, but organism-level experimentation is not possible. “Function”: potential for functional genomics (epigenomics, cellular and organoid models, genetic engineering, and so forth). “Sample size”: number of individuals that can be sampled, ranging from <100 (endangered species or laboratory animals requiring costly care) to millions (humans). “Sampling”: ease of collecting samples, ranging from only minimally invasive sampling in wild-caught individuals, to populations where euthanasia and tissue collection are feasible. In humans (midpoint), resources like electronic medical records make it possible, but not easy, to collect detailed phenotypes at scale. “Phenotyping”: ease of collecting phenotype data, ranging from only noninvasive phenotyping in natural environments, to invasive laboratory phenotyping. “Complexity”: genetic complexity of traits low in the laboratory mouse, with controlled genetic background and environment, and high in humans, where most traits are complex. Humans (midpoint) are outbred but less diverse than many species. ![]() “Diversity”: genetic diversity in populations, ranging from inbred (e.g., laboratory mice) to outbred/highly diverse. Different types of study populations have different strengths. Here, we describe potential for scientific discovery when comparative genomics works in close collaboration with a broad range of fields as well as the technical, scientific, and social constraints that must be addressed.įig. A new approach, integrating comparative genomics with cell and evolutionary biology, ecology, archaeology, anthropology, and conservation biology, is essential for understanding and protecting ourselves and our world. We have the technology to collect massive and exquisitely detailed datasets about the world, but expertise is siloed into distinct fields. Yet understanding how genomes work, and how genetic variation shapes phenotypes, requires a broad view that embraces the vast diversity of life. This imbalance reflects a perception that human studies are paramount in disease research. Fewer than 18,000 of ∼2,000,000 eukaryotic species (<1%) have a representative genome sequence in GenBank, and only a fraction of these have ancillary information on genome structure, genetic variation, gene expression, epigenetic modifications, and population diversity. To date, genomic research has focused on humans, a small number of agricultural species, and established laboratory models. Genomics encompasses the entire tree of life, both extinct and extant, and the evolutionary processes that shape this diversity.
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