Mexican Biobank advances population and medical genomics of diverse ancestries

Profiling the genetic diversity of the HLA system in Mexico using 9-locus allele and haplotype frequencies from donors in the NMDP Mexico donor center

Profiling the HLA diversity at the population level benefits multiple clinical and anthropological applications, such as tracing population migration, identifying genetic relationships between different groups, quantifying the added diversity in a global donor pool and matching for solid organ and stem cell transplantation. We calculated nine-locus HLA-A ∼ C ∼ B ∼ DRB1 ∼ DRB3/4/5 ∼ DQA1 ∼ DQB1 ∼ DPA1 ∼ DPB1 allele and haplotype frequencies in about 170,000 volunteer donor genotypes from the NMDP Mexico (NMDP MX, previously Be The Match Mexico) donor center. These donors are predominantly of Mexican ancestry recruited from multiple regions in Mexico. The goal of the study was to describe the HLA genetic profiles of the Mexican population and investigate the contribution of these donors' HLA in serving Mexican, US and international patients in need of hematopoietic cell transplants. Additionally, we estimated that almost all Mexican patients will have an available 5 of 8 or better matched donor in the NMDP MX donor center with matches also available for some of the Latino patients in the U.S. We demonstrate that Mexican populations clustered genetically and shared multiple frequent alleles and haplotypes with populations from the US Mexican or Chicano, US South/Central American Hispanic, and some Latino populations. Operationally, 78 % of NMDP Mexico donors contributed genotypes that were observed a total of three times or less on the registry, increasing the diversity of the overall NMDP registry. More than 300 donor collections were facilitated through the NMDP MX donor center serving mostly Hispanic/Latino patients in the US and abroad. This study highlights the importance of adding the NMDP MX donors to the worldwide donor pool and paves the way for a data-driven strategy for future planning and donor recruitment.

Expanding scope of genetic studies in the era of biobanks

Biobanks have become pivotal in genetic research, particularly through genome-wide association studies (GWAS), driving transformative insights into the genetic basis of complex diseases and traits through the integration of genetic data with phenotypic, environmental, family history, and behavioral information. This review explores the distinct design and utility of different biobanks, highlighting their unique contributions to genetic research. We further discuss the utility and methodological advances in combining data from disease-specific study or consortia with that of biobanks, especially focusing on summary statistics based meta-analysis. Subsequently we review the spectrum of additional advantages offered by biobanks in genetic studies in representing population differences, calibration of polygenic scores, assessment of pleiotropy and improving post-GWAS in silico analyses. Advances in sequencing technologies, particularly whole-exome and whole-genome sequencing, have further enabled the discovery of rare variants at biobank scale. Among recent developments, the integration of large-scale multi-omics data especially proteomics and metabolomics, within biobanks provides deeper insights into disease mechanisms and regulatory pathways. Despite challenges like ascertainment strategies and phenotypic misclassification, biobanks continue to evolve, driving methodological innovation and enabling precision medicine. We highlight the contributions of biobanks to genetic research, their growing integration with multi-omics, and finally discuss their future potential for advancing healthcare and therapeutic development.

Choice between DNA primer sets (A or B) of the ForenSeq kit: forensic evaluation in a Mexican admixed population sample

Massively parallel sequencing (MPS) overcomes many PCR-CE limitations to analyze STRs and allow simultaneous inclusion of SNPs in forensic cases. By MPS, the ForenSeq™ DNA Signature Prep kit analyzes 27 aSTRs, 7 X-STRs, 24Y-STRs, and 94 identity-informative SNPs (iiSNPs) with the DNA Primer Set-A (DPS-A). Optionally, the DNA Primer Set-B (DPS-B) adds to the analysis 56 ancestry-informative SNPs (aiSNPs) and 24 phenotype-informative SNPs (piSNPs), but diminishes from 96 to 32 the number of samples per sequencing run. We assessed the forensic informativity provided by the loci analyzed by these two DPS in admixed individuals from Mexico City (Center, Mexico). For STRs, we report length-based (LB) and sequence-based (SB) allele frequencies and forensic parameters of the 152 identity informative markers (DPS-A). For aSTRs, the combined PD of SB genotypes (PD ~ 100%) was ~ 2949 times larger than that from LB. Conversely, the observed phenotype distribution offered low PD levels (PD = 6.6% and 10.4%), whereas piSNPs predicted accurately only the modal brown eye and dark hair colors, respectively. Similarly, aiSNPs detected a large prevalence of admixed individuals (97.3%; PD = 5.4%). Although few individuals were inferred as Europeans and Native Americans (1.37% each), they were self-declared as admixed, which result confusing for HID purposes. In brief, SB genotypes increased significantly the informativity of STRs to solve complex cases (DPS-A), whereas aiSNPs and piSNPs added mostly irrelevant information (DPS-B). We provide useful cost-benefit criteria in one Latin American population to choose DPS-A (96 samples) instead of DPS-B (32 samples) of the Forenseq kit.

Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies

Genome-wide association studies (GWASs) in ancestrally diverse populations are rapidly expanding, opening up unique opportunities for novel gene discoveries and increased utility of genetic findings in non-European individuals. A popular technique to identify putative causal variants at GWAS loci is via statistical fine-mapping. Despite tremendous efforts, fine-mapping remains a very challenging task, even in the relatively simple scenario of studies with a single, homogeneous population. For studies with admixed individuals, such as within Latin America and the Caribbean, methods for gene discovery are still limited. Here, we propose a Bayesian model for fine-mapping in admixed populations, CARMA-X, that addresses some of the unique challenges of admixed individuals. The proposed method includes an estimation method for the linkage disequilibrium (LD) matrix that accounts for small reference panels for admixed individuals, heterogeneity across populations and cross-ancestry LD, and a Bayesian hypothesis test that leads to robust fine-mapping when relying on external reference panels of modest size for LD estimation. Using simulations, we compare performance with recently proposed fine-mapping methods for multi-ancestry studies and show that the proposed model provides higher power while controlling false discoveries, especially when using an out-of-sample LD matrix. We further illustrate our approach through applications to two Latin American genetic studies, the Estudio Familiar de Influencia Genética en Alzheimer (EFIGA) study in the Dominican Republic and the Mexican Biobank, where we show the benefit of modeling ancestry-specific effects by prioritizing putative causal variants and genes, including several findings driven by ancestry-specific effects in the African and Native American ancestries.

Genetic variants in TMPRSS2 influence SARS-CoV-2 infection susceptibility within Mexican Mestizos

Since host genetics is one of the primary factors contributing to COVID-19 susceptibility and its clinical progression, several studies have focused on analysing the implications of genetic polymorphisms associated with COVID-19. These studies particularly emphasise on common variants in genes that are involved in the viral mechanism of host entry and in the host's response to infection. In this study, we explored the participation of 24 single nucleotide polymorphisms located on the ACE, ADAM17, FURIN, IFITM3, TMPRSS2 and VDR genes in SARS-CoV-2 infection susceptibility. Three of these SNPs in TMPRSS2 (rs75603675, OR = 1.86, 95%CI = 1.29-2.66, p ≤ 0.001; rs4303795, OR = 1.98, 95%CI = 1.38-2.84, p ≤ 0.001 and rs8134378, OR = 2.59, 95%CI = 1.28-5.21, p ≤ 0.01) had a significant association with an increased risk of infection. When comparing haplotype frequency distributions, the haplotypes CAG (OR = 7.34, 95%CI = 5.51-9.77), AGA (OR = 2.46, 95%CI = 1.12-5.44), and AGG (OR = 1.59, 95%CI = 1.17-2.16) presented significant associations, suggesting that TMPRSS2 influences SARS-CoV-2 infection susceptibility within Mexican Mestizos. These risk alleles and their haplotypes were found more frequently in the case group than in the reference group, contributing to at least a twofold increase in the risk of SARS-CoV-2 infection, a finding that was reinforced by meta-analyses.

Major Histocompatibility Complex Class I and II Allele Frequencies and Disease Associations in Mexicans: A Systematic Review and Meta-Analysis

BACKGROUND

The major histocompatibility complex (MHC) plays a crucial role in immune responses and is associated with disease susceptibility. This study systematically reviews MHC class I and class II allele frequencies and their associations with diseases in the Mexican population from 1979 to 2023.

METHODS

A systematic review following PRISMA guidelines was conducted. Reports were obtained from the Allele Frequency Net Database and PubMed using keywords related to HLA and Mexican populations. A total of 776 reports were screened, and 214 were retained for final analysis. Seventy-six studies comprising allele frequency data from over 20,000 individuals across Mexican states and indigenous communities were analyzed. In addition, over 138 articles were examined to identify alleles associated with various diseases.

RESULTS

The analysis identified 117 alleles whose frequencies varied regionally within Mexico. While DPA1*01, DPB1*04:01, and DQA1*03 were predominant, DRB1*04, DQB1*03, and DQA1*05 were also prominent but variable. Certain alleles, such as A*02, B*35, C*04, and C*07, were relatively common in the population. Numerous disease correlations were uncovered, such as B*27's strong association with spondyloarthropathies. DRB1*15:01 and DRB1*04 conferred an increase in multiple sclerosis, while DRB1*04 may protect against some skin diseases.

CONCLUSION

This review improves the understanding of MHC allele frequencies and disease associations in Mexicans, highlighting genetic diversity. The findings lay the groundwork for future research on genetic predispositions and health outcomes, aiding healthcare strategies in this diverse population. Further studies are needed to address data gaps and refine genetic profiles for targeted medical applications.

Large-scale admixture mapping in the All of Us Research Program improves the characterization of cross-population phenotypic differences

Admixed individuals have largely been understudied in medical research due to their complex genetic ancestries. However, the consideration of admixture can help identify ancestry-enriched genetic associations, delineating some of the genetic underpinnings of cross-population phenotypic variation. To this end, we performed local ancestry inference within the All of Us Research Program to identify individuals with recent admixture between African (AFR) and European (EUR) populations (N=48,921). We identified evidence of local AFR ancestry enrichment at the HLA locus, suggestive of putative selection since admixture. Furthermore, we performed the largest admixture mapping (ADM) efforts in AFR-EUR Admixed individuals for 22 traits, identifying 71 associations between inferred local AFR ancestries and a trait. Variants from published GWAS could only account for 18 (25%) of the ADM associations, highlighting novel loci where ancestral haplotypes explained some phenotypic variation. Previous studies likely have not identified these loci due to the low availability of high-powered GWAS in populations genetically similar to AFR. One such loci was 9q21.33, associated with 1.4-fold risk of end-stage kidney disease (ESKD) for carriers of inferred local AFR ancestries at the region. This locus contains the gene SLC28A3, which has previously been linked to kidney function but has never been associated with cross-population ESKD prevalence differences. Together, our results expand upon the existing literature on phenotypic differences between populations, highlighting loci where genetic ancestries play a critical role in the genetic architecture of disease.

Psychiatric genetics in the diverse landscape of Latin American populations

Psychiatric disorders are highly heritable and polygenic, influenced by environmental factors and often comorbid. Large-scale genome-wide association studies (GWASs) through consortium efforts have identified genetic risk loci and revealed the underlying biology of psychiatric disorders and traits. However, over 85% of psychiatric GWAS participants are of European ancestry, limiting the applicability of these findings to non-European populations. Latin America and the Caribbean, regions marked by diverse genetic admixture, distinct environments and healthcare disparities, remain critically understudied in psychiatric genomics. This threatens access to precision psychiatry, where diversity is crucial for innovation and equity. This Review evaluates the current state and advancements in psychiatric genomics within Latin America and the Caribbean, discusses the prevalence and burden of psychiatric disorders, explores contributions to psychiatric GWASs from these regions and highlights methods that account for genetic diversity. We also identify existing gaps and challenges and propose recommendations to promote equity in psychiatric genomics.

Evolution, genetic diversity, and health

Human genetic diversity in today's world has been shaped by evolutionary history, demographic shifts and environmental exposures, influencing complex traits, disease susceptibility and drug responses. Capturing this diversity is essential for advancing precision medicine and promoting equitable healthcare. Despite the great progress achieved with initiatives such as the human Pangenome and large biobanks that aim for a better representation of human diversity, important challenges remain. In this Perspective, we discuss the importance of diversity in clinical genomics through an evolutionary lens. We highlight progress and challenges and outline key clinical applications of diverse genetic data. We argue that diversifying both datasets and methodologies-integrating ancestral and environmental factors-is crucial for fully understanding the genetic basis of human health and disease.

Genetic architecture in Greenland is shaped by demography, structure and selection

Greenlandic Inuit and other indigenous populations are underrepresented in genetic research1,2, leading to inequity in healthcare opportunities. To address this, we performed analyses of sequenced or imputed genomes of 5,996 Greenlanders with extensive phenotypes. We quantified their historical population bottleneck and how it has shaped their genetic architecture to have fewer, but more common, variable sites. Consequently, we find twice as many high-impact genome-wide associations to metabolic traits in Greenland compared with Europe. We infer that the high-impact variants arose after the population split from Native Americans and thus are Arctic-specific, and show that some of them are common due to not only genetic drift but also selection. We also find that European-derived polygenic scores for metabolic traits are only half as accurate in Greenlanders as in Europeans, and that adding Arctic-specific variants improves the overall accuracy to the same level as in Europeans. Similarly, lack of representation in public genetic databases makes genetic clinical screening harder in Greenlandic Inuit, but inclusion of Greenlandic data remedies this by reducing the number of non-causal candidate variants by sixfold. Finally, we identify pronounced genetic fine structure that explains differences in prevalence of monogenic diseases in Greenland and, together with recent changes in mobility, leads to a predicted future reduction in risk for certain recessive diseases. These results illustrate how including data from Greenlanders can greatly reduce inequity in genomic-based healthcare.

Biobanking with genetics shapes precision medicine and global health

Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.

The Biorepository and Integrative Genomics resource for inclusive genomics: insights from a diverse pediatric and admixed cohort

The Biorepository and Integrative Genomics (BIG) Initiative in Tennessee has developed a pioneering resource to address gaps in genomic research by linking genomic, phenotypic, and environmental data from a diverse Mid-South population, including underrepresented groups. We analyzed 13,152 exomes from BIG and found significant genetic diversity, with 50% of participants inferred to have non-European or several types of admixed ancestry. Ancestry within the BIG cohort is stratified, with distinct geographic and demographic patterns, as African ancestry is more common in urban areas, while European ancestry is more common in suburban regions. We observe ancestry-specific rates of novel genetic variants, which are enriched for functional or clinical relevance. Disease prevalence analysis linked ancestry and environmental factors, showing higher odds ratios for asthma and obesity in minority groups, particularly in the urban area. Finally, we observe discrepancies between self-reported race and genetic ancestry, with related individuals self-identifying in differing racial categories. These findings underscore the limitations of race as a biomedical variable. BIG has proven to be an effective model for community-centered precision medicine. We integrated genomics education, and fostered great trust among the contributing communities. Future goals include cohort expansion, and enhanced genomic analysis, to ensure equitable healthcare outcomes.

Leveraging protein structural information to improve variant effect prediction

Despite massive sequencing efforts, understanding the difference between human pathogenic and benign variants remains a challenge. Computational variant effect predictors (VEPs) have emerged as essential tools for assessing the impact of genetic variants, although their performance varies. Initially, sequence-based methods dominated the field, but recent advances, particularly in protein structure prediction technologies like AlphaFold, have led to an increased utilization of structural information by VEPs aimed at scoring human missense variants. This review highlights the progress in integrating structural information into VEPs, showcasing novel models such as AlphaMissense, PrimateAI-3D, and CPT-1 that demonstrate improved variant evaluation. Structural data offers more interpretability, especially for non-loss-of-function variants, and provides insights into complex variant interactions in vivo. As the field advances, utilizing biomolecular complex structures will be pivotal for future VEP development, with recent breakthroughs in protein-ligand and protein-nucleic acid complex prediction offering new avenues.

Haplotype Matching with GBWT for Pangenome Graphs

Traditionally, variations from a linear reference genome were used to represent large sets of haplotypes compactly. In the linear reference genome based paradigm, the positional Burrows-Wheeler transform (PBWT) has traditionally been used to perform efficient haplotype matching. Pangenome graphs have recently been proposed as an alternative to linear reference genomes for representing the full spectrum of variations in the human genome. However, haplotype matches in pangenome graph based haplotype sets are not trivially generalizable from haplotype matches in the linear reference genome based haplotype sets. Work has been done to represent large sets of haplotypes as paths through a pangenome graph. The graph Burrows-Wheeler transform (GBWT) is one such work. The GBWT essentially stores the haplotype paths in a run length compressed BWT with compressed local alphabets. Although efficient in practice count and locate queries on the GBWT were provided by the original authors, the efficient haplotype matching capabilities of the PBWT have never been shown on the GBWT. In this paper, we formally define the notion of haplotype matches in pangenome graph-based haplotype sets by generalizing from haplotype matches in linear reference genome-based haplotype sets. We also describe the relationship between set maximal matches, long matches, locally maximal matches, and text maximal matches on the GBWT, PBWT, and the BWT. We provide algorithms for outputting some of these matches by applying the data structures of the r-index (introduced by Gagie et al.) to the GBWT. We show that these structures enable set maximal match and long match queries on the GBWT in almost linear time and in space close to linear in the number of runs in the GBWT. We also provide multiple versions of the query algorithms for different combinations of the available data structures. The long match query algorithms presented here even run on the BWT in the same time complexity as the GBWT due to their similarity.

Understanding Sociodemographic Factors among Hispanics Through a Population-Based Study on Testicular Cancer in Mexico

Testicular cancer (TCa) is a rare malignancy affecting young men worldwide. Sociodemographic factors, especially socioeconomic level (SEL) and healthcare access, seem to impact TCa incidence and outcomes, particularly among Hispanic populations. However, limited research has explored these variables in Hispanic groups. This study aimed to investigate sociodemographic and clinical factors in Mexico and their role in health disparities among Hispanic TCa patients. We retrospectively analyzed 244 Mexican TCa cases between 2007 and 2020 of a representative cohort with diverse social backgrounds from a national reference cancer center. Logistic regression identified risk factors for fatality: non-seminoma histology, advanced stage, and lower education levels. Age showed a significant trend as a risk factor. Patient delay and healthcare distance lacked significant associations. Inadequate treatment response and chemotherapy resistance were more likely in advanced stages, while higher education positively impacted treatment response. Cox regression highlighted non-seminoma histology, below-median SEL, higher education, and advanced-stage survival rates. Survival disparities emerged based on tumor histology and patient SEL. This research underscores the importance of comprehensive approaches that integrate sociodemographic, biological, and environmental factors to address health disparities improving outcomes through personalized interventions in Hispanic individuals with TCa.

Microbial Communities in Agave Fermentations Vary by Local Biogeographic Regions

The production of traditional agave spirits in Mexico, such as mezcal, involves a process that uses environmental microorganisms to ferment the cooked must from agave plants. By analysing these microorganisms, researchers can understand the dynamics of microbial communities at the interface of natural and human-associated environments. This study involved 16S and ITS amplicon sequencing of 99 fermentation tanks from 42 distilleries across Mexico. The Agave species used, production methods, climatic conditions and biogeographic characteristics varied significantly among sites. However, certain taxa were found in most fermentations, indicating a core group of microorganisms common to these communities. The primary variable consistently associated with the composition of both bacterial and fungal communities was the distillery, suggesting that local production practices and site-specific attributes influence the microbiomes. The fermentation stage, climate and producing region also affected community composition but only for prokaryotes. Analysis of multiple tanks within three distilleries showed taxa enriched in specific fermentation stages or agave species. This research provides a detailed analysis of the microbiome of agave fermentations, offering important knowledge for its management and conservation.

Study design and the sampling of deleterious rare variants in biobank-scale datasets

One key component of study design in population genetics is the "geographic breadth" of a sample (i.e., how broad a region across which individuals are sampled). How the geographic breadth of a sample impacts observations of rare, deleterious variants is unclear, even though such variants are of particular interest for biomedical and evolutionary applications. Here, in order to gain insight into the effects of sample design on ascertained genetic variants, we formulate a stochastic model of dispersal, genetic drift, selection, mutation, and geographically concentrated sampling. We use this model to understand the effects of the geographic breadth of sampling effort on the discovery of negatively selected variants. We find that samples which are more geographically broad will discover a greater number variants as compared geographically narrow samples (an effect we label "discovery"); though the variants will be detected at lower average frequency than in narrow samples (e.g. as singletons, an effect we label "dilution"). Importantly, these effects are amplified for larger sample sizes and moderated by the magnitude of fitness effects. We validate these results using both population genetic simulations and empirical analyses in the UK Biobank. Our results are particularly important in two contexts: the association of large-effect rare variants with particular phenotypes and the inference of negative selection from allele frequency data. Overall, our findings emphasize the importance of considering geographic breadth when designing and carrying out genetic studies, especially at biobank scale.

A genealogy-based approach for revealing ancestry-specific structures in admixed populations

Elucidating ancestry-specific structures in admixed populations is crucial for comprehending population history and mitigating confounding effects in genome-wide association studies. Existing methods for elucidating the ancestry-specific structures generally rely on frequency-based estimates of genetic relationship matrix (GRM) among admixed individuals after masking segments from ancestry components not being targeted for investigation. However, these approaches disregard linkage information between markers, potentially limiting their resolution in revealing structure within an ancestry component. We introduce ancestry-specific expected GRM (as-eGRM), a novel framework for elucidating the relatedness within ancestry components between admixed individuals. The key design of as-eGRM consists of defining ancestry-specific pairwise relatedness between individuals based on genealogical trees encoded in the Ancestral Recombination Graph (ARG) and local ancestry calls and computing the expectation of the ancestry-specific relatedness across the genome. Comprehensive evaluations using both simulated stepping-stone models of population structure and empirical datasets based on three-way admixed Latino cohorts showed that analysis based on as-eGRM robustly outperforms existing methods in revealing the structure in admixed populations with diverse demographic histories. Taken together, as-eGRM has the promise to better reveal the fine-scale structure within an ancestry component of admixed individuals, which can help improve the robustness and interpretation of findings from association studies of disease or complex traits for these understudied populations.

Bayesian polygenic risk estimation approach to nuclear families with discordant sib-pairs for myelomeningocele

Myelomeningocele (MMC) is the most severe and disabling form of spina bifida with chronic health multisystem complications and social and economic family and health systems burden. In the present study, we aimed to investigate the genetic risk estimate for MMC in a cohort of 203 Mexican nuclear families with discordant siblings for the defect. Utilizing a custom Illumina array, we analyzed 656 single nucleotide polymorphisms (SNPs) of 395 candidate genes to identify a polygenic risk profile for MMC. Through a family-based analysis employing the transmission disequilibrium test (TDT) and Bayesian analysis, we assessed risk alleles transmission and calculated conditional probabilities estimating a polygenic risk for MMC. Our findings reveal significant associations of six genes related to neural tube closure (PSMB4, ATIC, DKK2, PSEN2, C2CD3, and PLCB2), showing differences in risk allele transmission between affected and unaffected siblings. Bayesian analysis identified changes in the risk profile after initiating folic acid fortification in Mexico, showing an evident decline in the conditional risk from 1/156 to 1/304 respectively. Despite the decline, this represents a 5.84-fold increase in risk before fortification and a 2.99-fold increase post-fortification compared to the baseline risk level (1/910). Our study highlights the advantage of incorporating a Bayesian analytical methodology in families with discordant sib-pairs, offering insights into the polygenic risk estimate for MMC and, most probably, for other congenital malformations.

Biobanks and data interoperability in Latin America: engendering high-quality evidence for the global research ecosystem

Currently, each biobank in Latin America operates with its own set of standards for database creation and management, resulting in a lack of regional and international interoperability. Furthermore, regulations concerning data protection, curation, and the transfer of biological samples and associated data vary significantly from country to country, by complicating efforts to create a unified data-sharing platform. To address these challenges, Latin America should promote the development of an integrated regional network of biobanks to generate high-quality evidence within the global research ecosystem. This initiative will combine regulatory science—focused on interoperability standards across semantic, technical, legal, and organizational dimensions—and meta-science, which assesses the quality of scientific practice. Evidence indicates that harmonized standards in biobanks lead to higher-quality, more reliable data, thereby facilitating the reproducibility of scientific studies. This paper aims to identify and address existing regulatory, policy, and infrastructure gaps in Latin America to establish harmonized interoperability criteria essential for reproducing biomedical studies. Additionally, it seeks to propose minimum standards for regulating biobank networks, which will promote the development of medical products on a global scale, thereby engendering high quality evidence for the global research ecosystem and enhancing Latin America’s integration into it.

Computational Genomics and Its Applications to Anthropological Questions

The advent of affordable genome sequencing and the development of new computational tools have established a new era of genomic knowledge. Sequenced human genomes number in the tens of thousands, including thousands of ancient human genomes. The abundance of data has been met with new analysis tools that can be used to understand populations' demographic and evolutionary histories. Thus, a variety of computational methods now exist that can be leveraged to answer anthropological questions. This includes novel likelihood and Bayesian methods, machine learning techniques, and a vast array of population simulators. These computational tools provide powerful insights gained from genomic datasets, although they are generally inaccessible to those with less computational experience. Here, we outline the theoretical workings behind computational genomics methods, limitations and other considerations when applying these computational methods, and examples of how computational methods have already been applied to anthropological questions. We hope this review will empower other anthropologists to utilize these powerful tools in their own research.

Improving GWAS performance in underrepresented groups by appropriate modeling of genetics, environment, and sociocultural factors

Genome-wide association studies (GWAS) and polygenic score (PGS) development are typically constrained by the data available in biobank repositories in which European cohorts are vastly overrepresented. Here, we increase the utility of non-European participant data within the UK Biobank (UKB) by characterizing the genetic affinities of UKB participants who self-identify as Bangladeshi, Indian, Pakistani, "White and Asian" (WA), and "Any Other Asian" (AOA), towards creating a more robust South Asian sample size for future genetic analyses. We assess the relationships between genetic structure and self-selected ethnic identities resulting in consistent patterns of clustering used to train a support vector machine (SVM). The SVM model was utilized to reassign n = 1,853 AOA and WA participants at the subcontinental level, and increase the sample size of the UKB South Asian group by 1,381 additional participants. We then leverage these samples to assess GWAS performance and PGS development. We further include environmental covariates in the height GWAS by implementing a rigorous covariate selection procedure, and compare the outputs of two GWAS models: GWASnull and GWASenv. We show that PGS performance derived from environmentally adjusted GWAS yields comparable prediction to PGS models developed with an order of magnitude larger training dataset (R 2=0.021 vs 0.026). Models with 7 - 8 environmental covariates double the variance explained by PGS alone. In summary, we demonstrate how GWAS performance can be improved by leveraging ambiguous ethnicity codes, ancestry matched imputation panels, and including environmental covariates.

Phenotypic evaluation of deep learning models for classifying germline variant pathogenicity

Deep learning models for predicting variant pathogenicity have not been thoroughly evaluated on real-world clinical phenotypes. Here, we apply state-of-the-art pathogenicity prediction models to hereditary breast cancer gene variants in UK Biobank participants. Model predictions for missense variants in BRCA1, BRCA2 and PALB2, but not ATM and CHEK2, were associated with breast cancer risk. However, deep learning models had limited clinical utility when specifically applied to variants of uncertain significance.

Population expansion, larger, and more homogeneous native American ancestry among Mexican mestizo populations based on 10 X-chromosome STR loci (X-STR decaplex system)

OBJECTIVE

To evaluate the genetic diversity, admixture, genetic relationships, and sex-biased demographic processes in Mexican Mestizo (admixed) populations based on 10 X-chromosome STRs (X-STRs).

METHODS

We analyzed the X-STRs Decaplex system in 104 Mexican Native Americans to obtain the ancestral reference needed to complete the demographic analyses above mentioned. We included reported Iberian and Latin American (admixed) populations from Central and South America, as well as datasets from Mexican Mestizos based on Y-linked STRs (Y-STRs), autosomal STRs (A-STRs), and mtDNA.

RESULTS

Higher X-linked Native American ancestry was observed among Latin American populations regarding that reported from A-STRs and Y-STRs. The interpopulation differentiation based on ancestry among Mexican Mestizos diminished according to the inheritance pattern: Y-STRs (highest), A-STRs, X-STRs, and mtDNA (lowest). This finding is related to the peculiar admixture process that occurred during and after the Spanish Conquest of Mexico (and most of Latin America), involving a large number of Spanish men (Y-chromosomes) with a lesser proportion of X-chromosomes than autosomes; besides to the limited number of Spanish women (XX) arrived in the Americas in subsequent and shorter periods. Population expansion was detected in Mexican Mestizos from all the country, except those from the southeast region characterized by elevated indigenous ancestry, marginalization, and poorness.

CONCLUSIONS

Population growth was detected in most Mexican Mestizos, besides more homogeneous and larger Native American ancestry based on X-linked inheritance than that based on autosomal STRs and Y-STRs.

Phenome-Wide Association Study of Latent Autoimmune Diabetes from a Southern Mexican Population Implicates rs7305229 with Plasmatic Anti-Glutamic Acid Decarboxylase Autoantibody (GADA) Levels

Latent autoimmune diabetes in adults (LADA) is characterized by the presence of glutamate decarboxylase autoantibodies (GADA). LADA has intermediate features between type 1 diabetes and type 2 diabetes. In addition, genetic risk factors for both types of diabetes are present in LADA. Nonetheless, evidence about the genetics of LADA in non-European populations is scarce. This study aims to perform a genome-wide association study with a phenome-wide association study of LADA in a southeastern Mexican population. We included 59 patients diagnosed with LADA from a previous study and 3121 individuals without diabetes from the MxGDAR/ENCODAT database. We utilized the GENESIS package in R to perform the genome-wide association study (GWAS) of LADA and PLINK for the phenome-wide association study (PheWAS) of LADA features. Nine polymorphisms reach the nominal association level (1 × 10-5) in the GWAS. The PheWAS showed that rs7305229 is genome-wide and associated with serum GADA levels in our sample (p = 1.84 × 10-8). rs7305229 is located downstream of the FAIM2 gene; previous reports associate FAIM2 variants with childhood obesity, body mass index, body adiposity measures, lymphocyte CD8+ activity, and anti-thyroid peroxidase antibodies. Our findings reveal that rs7305229 affects the GADA levels in patients with LADA from southeastern Mexico. More studies are needed to determine if this risk genotype exists in other populations with LADA.

Genetic associations with disease in populations with Indigenous American ancestries

The genetic architecture of complex diseases affecting populations with Indigenous American ancestries is poorly understood due to their underrepresentation in genomics studies. While most of the genetic diversity associated with disease trait variation is shared among worldwide populations, a fraction of this component is expected to be unique to each continental group, including Indigenous Americans. Here, I describe the current state of knowledge from genome-wide association studies on Indigenous populations, as well as non-Indigenous populations with partial Indigenous ancestries from the American continent, focusing on disease susceptibility and anthropometric traits. While some studies identified risk alleles unique to Indigenous populations, their effects on trait variation are mostly small. I suggest that the associations rendered by many inter-population studies are probably inflated due to the absence of socio-cultural-economic covariates in the association models. I encourage the inclusion of admixed individuals in future GWAS studies to control for inter-ancestry differences in environmental factors. I suggest that some complex diseases might have arisen as trade-off costs of adaptations to past evolutionary selective pressures. Finally, I discuss how expanding panels with Indigenous ancestries in GWAS studies is key to accurately assess genetic risk in populations from the American continent, thus decreasing global health disparities.

The need to diversify genomic studies: Insights from Andean highlanders and Amazonians

More globally diverse perspectives are needed in genomic studies and precision medicine practices on non-Europeans. Here, we illustrate this by discussing the distribution of clinically actionable genetic variants involved in drug response in Andean highlanders and Amazonians, considering their environment, history, genetic structure, and historical biases in the perception of biological diversity of Native Americans.

Promoting equity in polygenic risk assessment through global collaboration

The long delay before genomic technologies become available in low- and middle-income countries is a concern from both scientific and ethical standpoints. Polygenic risk scores (PRSs), a relatively recent advance in genomics, could have a substantial impact on promoting health by improving disease risk prediction and guiding preventive strategies. However, clinical use of PRSs in their current forms might widen global health disparities, as their portability to diverse groups is limited. This Perspective highlights the need for global collaboration to develop and implement PRSs that perform equitably across the world. Such collaboration requires capacity building and the generation of new data in low-resource settings, the sharing of harmonized genotype and phenotype data securely across borders, novel population genetics and statistical methods to improve PRS performance, and thoughtful clinical implementation in diverse settings. All this needs to occur while considering the ethical, legal and social implications, with support from regulatory and funding agencies and policymakers.

Nutrition, Other Environmental Influences, and Genetics in the Determination of Human Stature

Linear growth during three distinct stages of life determines attained stature in adulthood: namely, in utero, early postnatal life, and puberty and the adolescent period. Individual host factors, genetics, and the environment, including nutrition, influence attained human stature. Each period of physical growth has its specific biological and environmental considerations. Recent epidemiologic investigations reveal a strong influence of prenatal factors on linear size at birth that in turn influence the postnatal growth trajectory. Although average population height changes have been documented in high-income regions, stature as a complex human trait is not well understood or easily modified. This review summarizes the biology of linear growth and its major drivers, including nutrition from a life-course perspective, the genetics of programmed growth patterns or height, and gene-environment interactions that determine human stature in toto over the life span. Implications for public health interventions and knowledge gaps are discussed.

The role of the Latin American Professional Society of Genetic Counseling (SPLAGen): Advancing genetic counseling in Latin America

The increasing importance of genomics and its expanding clinical application underscore the significance of genetic counseling in disseminating this vital information. In many countries, including regions of Latin America and the Caribbean, there is a shortage of adequately trained genetics providers thus limiting access to these essential services. Moreover, providers practicing genetic counseling are scarce and experience a lack of professional support. To address these challenges, the Latin American Professional Society of Genetic Counseling (SPLAGen, for its Spanish and Portuguese abbreviation) was established with the objective to increase awareness of and access to genetic counseling services in the region. Through this commentary, we aim to provide insights into the formation and organizational structure of SPLAGen, outlining our impactful work, as well as the current and future obstacles we foresee in our endeavor. SPLAGen's efforts aim to serve as a critical step toward bridging the gap in genetic counseling services and promoting better health care outcomes for patients living in Latin America and the Caribbean.

ClOneHORT: Approaches for Improved Fidelity in Generative Models of Synthetic Genomes

Motivation

Deep generative models have the potential to overcome difficulties in sharing individual-level genomic data by producing synthetic genomes that preserve the genomic associations specific to a cohort while not violating the privacy of any individual cohort member. However, there is significant room for improvement in the fidelity and usability of existing synthetic genome approaches.

Results

We demonstrate that when combined with plentiful data and with population-specific selection criteria, deep generative models can produce synthetic genomes and cohorts that closely model the original populations. Our methods improve fidelity in the site-frequency spectra and linkage disequilibrium decay and yield synthetic genomes that can be substituted in downstream local ancestry inference analysis, recreating results with .91 to .94 accuracy.

Availability

The model described in this paper is freely available at github.com/rlaboulaye/clonehort .

Building national patient registries in Mexico: insights from the MexOMICS Consortium

Objective

To introduce MexOMICS, a Mexican Consortium focused on establishing electronic databases to collect, cross-reference, and share health-related and omics data on the Mexican population.

Methods

Since 2019, the MexOMICS Consortium has established three electronic-based registries: the Mexican Twin Registry (TwinsMX), Mexican Lupus Registry (LupusRGMX), and the Mexican Parkinson's Research Network (MEX-PD), designed and implemented using the Research Electronic Data Capture web-based application. Participants were enrolled through voluntary participation and on-site engagement with medical specialists. We also acquired DNA samples and Magnetic Resonance Imaging scans in subsets of participants.

Results

The registries have successfully enrolled a large number of participants from a variety of regions within Mexico: TwinsMX (n = 2,915), LupusRGMX (n = 1,761) and MEX-PD (n = 750). In addition to sociodemographic, psychosocial, and clinical data, MexOMICS has collected DNA samples to study the genetic biomarkers across the three registries. Cognitive function has been assessed with the Montreal Cognitive Assessment in a subset of 376 MEX-PD participants. Furthermore, a subset of 267 twins have participated in cognitive evaluations with the Creyos platform and in MRI sessions acquiring structural, functional, and spectroscopy brain imaging; comparable evaluations are planned for LupusRGMX and MEX-PD.

Conclusions

The MexOMICS registries offer a valuable repository of information concerning the potential interplay of genetic and environmental factors in health conditions among the Mexican population.

Personalized nutrition: the end of the one-diet-fits-all era

Personalized Nutrition emerged as a new trend for providing nutritional and food advice based on the individual's genetic composition, a field driven by the advancements in the multi-omic sciences throughout the last century. It intends not only to tailor the recommended daily allowances of nutrients and functional foods that a person may need but also to maintain the principles of sustainability and eco-friendliness. This principle implies the implementation of strategies within the healthcare system to advocate for the ending of the one-diet-fits-all paradigm by considering a personalized diet as an ally to prevent diet-related chronic diseases. In this Perspective, we highlight the potential benefits of such a paradigm within the region of Latin America, particularly Mexico, where the genetic admixture of the population, food biodiversity, and food culture provide unique opportunities to establish personalized nutrigenetic strategies. These strategies could play a crucial role in preventing chronic diseases and addressing the challenges confronted in the region.

Personalized medicine and nutrition in hepatology for preventing chronic liver disease in Mexico

Chronic liver disease is a global health issue. Patients with chronic liver disease require a fresh approach that focuses on the genetic and environmental factors that contribute to disease initiation and progression. Emerging knowledge in the fields of Genomic Medicine and Genomic Nutrition demonstrates differences between countries in terms of genetics and lifestyle risk factors such as diet, physical activity, and mental health in chronic liver disease, which serves as the foundation for the implementation of Personalized Medicine and Nutrition (PerMed-Nut) strategies. Most of the world's populations have descended from various ethnic groupings. Mexico's population has a tripartite ancestral background, consisting of Amerindian, European, and African lineages, which is common across Latin America's regional countries. The purpose of this review is to discuss the genetic and environmental components that could be incorporated into a PerMed-Nut model for metabolic-associated liver disease, viral hepatitis B and C, and hepatocellular carcinoma in Mexico. Additionally, the implementation of the PerMed-Nut approach will require updated medicine and nutrition education curricula. Training and equipping future health professionals and researchers with new clinical and investigative abilities focused on preventing liver illnesses in the field of genomic hepatology globally is a vision that clinicians and nutritionists should be concerned about.

A noncoding regulatory variant in IKZF1 increases acute lymphoblastic leukemia risk in Hispanic/Latino children

Hispanic/Latino children have the highest risk of acute lymphoblastic leukemia (ALL) in the US compared to other racial/ethnic groups, yet the basis of this remains incompletely understood. Through genetic fine-mapping analyses, we identified a new independent childhood ALL risk signal near IKZF1 in self-reported Hispanic/Latino individuals, but not in non-Hispanic White individuals, with an effect size of ∼1.44 (95% confidence interval = 1.33-1.55) and a risk allele frequency of ∼18% in Hispanic/Latino populations and <0.5% in European populations. This risk allele was positively associated with Indigenous American ancestry, showed evidence of selection in human history, and was associated with reduced IKZF1 expression. We identified a putative causal variant in a downstream enhancer that is most active in pro-B cells and interacts with the IKZF1 promoter. This variant disrupts IKZF1 autoregulation at this enhancer and results in reduced enhancer activity in B cell progenitors. Our study reveals a genetic basis for the increased ALL risk in Hispanic/Latino children.

Analysis of 26 STR loci (PowerPlex® Fusion 6C System) in a mestizo population from Mexico city

BACKGROUND

Short tandem repeats (STRs) are the most widely used genetic markers in forensic genetics. Therefore, it is essential to document genetic population data of new kits designed for human identification purposes to enable laboratories to use these genetic systems to interpret and solve forensic casework. However, in Mexico, there are no studies with the PowerPlex Fusion 6C System, which includes 26 STRs (23 autosomal STRs and 3 Y-STRs).

METHODS AND RESULTS

600 DNA samples from Mexico City were subjected to genotyping using the PowerPlex Fusion 6C System. For autosomal STRs, 312 different alleles were observed. Combined PE and PD were 99.999999809866% and 99.99999999999999999999999818795%, respectively. Genetic distances and AMOVA test showed low but significant differentiation between Mexican populations.

CONCLUSIONS

The results reported in this work demonstrate the efficacy of this system for human identification purposes in the population studied and justify its possible application in other Mexican Mestizo populations.

Cross-cultural perception of strength, attractiveness, aggressiveness and helpfulness of Maasai male faces calibrated to handgrip strength

Previous research has demonstrated that Maasai and Europeans tend to align in their ratings of the physical strength and aggressiveness of Maasai male faces, calibrated to hand grip strength (HGS). However, perceptions of attractiveness of these faces differed among populations. In this study, three morphs of young Maasai men created by means of geometric morphometrics, and depicting the average sample and two extrema (± 4 SD of HGS), were assessed by men and women from Tanzania, Czech Republic, Russia, Pakistan, China, and Mexico (total sample = 1540). The aim of this study was to test cross-cultural differences in the perception of young Maasai men's composites calibrated to HGS, focusing on four traits: physical strength, attractiveness, aggressiveness, and helpfulness. Individuals from all six cultures were able to distinguish between low, medium, and high HGS portraits. Across all study populations, portrait of Maasai men with lower HGS was perceived as less attractive, more aggressive, and less helpful. This suggests that people from diverse populations share similar perceptions of physical strength based on facial shape, as well as attribute similar social qualities like aggressiveness and helpfulness to these facial images. Participants from all samples rated the composite image of weak Maasai men as the least attractive.

Genetic interrogation for sequence and copy number variants in systemic lupus erythematosus

Early-onset systemic lupus erythematosus presents with a more severe disease and is associated with a greater genetic burden, especially in patients from Black, Asian or Hispanic ancestries. Next-generation sequencing techniques, notably whole exome sequencing, have been extensively used in genomic interrogation studies to identify causal disease variants that are increasingly implicated in the development of autoimmunity. This Review discusses the known casual variants of polygenic and monogenic systemic lupus erythematosus and its implications under certain genetic disparities while suggesting an age-based sequencing strategy to aid in clinical diagnostics and patient management for improved patient care.

Determinants of obesity in Latin America

Obesity rates are increasing almost everywhere in the world, although the pace and timing for this increase differ when populations from developed and developing countries are compared. The sharp and more recent increase in obesity rates in many Latin American countries is an example of that and results from regional characteristics that emerge from interactions between multiple factors. Aware of the complexity of enumerating these factors, we highlight eight main determinants (the physical environment, food exposure, economic and political interest, social inequity, limited access to scientific knowledge, culture, contextual behaviour and genetics) and discuss how they impact obesity rates in Latin American countries. We propose that initiatives aimed at understanding obesity and hampering obesity growth in Latin America should involve multidisciplinary, global approaches that consider these determinants to build more effective public policy and strategies, accounting for regional differences and disease complexity at the individual and systemic levels.

STAT4 Gene Variant rs7574865 Is Associated with Rheumatoid Arthritis Activity and Anti-CCP Levels in the Western but Not in the Southern Population of Mexico

Rheumatoid Arthritis (RA) is a multifactorial autoimmune disease. Currently, several genes play an important role in the development of the disease. The objective was to evaluate the association of the STAT4 rs7574865 and rs897200 gene variants with RA susceptibility, DAS28, RF, and anti-CCP in Western and Southern Mexico populations. Genotyping was performed on 476 samples (cases = 240; controls = 236) using the Taqman® system and qPCR probes. Disease activity was assessed using DAS28 and HAQ DI. CRP, ESR, RF, and anti-CCP were determined for clinical assessment. Our study showed there is a statistically significant association with susceptibility to RA for the rs7574865 variant in the Western population for the GT and TT genotypes. The same genotypes also showed a moderate-to-high activity according to DAS28 and positive anti-CCP compared to the control group. This association was not found in the Southern population. This work confirms the association of the rs7574865 variant with RA, as well as a moderate-to-high activity and positive anti-CCP in the Western population but not in the Southern population. No association of the rs897200 variant was found in any of the studied populations.

The genetic risk of acute lymphoblastic leukemia and its implications for children of Latin American origin

Acute lymphoblastic leukemia (ALL) is the most common cancer in children, and disproportionately affects children of Hispanic/Latino ethnicity in the United States, who have the highest incidence of disease compared with other racial/ethnic groups. Incidence of childhood ALL is similarly high in several Latin American countries, notably in Mexico, and of concern is the rising incidence of childhood ALL in some Hispanic/Latino populations that may further widen this disparity. Prior studies have implicated common germline genetic variants in the increased risk of ALL among Hispanic/Latino children. In this review, we describe the known disparities in ALL incidence as well as patient outcomes that disproportionately affect Hispanic/Latino children across the Americas, and we focus on the role of genetic variation as well as Indigenous American ancestry in the etiology of these disparities. Finally, we discuss future avenues of research to further our understanding of the causes of the disparities in ALL incidence and outcomes in children of Latin American origin, which will be required for future precision prevention efforts.

The Mexican Biobank Project promotes genetic discovery, inclusive science and local capacity building

Diversifying genotype-phenotype databases is essential to understanding complex trait and disease etiology across different environments and genetic ancestries. The rise of biobanks across the world is helping reveal the genetic and environmental architecture of multiple disease traits but the diversity they capture remains limited. To help close this gap, the Mexican Biobank (MXB) Project was recently generated, and has already revealed fine-scale genetic ancestries and demographic histories across the country, and their impact on trait-relevant genetic variation. This will help guide future genetic epidemiology and public health efforts, and has also improved polygenic prediction for several traits in Mexican populations compared with using data from other genome-wide association studies, such as the UK Biobank. The MXB illustrates the importance of transnational initiatives and funding calls that prioritize local leadership and capacity building to move towards inclusive genomic science.

Genetic risk prediction in Hispanics/Latinos: milestones, challenges, and social-ethical considerations

Genome-wide association studies (GWAS) have allowed the identification of disease-associated variants, which can be leveraged to build polygenic scores (PGSs). Even though PGSs can be a valuable tool in personalized medicine, their predictive power is limited in populations of non-European ancestry, particularly in admixed populations. Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. Given the recent increase of genomic studies that include Hispanics/Latinos, we review the milestones and efforts that focus on genetic risk prediction, summarize the potential for improving PGS transferability, and highlight the challenges yet to be addressed. Additionally, we summarize social-ethical considerations and provide ideas to promote genetic risk prediction models that can be implemented equitably.