The Human Pan-genome and Its Role in Understanding Global Disease Variability

The concept of a human pan-genome encompasses the full spectrum of genetic variation present within the human species. Traditionally, genetic studies have relied on a single reference genome for each species, but with the advent of more advanced genomic sequencing technologies and the recognition of the vast diversity present in human populations, it has become evident that a single reference genome is insufficient. The pan-genome is thus an assembly of all the DNA sequences occurring in a species, which includes not only the core genome shared by all individuals but also the variable parts unique to some individuals. Human Pan-genome and Global Genomic Diversity: The Human Pan-genome Project aims to create a more comprehensive and sophisticated human reference genome. The current reference genome, formed from a composite of haplotypes from over 20 individuals, has biases and errors and does not represent global human genomic variation. The project emphasizes a high-quality reference that encompasses common variants, including single-nucleotide variants, structural variants, and functional elements. By employing graph-based, telomere-to-telomere representation, the goal is to capture a more accurate and diverse representation of global genomic variation. This effort is crucial for improving gene-disease association studies across populations, expanding genomics research, and serving as a resource for future biomedical research and precision medicine​​​​​​​​. Genetic Basis of Diseases and Population Differences: Research shows that the risk of disease is shaped by multiple factors, including genetic, socio-economic, demographic, cultural, and environmental aspects. Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic determinants of disease risk. However, these studies have predominantly focused on individuals of European ancestry, with limited representation from other populations. This has led to a gap in understanding the genetic diversity and disease risk in non-European populations. The diversification of populations globally, driven by factors like population demography, genetic drift, and adaptation to environments, presents new opportunities for discovery and translation into therapies. It also aids in understanding population disease risk better​​. Polygenic Risk Scores and Disease Prevalence: Studies exploring the use of population-level polygenic risk scores (PRS) across different geographic populations have shed light on the genetic basis of phenotypic variation. For instance, traits like lactase persistence, melanoma, multiple sclerosis, and height have been studied in relation to their genetic complexity and prevalence across populations. These studies reveal significant variations in disease prevalence globally, influenced by factors like genetic architecture, environmental exposures, and allele frequencies. Such research is pivotal in understanding how population-specific alleles affect disease prevalence and can guide the development of more tailored medical interventions​​​​​​. The human pan-genome project and related studies underscore the importance of considering genetic diversity in understanding disease prevalence and developing precision medicine. They highlight the limitations of previous genomic studies and the need for a more inclusive approach that accounts for the diverse genetic makeup of global populations. Reference: Wang, T., Antonacci-Fulton, L., Howe, K., Lawson, H. A., Lucas, J. K., Phillippy, A. M., ... & Human Pangenome Reference Consortium. (2022). The Human Pangenome Project: a global resource to map genomic diversity. Nature, 604(7906), 437-446. Gurdasani, D., Barroso, I., Zeggini, E., & Sandhu, M. S. (2019). Genomics of disease risk in globally diverse populations. Nature Reviews Genetics, 20(9), 520-535. Graham, B. E., Plotkin, B., Muglia, L., Moore, J. H., & Williams, S. M. (2021). Estimating prevalence of human traits among populations from polygenic risk scores. Human Genomics, 15, 1-16.