Local and global ancestry inference and applications to genetic association analysis for admixed populations

Genetic Ancestry and Self-Reported "Skin Color/Race" in the Urban Admixed Population of São Paulo City, Brazil

Epidemiological studies frequently classify groups based on phenotypes like self-reported skin color/race, which inaccurately represent genetic ancestry and may lead to misclassification, particularly among individuals of multiracial backgrounds. This study aimed to characterize both global and local genome-wide genetic ancestries and to assess their relationship with self-reported skin color/race in an admixed population of Sao Paulo city. We analyzed 226,346 single-nucleotide polymorphisms from 841 individuals participating in the population-based ISA-Nutrition study. Our findings confirmed the admixed nature of the population, demonstrating substantial European, significant Sub-Saharan African, and minor Native American ancestries, irrespective of skin color. A correlation was observed between global genetic ancestry and self-reported color-race, which was more evident in the extreme proportions of African and European ancestries. Individuals with higher African ancestry tended to identify as Black, those with higher European ancestry tended to identify as White, and individuals with higher Native American ancestry were more likely to self-identify as Mixed, a group with diverse ancestral compositions. However, at the individual level, this correlation was notably weak, and no deviations were observed for specific regions throughout the individual's genome. Our findings emphasize the significance of accurately defining and thoroughly analyzing race and ancestry, especially within admixed populations.

Insights into Ancestral Diversity in Parkinsons Disease Risk: A Comparative Assessment of Polygenic Risk Scores

Objectives To evaluate and compare different polygenic risk score (PRS) models in predicting Parkinsons disease (PD) across diverse ancestries, focusing on identifying the most suitable approach for each population and potentially contributing to equitable advancements in precision medicine. Methods We constructed a total of 105 PRS across individual level data from seven diverse ancestries. First, a cross-ancestry conventional PRS comparison was implemented by utilizing the 90 known European risk loci with weighted effects from four independent summary statistics including European, East Asian, Latino/Admixed American, and African/Admixed. These models were adjusted by sex, age, and principal components (28 PRS) and by sex, age, and percentage of admixture (28 PRS) for comparison. Secondly, a novel and refined multi-ancestry best-fit PRS approach was then applied across the seven ancestries by leveraging multi-ancestry meta-analyzed summary statistics and using a p-value thresholding approach (49 PRS) to enhance prediction applicability in a global setting. Results European-based PRS models predicted disease status across all ancestries to differing degrees of accuracy. Ashkenazi Jewish had the highest Odds Ratio (OR): 1.96 (95% CI: 1.69-2.25, p < 0.0001) with an AUC (Area Under the Curve) of 68%. Conversely, the East Asian population, despite having fewer predictive variants (84 out of 90), had an OR of 1.37 (95% CI: 1.32-1.42) and an AUC of 62%, illustrating the cross-ancestry transferability of this model. Lower OR alongside broader confidence intervals were observed in other populations, including Africans (OR =1.38, 95% CI: 1.12-1.63, p=0.001). Adjustment by percentage of admixture did not outperform principal components. Multi-ancestry best-fit PRS models improved risk prediction in European, Ashkenazi Jewish, and African ancestries, yet didn't surpass conventional PRS in admixed populations such as Latino/American admixed and African admixed populations. Interpretation The present study represents a novel and comprehensive assessment of PRS performance across seven ancestries in PD, highlighting the inadequacy of a 'one size fits all' approach in genetic risk prediction. We demonstrated that European based PD PRS models are partially transferable to other ancestries and could be improved by a novel best-fit multi-ancestry PRS, especially in non-admixed populations.

Towards a Global View of Parkinson's Disease Genetics

Parkinson's disease (PD) is a global health challenge, yet historically studies of PD have taken place predominantly in European populations. Recent genetics research conducted in non-European populations has revealed novel population-specific genetic loci linked to PD risk, highlighting the importance of studying PD globally. These insights have broadened our understanding of PD etiology, which is crucial for developing disease-modifying interventions. This review comprehensively explores the global genetic landscape of PD, emphasizing the scientific rationale for studying underrepresented populations. It underscores challenges, such as genotype-phenotype heterogeneity and inclusion difficulties for non-European participants, emphasizing the ongoing need for diverse and inclusive research in PD. ANN NEUROL 2024;95:831-842.

Implementing Reporting Standards for Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease

PURPOSE OF REVIEW

There is considerable interest in using polygenic risk scores (PRSs) for assessing risk of atherosclerotic cardiovascular disease (ASCVD). A barrier to the clinical use of PRSs is heterogeneity in how PRS studies are reported. In this review, we summarize approaches to establish a uniform reporting framework for PRSs for coronary heart disease (CHD), the most common form of ASCVD.

RECENT FINDINGS

Reporting standards for PRSs need to be contextualized for disease specific applications. In addition to metrics of predictive performance, reporting standards for PRSs for CHD should include how cases/control were ascertained, degree of adjustment for conventional CHD risk factors, portability to diverse genetic ancestry groups and admixed individuals, and quality control measures for clinical deployment. Such a framework will enable PRSs to be optimized and benchmarked for clinical use.

Including diverse and admixed populations in genetic epidemiology research

The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.

MultiWaverX: modeling latent sex-biased admixture history

Sex-biased gene flow has been common in the demographic history of modern humans. However, the lack of sophisticated methods for delineating the detailed sex-biased admixture process prevents insights into complex admixture history and thus our understanding of the evolutionary mechanisms of genetic diversity. Here, we present a novel algorithm, MultiWaverX, for modeling complex admixture history with sex-biased gene flow. Systematic simulations showed that MultiWaverX is a powerful tool for modeling complex admixture history and inferring sex-biased gene flow. Application of MultiWaverX to empirical data of 17 typical admixed populations in America, Central Asia, and the Middle East revealed sex-biased admixture histories that were largely consistent with the historical records. Notably, fine-scale admixture process reconstruction enabled us to recognize latent sex-biased gene flow in certain populations that would likely be overlooked by much of the routine analysis with commonly used methods. An outstanding example in the real world is the Kazakh population that experienced complex admixture with sex-biased gene flow but in which the overall signature has been canceled due to biased gene flow from an opposite direction.

SALAI-Net: species-agnostic local ancestry inference network

MOTIVATION

Local ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.

RESULTS

We present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models' ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.

AVAILABILITY AND IMPLEMENTATION

We provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).

SUPPLEMENTARY INFORMATION

Supplementary data are available from Bioinformatics online.

Higher native Peruvian genetic ancestry proportion is associated with tuberculosis progression risk

We investigated whether ancestry-specific genetic factors affect tuberculosis (TB) progression risk in a cohort of admixed Peruvians. We genotyped 2,105 patients with TB and 1,320 household contacts (HHCs) who were infected with Mycobacterium tuberculosis (M. tb) but did not develop TB and inferred each individual’s proportion of native Peruvian genetic ancestry. Our HHC study design and our data on potential confounders allowed us to demonstrate increased risk independent of socioeconomic factors. A 10% increase in individual-level native Peruvian genetic ancestry proportion corresponded to a 25% increased TB progression risk. This corresponds to a 3-fold increased risk for individuals in the highest decile of native Peruvian genetic ancestry versus the lowest decile, making native Peruvian genetic ancestry comparable in effect to clinical factors such as diabetes. Our results suggest that genetic ancestry is a major contributor to TB progression risk and highlight the value of including diverse populations in host genetic studies.

Our understanding of how genetic differences among human populations may affect susceptibility to infectious diseases is very limited. Asgari et al. show that the proportion of native genetic ancestry in contemporary Peruvians affects the risk of progression from latent to active tuberculosis even after accounting for differences in socio-demographic factors.

Taurine and Indicine Haplotype Representation in Advanced Generation Individuals From Three American Breeds

Development of the American Breeds of beef cattle began in the 1920s as breeders and U. S. Experiment Station researchers began to create Bos taurus taurus × Bos taurus indicus hybrids using Brahman as the B. t. indicus source. By 1954, U.S. Breed Associations had been formed for Brangus (5/8 Angus × 3/8 Brahman), Beefmaster (½ Brahman × ¼ Shorthorn × ¼ Hereford), and Santa Gertrudis (5/8 Shorthorn × 3/8 Brahman). While these breeds were developed using mating designs expected to create base generation animals with the required genome contributions from progenitor breeds, each association has now registered advanced generation animals in which selection or drift may have caused the realized genome compositions to differ from initial expected proportions. The availability of high-density SNP genotypes for 9,161 Brangus, 3,762 Beefmaster, and 1,942 Santa Gertrudis animals allowed us to compare the realized genomic architectures of breed members to the base generation expectations. We used RFMix to estimate local ancestry and identify genomic regions in which the proportion of Brahman ancestry differed significantly from a priori expectations. For all three breeds, lower than expected levels of Brahman composition were found genome-wide, particularly in early-generation animals where we demonstrate that selection on beef production traits was likely responsible for the taurine enrichment. Using a proxy for generation number, we also contrasted the genomes of early- and advanced-generation animals and found that the indicine composition of the genome has increased with generation number likely due to selection on adaptive traits. Many of the most-highly differentiated genomic regions were breed specific, suggesting that differences in breeding objectives and selection intensities exist between the breeds. Global ancestry estimation is commonly performed in admixed animals to control for stratification in association studies. However, local ancestry estimation provides the opportunity to investigate the evolution of specific chromosomal segments and estimate haplotype effects on trait variation in admixed individuals. Investigating the genomic architecture of the American Breeds not only allows the estimation of indicine and taurine genome proportions genome-wide, but also the locations within the genome where either taurine or indicine alleles confer a selective advantage.

An Overview of Strategies for Detecting Genotype-Phenotype Associations Across Ancestrally Diverse Populations

Genome-wide association studies (GWAS) have been very successful at identifying genetic variants influencing a large number of traits. Although the great majority of these studies have been performed in European-descent individuals, it has been recognised that including populations with differing ancestries enhances the potential for identifying causal SNPs due to their differing patterns of linkage disequilibrium. However, when individuals from distinct ethnicities are included in a GWAS, it is necessary to implement a number of control steps to ensure that the identified associations are real genotype-phenotype relationships. In this Review, we discuss the analyses that are required when performing multi-ethnic studies, including methods for determining ancestry at the global and local level for sample exclusion, controlling for ancestry in association testing, and post-GWAS interrogation methods such as genomic control and meta-analysis. We hope that this overview provides a primer for those researchers interested in including distinct populations in their studies.

An immunogenetic view of COVID-19

Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.

Genetic Ancestry Inference and Its Application for the Genetic Mapping of Human Diseases

Admixed populations arise when two or more ancestral populations interbreed. As a result of this admixture, the genome of admixed populations is defined by tracts of variable size inherited from these parental groups and has particular genetic features that provide valuable information about their demographic history. Diverse methods can be used to derive the ancestry apportionment of admixed individuals, and such inferences can be leveraged for the discovery of genetic loci associated with diseases and traits, therefore having important biomedical implications. In this review article, we summarize the most common methods of global and local genetic ancestry estimation and discuss the use of admixture mapping studies in human diseases.

Comparing local ancestry inference models in populations of two- and three-way admixture

Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation that have been used in human complex trait genetics are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019). As users rather than developers, we sought to perform direct comparisons of accuracy, runtime, memory usage, and usability of these software tools to determine which is best for incorporation into association study pipelines. We find that in the majority of cases RFMix has the highest median accuracy with the ranking of the remaining software dependent on the ancestral architecture of the population tested. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software increase linearly with respect to sample size. The only exception is RFMix, which increases quadratically with respect to runtime and linearly with respect to memory. Effective local ancestry estimation tools are necessary to increase diversity and prevent population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform just as well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking.

locStra: Fast analysis of regional/global stratification in whole-genome sequencing studies

locStra is an R -package for the analysis of regional and global population stratification in whole-genome sequencing (WGS) studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard similarity matrix. Using a sliding window approach, the regional similarity matrices are compared with the global ones, based on user-defined window sizes and metrics, for example, the correlation between regional and global eigenvectors. An algorithm for the specification of the window size is provided. As the implementation fully exploits sparse matrix algebra and is written in C++, the analysis is highly efficient. Even on single cores, for realistic study sizes (several thousand subjects, several million rare variants per subject), the runtime for the genome-wide computation of all regional similarity matrices does typically not exceed one hour, enabling an unprecedented investigation of regional stratification across the entire genome. The package is applied to three WGS studies, illustrating the varying patterns of regional substructure across the genome and its beneficial effects on association testing.

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

BACKGROUND

Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization.

RESULTS

Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up.

CONCLUSIONS

We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

Prospective avenues for human population genomics and disease mapping in southern Africa

Population substructure within human populations is globally evident and a well-known confounding factor in many genetic studies. In contrast, admixture mapping exploits population stratification to detect genotype-phenotype correlations in admixed populations. Southern Africa has untapped potential for disease mapping of ancestry-specific disease risk alleles due to the distinct genetic diversity in its populations compared to other populations worldwide. This diversity contributes to a number of phenotypes, including ancestry-specific disease risk and response to pathogens. Although the 1000 Genomes Project significantly improved our understanding of genetic variation globally, southern African populations are still severely underrepresented in biomedical and human genetic studies due to insufficient large-scale publicly available data. In addition to a lack of genetic data in public repositories, existing software, algorithms and resources used for imputation and phasing of genotypic data (amongst others) are largely ineffective for populations with a complex genetic architecture such as that seen in southern Africa. This review article, therefore, aims to summarise the current limitations of conducting genetic studies on populations with a complex genetic architecture to identify potential areas for further research and development.

Genomic, Transcriptomic and Epigenomic Tools to Study the Domestication of Plants and Animals: A Field Guide for Beginners

In the last decade, genomics and the related fields of transcriptomics and epigenomics have revolutionized the study of the domestication process in plants and animals, leading to new discoveries and new unresolved questions. Given that some domesticated taxa have been more studied than others, the extent of genomic data can range from vast to nonexistent, depending on the domesticated taxon of interest. This review is meant as a rough guide for students and academics that want to start a domestication research project using modern genomic tools, as well as for researchers already conducting domestication studies that are interested in following a genomic approach and looking for alternate strategies (cheaper or more efficient) and future directions. We summarize the theoretical and technical background needed to carry out domestication genomics, starting from the acquisition of a reference genome and genome assembly, to the sampling design for population genomics, paleogenomics, transcriptomics, epigenomics and experimental validation of domestication-related genes. We also describe some examples of the aforementioned approaches and the relevant discoveries they made to understand the domestication of the studied taxa.

Identifying disease-causing mutations in genomes of single patients by computational approaches

Over the last decade next generation sequencing (NGS) has been extensively used to identify new pathogenic mutations and genes causing rare genetic diseases. The efficient analyses of NGS data is not trivial and requires a technically and biologically rigorous pipeline that addresses data quality control, accurate variant filtration to minimize false positives and false negatives, and prioritization of the remaining genes based on disease genomics and physiological knowledge. This review provides a pipeline including all these steps, describes popular software for each step of the analysis, and proposes a general framework for the identification of causal mutations and genes in individual patients of rare genetic diseases.

FRANC: a unified framework for multi-way local ancestry deconvolution with high density SNP data

Several thousand genomes have been completed with millions of variants identified in the human deoxyribonucleic acid sequences. These genomic variations, especially those introduced by admixture, significantly contribute to a remarkable phenotypic variability with medical and/or evolutionary implications. Elucidating local ancestry estimates is necessary for a better understanding of genomic variation patterns throughout modern human evolution and adaptive processes, and consequences in human heredity and health. However, existing local ancestry deconvolution tools are accessible as individual scripts, each requiring input and producing output in its own complex format. This limits the user’s ability to retrieve local ancestry estimates. We introduce a unified framework for multi-way local ancestry inference, FRANC, integrating eight existing state-of-the-art local ancestry deconvolution tools. FRANC is an adaptable, expandable and portable tool that manipulates tool-specific inputs, deconvolutes ancestry and standardizes tool-specific results. To facilitate both medical and population genetics studies, FRANC requires convenient and easy to manipulate input files and allows users to choose output formats to ease their use in further potential local ancestry deconvolution applications.

Genome-Wide Analysis of SNPs Is Consistent with No Domestic Dog Ancestry in the Endangered Mexican Wolf (Canis lupus baileyi)

The Mexican gray wolf (Canis lupus baileyi) was historically distributed throughout the southwestern United States and northern Mexico. Extensive predator removal campaigns during the early 20th century, however, resulted in its eventual extirpation by the mid 1980s. At this time, the Mexican wolf existed only in 3 separate captive lineages (McBride, Ghost Ranch, and Aragón) descended from 3, 2, and 2 founders, respectively. These lineages were merged in 1995 to increase the available genetic variation, and Mexican wolves were reintroduced into Arizona and New Mexico in 1998. Despite the ongoing management of the Mexican wolf population, it has been suggested that a proportion of the Mexican wolf ancestry may be recently derived from hybridization with domestic dogs. In this study, we genotyped 87 Mexican wolves, including individuals from all 3 captive lineages and cross-lineage wolves, for more than 172000 single nucleotide polymorphisms. We identified levels of genetic variation consistent with the pedigree record and effects of genetic rescue. To identify the potential to detect hybridization with domestic dogs, we compared our Mexican wolf genotypes with those from studies of domestic dogs and other gray wolves. The proportion of Mexican wolf ancestry assigned to domestic dogs was only between 0.06% (SD 0.23%) and 7.8% (SD 1.0%) for global and local ancestry estimates, respectively; and was consistent with simulated levels of incomplete lineage sorting. Overall, our results suggested that Mexican wolves lack biologically significant ancestry with dogs and have useful implications for the conservation and management of this endangered wolf subspecies.

An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines

Recent work points to a lack of diversity in genomics studies from genome-wide association studies to somatic (tumor) genome analyses. Yet, population-specific genetic variation has been shown to contribute to health disparities in cancer risk and outcomes. Immortalized cancer cell lines are widely used in cancer research, from mechanistic studies to drug screening. Larger collections of cancer cell lines better represent the genomic heterogeneity found in primary tumors. Yet, the genetic ancestral origin of cancer cell lines is rarely acknowledged and often unknown. Using genome-wide genotyping data from 1,393 cancer cell lines from the Catalogue of Somatic Mutations in Cancer (COSMIC) and Cancer Cell Line Encyclopedia (CCLE), we estimated the genetic ancestral origin for each cell line. Our data indicate that cancer cell line collections are not representative of the diverse ancestry and admixture characterizing human populations. We discuss the implications of genetic ancestry and diversity of cellular models for cancer research and present an interactive tool, Estimated Cell Line Ancestry (ECLA), where ancestry can be visualized with reference populations of the 1000 Genomes Project. Cancer researchers can use this resource to identify cell line models for their studies by taking ancestral origins into consideration.

Dating admixture events is unsolved problem in multi-way admixed populations

Advances in human sequencing technologies, coupled with statistical and computational tools, have fostered the development of methods for dating admixture events. These methods have merits and drawbacks in estimating admixture events in multi-way admixed populations. Here, we first provide a comprehensive review and comparison of current methods pertinent to dating admixture events. Second, we assess various admixture dating tools. We do so by performing various simulations. Third, we apply the top two assessed methods to real data of a uniquely admixed population from South Africa. Results reveal that current dating admixture models are not sufficiently equipped to estimate ancient admixtures events and to identify multi-faceted admixture events in complex multi-way admixed populations. We conclude with a discussion of research areas where further work on dating admixture-based methods is needed.

EPIGEN-Brazil Initiative resources: a Latin American imputation panel and the Scientific Workflow

EPIGEN-Brazil is one of the largest Latin American initiatives at the interface of human genomics, public health, and computational biology. Here, we present two resources to address two challenges to the global dissemination of precision medicine and the development of the bioinformatics know-how to support it. To address the underrepresentation of non-European individuals in human genome diversity studies, we present the EPIGEN-5M+1KGP imputation panel—the fusion of the public 1000 Genomes Project (1KGP) Phase 3 imputation panel with haplotypes derived from the EPIGEN-5M data set (a product of the genotyping of 4.3 million SNPs in 265 admixed individuals from the EPIGEN-Brazil Initiative). When we imputed a target SNPs data set (6487 admixed individuals genotyped for 2.2 million SNPs from the EPIGEN-Brazil project) with the EPIGEN-5M+1KGP panel, we gained 140,452 more SNPs in total than when using the 1KGP Phase 3 panel alone and 788,873 additional high confidence SNPs (info score ≥ 0.8). Thus, the major effect of the inclusion of the EPIGEN-5M data set in this new imputation panel is not only to gain more SNPs but also to improve the quality of imputation. To address the lack of transparency and reproducibility of bioinformatics protocols, we present a conceptual Scientific Workflow in the form of a website that models the scientific process (by including publications, flowcharts, masterscripts, documents, and bioinformatics protocols), making it accessible and interactive. Its applicability is shown in the context of the development of our EPIGEN-5M+1KGP imputation panel. The Scientific Workflow also serves as a repository of bioinformatics resources.

A Common Variation in the Caveolin 1 Gene Is Associated with High Serum Triglycerides and Metabolic Syndrome in an Admixed Latin American Population

Background: The caveolin 1 (CAV1) gene has been associated with metabolic traits in animal models and human cohorts. Recently, a prevalent variant in CAV1 has been found to be related to metabolic syndrome in Hispanics living in North America. Since Hispanics represent an admixed population at high risk for cardiovascular diseases, in this study a Latin American population with a similar genetic background was assessed.

Objective: To analyze a genetic association between CAV1 and metabolic traits in an admixed Latin American population.

Methods: A cross-sectional study was carried out with adults from the Colombian Caribbean Coast, selected in urban clusters and work places through a stratified sampling to include diverse ages and socioeconomic groups. Blood pressure and waist circumference were registered. Serum concentrations of glucose, triglycerides, and high-density lipoprotein cholesterol were measured from an 8-hr fasting whole-blood sample. Two previously analyzed CAV1 single nucleotide polymorphisms were genotyped (rs926198 and rs11773845). A logistic regression model was applied to estimate the associations. An admixture adjustment was performed through a Bayesian model.

Results: A total of 605 subjects were included. rs11773845 was associated with hypertriglyceridemia [odds ratio (OR) = 1.33, p = 0.001] and the metabolic syndrome (OR = 1.53, p = 0.02). When admixture adjustment was performed these genetic associations preserved their statistical significance. There were no significant associations between rs926198 and metabolic traits.

Conclusions: The CAV1 variation rs11773845 was found to be consistently associated with high serum triglycerides and the metabolic syndrome. This is the first report of a relationship between CAV1 variants and serum triglycerides in Latin America.

A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.

Conclusion: Special issue on genetic and alcohol use disorder research with diverse racial/ethnic groups: Key findings and potential next steps

BACKGROUND AND OBJECTIVES

This special issue brings together papers focusing on a wide range of topics relevant to the research and understanding of the role of race/ethnicity and genetic variation for the susceptibility of developing an alcohol use disorder (AUD).

METHODS

The key findings from the issue's 10 articles are reviewed and organized here around three topics: I: addictive behaviors and potential environmental influences; II: a focus on four racial/ethnic groups; and III: special methodologies.

RESULTS

Several potential next steps in improving effective research strategies are highlighted: (1) implementing best practices for outreach and community engagement may reduce reluctance to participate; (2) recruiting adequately sized and racially/ethnically diverse samples will require new collaborations with investigators who successfully work in diverse communities; (3) identifying and assessing environmental influences that are both unique to, and common among, racial/ethnic groups may inform preventions for AUD; (4) use of standardized measures will facilitate the generation of larger samples and meta-analysis of research findings; and (5) use of better analytic approaches and experimental methods will improve replication in gene finding research and help advance new areas of research.

CONCLUSIONS

Genetic research of AUD in diverse racial/ethnic populations is advancing. The articles in this issue examined the general theme of including diverse population groups in genetic studies and offered potential strategies for addressing some common problems.

SCIENTIFIC SIGNIFICANCE

Greater inclusion of diverse racial/ethnic populations in this research is important to ensure that the benefits of new knowledge and technology are equally shared. (Am J Addict 2017;26:532-537).

Variations in ADIPOR1 But Not ADIPOR2 are Associated With Hypertriglyceridemia and Diabetes in an Admixed Latin American Population

BACKGROUND

Adiponectin is a hormone secreted by adipose tissue. It regulates glycolysis and lipolysis and is involved in the pathophysiology of diabetes and related disorders. Its activity is mainly mediated by the transmembrane receptors AdipoR1 and AdipoR2, which are encoded by ADIPOR1 (1q32.1) and ADIPOR2 (12p13.33) genes, respectively. In genetic association studies, single nucleotide polymorphisms (SNPs) in or near these genes have been associated with metabolic alterations. However, these relationships are still controversial.

AIM

The aim of this work was to analyze possible associations between ADIPOR1/2 and diabetes and other metabolic disorders.

METHODS

A genetic association study was carried out in an admixed Latin American population. A sample of 200 adults was analyzed. Clinical and serum-biochemical characteristics were measured to diagnose obesity, abdominal obesity, hypertension, hyperglycemia, hypertriglyceridemia, low HDLc, insulin resistance (HOMA-IR), and diabetes. Three SNPs were genotyped in ADIPOR1 (rs10494839, rs12733285, and rs2275737) and ADIPOR2 (rs11061937, rs11612383, and rs2286383). For the association analysis, an additive model was assessed through logistic regression. An admixture adjustment was performed using a Monte-Carlo-Markov-Chain method, assuming a three-hybrid substructure (k = 3).

RESULTS

Two SNPs in ADIPOR1 were associated with diabetes: rs10494839 (OR = 3.88, adjusted p < 0.03) and rs12733285 (OR = 4.72, adjusted p < 0.03). Additionally, rs10494839 was associated with hypertriglyceridemia (OR = 2.16, adjusted p < 0.01). None of the SNPs in ADIPOR2 were associated with metabolic disorders.

CONCLUSIONS

ADIPOR1 was consistently associated with diabetes and hypertriglyceridemia. This association was maintained even after adjusting for genetic stratification. There were no significant associations involving ADIPOR2.

Family-based exome-wide association study of childhood acute lymphoblastic leukemia among Hispanics confirms role of ARID5B in susceptibility

We conducted an exome-wide association study of childhood acute lymphoblastic leukemia (ALL) among Hispanics to confirm and identify novel variants associated with disease risk in this population. We used a case-parent trio study design; unlike more commonly used case-control studies, this study design is ideal for avoiding issues with population stratification bias among this at-risk ethnic group. Using 710 individuals from 323 Guatemalan and US Hispanic families, two inherited SNPs in ARID5B reached genome-wide level significance: rs10821936, RR = 2.31, 95% CI = 1.70–3.14, p = 1.7×10⁻⁸ and rs7089424, RR = 2.22, 95% CI = 1.64–3.01, p = 5.2×10⁻⁸. Similar results were observed when restricting our analyses to those with the B-ALL subtype: ARID5B rs10821936 RR = 2.22, 95% CI = 1.63–3.02, p = 9.63×10⁻⁸ and ARID5B rs7089424 RR = 2.13, 95% CI = 1.57–2.88, p = 2.81×10⁻⁷. Notably, effect sizes observed for rs7089424 and rs10821936 in our study were >20% higher than those reported among non-Hispanic white populations in previous genetic association studies. Our results confirmed the role of ARID5B in childhood ALL susceptibility among Hispanics; however, our assessment did not reveal any strong novel inherited genetic risks for acute lymphoblastic leukemia among this ethnic group.

Evaluating genetic susceptibility to Staphylococcus aureus bacteremia in African Americans using admixture mapping

The incidence of Staphylococcus aureus bacteremia (SAB) is significantly higher in African American (AA) than in European-descended populations. We used admixture mapping (AM) to test the hypothesis that genomic variations with different frequencies in European and African ancestral genomes influence susceptibility to SAB in AAs. A total of 565 adult AAs (390 cases with SAB; 175 age-matched controls) were genotyped for AM analysis. A case-only admixture score and a mixed χ²(1df) score (MIX) to jointly evaluate both single-nucleotide polymorphism (SNP) and admixture association (P<5.00e-08) were computed using MIXSCORE. In addition, a permutation scheme was implemented to derive multiplicity adjusted P-values (genome-wide 0.05 significance threshold: P<9.46e-05). After empirical multiplicity adjustment, one region on chromosome 6 (52 SNPs, P=4.56e-05) in the HLA class II region was found to exhibit a genome-wide statistically significant increase in European ancestry. This region encodes genes involved in HLA-mediated immune response and these results provide additional evidence for genetic variation influencing HLA-mediated immunity, modulating susceptibility to SAB.

Model-free Estimation of Recent Genetic Relatedness

Genealogical inference from genetic data is essential for a variety of applications in human genetics. In genome-wide and sequencing association studies, for example, accurate inference on both recent genetic relatedness, such as family structure, and more distant genetic relatedness, such as population structure, is necessary for protection against spurious associations. Distinguishing familial relatedness from population structure with genotype data, however, is difficult because both manifest as genetic similarity through the sharing of alleles. Existing approaches for inference on recent genetic relatedness have limitations in the presence of population structure, where they either (1) make strong and simplifying assumptions about population structure, which are often untenable, or (2) require correct specification of and appropriate reference population panels for the ancestries in the sample, which might be unknown or not well defined. Here, we propose PC-Relate, a model-free approach for estimating commonly used measures of recent genetic relatedness, such as kinship coefficients and IBD sharing probabilities, in the presence of unspecified structure. PC-Relate uses principal components calculated from genome-screen data to partition genetic correlations among sampled individuals due to the sharing of recent ancestors and more distant common ancestry into two separate components, without requiring specification of the ancestral populations or reference population panels. In simulation studies with population structure, including admixture, we demonstrate that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. We further demonstrate the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity.

Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness

Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multidimensional scaling (MDS), and model-based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC-AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC-AiR utilizes genome-screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC-AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC-AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC-AiR provides better prediction of ancestry in a variety of structure settings than using 10 (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC-AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness.

Drinking from the Holy Grail: analysis of whole-genome sequencing from the Genetic Analysis Workshop 18

The Genetic Analysis Workshops distribute real and simulated human genetic data to allow the development and comparison of methods to detect genetic variants and genes related to biological traits; the results are then presented and discussed at a biennial meeting. The data made available for Genetic Analysis Workshop 18 (GAW18) included whole-genome sequence data for odd-numbered autosomes from 20 large Mexican American pedigrees selected through probands with type 2 diabetes. Real and simulated blood pressure phenotype data were provided to allow the comparison of methods to detect variants and genes associated with blood pressure. Some of the complexity present in the data includes related individuals, repeated quantitative trait outcomes, covariates, medication effects, pharmacokinetic effects, missing data, admixed population, and imputed genotypes. A wide range of analytic approaches were applied to the data. Contributions that focused only on a subset of up to 155 unrelated subjects from the pedigrees were faced with low power. One recommendation for future analysis is the use of the provided null phenotype to allow comparison of type I error across methods. Collaboration between statistical geneticists and molecular biologists or bioinformaticians would provide helpful input to place variants in genes for gene-based association tests.