Ciencias Exactas y Ciencias de la Salud

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Development of polygenic scores for the Mexican population for obesity, diabetes, and dyslipidemias
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10) Torres Treviño, David; Treviño Alvarado, Víctor Manuel; emipsanchez; Garza Hernández, Debora; Martínez Ledesma, Juan Emmanuel; School of Engineering and Sciences; Campus Monterrey
Genetic prediction estimates a risk for a diseases using genetic data, aiding with earlier diagnoses, prevention, and targeted treatments. Polygenic scores (PGS) estimate risk by multiple single nucleotide polymorphisms (SNPs), the most common form of genetic variation. Though each SNP has a small effect, their joint effect provides key insights into the risk for common diseases influenced by multiple genetic factors. A key limitation of PGS is that the majority have been trained on European populations, leading to a significant drop in predictive accuracy when applied to non-European groups. This study aimed to address this issue by improving the accuracy of PGS for type 2 diabetes (T2D), BMI, Triglycerides, Total Cholesterol, HDL, and LDL levels in the Mexican population through a series of strategies. We implemented various established methods for constructing PGS, including techniques that have shown success in non-European populations and ensemble models combining ancestrybased PGS scores to optimize accuracy across diverse populations. Our key innovation lies in applying shrinkage to the ancestry-based PGS according to each individual’s ancestry proportions, prioritizing ancestry-based scores that are genetically closer to the individual and enhancing the relevance of matched ancestry data. Our results showed no improvement, and in some cases, a decrease in accuracy when using multi-ethnic or Mexican training data, likely due to the underrepresentation of non-European individuals and the small sample size of the Mexican GWAS. However, notable exceptions included LDL and Triglycerides predictions, where the Mexican GWAS outperformed the European GWAS. This outcome may be attributed to genetic loci associated with lipid levels unique to Mexicans, some linked to Amerindian ancestry which explain a greater variance than the loci captured in the European GWAS. Moreover, ensembles incorporating both ancestry adjustment and the Mexican-based PGS underperformed compared to the European baseline model, whereas those excluding the Mexican-based PGS exceeded the European baseline’s performance. Ensembles constructed with Lassosum and LDpred2 fell short of the PRScsx ensemble’s results, suggesting an advantage to jointly modeling multiple populations rather than treating them separately. Introducing ancestry adjustment in PRScsx (RAW4) maintained accuracy and, in some traits, even improved it for subgroups with predominantly African ancestry, showing promise in the proposed ancestry-based shrinkage approach. However, despite these improvements, disparities in accuracy persisted across population subgroups, especially for individuals with a high proportion of African ancestry. These results highlight the current challenge of generalizability gaps in PGS models, even for methods designed for diverse populations like PRScsx. Future studies could focused on developing a sophisticated Bayesian framework for ancestry adjustment, refining ancestry estimation methods, or incorporating a Native American component to better capture the genetic diversity in the Mexican population.
Estimation of ancestry in the mexican population using informative genetic markers
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Valdez Alvarez, Héctor; Treviño Alvarado, Víctor Manuel; emipsanchez; Orozco Orozco, Lorena Sofía; García Ortiz, Humberto; Martínez Ledesma, Juan Emmanuel; Escuela de Ingeniería y Ciencias; Campus Monterrey; Garza Hernández, Debora
The study of genetic ancestry has become an essential component of modern genetics, offering insights into the origins and migrations of human populations. This thesis presents the development of a genetic ancestry panel specifically tailored for the Mexican population, a group characterized by its high genetic diversity and complex admixture. The primary objective of this research is to accurately estimate the proportions of ancestry in Mexicans using informative genetic markers, thereby addressing the underrepresentation of this population in Genome-Wide Association Studies (GWAS). In the initial phase, various genetic databases were considered, and three were selected for the development of the ancestry panel: the 1000 Genomes Project (1000G), the Human Genome Diversity Project (HGDP), and the Metabolic Analysis in an Indigenous Sample (MAIS). The integration of these datasets provided a comprehensive view of genetic diversity crucial for the panel's accuracy. Principal Component Analysis (PCA) was employed to visualize the genetic structure and verify the separation of ancestral groups. The results confirmed the integrity of the selected datasets. Three methods for selecting Ancestry Informative Markers (AIMs)—Top K, Balanced K, and SumInfo K—were developed and evaluated. Although Balanced K and SumInfo K showed better performance than Top K, integrating Mexican data (MAIS) posed significant challenges, particularly due to the influence of East Asian populations. To address these issues, a revised strategy was implemented, focusing on optimizing AIM selection and improving the robustness of the panel. This involved a detailed workflow and validation process, ensuring the final panel's reliability. Despite the challenges, the new strategy demonstrated promising results, and the final panel is expected to be completed soon. The developed ancestry panel has significant implications for forensic science, personalized medicine, and anthropological research. By accurately estimating ancestry proportions in the Mexican population, this research contributes to a broader understanding of genetic diversity and supports more effective medical and forensic applications. Future work will focus on finalizing the panel and applying it to the oriGen project, which aims to analyze genetic data from a large cohort of Mexicans, further enhancing the understanding of this population's genetic landscape.

Ciencias Exactas y Ciencias de la Salud

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