Immune checkpoint blockade (ICB) efficacy in clear cell renal cell carcinoma (ccRCC) is limited by tumor microenvironment (TME) heterogeneity. Because traditional bulk-derived models lack spatial resolution, we developed an integrated framework connecting macroscopic survival risks to microscopic TME structures. We applied ten algorithms to establish multi-omics subtypes and evaluated 101 machine-learning combinations across three independent cohorts to generate a Consensus Machine Learning-driven Signature (CMLS). The signature's spatial and cellular origins were decoded using spatial transcriptomics (ST) and a 140,000-cell scRNA-seq atlas. Expression of key genes was experimentally validated via RT-qPCR in 17 paired ccRCC clinical tissues. We identified two molecular subtypes with distinct clinical and epigenetic profiles. SuperPC optimization yielded a 24-gene CMLS serving as an independent prognostic factor. scRNA-seq and ST deconvolution revealed these signals predominantly originate from cancer-associated fibroblasts (CAFs) and malignant epithelial cells, which collaborate to drive spatial immune exclusion. RT-qPCR confirmed significant overexpression of five core CMLS genes in ccRCC versus adjacent normal tissues. Low CMLS scores correlated with enhanced ICB responsiveness, whereas high-CMLS tumors demonstrated specific vulnerability to dasatinib and dabrafenib. The CMLS translates spatial immune-exclusion dynamics into a quantifiable metric, outperforming tumor mutational burden in predicting ICB benefits, providing a robust tool for patient stratification in ccRCC.

The past decade has witnessed an unprecedented convergence of exposomic technologies, population-scale genomics, and AI-enabled data science, creating the conditions for a new integrative discipline. Here we introduce ExposoGenomics, defined as the integrative study of how the genome and exposome, treated as jointly dynamic systems, interact across the life course to shape health and disease. ExposoGenomics moves beyond classical gene-environment interaction models by embedding high-dimensional, temporally resolved exposure data within a multi-omic and AI-enabled analytical architecture oriented toward causal discovery, mechanistic understanding, and translational application. We describe the conceptual foundations of this framework, its mechanistic architecture linking external exposures to genomic responses through physiologically based kinetic models and adverse outcome networks, and the analytical approaches, including causal machine learning, graph-based integration, and foundation models, required to realize its potential. We emphasize that computational prediction must be accompanied by rigorous empirical validation, and that findings must be grounded in biologically plausible, causally supported mechanisms. In conjunction with this Perspective, Human Genomics formally launches ExposoGenomics as a dedicated article category and invites submissions that advance this integrative agenda.

The Roma people represent one of the largest and most historically enigmatic diasporas in Eurasia, illuminating human migration patterns and cultural resilience across continents. Despite extensive research on European Roma as the diaspora endpoint, the genetic legacy of their putative South Asian source populations remains critically underexplored, leaving fundamental gaps in understanding the pre-diaspora demographic structure and early dispersal dynamics. This study uniquely positions Pakistani Roma as a potential ancestral reservoir, offering a rare window into the pre-migration phase distinct from derived European Roma populations shaped by centuries of post-dispersal admixture. We analyze 82 Pakistani Roma from Punjab using high-resolution genome-wide SNP data and comprehensive mitochondrial haplogroup profiling to reconstruct their genetic origins, population structure, and historical trajectory. Analyses reveal a dual ancestry profile comprising 50-82% Indus Valley related, 20-30% Onge related, and up to 26% Steppe derived components, with three distinct subgroups exhibiting varying affinities along a South Asian to Central Western Eurasian continuum reflecting jati-like endogamy. A severe demographic bottleneck ~800 years ago coincides with medieval socio-political upheavals, while major Eurasian admixture is dated to ~660 years ago. Mitochondrial haplogroups H (45.12%) and M (26.83%) underscore dual maternal influences from West and South Eurasia. Pakistani Roma retain substantially higher South Asian ancestry than their European counterparts, establishing them as a genetically distinct population preserving the ancestral pre-diaspora state. These findings redefine the Romani origin narrative and underscore the critical value of understudied South Asian minorities in reconstructing complex human migration pathways and diaspora formation mechanisms.

National genome projects (NGPs) are increasingly shaping precision medicine by improving representation of population-specific genetic diversity. This review compiles findings from NGPs across Asia and Africa, regions that remain underrepresented in global genomic databases despite their extensive demographic and genetic diversity. A total of 53 studies from 24 countries were identified to understand (1) the genomic approach utilized, (2) novel findings that have emerged, and (3) strategies for improving research in these regions. The NGPs implement population-based variome databases (20 NGPs), linear reference genome assemblies (8 NGPs), and graph-based pangenome assemblies (1 NGP). Novel variants ranged between 0.28% (China) and 19.6% (Iran), whereas rare variants accounted for up to 88.9% of the detected variants in the Chinese population. Each NGP documents its country's evolutionary and migration history, which impacts disease frequency and pharmacogenomic variants. Clinically, NGPs revealed strong population stratification in disease-associated and pharmacogenomic variants. For example, the GJB2 rs72474224 hearing-loss variant ranged from 13% in Vietnam and 12% in Hong Kong to 0.0894% in Turkey, while the VKORC1 rs9923231 pharmacogenomic variant reached 89.2% in Taiwan but was 20%-25% in European-related Russian subpopulations. These findings demonstrate that clinically relevant allele frequencies, pathogenicity assessments, and drug-response markers differ substantially across ancestries. This review highlights ongoing efforts and strategies to enhance the representativeness of genomic data through NGPs in Asia and Africa. We also suggest future directions for national projects, including integrating family-based studies, multi-omic data, and standardized pipelines to accelerate discovery and support the equitable implementation of precision medicine.

The most commonly accepted scenario of early Earth includes: creation of the universe around 13.8 Ga (Giga-annus; or 10[9] years ago); establishment of our solar system ~ 4.60 Ga; and formation of Earth ~ 4.54 Ga. The earliest life forms on our planet so far observed to have existed, are microbes that left signals of their presence in rocks ~ 3.6 Ga - suggesting that Life forms existed within the first 940 million years after Earth's formation. However, an intriguing recent publication [1] infers that the last universal common ancestor (LUCA) likely existed by 4.2 Ga, and that the inferred LUCA had a genome of at least 2.5 Mb of DNA, encoding around 2,600 proteins; this suggests that sophisticated Life might have existed within the first 340 million years after Earth was formed. The commonly accepted geological history of early Earth suggests that the turbulent Hadean Eon lasted until 4.0 Ga, with the Late Heavy Bombardment (LHB) period occurring around 4.1 to 3.8 Ga. If Earth during the Hadean exhibited a molten surface, intense volcanic activity, and constant bombardment by asteroids and comets - how were sensitive molecules (e.g., nucleic acids, proteins) able to survive? Considering the "Lipid First" hypothesis [2], we propose that replicating lipid micelles are feasible candidates for having populated much of Earth's deep hydrothermal vents and turbulent surface within the first 340 million years of Earth's existence. These lipid micelles could therefore have provided a plausible form of "protective capsules" inside which early Life's sensitive molecules were able to evolve.

Exertional heat illness encompasses a continuum from heat exhaustion (EHE) to heat stroke (EHS), yet the molecular mechanisms remain poorly understood. DNA methylation offers a stable epigenetic signature linking environmental stress to gene regulation and long-term physiological outcomes. We profiled genome-wide DNA methylation in blood from active-duty service members hospitalized for EHE (n = 36), heat injury (EHI; n = 18), or EHS (n = 50). Blood was collected longitudinally and analyzed using the Illumina 850 K array, with normalization and time-course clustering (TCseq) to identify co-regulated CpG networks. Ingenuity Pathway Analysis was applied differentially methylated probes (p < 0.01, Δβ > 0.04). At diagnosis, both EHI and EHS shared hypomethylation in pathways regulating heat sensing, oxidative defense, and vascular tone. Over time, EHI exhibited adaptive methylation changes supporting neuronal repair, glucocorticoid regulation, and cytoskeletal stability. In contrast, EHS demonstrated sustained downregulation of metabolic and cardiovascular regulators, persistent inflammasome activation, and oxidative imbalance. Early patterns were shared between EHI and EHS, reflecting a common acute stress response, but later responses diverged: EHI showed partial epigenetic recovery, while EHS exhibited sustained metabolic suppression and inflammasome activation. These time-dependent methylation signatures identify potential molecular targets for promoting recovery and preventing long-term complications of heat illnesses.