Abstract
Background: Biochemical traits such as enzyme activity, metabolic capacity, and drug response are shaped by both genetic and environmental factors. While bioinformatics has advanced our understanding of population-level genetic variation, it often overlooks the influence of cultural identity, an important determinant of behavior, lifestyle, and environmental exposure. This study integrates cultural studies with biochemistry and bioinformatics to investigate how culturally defined human populations differ in their biochemical traits at the molecular level.
Methods: We analyzed genotype, transcriptomic, and biochemical biomarker data from three major pop ulation-scale datasets: the 1000 Genomes Project, GTEx, and the UK Biobank. Cultural groupings were inferred from population metadata, including ethnicity, region, and lifestyle proxies. We selected cultur ally relevant biochemical traits (e.g., lactase persistence, alcohol metabolism, xenobiotic detoxification) and performed principal component analysis (PCA), hierarchical clustering, SNP-trait association stud ies, pathway enrichment analysis, and machine learning classification to assess cultural stratification in molecular data.
Results: Cultural groups displayed distinctive biochemical signatures. For example, ALDH2 variants were enriched in East Asians, while LCT activity distinguished Northern Europeans. Enrichment analyses revealed culturally specific pathways, including acetaldehyde detoxification in East Asians and galactose metabolism in dairy-consuming populations. Machine learning models achieved moderate classification performance (e.g., AUC = 0.67 for East Asians and Northern Europeans), showing that biochemical traits can predict cultural affiliation to a degree. These findings were synthesized into a visual framework illustrating how culture intersects with biochemistry at the systems level.
Conclusion: Our study demonstrates that cultural identity is a meaningful dimension in molecular bio informatics. Cultural practices shape gene expression and metabolic function, leaving detectable traces in omics data. Incorporating cultural context into biomedical research enhances our understanding of human biochemical diversity and supports the development of more equitable, culturally sensitive ap proaches in precision medicine. This interdisciplinary model lays the foundation for future research at the interface of genomics, cultural anthropology, and systems biology.
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