Strategies for the Genomic Analysis of Admixed Populations

Tracing human genetic histories and natural selection with precise local ancestry inference

Local ancestry inference is crucial for unraveling demographic histories, discovering selection signals, and including admixed individuals in genomic studies for improved equity and portability. To date, the precision and resolution of local ancestry inference were limited by technical and dataset issues. To address them, we present Orchestra, a model we train on over 10,000 single-origin individuals from 35 worldwide populations that demonstrates superior accuracy in benchmarking analyzes. We employ Orchestra to shed light on the demographic history of Latin Americans, finding trace ancestries supported by historical records. We then deploy it to offer insight on the debated Ashkenazi Jewish origins, highlighting their South European heritage. Finally, Orchestra enables us to map selection signatures, identifying trace Scandinavian ancestry in British samples and unveiling an immune-rich region linked to respiratory infections passed down from the Viking conquests. Our work significantly advances the field of local ancestry inference, highlighting its use in admixed populations.

Methodological opportunities in genomic data analysis to advance health equity

The causes and consequences of inequities in genomic research and medicine are complex and widespread. However, it is widely acknowledged that underrepresentation of diverse populations in human genetics research risks exacerbating existing health disparities. Efforts to improve diversity are ongoing, but an often-overlooked source of inequity is the choice of analytical methods used to process, analyse and interpret genomic data. This choice can influence all areas of genomic research, from genome-wide association studies and polygenic score development to variant prioritization and functional genomics. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging within the wider genomic research and genomic medicine ecosystems. At this crucial time point, it is important to clarify where improvements in methods and practices can, or cannot, have a role in improving equity in genomics. Here, we review existing approaches to promote equity and fairness in statistical analysis for genomics, and propose future methodological developments that are likely to yield the most impact for equity.

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.

Experience using conventional compared to ancestry-based population descriptors in clinical genomics laboratories

Various scientific and professional groups, including the American Medical Association (AMA), American Society of Human Genetics (ASHG), American College of Medical Genetics (ACMG), and the National Academies of Sciences, Engineering, and Medicine (NASEM), have appropriately clarified that certain population descriptors, such as race and ethnicity, are social and cultural constructs with no basis in genetics. Nevertheless, these conventional population descriptors are routinely collected during the course of clinical genetic testing and may be used to interpret test results. Experts who have examined the use of population descriptors, both conventional and ancestry based, in human genetics and genomics have offered guidance on using these descriptors in research but not in clinical laboratory settings. This perspective piece is based on a decade of experience in a clinical genomics laboratory and provides insight into the relevance of conventional and ancestry-based population descriptors for clinical genetic testing, reporting, and clinical research on aggregated data. As clinicians, laboratory geneticists, genetic counselors, and researchers, we describe real-world experiences collecting conventional population descriptors in the course of clinical genetic testing and expose challenges in ensuring clarity and consistency in the use of population descriptors. Current practices in clinical genomics laboratories that are influenced by population descriptors are identified and discussed through case examples. In relation to this, we describe specific types of clinical research projects in which population descriptors were used and helped derive useful insights related to practicing and improving genomic medicine.

Influence of Cytokine-Related genetic variants in TNF, IL6, IL1β, and IFNγ genes in the thalidomide treatment for Erythema nodosum leprosum in a Brazilian population sample

Erythema nodosum leprosum (ENL), an inflammatory reaction in leprosy, causes painful nodules, fever, and malaise due to immune system activation. Thalidomide is an effective treatment, although associated with important adverse effects. We aimed to evaluate the association of genetic variants in genes encoding tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β) and interleukin-6 (IL-6) with the response to treatment of ENL with thalidomide. 148 patients from the South and Northeast regions of Brazil were included. Genomic DNA was isolated from blood and/or saliva samples using commercial kits, and genetic variants in TNF, IL6, IL1β, and IFNγ genes were genotyped by TaqMan system. We identified an association between polymorphisms in TNF (rs1799964C, rs1800630A, rs1799724T and rs1800629A) IL1β (rs4848306G, rs1143623G, rs16944A, and rs1143627A), IL6 (rs2069840C and rs2069845G) and IFNγ (rs2430561T) with thalidomide dose variation in a time-dependent manner. Associations of IL6 and TNF haplotypes with thalidomide dosage variation over the time of treatment were also observed. Polymorphisms in TNF, IL6, IL1β, and IFNγ genes may modulate their expression levels, potentially impacting the required dosage of thalidomide in the treatment of ENL. Our findings should be confirmed in further studies to estimate the size effect of these polymorphisms on ENL treatment with thalidomide.

Genome-wide local ancestry and the functional consequences of admixture in African and European cattle populations

Bos taurus (taurine) and Bos indicus (indicine) cattle diverged at least 150,000 years ago and, since that time, substantial genomic differences have evolved between the two lineages. During the last two millennia, genetic exchange in Africa has resulted in a complex tapestry of taurine-indicine ancestry, with most cattle populations exhibiting varying levels of admixture. Similarly, there are several Southern European cattle populations that also show evidence for historical gene flow from indicine cattle, the highest levels of which are found in the Central Italian White breeds. Here we use two different software tools (MOSAIC and ELAI) for local ancestry inference (LAI) with genome-wide high- and low-density SNP array data sets in hybrid African and residually admixed Southern European cattle populations and obtained broadly similar results despite critical differences in the two LAI methodologies used. Our analyses identified genomic regions with elevated levels of retained or introgressed ancestry from the African taurine, European taurine, and Asian indicine lineages. Functional enrichment of genes underlying these ancestry peaks highlighted biological processes relating to immunobiology and olfaction, some of which may relate to differing susceptibilities to infectious diseases, including bovine tuberculosis, East Coast fever, and tropical theileriosis. Notably, for retained African taurine ancestry in admixed trypanotolerant cattle we observed enrichment of genes associated with haemoglobin and oxygen transport. This may reflect positive selection of genomic variants that enhance control of severe anaemia, a debilitating feature of trypanosomiasis disease, which severely constrains cattle agriculture across much of sub-Saharan Africa.

Genes, culture, and scientific racism

Quantitative studies of cultural evolution and gene-culture coevolution (henceforth "CE" and "GCC") emerged in the 1970s, in the aftermath of the "race and intelligence quotient (IQ)" and "human sociobiology" debates, as a counter to extreme hereditarian positions. These studies incorporated cultural transmission and its interaction with genetics in contributing to patterns of human variation. Neither CE nor GCC results were consistent with racist claims of ubiquitous genetic differences between socially defined races. We summarize how genetic data refute the notion of racial substructure for human populations and address naive interpretations of race across the biological sciences, including those related to ancestry, health, and intelligence, that help to perpetuate racist ideas. A GCC perspective can refute reductionist and determinist claims while providing a more inclusive multidisciplinary framework in which to interpret human variation.

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

AncestryGrapher toolkit: Python command-line pipelines to visualize global- and local- ancestry inferences from the RFMIX version 2 software

SUMMARY

Admixture is a fundamental process that has shaped levels and patterns of genetic variation in human populations. RFMIX version 2 (RFMIX2) utilizes a robust modeling approach to identify the genetic ancestries in admixed populations. However, this software does not have a built-in method to visually summarize the results of analyses. Here, we introduce the AncestryGrapher toolkit, which converts the numerical output of RFMIX2 into graphical representations of global and local ancestry (i.e. the per-individual ancestry components and the genetic ancestry along chromosomes, respectively).

RESULTS

To demonstrate the utility of our methods, we applied the AncestryGrapher toolkit to visualize the global and local ancestry of individuals in the North African Mozabite Berber population from the Human Genome Diversity Panel. Our results showed that the Mozabite Berbers derived their ancestry from the Middle East, Europe, and sub-Saharan Africa (global ancestry). We also found that the population origin of ancestry varied considerably along chromosomes (local ancestry). For example, we observed variance in local ancestry in the genomic region on Chromosome 2 containing the regulatory sequence in the MCM6 gene associated with lactase persistence, a human trait tied to the cultural development of adult milk consumption. Overall, the AncestryGrapher toolkit facilitates the exploration, interpretation, and reporting of ancestry patterns in human populations.

AVAILABILITY AND IMPLEMENTATION

The AncestryGrapher toolkit is free and open source on https://github.com/alisi1989/RFmix2-Pipeline-to-plot.

Intronic Variants in the MSH2 (rs2303426 and rs10179950) and PMS2 (rs2286681 and rs62456178) Genes Are Not Associated with Colorectal Cancer in Mexican Patients

BACKGROUND/OBJECTIVES

In the origin and development of colorectal cancer (CRC), a global public health problem, a dysfunction mismatch repair system appears to be a key factor. The objective was to determine the association of intronic variants in the MSH2 and PMS2 genes with CRC in Mexican patients.

METHODS

Blood samples of 143 CRC patients and 146 reference individuals were genotyped through TaqMan® Genotyping Assays. Genotypic and allelic frequencies were determined by direct counting. To compare genotypic and allelic distributions, the chi-square test was used. For the association analysis, the risks of alleles and genotypes were estimated by odds ratio with 95% confidence intervals. Haplogroups were inferred with a Bayesian algorithm. Linkage disequilibrium was measured using D' and r2 with Arlequin v3.5.2. The in silico analysis was carried out using the SpliceAI, UCSC, JASPAR and TRRUST platforms. All statistical analyses were performed with SPSS v29.0.2.0.

RESULTS

In the CRC group, the mean age was 58.2 ± 14.7 years and 60.8% were men. No variant was associated with CRC or implicated in gene post-replicative processing. Linkage disequilibrium was observed for loci rs2303426 and rs10179950 in MSH2 and for loci rs2286681 and rs62456178 in PMS2.

CONCLUSIONS

The genotypic and allelic frequencies of the four variants are reported for the first time in Mexican patients with CRC. No association was found between gene variants and risk for CRC but there was a strong linkage disequilibrium between the loci of both MSH2 and PMS2 genes. None of the variants showed a possible repercussion on splicing.

Heterogeneity-aware integrative regression for ancestry-specific association studies

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.

FedGMMAT: Federated generalized linear mixed model association tests

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations

SUMMARY

Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic studies of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations.

AVAILABILITY AND IMPLEMENTATION

Admix-kit package is open-source and available at https://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available at https://github.com/UW-GAC/admix-kit_workflow.

Accurate identification of Helicoverpa armigera-Helicoverpa zea hybrids using genome admixture analysis: implications for genomic surveillance

Helicoverpa armigera, the cotton bollworm moth, is one of the world's most important crop pests, and is spreading throughout the New World from its original range in the Old World. In Brazil, invasive H. armigera has been reported to hybridize with local populations of Helicoverpa zea. The correct identification of H. armigera-H. zea hybrids is important in understanding the origin, spread and future outlook for New World regions that are affected by outbreaks, given that hybridization can potentially facilitate H. zea pesticide resistance and host plant range via introgression of H. armigera genes. Here, we present a genome admixture analysis of high quality genome sequences generated from two H. armigera-H. zea F1 hybrids generated in two different labs. Our admixture pipeline predicts 48.8% and 48.9% H. armigera for the two F1 hybrids, confirming its accuracy. Genome sequences from five H. zea and one H. armigera that were generated as part of the study show no evidence of hybridization. Interestingly, we show that four H. zea genomes generated from a previous study are predicted to possess a proportion of H. armigera genetic material. Using unsupervised clustering to identify non-hybridized H. armigera and H. zea genomes, 8511 ancestry informative markers (AIMs) were identified. Their relative frequencies are consistent with a minor H. armigera component in the four genomes, however its origin remains to be established. We show that the size and quality of genomic reference datasets are critical for accurate hybridization prediction. Consequently, we discuss potential pitfalls in genome admixture analysis of H. armigera-H. zea hybrids, and suggest measures that will improve such analyses.

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic study of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations.