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Abstract

Human genetic diversity is shaped by migration, isolation, and admixture, processes that are particularly complex in Latin America, one of the most admixed regions of the world. Many studies treat Latin American populations as a single category to control for stratification, but this approach overlooks heterogeneity generated by distinct colonial, Indigenous, and African demographic histories. Costa Rica represents an important but underexplored case: despite its small geographic size and cohesive national identity, historical evidence indicates regional differences in ancestry. Prior genetic studies relied on limited marker sets or small cohorts, restricting resolution of fine-scale population structure. This thesis applied genome-wide methods to characterize genetic ancestry and population structure in Costa Rica and to situate these findings within the broader Latin American context. Analyses included estimation of global and local ancestry proportions, fixation index (FST) to measure differentiation, and ancestry-specific multidimensional scaling (MDS) to trace continental origins. Results were interpreted in light of documented historical and demographic events, including Spanish colonization, displacement of Indigenous communities, and African migrations through slavery and Caribbean settlement. The analyses revealed clear regional differences in ancestry. Provinces with distinct settlement histories, such as Guanacaste and Limón, showed elevated African ancestry, while southern regions retained stronger Indigenous contributions. The Central Valley displayed predominantly European–Indigenous admixture, consistent with historical accounts of colonization and population growth. These findings demonstrate that local demographic processes left durable genetic signatures, highlighting Costa Rica as a case study of how admixture dynamics shape modern genomes. Beyond historical reconstruction, this work underscores the biomedical relevance of ancestry variation. Recognizing population substructure is essential to control stratification bias in association studies, improve polygenic risk score accuracy, and support equitable application of genomic tools. Limitations include incomplete representation of Indigenous ancestry in reference panels and uneven provincial sample sizes, but these results provide a foundation for expanded genomic research in Central America.