Since 1987, Mexico has conducted periodic National Health Surveys (ENSA) to monitor the country’s health status through population-based data. For this study, we use samples and data collected during the ENSA 2000, a nationwide health survey carried out by the Mexican Secretariat of Health between November 1999 and June 2000.
ENSA 2000 was a probabilistic, multi-stage, stratified, cluster household survey designed to be representative of the civilian, non-institutionalized Mexican population at both the state and national levels. Trained personnel conducted interviews and collected information on household and sociodemographic characteristics, current health status, health care usage, and behavioral factors. In addition, sera and buffy coat samples were obtained from 43,085 individuals aged 20 years or older.
To date, over 50 scientific publications have been derived from this survey, providing key insights into national health trends and even preliminary genetic characteristics of the Mexican population. Importantly, ENSA 2000 included individuals from remote and rural regions, making it one of the most geographically and demographically inclusive population health studies ever conducted in Mexico.
Given its large scale, sophisticated sampling design, and rich phenotypic data, the ENSA 2000 represents a valuable untapped genetic resource to link genetic markers with health outcomes. Its effective genetic characterization has now become feasible through the combined expertise of a multidisciplinary team spanning Public Health, Epidemiology, Immunology, Pediatrics, Population Genetics, and Computational Genomics.
ENSA datasets capture numerous traits relevant to cardiovascular and metabolic diseases, along with seroprevalence data for various infectious diseases. These insights have directly informed public health interventions, including vaccination strategies for hepatitis A and varicella-zoster virus (VZV).
Recent advances in Mexican genomics include the development of a fine-scale genetic map of ethnic variation, revealing extraordinary levels of genetic differentiation, especially among individuals of Indigenous ancestry. This diversity has been linked to physiological traits, such as lung function, underscoring the importance of studying population substructure in relation to health and disease.
The combination of detailed phenotypic data and deep genetic variation makes Mexico an unparalleled setting for exploring highly heritable traits and genetic risk factors with large effects. The Mexican Biobank project leverages this foundation to perform genome-wide association studies (GWAS) across diverse biomedical traits, using population substructure maps to refine and interpret genetic associations.
Using genome-wide data from nationally representative cohorts, we characterize fine-scale population structure and demographic history across Mexico. Our analyses dissect patterns of admixture among Indigenous, European, and African ancestries, resolving regional differentiation and historical migration events. This framework supports ancestry-aware genome-wide association studies and improves genetic discovery in diverse populations.
We link phenotypic variation to underlying genetic architecture and assess the distribution of previously reported disease-associated variants within the Mexican population. These studies provide insight into population-specific genetic risk and support more precise, ancestry-aware approaches to improving population health.
Using genome-wide data from the Mexican Biobank, we perform association studies across cardiometabolic, anthropometric, and infectious disease–related traits. By leveraging fine-scale population structure and nationally representative sampling, these studies identify genetic variants influencing complex traits while reducing bias from Eurocentric reference datasets.
We investigate how evolutionary forces have shaped genetic variation before and after admixture among Indigenous, European, African, and Asian ancestries. By integrating haplotype-based and ancestry-specific analyses, we identify signals of natural selection relevant to contemporary health and disease.
We analyze genomic data from archaeological human remains spanning pre-Hispanic and early colonial periods to reconstruct population history, migration, and continuity. By integrating ancient genomes with present-day data, we provide evolutionary context for modern genetic diversity in Mexico.
Serological data capture population-wide exposure to infectious diseases, vaccine coverage, and immune responses over time. Analyses of preserved serum samples reveal infection history across regions, age groups, and demographic backgrounds. National surveys show widespread exposure to SARS-CoV-2, strong vaccine-derived immunity to hepatitis B, low and declining hepatitis C prevalence, and low HIV seroprevalence concentrated in specific groups. Integrated with genomic data, these analyses inform immunity, disease risk, and public health impact.