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Genetics of Latin American Diversity (GLAD) Project: insights into population genetics and association studies in recently admixed groups in the Americas

Borda, V.; Loesch, D. P.; Guo, B.; Laboulaye, R.; Veliz-Otani, D.; French-Kwawu, J. N.; Peixoto Leal, T.; Gogarten, S. M.; Ikpe, S.; Gouveia, M. H.; Mendes, M.; Abecasis, G. R.; Alvim, I.; Arboleda-Bustos, C. E.; Arboleda, G.; Arboleda, H.; Barreto, M. L.; Barwick, L.; Bezzera, M. A.; Blangero, J.; Borges, V.; Caceres, O.; Cai, J.; Chana-Cuevas, P.; Chen, Z.; Custer, B.; Dean, M.; Dinardo, C.; Domingos, I.; Duggirala, R.; Dieguez, E.; Fernandez, W.; Ferraz, H. B.; Gilliland, F. D.; Guio, H.; Horta, B.; Curran, J. E.; Johnsen, J. M.; Kaplan, R. C.; Kelly, S.; Kenny, E. E.; Kittner, S.; Konkle,

2023-01-09 genetics
10.1101/2023.01.07.522490 bioRxiv
Show abstract

Latin America is underrepresented in genetic studies, which can exacerbate disparities in personalized genomic medicine. However, genetic data of thousands of Latin Americans are already publicly available, but require a bureaucratic maze to navigate all the data access and consenting issues. We present the Genetics of Latin American Diversity (GLAD) Project, a platform that compiles genome-wide information of 54,077 Latin Americans from 39 studies representing 45 geographical regions. Through GLAD, we identified heterogeneous ancestry composition and recent gene-flow across the Americas. Also, we developed a simulated-annealing-based algorithm to match the genetic background of external samples to our database and share summary statistics without transferring individual-level data. Finally, we demonstrate the potential of GLAD as a critical resource for evaluating statistical genetic softwares in the presence of admixture. By making this resource available, we promote genomic research in Latin Americans and contribute to the promises of personalized medicine to more people.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.